From f6a8a6bd62d32e6615e59594ad89bca585a66fa2 Mon Sep 17 00:00:00 2001 From: orensbruli Date: Fri, 15 Jun 2018 12:03:46 +0200 Subject: [PATCH 01/54] Moved to class --- HandDetection.py | 410 ++++++++++++++++++++++++++--------------------- 1 file changed, 225 insertions(+), 185 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index 55ad790..c33d125 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -2,196 +2,236 @@ import numpy as np import time -#Open Camera object -cap = cv2.VideoCapture(0) -#Decrease frame size -cap.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 1000) -cap.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 600) def nothing(x): pass + # Function to find angle between two vectors -def Angle(v1,v2): - dot = np.dot(v1,v2) - x_modulus = np.sqrt((v1*v1).sum()) - y_modulus = np.sqrt((v2*v2).sum()) - cos_angle = dot / x_modulus / y_modulus - angle = np.degrees(np.arccos(cos_angle)) - return angle +def Angle(v1, v2): + dot = np.dot(v1, v2) + x_modulus = np.sqrt((v1 * v1).sum()) + y_modulus = np.sqrt((v2 * v2).sum()) + cos_angle = dot / x_modulus / y_modulus + angle = np.degrees(np.arccos(cos_angle)) + return angle + # Function to find distance between two points in a list of lists -def FindDistance(A,B): - return np.sqrt(np.power((A[0][0]-B[0][0]),2) + np.power((A[0][1]-B[0][1]),2)) - - -# Creating a window for HSV track bars -cv2.namedWindow('HSV_TrackBar') - -# Starting with 100's to prevent error while masking -h,s,v = 100,100,100 - -# Creating track bar -cv2.createTrackbar('h', 'HSV_TrackBar',0,179,nothing) -cv2.createTrackbar('s', 'HSV_TrackBar',0,255,nothing) -cv2.createTrackbar('v', 'HSV_TrackBar',0,255,nothing) - -while(1): - - #Measure execution time - start_time = time.time() - - #Capture frames from the camera - ret, frame = cap.read() - - #Blur the image - blur = cv2.blur(frame,(3,3)) - - #Convert to HSV color space - hsv = cv2.cvtColor(blur,cv2.COLOR_BGR2HSV) - - #Create a binary image with where white will be skin colors and rest is black - mask2 = cv2.inRange(hsv,np.array([2,50,50]),np.array([15,255,255])) - - #Kernel matrices for morphological transformation - kernel_square = np.ones((11,11),np.uint8) - kernel_ellipse= cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5)) - - #Perform morphological transformations to filter out the background noise - #Dilation increase skin color area - #Erosion increase skin color area - dilation = cv2.dilate(mask2,kernel_ellipse,iterations = 1) - erosion = cv2.erode(dilation,kernel_square,iterations = 1) - dilation2 = cv2.dilate(erosion,kernel_ellipse,iterations = 1) - filtered = cv2.medianBlur(dilation2,5) - kernel_ellipse= cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(8,8)) - dilation2 = cv2.dilate(filtered,kernel_ellipse,iterations = 1) - kernel_ellipse= cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5)) - dilation3 = cv2.dilate(filtered,kernel_ellipse,iterations = 1) - median = cv2.medianBlur(dilation2,5) - ret,thresh = cv2.threshold(median,127,255,0) - - #Find contours of the filtered frame - contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) - - #Draw Contours - #cv2.drawContours(frame, cnt, -1, (122,122,0), 3) - #cv2.imshow('Dilation',median) - - #Find Max contour area (Assume that hand is in the frame) - max_area=100 - ci=0 - for i in range(len(contours)): - cnt=contours[i] - area = cv2.contourArea(cnt) - if(area>max_area): - max_area=area - ci=i - - #Largest area contour - cnts = contours[ci] - - #Find convex hull - hull = cv2.convexHull(cnts) - - #Find convex defects - hull2 = cv2.convexHull(cnts,returnPoints = False) - defects = cv2.convexityDefects(cnts,hull2) - - #Get defect points and draw them in the original image - FarDefect = [] - for i in range(defects.shape[0]): - s,e,f,d = defects[i,0] - start = tuple(cnts[s][0]) - end = tuple(cnts[e][0]) - far = tuple(cnts[f][0]) - FarDefect.append(far) - cv2.line(frame,start,end,[0,255,0],1) - cv2.circle(frame,far,10,[100,255,255],3) - - #Find moments of the largest contour - moments = cv2.moments(cnts) - - #Central mass of first order moments - if moments['m00']!=0: - cx = int(moments['m10']/moments['m00']) # cx = M10/M00 - cy = int(moments['m01']/moments['m00']) # cy = M01/M00 - centerMass=(cx,cy) - - #Draw center mass - cv2.circle(frame,centerMass,7,[100,0,255],2) - font = cv2.FONT_HERSHEY_SIMPLEX - cv2.putText(frame,'Center',tuple(centerMass),font,2,(255,255,255),2) - - #Distance from each finger defect(finger webbing) to the center mass - distanceBetweenDefectsToCenter = [] - for i in range(0,len(FarDefect)): - x = np.array(FarDefect[i]) - centerMass = np.array(centerMass) - distance = np.sqrt(np.power(x[0]-centerMass[0],2)+np.power(x[1]-centerMass[1],2)) - distanceBetweenDefectsToCenter.append(distance) - - #Get an average of three shortest distances from finger webbing to center mass - sortedDefectsDistances = sorted(distanceBetweenDefectsToCenter) - AverageDefectDistance = np.mean(sortedDefectsDistances[0:2]) - - #Get fingertip points from contour hull - #If points are in proximity of 80 pixels, consider as a single point in the group - finger = [] - for i in range(0,len(hull)-1): - if (np.absolute(hull[i][0][0] - hull[i+1][0][0]) > 80) or ( np.absolute(hull[i][0][1] - hull[i+1][0][1]) > 80): - if hull[i][0][1] <500 : - finger.append(hull[i][0]) - - #The fingertip points are 5 hull points with largest y coordinates - finger = sorted(finger,key=lambda x: x[1]) - fingers = finger[0:5] - - #Calculate distance of each finger tip to the center mass - fingerDistance = [] - for i in range(0,len(fingers)): - distance = np.sqrt(np.power(fingers[i][0]-centerMass[0],2)+np.power(fingers[i][1]-centerMass[0],2)) - fingerDistance.append(distance) - - #Finger is pointed/raised if the distance of between fingertip to the center mass is larger - #than the distance of average finger webbing to center mass by 130 pixels - result = 0 - for i in range(0,len(fingers)): - if fingerDistance[i] > AverageDefectDistance+130: - result = result +1 - - #Print number of pointed fingers - cv2.putText(frame,str(result),(100,100),font,2,(255,255,255),2) - - #show height raised fingers - #cv2.putText(frame,'finger1',tuple(finger[0]),font,2,(255,255,255),2) - #cv2.putText(frame,'finger2',tuple(finger[1]),font,2,(255,255,255),2) - #cv2.putText(frame,'finger3',tuple(finger[2]),font,2,(255,255,255),2) - #cv2.putText(frame,'finger4',tuple(finger[3]),font,2,(255,255,255),2) - #cv2.putText(frame,'finger5',tuple(finger[4]),font,2,(255,255,255),2) - #cv2.putText(frame,'finger6',tuple(finger[5]),font,2,(255,255,255),2) - #cv2.putText(frame,'finger7',tuple(finger[6]),font,2,(255,255,255),2) - #cv2.putText(frame,'finger8',tuple(finger[7]),font,2,(255,255,255),2) - - #Print bounding rectangle - x,y,w,h = cv2.boundingRect(cnts) - img = cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),2) - - cv2.drawContours(frame,[hull],-1,(255,255,255),2) - - ##### Show final image ######## - cv2.imshow('Dilation',frame) - ############################### - - #Print execution time - #print time.time()-start_time - - #close the output video by pressing 'ESC' - k = cv2.waitKey(5) & 0xFF - if k == 27: - break - - -cap.release() -cv2.destroyAllWindows() +def FindDistance(A, B): + return np.sqrt(np.power((A[0][0] - B[0][0]), 2) + np.power((A[0][1] - B[0][1]), 2)) + + +# # Creating a window for HSV track bars +# cv2.namedWindow('HSV_TrackBar') +# +# # Starting with 100's to prevent error while masking +# h, s, v = 100, 100, 100 +# +# # Creating track bar +# cv2.createTrackbar('h', 'HSV_TrackBar', 0, 179, nothing) +# cv2.createTrackbar('s', 'HSV_TrackBar', 0, 255, nothing) +# cv2.createTrackbar('v', 'HSV_TrackBar', 0, 255, nothing) + + +class HandDetector(): + def __init__(self): + # Open Camera object + self.capture = cv2.VideoCapture(0) + # Decrease frame size + # self.capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1000) + # self.capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 600) + + def compute(self): + while (True): + + # Measure execution time + start_time = time.time() + + # Capture frames from the camera + ret, frame = self.capture.read() + cv2.imshow("initial", frame) + + if ret: + # Create a binary image with where white will be skin colors and rest is black + hand_mask = self.create_hands_mask(frame) + cv2.imshow("hand_mask", hand_mask) + k = cv2.waitKey(5) & 0xFF + if k == 27: + break + continue + + # Kernel matrices for morphological transformation + kernel_square = np.ones((11, 11), np.uint8) + kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + + # Perform morphological transformations to filter out the background noise + # Dilation increase skin color area + # Erosion increase skin color area + dilation = cv2.dilate(hand_mask, kernel_ellipse, iterations=1) + erosion = cv2.erode(dilation, kernel_square, iterations=1) + dilation2 = cv2.dilate(erosion, kernel_ellipse, iterations=1) + filtered = cv2.medianBlur(dilation2, 5) + kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8, 8)) + dilation2 = cv2.dilate(filtered, kernel_ellipse, iterations=1) + kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + dilation3 = cv2.dilate(filtered, kernel_ellipse, iterations=1) + median = cv2.medianBlur(dilation2, 5) + ret, thresh = cv2.threshold(median, 127, 255, 0) + + # Find contours of the filtered frame + _, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + + # Draw Contours + # cv2.drawContours(frame, cnt, -1, (122,122,0), 3) + cv2.imshow('Dilation',median) + + if contours: + # Find Max contour area (Assume that hand is in the frame) + max_area = 100 + ci = 0 + for i in range(len(contours)): + cnt = contours[i] + area = cv2.contourArea(cnt) + if (area > max_area): + max_area = area + ci = i + + # Largest area contour + cnts = contours[ci] + + # Find convex hull + hull = cv2.convexHull(cnts) + + # Find convex defects + hull2 = cv2.convexHull(cnts, returnPoints=False) + defects = cv2.convexityDefects(cnts, hull2) + + # Get defect points and draw them in the original image + FarDefect = [] + print defects.shape + if defects.shape[0]>13: + for i in range(defects.shape[0]): + s, e, f, d = defects[i, 0] + start = tuple(cnts[s][0]) + end = tuple(cnts[e][0]) + far = tuple(cnts[f][0]) + FarDefect.append(far) + cv2.line(frame, start, end, [0, 255, 0], 1) + cv2.circle(frame, far, 10, [100, 255, 255], 3) + + # Find moments of the largest contour + moments = cv2.moments(cnts) + + # Central mass of first order moments + if moments['m00'] != 0: + cx = int(moments['m10'] / moments['m00']) # cx = M10/M00 + cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 + centerMass = (cx, cy) + + # Draw center mass + cv2.circle(frame, centerMass, 7, [100, 0, 255], 2) + font = cv2.FONT_HERSHEY_SIMPLEX + cv2.putText(frame, 'Center', tuple(centerMass), font, 2, (255, 255, 255), 2) + + # Distance from each finger defect(finger webbing) to the center mass + distanceBetweenDefectsToCenter = [] + for i in range(0, len(FarDefect)): + x = np.array(FarDefect[i]) + centerMass = np.array(centerMass) + distance = np.sqrt(np.power(x[0] - centerMass[0], 2) + np.power(x[1] - centerMass[1], 2)) + distanceBetweenDefectsToCenter.append(distance) + + # Get an average of three shortest distances from finger webbing to center mass + sortedDefectsDistances = sorted(distanceBetweenDefectsToCenter) + AverageDefectDistance = np.mean(sortedDefectsDistances[0:2]) + + # Get fingertip points from contour hull + # If points are in proximity of 80 pixels, consider as a single point in the group + finger = [] + for i in range(0, len(hull) - 1): + if (np.absolute(hull[i][0][0] - hull[i + 1][0][0]) > 80) or ( + np.absolute(hull[i][0][1] - hull[i + 1][0][1]) > 80): + if hull[i][0][1] < 500: + finger.append(hull[i][0]) + + # The fingertip points are 5 hull points with largest y coordinates + finger = sorted(finger, key=lambda x: x[1]) + fingers = finger[0:5] + + # Calculate distance of each finger tip to the center mass + fingerDistance = [] + for i in range(0, len(fingers)): + distance = np.sqrt(np.power(fingers[i][0] - centerMass[0], 2) + np.power(fingers[i][1] - centerMass[0], 2)) + fingerDistance.append(distance) + + # Finger is pointed/raised if the distance of between fingertip to the center mass is larger + # than the distance of average finger webbing to center mass by 130 pixels + result = 0 + for i in range(0, len(fingers)): + if fingerDistance[i] > AverageDefectDistance + 130: + result = result + 1 + + # Print number of pointed fingers + cv2.putText(frame, str(result), (100, 100), font, 2, (255, 255, 255), 2) + + # show height raised fingers + # cv2.putText(frame,'finger1',tuple(finger[0]),font,2,(255,255,255),2) + # cv2.putText(frame,'finger2',tuple(finger[1]),font,2,(255,255,255),2) + # cv2.putText(frame,'finger3',tuple(finger[2]),font,2,(255,255,255),2) + # cv2.putText(frame,'finger4',tuple(finger[3]),font,2,(255,255,255),2) + # cv2.putText(frame,'finger5',tuple(finger[4]),font,2,(255,255,255),2) + # cv2.putText(frame,'finger6',tuple(finger[5]),font,2,(255,255,255),2) + # cv2.putText(frame,'finger7',tuple(finger[6]),font,2,(255,255,255),2) + # cv2.putText(frame,'finger8',tuple(finger[7]),font,2,(255,255,255),2) + + # Print bounding rectangle + x, y, w, h = cv2.boundingRect(cnts) + img = cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) + + cv2.drawContours(frame, [hull], -1, (255, 255, 255), 2) + + ##### Show final image ######## + cv2.imshow('Dilation', frame) + ############################### + + # Print execution time + # print time.time()-start_time + + # close the output video by pressing 'ESC' + k = cv2.waitKey(5) & 0xFF + if k == 27: + break + + def exit(self): + self.capture.release() + cv2.destroyAllWindows() + + def create_hands_mask(self, image, mode="color"): + mask = None + if mode == "color": + # Blur the image + blur = cv2.blur(image, (3, 3)) + # Convert to HSV color space + hsv = cv2.cvtColor(blur, cv2.COLOR_BGR2HSV) + mask = cv2.inRange(hsv, np.array([2, 50, 50]), np.array([15, 255, 255])) + elif mode == "background_subtract": + fgbg = cv2.createBackgroundSubtractorMOG2() + # fgbg = cv2.BackgroundSubtractorMOG() + # gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + # blur = cv2.medianBlur(gray_image, 5) + cv2.imshow("blurred", image) + # blur = cv2.blur(image, (10, 10)) + mask = fgbg.apply(image,learningRate=0) + return mask + + +def main(): + hand_detector = HandDetector() + hand_detector.compute() + hand_detector.exit() + +if __name__ == "__main__": + main() + From fb18f2ed0faf976959d4e81817345a95397181f2 Mon Sep 17 00:00:00 2001 From: orensbruli Date: Fri, 15 Jun 2018 13:32:42 +0200 Subject: [PATCH 02/54] Modified the readme file according to the new class structure --- README.md | 24 +++++++++++------------- 1 file changed, 11 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index a055ba1..b332d30 100644 --- a/README.md +++ b/README.md @@ -1,28 +1,26 @@ Real-Time Hand Gesture Detection ================================ -The code captures frames using a web camera (tested on mac's camera) and outputs a video with a number designates the number of pointed finger. For example, a fist corresponds to 0 and an open hand to 5. +Python library offering a class for hand detection mixing diverse methods using opencv and inspired on this sources: +https://github.com/lzane/Fingers-Detection-using-OpenCV-and-Python/tree/py2_opencv2 +https://github.com/sashagaz/Hand_Detection + +The HandDetection class let the user get the hand detection from an image or the own class can get the camera capture +task. +Currently it only draw the points of the fingers on the original image. This project is written in Python 2.7. The following libraries are used in this project and neccessary to be add to your computer: 1) Time - usually comes with Python 2.7 2) OpenCV (2.4.9) - http://docs.opencv.org/trunk/doc/py_tutorials/py_setup/py_setup_in_windows/py_setup_in_windows.html 3) NumPy (1.8.0rc1) - http://www.numpy.org -The code is consist of a single file and can be executed from the commandline or terminal by calling: python HandDetection.py +The library can be executed for it own test with: +```python HandDetection.py``` When running the python file, you can expect to see the real time frame from your camera with a bounding rectangular framing your hand. The bounding rectangular will contain yellow circles corresponding to the fingertips and finger webbing. The center mass of your hand will also appear in the bounding rectangular. A number on the upper left side of the frame corresponds to the number of pointed fingers. To increase accuracy of the gesture recognition, it is recommended to run the code in a bright light room. Additionally, the code can analyze one hand (either left or right), and the hand needs to be in front of the camera. References: -opencv documentation http://docs.opencv.org/ -[2] Skin Detection using HSV color space - V. A. Oliveira, A. Conci -[3] OpenCv Documentation - Miscellaneous Image Transformation -http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html -[4] OpenCv Documentation - Morphological Operations http://docs.opencv.org/trunk/doc/py_tutorials/py_imgproc/py_morphological_ops/py_morphological_ops.html -[5] Suzuki, S. and Abe, K., Topological Structural Analysis of Digitized Binary Images by Border Following. CVGIP 30 1, pp 32-46 (1985) - -For any question related to the code please contact via email: -sasha7064570@gmail.com - -Sasha Gazman +https://github.com/lzane/Fingers-Detection-using-OpenCV-and-Python/tree/py2_opencv2 +https://github.com/sashagaz/Hand_Detection From 08c0c463e6fb42cb05c2fb468acb3b267a019cd4 Mon Sep 17 00:00:00 2001 From: orensbruli Date: Fri, 15 Jun 2018 14:10:09 +0200 Subject: [PATCH 03/54] Moved the detection code to the detect hand method and created and structure to contains the usefull information of the detected hand --- HandDetection.py | 310 ++++++++++++++++++++++++----------------------- 1 file changed, 161 insertions(+), 149 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index c33d125..f1d8fbb 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -39,6 +39,8 @@ class HandDetector(): def __init__(self): # Open Camera object self.capture = cv2.VideoCapture(0) + self.hands = [] #[{"fingers":None, "center_of_mass":None}] + self.font = cv2.FONT_HERSHEY_SIMPLEX # Decrease frame size # self.capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1000) # self.capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 600) @@ -53,156 +55,166 @@ def compute(self): ret, frame = self.capture.read() cv2.imshow("initial", frame) - if ret: - # Create a binary image with where white will be skin colors and rest is black - hand_mask = self.create_hands_mask(frame) - cv2.imshow("hand_mask", hand_mask) - k = cv2.waitKey(5) & 0xFF - if k == 27: - break - continue - - # Kernel matrices for morphological transformation - kernel_square = np.ones((11, 11), np.uint8) - kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) - - # Perform morphological transformations to filter out the background noise - # Dilation increase skin color area - # Erosion increase skin color area - dilation = cv2.dilate(hand_mask, kernel_ellipse, iterations=1) - erosion = cv2.erode(dilation, kernel_square, iterations=1) - dilation2 = cv2.dilate(erosion, kernel_ellipse, iterations=1) - filtered = cv2.medianBlur(dilation2, 5) - kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8, 8)) - dilation2 = cv2.dilate(filtered, kernel_ellipse, iterations=1) - kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) - dilation3 = cv2.dilate(filtered, kernel_ellipse, iterations=1) - median = cv2.medianBlur(dilation2, 5) - ret, thresh = cv2.threshold(median, 127, 255, 0) - - # Find contours of the filtered frame - _, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) - - # Draw Contours - # cv2.drawContours(frame, cnt, -1, (122,122,0), 3) - cv2.imshow('Dilation',median) - - if contours: + self.detect_hands(ret, frame) + + # Finger is pointed/raised if the distance of between fingertip to the center mass is larger + # than the distance of average finger webbing to center mass by 130 pixels + result = 0 + if len(self.hands) > 0: + for i in range(0, len(self.hands[0]["fingers"])): + if self.hands[0]['finger_distance'][i] > self.hands[0]['average_defects_distance'] + 130: + result = result + 1 + + # Print number of pointed fingers + cv2.putText(frame, str(result), (100, 100), self.font, 2, (255, 255, 255), 2) + + # show height raised fingers + # cv2.putText(frame,'finger1',tuple(finger[0]),self.font,2,(255,255,255),2) + # cv2.putText(frame,'finger2',tuple(finger[1]),self.font,2,(255,255,255),2) + # cv2.putText(frame,'finger3',tuple(finger[2]),self.font,2,(255,255,255),2) + # cv2.putText(frame,'finger4',tuple(finger[3]),self.font,2,(255,255,255),2) + # cv2.putText(frame,'finger5',tuple(finger[4]),self.font,2,(255,255,255),2) + # cv2.putText(frame,'finger6',tuple(finger[5]),self.font,2,(255,255,255),2) + # cv2.putText(frame,'finger7',tuple(finger[6]),self.font,2,(255,255,255),2) + # cv2.putText(frame,'finger8',tuple(finger[7]),self.font,2,(255,255,255),2) + x,y,w,h = self.hands[0]['bounding_rect'] + img = cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) + + cv2.drawContours(frame, [self.hands[0]['hull']], -1, (255, 255, 255), 2) + + ##### Show final image ######## + cv2.imshow('Dilation', frame) + ############################### + + # Print execution time + # print time.time()-start_time + + # close the output video by pressing 'ESC' + k = cv2.waitKey(5) & 0xFF + if k == 27: + break + + def detect_hands(self, ret, frame): + + if ret: + # Create a binary image with where white will be skin colors and rest is black + hand_mask = self.create_hands_mask(frame) + cv2.imshow("hand_mask", hand_mask) + + # Kernel matrices for morphological transformation + kernel_square = np.ones((11, 11), np.uint8) + kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + + # Perform morphological transformations to filter out the background noise + # Dilation increase skin color area + # Erosion increase skin color area + dilation = cv2.dilate(hand_mask, kernel_ellipse, iterations=1) + erosion = cv2.erode(dilation, kernel_square, iterations=1) + dilation2 = cv2.dilate(erosion, kernel_ellipse, iterations=1) + filtered = cv2.medianBlur(dilation2, 5) + kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8, 8)) + dilation2 = cv2.dilate(filtered, kernel_ellipse, iterations=1) + kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + dilation3 = cv2.dilate(filtered, kernel_ellipse, iterations=1) + median = cv2.medianBlur(dilation2, 5) + ret, thresh = cv2.threshold(median, 127, 255, 0) + + # Find contours of the filtered frame + _, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + + # Draw Contours + # cv2.drawContours(frame, cnt, -1, (122,122,0), 3) + cv2.imshow('Dilation', median) + + if contours: # Find Max contour area (Assume that hand is in the frame) - max_area = 100 - ci = 0 - for i in range(len(contours)): - cnt = contours[i] - area = cv2.contourArea(cnt) - if (area > max_area): - max_area = area - ci = i - - # Largest area contour - cnts = contours[ci] - - # Find convex hull - hull = cv2.convexHull(cnts) - - # Find convex defects - hull2 = cv2.convexHull(cnts, returnPoints=False) - defects = cv2.convexityDefects(cnts, hull2) - - # Get defect points and draw them in the original image - FarDefect = [] - print defects.shape - if defects.shape[0]>13: - for i in range(defects.shape[0]): - s, e, f, d = defects[i, 0] - start = tuple(cnts[s][0]) - end = tuple(cnts[e][0]) - far = tuple(cnts[f][0]) - FarDefect.append(far) - cv2.line(frame, start, end, [0, 255, 0], 1) - cv2.circle(frame, far, 10, [100, 255, 255], 3) - - # Find moments of the largest contour - moments = cv2.moments(cnts) - - # Central mass of first order moments - if moments['m00'] != 0: - cx = int(moments['m10'] / moments['m00']) # cx = M10/M00 - cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 - centerMass = (cx, cy) - - # Draw center mass - cv2.circle(frame, centerMass, 7, [100, 0, 255], 2) - font = cv2.FONT_HERSHEY_SIMPLEX - cv2.putText(frame, 'Center', tuple(centerMass), font, 2, (255, 255, 255), 2) - - # Distance from each finger defect(finger webbing) to the center mass - distanceBetweenDefectsToCenter = [] - for i in range(0, len(FarDefect)): - x = np.array(FarDefect[i]) - centerMass = np.array(centerMass) - distance = np.sqrt(np.power(x[0] - centerMass[0], 2) + np.power(x[1] - centerMass[1], 2)) - distanceBetweenDefectsToCenter.append(distance) - - # Get an average of three shortest distances from finger webbing to center mass - sortedDefectsDistances = sorted(distanceBetweenDefectsToCenter) - AverageDefectDistance = np.mean(sortedDefectsDistances[0:2]) - - # Get fingertip points from contour hull - # If points are in proximity of 80 pixels, consider as a single point in the group - finger = [] - for i in range(0, len(hull) - 1): - if (np.absolute(hull[i][0][0] - hull[i + 1][0][0]) > 80) or ( - np.absolute(hull[i][0][1] - hull[i + 1][0][1]) > 80): - if hull[i][0][1] < 500: - finger.append(hull[i][0]) - - # The fingertip points are 5 hull points with largest y coordinates - finger = sorted(finger, key=lambda x: x[1]) - fingers = finger[0:5] - - # Calculate distance of each finger tip to the center mass - fingerDistance = [] - for i in range(0, len(fingers)): - distance = np.sqrt(np.power(fingers[i][0] - centerMass[0], 2) + np.power(fingers[i][1] - centerMass[0], 2)) - fingerDistance.append(distance) - - # Finger is pointed/raised if the distance of between fingertip to the center mass is larger - # than the distance of average finger webbing to center mass by 130 pixels - result = 0 - for i in range(0, len(fingers)): - if fingerDistance[i] > AverageDefectDistance + 130: - result = result + 1 - - # Print number of pointed fingers - cv2.putText(frame, str(result), (100, 100), font, 2, (255, 255, 255), 2) - - # show height raised fingers - # cv2.putText(frame,'finger1',tuple(finger[0]),font,2,(255,255,255),2) - # cv2.putText(frame,'finger2',tuple(finger[1]),font,2,(255,255,255),2) - # cv2.putText(frame,'finger3',tuple(finger[2]),font,2,(255,255,255),2) - # cv2.putText(frame,'finger4',tuple(finger[3]),font,2,(255,255,255),2) - # cv2.putText(frame,'finger5',tuple(finger[4]),font,2,(255,255,255),2) - # cv2.putText(frame,'finger6',tuple(finger[5]),font,2,(255,255,255),2) - # cv2.putText(frame,'finger7',tuple(finger[6]),font,2,(255,255,255),2) - # cv2.putText(frame,'finger8',tuple(finger[7]),font,2,(255,255,255),2) - - # Print bounding rectangle - x, y, w, h = cv2.boundingRect(cnts) - img = cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) - - cv2.drawContours(frame, [hull], -1, (255, 255, 255), 2) - - ##### Show final image ######## - cv2.imshow('Dilation', frame) - ############################### - - # Print execution time - # print time.time()-start_time - - # close the output video by pressing 'ESC' - k = cv2.waitKey(5) & 0xFF - if k == 27: - break + max_area = 100 + ci = 0 + for i in range(len(contours)): + cnt = contours[i] + area = cv2.contourArea(cnt) + if (area > max_area): + max_area = area + ci = i + + # Largest area contour + cnts = contours[ci] + + # Find convex hull + hull = cv2.convexHull(cnts) + + # Find convex defects + hull2 = cv2.convexHull(cnts, returnPoints=False) + defects = cv2.convexityDefects(cnts, hull2) + + # Get defect points and draw them in the original image + FarDefect = [] + # print defects.shape + if defects.shape[0] > 13: + for i in range(defects.shape[0]): + s, e, f, d = defects[i, 0] + start = tuple(cnts[s][0]) + end = tuple(cnts[e][0]) + far = tuple(cnts[f][0]) + FarDefect.append(far) + cv2.line(frame, start, end, [0, 255, 0], 1) + cv2.circle(frame, far, 10, [100, 255, 255], 3) + + # Find moments of the largest contour + moments = cv2.moments(cnts) + + # Central mass of first order moments + if moments['m00'] != 0: + cx = int(moments['m10'] / moments['m00']) # cx = M10/M00 + cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 + centerMass = (cx, cy) + + # Draw center mass + cv2.circle(frame, centerMass, 7, [100, 0, 255], 2) + cv2.putText(frame, 'Center', tuple(centerMass), self.font, 2, (255, 255, 255), 2) + + # Distance from each finger defect(finger webbing) to the center mass + distanceBetweenDefectsToCenter = [] + for i in range(0, len(FarDefect)): + x = np.array(FarDefect[i]) + centerMass = np.array(centerMass) + distance = np.sqrt(np.power(x[0] - centerMass[0], 2) + np.power(x[1] - centerMass[1], 2)) + distanceBetweenDefectsToCenter.append(distance) + + # Get an average of three shortest distances from finger webbing to center mass + sortedDefectsDistances = sorted(distanceBetweenDefectsToCenter) + AverageDefectDistance = np.mean(sortedDefectsDistances[0:2]) + + # Get fingertip points from contour hull + # If points are in proximity of 80 pixels, consider as a single point in the group + finger = [] + for i in range(0, len(hull) - 1): + if (np.absolute(hull[i][0][0] - hull[i + 1][0][0]) > 80) or ( + np.absolute(hull[i][0][1] - hull[i + 1][0][1]) > 80): + if hull[i][0][1] < 500: + finger.append(hull[i][0]) + + # The fingertip points are 5 hull points with largest y coordinates + finger = sorted(finger, key=lambda x: x[1]) + fingers = finger[0:5] + + # Calculate distance of each finger tip to the center mass + finger_distances = [] + for i in range(0, len(fingers)): + distance = np.sqrt( + np.power(fingers[i][0] - centerMass[0], 2) + np.power(fingers[i][1] - centerMass[0], 2)) + finger_distances.append(distance) + hand={ + 'fingers': fingers, + 'center_of_mass':centerMass, + 'finger_distances':finger_distances, + 'bounding_rect': (cv2.boundingRect(cnts)), + 'average_defect_distance': AverageDefectDistance, + 'hull': hull + } + self.hands.append(hand) + + def exit(self): self.capture.release() From 63e6cd50b3b1c386a842b1f46d362648c9de16a8 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Mon, 18 Jun 2018 09:46:56 +0200 Subject: [PATCH 04/54] References updated --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index b332d30..578c842 100644 --- a/README.md +++ b/README.md @@ -23,4 +23,5 @@ To increase accuracy of the gesture recognition, it is recommended to run the co References: https://github.com/lzane/Fingers-Detection-using-OpenCV-and-Python/tree/py2_opencv2 https://github.com/sashagaz/Hand_Detection +http://www.tmroyal.com/a-high-level-description-of-two-fingertip-tracking-techniques-k-curvature-and-convexity-defects.html From 78d1d795476eac73a87cfda84ba495757c5a7dd5 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Mon, 18 Jun 2018 17:02:12 +0200 Subject: [PATCH 05/54] New reference --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 578c842..7222a02 100644 --- a/README.md +++ b/README.md @@ -24,4 +24,4 @@ References: https://github.com/lzane/Fingers-Detection-using-OpenCV-and-Python/tree/py2_opencv2 https://github.com/sashagaz/Hand_Detection http://www.tmroyal.com/a-high-level-description-of-two-fingertip-tracking-techniques-k-curvature-and-convexity-defects.html - +http://fivedots.coe.psu.ac.th/~ad/jg/nui055/ From a6a15d7f6ccca30f27066e0504fb1bb3ade951d7 Mon Sep 17 00:00:00 2001 From: orensbruli Date: Mon, 18 Jun 2018 19:27:12 +0200 Subject: [PATCH 06/54] Improved detection of hand fingertips and hand intertips. Added a new substract method using first frames to get the background --- HandDetection.py | 193 +++++++++++++++++++++++++++++++++-------------- 1 file changed, 136 insertions(+), 57 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index f1d8fbb..cbdefe1 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -1,3 +1,6 @@ +import copy +import math + import cv2 import numpy as np import time @@ -9,7 +12,7 @@ def nothing(x): # Function to find angle between two vectors -def Angle(v1, v2): +def calculate_angle(v1, v2): dot = np.dot(v1, v2) x_modulus = np.sqrt((v1 * v1).sum()) y_modulus = np.sqrt((v2 * v2).sum()) @@ -19,7 +22,7 @@ def Angle(v1, v2): # Function to find distance between two points in a list of lists -def FindDistance(A, B): +def find_distance(A, B): return np.sqrt(np.power((A[0][0] - B[0][0]), 2) + np.power((A[0][1] - B[0][1]), 2)) @@ -41,12 +44,14 @@ def __init__(self): self.capture = cv2.VideoCapture(0) self.hands = [] #[{"fingers":None, "center_of_mass":None}] self.font = cv2.FONT_HERSHEY_SIMPLEX + self.first_frame = None + self.discarded_frames = 15 # Decrease frame size # self.capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1000) # self.capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 600) def compute(self): - while (True): + while True: # Measure execution time start_time = time.time() @@ -59,22 +64,21 @@ def compute(self): # Finger is pointed/raised if the distance of between fingertip to the center mass is larger # than the distance of average finger webbing to center mass by 130 pixels - result = 0 if len(self.hands) > 0: - for i in range(0, len(self.hands[0]["fingers"])): - if self.hands[0]['finger_distance'][i] > self.hands[0]['average_defects_distance'] + 130: - result = result + 1 + for i in range(0, len(self.hands[0]["fingertips"])): + cv2.putText(frame, 'finger'+str(i), tuple(self.hands[0]["fingertips"][i]), self.font, 0.5, (255, 255, 255), + 1) # Print number of pointed fingers - cv2.putText(frame, str(result), (100, 100), self.font, 2, (255, 255, 255), 2) + cv2.putText(frame, str(len(self.hands[0]["fingertips"])), (100, 100), self.font, 2, (0, 0, 0), 2) # show height raised fingers - # cv2.putText(frame,'finger1',tuple(finger[0]),self.font,2,(255,255,255),2) - # cv2.putText(frame,'finger2',tuple(finger[1]),self.font,2,(255,255,255),2) - # cv2.putText(frame,'finger3',tuple(finger[2]),self.font,2,(255,255,255),2) - # cv2.putText(frame,'finger4',tuple(finger[3]),self.font,2,(255,255,255),2) - # cv2.putText(frame,'finger5',tuple(finger[4]),self.font,2,(255,255,255),2) - # cv2.putText(frame,'finger6',tuple(finger[5]),self.font,2,(255,255,255),2) + # cv2.putText(frame,'finger1',tuple(self.hands[0]["fingers"][0]),self.font,2,(255,255,255),2) + # cv2.putText(frame,'finger2',tuple(self.hands[0]["fingers"][1]),self.font,2,(255,255,255),2) + # cv2.putText(frame,'finger3',tuple(self.hands[0]["fingers"][2]),self.font,2,(255,255,255),2) + # cv2.putText(frame,'finger4',tuple(self.hands[0]["fingers"][3]),self.font,2,(255,255,255),2) + # cv2.putText(frame,'finger5',tuple(self.hands[0]["fingers"][4]),self.font,2,(255,255,255),2) + # cv2.putText(frame,'finger6',tuple(self.hands[0]["fingers"][5]),self.font,2,(255,255,255),2) # cv2.putText(frame,'finger7',tuple(finger[6]),self.font,2,(255,255,255),2) # cv2.putText(frame,'finger8',tuple(finger[7]),self.font,2,(255,255,255),2) x,y,w,h = self.hands[0]['bounding_rect'] @@ -94,11 +98,39 @@ def compute(self): if k == 27: break - def detect_hands(self, ret, frame): + # def calculate_hand_interst_points(self, frame, cnts): + # # convexity defect + # + # drawing = copy.deepcopy(frame) + # hull = cv2.convexHull(cnts, returnPoints=False) + # if len(hull) > 3: + # defects = cv2.convexityDefects(cnts, hull) + # if type(defects) != type(None): # avoid crashing. (BUG not found) + # + # cnt = 0 + # for i in range(defects.shape[0]): # calculate the angle + # s, e, f, d = defects[i][0] + # start = tuple(cnts[s][0]) + # end = tuple(cnts[e][0]) + # far = tuple(cnts[f][0]) + # a = math.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2) + # b = math.sqrt((far[0] - start[0]) ** 2 + (far[1] - start[1]) ** 2) + # c = math.sqrt((end[0] - far[0]) ** 2 + (end[1] - far[1]) ** 2) + # angle = math.acos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c)) # cosine theorem + # if angle <= math.pi / 2: # angle less than 90 degree, treat as fingers + # cnt += 1 + # cv2.circle(drawing, far, 8, [211, 84, 0], -1) + # interdigs.append(far) + # cv2.imshow("detect_fingers", drawing) + # return interdigs + def detect_hands(self, ret, frame): if ret: + self.hands = [] # Create a binary image with where white will be skin colors and rest is black hand_mask = self.create_hands_mask(frame) + if hand_mask is None: + return cv2.imshow("hand_mask", hand_mask) # Kernel matrices for morphological transformation @@ -124,7 +156,6 @@ def detect_hands(self, ret, frame): # Draw Contours # cv2.drawContours(frame, cnt, -1, (122,122,0), 3) - cv2.imshow('Dilation', median) if contours: # Find Max contour area (Assume that hand is in the frame) @@ -140,6 +171,8 @@ def detect_hands(self, ret, frame): # Largest area contour cnts = contours[ci] + # self.detect_fingers(frame, cnts) + # Find convex hull hull = cv2.convexHull(cnts) @@ -147,10 +180,24 @@ def detect_hands(self, ret, frame): hull2 = cv2.convexHull(cnts, returnPoints=False) defects = cv2.convexityDefects(cnts, hull2) + cv2.drawContours(frame, [cnts], 0, (255,0, 0), 2) + # cv2.drawContours(frame, [hull], 0, (0, 0, 255), 3) + + # Find moments of the largest contour + moments = cv2.moments(cnts) + + # Central mass of first order moments + if moments['m00'] != 0: + cx = int(moments['m10'] / moments['m00']) # cx = M10/M00 + cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 + centerMass = (cx, cy) + # Get defect points and draw them in the original image - FarDefect = [] - # print defects.shape - if defects.shape[0] > 13: + if defects is not None: + intertips_coords = [] + FarDefect = [] + fingertips_coords = [] + distanceBetweenDefectsToCenter = [] for i in range(defects.shape[0]): s, e, f, d = defects[i, 0] start = tuple(cnts[s][0]) @@ -158,54 +205,73 @@ def detect_hands(self, ret, frame): far = tuple(cnts[f][0]) FarDefect.append(far) cv2.line(frame, start, end, [0, 255, 0], 1) - cv2.circle(frame, far, 10, [100, 255, 255], 3) - - # Find moments of the largest contour - moments = cv2.moments(cnts) + a = math.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2) + b = math.sqrt((far[0] - start[0]) ** 2 + (far[1] - start[1]) ** 2) + c = math.sqrt((end[0] - far[0]) ** 2 + (end[1] - far[1]) ** 2) + angle = math.acos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c)) # cosine theorem + #get tips and intertips coordinates + if angle <= math.pi / 2: # angle less than 90 degree, treat as fingers + cnt += 1 + cv2.circle(frame, far, 8, [211, 84, 0], -1) + intertips_coords.append(far) + if len(fingertips_coords) > 0: + from scipy.spatial import distance + #calculate distances from start and end to the already known tips + start_distance, end_distance = tuple(distance.cdist(fingertips_coords, [start, end]).min(axis=0)) + + if start_distance> 10: + fingertips_coords.append(start) + cv2.circle(frame, start, 10, [255, 100, 255], 3) + if end_distance>10: + fingertips_coords.append(end) + cv2.circle(frame, end, 10, [255, 100, 255], 3) + else: + fingertips_coords.append(start) + cv2.circle(frame, start, 10, [255, 100, 255], 3) + fingertips_coords.append(end) + cv2.circle(frame, end, 10, [255, 100, 255], 3) + + # Distance from each finger defect(finger webbing) to the center mass + x = np.array(far) + center_mass_array = np.array(centerMass) + distance = np.sqrt( + np.power(x[0] - center_mass_array[0], 2) + np.power(x[1] - center_mass_array[1], 2)) + distanceBetweenDefectsToCenter.append(distance) + # cv2.circle(frame, far, 10, [100, 255, 255], 3) - # Central mass of first order moments - if moments['m00'] != 0: - cx = int(moments['m10'] / moments['m00']) # cx = M10/M00 - cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 - centerMass = (cx, cy) # Draw center mass cv2.circle(frame, centerMass, 7, [100, 0, 255], 2) cv2.putText(frame, 'Center', tuple(centerMass), self.font, 2, (255, 255, 255), 2) - # Distance from each finger defect(finger webbing) to the center mass - distanceBetweenDefectsToCenter = [] - for i in range(0, len(FarDefect)): - x = np.array(FarDefect[i]) - centerMass = np.array(centerMass) - distance = np.sqrt(np.power(x[0] - centerMass[0], 2) + np.power(x[1] - centerMass[1], 2)) - distanceBetweenDefectsToCenter.append(distance) # Get an average of three shortest distances from finger webbing to center mass sortedDefectsDistances = sorted(distanceBetweenDefectsToCenter) AverageDefectDistance = np.mean(sortedDefectsDistances[0:2]) - # Get fingertip points from contour hull - # If points are in proximity of 80 pixels, consider as a single point in the group - finger = [] - for i in range(0, len(hull) - 1): - if (np.absolute(hull[i][0][0] - hull[i + 1][0][0]) > 80) or ( - np.absolute(hull[i][0][1] - hull[i + 1][0][1]) > 80): - if hull[i][0][1] < 500: - finger.append(hull[i][0]) - - # The fingertip points are 5 hull points with largest y coordinates - finger = sorted(finger, key=lambda x: x[1]) - fingers = finger[0:5] + # # Get fingertip points from contour hull + # # If points are in proximity of 80 pixels, consider as a single point in the group + # finger = [] + # for i in range(0, len(hull) - 1): + # if (np.absolute(hull[i][0][0] - hull[i + 1][0][0]) > 10) or ( + # np.absolute(hull[i][0][1] - hull[i + 1][0][1]) > 10): + # if hull[i][0][1] < 500: + # finger.append(hull[i][0]) + # + # + # # The fingertip points are 5 hull points with largest y coordinates + # finger = sorted(finger, key=lambda x: x[1]) + # fingers = finger[0:5] # Calculate distance of each finger tip to the center mass finger_distances = [] - for i in range(0, len(fingers)): + for i in range(0, len(fingertips_coords)): distance = np.sqrt( - np.power(fingers[i][0] - centerMass[0], 2) + np.power(fingers[i][1] - centerMass[0], 2)) + np.power(fingertips_coords[i][0] - centerMass[0], 2) + np.power(fingertips_coords[i][1] - centerMass[0], 2)) finger_distances.append(distance) hand={ - 'fingers': fingers, + 'fingertips': fingertips_coords, + 'intertips':intertips_coords, 'center_of_mass':centerMass, 'finger_distances':finger_distances, 'bounding_rect': (cv2.boundingRect(cnts)), @@ -220,7 +286,7 @@ def exit(self): self.capture.release() cv2.destroyAllWindows() - def create_hands_mask(self, image, mode="color"): + def create_hands_mask(self, image, mode="diff"): mask = None if mode == "color": # Blur the image @@ -228,14 +294,27 @@ def create_hands_mask(self, image, mode="color"): # Convert to HSV color space hsv = cv2.cvtColor(blur, cv2.COLOR_BGR2HSV) mask = cv2.inRange(hsv, np.array([2, 50, 50]), np.array([15, 255, 255])) - elif mode == "background_subtract": - fgbg = cv2.createBackgroundSubtractorMOG2() + elif mode == "MOG2": + fgbg = cv2.createBackgroundSubtractorMOG2(detectShadows=False) + # fgbg= cv2.BackgroundSubtractorMOG2(0, 50) + # fgbg=cv2.BackgroundSubtractor() # fgbg = cv2.BackgroundSubtractorMOG() # gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) - # blur = cv2.medianBlur(gray_image, 5) - cv2.imshow("blurred", image) - # blur = cv2.blur(image, (10, 10)) - mask = fgbg.apply(image,learningRate=0) + # blur = cv2.medianBlur(gray_image, 100) + # blur = cv2.blur(gray_image, (5, 5)) + # cv2.imshow("blurred", blur) + mask = fgbg.apply(image) + elif mode == "diff": + blurred = cv2.GaussianBlur(image, (5, 5), 0) + if self.first_frame is None or self.discarded_frames != 0: + self.discarded_frames -= 1 + self.first_frame = image + else: + diff = cv2.absdiff(blurred, self.first_frame) + # print "diff" + gray_diff = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY) + cv2.imshow("diff", gray_diff) + _, mask = cv2.threshold(gray_diff, 40, 255, cv2.THRESH_BINARY) return mask From d13c6f9c46f58e3743e9cb70e1fbdc34fd464230 Mon Sep 17 00:00:00 2001 From: orensbruli Date: Mon, 18 Jun 2018 19:57:31 +0200 Subject: [PATCH 07/54] Added some TODO comments on the variables that depends on the enviroment (lighting, camera, distances, etc) --- HandDetection.py | 79 +++++++++++++++++++++++++++--------------------- 1 file changed, 44 insertions(+), 35 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index cbdefe1..e5cb18b 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -6,7 +6,6 @@ import time - def nothing(x): pass @@ -42,9 +41,10 @@ class HandDetector(): def __init__(self): # Open Camera object self.capture = cv2.VideoCapture(0) - self.hands = [] #[{"fingers":None, "center_of_mass":None}] + self.hands = [] # [{"fingers":None, "center_of_mass":None}] self.font = cv2.FONT_HERSHEY_SIMPLEX self.first_frame = None + # TODO: ENV_DEPENDENCE: depending on the environment and camera it would be more or less frames to discard self.discarded_frames = 15 # Decrease frame size # self.capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1000) @@ -66,8 +66,9 @@ def compute(self): # than the distance of average finger webbing to center mass by 130 pixels if len(self.hands) > 0: for i in range(0, len(self.hands[0]["fingertips"])): - cv2.putText(frame, 'finger'+str(i), tuple(self.hands[0]["fingertips"][i]), self.font, 0.5, (255, 255, 255), - 1) + cv2.putText(frame, 'finger' + str(i), tuple(self.hands[0]["fingertips"][i]), self.font, 0.5, + (255, 255, 255), + 1) # Print number of pointed fingers cv2.putText(frame, str(len(self.hands[0]["fingertips"])), (100, 100), self.font, 2, (0, 0, 0), 2) @@ -81,7 +82,7 @@ def compute(self): # cv2.putText(frame,'finger6',tuple(self.hands[0]["fingers"][5]),self.font,2,(255,255,255),2) # cv2.putText(frame,'finger7',tuple(finger[6]),self.font,2,(255,255,255),2) # cv2.putText(frame,'finger8',tuple(finger[7]),self.font,2,(255,255,255),2) - x,y,w,h = self.hands[0]['bounding_rect'] + x, y, w, h = self.hands[0]['bounding_rect'] img = cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) cv2.drawContours(frame, [self.hands[0]['hull']], -1, (255, 255, 255), 2) @@ -159,6 +160,7 @@ def detect_hands(self, ret, frame): if contours: # Find Max contour area (Assume that hand is in the frame) + # TODO: ENV_DEPENDENCE: depends on the camera resolution, distance to the background, noisy areas sizes max_area = 100 ci = 0 for i in range(len(contours)): @@ -180,7 +182,7 @@ def detect_hands(self, ret, frame): hull2 = cv2.convexHull(cnts, returnPoints=False) defects = cv2.convexityDefects(cnts, hull2) - cv2.drawContours(frame, [cnts], 0, (255,0, 0), 2) + cv2.drawContours(frame, [cnts], 0, (255, 0, 0), 2) # cv2.drawContours(frame, [hull], 0, (0, 0, 255), 3) # Find moments of the largest contour @@ -209,20 +211,24 @@ def detect_hands(self, ret, frame): b = math.sqrt((far[0] - start[0]) ** 2 + (far[1] - start[1]) ** 2) c = math.sqrt((end[0] - far[0]) ** 2 + (end[1] - far[1]) ** 2) angle = math.acos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c)) # cosine theorem - #get tips and intertips coordinates - if angle <= math.pi / 2: # angle less than 90 degree, treat as fingers + # Get tips and intertips coordinates + # TODO: ENV_DEPENDENCE: this angle > 90ยบ determinate if two points are considerated fingertips or not and 90 make thumb to fail in some occasions + intertips_max_angle = math.pi / 1.7 + if angle <= intertips_max_angle: # angle less than 90 degree, treat as fingers cnt += 1 cv2.circle(frame, far, 8, [211, 84, 0], -1) intertips_coords.append(far) if len(fingertips_coords) > 0: from scipy.spatial import distance - #calculate distances from start and end to the already known tips - start_distance, end_distance = tuple(distance.cdist(fingertips_coords, [start, end]).min(axis=0)) - - if start_distance> 10: + # calculate distances from start and end to the already known tips + start_distance, end_distance = tuple( + distance.cdist(fingertips_coords, [start, end]).min(axis=0)) + # TODO: ENV_DEPENDENCE: it determinate the pixels distance to consider two points the same. It depends on camera resolution and distance from the hand to the camera + same_fingertip_radius = 10 + if start_distance > same_fingertip_radius: fingertips_coords.append(start) cv2.circle(frame, start, 10, [255, 100, 255], 3) - if end_distance>10: + if end_distance > same_fingertip_radius: fingertips_coords.append(end) cv2.circle(frame, end, 10, [255, 100, 255], 3) else: @@ -239,12 +245,10 @@ def detect_hands(self, ret, frame): distanceBetweenDefectsToCenter.append(distance) # cv2.circle(frame, far, 10, [100, 255, 255], 3) - # Draw center mass cv2.circle(frame, centerMass, 7, [100, 0, 255], 2) cv2.putText(frame, 'Center', tuple(centerMass), self.font, 2, (255, 255, 255), 2) - # Get an average of three shortest distances from finger webbing to center mass sortedDefectsDistances = sorted(distanceBetweenDefectsToCenter) AverageDefectDistance = np.mean(sortedDefectsDistances[0:2]) @@ -267,45 +271,50 @@ def detect_hands(self, ret, frame): finger_distances = [] for i in range(0, len(fingertips_coords)): distance = np.sqrt( - np.power(fingertips_coords[i][0] - centerMass[0], 2) + np.power(fingertips_coords[i][1] - centerMass[0], 2)) + np.power(fingertips_coords[i][0] - centerMass[0], 2) + np.power( + fingertips_coords[i][1] - centerMass[0], 2)) finger_distances.append(distance) - hand={ - 'fingertips': fingertips_coords, - 'intertips':intertips_coords, - 'center_of_mass':centerMass, - 'finger_distances':finger_distances, - 'bounding_rect': (cv2.boundingRect(cnts)), - 'average_defect_distance': AverageDefectDistance, - 'hull': hull - } + hand = { + 'fingertips': fingertips_coords, + 'intertips': intertips_coords, + 'center_of_mass': centerMass, + 'finger_distances': finger_distances, + 'bounding_rect': (cv2.boundingRect(cnts)), + 'average_defect_distance': AverageDefectDistance, + 'hull': hull + } self.hands.append(hand) - - def exit(self): self.capture.release() cv2.destroyAllWindows() - def create_hands_mask(self, image, mode="diff"): + def create_hands_mask(self, image, mode="diff"): mask = None if mode == "color": # Blur the image - blur = cv2.blur(image, (3, 3)) + blur_radius = 5 + blurred = cv2.GaussianBlur(image, (blur_radius, blur_radius), 0) # Convert to HSV color space - hsv = cv2.cvtColor(blur, cv2.COLOR_BGR2HSV) + hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV) mask = cv2.inRange(hsv, np.array([2, 50, 50]), np.array([15, 255, 255])) elif mode == "MOG2": + # TODO: not working (it considers everything shadows or moves (too dark background) fgbg = cv2.createBackgroundSubtractorMOG2(detectShadows=False) # fgbg= cv2.BackgroundSubtractorMOG2(0, 50) # fgbg=cv2.BackgroundSubtractor() # fgbg = cv2.BackgroundSubtractorMOG() - # gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + # TODO: ENV_DEPENDENCE: it could depend on the camera quality # blur = cv2.medianBlur(gray_image, 100) - # blur = cv2.blur(gray_image, (5, 5)) + blur_radius = 5 + blurred = cv2.GaussianBlur(image, (blur_radius, blur_radius), 0) # cv2.imshow("blurred", blur) - mask = fgbg.apply(image) + mask = fgbg.apply(blurred) elif mode == "diff": - blurred = cv2.GaussianBlur(image, (5, 5), 0) + # TODO: ENV_DEPENDENCE: it could depend on the camera quality + blur_radius = 5 + blurred = cv2.GaussianBlur(image, (blur_radius, blur_radius), 0) if self.first_frame is None or self.discarded_frames != 0: self.discarded_frames -= 1 self.first_frame = image @@ -323,6 +332,6 @@ def main(): hand_detector.compute() hand_detector.exit() + if __name__ == "__main__": main() - From 9c7f06f6f9f890c2c3c70e5f16d97d2be7a84faa Mon Sep 17 00:00:00 2001 From: orensbruli Date: Mon, 18 Jun 2018 20:18:05 +0200 Subject: [PATCH 08/54] Added new reference --- README.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/README.md b/README.md index 7222a02..d3bb6f0 100644 --- a/README.md +++ b/README.md @@ -21,7 +21,13 @@ When running the python file, you can expect to see the real time frame from you To increase accuracy of the gesture recognition, it is recommended to run the code in a bright light room. Additionally, the code can analyze one hand (either left or right), and the hand needs to be in front of the camera. References: + https://github.com/lzane/Fingers-Detection-using-OpenCV-and-Python/tree/py2_opencv2 + https://github.com/sashagaz/Hand_Detection + http://www.tmroyal.com/a-high-level-description-of-two-fingertip-tracking-techniques-k-curvature-and-convexity-defects.html + http://fivedots.coe.psu.ac.th/~ad/jg/nui055/ + +https://link.springer.com/article/10.1007/s10489-015-0680-z From a41b55493fac49c58162c36af2ce5d496066e007 Mon Sep 17 00:00:00 2001 From: orensbruli Date: Tue, 19 Jun 2018 17:56:10 +0200 Subject: [PATCH 09/54] Created new mixed mode with color and background substraction to get the hand mask. --- HandDetection.py | 119 ++++++++++++++++++++++++++++------------------- 1 file changed, 71 insertions(+), 48 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index e5cb18b..7d99070 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -1,4 +1,6 @@ -import copy +#!/usr/bin/env python +# -*- coding: utf-8 -*- + import math import cv2 @@ -129,10 +131,10 @@ def detect_hands(self, ret, frame): if ret: self.hands = [] # Create a binary image with where white will be skin colors and rest is black - hand_mask = self.create_hands_mask(frame) - if hand_mask is None: + hands_mask = self.create_hands_mask(frame) + if hands_mask is None: return - cv2.imshow("hand_mask", hand_mask) + cv2.imshow("hands_mask", hands_mask) # Kernel matrices for morphological transformation kernel_square = np.ones((11, 11), np.uint8) @@ -141,7 +143,7 @@ def detect_hands(self, ret, frame): # Perform morphological transformations to filter out the background noise # Dilation increase skin color area # Erosion increase skin color area - dilation = cv2.dilate(hand_mask, kernel_ellipse, iterations=1) + dilation = cv2.dilate(hands_mask, kernel_ellipse, iterations=1) erosion = cv2.erode(dilation, kernel_square, iterations=1) dilation2 = cv2.dilate(erosion, kernel_ellipse, iterations=1) filtered = cv2.medianBlur(dilation2, 5) @@ -149,7 +151,7 @@ def detect_hands(self, ret, frame): dilation2 = cv2.dilate(filtered, kernel_ellipse, iterations=1) kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) dilation3 = cv2.dilate(filtered, kernel_ellipse, iterations=1) - median = cv2.medianBlur(dilation2, 5) + median = cv2.medianBlur(dilation3, 5) ret, thresh = cv2.threshold(median, 127, 255, 0) # Find contours of the filtered frame @@ -170,23 +172,23 @@ def detect_hands(self, ret, frame): max_area = area ci = i - # Largest area contour - cnts = contours[ci] + # Largest area contour + hand_contour = contours[ci] # self.detect_fingers(frame, cnts) # Find convex hull - hull = cv2.convexHull(cnts) + hull = cv2.convexHull(hand_contour) # Find convex defects - hull2 = cv2.convexHull(cnts, returnPoints=False) - defects = cv2.convexityDefects(cnts, hull2) + hull2 = cv2.convexHull(hand_contour, returnPoints=False) + defects = cv2.convexityDefects(hand_contour, hull2) - cv2.drawContours(frame, [cnts], 0, (255, 0, 0), 2) + cv2.drawContours(frame, [hand_contour], 0, (255, 0, 0), 2) # cv2.drawContours(frame, [hull], 0, (0, 0, 255), 3) # Find moments of the largest contour - moments = cv2.moments(cnts) + moments = cv2.moments(hand_contour) # Central mass of first order moments if moments['m00'] != 0: @@ -202,9 +204,9 @@ def detect_hands(self, ret, frame): distanceBetweenDefectsToCenter = [] for i in range(defects.shape[0]): s, e, f, d = defects[i, 0] - start = tuple(cnts[s][0]) - end = tuple(cnts[e][0]) - far = tuple(cnts[f][0]) + start = tuple(hand_contour[s][0]) + end = tuple(hand_contour[e][0]) + far = tuple(hand_contour[f][0]) FarDefect.append(far) cv2.line(frame, start, end, [0, 255, 0], 1) a = math.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2) @@ -279,7 +281,7 @@ def detect_hands(self, ret, frame): 'intertips': intertips_coords, 'center_of_mass': centerMass, 'finger_distances': finger_distances, - 'bounding_rect': (cv2.boundingRect(cnts)), + 'bounding_rect': (cv2.boundingRect(hand_contour)), 'average_defect_distance': AverageDefectDistance, 'hull': hull } @@ -289,43 +291,64 @@ def exit(self): self.capture.release() cv2.destroyAllWindows() - def create_hands_mask(self, image, mode="diff"): + def create_hands_mask(self, image, mode="mixed"): mask = None if mode == "color": - # Blur the image - blur_radius = 5 - blurred = cv2.GaussianBlur(image, (blur_radius, blur_radius), 0) - # Convert to HSV color space - hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV) - mask = cv2.inRange(hsv, np.array([2, 50, 50]), np.array([15, 255, 255])) + mask = self.get_color_mask(image) elif mode == "MOG2": - # TODO: not working (it considers everything shadows or moves (too dark background) - fgbg = cv2.createBackgroundSubtractorMOG2(detectShadows=False) - # fgbg= cv2.BackgroundSubtractorMOG2(0, 50) - # fgbg=cv2.BackgroundSubtractor() - # fgbg = cv2.BackgroundSubtractorMOG() - gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) - # TODO: ENV_DEPENDENCE: it could depend on the camera quality - # blur = cv2.medianBlur(gray_image, 100) - blur_radius = 5 - blurred = cv2.GaussianBlur(image, (blur_radius, blur_radius), 0) - # cv2.imshow("blurred", blur) - mask = fgbg.apply(blurred) + mask = self.get_MOG2_mask(image) elif mode == "diff": - # TODO: ENV_DEPENDENCE: it could depend on the camera quality - blur_radius = 5 - blurred = cv2.GaussianBlur(image, (blur_radius, blur_radius), 0) - if self.first_frame is None or self.discarded_frames != 0: - self.discarded_frames -= 1 - self.first_frame = image - else: - diff = cv2.absdiff(blurred, self.first_frame) - # print "diff" - gray_diff = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY) - cv2.imshow("diff", gray_diff) - _, mask = cv2.threshold(gray_diff, 40, 255, cv2.THRESH_BINARY) + mask = self.get_simple_diff_mask(image) + elif mode == "mixed": + diff_mask = self.get_simple_diff_mask(image) + + color_mask = self.get_color_mask(image) + + if diff_mask is not None and color_mask is not None: + mask = cv2.bitwise_and(diff_mask,color_mask) + cv2.imshow("diff_mask", diff_mask) + cv2.imshow("color_mask", color_mask) + return mask + + def get_color_mask(self, image): + # Blur the image + blur_radius = 5 + blurred = cv2.GaussianBlur(image, (blur_radius, blur_radius), 0) + # Convert to HSV color space + hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV) + mask = cv2.inRange(hsv, np.array([2, 50, 50]), np.array([15, 255, 255])) return mask + def get_simple_diff_mask(self, image): + mask = None + # TODO: ENV_DEPENDENCE: it could depend on the camera quality + blur_radius = 5 + blurred = cv2.GaussianBlur(image, (blur_radius, blur_radius), 0) + if self.first_frame is None or self.discarded_frames != 0: + self.discarded_frames -= 1 + self.first_frame = image + else: + diff = cv2.absdiff(blurred, self.first_frame) + # print "diff" + gray_diff = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY) + cv2.imshow("diff", gray_diff) + _, mask = cv2.threshold(gray_diff, 40, 255, cv2.THRESH_BINARY) + return mask + + def get_MOG2_mask(self, image): + # TODO: not working (it considers everything shadows or moves (too dark background) + fgbg = cv2.createBackgroundSubtractorMOG2(detectShadows=False) + # fgbg= cv2.BackgroundSubtractorMOG2(0, 50) + # fgbg=cv2.BackgroundSubtractor() + # fgbg = cv2.BackgroundSubtractorMOG() + gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + # TODO: ENV_DEPENDENCE: it could depend on the camera quality + # blur = cv2.medianBlur(gray_image, 100) + blur_radius = 5 + blurred = cv2.GaussianBlur(image, (blur_radius, blur_radius), 0) + # cv2.imshow("blurred", blur) + mask = fgbg.apply(blurred) + return mask def main(): hand_detector = HandDetector() From 3e0c85e95daa95f4de50aa3ced615f0f04d97564 Mon Sep 17 00:00:00 2001 From: orensbruli Date: Thu, 21 Jun 2018 21:38:03 +0200 Subject: [PATCH 10/54] Many changes. Added multiple hands tracking. Added tracking methods. Added ID to the hand structure. --- HandDetection.py | 852 +++++++++++++++++++++++++++++++++++++---------- 1 file changed, 682 insertions(+), 170 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index 7d99070..8c3d1a2 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -2,10 +2,15 @@ # -*- coding: utf-8 -*- import math +from copy import copy, deepcopy import cv2 import numpy as np import time +from collections import deque + +from numpy import trace +from scipy.linalg._fblas import ccopy def nothing(x): @@ -40,67 +45,131 @@ def find_distance(A, B): class HandDetector(): - def __init__(self): + def __init__(self, source=0): # Open Camera object - self.capture = cv2.VideoCapture(0) + # self.capture = cv2.VideoCapture(0) + # TODO: For testing only + self.capture = cv2.VideoCapture(source) self.hands = [] # [{"fingers":None, "center_of_mass":None}] self.font = cv2.FONT_HERSHEY_SIMPLEX self.first_frame = None + self.next_hand_id = 0 # TODO: ENV_DEPENDENCE: depending on the environment and camera it would be more or less frames to discard - self.discarded_frames = 15 + self.discarded_frames = 10 + self.last_frames = deque(maxlen=self.discarded_frames) # Decrease frame size # self.capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1000) # self.capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 600) def compute(self): - while True: + while self.capture.isOpened(): # Measure execution time start_time = time.time() # Capture frames from the camera ret, frame = self.capture.read() - cv2.imshow("initial", frame) - - self.detect_hands(ret, frame) - - # Finger is pointed/raised if the distance of between fingertip to the center mass is larger - # than the distance of average finger webbing to center mass by 130 pixels - if len(self.hands) > 0: - for i in range(0, len(self.hands[0]["fingertips"])): - cv2.putText(frame, 'finger' + str(i), tuple(self.hands[0]["fingertips"][i]), self.font, 0.5, - (255, 255, 255), - 1) - - # Print number of pointed fingers - cv2.putText(frame, str(len(self.hands[0]["fingertips"])), (100, 100), self.font, 2, (0, 0, 0), 2) - - # show height raised fingers - # cv2.putText(frame,'finger1',tuple(self.hands[0]["fingers"][0]),self.font,2,(255,255,255),2) - # cv2.putText(frame,'finger2',tuple(self.hands[0]["fingers"][1]),self.font,2,(255,255,255),2) - # cv2.putText(frame,'finger3',tuple(self.hands[0]["fingers"][2]),self.font,2,(255,255,255),2) - # cv2.putText(frame,'finger4',tuple(self.hands[0]["fingers"][3]),self.font,2,(255,255,255),2) - # cv2.putText(frame,'finger5',tuple(self.hands[0]["fingers"][4]),self.font,2,(255,255,255),2) - # cv2.putText(frame,'finger6',tuple(self.hands[0]["fingers"][5]),self.font,2,(255,255,255),2) - # cv2.putText(frame,'finger7',tuple(finger[6]),self.font,2,(255,255,255),2) - # cv2.putText(frame,'finger8',tuple(finger[7]),self.font,2,(255,255,255),2) - x, y, w, h = self.hands[0]['bounding_rect'] - img = cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) - - cv2.drawContours(frame, [self.hands[0]['hull']], -1, (255, 255, 255), 2) - - ##### Show final image ######## - cv2.imshow('Dilation', frame) - ############################### - - # Print execution time - # print time.time()-start_time + if ret is True: + new_hands = self.detect_hands(frame) + for hand1 in new_hands: + if len(self.hands) > 0: + existing_hand = False + for hand2 in self.hands: + intersection_value, _ = self.calculate_bounding_rects_intersection(hand1['bounding_rect'], hand2['bounding_rect']) + if intersection_value >0.1 : + existing_hand=True + break + if not existing_hand: + hand1['ID']=str(self.next_hand_id) + self.next_hand_id+=1 + self.hands.append(hand1) + print "New hand" + else: + hand1['ID'] = str(self.next_hand_id) + self.next_hand_id += 1 + self.hands.append(hand1) + print "New hand" + + if len(self.hands) > 0: + for index, hand in enumerate(self.hands): + hands_mask = self.create_hands_mask(frame) + ret, bounding_rect = self.follow(frame,hands_mask,hand["bounding_rect"]) + if ret: + print "updating hand" + hand["bounding_rect"] = bounding_rect + updated_hand = self.update_hand_charasteristics(frame, hand) + if updated_hand is not None: + self.hands[index] = updated_hand + else: + #TODO: How to decided when to remove + if hand['tracking_fails'] > 10: + print "removing hand" + self.hands.remove(hand) + else: + hand['tracking_fails']+=1 + else: + # TODO: How to decided when to remove + if hand['tracking_fails'] > 10: + print "removing hand" + self.hands.remove(hand) + else: + hand['tracking_fails'] += 1 + + + # Finger is pointed/raised if the distance of between fingertip to the center mass is larger + # than the distance of average finger webbing to center mass by 130 pixels + overlayed_frame = frame.copy() + for hand in self.hands: + overlayed_frame = self.draw_hand_overlay(frame, hand) + pass + + ##### Show final image ######## + cv2.imshow('Detection', overlayed_frame) + ############################### + # Print execution time + # print time.time()-start_time + else: + print "No video detected" # close the output video by pressing 'ESC' k = cv2.waitKey(5) & 0xFF if k == 27: break + + def calculate_bounding_rects_intersection(self, bounding_rect_1, bounding_rect_2): + b1_x_left = bounding_rect_1[0] + b1_y_top = bounding_rect_1[1] + b1_x_right = bounding_rect_1[0] + bounding_rect_1[2] + b1_y_bottom = bounding_rect_1[1] + bounding_rect_1[3] + b2_x_left = bounding_rect_2[0] + b2_y_top = bounding_rect_2[1] + b2_x_right = bounding_rect_2[0] + bounding_rect_2[2] + b2_y_bottom = bounding_rect_2[1] + bounding_rect_2[3] + x_left = max(b1_x_left, b2_x_left) + y_top = max(b1_y_top, b2_y_top) + x_right = min(b1_x_right, b2_x_right) + y_bottom = min(b1_y_bottom, b2_y_bottom) + + if x_left > x_right or y_top > y_bottom: + return 0, None + + # compute the area of intersection rectangle + interArea = (x_right - x_left + 1) * (y_bottom - y_top + 1) + + # compute the area of both the prediction and ground-truth + # rectangles + boxAArea = (b1_x_right - b1_x_left + 1) * (b1_y_bottom - b1_y_top + 1) + boxBArea = (b2_x_right - b2_x_left + 1) * (b2_y_bottom - b2_y_top + 1) + + # compute the intersection over union by taking the intersection + # area and dividing it by the sum of prediction + ground-truth + # areas - the interesection area + iou = interArea / float(boxAArea + boxBArea - interArea) + + # return the intersection over union value + return iou, (x_left, y_top, x_right, y_bottom) + # def calculate_hand_interst_points(self, frame, cnts): # # convexity defect # @@ -127,99 +196,416 @@ def compute(self): # cv2.imshow("detect_fingers", drawing) # return interdigs - def detect_hands(self, ret, frame): - if ret: - self.hands = [] - # Create a binary image with where white will be skin colors and rest is black - hands_mask = self.create_hands_mask(frame) - if hands_mask is None: - return - cv2.imshow("hands_mask", hands_mask) - - # Kernel matrices for morphological transformation - kernel_square = np.ones((11, 11), np.uint8) - kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) - - # Perform morphological transformations to filter out the background noise - # Dilation increase skin color area - # Erosion increase skin color area - dilation = cv2.dilate(hands_mask, kernel_ellipse, iterations=1) - erosion = cv2.erode(dilation, kernel_square, iterations=1) - dilation2 = cv2.dilate(erosion, kernel_ellipse, iterations=1) - filtered = cv2.medianBlur(dilation2, 5) - kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8, 8)) - dilation2 = cv2.dilate(filtered, kernel_ellipse, iterations=1) - kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) - dilation3 = cv2.dilate(filtered, kernel_ellipse, iterations=1) - median = cv2.medianBlur(dilation3, 5) - ret, thresh = cv2.threshold(median, 127, 255, 0) - - # Find contours of the filtered frame - _, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) - - # Draw Contours - # cv2.drawContours(frame, cnt, -1, (122,122,0), 3) - - if contours: - # Find Max contour area (Assume that hand is in the frame) - # TODO: ENV_DEPENDENCE: depends on the camera resolution, distance to the background, noisy areas sizes - max_area = 100 - ci = 0 - for i in range(len(contours)): - cnt = contours[i] - area = cv2.contourArea(cnt) - if (area > max_area): - max_area = area - ci = i - - # Largest area contour - hand_contour = contours[ci] - - # self.detect_fingers(frame, cnts) - - # Find convex hull - hull = cv2.convexHull(hand_contour) - - # Find convex defects + def draw_hand_overlay(self, frame, hand): + for finger_number, fingertip in enumerate(hand["fingertips"]): + cv2.circle(frame, tuple(fingertip), 10, [255, 100, 255], 3) + cv2.putText(frame, 'finger' + str(finger_number), tuple(fingertip), self.font, 0.5, + (255, 255, 255), + 1) + for defect in hand["intertips"]: + cv2.circle(frame, tuple(defect), 8, [211, 84, 0], -1) + + # Print number of pointed fingers + cv2.putText(frame, str(len(self.hands[0]["fingertips"])), (100, 100), self.font, 2, (0, 0, 0), 2) + + x, y, w, h = hand['bounding_rect'] + cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) + + cv2.drawContours(frame, [hand['contour']], -1, (255, 255, 255), 2) + + if hand['center_of_mass'] is not None: + # Draw center mass + cv2.circle(frame, hand['center_of_mass'], 7, [100, 0, 255], 2) + cv2.putText(frame, 'Center', tuple(hand['center_of_mass']), self.font, 0.5, (255, 255, 255), 1) + + cv2.putText(frame, hand['ID'], (x,y), self.font, 0.5, (255, 255, 255), 1) + return frame + + + + def detect_hands(self, frame): + new_hands = [] + + # Create a binary image with where white will be skin colors and rest is black + hands_mask = self.create_hands_mask(frame) + if hands_mask is None: + return new_hands + cv2.imshow("hands_mask", hands_mask) + + ret, thresh = cv2.threshold(hands_mask, 127, 255, 0) + + # Find contours of the filtered frame + _, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + + # Draw Contours + # cv2.drawContours(frame, cnt, -1, (122,122,0), 3) + + if contours: + # Find Max contour area (Assume that hand is in the frame) + # TODO: ENV_DEPENDENCE: depends on the camera resolution, distance to the background, noisy areas sizes + min_area = 100 + + for contour in contours: + area = cv2.contourArea(contour) + if area > min_area: + # Largest area contour + hand_contour = contour + + # Find convex hull + # hull = cv2.convexHull(hand_contour) + # for point in hull: + # cv2.circle(frame, tuple(point[0]), 8, [255, 100, 10], -1) + + # Find convex defects + hull2 = cv2.convexHull(hand_contour, returnPoints=False) + defects = cv2.convexityDefects(hand_contour, hull2) + + + # Get defect points and draw them in the original image + if defects is not None: + intertips_coords = [] + intertips_indexes = [] + FarDefect = [] + fingertips_coords = [] + fingertips_indexes = [] + defect_indices = [] + for defect_index in range(defects.shape[0]): + s, e, f, d = defects[defect_index, 0] + start = tuple(hand_contour[s][0]) + end = tuple(hand_contour[e][0]) + far = tuple(hand_contour[f][0]) + FarDefect.append(far) + # cv2.line(frame, start, end, [0, 255, 0], 1) + a = math.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2) + b = math.sqrt((far[0] - start[0]) ** 2 + (far[1] - start[1]) ** 2) + c = math.sqrt((end[0] - far[0]) ** 2 + (end[1] - far[1]) ** 2) + angle = math.acos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c)) # cosine theorem + # cv2.circle(frame, far, 8, [211, 84, 125], -1) + # cv2.circle(frame, start, 8, [0, 84, 125], -1) + # cv2.circle(frame, end, 8, [0, 84, 125], -1) + # Get tips and intertips coordinates + # TODO: ENV_DEPENDENCE: this angle > 90ยบ determinate if two points are considered fingertips or not and 90 make thumb to fail in some occasions + intertips_max_angle = math.pi / 1.7 + if angle <= intertips_max_angle: # angle less than 90 degree, treat as fingers + defect_indices.append(defect_index) + # cv2.circle(frame, far, 8, [211, 84, 0], -1) + intertips_coords.append(far) + intertips_indexes.append(f) + # cv2.putText(frame, str(s), start, self.font, 0.7, (255, 255, 255), 1) + # cv2.putText(frame, str(e), end, self.font, 0.7, (255, 255, 200), 1) + if len(fingertips_coords) > 0: + from scipy.spatial import distance + # calculate distances from start and end to the already known tips + start_distance, end_distance = tuple( + distance.cdist(fingertips_coords, [start, end]).min(axis=0)) + # TODO: ENV_DEPENDENCE: it determinate the pixels distance to consider two points the same. It depends on camera resolution and distance from the hand to the camera + same_fingertip_radius = 10 + if start_distance > same_fingertip_radius: + fingertips_coords.append(start) + fingertips_indexes.append(s) + + # cv2.circle(frame, start, 10, [255, 100, 255], 3) + if end_distance > same_fingertip_radius: + fingertips_coords.append(end) + fingertips_indexes.append(e) + + # cv2.circle(frame, end, 10, [255, 100, 255], 3) + else: + fingertips_coords.append(start) + fingertips_indexes.append(s) + + # cv2.circle(frame, start, 10, [255, 100, 255], 3) + fingertips_coords.append(end) + fingertips_indexes.append(e) + + # cv2.circle(frame, end, 10, [255, 100, 255], 3) + + # cv2.circle(frame, far, 10, [100, 255, 255], 3) + # cv2.drawContours(frame, [hand_contour], 0, (255, 0, 0), 2) + if len(fingertips_coords)==5 and len(fingertips_coords)==len(intertips_coords)+1: + # print defect_indices + # new_defect_indices = [] + # if min(defect_indices)-1 <= 0: + # new_defect_indices.append(len(defects)-1) + # else: + # new_defect_indices.append(min(defect_indices)-1) + # if max(defect_indices)+1 < len(defects): + # new_defect_indices.append(max(defect_indices)+1) + # else: + # new_defect_indices.append(0) + # print len(defects) + # print new_defect_indices + # contour_indices = [] + # min_x_index = 5000 + # max_x_index = 0 + # min_x = 5000 + # max_x = 0 + # for defect_index in list(set(defect_indices)): + # s, e, f, d = defects[defect_index, 0] + # start = tuple(hand_contour[s][0]) + # end = tuple(hand_contour[e][0]) + # far = tuple(hand_contour[f][0]) + # + # if start[0]max_x: + # max_x=start[0] + # max_x_index = s + # if end[0]>max_x: + # max_x=end[0] + # max_x_index = e + # if far[0]>max_x: + # max_x=far[0] + # max_x_index = f + # + # # Pretty complex way to add contour points from start to far and from far to end + # # It consider the possibility of the 0 index to be somewhere in between + # # First it create the full range of possible indexes + # hand_contour_range = range(len(hand_contour)) + # # then it roll this range to get the s index in the first position + # rolled_for_s = np.roll(hand_contour_range, -s) + # # then it takes the indexes from s (at 0) to the index where f is found (np.where(rolled_for_s == f) + # start_to_far_range = rolled_for_s[:np.where(rolled_for_s == f)[0][0]] + # # insert the new range of indexes for the new contour + # contour_indices.extend(start_to_far_range) + # # Does the same for the range from f to e + # rolled_for_f = np.roll(hand_contour_range, -f) + # far_to_end_range = rolled_for_f[:np.where(rolled_for_f == e)[0][0]+1] + # contour_indices.extend(far_to_end_range) + # + # # contour_indices.append(s) + # # contour_indices.append(f) + # # contour_indices.append(e) + fingers_contour = np.take(hand_contour, fingertips_indexes+intertips_indexes, axis=0, mode="wrap") + (x, y), radius = cv2.minEnclosingCircle(fingers_contour) + center = (int(x), int(y)) + radius = int(radius)+10 + hand_contour = self.extract_contour_inside_circle(hand_contour, (center, radius)) + hand_bounding_rect = cv2.boundingRect(hand_contour) + # self.follow(frame,hands_mask,(int(max(y-radius,0)), int(max(x-radius,0)) ,int(radius*2),int(radius*2))) + # other = frame.copy() + # cv2.circle(frame, center, radius, (0, 255, 0), 2) + # cv2.imshow("Hand circunferences", other) + # # x, y, w, h = cv2.boundingRect(fingers_contour) + # # cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) + # print contour_indices + # size_to_extend_contour = 40 + # index_of_min_x_index = contour_indices.index(min_x_index) + # new_section = range(min_x_index+1,min_x_index+size_to_extend_contour) + # for new_index in reversed(new_section): + # contour_indices.insert(index_of_min_x_index+1, new_index) + # + # index_of_max_x_index = contour_indices.index(max_x_index) + # new_section = range(max_x_index-1,max_x_index-size_to_extend_contour-1, -1) + # for new_index in new_section: + # contour_indices.insert(index_of_max_x_index, new_index) + # + # # if min_x_index-size_to_extend_contour < 0: + # # half_section = range(0,min_x_index) + # # half_section_size = len(half_section) + # # index_of_min_x_index = contour_indices.index(min_x_index) + # # for new_index in reversed(half_section): + # # contour_indices.insert(index_of_min_x_index, new_index) + # # other_half = range((len(hand_contour))-(size_to_extend_contour-half_section_size),len(hand_contour)) + # # for new_index in reversed(other_half): + # # contour_indices.insert(index_of_min_x_index,new_index) + # # else: + # # new_section = range(min_x_index-size_to_extend_contour,min_x_index) + # # index_of_min_x_index = contour_indices.index(min_x_index) + # # for new_index in reversed(new_section): + # # contour_indices.insert(index_of_min_x_index, new_index) + # # + # # if max_x_index + size_to_extend_contour > len(hand_contour)-1: + # # half_section = range(max_x_index, len(hand_contour)) + # # half_section_size = len(half_section) + # # index_of_max_x_index = contour_indices.index(max_x_index) + # # for new_index in reversed(half_section): + # # contour_indices.insert(index_of_max_x_index, new_index) + # # other_half = range(0,(size_to_extend_contour - half_section_size)) + # # for new_index in reversed(other_half): + # # contour_indices.insert(index_of_max_x_index, new_index) + # # else: + # # new_section = range(max_x_index , max_x_index+size_to_extend_contour) + # # index_of_max_x_index = contour_indices.index(max_x_index) + # # for new_index in reversed(new_section): + # # contour_indices.insert(index_of_max_x_index, new_index) + # + # + # # min_index= min(contour_indices) + # # max_index = max(contour_indices) + # # if min_index -1 >= 0: + # # contour_indices.append(min_index-1) + # # else: + # # contour_indices.append(len(hand_contour)-1) + # # if max_index+1 < len(hand_contour): + # # contour_indices.append(max_index+1) + # # else: + # # contour_indices.append(0) + # # contour_indices = sorted(set(contour_indices)) + # + # + # # for i in sorted(set(new_defect_indices)): + # # s, e, f, d = defects[i, 0] + # # contour_indices.append(s) + # # contour_indices.append(e) + # # hand_contour = np.take(hand_contour, contour_indices, axis=0, mode="wrap") + cv2.drawContours(frame, [hand_contour], 0, (0,255, 0), 1) + + # cv2.drawContours(frame, [hull], 0, (0, 0, 255), 3) + + # Find moments of the largest contour + moments = cv2.moments(hand_contour) + centerMass = None + finger_distances = None + AverageDefectDistance = None + # Central mass of first order moments + if moments['m00'] != 0: + cx = int(moments['m10'] / moments['m00']) # cx = M10/M00 + cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 + centerMass = (cx, cy) + + # Distance from each finger defect(finger webbing) to the center mass + distanceBetweenDefectsToCenter = [] + for far in intertips_coords: + x = np.array(far) + center_mass_array = np.array(centerMass) + distance = np.sqrt( + np.power(x[0] - center_mass_array[0], 2) + np.power(x[1] - center_mass_array[1], 2)) + distanceBetweenDefectsToCenter.append(distance) + + + + # Get an average of three shortest distances from finger webbing to center mass + sortedDefectsDistances = sorted(distanceBetweenDefectsToCenter) + AverageDefectDistance = np.mean(sortedDefectsDistances[0:2]) + + # # Get fingertip points from contour hull + # # If points are in proximity of 80 pixels, consider as a single point in the group + # finger = [] + # for i in range(0, len(hull) - 1): + # if (np.absolute(hull[i][0][0] - hull[i + 1][0][0]) > 10) or ( + # np.absolute(hull[i][0][1] - hull[i + 1][0][1]) > 10): + # if hull[i][0][1] < 500: + # finger.append(hull[i][0]) + # + # + # # The fingertip points are 5 hull points with largest y coordinates + # finger = sorted(finger, key=lambda x: x[1]) + # fingers = finger[0:5] + + # Calculate distance of each finger tip to the center mass + finger_distances = [] + for i in range(0, len(fingertips_coords)): + distance = np.sqrt( + np.power(fingertips_coords[i][0] - centerMass[0], 2) + np.power( + fingertips_coords[i][1] - centerMass[0], 2)) + finger_distances.append(distance) + hand = { + 'fingertips': fingertips_coords, + 'intertips': intertips_coords, + 'center_of_mass': centerMass, + 'finger_distances': finger_distances, + 'average_defect_distance': AverageDefectDistance, + 'contour': hand_contour, + 'bounding_rect': hand_bounding_rect, + 'tracking_fails': 0 + } + new_hands.append(hand) + return new_hands + + def update_hand_charasteristics(self, frame, hand ): + updated_hand = { + 'ID': hand['ID'], + 'fingertips': [], + 'intertips': [], + 'center_of_mass': None, + 'finger_distances': [], + 'average_defect_distance': [], + 'contour': None, + 'bounding_rect': hand['bounding_rect'], + 'tracking_fails': hand['tracking_fails'] + } + # Create a binary image with where white will be skin colors and rest is black + hands_mask = self.create_hands_mask(frame) + current_hand_roi_mask = np.zeros(hands_mask.shape, dtype='uint8') + x,y,w,h = hand['bounding_rect'] + current_hand_roi_mask[y:y+h,x:x+w] = 255 + current_hand_mask = cv2.bitwise_and(hands_mask,current_hand_roi_mask) + + cv2.imshow("current hands_mask"+str(hand['ID']), current_hand_mask) + + ret, thresh = cv2.threshold(current_hand_mask, 127, 255, 0) + + # Find contours of the filtered frame + _, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + + # Draw Contours + # cv2.drawContours(frame, cnt, -1, (122,122,0), 3) + + if contours: + # Find Max contour area (Assume that hand is in the frame) + # TODO: ENV_DEPENDENCE: depends on the camera resolution, distance to the background, noisy areas sizes + max_area = 0 + hand_contour = None + for contour_index in range(len(contours)): + cnt = contours[contour_index] + area = cv2.contourArea(cnt) + if area >= max_area: + max_area = area + # Largest area contour + hand_contour = contours[contour_index] + + # Find convex hull + # hull = cv2.convexHull(hand_contour) + # for point in hull: + # cv2.circle(frame, tuple(point[0]), 8, [255, 100, 10], -1) + + # Find convex defects + if hand_contour is not None: + updated_hand['contour'] = hand_contour + updated_hand['tracking_fails'] = 0 hull2 = cv2.convexHull(hand_contour, returnPoints=False) defects = cv2.convexityDefects(hand_contour, hull2) - cv2.drawContours(frame, [hand_contour], 0, (255, 0, 0), 2) - # cv2.drawContours(frame, [hull], 0, (0, 0, 255), 3) - - # Find moments of the largest contour - moments = cv2.moments(hand_contour) - - # Central mass of first order moments - if moments['m00'] != 0: - cx = int(moments['m10'] / moments['m00']) # cx = M10/M00 - cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 - centerMass = (cx, cy) - # Get defect points and draw them in the original image if defects is not None: intertips_coords = [] + intertips_indexes = [] FarDefect = [] fingertips_coords = [] - distanceBetweenDefectsToCenter = [] - for i in range(defects.shape[0]): - s, e, f, d = defects[i, 0] + fingertips_indexes = [] + defect_indices = [] + for defect_index in range(defects.shape[0]): + s, e, f, d = defects[defect_index, 0] start = tuple(hand_contour[s][0]) end = tuple(hand_contour[e][0]) far = tuple(hand_contour[f][0]) FarDefect.append(far) - cv2.line(frame, start, end, [0, 255, 0], 1) + # cv2.line(frame, start, end, [0, 255, 0], 1) a = math.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2) b = math.sqrt((far[0] - start[0]) ** 2 + (far[1] - start[1]) ** 2) c = math.sqrt((end[0] - far[0]) ** 2 + (end[1] - far[1]) ** 2) angle = math.acos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c)) # cosine theorem + # cv2.circle(frame, far, 8, [211, 84, 125], -1) + # cv2.circle(frame, start, 8, [0, 84, 125], -1) + # cv2.circle(frame, end, 8, [0, 84, 125], -1) # Get tips and intertips coordinates - # TODO: ENV_DEPENDENCE: this angle > 90ยบ determinate if two points are considerated fingertips or not and 90 make thumb to fail in some occasions + # TODO: ENV_DEPENDENCE: this angle > 90ยบ determinate if two points are considered fingertips or not and 90 make thumb to fail in some occasions intertips_max_angle = math.pi / 1.7 if angle <= intertips_max_angle: # angle less than 90 degree, treat as fingers - cnt += 1 - cv2.circle(frame, far, 8, [211, 84, 0], -1) + defect_indices.append(defect_index) + # cv2.circle(frame, far, 8, [211, 84, 0], -1) intertips_coords.append(far) + intertips_indexes.append(f) + # cv2.putText(frame, str(s), start, self.font, 0.7, (255, 255, 255), 1) + # cv2.putText(frame, str(e), end, self.font, 0.7, (255, 255, 200), 1) if len(fingertips_coords) > 0: from scipy.spatial import distance # calculate distances from start and end to the already known tips @@ -229,69 +615,105 @@ def detect_hands(self, ret, frame): same_fingertip_radius = 10 if start_distance > same_fingertip_radius: fingertips_coords.append(start) - cv2.circle(frame, start, 10, [255, 100, 255], 3) + fingertips_indexes.append(s) + + # cv2.circle(frame, start, 10, [255, 100, 255], 3) if end_distance > same_fingertip_radius: fingertips_coords.append(end) - cv2.circle(frame, end, 10, [255, 100, 255], 3) + fingertips_indexes.append(e) + + # cv2.circle(frame, end, 10, [255, 100, 255], 3) else: fingertips_coords.append(start) - cv2.circle(frame, start, 10, [255, 100, 255], 3) + fingertips_indexes.append(s) + + # cv2.circle(frame, start, 10, [255, 100, 255], 3) fingertips_coords.append(end) - cv2.circle(frame, end, 10, [255, 100, 255], 3) + fingertips_indexes.append(e) + + # cv2.circle(frame, end, 10, [255, 100, 255], 3) - # Distance from each finger defect(finger webbing) to the center mass - x = np.array(far) - center_mass_array = np.array(centerMass) - distance = np.sqrt( - np.power(x[0] - center_mass_array[0], 2) + np.power(x[1] - center_mass_array[1], 2)) - distanceBetweenDefectsToCenter.append(distance) # cv2.circle(frame, far, 10, [100, 255, 255], 3) + # cv2.drawContours(frame, [hand_contour], 0, (255, 0, 0), 2) + if len(fingertips_coords) <= 5 and len(fingertips_coords) > 2 and len(fingertips_coords) == len(intertips_coords) + 1: + updated_hand['fingertips']= fingertips_coords + updated_hand['intertips'] = intertips_coords + + if len(fingertips_coords) ==5: + fingers_contour = np.take(hand_contour, fingertips_indexes + intertips_indexes, axis=0, + mode="wrap") + (x, y), radius = cv2.minEnclosingCircle(fingers_contour) + center = (int(x), int(y)) + radius = int(radius) + 10 + hand_contour = self.extract_contour_inside_circle(hand_contour, (center, radius)) + updated_hand['contour'] = hand_contour + + # Find moments of the largest contour + moments = cv2.moments(hand_contour) + centerMass = None + finger_distances = None + AverageDefectDistance = None + # Central mass of first order moments + if moments['m00'] != 0: + cx = int(moments['m10'] / moments['m00']) # cx = M10/M00 + cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 + centerMass = (cx, cy) + updated_hand['center_of_mass'] = centerMass + + if centerMass is not None and len(intertips_coords)>0: + # Distance from each finger defect(finger webbing) to the center mass + distanceBetweenDefectsToCenter = [] + for far in intertips_coords: + x = np.array(far) + center_mass_array = np.array(centerMass) + distance = np.sqrt( + np.power(x[0] - center_mass_array[0], 2) + np.power(x[1] - center_mass_array[1], + 2)) + distanceBetweenDefectsToCenter.append(distance) + + # Get an average of three shortest distances from finger webbing to center mass + sortedDefectsDistances = sorted(distanceBetweenDefectsToCenter) + AverageDefectDistance = np.mean(sortedDefectsDistances[0:2]) + updated_hand['average_defect_distance']= AverageDefectDistance + # # Get fingertip points from contour hull + # # If points are in proximity of 80 pixels, consider as a single point in the group + # finger = [] + # for i in range(0, len(hull) - 1): + # if (np.absolute(hull[i][0][0] - hull[i + 1][0][0]) > 10) or ( + # np.absolute(hull[i][0][1] - hull[i + 1][0][1]) > 10): + # if hull[i][0][1] < 500: + # finger.append(hull[i][0]) + # + # + # # The fingertip points are 5 hull points with largest y coordinates + # finger = sorted(finger, key=lambda x: x[1]) + # fingers = finger[0:5] + if centerMass is not None and len(fingertips_coords) > 0: + # Calculate distance of each finger tip to the center mass + finger_distances = [] + for i in range(0, len(fingertips_coords)): + distance = np.sqrt( + np.power(fingertips_coords[i][0] - centerMass[0], 2) + np.power( + fingertips_coords[i][1] - centerMass[0], 2)) + finger_distances.append(distance) + updated_hand['finger_distances']= finger_distances + return updated_hand + + def extract_contour_inside_circle(self, full_contour, circle): + center, radius = circle + new_contour = [] + for point in full_contour: + if (point[0][0] - center[0]) ** 2 + (point[0][1] - center[1]) ** 2 < radius ** 2: + new_contour.append(point) + return np.array(new_contour) + - # Draw center mass - cv2.circle(frame, centerMass, 7, [100, 0, 255], 2) - cv2.putText(frame, 'Center', tuple(centerMass), self.font, 2, (255, 255, 255), 2) - - # Get an average of three shortest distances from finger webbing to center mass - sortedDefectsDistances = sorted(distanceBetweenDefectsToCenter) - AverageDefectDistance = np.mean(sortedDefectsDistances[0:2]) - - # # Get fingertip points from contour hull - # # If points are in proximity of 80 pixels, consider as a single point in the group - # finger = [] - # for i in range(0, len(hull) - 1): - # if (np.absolute(hull[i][0][0] - hull[i + 1][0][0]) > 10) or ( - # np.absolute(hull[i][0][1] - hull[i + 1][0][1]) > 10): - # if hull[i][0][1] < 500: - # finger.append(hull[i][0]) - # - # - # # The fingertip points are 5 hull points with largest y coordinates - # finger = sorted(finger, key=lambda x: x[1]) - # fingers = finger[0:5] - - # Calculate distance of each finger tip to the center mass - finger_distances = [] - for i in range(0, len(fingertips_coords)): - distance = np.sqrt( - np.power(fingertips_coords[i][0] - centerMass[0], 2) + np.power( - fingertips_coords[i][1] - centerMass[0], 2)) - finger_distances.append(distance) - hand = { - 'fingertips': fingertips_coords, - 'intertips': intertips_coords, - 'center_of_mass': centerMass, - 'finger_distances': finger_distances, - 'bounding_rect': (cv2.boundingRect(hand_contour)), - 'average_defect_distance': AverageDefectDistance, - 'hull': hull - } - self.hands.append(hand) def exit(self): self.capture.release() cv2.destroyAllWindows() - def create_hands_mask(self, image, mode="mixed"): + def create_hands_mask(self, image, mode="diff"): mask = None if mode == "color": mask = self.get_color_mask(image) @@ -303,11 +725,54 @@ def create_hands_mask(self, image, mode="mixed"): diff_mask = self.get_simple_diff_mask(image) color_mask = self.get_color_mask(image) + color_mask = self.clean_mask_noise(color_mask) if diff_mask is not None and color_mask is not None: mask = cv2.bitwise_and(diff_mask,color_mask) cv2.imshow("diff_mask", diff_mask) cv2.imshow("color_mask", color_mask) + elif mode == "movement_buffer": + # Absolutly unusefull + mask = self.get_movement_buffer_mask(image) + return mask + + def clean_mask_noise(self, mask): + + # Kernel matrices for morphological transformation + kernel_square = np.ones((11, 11), np.uint8) + kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + + # Perform morphological transformations to filter out the background noise + # Dilation increase skin color area + # Erosion increase skin color area + dilation = cv2.dilate(mask, kernel_ellipse, iterations=1) + erosion = cv2.erode(dilation, kernel_square, iterations=1) + dilation2 = cv2.dilate(erosion, kernel_ellipse, iterations=1) + # filtered = cv2.medianBlur(dilation2, 5) + # kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8, 8)) + # dilation2 = cv2.dilate(filtered, kernel_ellipse, iterations=1) + # kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + # dilation3 = cv2.dilate(filtered, kernel_ellipse, iterations=1) + median = cv2.medianBlur(dilation2, 5) + return median + + def get_movement_buffer_mask(self, image): + mask = None + self.last_frames.append(image) + mean_image = np.zeros(image.shape, dtype=np.float32) + for old_image in self.last_frames: + cv2.accumulate(old_image, mean_image) + mean_image = mean_image / len(self.last_frames) + + blur_radius = 5 + blurred_image = cv2.GaussianBlur(image, (blur_radius, blur_radius), 0) + blurred_background = cv2.GaussianBlur(mean_image, (blur_radius, blur_radius), 0) + diff = cv2.absdiff(blurred_image, blurred_background.astype(np.uint8)) + # print "diff" + gray_diff = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY) + cv2.imshow("diff", gray_diff) + # TODO: ENV_DEPENDENCE: it could depend on the lighting and environment + _, mask = cv2.threshold(gray_diff, 40, 255, cv2.THRESH_BINARY) return mask def get_color_mask(self, image): @@ -321,19 +786,65 @@ def get_color_mask(self, image): def get_simple_diff_mask(self, image): mask = None - # TODO: ENV_DEPENDENCE: it could depend on the camera quality - blur_radius = 5 - blurred = cv2.GaussianBlur(image, (blur_radius, blur_radius), 0) - if self.first_frame is None or self.discarded_frames != 0: - self.discarded_frames -= 1 - self.first_frame = image + if len(self.last_frames) Date: Fri, 22 Jun 2018 12:18:30 +0200 Subject: [PATCH 11/54] Moved the hand structure from a dict structure to a class --- HandDetection.py | 416 ++++++++++++++++++++++++----------------------- 1 file changed, 213 insertions(+), 203 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index 8c3d1a2..a974d09 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -2,19 +2,11 @@ # -*- coding: utf-8 -*- import math -from copy import copy, deepcopy - -import cv2 -import numpy as np import time from collections import deque -from numpy import trace -from scipy.linalg._fblas import ccopy - - -def nothing(x): - pass +import cv2 +import numpy as np # Function to find angle between two vectors @@ -28,8 +20,8 @@ def calculate_angle(v1, v2): # Function to find distance between two points in a list of lists -def find_distance(A, B): - return np.sqrt(np.power((A[0][0] - B[0][0]), 2) + np.power((A[0][1] - B[0][1]), 2)) +def find_distance(point_a, point_b): + return np.sqrt(np.power((point_a[0][0] - point_b[0][0]), 2) + np.power((point_a[0][1] - point_b[0][1]), 2)) # # Creating a window for HSV track bars @@ -44,7 +36,102 @@ def find_distance(A, B): # cv2.createTrackbar('v', 'HSV_TrackBar', 0, 255, nothing) -class HandDetector(): +class Hand: + id = None + fingertips = [] + intertips = [] + center_of_mass = None + finger_distances = [] + average_defect_distance = [] + contour = None, + bounding_rect = None + tracking_fails = None + + def __init__(self): + pass + + +def clean_mask_noise(mask): + + # Kernel matrices for morphological transformation + kernel_square = np.ones((11, 11), np.uint8) + kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + + # Perform morphological transformations to filter out the background noise + # Dilation increase skin color area + # Erosion increase skin color area + dilation = cv2.dilate(mask, kernel_ellipse, iterations=1) + erosion = cv2.erode(dilation, kernel_square, iterations=1) + dilation2 = cv2.dilate(erosion, kernel_ellipse, iterations=1) + # filtered = cv2.medianBlur(dilation2, 5) + # kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8, 8)) + # dilation2 = cv2.dilate(filtered, kernel_ellipse, iterations=1) + # kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + # dilation3 = cv2.dilate(filtered, kernel_ellipse, iterations=1) + median = cv2.medianBlur(dilation2, 5) + return median + + +def get_color_mask(image): + # Blur the image + blur_radius = 5 + blurred = cv2.GaussianBlur(image, (blur_radius, blur_radius), 0) + # Convert to HSV color space + hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV) + mask = cv2.inRange(hsv, np.array([2, 50, 50]), np.array([15, 255, 255])) + return mask + + +def follow(frame, mask, bounding_rect): + upscaled_bounding_rect = bounding_rect # self.upscale_bounding_rec(bounding_rect,frame.shape,20) + x, y, w, h = upscaled_bounding_rect + track_window = upscaled_bounding_rect + # set up the ROI for tracking + roi = frame[y:y + h, x:x + w] + hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV) + # mask = cv2.inRange(hsv_roi, np.array((0., 60., 32.)), np.array((180., 255., 255.))) + roi_mask = mask[y:y + h, x:x + w] + cv2.imshow("roi_follow_mask", roi_mask) + roi_hist = cv2.calcHist([hsv_roi], [0], roi_mask, [180], [0, 180]) + cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX) + # Setup the termination criteria, either 10 iteration or move by atleast 1 pt + term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1) + hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) + dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1) + # apply meanshift to get the new location + ret, track_window = cv2.meanShift(dst, track_window, term_crit) + return ret, track_window + # return ret, (max(newx-30,0), max(newy-30,0), min(neww+30,frame.shape[1]), min(newh+30,frame.shape[0])) + + +def upscale_bounding_rec(bounding_rect, frame_shape, upscaled_pixels): + x, y, w, h = bounding_rect + new_x = max(x - int(upscaled_pixels / 2), 0) + new_y = max(y - int(upscaled_pixels / 2), 0) + if x + w + int(upscaled_pixels / 2) < frame_shape[1]: + new_w = w + int(upscaled_pixels / 2) + else: + exceded_pixels = x + w + int(upscaled_pixels / 2) - frame_shape[1] + new_w = w + exceded_pixels + if y + h + int(upscaled_pixels / 2) < frame_shape[0]: + new_h = h + int(upscaled_pixels / 2) + else: + exceded_pixels = y + h + int(upscaled_pixels / 2) - frame_shape[0] + new_h = h + exceded_pixels + upscaled_bounding_rect = (new_x, new_y, new_w, new_h) + return upscaled_bounding_rect + + +def extract_contour_inside_circle(full_contour, circle): + center, radius = circle + new_contour = [] + for point in full_contour: + if (point[0][0] - center[0]) ** 2 + (point[0][1] - center[1]) ** 2 < radius ** 2: + new_contour.append(point) + return np.array(new_contour) + + +class HandDetector: def __init__(self, source=0): # Open Camera object # self.capture = cv2.VideoCapture(0) @@ -65,7 +152,7 @@ def compute(self): while self.capture.isOpened(): # Measure execution time - start_time = time.time() + # start_time = time.time() # Capture frames from the camera ret, frame = self.capture.read() @@ -75,17 +162,18 @@ def compute(self): if len(self.hands) > 0: existing_hand = False for hand2 in self.hands: - intersection_value, _ = self.calculate_bounding_rects_intersection(hand1['bounding_rect'], hand2['bounding_rect']) - if intersection_value >0.1 : - existing_hand=True + intersection_value, _ = self.calculate_bounding_rects_intersection(hand1.bounding_rect, + hand2.bounding_rect) + if intersection_value > 0.1: + existing_hand = True break if not existing_hand: - hand1['ID']=str(self.next_hand_id) - self.next_hand_id+=1 + hand1.id = str(self.next_hand_id) + self.next_hand_id += 1 self.hands.append(hand1) print "New hand" else: - hand1['ID'] = str(self.next_hand_id) + hand1.id = str(self.next_hand_id) self.next_hand_id += 1 self.hands.append(hand1) print "New hand" @@ -93,28 +181,27 @@ def compute(self): if len(self.hands) > 0: for index, hand in enumerate(self.hands): hands_mask = self.create_hands_mask(frame) - ret, bounding_rect = self.follow(frame,hands_mask,hand["bounding_rect"]) + ret, bounding_rect = follow(frame, hands_mask, hand.bounding_rect) if ret: print "updating hand" - hand["bounding_rect"] = bounding_rect + hand.bounding_rect = bounding_rect updated_hand = self.update_hand_charasteristics(frame, hand) if updated_hand is not None: self.hands[index] = updated_hand else: - #TODO: How to decided when to remove - if hand['tracking_fails'] > 10: + # TODO: How to decided when to remove + if hand.tracking_fails > 10: print "removing hand" self.hands.remove(hand) else: - hand['tracking_fails']+=1 + hand.tracking_fails += 1 else: # TODO: How to decided when to remove - if hand['tracking_fails'] > 10: + if hand.tracking_fails > 10: print "removing hand" self.hands.remove(hand) else: - hand['tracking_fails'] += 1 - + hand.tracking_fails += 1 # Finger is pointed/raised if the distance of between fingertip to the center mass is larger # than the distance of average finger webbing to center mass by 130 pixels @@ -136,7 +223,6 @@ def compute(self): if k == 27: break - def calculate_bounding_rects_intersection(self, bounding_rect_1, bounding_rect_2): b1_x_left = bounding_rect_1[0] b1_y_top = bounding_rect_1[1] @@ -155,17 +241,17 @@ def calculate_bounding_rects_intersection(self, bounding_rect_1, bounding_rect_2 return 0, None # compute the area of intersection rectangle - interArea = (x_right - x_left + 1) * (y_bottom - y_top + 1) + intersection_area = (x_right - x_left + 1) * (y_bottom - y_top + 1) # compute the area of both the prediction and ground-truth # rectangles - boxAArea = (b1_x_right - b1_x_left + 1) * (b1_y_bottom - b1_y_top + 1) - boxBArea = (b2_x_right - b2_x_left + 1) * (b2_y_bottom - b2_y_top + 1) + box_a_area = (b1_x_right - b1_x_left + 1) * (b1_y_bottom - b1_y_top + 1) + box_b_area = (b2_x_right - b2_x_left + 1) * (b2_y_bottom - b2_y_top + 1) # compute the intersection over union by taking the intersection # area and dividing it by the sum of prediction + ground-truth # areas - the interesection area - iou = interArea / float(boxAArea + boxBArea - interArea) + iou = intersection_area / float(box_a_area + box_b_area - intersection_area) # return the intersection over union value return iou, (x_left, y_top, x_right, y_bottom) @@ -197,32 +283,30 @@ def calculate_bounding_rects_intersection(self, bounding_rect_1, bounding_rect_2 # return interdigs def draw_hand_overlay(self, frame, hand): - for finger_number, fingertip in enumerate(hand["fingertips"]): + for finger_number, fingertip in enumerate(hand.fingertips): cv2.circle(frame, tuple(fingertip), 10, [255, 100, 255], 3) cv2.putText(frame, 'finger' + str(finger_number), tuple(fingertip), self.font, 0.5, (255, 255, 255), 1) - for defect in hand["intertips"]: + for defect in hand.intertips: cv2.circle(frame, tuple(defect), 8, [211, 84, 0], -1) # Print number of pointed fingers - cv2.putText(frame, str(len(self.hands[0]["fingertips"])), (100, 100), self.font, 2, (0, 0, 0), 2) + cv2.putText(frame, str(len(self.hands[0].fingertips)), (100, 100), self.font, 2, (0, 0, 0), 2) - x, y, w, h = hand['bounding_rect'] + x, y, w, h = hand.bounding_rect cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) - cv2.drawContours(frame, [hand['contour']], -1, (255, 255, 255), 2) + cv2.drawContours(frame, [hand.contour], -1, (255, 255, 255), 2) - if hand['center_of_mass'] is not None: + if hand.center_of_mass is not None: # Draw center mass - cv2.circle(frame, hand['center_of_mass'], 7, [100, 0, 255], 2) - cv2.putText(frame, 'Center', tuple(hand['center_of_mass']), self.font, 0.5, (255, 255, 255), 1) + cv2.circle(frame, hand.center_of_mass, 7, [100, 0, 255], 2) + cv2.putText(frame, 'Center', tuple(hand.center_of_mass), self.font, 0.5, (255, 255, 255), 1) - cv2.putText(frame, hand['ID'], (x,y), self.font, 0.5, (255, 255, 255), 1) + cv2.putText(frame, hand.id, (x, y), self.font, 0.5, (255, 255, 255), 1) return frame - - def detect_hands(self, frame): new_hands = [] @@ -260,12 +344,11 @@ def detect_hands(self, frame): hull2 = cv2.convexHull(hand_contour, returnPoints=False) defects = cv2.convexityDefects(hand_contour, hull2) - # Get defect points and draw them in the original image if defects is not None: intertips_coords = [] intertips_indexes = [] - FarDefect = [] + far_defect = [] fingertips_coords = [] fingertips_indexes = [] defect_indices = [] @@ -274,7 +357,7 @@ def detect_hands(self, frame): start = tuple(hand_contour[s][0]) end = tuple(hand_contour[e][0]) far = tuple(hand_contour[f][0]) - FarDefect.append(far) + far_defect.append(far) # cv2.line(frame, start, end, [0, 255, 0], 1) a = math.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2) b = math.sqrt((far[0] - start[0]) ** 2 + (far[1] - start[1]) ** 2) @@ -322,7 +405,7 @@ def detect_hands(self, frame): # cv2.circle(frame, far, 10, [100, 255, 255], 3) # cv2.drawContours(frame, [hand_contour], 0, (255, 0, 0), 2) - if len(fingertips_coords)==5 and len(fingertips_coords)==len(intertips_coords)+1: + if len(fingertips_coords) == 5 and len(fingertips_coords) == len(intertips_coords) + 1: # print defect_indices # new_defect_indices = [] # if min(defect_indices)-1 <= 0: @@ -383,11 +466,12 @@ def detect_hands(self, frame): # # contour_indices.append(s) # # contour_indices.append(f) # # contour_indices.append(e) - fingers_contour = np.take(hand_contour, fingertips_indexes+intertips_indexes, axis=0, mode="wrap") + fingers_contour = np.take(hand_contour, fingertips_indexes + intertips_indexes, axis=0, + mode="wrap") (x, y), radius = cv2.minEnclosingCircle(fingers_contour) center = (int(x), int(y)) - radius = int(radius)+10 - hand_contour = self.extract_contour_inside_circle(hand_contour, (center, radius)) + radius = int(radius) + 10 + hand_contour = extract_contour_inside_circle(hand_contour, (center, radius)) hand_bounding_rect = cv2.boundingRect(hand_contour) # self.follow(frame,hands_mask,(int(max(y-radius,0)), int(max(x-radius,0)) ,int(radius*2),int(radius*2))) # other = frame.copy() @@ -456,35 +540,34 @@ def detect_hands(self, frame): # # contour_indices.append(s) # # contour_indices.append(e) # # hand_contour = np.take(hand_contour, contour_indices, axis=0, mode="wrap") - cv2.drawContours(frame, [hand_contour], 0, (0,255, 0), 1) + cv2.drawContours(frame, [hand_contour], 0, (0, 255, 0), 1) # cv2.drawContours(frame, [hull], 0, (0, 0, 255), 3) # Find moments of the largest contour moments = cv2.moments(hand_contour) - centerMass = None + center_mass = None finger_distances = None - AverageDefectDistance = None + average_defect_distance = None # Central mass of first order moments if moments['m00'] != 0: cx = int(moments['m10'] / moments['m00']) # cx = M10/M00 cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 - centerMass = (cx, cy) + center_mass = (cx, cy) # Distance from each finger defect(finger webbing) to the center mass - distanceBetweenDefectsToCenter = [] + distance_between_defects_to_center = [] for far in intertips_coords: x = np.array(far) - center_mass_array = np.array(centerMass) + center_mass_array = np.array(center_mass) distance = np.sqrt( - np.power(x[0] - center_mass_array[0], 2) + np.power(x[1] - center_mass_array[1], 2)) - distanceBetweenDefectsToCenter.append(distance) - - + np.power(x[0] - center_mass_array[0], 2) + np.power(x[1] - center_mass_array[1], + 2)) + distance_between_defects_to_center.append(distance) # Get an average of three shortest distances from finger webbing to center mass - sortedDefectsDistances = sorted(distanceBetweenDefectsToCenter) - AverageDefectDistance = np.mean(sortedDefectsDistances[0:2]) + sorted_defects_distances = sorted(distance_between_defects_to_center) + average_defect_distance = np.mean(sorted_defects_distances[0:2]) # # Get fingertip points from contour hull # # If points are in proximity of 80 pixels, consider as a single point in the group @@ -504,42 +587,40 @@ def detect_hands(self, frame): finger_distances = [] for i in range(0, len(fingertips_coords)): distance = np.sqrt( - np.power(fingertips_coords[i][0] - centerMass[0], 2) + np.power( - fingertips_coords[i][1] - centerMass[0], 2)) + np.power(fingertips_coords[i][0] - center_mass[0], 2) + np.power( + fingertips_coords[i][1] - center_mass[0], 2)) finger_distances.append(distance) - hand = { - 'fingertips': fingertips_coords, - 'intertips': intertips_coords, - 'center_of_mass': centerMass, - 'finger_distances': finger_distances, - 'average_defect_distance': AverageDefectDistance, - 'contour': hand_contour, - 'bounding_rect': hand_bounding_rect, - 'tracking_fails': 0 - } + hand = Hand() + hand.fingertips = fingertips_coords + hand.intertips = intertips_coords + hand.center_of_mass = center_mass + hand.finger_distances = finger_distances + hand.average_defect_distance = average_defect_distance + hand.contour = hand_contour + hand.bounding_rect = hand_bounding_rect + hand.tracking_fails = 0 new_hands.append(hand) return new_hands - def update_hand_charasteristics(self, frame, hand ): - updated_hand = { - 'ID': hand['ID'], - 'fingertips': [], - 'intertips': [], - 'center_of_mass': None, - 'finger_distances': [], - 'average_defect_distance': [], - 'contour': None, - 'bounding_rect': hand['bounding_rect'], - 'tracking_fails': hand['tracking_fails'] - } + def update_hand_charasteristics(self, frame, hand): + updated_hand = Hand() + updated_hand.id = hand.id + updated_hand.fingertips = [] + updated_hand.intertips = [] + updated_hand.center_of_mass = None + updated_hand.finger_distances = [] + updated_hand.average_defect_distance = [] + updated_hand.contour = None + updated_hand.bounding_rect = hand.bounding_rect + updated_hand.tracking_fails = hand.tracking_fails # Create a binary image with where white will be skin colors and rest is black hands_mask = self.create_hands_mask(frame) current_hand_roi_mask = np.zeros(hands_mask.shape, dtype='uint8') - x,y,w,h = hand['bounding_rect'] - current_hand_roi_mask[y:y+h,x:x+w] = 255 - current_hand_mask = cv2.bitwise_and(hands_mask,current_hand_roi_mask) + x, y, w, h = hand.bounding_rect + current_hand_roi_mask[y:y + h, x:x + w] = 255 + current_hand_mask = cv2.bitwise_and(hands_mask, current_hand_roi_mask) - cv2.imshow("current hands_mask"+str(hand['ID']), current_hand_mask) + cv2.imshow("current hands_mask" + str(hand.id), current_hand_mask) ret, thresh = cv2.threshold(current_hand_mask, 127, 255, 0) @@ -569,8 +650,8 @@ def update_hand_charasteristics(self, frame, hand ): # Find convex defects if hand_contour is not None: - updated_hand['contour'] = hand_contour - updated_hand['tracking_fails'] = 0 + updated_hand.contour = hand_contour + updated_hand.tracking_fails = 0 hull2 = cv2.convexHull(hand_contour, returnPoints=False) defects = cv2.convexityDefects(hand_contour, hull2) @@ -578,7 +659,7 @@ def update_hand_charasteristics(self, frame, hand ): if defects is not None: intertips_coords = [] intertips_indexes = [] - FarDefect = [] + far_defect = [] fingertips_coords = [] fingertips_indexes = [] defect_indices = [] @@ -587,7 +668,7 @@ def update_hand_charasteristics(self, frame, hand ): start = tuple(hand_contour[s][0]) end = tuple(hand_contour[e][0]) far = tuple(hand_contour[f][0]) - FarDefect.append(far) + far_defect.append(far) # cv2.line(frame, start, end, [0, 255, 0], 1) a = math.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2) b = math.sqrt((far[0] - start[0]) ** 2 + (far[1] - start[1]) ** 2) @@ -635,46 +716,49 @@ def update_hand_charasteristics(self, frame, hand ): # cv2.circle(frame, far, 10, [100, 255, 255], 3) # cv2.drawContours(frame, [hand_contour], 0, (255, 0, 0), 2) - if len(fingertips_coords) <= 5 and len(fingertips_coords) > 2 and len(fingertips_coords) == len(intertips_coords) + 1: - updated_hand['fingertips']= fingertips_coords - updated_hand['intertips'] = intertips_coords - - if len(fingertips_coords) ==5: - fingers_contour = np.take(hand_contour, fingertips_indexes + intertips_indexes, axis=0, + if 5 >= len(fingertips_coords) > 2 and len(fingertips_coords) == len(intertips_coords) + 1: + updated_hand.fingertips = fingertips_coords + updated_hand.intertips = intertips_coords + + if len(fingertips_coords) == 5: + fingers_contour = np.take(hand_contour, + fingertips_indexes + intertips_indexes, + axis=0, mode="wrap") (x, y), radius = cv2.minEnclosingCircle(fingers_contour) center = (int(x), int(y)) radius = int(radius) + 10 - hand_contour = self.extract_contour_inside_circle(hand_contour, (center, radius)) - updated_hand['contour'] = hand_contour + hand_contour = extract_contour_inside_circle(hand_contour, (center, radius)) + updated_hand.contour = hand_contour # Find moments of the largest contour moments = cv2.moments(hand_contour) - centerMass = None - finger_distances = None - AverageDefectDistance = None + center_of_mass = None + finger_distances = [] + average_defect_distance = None # Central mass of first order moments if moments['m00'] != 0: cx = int(moments['m10'] / moments['m00']) # cx = M10/M00 cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 - centerMass = (cx, cy) - updated_hand['center_of_mass'] = centerMass + center_of_mass = (cx, cy) + updated_hand.center_of_mass = center_of_mass - if centerMass is not None and len(intertips_coords)>0: + if center_of_mass is not None and len(intertips_coords) > 0: # Distance from each finger defect(finger webbing) to the center mass - distanceBetweenDefectsToCenter = [] + distance_between_defects_to_center = [] for far in intertips_coords: x = np.array(far) - center_mass_array = np.array(centerMass) + center_mass_array = np.array(center_of_mass) distance = np.sqrt( - np.power(x[0] - center_mass_array[0], 2) + np.power(x[1] - center_mass_array[1], - 2)) - distanceBetweenDefectsToCenter.append(distance) + np.power(x[0] - center_mass_array[0], + 2) + np.power(x[1] - center_mass_array[1], 2) + ) + distance_between_defects_to_center.append(distance) # Get an average of three shortest distances from finger webbing to center mass - sortedDefectsDistances = sorted(distanceBetweenDefectsToCenter) - AverageDefectDistance = np.mean(sortedDefectsDistances[0:2]) - updated_hand['average_defect_distance']= AverageDefectDistance + sorted_defects_distances = sorted(distance_between_defects_to_center) + average_defect_distance = np.mean(sorted_defects_distances[0:2]) + updated_hand.average_defect_distance = average_defect_distance # # Get fingertip points from contour hull # # If points are in proximity of 80 pixels, consider as a single point in the group # finger = [] @@ -688,27 +772,17 @@ def update_hand_charasteristics(self, frame, hand ): # # The fingertip points are 5 hull points with largest y coordinates # finger = sorted(finger, key=lambda x: x[1]) # fingers = finger[0:5] - if centerMass is not None and len(fingertips_coords) > 0: + if center_of_mass is not None and len(fingertips_coords) > 0: # Calculate distance of each finger tip to the center mass finger_distances = [] for i in range(0, len(fingertips_coords)): distance = np.sqrt( - np.power(fingertips_coords[i][0] - centerMass[0], 2) + np.power( - fingertips_coords[i][1] - centerMass[0], 2)) + np.power(fingertips_coords[i][0] - center_of_mass[0], 2) + np.power( + fingertips_coords[i][1] - center_of_mass[0], 2)) finger_distances.append(distance) - updated_hand['finger_distances']= finger_distances + updated_hand.finger_distances = finger_distances return updated_hand - def extract_contour_inside_circle(self, full_contour, circle): - center, radius = circle - new_contour = [] - for point in full_contour: - if (point[0][0] - center[0]) ** 2 + (point[0][1] - center[1]) ** 2 < radius ** 2: - new_contour.append(point) - return np.array(new_contour) - - - def exit(self): self.capture.release() cv2.destroyAllWindows() @@ -716,7 +790,7 @@ def exit(self): def create_hands_mask(self, image, mode="diff"): mask = None if mode == "color": - mask = self.get_color_mask(image) + mask = get_color_mask(image) elif mode == "MOG2": mask = self.get_MOG2_mask(image) elif mode == "diff": @@ -724,11 +798,11 @@ def create_hands_mask(self, image, mode="diff"): elif mode == "mixed": diff_mask = self.get_simple_diff_mask(image) - color_mask = self.get_color_mask(image) - color_mask = self.clean_mask_noise(color_mask) + color_mask = get_color_mask(image) + color_mask = clean_mask_noise(color_mask) if diff_mask is not None and color_mask is not None: - mask = cv2.bitwise_and(diff_mask,color_mask) + mask = cv2.bitwise_and(diff_mask, color_mask) cv2.imshow("diff_mask", diff_mask) cv2.imshow("color_mask", color_mask) elif mode == "movement_buffer": @@ -736,26 +810,6 @@ def create_hands_mask(self, image, mode="diff"): mask = self.get_movement_buffer_mask(image) return mask - def clean_mask_noise(self, mask): - - # Kernel matrices for morphological transformation - kernel_square = np.ones((11, 11), np.uint8) - kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) - - # Perform morphological transformations to filter out the background noise - # Dilation increase skin color area - # Erosion increase skin color area - dilation = cv2.dilate(mask, kernel_ellipse, iterations=1) - erosion = cv2.erode(dilation, kernel_square, iterations=1) - dilation2 = cv2.dilate(erosion, kernel_ellipse, iterations=1) - # filtered = cv2.medianBlur(dilation2, 5) - # kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8, 8)) - # dilation2 = cv2.dilate(filtered, kernel_ellipse, iterations=1) - # kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) - # dilation3 = cv2.dilate(filtered, kernel_ellipse, iterations=1) - median = cv2.medianBlur(dilation2, 5) - return median - def get_movement_buffer_mask(self, image): mask = None self.last_frames.append(image) @@ -775,18 +829,10 @@ def get_movement_buffer_mask(self, image): _, mask = cv2.threshold(gray_diff, 40, 255, cv2.THRESH_BINARY) return mask - def get_color_mask(self, image): - # Blur the image - blur_radius = 5 - blurred = cv2.GaussianBlur(image, (blur_radius, blur_radius), 0) - # Convert to HSV color space - hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV) - mask = cv2.inRange(hsv, np.array([2, 50, 50]), np.array([15, 255, 255])) - return mask - def get_simple_diff_mask(self, image): + # type: (object) -> object mask = None - if len(self.last_frames) Date: Fri, 22 Jun 2018 21:17:26 +0200 Subject: [PATCH 12/54] Improved the hand tracking Fixed several bugs Implemented some functions to remove failed matches. --- HandDetection.py | 255 ++++++++++++++++++++++++++++++----------------- 1 file changed, 164 insertions(+), 91 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index a974d09..f88dbd6 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -7,6 +7,24 @@ import cv2 import numpy as np +import random + + +def get_random_color(n=1): + ret = [] + r = int(random.random() * 256) + g = int(random.random() * 256) + b = int(random.random() * 256) + step = 256 / n + for i in range(n): + r += step + g += step + b += step + r = int(r) % 256 + g = int(g) % 256 + b = int(b) % 256 + ret.append((r, g, b)) + return ret # Function to find angle between two vectors @@ -37,22 +55,26 @@ def find_distance(point_a, point_b): class Hand: - id = None - fingertips = [] - intertips = [] - center_of_mass = None - finger_distances = [] - average_defect_distance = [] - contour = None, - bounding_rect = None - tracking_fails = None - def __init__(self): - pass + self.id = None + self.fingertips = [] + self.intertips = [] + self.center_of_mass = None + self.finger_distances = [] + self.average_defect_distance = [] + self.contour = None, + self.bounding_rect = None + self.tracking_fails = 0 + self.detection_fail = 0 + self.frame_count = 0 + self.tracking_window = None + self.tracked = False + self.detected = True + self.position_history = [] + self.color = get_random_color()[0] def clean_mask_noise(mask): - # Kernel matrices for morphological transformation kernel_square = np.ones((11, 11), np.uint8) kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) @@ -82,28 +104,6 @@ def get_color_mask(image): return mask -def follow(frame, mask, bounding_rect): - upscaled_bounding_rect = bounding_rect # self.upscale_bounding_rec(bounding_rect,frame.shape,20) - x, y, w, h = upscaled_bounding_rect - track_window = upscaled_bounding_rect - # set up the ROI for tracking - roi = frame[y:y + h, x:x + w] - hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV) - # mask = cv2.inRange(hsv_roi, np.array((0., 60., 32.)), np.array((180., 255., 255.))) - roi_mask = mask[y:y + h, x:x + w] - cv2.imshow("roi_follow_mask", roi_mask) - roi_hist = cv2.calcHist([hsv_roi], [0], roi_mask, [180], [0, 180]) - cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX) - # Setup the termination criteria, either 10 iteration or move by atleast 1 pt - term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1) - hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) - dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1) - # apply meanshift to get the new location - ret, track_window = cv2.meanShift(dst, track_window, term_crit) - return ret, track_window - # return ret, (max(newx-30,0), max(newy-30,0), min(neww+30,frame.shape[1]), min(newh+30,frame.shape[0])) - - def upscale_bounding_rec(bounding_rect, frame_shape, upscaled_pixels): x, y, w, h = bounding_rect new_x = max(x - int(upscaled_pixels / 2), 0) @@ -122,6 +122,9 @@ def upscale_bounding_rec(bounding_rect, frame_shape, upscaled_pixels): return upscaled_bounding_rect + + + def extract_contour_inside_circle(full_contour, circle): center, radius = circle new_contour = [] @@ -148,6 +151,60 @@ def __init__(self, source=0): # self.capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1000) # self.capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 600) + def update_detection(self, frame): + for hand in self.hands: + hand.detected = False + hand.frame_count += 1 + detected_hands = self.detect_hands(frame) + for detected_hand in detected_hands: + if len(self.hands) > 0: + hand_exists = False + for existing_hand_index, existing_hand in enumerate(self.hands): + intersection_value, _ = self.calculate_bounding_rects_intersection( + detected_hand.bounding_rect, + existing_hand.bounding_rect) + if intersection_value > 0.1: + hand_exists = True + detected_hand.detected = True + detected_hand.id = existing_hand.id + detected_hand.position_history = existing_hand.position_history + detected_hand.color = existing_hand.color + self.hands[existing_hand_index] = detected_hand + break + if not hand_exists: + detected_hand.id = str(self.next_hand_id) + detected_hand.detected = True + self.next_hand_id += 1 + self.hands.append(detected_hand) + print "New hand" + else: + detected_hand.id = str(self.next_hand_id) + detected_hand.detected = True + self.next_hand_id += 1 + self.hands.append(detected_hand) + print "New hand" + + def update_tracking(self, frame): + if len(self.hands) > 0: + for index, hand in enumerate(self.hands): + hand.tracked = False + hands_mask = self.create_hands_mask(frame) + ret, tracking_window = self.follow(frame, hands_mask, hand.bounding_rect) + if ret and tracking_window is not None: + updated_hand = self.update_hand_charasteristics(frame, hand) + updated_hand.detected = hand.detected + updated_hand.tracking_window = tracking_window + updated_hand.detection_fail = hand.detection_fail + if updated_hand is not None: + updated_hand.tracked = True + self.hands[index] = updated_hand + else: + hand.tracked = False + hand.tracking_fails += 1 + else: + hand.tracked = False + hand.tracking_fails += 1 + def compute(self): while self.capture.isOpened(): @@ -156,55 +213,24 @@ def compute(self): # Capture frames from the camera ret, frame = self.capture.read() + if ret is True: - new_hands = self.detect_hands(frame) - for hand1 in new_hands: - if len(self.hands) > 0: - existing_hand = False - for hand2 in self.hands: - intersection_value, _ = self.calculate_bounding_rects_intersection(hand1.bounding_rect, - hand2.bounding_rect) - if intersection_value > 0.1: - existing_hand = True - break - if not existing_hand: - hand1.id = str(self.next_hand_id) - self.next_hand_id += 1 - self.hands.append(hand1) - print "New hand" - else: - hand1.id = str(self.next_hand_id) - self.next_hand_id += 1 - self.hands.append(hand1) - print "New hand" - - if len(self.hands) > 0: - for index, hand in enumerate(self.hands): - hands_mask = self.create_hands_mask(frame) - ret, bounding_rect = follow(frame, hands_mask, hand.bounding_rect) - if ret: - print "updating hand" - hand.bounding_rect = bounding_rect - updated_hand = self.update_hand_charasteristics(frame, hand) - if updated_hand is not None: - self.hands[index] = updated_hand - else: - # TODO: How to decided when to remove - if hand.tracking_fails > 10: - print "removing hand" - self.hands.remove(hand) - else: - hand.tracking_fails += 1 + self.update_detection(frame) + + self.update_tracking(frame) + + for index, hand in enumerate(self.hands): + if hand.detected is False: + hand.detection_fail += 1 + if hand.tracked: + hand.bounding_rect = hand.tracking_window else: - # TODO: How to decided when to remove - if hand.tracking_fails > 10: - print "removing hand" - self.hands.remove(hand) - else: - hand.tracking_fails += 1 + print "_____________No updated information" + # TODO: How to decided when to remove + if (hand.tracking_fails > 20 and hand.detected is False) or (hand.detection_fail > 20): + print "removing hand" + self.hands.remove(hand) - # Finger is pointed/raised if the distance of between fingertip to the center mass is larger - # than the distance of average finger webbing to center mass by 130 pixels overlayed_frame = frame.copy() for hand in self.hands: overlayed_frame = self.draw_hand_overlay(frame, hand) @@ -219,7 +245,7 @@ def compute(self): print "No video detected" # close the output video by pressing 'ESC' - k = cv2.waitKey(5) & 0xFF + k = cv2.waitKey(50) & 0xFF if k == 27: break @@ -256,6 +282,27 @@ def calculate_bounding_rects_intersection(self, bounding_rect_1, bounding_rect_2 # return the intersection over union value return iou, (x_left, y_top, x_right, y_bottom) + def follow(self, frame, mask, bounding_rect): + upscaled_bounding_rect = bounding_rect # self.upscale_bounding_rec(bounding_rect,frame.shape,20) + x, y, w, h = upscaled_bounding_rect + track_window = upscaled_bounding_rect + # set up the ROI for tracking + roi = frame[y:y + h, x:x + w] + hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV) + # mask = cv2.inRange(hsv_roi, np.array((0., 60., 32.)), np.array((180., 255., 255.))) + roi_mask = mask[y:y + h, x:x + w] + cv2.imshow("roi_follow_mask", roi_mask) + roi_hist = cv2.calcHist([hsv_roi], [0], roi_mask, [180], [0, 180]) + cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX) + # Setup the termination criteria, either 10 iteration or move by atleast 1 pt + term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1) + hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) + dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1) + # apply meanshift to get the new location + ret, track_window = cv2.meanShift(dst, track_window, term_crit) + return ret, track_window + # return ret, (max(newx-30,0), max(newy-30,0), min(neww+30,frame.shape[1]), min(newh+30,frame.shape[0])) + # def calculate_hand_interst_points(self, frame, cnts): # # convexity defect # @@ -291,20 +338,26 @@ def draw_hand_overlay(self, frame, hand): for defect in hand.intertips: cv2.circle(frame, tuple(defect), 8, [211, 84, 0], -1) - # Print number of pointed fingers - cv2.putText(frame, str(len(self.hands[0].fingertips)), (100, 100), self.font, 2, (0, 0, 0), 2) - x, y, w, h = hand.bounding_rect + # cv2.putText(frame, (str(w)), (x + w, y), self.font, 0.3, [255, 255, 255], 1) + # cv2.putText(frame, (str(h)), (x + w, y + h), self.font, 0.3, [255, 255, 255], 1) + # cv2.putText(frame, (str(w * h)), (x + w / 2, y + h / 2), self.font, 0.3, [255, 255, 255], 1) + # cv2.putText(frame, (str(x)+", "+str(y)), (x-10, y-10), self.font, 0.3, [255, 255, 255], 1) cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) cv2.drawContours(frame, [hand.contour], -1, (255, 255, 255), 2) + points = np.array(hand.position_history) + cv2.polylines(img=frame, pts=np.int32([points]), isClosed=False, color=hand.color) + if hand.center_of_mass is not None: # Draw center mass cv2.circle(frame, hand.center_of_mass, 7, [100, 0, 255], 2) cv2.putText(frame, 'Center', tuple(hand.center_of_mass), self.font, 0.5, (255, 255, 255), 1) - cv2.putText(frame, hand.id, (x, y), self.font, 0.5, (255, 255, 255), 1) + hand_string = "hand " + str(hand.id) + ": detected =" + str(hand.detected) + " tracked =" + str( + hand.tracked) + " at " + str(hand.center_of_mass) + cv2.putText(frame, hand_string, (10, 30 + 15 * int(hand.id)), self.font, 0.5, (255, 255, 255), 1) return frame def detect_hands(self, frame): @@ -474,9 +527,19 @@ def detect_hands(self, frame): hand_contour = extract_contour_inside_circle(hand_contour, (center, radius)) hand_bounding_rect = cv2.boundingRect(hand_contour) # self.follow(frame,hands_mask,(int(max(y-radius,0)), int(max(x-radius,0)) ,int(radius*2),int(radius*2))) - # other = frame.copy() - # cv2.circle(frame, center, radius, (0, 255, 0), 2) - # cv2.imshow("Hand circunferences", other) + other = frame.copy() + x, y, w, h = hand_bounding_rect + cv2.circle(other, center, radius, (255, 255, 0), 2) + cv2.drawContours(other, [hand_contour], 0, (255, 255, 0)) + cv2.rectangle(other, (hand_bounding_rect[0], hand_bounding_rect[1]), ( + hand_bounding_rect[0] + hand_bounding_rect[2], + hand_bounding_rect[1] + hand_bounding_rect[3]), (255, 255, 0), 1) + cv2.putText(other, (str(w)), (x + w, y), self.font, 0.3, [255, 255, 255], 1) + cv2.putText(other, (str(h)), (x + w, y + h), self.font, 0.3, [100, 255, 255], 1) + cv2.putText(other, (str(w * h)), (x + w / 2, y + h / 2), self.font, 0.3, [100, 100, 255], 1) + cv2.putText(other, (str(x) + ", " + str(y)), (x - 10, y - 10), self.font, 0.3, + [255, 255, 255], 1) + cv2.imshow("Hand circunferences", other) # # x, y, w, h = cv2.boundingRect(fingers_contour) # # cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) # print contour_indices @@ -540,7 +603,7 @@ def detect_hands(self, frame): # # contour_indices.append(s) # # contour_indices.append(e) # # hand_contour = np.take(hand_contour, contour_indices, axis=0, mode="wrap") - cv2.drawContours(frame, [hand_contour], 0, (0, 255, 0), 1) + # cv2.drawContours(frame, [hand_contour], 0, (0, 255, 0), 1) # cv2.drawContours(frame, [hull], 0, (0, 0, 255), 3) @@ -599,6 +662,7 @@ def detect_hands(self, frame): hand.contour = hand_contour hand.bounding_rect = hand_bounding_rect hand.tracking_fails = 0 + # hand.position_history.append(hand.center_of_mass) new_hands.append(hand) return new_hands @@ -613,14 +677,20 @@ def update_hand_charasteristics(self, frame, hand): updated_hand.contour = None updated_hand.bounding_rect = hand.bounding_rect updated_hand.tracking_fails = hand.tracking_fails + updated_hand.position_history = hand.position_history + updated_hand.color = hand.color # Create a binary image with where white will be skin colors and rest is black hands_mask = self.create_hands_mask(frame) current_hand_roi_mask = np.zeros(hands_mask.shape, dtype='uint8') x, y, w, h = hand.bounding_rect current_hand_roi_mask[y:y + h, x:x + w] = 255 current_hand_mask = cv2.bitwise_and(hands_mask, current_hand_roi_mask) - - cv2.imshow("current hands_mask" + str(hand.id), current_hand_mask) + to_show = cv2.resize(current_hand_mask, None, fx=.3, fy=.3, interpolation=cv2.INTER_CUBIC) + # cv2.putText(to_show, (str(w)), (x + w, y), self.font, 0.3, [255, 255, 255], 1) + # cv2.putText(to_show, (str(h)), (x + w, y + h), self.font, 0.3, [100, 255, 255], 1) + # cv2.putText(to_show, (str(w * h)), (x + w / 2, y + h / 2), self.font, 0.3, [100, 100, 255], 1) + # cv2.putText(to_show, (str(x)+", "+str(y)), (x-10, y-10), self.font, 0.3, [255, 255, 255], 1) + cv2.imshow("current hands_mask" + str(hand.id), to_show) ret, thresh = cv2.threshold(current_hand_mask, 127, 255, 0) @@ -742,6 +812,7 @@ def update_hand_charasteristics(self, frame, hand): cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 center_of_mass = (cx, cy) updated_hand.center_of_mass = center_of_mass + updated_hand.position_history.append(center_of_mass) if center_of_mass is not None and len(intertips_coords) > 0: # Distance from each finger defect(finger webbing) to the center mass @@ -781,6 +852,8 @@ def update_hand_charasteristics(self, frame, hand): fingertips_coords[i][1] - center_of_mass[0], 2)) finger_distances.append(distance) updated_hand.finger_distances = finger_distances + else: + return None return updated_hand def exit(self): From e6ef4aa0559be93e1a7f7e1c03087a4b6db44fe2 Mon Sep 17 00:00:00 2001 From: orensbruli Date: Mon, 25 Jun 2018 16:00:29 +0200 Subject: [PATCH 13/54] Created new methods to avoid code duplications --- HandDetection.py | 594 +++++++++++++++++------------------------------ 1 file changed, 209 insertions(+), 385 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index f88dbd6..086c3fb 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -1,6 +1,6 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- - +import copy import math import time from collections import deque @@ -73,6 +73,21 @@ def __init__(self): self.position_history = [] self.color = get_random_color()[0] + def copy_main_attributes(self): + updated_hand = Hand() + updated_hand.id = self.id + updated_hand.fingertips = [] + updated_hand.intertips = [] + updated_hand.center_of_mass = None + updated_hand.finger_distances = [] + updated_hand.average_defect_distance = [] + updated_hand.contour = None + updated_hand.bounding_rect = self.bounding_rect + updated_hand.tracking_fails = self.tracking_fails + updated_hand.position_history = self.position_history + updated_hand.color = self.color + return updated_hand + def clean_mask_noise(mask): # Kernel matrices for morphological transformation @@ -155,7 +170,7 @@ def update_detection(self, frame): for hand in self.hands: hand.detected = False hand.frame_count += 1 - detected_hands = self.detect_hands(frame) + detected_hands = self.detect_hands_in_frame(frame) for detected_hand in detected_hands: if len(self.hands) > 0: hand_exists = False @@ -330,13 +345,14 @@ def follow(self, frame, mask, bounding_rect): # return interdigs def draw_hand_overlay(self, frame, hand): - for finger_number, fingertip in enumerate(hand.fingertips): - cv2.circle(frame, tuple(fingertip), 10, [255, 100, 255], 3) - cv2.putText(frame, 'finger' + str(finger_number), tuple(fingertip), self.font, 0.5, - (255, 255, 255), - 1) - for defect in hand.intertips: - cv2.circle(frame, tuple(defect), 8, [211, 84, 0], -1) + if hand.detected: + for finger_number, fingertip in enumerate(hand.fingertips): + cv2.circle(frame, tuple(fingertip), 10, [255, 100, 255], 3) + cv2.putText(frame, 'finger' + str(finger_number), tuple(fingertip), self.font, 0.5, + (255, 255, 255), + 1) + for defect in hand.intertips: + cv2.circle(frame, tuple(defect), 8, [211, 84, 0], -1) x, y, w, h = hand.bounding_rect # cv2.putText(frame, (str(w)), (x + w, y), self.font, 0.3, [255, 255, 255], 1) @@ -345,7 +361,8 @@ def draw_hand_overlay(self, frame, hand): # cv2.putText(frame, (str(x)+", "+str(y)), (x-10, y-10), self.font, 0.3, [255, 255, 255], 1) cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) - cv2.drawContours(frame, [hand.contour], -1, (255, 255, 255), 2) + if hand.detected or hand.tracked: + cv2.drawContours(frame, [hand.contour], -1, (255, 255, 255), 2) points = np.array(hand.position_history) cv2.polylines(img=frame, pts=np.int32([points]), isClosed=False, color=hand.color) @@ -360,24 +377,40 @@ def draw_hand_overlay(self, frame, hand): cv2.putText(frame, hand_string, (10, 30 + 15 * int(hand.id)), self.font, 0.5, (255, 255, 255), 1) return frame - def detect_hands(self, frame): - new_hands = [] - + def create_contours(self, frame, roi_mask=None): # Create a binary image with where white will be skin colors and rest is black hands_mask = self.create_hands_mask(frame) if hands_mask is None: - return new_hands + return [] cv2.imshow("hands_mask", hands_mask) + if roi_mask is not None: + current_roi_mask = np.zeros(hands_mask.shape, dtype='uint8') + x, y, w, h = roi_mask + current_roi_mask[y:y + h, x:x + w] = 255 + hands_mask = cv2.bitwise_and(hands_mask, current_roi_mask) + # to_show = cv2.resize(hands_mask, None, fx=.3, fy=.3, interpolation=cv2.INTER_CUBIC) + # # cv2.putText(to_show, (str(w)), (x + w, y), self.font, 0.3, [255, 255, 255], 1) + # # cv2.putText(to_show, (str(h)), (x + w, y + h), self.font, 0.3, [100, 255, 255], 1) + # # cv2.putText(to_show, (str(w * h)), (x + w / 2, y + h / 2), self.font, 0.3, [100, 100, 255], 1) + # # cv2.putText(to_show, (str(x)+", "+str(y)), (x-10, y-10), self.font, 0.3, [255, 255, 255], 1) + # cv2.imshow("current hands_mask" + str(hand.id), to_show) + ret, thresh = cv2.threshold(hands_mask, 127, 255, 0) # Find contours of the filtered frame _, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + return contours + + def detect_hands_in_frame(self, frame): + new_hands = [] + + contours = self.create_contours(frame) # Draw Contours # cv2.drawContours(frame, cnt, -1, (122,122,0), 3) - if contours: + if len(contours) > 0: # Find Max contour area (Assume that hand is in the frame) # TODO: ENV_DEPENDENCE: depends on the camera resolution, distance to the background, noisy areas sizes min_area = 100 @@ -385,7 +418,6 @@ def detect_hands(self, frame): for contour in contours: area = cv2.contourArea(contour) if area > min_area: - # Largest area contour hand_contour = contour # Find convex hull @@ -399,136 +431,16 @@ def detect_hands(self, frame): # Get defect points and draw them in the original image if defects is not None: - intertips_coords = [] - intertips_indexes = [] - far_defect = [] - fingertips_coords = [] - fingertips_indexes = [] - defect_indices = [] - for defect_index in range(defects.shape[0]): - s, e, f, d = defects[defect_index, 0] - start = tuple(hand_contour[s][0]) - end = tuple(hand_contour[e][0]) - far = tuple(hand_contour[f][0]) - far_defect.append(far) - # cv2.line(frame, start, end, [0, 255, 0], 1) - a = math.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2) - b = math.sqrt((far[0] - start[0]) ** 2 + (far[1] - start[1]) ** 2) - c = math.sqrt((end[0] - far[0]) ** 2 + (end[1] - far[1]) ** 2) - angle = math.acos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c)) # cosine theorem - # cv2.circle(frame, far, 8, [211, 84, 125], -1) - # cv2.circle(frame, start, 8, [0, 84, 125], -1) - # cv2.circle(frame, end, 8, [0, 84, 125], -1) - # Get tips and intertips coordinates - # TODO: ENV_DEPENDENCE: this angle > 90ยบ determinate if two points are considered fingertips or not and 90 make thumb to fail in some occasions - intertips_max_angle = math.pi / 1.7 - if angle <= intertips_max_angle: # angle less than 90 degree, treat as fingers - defect_indices.append(defect_index) - # cv2.circle(frame, far, 8, [211, 84, 0], -1) - intertips_coords.append(far) - intertips_indexes.append(f) - # cv2.putText(frame, str(s), start, self.font, 0.7, (255, 255, 255), 1) - # cv2.putText(frame, str(e), end, self.font, 0.7, (255, 255, 200), 1) - if len(fingertips_coords) > 0: - from scipy.spatial import distance - # calculate distances from start and end to the already known tips - start_distance, end_distance = tuple( - distance.cdist(fingertips_coords, [start, end]).min(axis=0)) - # TODO: ENV_DEPENDENCE: it determinate the pixels distance to consider two points the same. It depends on camera resolution and distance from the hand to the camera - same_fingertip_radius = 10 - if start_distance > same_fingertip_radius: - fingertips_coords.append(start) - fingertips_indexes.append(s) - - # cv2.circle(frame, start, 10, [255, 100, 255], 3) - if end_distance > same_fingertip_radius: - fingertips_coords.append(end) - fingertips_indexes.append(e) - - # cv2.circle(frame, end, 10, [255, 100, 255], 3) - else: - fingertips_coords.append(start) - fingertips_indexes.append(s) - - # cv2.circle(frame, start, 10, [255, 100, 255], 3) - fingertips_coords.append(end) - fingertips_indexes.append(e) - - # cv2.circle(frame, end, 10, [255, 100, 255], 3) - - # cv2.circle(frame, far, 10, [100, 255, 255], 3) + fingertips_coords, fingertips_indexes, intertips_coords, intertips_indexes = self.get_hand_fingertips(hand_contour, defects) # cv2.drawContours(frame, [hand_contour], 0, (255, 0, 0), 2) if len(fingertips_coords) == 5 and len(fingertips_coords) == len(intertips_coords) + 1: - # print defect_indices - # new_defect_indices = [] - # if min(defect_indices)-1 <= 0: - # new_defect_indices.append(len(defects)-1) - # else: - # new_defect_indices.append(min(defect_indices)-1) - # if max(defect_indices)+1 < len(defects): - # new_defect_indices.append(max(defect_indices)+1) - # else: - # new_defect_indices.append(0) - # print len(defects) - # print new_defect_indices - # contour_indices = [] - # min_x_index = 5000 - # max_x_index = 0 - # min_x = 5000 - # max_x = 0 - # for defect_index in list(set(defect_indices)): - # s, e, f, d = defects[defect_index, 0] - # start = tuple(hand_contour[s][0]) - # end = tuple(hand_contour[e][0]) - # far = tuple(hand_contour[f][0]) - # - # if start[0]max_x: - # max_x=start[0] - # max_x_index = s - # if end[0]>max_x: - # max_x=end[0] - # max_x_index = e - # if far[0]>max_x: - # max_x=far[0] - # max_x_index = f - # - # # Pretty complex way to add contour points from start to far and from far to end - # # It consider the possibility of the 0 index to be somewhere in between - # # First it create the full range of possible indexes - # hand_contour_range = range(len(hand_contour)) - # # then it roll this range to get the s index in the first position - # rolled_for_s = np.roll(hand_contour_range, -s) - # # then it takes the indexes from s (at 0) to the index where f is found (np.where(rolled_for_s == f) - # start_to_far_range = rolled_for_s[:np.where(rolled_for_s == f)[0][0]] - # # insert the new range of indexes for the new contour - # contour_indices.extend(start_to_far_range) - # # Does the same for the range from f to e - # rolled_for_f = np.roll(hand_contour_range, -f) - # far_to_end_range = rolled_for_f[:np.where(rolled_for_f == e)[0][0]+1] - # contour_indices.extend(far_to_end_range) - # - # # contour_indices.append(s) - # # contour_indices.append(f) - # # contour_indices.append(e) + fingers_contour = np.take(hand_contour, fingertips_indexes + intertips_indexes, axis=0, mode="wrap") - (x, y), radius = cv2.minEnclosingCircle(fingers_contour) - center = (int(x), int(y)) - radius = int(radius) + 10 - hand_contour = extract_contour_inside_circle(hand_contour, (center, radius)) - hand_bounding_rect = cv2.boundingRect(hand_contour) - # self.follow(frame,hands_mask,(int(max(y-radius,0)), int(max(x-radius,0)) ,int(radius*2),int(radius*2))) + hand_bounding_rect, hand_circle, hand_contour = self.get_hand_bounding_rect(hand_contour, fingers_contour) other = frame.copy() x, y, w, h = hand_bounding_rect + center, radius = hand_circle cv2.circle(other, center, radius, (255, 255, 0), 2) cv2.drawContours(other, [hand_contour], 0, (255, 255, 0)) cv2.rectangle(other, (hand_bounding_rect[0], hand_bounding_rect[1]), ( @@ -540,72 +452,6 @@ def detect_hands(self, frame): cv2.putText(other, (str(x) + ", " + str(y)), (x - 10, y - 10), self.font, 0.3, [255, 255, 255], 1) cv2.imshow("Hand circunferences", other) - # # x, y, w, h = cv2.boundingRect(fingers_contour) - # # cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) - # print contour_indices - # size_to_extend_contour = 40 - # index_of_min_x_index = contour_indices.index(min_x_index) - # new_section = range(min_x_index+1,min_x_index+size_to_extend_contour) - # for new_index in reversed(new_section): - # contour_indices.insert(index_of_min_x_index+1, new_index) - # - # index_of_max_x_index = contour_indices.index(max_x_index) - # new_section = range(max_x_index-1,max_x_index-size_to_extend_contour-1, -1) - # for new_index in new_section: - # contour_indices.insert(index_of_max_x_index, new_index) - # - # # if min_x_index-size_to_extend_contour < 0: - # # half_section = range(0,min_x_index) - # # half_section_size = len(half_section) - # # index_of_min_x_index = contour_indices.index(min_x_index) - # # for new_index in reversed(half_section): - # # contour_indices.insert(index_of_min_x_index, new_index) - # # other_half = range((len(hand_contour))-(size_to_extend_contour-half_section_size),len(hand_contour)) - # # for new_index in reversed(other_half): - # # contour_indices.insert(index_of_min_x_index,new_index) - # # else: - # # new_section = range(min_x_index-size_to_extend_contour,min_x_index) - # # index_of_min_x_index = contour_indices.index(min_x_index) - # # for new_index in reversed(new_section): - # # contour_indices.insert(index_of_min_x_index, new_index) - # # - # # if max_x_index + size_to_extend_contour > len(hand_contour)-1: - # # half_section = range(max_x_index, len(hand_contour)) - # # half_section_size = len(half_section) - # # index_of_max_x_index = contour_indices.index(max_x_index) - # # for new_index in reversed(half_section): - # # contour_indices.insert(index_of_max_x_index, new_index) - # # other_half = range(0,(size_to_extend_contour - half_section_size)) - # # for new_index in reversed(other_half): - # # contour_indices.insert(index_of_max_x_index, new_index) - # # else: - # # new_section = range(max_x_index , max_x_index+size_to_extend_contour) - # # index_of_max_x_index = contour_indices.index(max_x_index) - # # for new_index in reversed(new_section): - # # contour_indices.insert(index_of_max_x_index, new_index) - # - # - # # min_index= min(contour_indices) - # # max_index = max(contour_indices) - # # if min_index -1 >= 0: - # # contour_indices.append(min_index-1) - # # else: - # # contour_indices.append(len(hand_contour)-1) - # # if max_index+1 < len(hand_contour): - # # contour_indices.append(max_index+1) - # # else: - # # contour_indices.append(0) - # # contour_indices = sorted(set(contour_indices)) - # - # - # # for i in sorted(set(new_defect_indices)): - # # s, e, f, d = defects[i, 0] - # # contour_indices.append(s) - # # contour_indices.append(e) - # # hand_contour = np.take(hand_contour, contour_indices, axis=0, mode="wrap") - # cv2.drawContours(frame, [hand_contour], 0, (0, 255, 0), 1) - - # cv2.drawContours(frame, [hull], 0, (0, 0, 255), 3) # Find moments of the largest contour moments = cv2.moments(hand_contour) @@ -632,20 +478,6 @@ def detect_hands(self, frame): sorted_defects_distances = sorted(distance_between_defects_to_center) average_defect_distance = np.mean(sorted_defects_distances[0:2]) - # # Get fingertip points from contour hull - # # If points are in proximity of 80 pixels, consider as a single point in the group - # finger = [] - # for i in range(0, len(hull) - 1): - # if (np.absolute(hull[i][0][0] - hull[i + 1][0][0]) > 10) or ( - # np.absolute(hull[i][0][1] - hull[i + 1][0][1]) > 10): - # if hull[i][0][1] < 500: - # finger.append(hull[i][0]) - # - # - # # The fingertip points are 5 hull points with largest y coordinates - # finger = sorted(finger, key=lambda x: x[1]) - # fingers = finger[0:5] - # Calculate distance of each finger tip to the center mass finger_distances = [] for i in range(0, len(fingertips_coords)): @@ -666,41 +498,162 @@ def detect_hands(self, frame): new_hands.append(hand) return new_hands - def update_hand_charasteristics(self, frame, hand): - updated_hand = Hand() - updated_hand.id = hand.id - updated_hand.fingertips = [] - updated_hand.intertips = [] - updated_hand.center_of_mass = None - updated_hand.finger_distances = [] - updated_hand.average_defect_distance = [] - updated_hand.contour = None - updated_hand.bounding_rect = hand.bounding_rect - updated_hand.tracking_fails = hand.tracking_fails - updated_hand.position_history = hand.position_history - updated_hand.color = hand.color - # Create a binary image with where white will be skin colors and rest is black - hands_mask = self.create_hands_mask(frame) - current_hand_roi_mask = np.zeros(hands_mask.shape, dtype='uint8') - x, y, w, h = hand.bounding_rect - current_hand_roi_mask[y:y + h, x:x + w] = 255 - current_hand_mask = cv2.bitwise_and(hands_mask, current_hand_roi_mask) - to_show = cv2.resize(current_hand_mask, None, fx=.3, fy=.3, interpolation=cv2.INTER_CUBIC) - # cv2.putText(to_show, (str(w)), (x + w, y), self.font, 0.3, [255, 255, 255], 1) - # cv2.putText(to_show, (str(h)), (x + w, y + h), self.font, 0.3, [100, 255, 255], 1) - # cv2.putText(to_show, (str(w * h)), (x + w / 2, y + h / 2), self.font, 0.3, [100, 100, 255], 1) - # cv2.putText(to_show, (str(x)+", "+str(y)), (x-10, y-10), self.font, 0.3, [255, 255, 255], 1) - cv2.imshow("current hands_mask" + str(hand.id), to_show) - - ret, thresh = cv2.threshold(current_hand_mask, 127, 255, 0) - - # Find contours of the filtered frame - _, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + def get_hand_fingertips(self, hand_contour, defects): + intertips_coords = [] + intertips_indexes = [] + far_defect = [] + fingertips_coords = [] + fingertips_indexes = [] + defect_indices = [] + for defect_index in range(defects.shape[0]): + s, e, f, d = defects[defect_index, 0] + start = tuple(hand_contour[s][0]) + end = tuple(hand_contour[e][0]) + far = tuple(hand_contour[f][0]) + far_defect.append(far) + # cv2.line(frame, start, end, [0, 255, 0], 1) + a = math.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2) + b = math.sqrt((far[0] - start[0]) ** 2 + (far[1] - start[1]) ** 2) + c = math.sqrt((end[0] - far[0]) ** 2 + (end[1] - far[1]) ** 2) + angle = math.acos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c)) # cosine theorem + # cv2.circle(frame, far, 8, [211, 84, 125], -1) + # cv2.circle(frame, start, 8, [0, 84, 125], -1) + # cv2.circle(frame, end, 8, [0, 84, 125], -1) + # Get tips and intertips coordinates + # TODO: ENV_DEPENDENCE: this angle > 90ยบ determinate if two points are considered fingertips or not and 90 make thumb to fail in some occasions + intertips_max_angle = math.pi / 1.7 + if angle <= intertips_max_angle: # angle less than 90 degree, treat as fingers + defect_indices.append(defect_index) + # cv2.circle(frame, far, 8, [211, 84, 0], -1) + intertips_coords.append(far) + intertips_indexes.append(f) + # cv2.putText(frame, str(s), start, self.font, 0.7, (255, 255, 255), 1) + # cv2.putText(frame, str(e), end, self.font, 0.7, (255, 255, 200), 1) + if len(fingertips_coords) > 0: + from scipy.spatial import distance + # calculate distances from start and end to the already known tips + start_distance, end_distance = tuple( + distance.cdist(fingertips_coords, [start, end]).min(axis=0)) + # TODO: ENV_DEPENDENCE: it determinate the pixels distance to consider two points the same. It depends on camera resolution and distance from the hand to the camera + same_fingertip_radius = 10 + if start_distance > same_fingertip_radius: + fingertips_coords.append(start) + fingertips_indexes.append(s) + + # cv2.circle(frame, start, 10, [255, 100, 255], 3) + if end_distance > same_fingertip_radius: + fingertips_coords.append(end) + fingertips_indexes.append(e) + + # cv2.circle(frame, end, 10, [255, 100, 255], 3) + else: + fingertips_coords.append(start) + fingertips_indexes.append(s) + + # cv2.circle(frame, start, 10, [255, 100, 255], 3) + fingertips_coords.append(end) + fingertips_indexes.append(e) + + # cv2.circle(frame, end, 10, [255, 100, 255], 3) + + # cv2.circle(frame, far, 10, [100, 255, 255], 3) + return fingertips_coords, fingertips_indexes, intertips_coords, intertips_indexes + + def get_hand_bounding_rect(self, hand_contour, fingers_contour): + (x, y), radius = cv2.minEnclosingCircle(fingers_contour) + center = (int(x), int(y)) + radius = int(radius) + 10 + hand_contour = extract_contour_inside_circle(hand_contour, (center, radius)) + hand_bounding_rect = cv2.boundingRect(hand_contour) + return hand_bounding_rect, ((int(x), int(y)), radius), hand_contour + + def contour_to_hand(self, frame, hand_contour, hand_to_update = None): + if hand_to_update is not None: + updated_hand = copy.deepcopy(hand_to_update) + updated_hand.contour = hand_contour + else: + updated_hand = Hand() + + hull2 = cv2.convexHull(hand_contour, returnPoints=False) + defects = cv2.convexityDefects(hand_contour, hull2) + + # Get defect points and draw them in the original image + if defects is not None: + fingertips_coords, \ + fingertips_indexes, \ + intertips_coords, \ + intertips_indexes = self.get_hand_fingertips(hand_contour, defects) + + if 5 >= len(fingertips_coords) > 2 and len(fingertips_coords) == len(intertips_coords) + 1: + updated_hand.fingertips = fingertips_coords + updated_hand.intertips = intertips_coords + + if len(fingertips_coords) == 5: + fingers_contour = np.take(hand_contour, + fingertips_indexes + intertips_indexes, + axis=0, + mode="wrap") + hand_bounding_rect, hand_circle, hand_contour = self.get_hand_bounding_rect(hand_contour, fingers_contour) + updated_hand.contour = hand_contour + + # Find moments of the largest contour + moments = cv2.moments(hand_contour) + center_of_mass = None + finger_distances = [] + average_defect_distance = None + # Central mass of first order moments + if moments['m00'] != 0: + cx = int(moments['m10'] / moments['m00']) # cx = M10/M00 + cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 + center_of_mass = (cx, cy) + updated_hand.center_of_mass = center_of_mass + updated_hand.position_history.append(center_of_mass) + + if center_of_mass is not None and len(intertips_coords) > 0: + # Distance from each finger defect(finger webbing) to the center mass + distance_between_defects_to_center = [] + for far in intertips_coords: + x = np.array(far) + center_mass_array = np.array(center_of_mass) + distance = np.sqrt( + np.power(x[0] - center_mass_array[0], + 2) + np.power(x[1] - center_mass_array[1], 2) + ) + distance_between_defects_to_center.append(distance) + + # Get an average of three shortest distances from finger webbing to center mass + sorted_defects_distances = sorted(distance_between_defects_to_center) + average_defect_distance = np.mean(sorted_defects_distances[0:2]) + updated_hand.average_defect_distance = average_defect_distance + # # Get fingertip points from contour hull + # # If points are in proximity of 80 pixels, consider as a single point in the group + # finger = [] + # for i in range(0, len(hull) - 1): + # if (np.absolute(hull[i][0][0] - hull[i + 1][0][0]) > 10) or ( + # np.absolute(hull[i][0][1] - hull[i + 1][0][1]) > 10): + # if hull[i][0][1] < 500: + # finger.append(hull[i][0]) + # + # + # # The fingertip points are 5 hull points with largest y coordinates + # finger = sorted(finger, key=lambda x: x[1]) + # fingers = finger[0:5] + if center_of_mass is not None and len(fingertips_coords) > 0: + # Calculate distance of each finger tip to the center mass + finger_distances = [] + for i in range(0, len(fingertips_coords)): + distance = np.sqrt( + np.power(fingertips_coords[i][0] - center_of_mass[0], 2) + np.power( + fingertips_coords[i][1] - center_of_mass[0], 2)) + finger_distances.append(distance) + updated_hand.finger_distances = finger_distances + return updated_hand - # Draw Contours - # cv2.drawContours(frame, cnt, -1, (122,122,0), 3) + def update_hand_charasteristics(self, frame, hand): + updated_hand = copy.deepcopy(hand) - if contours: + contours = self.create_contours(frame, hand.bounding_rect) + if len(contours) > 0: # Find Max contour area (Assume that hand is in the frame) # TODO: ENV_DEPENDENCE: depends on the camera resolution, distance to the background, noisy areas sizes max_area = 0 @@ -720,138 +673,9 @@ def update_hand_charasteristics(self, frame, hand): # Find convex defects if hand_contour is not None: - updated_hand.contour = hand_contour - updated_hand.tracking_fails = 0 - hull2 = cv2.convexHull(hand_contour, returnPoints=False) - defects = cv2.convexityDefects(hand_contour, hull2) - - # Get defect points and draw them in the original image - if defects is not None: - intertips_coords = [] - intertips_indexes = [] - far_defect = [] - fingertips_coords = [] - fingertips_indexes = [] - defect_indices = [] - for defect_index in range(defects.shape[0]): - s, e, f, d = defects[defect_index, 0] - start = tuple(hand_contour[s][0]) - end = tuple(hand_contour[e][0]) - far = tuple(hand_contour[f][0]) - far_defect.append(far) - # cv2.line(frame, start, end, [0, 255, 0], 1) - a = math.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2) - b = math.sqrt((far[0] - start[0]) ** 2 + (far[1] - start[1]) ** 2) - c = math.sqrt((end[0] - far[0]) ** 2 + (end[1] - far[1]) ** 2) - angle = math.acos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c)) # cosine theorem - # cv2.circle(frame, far, 8, [211, 84, 125], -1) - # cv2.circle(frame, start, 8, [0, 84, 125], -1) - # cv2.circle(frame, end, 8, [0, 84, 125], -1) - # Get tips and intertips coordinates - # TODO: ENV_DEPENDENCE: this angle > 90ยบ determinate if two points are considered fingertips or not and 90 make thumb to fail in some occasions - intertips_max_angle = math.pi / 1.7 - if angle <= intertips_max_angle: # angle less than 90 degree, treat as fingers - defect_indices.append(defect_index) - # cv2.circle(frame, far, 8, [211, 84, 0], -1) - intertips_coords.append(far) - intertips_indexes.append(f) - # cv2.putText(frame, str(s), start, self.font, 0.7, (255, 255, 255), 1) - # cv2.putText(frame, str(e), end, self.font, 0.7, (255, 255, 200), 1) - if len(fingertips_coords) > 0: - from scipy.spatial import distance - # calculate distances from start and end to the already known tips - start_distance, end_distance = tuple( - distance.cdist(fingertips_coords, [start, end]).min(axis=0)) - # TODO: ENV_DEPENDENCE: it determinate the pixels distance to consider two points the same. It depends on camera resolution and distance from the hand to the camera - same_fingertip_radius = 10 - if start_distance > same_fingertip_radius: - fingertips_coords.append(start) - fingertips_indexes.append(s) - - # cv2.circle(frame, start, 10, [255, 100, 255], 3) - if end_distance > same_fingertip_radius: - fingertips_coords.append(end) - fingertips_indexes.append(e) - - # cv2.circle(frame, end, 10, [255, 100, 255], 3) - else: - fingertips_coords.append(start) - fingertips_indexes.append(s) - - # cv2.circle(frame, start, 10, [255, 100, 255], 3) - fingertips_coords.append(end) - fingertips_indexes.append(e) - - # cv2.circle(frame, end, 10, [255, 100, 255], 3) - - # cv2.circle(frame, far, 10, [100, 255, 255], 3) - # cv2.drawContours(frame, [hand_contour], 0, (255, 0, 0), 2) - if 5 >= len(fingertips_coords) > 2 and len(fingertips_coords) == len(intertips_coords) + 1: - updated_hand.fingertips = fingertips_coords - updated_hand.intertips = intertips_coords - - if len(fingertips_coords) == 5: - fingers_contour = np.take(hand_contour, - fingertips_indexes + intertips_indexes, - axis=0, - mode="wrap") - (x, y), radius = cv2.minEnclosingCircle(fingers_contour) - center = (int(x), int(y)) - radius = int(radius) + 10 - hand_contour = extract_contour_inside_circle(hand_contour, (center, radius)) - updated_hand.contour = hand_contour - - # Find moments of the largest contour - moments = cv2.moments(hand_contour) - center_of_mass = None - finger_distances = [] - average_defect_distance = None - # Central mass of first order moments - if moments['m00'] != 0: - cx = int(moments['m10'] / moments['m00']) # cx = M10/M00 - cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 - center_of_mass = (cx, cy) - updated_hand.center_of_mass = center_of_mass - updated_hand.position_history.append(center_of_mass) - - if center_of_mass is not None and len(intertips_coords) > 0: - # Distance from each finger defect(finger webbing) to the center mass - distance_between_defects_to_center = [] - for far in intertips_coords: - x = np.array(far) - center_mass_array = np.array(center_of_mass) - distance = np.sqrt( - np.power(x[0] - center_mass_array[0], - 2) + np.power(x[1] - center_mass_array[1], 2) - ) - distance_between_defects_to_center.append(distance) - - # Get an average of three shortest distances from finger webbing to center mass - sorted_defects_distances = sorted(distance_between_defects_to_center) - average_defect_distance = np.mean(sorted_defects_distances[0:2]) - updated_hand.average_defect_distance = average_defect_distance - # # Get fingertip points from contour hull - # # If points are in proximity of 80 pixels, consider as a single point in the group - # finger = [] - # for i in range(0, len(hull) - 1): - # if (np.absolute(hull[i][0][0] - hull[i + 1][0][0]) > 10) or ( - # np.absolute(hull[i][0][1] - hull[i + 1][0][1]) > 10): - # if hull[i][0][1] < 500: - # finger.append(hull[i][0]) - # - # - # # The fingertip points are 5 hull points with largest y coordinates - # finger = sorted(finger, key=lambda x: x[1]) - # fingers = finger[0:5] - if center_of_mass is not None and len(fingertips_coords) > 0: - # Calculate distance of each finger tip to the center mass - finger_distances = [] - for i in range(0, len(fingertips_coords)): - distance = np.sqrt( - np.power(fingertips_coords[i][0] - center_of_mass[0], 2) + np.power( - fingertips_coords[i][1] - center_of_mass[0], 2)) - finger_distances.append(distance) - updated_hand.finger_distances = finger_distances + updated_hand = self.contour_to_hand(frame,hand_contour,updated_hand) + else: + updated_hand.contour = [] else: return None return updated_hand From 0d33fbca4cdaa2626778fb8b1a0e37ac786703ed Mon Sep 17 00:00:00 2001 From: orensbruli Date: Mon, 25 Jun 2018 17:30:35 +0200 Subject: [PATCH 14/54] Created tw new methods to get the truth value of the class hand depending on the number of frames, the detections and the traking. --- HandDetection.py | 49 +++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 44 insertions(+), 5 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index 086c3fb..684d313 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -4,6 +4,7 @@ import math import time from collections import deque +from datetime import datetime import cv2 import numpy as np @@ -53,6 +54,13 @@ def find_distance(point_a, point_b): # cv2.createTrackbar('s', 'HSV_TrackBar', 0, 255, nothing) # cv2.createTrackbar('v', 'HSV_TrackBar', 0, 255, nothing) +MAX_UNDETECTED_FRAMES = 30*10 # 30 FPS * 10 seconds +MAX_UNDETECTED_SECONDS = 10 +DETECTION_TRUTH_FACTOR = 2. # points of life to recover if detected in one iteration +TRACKING_TRUTH_FACTOR = .1 # points of life to recover if tracked in one iteration +UNDETECTION_TRUTH_FACTOR = 3. # points of life to recover if detected in one iteration +UNTRACKING_TRUTH_FACTOR = .1 # points of life to recover if tracked in one iteration +MAX_TRUTH_VALUE = 100. class Hand: def __init__(self): @@ -72,6 +80,37 @@ def __init__(self): self.detected = True self.position_history = [] self.color = get_random_color()[0] + self.truth_value = 100 + + def update_truth_value_by_time(self): + if self.last_time_update is not None: + elapsed_time = datetime.now()-self.last_time_update + elapsed_miliseconds = int(elapsed_time.total_seconds() * 1000) + truth_subtraction = elapsed_miliseconds*MAX_TRUTH_VALUE /MAX_UNDETECTED_SECONDS*1000 + detection_adition = DETECTION_TRUTH_FACTOR if self.detected is True else 0 + tracking_adition = TRACKING_TRUTH_FACTOR if self.tracked is True else 0 + self.truth_value = self.truth_value - truth_subtraction + detection_adition + tracking_adition + self.last_time_update = datetime.now() + + + def update_truth_value_by_frame(self): + one_frame_truth_subtraction = MAX_TRUTH_VALUE / MAX_UNDETECTED_FRAMES + detection_adition = 0 + if self.detected: + detection_adition = DETECTION_TRUTH_FACTOR*one_frame_truth_subtraction + else: + detection_adition = -1 * UNDETECTION_TRUTH_FACTOR * one_frame_truth_subtraction + tracking_adition = 0 + if self.tracked: + tracking_adition = TRACKING_TRUTH_FACTOR*one_frame_truth_subtraction + else: + tracking_adition = -1 *UNTRACKING_TRUTH_FACTOR * one_frame_truth_subtraction + new_truth_value = self.truth_value - one_frame_truth_subtraction + detection_adition + tracking_adition + if new_truth_value <= 100: + self.truth_value = new_truth_value + else: + self.truth_value = 100 + def copy_main_attributes(self): updated_hand = Hand() @@ -241,8 +280,8 @@ def compute(self): hand.bounding_rect = hand.tracking_window else: print "_____________No updated information" - # TODO: How to decided when to remove - if (hand.tracking_fails > 20 and hand.detected is False) or (hand.detection_fail > 20): + hand.update_truth_value_by_frame() + if hand.truth_value<=0: print "removing hand" self.hands.remove(hand) @@ -373,7 +412,7 @@ def draw_hand_overlay(self, frame, hand): cv2.putText(frame, 'Center', tuple(hand.center_of_mass), self.font, 0.5, (255, 255, 255), 1) hand_string = "hand " + str(hand.id) + ": detected =" + str(hand.detected) + " tracked =" + str( - hand.tracked) + " at " + str(hand.center_of_mass) + hand.tracked) + " at " + str(hand.center_of_mass) +" truth: "+str(hand.truth_value)+"%" cv2.putText(frame, hand_string, (10, 30 + 15 * int(hand.id)), self.font, 0.5, (255, 255, 255), 1) return frame @@ -769,8 +808,8 @@ def get_MOG2_mask(self, image): def main(): - # hand_detector = HandDetector() - hand_detector = HandDetector('/home/robolab/PycharmProjects/TVGames/libs/Hand_Detection/hand_on_screen2.mp4') + hand_detector = HandDetector() + # hand_detector = HandDetector('/home/robolab/PycharmProjects/TVGames/libs/Hand_Detection/hand_on_screen2.mp4') hand_detector.compute() hand_detector.exit() From ed6ec5c1fabc01d0c56b03e175612303d55b71d1 Mon Sep 17 00:00:00 2001 From: orensbruli Date: Tue, 26 Jun 2018 11:59:13 +0200 Subject: [PATCH 15/54] Fixed a bug with the hand detection update. --- HandDetection.py | 21 ++++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index 684d313..3c23869 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -82,6 +82,17 @@ def __init__(self): self.color = get_random_color()[0] self.truth_value = 100 + def update_attributes_from_detected(self, other_hand): + self.fingertips = other_hand.fingertips + self.intertips = other_hand.intertips + self.center_of_mass = other_hand.center_of_mass + self.finger_distances = other_hand.finger_distances + self.average_defect_distance = other_hand.average_defect_distance + self.contour = other_hand.contour + self.bounding_rect = other_hand.bounding_rect + self.detected = True + + def update_truth_value_by_time(self): if self.last_time_update is not None: elapsed_time = datetime.now()-self.last_time_update @@ -219,11 +230,7 @@ def update_detection(self, frame): existing_hand.bounding_rect) if intersection_value > 0.1: hand_exists = True - detected_hand.detected = True - detected_hand.id = existing_hand.id - detected_hand.position_history = existing_hand.position_history - detected_hand.color = existing_hand.color - self.hands[existing_hand_index] = detected_hand + existing_hand.update_attributes_from_detected(detected_hand) break if not hand_exists: detected_hand.id = str(self.next_hand_id) @@ -808,8 +815,8 @@ def get_MOG2_mask(self, image): def main(): - hand_detector = HandDetector() - # hand_detector = HandDetector('/home/robolab/PycharmProjects/TVGames/libs/Hand_Detection/hand_on_screen2.mp4') + # hand_detector = HandDetector() + hand_detector = HandDetector('/home/robolab/PycharmProjects/TVGames/libs/Hand_Detection/hand_on_screen2.mp4') hand_detector.compute() hand_detector.exit() From 0304db5b74f267a376c79ed8bce1203734f86ca1 Mon Sep 17 00:00:00 2001 From: orensbruli Date: Tue, 26 Jun 2018 13:07:05 +0200 Subject: [PATCH 16/54] Fixed some bugs Sumarized some duplicated code on a new generic method contour_to_hand --- HandDetection.py | 264 ++++++++++++++++++----------------------------- 1 file changed, 102 insertions(+), 162 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index 3c23869..7872154 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -10,6 +10,8 @@ import numpy as np import random +from scipy.linalg import fractional_matrix_power + def get_random_color(n=1): ret = [] @@ -54,14 +56,15 @@ def find_distance(point_a, point_b): # cv2.createTrackbar('s', 'HSV_TrackBar', 0, 255, nothing) # cv2.createTrackbar('v', 'HSV_TrackBar', 0, 255, nothing) -MAX_UNDETECTED_FRAMES = 30*10 # 30 FPS * 10 seconds +MAX_UNDETECTED_FRAMES = 30 * 10 # 30 FPS * 10 seconds MAX_UNDETECTED_SECONDS = 10 -DETECTION_TRUTH_FACTOR = 2. # points of life to recover if detected in one iteration -TRACKING_TRUTH_FACTOR = .1 # points of life to recover if tracked in one iteration -UNDETECTION_TRUTH_FACTOR = 3. # points of life to recover if detected in one iteration -UNTRACKING_TRUTH_FACTOR = .1 # points of life to recover if tracked in one iteration +DETECTION_TRUTH_FACTOR = 2. # points of life to recover if detected in one iteration +TRACKING_TRUTH_FACTOR = .1 # points of life to recover if tracked in one iteration +UNDETECTION_TRUTH_FACTOR = 3. # points of life to recover if detected in one iteration +UNTRACKING_TRUTH_FACTOR = .1 # points of life to recover if tracked in one iteration MAX_TRUTH_VALUE = 100. + class Hand: def __init__(self): self.id = None @@ -70,7 +73,7 @@ def __init__(self): self.center_of_mass = None self.finger_distances = [] self.average_defect_distance = [] - self.contour = None, + self.contour = None self.bounding_rect = None self.tracking_fails = 0 self.detection_fail = 0 @@ -92,36 +95,34 @@ def update_attributes_from_detected(self, other_hand): self.bounding_rect = other_hand.bounding_rect self.detected = True - def update_truth_value_by_time(self): if self.last_time_update is not None: - elapsed_time = datetime.now()-self.last_time_update + elapsed_time = datetime.now() - self.last_time_update elapsed_miliseconds = int(elapsed_time.total_seconds() * 1000) - truth_subtraction = elapsed_miliseconds*MAX_TRUTH_VALUE /MAX_UNDETECTED_SECONDS*1000 + truth_subtraction = elapsed_miliseconds * MAX_TRUTH_VALUE / MAX_UNDETECTED_SECONDS * 1000 detection_adition = DETECTION_TRUTH_FACTOR if self.detected is True else 0 tracking_adition = TRACKING_TRUTH_FACTOR if self.tracked is True else 0 self.truth_value = self.truth_value - truth_subtraction + detection_adition + tracking_adition self.last_time_update = datetime.now() - def update_truth_value_by_frame(self): one_frame_truth_subtraction = MAX_TRUTH_VALUE / MAX_UNDETECTED_FRAMES detection_adition = 0 if self.detected: - detection_adition = DETECTION_TRUTH_FACTOR*one_frame_truth_subtraction + detection_adition = DETECTION_TRUTH_FACTOR * one_frame_truth_subtraction else: detection_adition = -1 * UNDETECTION_TRUTH_FACTOR * one_frame_truth_subtraction tracking_adition = 0 if self.tracked: - tracking_adition = TRACKING_TRUTH_FACTOR*one_frame_truth_subtraction + tracking_adition = TRACKING_TRUTH_FACTOR * one_frame_truth_subtraction else: - tracking_adition = -1 *UNTRACKING_TRUTH_FACTOR * one_frame_truth_subtraction + tracking_adition = -1 * UNTRACKING_TRUTH_FACTOR * one_frame_truth_subtraction new_truth_value = self.truth_value - one_frame_truth_subtraction + detection_adition + tracking_adition if new_truth_value <= 100: self.truth_value = new_truth_value else: self.truth_value = 100 - + self.frame_count += 1 def copy_main_attributes(self): updated_hand = Hand() @@ -187,9 +188,6 @@ def upscale_bounding_rec(bounding_rect, frame_shape, upscaled_pixels): return upscaled_bounding_rect - - - def extract_contour_inside_circle(full_contour, circle): center, radius = circle new_contour = [] @@ -219,7 +217,6 @@ def __init__(self, source=0): def update_detection(self, frame): for hand in self.hands: hand.detected = False - hand.frame_count += 1 detected_hands = self.detect_hands_in_frame(frame) for detected_hand in detected_hands: if len(self.hands) > 0: @@ -253,10 +250,10 @@ def update_tracking(self, frame): ret, tracking_window = self.follow(frame, hands_mask, hand.bounding_rect) if ret and tracking_window is not None: updated_hand = self.update_hand_charasteristics(frame, hand) - updated_hand.detected = hand.detected - updated_hand.tracking_window = tracking_window - updated_hand.detection_fail = hand.detection_fail if updated_hand is not None: + updated_hand.detected = hand.detected + updated_hand.tracking_window = tracking_window + updated_hand.detection_fail = hand.detection_fail updated_hand.tracked = True self.hands[index] = updated_hand else: @@ -288,7 +285,7 @@ def compute(self): else: print "_____________No updated information" hand.update_truth_value_by_frame() - if hand.truth_value<=0: + if hand.truth_value <= 0: print "removing hand" self.hands.remove(hand) @@ -419,15 +416,15 @@ def draw_hand_overlay(self, frame, hand): cv2.putText(frame, 'Center', tuple(hand.center_of_mass), self.font, 0.5, (255, 255, 255), 1) hand_string = "hand " + str(hand.id) + ": detected =" + str(hand.detected) + " tracked =" + str( - hand.tracked) + " at " + str(hand.center_of_mass) +" truth: "+str(hand.truth_value)+"%" + hand.tracked) + " at " + str(hand.center_of_mass) + " truth: " + str(hand.truth_value) + "%" cv2.putText(frame, hand_string, (10, 30 + 15 * int(hand.id)), self.font, 0.5, (255, 255, 255), 1) return frame - def create_contours(self, frame, roi_mask=None): + def create_contours_and_mask(self, frame, roi_mask=None): # Create a binary image with where white will be skin colors and rest is black hands_mask = self.create_hands_mask(frame) if hands_mask is None: - return [] + return ([], []) cv2.imshow("hands_mask", hands_mask) if roi_mask is not None: @@ -446,12 +443,12 @@ def create_contours(self, frame, roi_mask=None): # Find contours of the filtered frame _, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) - return contours + return (contours, hands_mask) def detect_hands_in_frame(self, frame): new_hands = [] - contours = self.create_contours(frame) + contours, _ = self.create_contours_and_mask(frame) # Draw Contours # cv2.drawContours(frame, cnt, -1, (122,122,0), 3) @@ -465,85 +462,17 @@ def detect_hands_in_frame(self, frame): area = cv2.contourArea(contour) if area > min_area: hand_contour = contour - - # Find convex hull - # hull = cv2.convexHull(hand_contour) - # for point in hull: - # cv2.circle(frame, tuple(point[0]), 8, [255, 100, 10], -1) - - # Find convex defects - hull2 = cv2.convexHull(hand_contour, returnPoints=False) - defects = cv2.convexityDefects(hand_contour, hull2) - - # Get defect points and draw them in the original image - if defects is not None: - fingertips_coords, fingertips_indexes, intertips_coords, intertips_indexes = self.get_hand_fingertips(hand_contour, defects) - # cv2.drawContours(frame, [hand_contour], 0, (255, 0, 0), 2) - if len(fingertips_coords) == 5 and len(fingertips_coords) == len(intertips_coords) + 1: - - fingers_contour = np.take(hand_contour, fingertips_indexes + intertips_indexes, axis=0, - mode="wrap") - hand_bounding_rect, hand_circle, hand_contour = self.get_hand_bounding_rect(hand_contour, fingers_contour) - other = frame.copy() - x, y, w, h = hand_bounding_rect - center, radius = hand_circle - cv2.circle(other, center, radius, (255, 255, 0), 2) - cv2.drawContours(other, [hand_contour], 0, (255, 255, 0)) - cv2.rectangle(other, (hand_bounding_rect[0], hand_bounding_rect[1]), ( - hand_bounding_rect[0] + hand_bounding_rect[2], - hand_bounding_rect[1] + hand_bounding_rect[3]), (255, 255, 0), 1) - cv2.putText(other, (str(w)), (x + w, y), self.font, 0.3, [255, 255, 255], 1) - cv2.putText(other, (str(h)), (x + w, y + h), self.font, 0.3, [100, 255, 255], 1) - cv2.putText(other, (str(w * h)), (x + w / 2, y + h / 2), self.font, 0.3, [100, 100, 255], 1) - cv2.putText(other, (str(x) + ", " + str(y)), (x - 10, y - 10), self.font, 0.3, - [255, 255, 255], 1) - cv2.imshow("Hand circunferences", other) - - # Find moments of the largest contour - moments = cv2.moments(hand_contour) - center_mass = None - finger_distances = None - average_defect_distance = None - # Central mass of first order moments - if moments['m00'] != 0: - cx = int(moments['m10'] / moments['m00']) # cx = M10/M00 - cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 - center_mass = (cx, cy) - - # Distance from each finger defect(finger webbing) to the center mass - distance_between_defects_to_center = [] - for far in intertips_coords: - x = np.array(far) - center_mass_array = np.array(center_mass) - distance = np.sqrt( - np.power(x[0] - center_mass_array[0], 2) + np.power(x[1] - center_mass_array[1], - 2)) - distance_between_defects_to_center.append(distance) - - # Get an average of three shortest distances from finger webbing to center mass - sorted_defects_distances = sorted(distance_between_defects_to_center) - average_defect_distance = np.mean(sorted_defects_distances[0:2]) - - # Calculate distance of each finger tip to the center mass - finger_distances = [] - for i in range(0, len(fingertips_coords)): - distance = np.sqrt( - np.power(fingertips_coords[i][0] - center_mass[0], 2) + np.power( - fingertips_coords[i][1] - center_mass[0], 2)) - finger_distances.append(distance) - hand = Hand() - hand.fingertips = fingertips_coords - hand.intertips = intertips_coords - hand.center_of_mass = center_mass - hand.finger_distances = finger_distances - hand.average_defect_distance = average_defect_distance - hand.contour = hand_contour - hand.bounding_rect = hand_bounding_rect - hand.tracking_fails = 0 - # hand.position_history.append(hand.center_of_mass) - new_hands.append(hand) + hand = self.contour_to_hand(frame, hand_contour) + if hand is not None: + new_hands.append(hand) return new_hands + def is_it_hand(self, fingertips, intertips, initial=True): + if initial: + return len(fingertips) == 5 and len(fingertips) == len(intertips) + 1 + else: + return 5 >= len(fingertips) > 2 and len(fingertips) == len(intertips) + 1 + def get_hand_fingertips(self, hand_contour, defects): intertips_coords = [] intertips_indexes = [] @@ -613,10 +542,13 @@ def get_hand_bounding_rect(self, hand_contour, fingers_contour): hand_bounding_rect = cv2.boundingRect(hand_contour) return hand_bounding_rect, ((int(x), int(y)), radius), hand_contour - def contour_to_hand(self, frame, hand_contour, hand_to_update = None): - if hand_to_update is not None: + def contour_to_hand(self, frame, hand_contour, hand_to_update=None): + + initial = (hand_to_update == None) + if not initial: updated_hand = copy.deepcopy(hand_to_update) updated_hand.contour = hand_contour + hand_bounding_rect = cv2.boundingRect(hand_contour) else: updated_hand = Hand() @@ -630,7 +562,8 @@ def contour_to_hand(self, frame, hand_contour, hand_to_update = None): intertips_coords, \ intertips_indexes = self.get_hand_fingertips(hand_contour, defects) - if 5 >= len(fingertips_coords) > 2 and len(fingertips_coords) == len(intertips_coords) + 1: + is_hand = self.is_it_hand(fingertips_coords, intertips_coords, initial=initial) + if is_hand: updated_hand.fingertips = fingertips_coords updated_hand.intertips = intertips_coords @@ -639,66 +572,73 @@ def contour_to_hand(self, frame, hand_contour, hand_to_update = None): fingertips_indexes + intertips_indexes, axis=0, mode="wrap") - hand_bounding_rect, hand_circle, hand_contour = self.get_hand_bounding_rect(hand_contour, fingers_contour) - updated_hand.contour = hand_contour - - # Find moments of the largest contour - moments = cv2.moments(hand_contour) - center_of_mass = None - finger_distances = [] - average_defect_distance = None - # Central mass of first order moments - if moments['m00'] != 0: - cx = int(moments['m10'] / moments['m00']) # cx = M10/M00 - cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 - center_of_mass = (cx, cy) - updated_hand.center_of_mass = center_of_mass - updated_hand.position_history.append(center_of_mass) - - if center_of_mass is not None and len(intertips_coords) > 0: - # Distance from each finger defect(finger webbing) to the center mass - distance_between_defects_to_center = [] - for far in intertips_coords: - x = np.array(far) - center_mass_array = np.array(center_of_mass) - distance = np.sqrt( - np.power(x[0] - center_mass_array[0], - 2) + np.power(x[1] - center_mass_array[1], 2) - ) - distance_between_defects_to_center.append(distance) - - # Get an average of three shortest distances from finger webbing to center mass - sorted_defects_distances = sorted(distance_between_defects_to_center) - average_defect_distance = np.mean(sorted_defects_distances[0:2]) - updated_hand.average_defect_distance = average_defect_distance - # # Get fingertip points from contour hull - # # If points are in proximity of 80 pixels, consider as a single point in the group - # finger = [] - # for i in range(0, len(hull) - 1): - # if (np.absolute(hull[i][0][0] - hull[i + 1][0][0]) > 10) or ( - # np.absolute(hull[i][0][1] - hull[i + 1][0][1]) > 10): - # if hull[i][0][1] < 500: - # finger.append(hull[i][0]) - # - # - # # The fingertip points are 5 hull points with largest y coordinates - # finger = sorted(finger, key=lambda x: x[1]) - # fingers = finger[0:5] - if center_of_mass is not None and len(fingertips_coords) > 0: - # Calculate distance of each finger tip to the center mass + hand_bounding_rect, hand_circle, hand_contour = self.get_hand_bounding_rect(hand_contour, + fingers_contour) + + if not initial or is_hand: + updated_hand.contour = hand_contour + updated_hand.bounding_rect = hand_bounding_rect + # Find moments of the largest contour + moments = cv2.moments(hand_contour) + center_of_mass = None finger_distances = [] - for i in range(0, len(fingertips_coords)): - distance = np.sqrt( - np.power(fingertips_coords[i][0] - center_of_mass[0], 2) + np.power( - fingertips_coords[i][1] - center_of_mass[0], 2)) - finger_distances.append(distance) - updated_hand.finger_distances = finger_distances + average_defect_distance = None + # Central mass of first order moments + if moments['m00'] != 0: + cx = int(moments['m10'] / moments['m00']) # cx = M10/M00 + cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 + center_of_mass = (cx, cy) + updated_hand.center_of_mass = center_of_mass + updated_hand.position_history.append(center_of_mass) + + if center_of_mass is not None and len(intertips_coords) > 0: + # Distance from each finger defect(finger webbing) to the center mass + distance_between_defects_to_center = [] + for far in intertips_coords: + x = np.array(far) + center_mass_array = np.array(center_of_mass) + distance = np.sqrt( + np.power(x[0] - center_mass_array[0], + 2) + np.power(x[1] - center_mass_array[1], 2) + ) + distance_between_defects_to_center.append(distance) + + # Get an average of three shortest distances from finger webbing to center mass + sorted_defects_distances = sorted(distance_between_defects_to_center) + average_defect_distance = np.mean(sorted_defects_distances[0:2]) + updated_hand.average_defect_distance = average_defect_distance + # # Get fingertip points from contour hull + # # If points are in proximity of 80 pixels, consider as a single point in the group + # finger = [] + # for i in range(0, len(hull) - 1): + # if (np.absolute(hull[i][0][0] - hull[i + 1][0][0]) > 10) or ( + # np.absolute(hull[i][0][1] - hull[i + 1][0][1]) > 10): + # if hull[i][0][1] < 500: + # finger.append(hull[i][0]) + # + # + # # The fingertip points are 5 hull points with largest y coordinates + # finger = sorted(finger, key=lambda x: x[1]) + # fingers = finger[0:5] + if center_of_mass is not None and len(fingertips_coords) > 0: + # Calculate distance of each finger tip to the center mass + finger_distances = [] + for i in range(0, len(fingertips_coords)): + distance = np.sqrt( + np.power(fingertips_coords[i][0] - center_of_mass[0], 2) + np.power( + fingertips_coords[i][1] - center_of_mass[0], 2)) + finger_distances.append(distance) + updated_hand.finger_distances = finger_distances + else: + return None + elif initial: + return None return updated_hand def update_hand_charasteristics(self, frame, hand): updated_hand = copy.deepcopy(hand) - contours = self.create_contours(frame, hand.bounding_rect) + contours, _ = self.create_contours_and_mask(frame, hand.bounding_rect) if len(contours) > 0: # Find Max contour area (Assume that hand is in the frame) # TODO: ENV_DEPENDENCE: depends on the camera resolution, distance to the background, noisy areas sizes @@ -719,7 +659,7 @@ def update_hand_charasteristics(self, frame, hand): # Find convex defects if hand_contour is not None: - updated_hand = self.contour_to_hand(frame,hand_contour,updated_hand) + updated_hand = self.contour_to_hand(frame, hand_contour, updated_hand) else: updated_hand.contour = [] else: From a9ffee9ba944e2839957b464df5eb4b24779df20 Mon Sep 17 00:00:00 2001 From: orensbruli Date: Tue, 26 Jun 2018 20:00:28 +0200 Subject: [PATCH 17/54] Massive code resortintg. Created new method to manually add new hands to the tracking. This makes the percentage of false positives much lower than before. Fixed some bugs. Implemented a new masking method by diff with black pixel percentage checking. --- HandDetection.py | 981 +++++++++++++++++++++++++++++++---------------- 1 file changed, 648 insertions(+), 333 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index 7872154..f34b275 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -76,7 +76,7 @@ def __init__(self): self.contour = None self.bounding_rect = None self.tracking_fails = 0 - self.detection_fail = 0 + self.detection_fails = 0 self.frame_count = 0 self.tracking_window = None self.tracked = False @@ -111,11 +111,13 @@ def update_truth_value_by_frame(self): if self.detected: detection_adition = DETECTION_TRUTH_FACTOR * one_frame_truth_subtraction else: + self.detection_fails += 1 detection_adition = -1 * UNDETECTION_TRUTH_FACTOR * one_frame_truth_subtraction tracking_adition = 0 if self.tracked: tracking_adition = TRACKING_TRUTH_FACTOR * one_frame_truth_subtraction else: + self.tracking_fails += 1 tracking_adition = -1 * UNTRACKING_TRUTH_FACTOR * one_frame_truth_subtraction new_truth_value = self.truth_value - one_frame_truth_subtraction + detection_adition + tracking_adition if new_truth_value <= 100: @@ -173,16 +175,19 @@ def get_color_mask(image): def upscale_bounding_rec(bounding_rect, frame_shape, upscaled_pixels): x, y, w, h = bounding_rect new_x = max(x - int(upscaled_pixels / 2), 0) + new_y = max(y - int(upscaled_pixels / 2), 0) - if x + w + int(upscaled_pixels / 2) < frame_shape[1]: - new_w = w + int(upscaled_pixels / 2) + + if x + w + upscaled_pixels < frame_shape[1]: + new_w = w + upscaled_pixels else: - exceded_pixels = x + w + int(upscaled_pixels / 2) - frame_shape[1] + exceded_pixels = x + w + upscaled_pixels - frame_shape[1] new_w = w + exceded_pixels - if y + h + int(upscaled_pixels / 2) < frame_shape[0]: - new_h = h + int(upscaled_pixels / 2) + + if y + h + upscaled_pixels < frame_shape[0]: + new_h = h + upscaled_pixels else: - exceded_pixels = y + h + int(upscaled_pixels / 2) - frame_shape[0] + exceded_pixels = y + h + upscaled_pixels - frame_shape[0] new_h = h + exceded_pixels upscaled_bounding_rect = (new_x, new_y, new_w, new_h) return upscaled_bounding_rect @@ -214,98 +219,71 @@ def __init__(self, source=0): # self.capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1000) # self.capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 600) - def update_detection(self, frame): - for hand in self.hands: - hand.detected = False - detected_hands = self.detect_hands_in_frame(frame) - for detected_hand in detected_hands: - if len(self.hands) > 0: - hand_exists = False - for existing_hand_index, existing_hand in enumerate(self.hands): - intersection_value, _ = self.calculate_bounding_rects_intersection( - detected_hand.bounding_rect, - existing_hand.bounding_rect) - if intersection_value > 0.1: - hand_exists = True - existing_hand.update_attributes_from_detected(detected_hand) - break - if not hand_exists: - detected_hand.id = str(self.next_hand_id) - detected_hand.detected = True - self.next_hand_id += 1 - self.hands.append(detected_hand) - print "New hand" - else: - detected_hand.id = str(self.next_hand_id) - detected_hand.detected = True - self.next_hand_id += 1 - self.hands.append(detected_hand) - print "New hand" - - def update_tracking(self, frame): - if len(self.hands) > 0: - for index, hand in enumerate(self.hands): - hand.tracked = False - hands_mask = self.create_hands_mask(frame) - ret, tracking_window = self.follow(frame, hands_mask, hand.bounding_rect) - if ret and tracking_window is not None: - updated_hand = self.update_hand_charasteristics(frame, hand) - if updated_hand is not None: - updated_hand.detected = hand.detected - updated_hand.tracking_window = tracking_window - updated_hand.detection_fail = hand.detection_fail - updated_hand.tracked = True - self.hands[index] = updated_hand - else: - hand.tracked = False - hand.tracking_fails += 1 - else: - hand.tracked = False - hand.tracking_fails += 1 - - def compute(self): - while self.capture.isOpened(): - - # Measure execution time - # start_time = time.time() - - # Capture frames from the camera - ret, frame = self.capture.read() - - if ret is True: - self.update_detection(frame) - - self.update_tracking(frame) - - for index, hand in enumerate(self.hands): - if hand.detected is False: - hand.detection_fail += 1 - if hand.tracked: - hand.bounding_rect = hand.tracking_window - else: - print "_____________No updated information" - hand.update_truth_value_by_frame() - if hand.truth_value <= 0: - print "removing hand" - self.hands.remove(hand) + def add_hand(self, frame): + hand_mask = None + # Load the hand template + hand_template = cv2.imread("/home/robolab/Desktop/hand_mask.png") + hand_template_contour, hand_template_roi, hand_template_roi_image = self.calculate_max_contour(hand_template) + + template_x, template_y, template_w, template_h = hand_template_roi + # Get the contour and mask of the frame + frame_contours, frame_mask = self.create_contours_and_mask(frame, hand_template_roi) + masked_frame = frame.copy() + + if len(frame_contours) > 0 and len(frame_mask) > 0: + # Get the maximum area contour + min_area = 100 + hand_contour = None + for i in range(len(frame_contours)): + cnt = frame_contours[i] + area = cv2.contourArea(cnt) + if area > min_area: + min_area = area + hand_contour = frame_contours[i] - overlayed_frame = frame.copy() - for hand in self.hands: - overlayed_frame = self.draw_hand_overlay(frame, hand) - pass + if hand_contour is not None: + # cv2.drawContours(frame, [hand_contour], -1, (0, 255, 255), 2) + detected_hand_bounding_rect = cv2.boundingRect(hand_contour) + detected_hand_x, detected_hand_y, detected_hand_w, detected_hand_h = detected_hand_bounding_rect + frame_mask_roi_image = frame_mask[template_y:template_y + template_h, + template_x:template_x + template_w] + frame_mask_roi_image_contour, _, _ = self.calculate_max_contour(frame_mask_roi_image, to_binary=False) + + if frame_mask_roi_image is not None: + # realtime_handmask = np.zeros((frame_mask_roi_image.shape)) + # cv2.fillPoly(realtime_handmask, pts=[hand_contour], color=(255, 255, 255)) + # cv2.imshow("realtime hand mask", realtime_handmask) + diff = cv2.absdiff(hand_template_roi_image.astype(np.uint8), frame_mask_roi_image.astype(np.uint8)) + cv2.imshow("diff", diff) + result = cv2.matchShapes(frame_mask_roi_image_contour, hand_template_contour, 1, 0) + print result + if frame_mask_roi_image_contour is not None: + aux_detected_cnt = frame_mask_roi_image_contour + # Put in the original X and Y + aux_detected_cnt[:, :, 0] += hand_template_roi[0] + aux_detected_cnt[:, :, 1] += hand_template_roi[1] + cv2.drawContours(masked_frame, [aux_detected_cnt], -1, (0, 255, 0), 2) + if result < 0.05: + print "Match!!! " + str(result) + hand = Hand() + hand.id = len(self.hands) + hand.contour = hand_contour + hand.bounding_rect = detected_hand_bounding_rect + self.hands.append(hand) + cv2.putText(masked_frame, "HAND FOUND", + (template_x, template_y + template_h + 10), + self.font, 1, [255, 255, 255], 2) + if len(self.hands)<1: + cv2.putText(masked_frame, "CENTER YOUR HAND", + (template_x, template_y + template_h + 10), + self.font, 1, [255, 255, 255], 2) + else: + cv2.putText(masked_frame, "PLEASE PUT YOUR HAND HERE", (template_x - 100, template_y + template_h + 10), + self.font, 1, [255, 255, 255], 2) - ##### Show final image ######## - cv2.imshow('Detection', overlayed_frame) - ############################### - # Print execution time - # print time.time()-start_time - else: - print "No video detected" - # close the output video by pressing 'ESC' - k = cv2.waitKey(50) & 0xFF - if k == 27: - break + masked_frame = cv2.addWeighted(masked_frame, 0.7, hand_template, 0.3, 0) + cv2.imshow('masked_frame', masked_frame) def calculate_bounding_rects_intersection(self, bounding_rect_1, bounding_rect_2): b1_x_left = bounding_rect_1[0] @@ -340,85 +318,47 @@ def calculate_bounding_rects_intersection(self, bounding_rect_1, bounding_rect_2 # return the intersection over union value return iou, (x_left, y_top, x_right, y_bottom) - def follow(self, frame, mask, bounding_rect): - upscaled_bounding_rect = bounding_rect # self.upscale_bounding_rec(bounding_rect,frame.shape,20) - x, y, w, h = upscaled_bounding_rect - track_window = upscaled_bounding_rect - # set up the ROI for tracking - roi = frame[y:y + h, x:x + w] - hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV) - # mask = cv2.inRange(hsv_roi, np.array((0., 60., 32.)), np.array((180., 255., 255.))) - roi_mask = mask[y:y + h, x:x + w] - cv2.imshow("roi_follow_mask", roi_mask) - roi_hist = cv2.calcHist([hsv_roi], [0], roi_mask, [180], [0, 180]) - cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX) - # Setup the termination criteria, either 10 iteration or move by atleast 1 pt - term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1) - hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) - dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1) - # apply meanshift to get the new location - ret, track_window = cv2.meanShift(dst, track_window, term_crit) - return ret, track_window - # return ret, (max(newx-30,0), max(newy-30,0), min(neww+30,frame.shape[1]), min(newh+30,frame.shape[0])) - - # def calculate_hand_interst_points(self, frame, cnts): - # # convexity defect - # - # drawing = copy.deepcopy(frame) - # hull = cv2.convexHull(cnts, returnPoints=False) - # if len(hull) > 3: - # defects = cv2.convexityDefects(cnts, hull) - # if type(defects) != type(None): # avoid crashing. (BUG not found) - # - # cnt = 0 - # for i in range(defects.shape[0]): # calculate the angle - # s, e, f, d = defects[i][0] - # start = tuple(cnts[s][0]) - # end = tuple(cnts[e][0]) - # far = tuple(cnts[f][0]) - # a = math.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2) - # b = math.sqrt((far[0] - start[0]) ** 2 + (far[1] - start[1]) ** 2) - # c = math.sqrt((end[0] - far[0]) ** 2 + (end[1] - far[1]) ** 2) - # angle = math.acos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c)) # cosine theorem - # if angle <= math.pi / 2: # angle less than 90 degree, treat as fingers - # cnt += 1 - # cv2.circle(drawing, far, 8, [211, 84, 0], -1) - # interdigs.append(far) - # cv2.imshow("detect_fingers", drawing) - # return interdigs - - def draw_hand_overlay(self, frame, hand): - if hand.detected: - for finger_number, fingertip in enumerate(hand.fingertips): - cv2.circle(frame, tuple(fingertip), 10, [255, 100, 255], 3) - cv2.putText(frame, 'finger' + str(finger_number), tuple(fingertip), self.font, 0.5, - (255, 255, 255), - 1) - for defect in hand.intertips: - cv2.circle(frame, tuple(defect), 8, [211, 84, 0], -1) - - x, y, w, h = hand.bounding_rect - # cv2.putText(frame, (str(w)), (x + w, y), self.font, 0.3, [255, 255, 255], 1) - # cv2.putText(frame, (str(h)), (x + w, y + h), self.font, 0.3, [255, 255, 255], 1) - # cv2.putText(frame, (str(w * h)), (x + w / 2, y + h / 2), self.font, 0.3, [255, 255, 255], 1) - # cv2.putText(frame, (str(x)+", "+str(y)), (x-10, y-10), self.font, 0.3, [255, 255, 255], 1) - cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) - - if hand.detected or hand.tracked: - cv2.drawContours(frame, [hand.contour], -1, (255, 255, 255), 2) - - points = np.array(hand.position_history) - cv2.polylines(img=frame, pts=np.int32([points]), isClosed=False, color=hand.color) - - if hand.center_of_mass is not None: - # Draw center mass - cv2.circle(frame, hand.center_of_mass, 7, [100, 0, 255], 2) - cv2.putText(frame, 'Center', tuple(hand.center_of_mass), self.font, 0.5, (255, 255, 255), 1) - - hand_string = "hand " + str(hand.id) + ": detected =" + str(hand.detected) + " tracked =" + str( - hand.tracked) + " at " + str(hand.center_of_mass) + " truth: " + str(hand.truth_value) + "%" - cv2.putText(frame, hand_string, (10, 30 + 15 * int(hand.id)), self.font, 0.5, (255, 255, 255), 1) - return frame + def calculate_max_contour(self, image, to_binary=True): + bounding_rect = None + image_roi = None + if to_binary: + gray_diff = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + _, mask = cv2.threshold(gray_diff, 40, 255, cv2.THRESH_BINARY) + else: + mask = image + kernel_square = np.ones((11, 11), np.uint8) + kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + + # Perform morphological transformations to filter out the background noise + # Dilation increase skin color area + # Erosion increase skin color area + dilation = cv2.dilate(mask, kernel_ellipse, iterations=1) + erosion = cv2.erode(dilation, kernel_square, iterations=1) + dilation2 = cv2.dilate(erosion, kernel_ellipse, iterations=1) + filtered = cv2.medianBlur(dilation2.astype(np.uint8), 5) + kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8, 8)) + dilation2 = cv2.dilate(filtered, kernel_ellipse, iterations=1) + kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + dilation3 = cv2.dilate(filtered, kernel_ellipse, iterations=1) + median = cv2.medianBlur(dilation2, 5) + ret, thresh = cv2.threshold(median, 127, 255, 0) + cnts = None + max_area = 100 + ci = 0 + # Find contours of the filtered frame + _, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + if contours: + for i in range(len(contours)): + cnt = contours[i] + area = cv2.contourArea(cnt) + if area > max_area: + max_area = area + ci = i + cnts = contours[ci] + bounding_rect = cv2.boundingRect(cnts) + x, y, w, h = bounding_rect + image_roi = mask[y:y + h, x:x + w] + return cnts, bounding_rect, image_roi def create_contours_and_mask(self, frame, roi_mask=None): # Create a binary image with where white will be skin colors and rest is black @@ -430,14 +370,18 @@ def create_contours_and_mask(self, frame, roi_mask=None): if roi_mask is not None: current_roi_mask = np.zeros(hands_mask.shape, dtype='uint8') x, y, w, h = roi_mask + current_roi_mask[y:y + h, x:x + w] = 255 hands_mask = cv2.bitwise_and(hands_mask, current_roi_mask) - # to_show = cv2.resize(hands_mask, None, fx=.3, fy=.3, interpolation=cv2.INTER_CUBIC) - # # cv2.putText(to_show, (str(w)), (x + w, y), self.font, 0.3, [255, 255, 255], 1) - # # cv2.putText(to_show, (str(h)), (x + w, y + h), self.font, 0.3, [100, 255, 255], 1) - # # cv2.putText(to_show, (str(w * h)), (x + w / 2, y + h / 2), self.font, 0.3, [100, 100, 255], 1) - # # cv2.putText(to_show, (str(x)+", "+str(y)), (x-10, y-10), self.font, 0.3, [255, 255, 255], 1) - # cv2.imshow("current hands_mask" + str(hand.id), to_show) + to_show = cv2.resize(hands_mask, None, fx=.3, fy=.3, interpolation=cv2.INTER_CUBIC) + to_show = hands_mask.copy() + # cv2.putText(to_show, (str(w)), (x + w, y), self.font, 0.3, [255, 255, 255], 1) + # cv2.putText(to_show, (str(h)), (x + w, y + h), self.font, 0.3, [100, 255, 255], 1) + # cv2.putText(to_show, (str(w * h)), (x + w / 2, y + h / 2), self.font, 0.3, [100, 100, 255], 1) + # cv2.putText(to_show, (str(x)+", "+str(y)), (x-10, y-10), self.font, 0.3, [255, 255, 255], 1) + cv2.rectangle(to_show, (x, y), (x + w, y + h), [255, 255, 255]) + + cv2.imshow("current hands_mask", to_show) ret, thresh = cv2.threshold(hands_mask, 127, 255, 0) @@ -445,105 +389,198 @@ def create_contours_and_mask(self, frame, roi_mask=None): _, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) return (contours, hands_mask) - def detect_hands_in_frame(self, frame): - new_hands = [] + def create_hands_mask(self, image, mode="diff"): + mask = None + if mode == "color": + mask = get_color_mask(image) + elif mode == "MOG2": + mask = self.get_MOG2_mask(image) + elif mode == "diff": + mask = self.get_simple_diff_mask2(image) + elif mode == "mixed": + diff_mask = self.get_simple_diff_mask(image) - contours, _ = self.create_contours_and_mask(frame) + color_mask = get_color_mask(image) + color_mask = clean_mask_noise(color_mask) - # Draw Contours - # cv2.drawContours(frame, cnt, -1, (122,122,0), 3) + if diff_mask is not None and color_mask is not None: + mask = cv2.bitwise_and(diff_mask, color_mask) + cv2.imshow("diff_mask", diff_mask) + cv2.imshow("color_mask", color_mask) + elif mode == "movement_buffer": + # Absolutly unusefull + mask = self.get_movement_buffer_mask(image) + return mask - if len(contours) > 0: - # Find Max contour area (Assume that hand is in the frame) - # TODO: ENV_DEPENDENCE: depends on the camera resolution, distance to the background, noisy areas sizes - min_area = 100 + def compute(self): + while self.capture.isOpened(): - for contour in contours: - area = cv2.contourArea(contour) - if area > min_area: - hand_contour = contour - hand = self.contour_to_hand(frame, hand_contour) - if hand is not None: - new_hands.append(hand) - return new_hands + # Measure execution time + # start_time = time.time() - def is_it_hand(self, fingertips, intertips, initial=True): - if initial: - return len(fingertips) == 5 and len(fingertips) == len(intertips) + 1 - else: - return 5 >= len(fingertips) > 2 and len(fingertips) == len(intertips) + 1 + # Capture frames from the camera + ret, frame = self.capture.read() - def get_hand_fingertips(self, hand_contour, defects): - intertips_coords = [] - intertips_indexes = [] - far_defect = [] - fingertips_coords = [] - fingertips_indexes = [] - defect_indices = [] - for defect_index in range(defects.shape[0]): - s, e, f, d = defects[defect_index, 0] - start = tuple(hand_contour[s][0]) - end = tuple(hand_contour[e][0]) - far = tuple(hand_contour[f][0]) - far_defect.append(far) - # cv2.line(frame, start, end, [0, 255, 0], 1) - a = math.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2) - b = math.sqrt((far[0] - start[0]) ** 2 + (far[1] - start[1]) ** 2) - c = math.sqrt((end[0] - far[0]) ** 2 + (end[1] - far[1]) ** 2) - angle = math.acos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c)) # cosine theorem - # cv2.circle(frame, far, 8, [211, 84, 125], -1) - # cv2.circle(frame, start, 8, [0, 84, 125], -1) - # cv2.circle(frame, end, 8, [0, 84, 125], -1) - # Get tips and intertips coordinates - # TODO: ENV_DEPENDENCE: this angle > 90ยบ determinate if two points are considered fingertips or not and 90 make thumb to fail in some occasions - intertips_max_angle = math.pi / 1.7 - if angle <= intertips_max_angle: # angle less than 90 degree, treat as fingers - defect_indices.append(defect_index) - # cv2.circle(frame, far, 8, [211, 84, 0], -1) - intertips_coords.append(far) - intertips_indexes.append(f) - # cv2.putText(frame, str(s), start, self.font, 0.7, (255, 255, 255), 1) - # cv2.putText(frame, str(e), end, self.font, 0.7, (255, 255, 200), 1) - if len(fingertips_coords) > 0: - from scipy.spatial import distance - # calculate distances from start and end to the already known tips - start_distance, end_distance = tuple( - distance.cdist(fingertips_coords, [start, end]).min(axis=0)) - # TODO: ENV_DEPENDENCE: it determinate the pixels distance to consider two points the same. It depends on camera resolution and distance from the hand to the camera - same_fingertip_radius = 10 - if start_distance > same_fingertip_radius: - fingertips_coords.append(start) - fingertips_indexes.append(s) + if ret is True: + self.update_detection(frame) - # cv2.circle(frame, start, 10, [255, 100, 255], 3) - if end_distance > same_fingertip_radius: - fingertips_coords.append(end) - fingertips_indexes.append(e) + self.update_tracking(frame) - # cv2.circle(frame, end, 10, [255, 100, 255], 3) - else: - fingertips_coords.append(start) - fingertips_indexes.append(s) + for index, hand in enumerate(self.hands): + if hand.detected is False: + hand.detection_fail += 1 + if hand.tracked: + hand.bounding_rect = hand.tracking_window + else: + print "_____________No updated information" + hand.update_truth_value_by_frame() + if hand.truth_value <= 0: + print "removing hand" + self.hands.remove(hand) - # cv2.circle(frame, start, 10, [255, 100, 255], 3) - fingertips_coords.append(end) - fingertips_indexes.append(e) + overlayed_frame = frame.copy() + for hand in self.hands: + overlayed_frame = self.draw_hand_overlay(frame, hand) + pass - # cv2.circle(frame, end, 10, [255, 100, 255], 3) + ##### Show final image ######## + cv2.imshow('Detection', overlayed_frame) + ############################### + # Print execution time + # print time.time()-start_time + else: + print "No video detected" - # cv2.circle(frame, far, 10, [100, 255, 255], 3) - return fingertips_coords, fingertips_indexes, intertips_coords, intertips_indexes + # close the output video by pressing 'ESC' + k = cv2.waitKey(50) & 0xFF + if k == 27: + break - def get_hand_bounding_rect(self, hand_contour, fingers_contour): - (x, y), radius = cv2.minEnclosingCircle(fingers_contour) - center = (int(x), int(y)) - radius = int(radius) + 10 - hand_contour = extract_contour_inside_circle(hand_contour, (center, radius)) - hand_bounding_rect = cv2.boundingRect(hand_contour) - return hand_bounding_rect, ((int(x), int(y)), radius), hand_contour + def compute2(self): + while self.capture.isOpened(): - def contour_to_hand(self, frame, hand_contour, hand_to_update=None): + # Measure execution time + # start_time = time.time() + + # Capture frames from the camera + ret, frame = self.capture.read() + + if ret is True: + if len(self.hands) != 1: + self.add_hand(frame) + + self.update_detection_and_tracking(frame) + + overlayed_frame = frame.copy() + for hand in self.hands: + overlayed_frame = self.draw_hand_overlay(frame, hand) + pass + + ##### Show final image ######## + cv2.imshow('Detection', overlayed_frame) + ############################### + # Print execution time + # print time.time()-start_time + else: + print "No video detected" + + # close the output video by pressing 'ESC' + k = cv2.waitKey(50) & 0xFF + if k == 27: + break + + def contour_to_hand_strict(self, frame, hand_contour, hand_to_update=None, strict=True): + if hand_to_update is not None: + updated_hand = copy.deepcopy(hand_to_update) + updated_hand.contour = hand_contour + hand_bounding_rect = cv2.boundingRect(hand_contour) + else: + updated_hand = Hand() + + hull2 = cv2.convexHull(hand_contour, returnPoints=False) + defects = cv2.convexityDefects(hand_contour, hull2) + + # Get defect points and draw them in the original image + if defects is not None: + fingertips_coords, \ + fingertips_indexes, \ + intertips_coords, \ + intertips_indexes = self.get_hand_fingertips(hand_contour, defects) + is_hand = self.is_hand(fingertips_coords, intertips_coords, strict=strict) + if is_hand: + updated_hand.fingertips = fingertips_coords + updated_hand.intertips = intertips_coords + + if len(fingertips_coords) == 5: + fingers_contour = np.take(hand_contour, + fingertips_indexes + intertips_indexes, + axis=0, + mode="wrap") + hand_bounding_rect, hand_circle, hand_contour = self.get_hand_bounding_rect(hand_contour, + fingers_contour) + elif strict: + return None + + updated_hand.contour = hand_contour + updated_hand.bounding_rect = hand_bounding_rect + # Find moments of the largest contour + moments = cv2.moments(hand_contour) + center_of_mass = None + finger_distances = [] + average_defect_distance = None + # Central mass of first order moments + if moments['m00'] != 0: + cx = int(moments['m10'] / moments['m00']) # cx = M10/M00 + cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 + center_of_mass = (cx, cy) + updated_hand.center_of_mass = center_of_mass + updated_hand.position_history.append(center_of_mass) + + if center_of_mass is not None and len(intertips_coords) > 0: + # Distance from each finger defect(finger webbing) to the center mass + distance_between_defects_to_center = [] + for far in intertips_coords: + x = np.array(far) + center_mass_array = np.array(center_of_mass) + distance = np.sqrt( + np.power(x[0] - center_mass_array[0], + 2) + np.power(x[1] - center_mass_array[1], 2) + ) + distance_between_defects_to_center.append(distance) + + # Get an average of three shortest distances from finger webbing to center mass + sorted_defects_distances = sorted(distance_between_defects_to_center) + average_defect_distance = np.mean(sorted_defects_distances[0:2]) + updated_hand.average_defect_distance = average_defect_distance + # # Get fingertip points from contour hull + # # If points are in proximity of 80 pixels, consider as a single point in the group + # finger = [] + # for i in range(0, len(hull) - 1): + # if (np.absolute(hull[i][0][0] - hull[i + 1][0][0]) > 10) or ( + # np.absolute(hull[i][0][1] - hull[i + 1][0][1]) > 10): + # if hull[i][0][1] < 500: + # finger.append(hull[i][0]) + # + # + # # The fingertip points are 5 hull points with largest y coordinates + # finger = sorted(finger, key=lambda x: x[1]) + # fingers = finger[0:5] + if center_of_mass is not None and len(fingertips_coords) > 0: + # Calculate distance of each finger tip to the center mass + finger_distances = [] + for i in range(0, len(fingertips_coords)): + distance = np.sqrt( + np.power(fingertips_coords[i][0] - center_of_mass[0], 2) + np.power( + fingertips_coords[i][1] - center_of_mass[0], 2)) + finger_distances.append(distance) + updated_hand.finger_distances = finger_distances + + else: + return None + return updated_hand + + def contour_to_hand(self, frame, hand_contour, hand_to_update=None): initial = (hand_to_update == None) if not initial: updated_hand = copy.deepcopy(hand_to_update) @@ -562,7 +599,7 @@ def contour_to_hand(self, frame, hand_contour, hand_to_update=None): intertips_coords, \ intertips_indexes = self.get_hand_fingertips(hand_contour, defects) - is_hand = self.is_it_hand(fingertips_coords, intertips_coords, initial=initial) + is_hand = self.is_hand(fingertips_coords, intertips_coords, strict=initial) if is_hand: updated_hand.fingertips = fingertips_coords updated_hand.intertips = intertips_coords @@ -574,7 +611,6 @@ def contour_to_hand(self, frame, hand_contour, hand_to_update=None): mode="wrap") hand_bounding_rect, hand_circle, hand_contour = self.get_hand_bounding_rect(hand_contour, fingers_contour) - if not initial or is_hand: updated_hand.contour = hand_contour updated_hand.bounding_rect = hand_bounding_rect @@ -635,62 +671,77 @@ def contour_to_hand(self, frame, hand_contour, hand_to_update=None): return None return updated_hand - def update_hand_charasteristics(self, frame, hand): - updated_hand = copy.deepcopy(hand) + def draw_hand_overlay(self, frame, hand): + if hand.detected: + for finger_number, fingertip in enumerate(hand.fingertips): + cv2.circle(frame, tuple(fingertip), 10, [255, 100, 255], 3) + cv2.putText(frame, 'finger' + str(finger_number), tuple(fingertip), self.font, 0.5, + (255, 255, 255), + 1) + for defect in hand.intertips: + cv2.circle(frame, tuple(defect), 8, [211, 84, 0], -1) - contours, _ = self.create_contours_and_mask(frame, hand.bounding_rect) - if len(contours) > 0: - # Find Max contour area (Assume that hand is in the frame) - # TODO: ENV_DEPENDENCE: depends on the camera resolution, distance to the background, noisy areas sizes - max_area = 0 - hand_contour = None - for contour_index in range(len(contours)): - cnt = contours[contour_index] - area = cv2.contourArea(cnt) - if area >= max_area: - max_area = area - # Largest area contour - hand_contour = contours[contour_index] + x, y, w, h = hand.bounding_rect + # cv2.putText(frame, (str(w)), (x + w, y), self.font, 0.3, [255, 255, 255], 1) + # cv2.putText(frame, (str(h)), (x + w, y + h), self.font, 0.3, [255, 255, 255], 1) + # cv2.putText(frame, (str(w * h)), (x + w / 2, y + h / 2), self.font, 0.3, [255, 255, 255], 1) + # cv2.putText(frame, (str(x)+", "+str(y)), (x-10, y-10), self.font, 0.3, [255, 255, 255], 1) + cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) - # Find convex hull - # hull = cv2.convexHull(hand_contour) - # for point in hull: - # cv2.circle(frame, tuple(point[0]), 8, [255, 100, 10], -1) + if hand.detected or hand.tracked: + cv2.drawContours(frame, [hand.contour], -1, (255, 255, 255), 2) - # Find convex defects - if hand_contour is not None: - updated_hand = self.contour_to_hand(frame, hand_contour, updated_hand) - else: - updated_hand.contour = [] - else: - return None - return updated_hand + points = np.array(hand.position_history) + cv2.polylines(img=frame, pts=np.int32([points]), isClosed=False, color=hand.color) + + if hand.center_of_mass is not None: + # Draw center mass + cv2.circle(frame, hand.center_of_mass, 7, [100, 0, 255], 2) + cv2.putText(frame, 'Center', tuple(hand.center_of_mass), self.font, 0.5, (255, 255, 255), 1) + + hand_string = "hand " + str(hand.id) + ": detected =" + str(hand.detected) + " tracked =" + str( + hand.tracked) + " at " + str(hand.center_of_mass) + " truth: " + str(hand.truth_value) + "%" + cv2.putText(frame, hand_string, (10, 30 + 15 * int(hand.id)), self.font, 0.5, (255, 255, 255), 1) + return frame def exit(self): self.capture.release() cv2.destroyAllWindows() - def create_hands_mask(self, image, mode="diff"): - mask = None - if mode == "color": - mask = get_color_mask(image) - elif mode == "MOG2": - mask = self.get_MOG2_mask(image) - elif mode == "diff": - mask = self.get_simple_diff_mask(image) - elif mode == "mixed": - diff_mask = self.get_simple_diff_mask(image) - - color_mask = get_color_mask(image) - color_mask = clean_mask_noise(color_mask) + def follow(self, frame, mask, bounding_rect): + upscaled_bounding_rect = bounding_rect # self.upscale_bounding_rec(bounding_rect,frame.shape,20) + x, y, w, h = upscaled_bounding_rect + track_window = upscaled_bounding_rect + # set up the ROI for tracking + roi = frame[y:y + h, x:x + w] + hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV) + # mask = cv2.inRange(hsv_roi, np.array((0., 60., 32.)), np.array((180., 255., 255.))) + roi_mask = mask[y:y + h, x:x + w] + cv2.imshow("roi_follow_mask", roi_mask) + roi_hist = cv2.calcHist([hsv_roi], [0], roi_mask, [180], [0, 180]) + cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX) + # Setup the termination criteria, either 10 iteration or move by atleast 1 pt + term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1) + hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) + dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1) + # apply meanshift to get the new location + ret, track_window = cv2.meanShift(dst, track_window, term_crit) + return ret, track_window + # return ret, (max(newx-30,0), max(newy-30,0), min(neww+30,frame.shape[1]), min(newh+30,frame.shape[0])) - if diff_mask is not None and color_mask is not None: - mask = cv2.bitwise_and(diff_mask, color_mask) - cv2.imshow("diff_mask", diff_mask) - cv2.imshow("color_mask", color_mask) - elif mode == "movement_buffer": - # Absolutly unusefull - mask = self.get_movement_buffer_mask(image) + def get_MOG2_mask(self, image): + # TODO: not working (it considers everything shadows or moves (too dark background) + fgbg = cv2.createBackgroundSubtractorMOG2(detectShadows=False) + # fgbg= cv2.BackgroundSubtractorMOG2(0, 50) + # fgbg=cv2.BackgroundSubtractor() + # fgbg = cv2.BackgroundSubtractorMOG() + gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + # TODO: ENV_DEPENDENCE: it could depend on the camera quality + # blur = cv2.medianBlur(gray_image, 100) + blur_radius = 5 + blurred = cv2.GaussianBlur(image, (blur_radius, blur_radius), 0) + # cv2.imshow("blurred", blur) + mask = fgbg.apply(blurred) return mask def get_movement_buffer_mask(self, image): @@ -713,7 +764,6 @@ def get_movement_buffer_mask(self, image): return mask def get_simple_diff_mask(self, image): - # type: (object) -> object mask = None if len(self.last_frames) < self.discarded_frames: self.last_frames.append(image) @@ -733,31 +783,296 @@ def get_simple_diff_mask(self, image): _, mask = cv2.threshold(gray_diff, 40, 255, cv2.THRESH_BINARY) return mask + def get_simple_diff_mask2(self, image): + mask = None + # TODO: ENV_DEPENDENCE: it could depend on the lighting and environment + blur_radius = 5 + if len(self.last_frames) > 0: + mean_image = np.zeros(image.shape, dtype=np.float32) + for old_image in self.last_frames: + cv2.accumulate(old_image, mean_image) + result_image = mean_image / len(self.last_frames) + result_image = result_image.astype(np.uint8) + # cv2.imshow("mean_image", result_image.astype(np.uint8)) + # cv2.imshow("the image", image) + blurred_image = cv2.GaussianBlur(image, (blur_radius, blur_radius), 0) + blurred_background = cv2.GaussianBlur(result_image, (blur_radius, blur_radius), 0) + # cv2.imshow("b_mean_image", blurred_background) + # cv2.imshow("b_the image", blurred_image) + diff = cv2.absdiff(blurred_image, blurred_background.astype(np.uint8)) + # cv2.imshow("diff", diff) + # print "diff" + gray_diff = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY) + # cv2.imshow("gray_diff", gray_diff) + # TODO: ENV_DEPENDENCE: it could depend on the lighting and environment + _, mask = cv2.threshold(gray_diff, 40, 255, cv2.THRESH_BINARY) + non_zero_pixels = cv2.countNonZero(mask) + non_zero_percent = non_zero_pixels * 100 / image.shape[0] * image.shape[1] + if non_zero_percent < 10: + self.first_frame = result_image + if self.first_frame is not None: + + blurred_image = cv2.GaussianBlur(image, (blur_radius, blur_radius), 0) + blurred_background = cv2.GaussianBlur(self.first_frame, (blur_radius, blur_radius), 0) + diff = cv2.absdiff(blurred_image, blurred_background.astype(np.uint8)) + # print "diff" + gray_diff = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY) + # cv2.imshow("diff", gray_diff) + # TODO: ENV_DEPENDENCE: it could depend on the lighting and environment + _, mask = cv2.threshold(gray_diff, 40, 255, cv2.THRESH_BINARY) + else: + self.last_frames.append(image) + return mask + # # # scale_up = 20 # # current_hand_roi_mask[max(y - scale_up, 0):min(y + h + scale_up, frame.shape[0]), # # max(x - scale_up, 0):min(x + w + scale_up, frame.shape[1])] = 255 - def get_MOG2_mask(self, image): - # TODO: not working (it considers everything shadows or moves (too dark background) - fgbg = cv2.createBackgroundSubtractorMOG2(detectShadows=False) - # fgbg= cv2.BackgroundSubtractorMOG2(0, 50) - # fgbg=cv2.BackgroundSubtractor() - # fgbg = cv2.BackgroundSubtractorMOG() - gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) - # TODO: ENV_DEPENDENCE: it could depend on the camera quality - # blur = cv2.medianBlur(gray_image, 100) - blur_radius = 5 - blurred = cv2.GaussianBlur(image, (blur_radius, blur_radius), 0) - # cv2.imshow("blurred", blur) - mask = fgbg.apply(blurred) - return mask + # def calculate_hand_interst_points(self, frame, cnts): + # # convexity defect + # + # drawing = copy.deepcopy(frame) + # hull = cv2.convexHull(cnts, returnPoints=False) + # if len(hull) > 3: + # defects = cv2.convexityDefects(cnts, hull) + # if type(defects) != type(None): # avoid crashing. (BUG not found) + # + # cnt = 0 + # for i in range(defects.shape[0]): # calculate the angle + # s, e, f, d = defects[i][0] + # start = tuple(cnts[s][0]) + # end = tuple(cnts[e][0]) + # far = tuple(cnts[f][0]) + # a = math.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2) + # b = math.sqrt((far[0] - start[0]) ** 2 + (far[1] - start[1]) ** 2) + # c = math.sqrt((end[0] - far[0]) ** 2 + (end[1] - far[1]) ** 2) + # angle = math.acos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c)) # cosine theorem + # if angle <= math.pi / 2: # angle less than 90 degree, treat as fingers + # cnt += 1 + # cv2.circle(drawing, far, 8, [211, 84, 0], -1) + # interdigs.append(far) + # cv2.imshow("detect_fingers", drawing) + # return interdigs + + def update_hand_charasteristics(self, frame, hand, strict=True): + updated_hand = copy.deepcopy(hand) + + extended_bounding_rect = upscale_bounding_rec(hand.bounding_rect, frame.shape, 20) + contours, _ = self.create_contours_and_mask(frame, extended_bounding_rect) + if len(contours) > 0: + # Find Max contour area (Assume that hand is in the frame) + # TODO: ENV_DEPENDENCE: depends on the camera resolution, distance to the background, noisy areas sizes + max_area = 0 + hand_contour = None + for contour_index in range(len(contours)): + cnt = contours[contour_index] + area = cv2.contourArea(cnt) + if area >= max_area: + max_area = area + # Largest area contour + hand_contour = contours[contour_index] + + # Find convex hull + # hull = cv2.convexHull(hand_contour) + # for point in hull: + # cv2.circle(frame, tuple(point[0]), 8, [255, 100, 10], -1) + + # Find convex defects + if hand_contour is not None: + updated_hand = self.contour_to_hand_strict(frame, hand_contour, updated_hand, strict) + else: + updated_hand.contour = [] + else: + return None + return updated_hand + + def detect_hands_in_frame(self, frame, roi=None): + new_hands = [] + + contours, _ = self.create_contours_and_mask(frame, roi) + + # Draw Contours + # cv2.drawContours(frame, cnt, -1, (122,122,0), 3) + + if len(contours) > 0: + # Find Max contour area (Assume that hand is in the frame) + # TODO: ENV_DEPENDENCE: depends on the camera resolution, distance to the background, noisy areas sizes + min_area = 100 + + for contour in contours: + area = cv2.contourArea(contour) + if area > min_area: + hand_contour = contour + hand = self.contour_to_hand(frame, hand_contour) + if hand is not None: + new_hands.append(hand) + return new_hands + + def get_hand_fingertips(self, hand_contour, defects): + intertips_coords = [] + intertips_indexes = [] + far_defect = [] + fingertips_coords = [] + fingertips_indexes = [] + defect_indices = [] + for defect_index in range(defects.shape[0]): + s, e, f, d = defects[defect_index, 0] + start = tuple(hand_contour[s][0]) + end = tuple(hand_contour[e][0]) + far = tuple(hand_contour[f][0]) + far_defect.append(far) + # cv2.line(frame, start, end, [0, 255, 0], 1) + a = math.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2) + b = math.sqrt((far[0] - start[0]) ** 2 + (far[1] - start[1]) ** 2) + c = math.sqrt((end[0] - far[0]) ** 2 + (end[1] - far[1]) ** 2) + angle = math.acos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c)) # cosine theorem + # cv2.circle(frame, far, 8, [211, 84, 125], -1) + # cv2.circle(frame, start, 8, [0, 84, 125], -1) + # cv2.circle(frame, end, 8, [0, 84, 125], -1) + # Get tips and intertips coordinates + # TODO: ENV_DEPENDENCE: this angle > 90ยบ determinate if two points are considered fingertips or not and 90 make thumb to fail in some occasions + intertips_max_angle = math.pi / 1.7 + if angle <= intertips_max_angle: # angle less than 90 degree, treat as fingers + defect_indices.append(defect_index) + # cv2.circle(frame, far, 8, [211, 84, 0], -1) + intertips_coords.append(far) + intertips_indexes.append(f) + # cv2.putText(frame, str(s), start, self.font, 0.7, (255, 255, 255), 1) + # cv2.putText(frame, str(e), end, self.font, 0.7, (255, 255, 200), 1) + if len(fingertips_coords) > 0: + from scipy.spatial import distance + # calculate distances from start and end to the already known tips + start_distance, end_distance = tuple( + distance.cdist(fingertips_coords, [start, end]).min(axis=0)) + # TODO: ENV_DEPENDENCE: it determinate the pixels distance to consider two points the same. It depends on camera resolution and distance from the hand to the camera + same_fingertip_radius = 10 + if start_distance > same_fingertip_radius: + fingertips_coords.append(start) + fingertips_indexes.append(s) + + # cv2.circle(frame, start, 10, [255, 100, 255], 3) + if end_distance > same_fingertip_radius: + fingertips_coords.append(end) + fingertips_indexes.append(e) + + # cv2.circle(frame, end, 10, [255, 100, 255], 3) + else: + fingertips_coords.append(start) + fingertips_indexes.append(s) + + # cv2.circle(frame, start, 10, [255, 100, 255], 3) + fingertips_coords.append(end) + fingertips_indexes.append(e) + + # cv2.circle(frame, end, 10, [255, 100, 255], 3) + + # cv2.circle(frame, far, 10, [100, 255, 255], 3) + return fingertips_coords, fingertips_indexes, intertips_coords, intertips_indexes + + def get_hand_bounding_rect(self, hand_contour, fingers_contour): + (x, y), radius = cv2.minEnclosingCircle(fingers_contour) + center = (int(x), int(y)) + radius = int(radius) + 10 + hand_contour = extract_contour_inside_circle(hand_contour, (center, radius)) + hand_bounding_rect = cv2.boundingRect(hand_contour) + return hand_bounding_rect, ((int(x), int(y)), radius), hand_contour + + def is_hand(self, fingertips, intertips, strict=True): + if strict: + return len(fingertips) == 5 and len(intertips) > 2 + else: + return 5 >= len(fingertips) > 2 + + def update_detection(self, frame): + for hand in self.hands: + hand.detected = False + detected_hands = self.detect_hands_in_frame(frame) + for detected_hand in detected_hands: + if len(self.hands) > 0: + hand_exists = False + for existing_hand_index, existing_hand in enumerate(self.hands): + intersection_value, _ = self.calculate_bounding_rects_intersection( + detected_hand.bounding_rect, + existing_hand.bounding_rect) + if intersection_value > 0.1: + hand_exists = True + existing_hand.update_attributes_from_detected(detected_hand) + break + if not hand_exists: + detected_hand.id = str(self.next_hand_id) + detected_hand.detected = True + self.next_hand_id += 1 + self.hands.append(detected_hand) + print "New hand" + else: + detected_hand.id = str(self.next_hand_id) + detected_hand.detected = True + self.next_hand_id += 1 + self.hands.append(detected_hand) + print "New hand" + + def update_detection_and_tracking(self, frame): + if len(self.hands) > 0: + for index, existing_hand in enumerate(self.hands): + existing_hand.tracked = False + existing_hand.detected = False + hands_mask = self.create_hands_mask(frame) + ret, tracking_window = self.follow(frame, hands_mask, existing_hand.bounding_rect) + updated_hand = self.update_hand_charasteristics(frame, existing_hand) + if updated_hand is not None: + existing_hand.update_attributes_from_detected(updated_hand) + existing_hand.detected = True + if ret and tracking_window is not None: + existing_hand.tracking_window = tracking_window + existing_hand.tracked = True + + if existing_hand.detected is False: + if existing_hand.tracked: + existing_hand.bounding_rect = existing_hand.tracking_window + updated_hand = self.update_hand_charasteristics(frame, existing_hand, strict=False) + if updated_hand is not None: + existing_hand.update_attributes_from_detected(updated_hand) + existing_hand.detected = False + else: + print "_____________No updated information" + existing_hand.update_truth_value_by_frame() + if existing_hand.truth_value <= 0: + print "removing hand" + self.hands.remove(existing_hand) + + def update_detection2(self, frame): + for existing_hand in self.hands: + existing_hand.detected = False + # new_bounding_rect = upscale_bounding_rec(existing_hand.bounding_rect,frame.shape, 100) + updated_hand = self.update_hand_charasteristics(frame, existing_hand) + if updated_hand is not None: + existing_hand.update_attributes_from_detected(updated_hand) + + def update_tracking(self, frame): + if len(self.hands) > 0: + for index, hand in enumerate(self.hands): + hand.tracked = False + hands_mask = self.create_hands_mask(frame) + ret, tracking_window = self.follow(frame, hands_mask, hand.bounding_rect) + if ret and tracking_window is not None: + updated_hand = self.update_hand_charasteristics(frame, hand) + if updated_hand is not None: + updated_hand.detected = hand.detected + updated_hand.tracking_window = tracking_window + updated_hand.detection_fails = hand.detection_fail + updated_hand.tracked = True + self.hands[index] = updated_hand + else: + hand.tracked = False + else: + hand.tracked = False def main(): - # hand_detector = HandDetector() - hand_detector = HandDetector('/home/robolab/PycharmProjects/TVGames/libs/Hand_Detection/hand_on_screen2.mp4') - hand_detector.compute() + hand_detector = HandDetector() + # hand_detector = HandDetector('/home/robolab/PycharmProjects/TVGames/libs/Hand_Detection/hand_on_screen2.mp4') + hand_detector.compute2() hand_detector.exit() From fb09f9b3313e87a20770b4b315a77353a3a3c4bd Mon Sep 17 00:00:00 2001 From: orensbruli Date: Wed, 27 Jun 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z<4JCDjNKqadH_E&$`-9(^OP>2!fTN$fF+qo@2w${aLP4XHlmi!bWJ`QGx-B@*Rv{$ z*?~hdVZ;u;>7l|@?w=oEs3#KO4@5dV}lMJvPZJu`j@|(5u z-%ZW0|{VEz-+QphhT@YUAHkp|vuzjO;dsvg^ z9UGF64`!gIPo^a+yzIf_1KL~92C^G9K>^n>`8p$0HvR7)pszs?VE%uT9U}nvFlvW? zjj<+-mib@2@m~zT`B?oA<(2tc={#@#q0*qgmF?reAFA= Date: Fri, 29 Jun 2018 19:42:39 +0200 Subject: [PATCH 19/54] Created new truth method for the hand (need fixes) Fixed some typos in some method names Created a method to downscale bounding rects Created method extract_contour_inside_rect Added debug attribute for the HandDetector class Added method addHand2 to look for hands on a roi ignorin the contours Added method compute_overlayed_frame to get an image with the hands information over the initial frame Fixed a bug with the position history of the hands Added an option to the follow method to use camshift instead meanshift for the tracking Added a method to reset the background and some conditions in the method to get the mask to reset if needed. Added a new method to "detect" fist based on the convex hull and defects points and taking the bigges of two clusters created with kmeans --- HandDetection.py | 701 ++++++++++++++++++++++++++++++++++------------- __init__.py | 0 2 files changed, 514 insertions(+), 187 deletions(-) create mode 100644 __init__.py diff --git a/HandDetection.py b/HandDetection.py index f34b275..64835ad 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -2,15 +2,13 @@ # -*- coding: utf-8 -*- import copy import math -import time +import os +import random from collections import deque from datetime import datetime import cv2 import numpy as np -import random - -from scipy.linalg import fractional_matrix_power def get_random_color(n=1): @@ -61,7 +59,7 @@ def find_distance(point_a, point_b): DETECTION_TRUTH_FACTOR = 2. # points of life to recover if detected in one iteration TRACKING_TRUTH_FACTOR = .1 # points of life to recover if tracked in one iteration UNDETECTION_TRUTH_FACTOR = 3. # points of life to recover if detected in one iteration -UNTRACKING_TRUTH_FACTOR = .1 # points of life to recover if tracked in one iteration +UNTRACKING_TRUTH_FACTOR = 2 # points of life to recover if tracked in one iteration MAX_TRUTH_VALUE = 100. @@ -126,6 +124,23 @@ def update_truth_value_by_frame(self): self.truth_value = 100 self.frame_count += 1 + def update_truth_value_by_frame2(self): + one_frame_truth_subtraction = MAX_TRUTH_VALUE / MAX_UNDETECTED_FRAMES + if not self.detected: + self.detection_fails += 1 + if not self.tracked: + self.tracking_fails += 1 + if not self.detected and not self.tracked: + substraction = -1 * UNDETECTION_TRUTH_FACTOR * UNTRACKING_TRUTH_FACTOR * one_frame_truth_subtraction + else: + substraction = 0 + new_truth_value = self.truth_value + substraction + if new_truth_value <= 100: + self.truth_value = new_truth_value + else: + self.truth_value = 100 + self.frame_count += 1 + def copy_main_attributes(self): updated_hand = Hand() updated_hand.id = self.id @@ -172,7 +187,7 @@ def get_color_mask(image): return mask -def upscale_bounding_rec(bounding_rect, frame_shape, upscaled_pixels): +def upscale_bounding_rect(bounding_rect, frame_shape, upscaled_pixels): x, y, w, h = bounding_rect new_x = max(x - int(upscaled_pixels / 2), 0) @@ -193,6 +208,25 @@ def upscale_bounding_rec(bounding_rect, frame_shape, upscaled_pixels): return upscaled_bounding_rect +def downscale_bounding_rect(bounding_rect, frame_shape, downscaled_pixels): + x, y, w, h = bounding_rect + new_x = min(x + int(downscaled_pixels / 2), frame_shape[1]) + + new_y = min(y + int(downscaled_pixels / 2), frame_shape[0]) + + if w - downscaled_pixels > 0: + new_w = w - downscaled_pixels + else: + new_w = 0 + + if h - downscaled_pixels > 0: + new_h = h + downscaled_pixels + else: + new_h = 0 + downscaled_bounding_rect = (new_x, new_y, new_w, new_h) + return downscaled_bounding_rect + + def extract_contour_inside_circle(full_contour, circle): center, radius = circle new_contour = [] @@ -202,12 +236,27 @@ def extract_contour_inside_circle(full_contour, circle): return np.array(new_contour) +def extract_contour_inside_rect(full_contour, rect): + x1, y1, w, h = rect + x2 = x1 + w + y2 = y1 + h + new_contour = [] + for point in full_contour: + if x1 < point[0][0] < x2 and y1 < point[0][1] < y2: + new_contour.append(point) + return np.array(new_contour) + + class HandDetector: def __init__(self, source=0): # Open Camera object # self.capture = cv2.VideoCapture(0) # TODO: For testing only - self.capture = cv2.VideoCapture(source) + if source != -1: + self.capture = cv2.VideoCapture(source) + else: + self.capture = None + self.hands = [] # [{"fingers":None, "center_of_mass":None}] self.font = cv2.FONT_HERSHEY_SIMPLEX self.first_frame = None @@ -215,21 +264,66 @@ def __init__(self, source=0): # TODO: ENV_DEPENDENCE: depending on the environment and camera it would be more or less frames to discard self.discarded_frames = 10 self.last_frames = deque(maxlen=self.discarded_frames) + self.debug = True # Decrease frame size # self.capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1000) # self.capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 600) + def add_hand2(self, frame): + hand_template_roi = (frame.shape[1] / 2 - 100, frame.shape[0] / 2 - 100, 200, 200) + template_x, template_y, template_w, template_h = hand_template_roi + frame_contours, frame_mask = self.create_contours_and_mask(frame, hand_template_roi) + masked_frame = frame.copy() + masked_frame = np.zeros(frame.shape, dtype="uint8") + masked_frame[::] = 255 + if len(frame_contours) > 0 and len(frame_mask) > 0: + # Get the maximum area contour + min_area = 100 + hand_contour = None + for i in range(len(frame_contours)): + cnt = frame_contours[i] + area = cv2.contourArea(cnt) + if area > min_area: + min_area = area + hand_contour = frame_contours[i] + + if hand_contour is not None: + # cv2.drawContours(frame, [hand_contour], -1, (0, 255, 255), 2) + detected_hand_bounding_rect = cv2.boundingRect(hand_contour) + detected_hand_x, detected_hand_y, detected_hand_w, detected_hand_h = detected_hand_bounding_rect + frame_mask_roi_image = frame_mask[template_y:template_y + template_h, + template_x:template_x + template_w] + frame_mask_roi_image_contour, _, _ = self.calculate_max_contour(frame_mask_roi_image, to_binary=False) + new_hand = self.contour_to_new_hand(frame, hand_contour) + if new_hand is not None: + new_hand.id = len(self.hands) + self.hands.append(new_hand) + cv2.putText(masked_frame, "HAND FOUND", + (template_x, template_y + template_h + 10), + self.font, 1, [0, 0, 0], 2) + else: + cv2.putText(masked_frame, "CENTER YOUR HAND", + (template_x, template_y + template_h + 10), + self.font, 1, [0, 0, 0], 2) + else: + cv2.putText(masked_frame, "PLEASE PUT YOUR HAND HERE", (template_x - 100, template_y + template_h + 10), + self.font, 1, [0, 0, 0], 2) + masked_frame = cv2.rectangle(masked_frame, (template_x, template_y), + (template_x + template_w, template_y + template_h), [0, 0, 0]) + # cv2.imshow('masked_frame', masked_frame) + return masked_frame + def add_hand(self, frame): - hand_mask = None # Load the hand template - hand_template = cv2.imread("/home/robolab/Desktop/hand_mask.png") + template_path = os.path.join(os.path.dirname(__file__), './resources/right_hand_mask.png') + hand_template = cv2.imread(template_path) hand_template_contour, hand_template_roi, hand_template_roi_image = self.calculate_max_contour(hand_template) template_x, template_y, template_w, template_h = hand_template_roi # Get the contour and mask of the frame frame_contours, frame_mask = self.create_contours_and_mask(frame, hand_template_roi) masked_frame = frame.copy() - + if len(frame_contours) > 0 and len(frame_mask) > 0: # Get the maximum area contour min_area = 100 @@ -254,7 +348,8 @@ def add_hand(self, frame): # cv2.fillPoly(realtime_handmask, pts=[hand_contour], color=(255, 255, 255)) # cv2.imshow("realtime hand mask", realtime_handmask) diff = cv2.absdiff(hand_template_roi_image.astype(np.uint8), frame_mask_roi_image.astype(np.uint8)) - cv2.imshow("diff", diff) + if self.debug: + cv2.imshow("diff", diff) result = cv2.matchShapes(frame_mask_roi_image_contour, hand_template_contour, 1, 0) print result if frame_mask_roi_image_contour is not None: @@ -273,17 +368,17 @@ def add_hand(self, frame): cv2.putText(masked_frame, "HAND FOUND", (template_x, template_y + template_h + 10), self.font, 1, [255, 255, 255], 2) - if len(self.hands)<1: + if len(self.hands) < 1: cv2.putText(masked_frame, "CENTER YOUR HAND", - (template_x, template_y + template_h + 10), - self.font, 1, [255, 255, 255], 2) + (template_x, template_y + template_h + 10), + self.font, 1, [255, 255, 255], 2) else: cv2.putText(masked_frame, "PLEASE PUT YOUR HAND HERE", (template_x - 100, template_y + template_h + 10), self.font, 1, [255, 255, 255], 2) - masked_frame = cv2.addWeighted(masked_frame, 0.7, hand_template, 0.3, 0) - cv2.imshow('masked_frame', masked_frame) + # cv2.imshow('masked_frame', masked_frame) + return masked_frame def calculate_bounding_rects_intersection(self, bounding_rect_1, bounding_rect_2): b1_x_left = bounding_rect_1[0] @@ -365,7 +460,8 @@ def create_contours_and_mask(self, frame, roi_mask=None): hands_mask = self.create_hands_mask(frame) if hands_mask is None: return ([], []) - cv2.imshow("hands_mask", hands_mask) + if self.debug: + cv2.imshow("create_contours_and_mask (Frame Mask)", hands_mask) if roi_mask is not None: current_roi_mask = np.zeros(hands_mask.shape, dtype='uint8') @@ -380,8 +476,8 @@ def create_contours_and_mask(self, frame, roi_mask=None): # cv2.putText(to_show, (str(w * h)), (x + w / 2, y + h / 2), self.font, 0.3, [100, 100, 255], 1) # cv2.putText(to_show, (str(x)+", "+str(y)), (x-10, y-10), self.font, 0.3, [255, 255, 255], 1) cv2.rectangle(to_show, (x, y), (x + w, y + h), [255, 255, 255]) - - cv2.imshow("current hands_mask", to_show) + if self.debug: + cv2.imshow("create_contours_and_mask (ROIed Mask)", to_show) ret, thresh = cv2.threshold(hands_mask, 127, 255, 0) @@ -405,8 +501,9 @@ def create_hands_mask(self, image, mode="diff"): if diff_mask is not None and color_mask is not None: mask = cv2.bitwise_and(diff_mask, color_mask) - cv2.imshow("diff_mask", diff_mask) - cv2.imshow("color_mask", color_mask) + if self.debug: + cv2.imshow("diff_mask", diff_mask) + cv2.imshow("color_mask", color_mask) elif mode == "movement_buffer": # Absolutly unusefull mask = self.get_movement_buffer_mask(image) @@ -444,7 +541,8 @@ def compute(self): pass ##### Show final image ######## - cv2.imshow('Detection', overlayed_frame) + if self.debug: + cv2.imshow('Detection', overlayed_frame) ############################### # Print execution time # print time.time()-start_time @@ -467,17 +565,15 @@ def compute2(self): if ret is True: if len(self.hands) != 1: - self.add_hand(frame) + self.add_hand2(frame) self.update_detection_and_tracking(frame) - overlayed_frame = frame.copy() - for hand in self.hands: - overlayed_frame = self.draw_hand_overlay(frame, hand) - pass + overlayed_frame = self.compute_overlayed_frame(frame) ##### Show final image ######## - cv2.imshow('Detection', overlayed_frame) + if self.debug: + cv2.imshow('Detection', overlayed_frame) ############################### # Print execution time # print time.time()-start_time @@ -485,18 +581,21 @@ def compute2(self): print "No video detected" # close the output video by pressing 'ESC' - k = cv2.waitKey(50) & 0xFF + if self.debug and len(self.hands) > 0: + k = cv2.waitKey(0) & 0xFF + else: + k = cv2.waitKey(5) & 0xFF if k == 27: break - def contour_to_hand_strict(self, frame, hand_contour, hand_to_update=None, strict=True): - if hand_to_update is not None: - updated_hand = copy.deepcopy(hand_to_update) - updated_hand.contour = hand_contour - hand_bounding_rect = cv2.boundingRect(hand_contour) - else: - updated_hand = Hand() + def compute_overlayed_frame(self, frame): + overlayed_frame = frame.copy() + for hand in self.hands: + overlayed_frame = self.draw_hand_overlay(frame, hand) + return overlayed_frame + def contour_to_new_hand(self, frame, hand_contour): + hand = Hand() hull2 = cv2.convexHull(hand_contour, returnPoints=False) defects = cv2.convexityDefects(hand_contour, hull2) @@ -507,23 +606,23 @@ def contour_to_hand_strict(self, frame, hand_contour, hand_to_update=None, stric intertips_coords, \ intertips_indexes = self.get_hand_fingertips(hand_contour, defects) - is_hand = self.is_hand(fingertips_coords, intertips_coords, strict=strict) + is_hand = self.is_hand(fingertips_coords, intertips_coords, strict=True) if is_hand: - updated_hand.fingertips = fingertips_coords - updated_hand.intertips = intertips_coords + hand.fingertips = fingertips_coords + hand.intertips = intertips_coords if len(fingertips_coords) == 5: fingers_contour = np.take(hand_contour, fingertips_indexes + intertips_indexes, axis=0, mode="wrap") - hand_bounding_rect, hand_circle, hand_contour = self.get_hand_bounding_rect(hand_contour, - fingers_contour) - elif strict: + hand.bounding_rect, hand_circle, hand.contour = self.get_hand_bounding_rect_from_fingers( + hand_contour, + fingers_contour) + else: + return None + else: return None - - updated_hand.contour = hand_contour - updated_hand.bounding_rect = hand_bounding_rect # Find moments of the largest contour moments = cv2.moments(hand_contour) center_of_mass = None @@ -534,8 +633,8 @@ def contour_to_hand_strict(self, frame, hand_contour, hand_to_update=None, stric cx = int(moments['m10'] / moments['m00']) # cx = M10/M00 cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 center_of_mass = (cx, cy) - updated_hand.center_of_mass = center_of_mass - updated_hand.position_history.append(center_of_mass) + hand.center_of_mass = center_of_mass + hand.position_history.append(center_of_mass) if center_of_mass is not None and len(intertips_coords) > 0: # Distance from each finger defect(finger webbing) to the center mass @@ -552,7 +651,7 @@ def contour_to_hand_strict(self, frame, hand_contour, hand_to_update=None, stric # Get an average of three shortest distances from finger webbing to center mass sorted_defects_distances = sorted(distance_between_defects_to_center) average_defect_distance = np.mean(sorted_defects_distances[0:2]) - updated_hand.average_defect_distance = average_defect_distance + hand.average_defect_distance = average_defect_distance # # Get fingertip points from contour hull # # If points are in proximity of 80 pixels, consider as a single point in the group # finger = [] @@ -574,21 +673,15 @@ def contour_to_hand_strict(self, frame, hand_contour, hand_to_update=None, stric np.power(fingertips_coords[i][0] - center_of_mass[0], 2) + np.power( fingertips_coords[i][1] - center_of_mass[0], 2)) finger_distances.append(distance) - updated_hand.finger_distances = finger_distances - + hand.finger_distances = finger_distances else: return None - return updated_hand - - def contour_to_hand(self, frame, hand_contour, hand_to_update=None): - initial = (hand_to_update == None) - if not initial: - updated_hand = copy.deepcopy(hand_to_update) - updated_hand.contour = hand_contour - hand_bounding_rect = cv2.boundingRect(hand_contour) - else: - updated_hand = Hand() + return hand + def update_hand_with_contour(self, frame, hand_contour, hand_to_update): + hand = copy.deepcopy(hand_to_update) + hand.contour = hand_contour + hand.bounding_rect = cv2.boundingRect(hand_contour) hull2 = cv2.convexHull(hand_contour, returnPoints=False) defects = cv2.convexityDefects(hand_contour, hull2) @@ -599,77 +692,171 @@ def contour_to_hand(self, frame, hand_contour, hand_to_update=None): intertips_coords, \ intertips_indexes = self.get_hand_fingertips(hand_contour, defects) - is_hand = self.is_hand(fingertips_coords, intertips_coords, strict=initial) + is_hand = self.is_hand(fingertips_coords, intertips_coords, strict=False) if is_hand: - updated_hand.fingertips = fingertips_coords - updated_hand.intertips = intertips_coords + hand.fingertips = fingertips_coords + hand.intertips = intertips_coords if len(fingertips_coords) == 5: fingers_contour = np.take(hand_contour, fingertips_indexes + intertips_indexes, axis=0, mode="wrap") - hand_bounding_rect, hand_circle, hand_contour = self.get_hand_bounding_rect(hand_contour, - fingers_contour) - if not initial or is_hand: - updated_hand.contour = hand_contour - updated_hand.bounding_rect = hand_bounding_rect - # Find moments of the largest contour - moments = cv2.moments(hand_contour) - center_of_mass = None + hand.bounding_rect, hand_circle, hand.contour = self.get_hand_bounding_rect_from_fingers( + hand_contour, + fingers_contour) + + # Find moments of the largest contour + moments = cv2.moments(hand_contour) + center_of_mass = None + finger_distances = [] + average_defect_distance = None + # Central mass of first order moments + if moments['m00'] != 0: + cx = int(moments['m10'] / moments['m00']) # cx = M10/M00 + cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 + center_of_mass = (cx, cy) + hand.center_of_mass = center_of_mass + hand.position_history.append(center_of_mass) + + if center_of_mass is not None and len(intertips_coords) > 0: + # Distance from each finger defect(finger webbing) to the center mass + distance_between_defects_to_center = [] + for far in intertips_coords: + x = np.array(far) + center_mass_array = np.array(center_of_mass) + distance = np.sqrt( + np.power(x[0] - center_mass_array[0], + 2) + np.power(x[1] - center_mass_array[1], 2) + ) + distance_between_defects_to_center.append(distance) + + # Get an average of three shortest distances from finger webbing to center mass + sorted_defects_distances = sorted(distance_between_defects_to_center) + average_defect_distance = np.mean(sorted_defects_distances[0:2]) + hand.average_defect_distance = average_defect_distance + # # Get fingertip points from contour hull + # # If points are in proximity of 80 pixels, consider as a single point in the group + # finger = [] + # for i in range(0, len(hull) - 1): + # if (np.absolute(hull[i][0][0] - hull[i + 1][0][0]) > 10) or ( + # np.absolute(hull[i][0][1] - hull[i + 1][0][1]) > 10): + # if hull[i][0][1] < 500: + # finger.append(hull[i][0]) + # + # + # # The fingertip points are 5 hull points with largest y coordinates + # finger = sorted(finger, key=lambda x: x[1]) + # fingers = finger[0:5] + if center_of_mass is not None and len(fingertips_coords) > 0: + # Calculate distance of each finger tip to the center mass finger_distances = [] - average_defect_distance = None - # Central mass of first order moments - if moments['m00'] != 0: - cx = int(moments['m10'] / moments['m00']) # cx = M10/M00 - cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 - center_of_mass = (cx, cy) - updated_hand.center_of_mass = center_of_mass - updated_hand.position_history.append(center_of_mass) - - if center_of_mass is not None and len(intertips_coords) > 0: - # Distance from each finger defect(finger webbing) to the center mass - distance_between_defects_to_center = [] - for far in intertips_coords: - x = np.array(far) - center_mass_array = np.array(center_of_mass) - distance = np.sqrt( - np.power(x[0] - center_mass_array[0], - 2) + np.power(x[1] - center_mass_array[1], 2) - ) - distance_between_defects_to_center.append(distance) - - # Get an average of three shortest distances from finger webbing to center mass - sorted_defects_distances = sorted(distance_between_defects_to_center) - average_defect_distance = np.mean(sorted_defects_distances[0:2]) - updated_hand.average_defect_distance = average_defect_distance - # # Get fingertip points from contour hull - # # If points are in proximity of 80 pixels, consider as a single point in the group - # finger = [] - # for i in range(0, len(hull) - 1): - # if (np.absolute(hull[i][0][0] - hull[i + 1][0][0]) > 10) or ( - # np.absolute(hull[i][0][1] - hull[i + 1][0][1]) > 10): - # if hull[i][0][1] < 500: - # finger.append(hull[i][0]) - # - # - # # The fingertip points are 5 hull points with largest y coordinates - # finger = sorted(finger, key=lambda x: x[1]) - # fingers = finger[0:5] - if center_of_mass is not None and len(fingertips_coords) > 0: - # Calculate distance of each finger tip to the center mass - finger_distances = [] - for i in range(0, len(fingertips_coords)): - distance = np.sqrt( - np.power(fingertips_coords[i][0] - center_of_mass[0], 2) + np.power( - fingertips_coords[i][1] - center_of_mass[0], 2)) - finger_distances.append(distance) - updated_hand.finger_distances = finger_distances - else: - return None - elif initial: - return None - return updated_hand + for i in range(0, len(fingertips_coords)): + distance = np.sqrt( + np.power(fingertips_coords[i][0] - center_of_mass[0], 2) + np.power( + fingertips_coords[i][1] - center_of_mass[0], 2)) + finger_distances.append(distance) + hand.finger_distances = finger_distances + return hand + + # + # def contour_to_hand(self, frame, hand_contour, hand_to_update=None): + # initial = (hand_to_update == None) + # if not initial: + # hand = copy.deepcopy(hand_to_update) + # updated_hand.contour = hand_contour + # hand_bounding_rect = cv2.boundingRect(hand_contour) + # else: + # updated_hand = Hand() + # + # hull2 = cv2.convexHull(hand_contour, returnPoints=False) + # defects = cv2.convexityDefects(hand_contour, hull2) + # + # # Get defect points and draw them in the original image + # if defects is not None: + # fingertips_coords, \ + # fingertips_indexes, \ + # intertips_coords, \ + # intertips_indexes = self.get_hand_fingertips(hand_contour, defects) + # + # is_hand = self.is_hand(fingertips_coords, intertips_coords, strict=initial) + # if is_hand: + # updated_hand.fingertips = fingertips_coords + # updated_hand.intertips = intertips_coords + # + # if len(fingertips_coords) == 5: + # fingers_contour = np.take(hand_contour, + # fingertips_indexes + intertips_indexes, + # axis=0, + # mode="wrap") + # hand_bounding_rect, hand_circle, hand_contour = self.get_hand_bounding_rect_from_fingers(hand_contour, + # fingers_contour) + # # elif hand_to_update is not None and hand_to_update.center_of_mass is not None and len(hand_to_update.finger_distances)>0: + # # hand_bounding_rect, hand_circle, hand_contour = \ + # # self.get_hand_bounding_rect_from_center_of_mass( + # # hand_contour, + # # hand_to_update.center_of_mass, + # # max(hand_to_update.finger_distances)) + # + # if not initial or is_hand: + # updated_hand.contour = hand_contour + # updated_hand.bounding_rect = hand_bounding_rect + # # Find moments of the largest contour + # moments = cv2.moments(hand_contour) + # center_of_mass = None + # finger_distances = [] + # average_defect_distance = None + # # Central mass of first order moments + # if moments['m00'] != 0: + # cx = int(moments['m10'] / moments['m00']) # cx = M10/M00 + # cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 + # center_of_mass = (cx, cy) + # updated_hand.center_of_mass = center_of_mass + # updated_hand.position_history.append(center_of_mass) + # + # if center_of_mass is not None and len(intertips_coords) > 0: + # # Distance from each finger defect(finger webbing) to the center mass + # distance_between_defects_to_center = [] + # for far in intertips_coords: + # x = np.array(far) + # center_mass_array = np.array(center_of_mass) + # distance = np.sqrt( + # np.power(x[0] - center_mass_array[0], + # 2) + np.power(x[1] - center_mass_array[1], 2) + # ) + # distance_between_defects_to_center.append(distance) + # + # # Get an average of three shortest distances from finger webbing to center mass + # sorted_defects_distances = sorted(distance_between_defects_to_center) + # average_defect_distance = np.mean(sorted_defects_distances[0:2]) + # updated_hand.average_defect_distance = average_defect_distance + # # # Get fingertip points from contour hull + # # # If points are in proximity of 80 pixels, consider as a single point in the group + # # finger = [] + # # for i in range(0, len(hull) - 1): + # # if (np.absolute(hull[i][0][0] - hull[i + 1][0][0]) > 10) or ( + # # np.absolute(hull[i][0][1] - hull[i + 1][0][1]) > 10): + # # if hull[i][0][1] < 500: + # # finger.append(hull[i][0]) + # # + # # + # # # The fingertip points are 5 hull points with largest y coordinates + # # finger = sorted(finger, key=lambda x: x[1]) + # # fingers = finger[0:5] + # if center_of_mass is not None and len(fingertips_coords) > 0: + # # Calculate distance of each finger tip to the center mass + # finger_distances = [] + # for i in range(0, len(fingertips_coords)): + # distance = np.sqrt( + # np.power(fingertips_coords[i][0] - center_of_mass[0], 2) + np.power( + # fingertips_coords[i][1] - center_of_mass[0], 2)) + # finger_distances.append(distance) + # updated_hand.finger_distances = finger_distances + # else: + # return None + # elif initial: + # return None + # return updated_hand def draw_hand_overlay(self, frame, hand): if hand.detected: @@ -693,14 +880,21 @@ def draw_hand_overlay(self, frame, hand): points = np.array(hand.position_history) cv2.polylines(img=frame, pts=np.int32([points]), isClosed=False, color=hand.color) + tail_length = 15 + if len(points) > tail_length: + for i in np.arange(1, tail_length): + ci = len(points) - tail_length + i + thickness = int((float(i) / tail_length) * 13) + 1 + cv2.line(frame, tuple(points[ci - 1]), tuple(points[ci]), (0, 0, 255), thickness) if hand.center_of_mass is not None: # Draw center mass cv2.circle(frame, hand.center_of_mass, 7, [100, 0, 255], 2) cv2.putText(frame, 'Center', tuple(hand.center_of_mass), self.font, 0.5, (255, 255, 255), 1) - hand_string = "hand " + str(hand.id) + ": detected =" + str(hand.detected) + " tracked =" + str( - hand.tracked) + " at " + str(hand.center_of_mass) + " truth: " + str(hand.truth_value) + "%" + hand_string = "hand %d %s: D=%s|T=%s|L=%s|F=%d" % ( + hand.id, str(hand.center_of_mass), str(hand.detected), str(hand.tracked), str(hand.truth_value), + hand.frame_count) cv2.putText(frame, hand_string, (10, 30 + 15 * int(hand.id)), self.font, 0.5, (255, 255, 255), 1) return frame @@ -708,16 +902,16 @@ def exit(self): self.capture.release() cv2.destroyAllWindows() - def follow(self, frame, mask, bounding_rect): - upscaled_bounding_rect = bounding_rect # self.upscale_bounding_rec(bounding_rect,frame.shape,20) - x, y, w, h = upscaled_bounding_rect - track_window = upscaled_bounding_rect + def follow(self, frame, mask, bounding_rect, method="camshift"): + x, y, w, h = bounding_rect + track_window = bounding_rect # set up the ROI for tracking roi = frame[y:y + h, x:x + w] hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV) # mask = cv2.inRange(hsv_roi, np.array((0., 60., 32.)), np.array((180., 255., 255.))) roi_mask = mask[y:y + h, x:x + w] - cv2.imshow("roi_follow_mask", roi_mask) + if self.debug: + cv2.imshow("follow (ROI extracted mask)", roi_mask) roi_hist = cv2.calcHist([hsv_roi], [0], roi_mask, [180], [0, 180]) cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX) # Setup the termination criteria, either 10 iteration or move by atleast 1 pt @@ -725,7 +919,10 @@ def follow(self, frame, mask, bounding_rect): hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1) # apply meanshift to get the new location - ret, track_window = cv2.meanShift(dst, track_window, term_crit) + if method == "meanshift": + ret, track_window = cv2.meanShift(dst, track_window, term_crit) + else: + ret, track_window = cv2.CamShift(dst, track_window, term_crit) return ret, track_window # return ret, (max(newx-30,0), max(newy-30,0), min(neww+30,frame.shape[1]), min(newh+30,frame.shape[0])) @@ -758,7 +955,8 @@ def get_movement_buffer_mask(self, image): diff = cv2.absdiff(blurred_image, blurred_background.astype(np.uint8)) # print "diff" gray_diff = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY) - cv2.imshow("diff", gray_diff) + if self.debug: + cv2.imshow("diff", gray_diff) # TODO: ENV_DEPENDENCE: it could depend on the lighting and environment _, mask = cv2.threshold(gray_diff, 40, 255, cv2.THRESH_BINARY) return mask @@ -778,7 +976,8 @@ def get_simple_diff_mask(self, image): diff = cv2.absdiff(blurred_image, blurred_background.astype(np.uint8)) # print "diff" gray_diff = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY) - cv2.imshow("diff", gray_diff) + if self.debug: + cv2.imshow("diff", gray_diff) # TODO: ENV_DEPENDENCE: it could depend on the lighting and environment _, mask = cv2.threshold(gray_diff, 40, 255, cv2.THRESH_BINARY) return mask @@ -807,9 +1006,13 @@ def get_simple_diff_mask2(self, image): # TODO: ENV_DEPENDENCE: it could depend on the lighting and environment _, mask = cv2.threshold(gray_diff, 40, 255, cv2.THRESH_BINARY) non_zero_pixels = cv2.countNonZero(mask) - non_zero_percent = non_zero_pixels * 100 / image.shape[0] * image.shape[1] - if non_zero_percent < 10: + non_zero_percent = (non_zero_pixels * 100.) / (image.shape[0] * image.shape[1]) + if non_zero_percent < 1: self.first_frame = result_image + elif non_zero_percent > 95: + self.reset_background() + # print "Non zero pixel percentage %d" % non_zero_percent + if self.first_frame is not None: blurred_image = cv2.GaussianBlur(image, (blur_radius, blur_radius), 0) @@ -855,45 +1058,19 @@ def get_simple_diff_mask2(self, image): # cv2.imshow("detect_fingers", drawing) # return interdigs - def update_hand_charasteristics(self, frame, hand, strict=True): - updated_hand = copy.deepcopy(hand) - - extended_bounding_rect = upscale_bounding_rec(hand.bounding_rect, frame.shape, 20) - contours, _ = self.create_contours_and_mask(frame, extended_bounding_rect) - if len(contours) > 0: - # Find Max contour area (Assume that hand is in the frame) - # TODO: ENV_DEPENDENCE: depends on the camera resolution, distance to the background, noisy areas sizes - max_area = 0 - hand_contour = None - for contour_index in range(len(contours)): - cnt = contours[contour_index] - area = cv2.contourArea(cnt) - if area >= max_area: - max_area = area - # Largest area contour - hand_contour = contours[contour_index] - - # Find convex hull - # hull = cv2.convexHull(hand_contour) - # for point in hull: - # cv2.circle(frame, tuple(point[0]), 8, [255, 100, 10], -1) - - # Find convex defects - if hand_contour is not None: - updated_hand = self.contour_to_hand_strict(frame, hand_contour, updated_hand, strict) - else: - updated_hand.contour = [] - else: - return None - return updated_hand - def detect_hands_in_frame(self, frame, roi=None): new_hands = [] contours, _ = self.create_contours_and_mask(frame, roi) # Draw Contours - # cv2.drawContours(frame, cnt, -1, (122,122,0), 3) + if self.debug: + to_show = frame.copy() + cv2.drawContours(to_show, contours, -1, (122, 122, 0), 1) + if roi is not None: + cv2.rectangle(to_show, (roi[0], roi[1]), (roi[0] + roi[2], roi[1] + roi[3]), (122, 122, 255)) + cv2.imshow("detect_hands_in_frame (Detected Contours)", to_show) + k = cv2.waitKey(1) if len(contours) > 0: # Find Max contour area (Assume that hand is in the frame) @@ -904,7 +1081,7 @@ def detect_hands_in_frame(self, frame, roi=None): area = cv2.contourArea(contour) if area > min_area: hand_contour = contour - hand = self.contour_to_hand(frame, hand_contour) + hand = self.contour_to_new_hand(frame, hand_contour) if hand is not None: new_hands.append(hand) return new_hands @@ -970,7 +1147,7 @@ def get_hand_fingertips(self, hand_contour, defects): # cv2.circle(frame, far, 10, [100, 255, 255], 3) return fingertips_coords, fingertips_indexes, intertips_coords, intertips_indexes - def get_hand_bounding_rect(self, hand_contour, fingers_contour): + def get_hand_bounding_rect_from_fingers(self, hand_contour, fingers_contour): (x, y), radius = cv2.minEnclosingCircle(fingers_contour) center = (int(x), int(y)) radius = int(radius) + 10 @@ -978,12 +1155,31 @@ def get_hand_bounding_rect(self, hand_contour, fingers_contour): hand_bounding_rect = cv2.boundingRect(hand_contour) return hand_bounding_rect, ((int(x), int(y)), radius), hand_contour + def get_hand_bounding_rect_from_rect(self, hand_contour, bounding_rect): + hand_contour = extract_contour_inside_rect(hand_contour, bounding_rect) + hand_bounding_rect = cv2.boundingRect(hand_contour) + return hand_bounding_rect, hand_contour + + def get_hand_bounding_rect_from_center_of_mass(self, hand_contour, center_of_mass, average_distance): + (x, y) = center_of_mass + radius = average_distance + center = (int(x), int(y)) + radius = int(radius) + 10 + hand_contour = extract_contour_inside_circle(hand_contour, (center, radius)) + hand_bounding_rect = cv2.boundingRect(hand_contour) + return hand_bounding_rect, ((int(x), int(y)), radius), hand_contour + def is_hand(self, fingertips, intertips, strict=True): if strict: return len(fingertips) == 5 and len(intertips) > 2 else: return 5 >= len(fingertips) > 2 + def reset_background(self): + # TODO test it better + self.last_frames = deque(maxlen=self.discarded_frames) + self.first_frame = None + def update_detection(self, frame): for hand in self.hands: hand.detected = False @@ -1013,42 +1209,173 @@ def update_detection(self, frame): print "New hand" def update_detection_and_tracking(self, frame): + if len(self.hands) > 0: for index, existing_hand in enumerate(self.hands): existing_hand.tracked = False - existing_hand.detected = False hands_mask = self.create_hands_mask(frame) ret, tracking_window = self.follow(frame, hands_mask, existing_hand.bounding_rect) - updated_hand = self.update_hand_charasteristics(frame, existing_hand) - if updated_hand is not None: - existing_hand.update_attributes_from_detected(updated_hand) - existing_hand.detected = True if ret and tracking_window is not None: existing_hand.tracking_window = tracking_window existing_hand.tracked = True + # detecction by intersection with hands found in frame + extended_roi = upscale_bounding_rect(existing_hand.bounding_rect, frame.shape, 60) + # if existing_hand.detected: + # extended_roi = upscale_bounding_rect(existing_hand.bounding_rect, frame.shape, 60) + # else: + # extended_roi = None + detected_hands = self.detect_hands_in_frame(frame, extended_roi) + existing_hand.detected = False + for detected_hand in detected_hands: + intersection_value, _ = self.calculate_bounding_rects_intersection( + detected_hand.bounding_rect, + existing_hand.bounding_rect) + if intersection_value > 0.1: + existing_hand.update_attributes_from_detected(detected_hand) + existing_hand.position_history.extend(detected_hand.position_history) + existing_hand.detected = True + break if existing_hand.detected is False: - if existing_hand.tracked: - existing_hand.bounding_rect = existing_hand.tracking_window - updated_hand = self.update_hand_charasteristics(frame, existing_hand, strict=False) - if updated_hand is not None: - existing_hand.update_attributes_from_detected(updated_hand) - existing_hand.detected = False - else: - print "_____________No updated information" - existing_hand.update_truth_value_by_frame() + extended_roi = extended_roi if extended_roi is not None else existing_hand.bounding_rect + + fist_bounding_rect, existing_hand.contour = self.detect_fist(frame, extended_roi) + existing_hand.bounding_rect = upscale_bounding_rect(fist_bounding_rect, frame.shape, 50) + # if existing_hand.tracked: + # existing_hand.bounding_rect = existing_hand.tracking_window + # updated_hand = self.update_hand_attributes(frame, existing_hand, strict=False) + # if updated_hand is not None: + # existing_hand.update_attributes_from_detected(updated_hand) + # existing_hand.position_history = updated_hand.position_history + # existing_hand.detected = False + # else: + # print "_____________No updated information" + existing_hand.update_truth_value_by_frame2() if existing_hand.truth_value <= 0: print "removing hand" self.hands.remove(existing_hand) + def detect_fist(self, frame, roi): + to_show = frame.copy() + contours, _ = self.create_contours_and_mask(frame, roi) + hand_contour = None + if len(contours) > 0: + # Get the maximum area contour + min_area = 100 + hand_contour = None + for i in range(len(contours)): + cnt = contours[i] + area = cv2.contourArea(cnt) + if area > min_area: + min_area = area + hand_contour = contours[i] + + if hand_contour is not None: + cv2.drawContours(to_show, [hand_contour], -1, (0, 255, 255), 1) + cv2.imshow("detect_fist (Hand with contour)", to_show) + hull = cv2.convexHull(hand_contour, returnPoints=False) + new_contour = [] + # for index in hull: + # cv2.circle(to_show,tuple(hand_contour[index][0][0]),5,(255,255,255),2) + defects = cv2.convexityDefects(hand_contour, hull) + for defect_index in range(defects.shape[0]): + s, e, f, d = defects[defect_index, 0] + start = tuple(hand_contour[s][0]) + end = tuple(hand_contour[e][0]) + far = tuple(hand_contour[f][0]) + # cv2.circle(to_show,start,5,(0,99,255),2) + # cv2.circle(to_show, end, 5, (0, 99, 255), 2) + # cv2.circle(to_show, far, 5, (0, 99, 255), 2) + new_contour.append(hand_contour[s]) + new_contour.append(hand_contour[f]) + new_contour.append(hand_contour[e]) + + # define criteria and apply kmeans() + criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0) + new_contour_2d_points = np.float32(np.array(new_contour).reshape(len(new_contour), 2)) + ret, label, center = cv2.kmeans(new_contour_2d_points, 2, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS) + + # Now separate the data, Note the ravel() + max_group_len = 0 + max_group = None + + for label_number in range(2): + + group = new_contour_2d_points[label.ravel() == label_number] + rand_color = get_random_color() + for point in group: + cv2.circle(to_show, tuple(point), 5, rand_color[0], 2) + if len(group) > max_group_len: + max_group_len = len(group) + max_group = group + + (x, y), radius = cv2.minEnclosingCircle(max_group) + center = (int(x), int(y)) + radius = int(radius) + 20 + + cv2.circle(to_show, center, radius, (122, 122, 0), 1) + + for point in max_group: + cv2.circle(to_show, tuple(point), 5, (0, 255, 255), 2) + + cv2.imshow("detect_fist (Fist_ring)", to_show) + new_contour = extract_contour_inside_circle(hand_contour, (center, radius)) + return cv2.boundingRect(new_contour), new_contour + def update_detection2(self, frame): for existing_hand in self.hands: existing_hand.detected = False # new_bounding_rect = upscale_bounding_rec(existing_hand.bounding_rect,frame.shape, 100) - updated_hand = self.update_hand_charasteristics(frame, existing_hand) + updated_hand = self.update_hand_attributes(frame, existing_hand) if updated_hand is not None: existing_hand.update_attributes_from_detected(updated_hand) + def update_hand_attributes(self, frame, hand, strict=True): + updated_hand = copy.deepcopy(hand) + # if hand.detected or (not hand.detected and not hand.tracked): + # extended_bounding_rect = hand.bounding_rect + # else : + # extended_bounding_rect = upscale_bounding_rect(hand.tracking_window, frame.shape, 20) + # if hand.bounding_rect[2] > extended_bounding_rect[2] and hand.bounding_rect[3] > extended_bounding_rect[3]: + # extended_bounding_rect = (extended_bounding_rect[0], extended_bounding_rect[1],hand.bounding_rect[2], hand.bounding_rect[3]) + # if hand.detected or (not hand.detected and not hand.tracked): + contours, _ = self.create_contours_and_mask(frame, hand.bounding_rect) + # else: + # # upscaled_tracking_window = upscale_bounding_rect(hand.bounding_rect, frame.shape, 100) + # new_tracking_window = (hand.tracking_window[0], hand.tracking_window[1], hand.bounding_rect[2], hand.bounding_rect[3]) + # contours, _ = self.create_contours_and_mask(frame, new_tracking_window) + + if len(contours) > 0: + # Find Max contour area (Assume that hand is in the frame) + # TODO: ENV_DEPENDENCE: depends on the camera resolution, distance to the background, noisy areas sizes + max_area = 0 + hand_contour = None + for contour_index in range(len(contours)): + cnt = contours[contour_index] + area = cv2.contourArea(cnt) + if area >= max_area: + max_area = area + # Largest area contour + hand_contour = contours[contour_index] + + # Find convex hull + # hull = cv2.convexHull(hand_contour) + # for point in hull: + # cv2.circle(frame, tuple(point[0]), 8, [255, 100, 10], -1) + + # Find convex defects + if hand_contour is not None: + if self.debug: + to_show = frame.copy() + cv2.drawContours(to_show, contours, -1, 255) + cv2.imshow("update_hand_charasteristics (New Contour)", to_show) + updated_hand = self.update_hand_with_contour(frame, hand_contour, updated_hand) + else: + return None + else: + return None + return updated_hand + def update_tracking(self, frame): if len(self.hands) > 0: for index, hand in enumerate(self.hands): @@ -1056,7 +1383,7 @@ def update_tracking(self, frame): hands_mask = self.create_hands_mask(frame) ret, tracking_window = self.follow(frame, hands_mask, hand.bounding_rect) if ret and tracking_window is not None: - updated_hand = self.update_hand_charasteristics(frame, hand) + updated_hand = self.update_hand_attributes(frame, hand) if updated_hand is not None: updated_hand.detected = hand.detected updated_hand.tracking_window = tracking_window @@ -1071,7 +1398,7 @@ def update_tracking(self, frame): def main(): hand_detector = HandDetector() - # hand_detector = HandDetector('/home/robolab/PycharmProjects/TVGames/libs/Hand_Detection/hand_on_screen2.mp4') + # hand_detector = HandDetector('./resources/testing_hand_video2.mp4') hand_detector.compute2() hand_detector.exit() diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000..e69de29 From cc4a9da75e8c7279e0c2186711591b1b58c00108 Mon Sep 17 00:00:00 2001 From: orensbruli Date: Mon, 2 Jul 2018 19:02:36 +0200 Subject: [PATCH 20/54] Added new method draw_contour_features to visualize contour features of the hand. Added code to update the attributes of the hand when a fist is detected --- HandDetection.py | 48 ++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 46 insertions(+), 2 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index 64835ad..f89ca9c 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -867,7 +867,7 @@ def draw_hand_overlay(self, frame, hand): 1) for defect in hand.intertips: cv2.circle(frame, tuple(defect), 8, [211, 84, 0], -1) - + self.draw_contour_features(frame, hand.contour) x, y, w, h = hand.bounding_rect # cv2.putText(frame, (str(w)), (x + w, y), self.font, 0.3, [255, 255, 255], 1) # cv2.putText(frame, (str(h)), (x + w, y + h), self.font, 0.3, [255, 255, 255], 1) @@ -898,6 +898,45 @@ def draw_hand_overlay(self, frame, hand): cv2.putText(frame, hand_string, (10, 30 + 15 * int(hand.id)), self.font, 0.5, (255, 255, 255), 1) return frame + def draw_contour_features(self, to_show, hand_contour): + perimeter = cv2.arcLength(hand_contour, True) + print perimeter + hull = cv2.convexHull(hand_contour, returnPoints=False) + new_contour = [] + # for index in hull: + # cv2.circle(to_show,tuple(hand_contour[index][0][0]),5,(255,255,255),2) + defects = cv2.convexityDefects(hand_contour, hull) + for defect_index in range(defects.shape[0]): + s, e, f, d = defects[defect_index, 0] + # cv2.circle(to_show,start,5,(0,99,255),2) + # cv2.circle(to_show, end, 5, (0, 99, 255), 2) + # cv2.circle(to_show, far, 5, (0, 99, 255), 2) + new_contour.append(hand_contour[s]) + new_contour.append(hand_contour[f]) + new_contour.append(hand_contour[e]) + + x, y, w, h = cv2.boundingRect(hand_contour) + cv2.rectangle(to_show, (x, y), (x + w, y + h), (0, 255, 0), 2) + rect = cv2.minAreaRect(hand_contour) + box = cv2.boxPoints(rect) + box = np.int0(box) + cv2.drawContours(to_show, [box], 0, (0, 0, 255), 2) + + (x, y), radius = cv2.minEnclosingCircle(hand_contour) + center = (int(x), int(y)) + radius = int(radius) + cv2.circle(to_show, center, radius, (255, 0, 0), 2) + ellipse = cv2.fitEllipse(hand_contour) + cv2.ellipse(to_show, ellipse, (100, 48, 170), 2) + + rows, cols = to_show.shape[:2] + [vx, vy, x, y] = cv2.fitLine(hand_contour, cv2.DIST_L2, 0, 0.01, 0.01) + lefty = int((-x * vy / vx) + y) + righty = int(((cols - x) * vy / vx) + y) + cv2.line(to_show, (cols - 1, righty), (0, lefty), (0, 255, 0), 2) + + cv2.imshow("Hand with contour", to_show) + def exit(self): self.capture.release() cv2.destroyAllWindows() @@ -1238,9 +1277,14 @@ def update_detection_and_tracking(self, frame): if existing_hand.detected is False: extended_roi = extended_roi if extended_roi is not None else existing_hand.bounding_rect - fist_bounding_rect, existing_hand.contour = self.detect_fist(frame, extended_roi) existing_hand.bounding_rect = upscale_bounding_rect(fist_bounding_rect, frame.shape, 50) + + updated_hand = self.update_hand_attributes(frame, existing_hand, strict=False) + if updated_hand is not None: + existing_hand.update_attributes_from_detected(updated_hand) + existing_hand.position_history = updated_hand.position_history + existing_hand.detected = False # if existing_hand.tracked: # existing_hand.bounding_rect = existing_hand.tracking_window # updated_hand = self.update_hand_attributes(frame, existing_hand, strict=False) From d6f0f517632f896e98c14cc787e7f3e218f1b1d3 Mon Sep 17 00:00:00 2001 From: orensbruli Date: Fri, 6 Jul 2018 12:37:29 +0200 Subject: [PATCH 21/54] Change of the debug default value to False Removed the default call to the method draw_contour_features Fixed a bug with the detecction of the fist when no hand contour is found. --- HandDetection.py | 125 +++++++++++++++++++++++++---------------------- 1 file changed, 67 insertions(+), 58 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index f89ca9c..3de0ed8 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -264,7 +264,7 @@ def __init__(self, source=0): # TODO: ENV_DEPENDENCE: depending on the environment and camera it would be more or less frames to discard self.discarded_frames = 10 self.last_frames = deque(maxlen=self.discarded_frames) - self.debug = True + self.debug = False # Decrease frame size # self.capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1000) # self.capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 600) @@ -867,7 +867,7 @@ def draw_hand_overlay(self, frame, hand): 1) for defect in hand.intertips: cv2.circle(frame, tuple(defect), 8, [211, 84, 0], -1) - self.draw_contour_features(frame, hand.contour) + # self.draw_contour_features(frame, hand.contour) x, y, w, h = hand.bounding_rect # cv2.putText(frame, (str(w)), (x + w, y), self.font, 0.3, [255, 255, 255], 1) # cv2.putText(frame, (str(h)), (x + w, y + h), self.font, 0.3, [255, 255, 255], 1) @@ -1277,15 +1277,21 @@ def update_detection_and_tracking(self, frame): if existing_hand.detected is False: extended_roi = extended_roi if extended_roi is not None else existing_hand.bounding_rect - fist_bounding_rect, existing_hand.contour = self.detect_fist(frame, extended_roi) - existing_hand.bounding_rect = upscale_bounding_rect(fist_bounding_rect, frame.shape, 50) - - updated_hand = self.update_hand_attributes(frame, existing_hand, strict=False) - if updated_hand is not None: - existing_hand.update_attributes_from_detected(updated_hand) - existing_hand.position_history = updated_hand.position_history - existing_hand.detected = False + fist_bounding_rect, new_contour = self.detect_fist(frame, extended_roi) + if fist_bounding_rect is not None and new_contour is not None: + existing_hand.contour = new_contour + existing_hand.bounding_rect = upscale_bounding_rect(fist_bounding_rect, frame.shape, 50) + updated_hand = self.update_hand_attributes(frame, existing_hand, strict=False) + if updated_hand is not None: + existing_hand.update_attributes_from_detected(updated_hand) + existing_hand.position_history = updated_hand.position_history + existing_hand.detected = False + existing_hand.tracked=True + else: + existing_hand.tracked = False # if existing_hand.tracked: + else: + existing_hand.tracked = False # existing_hand.bounding_rect = existing_hand.tracking_window # updated_hand = self.update_hand_attributes(frame, existing_hand, strict=False) # if updated_hand is not None: @@ -1317,54 +1323,56 @@ def detect_fist(self, frame, roi): if hand_contour is not None: cv2.drawContours(to_show, [hand_contour], -1, (0, 255, 255), 1) cv2.imshow("detect_fist (Hand with contour)", to_show) - hull = cv2.convexHull(hand_contour, returnPoints=False) - new_contour = [] - # for index in hull: - # cv2.circle(to_show,tuple(hand_contour[index][0][0]),5,(255,255,255),2) - defects = cv2.convexityDefects(hand_contour, hull) - for defect_index in range(defects.shape[0]): - s, e, f, d = defects[defect_index, 0] - start = tuple(hand_contour[s][0]) - end = tuple(hand_contour[e][0]) - far = tuple(hand_contour[f][0]) - # cv2.circle(to_show,start,5,(0,99,255),2) - # cv2.circle(to_show, end, 5, (0, 99, 255), 2) - # cv2.circle(to_show, far, 5, (0, 99, 255), 2) - new_contour.append(hand_contour[s]) - new_contour.append(hand_contour[f]) - new_contour.append(hand_contour[e]) - - # define criteria and apply kmeans() - criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0) - new_contour_2d_points = np.float32(np.array(new_contour).reshape(len(new_contour), 2)) - ret, label, center = cv2.kmeans(new_contour_2d_points, 2, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS) - - # Now separate the data, Note the ravel() - max_group_len = 0 - max_group = None - - for label_number in range(2): - - group = new_contour_2d_points[label.ravel() == label_number] - rand_color = get_random_color() - for point in group: - cv2.circle(to_show, tuple(point), 5, rand_color[0], 2) - if len(group) > max_group_len: - max_group_len = len(group) - max_group = group - - (x, y), radius = cv2.minEnclosingCircle(max_group) - center = (int(x), int(y)) - radius = int(radius) + 20 - - cv2.circle(to_show, center, radius, (122, 122, 0), 1) - - for point in max_group: - cv2.circle(to_show, tuple(point), 5, (0, 255, 255), 2) - - cv2.imshow("detect_fist (Fist_ring)", to_show) - new_contour = extract_contour_inside_circle(hand_contour, (center, radius)) - return cv2.boundingRect(new_contour), new_contour + hull = cv2.convexHull(hand_contour, returnPoints=False) + new_contour = [] + # for index in hull: + # cv2.circle(to_show,tuple(hand_contour[index][0][0]),5,(255,255,255),2) + defects = cv2.convexityDefects(hand_contour, hull) + for defect_index in range(defects.shape[0]): + s, e, f, d = defects[defect_index, 0] + start = tuple(hand_contour[s][0]) + end = tuple(hand_contour[e][0]) + far = tuple(hand_contour[f][0]) + # cv2.circle(to_show,start,5,(0,99,255),2) + # cv2.circle(to_show, end, 5, (0, 99, 255), 2) + # cv2.circle(to_show, far, 5, (0, 99, 255), 2) + new_contour.append(hand_contour[s]) + new_contour.append(hand_contour[f]) + new_contour.append(hand_contour[e]) + + # define criteria and apply kmeans() + criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0) + new_contour_2d_points = np.float32(np.array(new_contour).reshape(len(new_contour), 2)) + ret, label, center = cv2.kmeans(new_contour_2d_points, 2, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS) + + # Now separate the data, Note the ravel() + max_group_len = 0 + max_group = None + + for label_number in range(2): + + group = new_contour_2d_points[label.ravel() == label_number] + rand_color = get_random_color() + for point in group: + cv2.circle(to_show, tuple(point), 5, rand_color[0], 2) + if len(group) > max_group_len: + max_group_len = len(group) + max_group = group + + (x, y), radius = cv2.minEnclosingCircle(max_group) + center = (int(x), int(y)) + radius = int(radius) + 20 + + cv2.circle(to_show, center, radius, (122, 122, 0), 1) + + for point in max_group: + cv2.circle(to_show, tuple(point), 5, (0, 255, 255), 2) + + cv2.imshow("detect_fist (Fist_ring)", to_show) + new_contour = extract_contour_inside_circle(hand_contour, (center, radius)) + return cv2.boundingRect(new_contour), new_contour + else: + return None, None def update_detection2(self, frame): for existing_hand in self.hands: @@ -1442,6 +1450,7 @@ def update_tracking(self, frame): def main(): hand_detector = HandDetector() + hand_detector.debug = True # hand_detector = HandDetector('./resources/testing_hand_video2.mp4') hand_detector.compute2() hand_detector.exit() From b5a1eca8af000da847438856cf8f74aa52c99455 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Mon, 9 Jul 2018 12:11:55 +0200 Subject: [PATCH 22/54] Added ignore of *pyc files --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 .gitignore diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..fd20fdd --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ + +*.pyc From 406e8da4c80cda6c7ca87154b98c18eefcb3ba90 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Mon, 9 Jul 2018 14:04:58 +0200 Subject: [PATCH 23/54] removed commented line --- HandDetection.py | 1 - 1 file changed, 1 deletion(-) diff --git a/HandDetection.py b/HandDetection.py index 3de0ed8..9913338 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -310,7 +310,6 @@ def add_hand2(self, frame): self.font, 1, [0, 0, 0], 2) masked_frame = cv2.rectangle(masked_frame, (template_x, template_y), (template_x + template_w, template_y + template_h), [0, 0, 0]) - # cv2.imshow('masked_frame', masked_frame) return masked_frame def add_hand(self, frame): From ca3e73fb65f9d983587f670b3aa252a1061a8317 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Tue, 10 Jul 2018 12:43:21 +0200 Subject: [PATCH 24/54] Added roi parameter to the addHand2 method. --- HandDetection.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index 9913338..21d5f2c 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -269,11 +269,13 @@ def __init__(self, source=0): # self.capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1000) # self.capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 600) - def add_hand2(self, frame): - hand_template_roi = (frame.shape[1] / 2 - 100, frame.shape[0] / 2 - 100, 200, 200) - template_x, template_y, template_w, template_h = hand_template_roi - frame_contours, frame_mask = self.create_contours_and_mask(frame, hand_template_roi) - masked_frame = frame.copy() + def add_hand2(self, frame, roi = None): + if roi is None: + search_roi = (frame.shape[1] / 2 - 100, frame.shape[0] / 2 - 100, 200, 200) + else: + search_roi = roi + template_x, template_y, template_w, template_h = search_roi + frame_contours, frame_mask = self.create_contours_and_mask(frame, search_roi) masked_frame = np.zeros(frame.shape, dtype="uint8") masked_frame[::] = 255 if len(frame_contours) > 0 and len(frame_mask) > 0: From d0c2e2a06c9af46c9dcfcc83b835646c62628cf2 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Tue, 10 Jul 2018 20:27:10 +0200 Subject: [PATCH 25/54] Fixed two bug. imshow would be inside an if self.debug condition. . --- HandDetection.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index 21d5f2c..266b09a 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -1323,7 +1323,8 @@ def detect_fist(self, frame, roi): if hand_contour is not None: cv2.drawContours(to_show, [hand_contour], -1, (0, 255, 255), 1) - cv2.imshow("detect_fist (Hand with contour)", to_show) + if self.debug: + cv2.imshow("detect_fist (Hand with contour)", to_show) hull = cv2.convexHull(hand_contour, returnPoints=False) new_contour = [] # for index in hull: @@ -1368,8 +1369,8 @@ def detect_fist(self, frame, roi): for point in max_group: cv2.circle(to_show, tuple(point), 5, (0, 255, 255), 2) - - cv2.imshow("detect_fist (Fist_ring)", to_show) + if self.debug: + cv2.imshow("detect_fist (Fist_ring)", to_show) new_contour = extract_contour_inside_circle(hand_contour, (center, radius)) return cv2.boundingRect(new_contour), new_contour else: From 8732646de3867aa886d6e77fe5a20ec78b4e9b86 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Fri, 20 Jul 2018 12:23:46 +0200 Subject: [PATCH 26/54] Adding code to use the depth of a rgbd camera to create the hand mask. --- HandDetection.py | 24 +++++++++++++++++++++++- 1 file changed, 23 insertions(+), 1 deletion(-) diff --git a/HandDetection.py b/HandDetection.py index 266b09a..328fa5d 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -265,6 +265,9 @@ def __init__(self, source=0): self.discarded_frames = 10 self.last_frames = deque(maxlen=self.discarded_frames) self.debug = False + self.mask_mode = "rgbd" + # Only used with RGBD cameras to create the mask. + self.depth_mask = None # Decrease frame size # self.capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1000) # self.capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 600) @@ -275,6 +278,7 @@ def add_hand2(self, frame, roi = None): else: search_roi = roi template_x, template_y, template_w, template_h = search_roi + frame_contours, frame_mask = self.create_contours_and_mask(frame, search_roi) masked_frame = np.zeros(frame.shape, dtype="uint8") masked_frame[::] = 255 @@ -486,7 +490,21 @@ def create_contours_and_mask(self, frame, roi_mask=None): _, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) return (contours, hands_mask) - def create_hands_mask(self, image, mode="diff"): + def set_mask_mode(self, mode): + self.mask_mode = mode + + def set_depth_mask(self, depth_mask): + if self.debug: + cv2.imshow("depth_image", depth_mask) + + self.depth_mask = depth_mask + + + def create_hands_mask(self, image, mode=None): + + if mode is None: + mode = self.mask_mode + # print "create_hands_mask %s" % mode mask = None if mode == "color": mask = get_color_mask(image) @@ -508,6 +526,10 @@ def create_hands_mask(self, image, mode="diff"): elif mode == "movement_buffer": # Absolutly unusefull mask = self.get_movement_buffer_mask(image) + elif mode == "depth": + print "Mode depth" + assert self.depth_mask is not None, "Depth mask must be set with set_depth_mask method. Use this method only with RGBD cameras" + _, mask = cv2.threshold(self.depth_mask, 40, 255, cv2.THRESH_BINARY) return mask def compute(self): From ce56d2c794201d9289c47871bc9d13ba40eeab0f Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Wed, 25 Jul 2018 19:57:57 +0200 Subject: [PATCH 27/54] Added a option to change the amount of blur in the clean_mask_noise function. Fixed the implementation of the depth mask. --- HandDetection.py | 23 ++++++++++++++++++----- 1 file changed, 18 insertions(+), 5 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index 328fa5d..6cf4226 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -157,7 +157,7 @@ def copy_main_attributes(self): return updated_hand -def clean_mask_noise(mask): +def clean_mask_noise(mask, blur=5): # Kernel matrices for morphological transformation kernel_square = np.ones((11, 11), np.uint8) kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) @@ -173,7 +173,7 @@ def clean_mask_noise(mask): # dilation2 = cv2.dilate(filtered, kernel_ellipse, iterations=1) # kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) # dilation3 = cv2.dilate(filtered, kernel_ellipse, iterations=1) - median = cv2.medianBlur(dilation2, 5) + median = cv2.medianBlur(dilation2, blur) return median @@ -496,7 +496,6 @@ def set_mask_mode(self, mode): def set_depth_mask(self, depth_mask): if self.debug: cv2.imshow("depth_image", depth_mask) - self.depth_mask = depth_mask @@ -529,7 +528,21 @@ def create_hands_mask(self, image, mode=None): elif mode == "depth": print "Mode depth" assert self.depth_mask is not None, "Depth mask must be set with set_depth_mask method. Use this method only with RGBD cameras" - _, mask = cv2.threshold(self.depth_mask, 40, 255, cv2.THRESH_BINARY) + #TODO: ENV_DEPENDENCE: the second value depends on the distance from the camera to the maximum depth where it can be found in a scale of 0-255 + mask = cv2.inRange(self.depth_mask,1,109) + # Kernel matrices for morphological transformation + kernel_square = np.ones((11, 11), np.uint8) + kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + dilation = cv2.dilate(mask, kernel_ellipse, iterations=1) + erosion = cv2.erode(dilation, kernel_square, iterations=1) + # dilation2 = cv2.dilate(erosion, kernel_ellipse, iterations=1) + # filtered = cv2.medianBlur(dilation2, 5) + # kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8, 8)) + # dilation2 = cv2.dilate(filtered, kernel_ellipse, iterations=1) + # kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + # dilation3 = cv2.dilate(filtered, kernel_ellipse, iterations=1) + mask = cv2.medianBlur(erosion, 3) + # _, mask = cv2.threshold(mask, 100, 255, cv2.THRESH_BINARY) return mask def compute(self): @@ -923,7 +936,7 @@ def draw_hand_overlay(self, frame, hand): def draw_contour_features(self, to_show, hand_contour): perimeter = cv2.arcLength(hand_contour, True) - print perimeter + # print perimeter hull = cv2.convexHull(hand_contour, returnPoints=False) new_contour = [] # for index in hull: From efa529f3b28bdeb5c85de8bcce97e7dc959e971b Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Thu, 26 Jul 2018 14:26:06 +0200 Subject: [PATCH 28/54] Fixing the depth mask mode. --- HandDetection.py | 21 ++++++++++++++++----- 1 file changed, 16 insertions(+), 5 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index 6cf4226..e6ce469 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -268,6 +268,7 @@ def __init__(self, source=0): self.mask_mode = "rgbd" # Only used with RGBD cameras to create the mask. self.depth_mask = None + self.depth_threshold = 600 # Decrease frame size # self.capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1000) # self.capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 600) @@ -493,14 +494,15 @@ def create_contours_and_mask(self, frame, roi_mask=None): def set_mask_mode(self, mode): self.mask_mode = mode - def set_depth_mask(self, depth_mask): - if self.debug: - cv2.imshow("depth_image", depth_mask) + def set_depth_mask(self, depth_mask, threshold=600): self.depth_mask = depth_mask + self.depth_threshold = threshold + def set_depth_threshold(self, threshold): + self.depth_threshold = threshold - def create_hands_mask(self, image, mode=None): + def create_hands_mask(self, image, mode=None): if mode is None: mode = self.mask_mode # print "create_hands_mask %s" % mode @@ -529,7 +531,8 @@ def create_hands_mask(self, image, mode=None): print "Mode depth" assert self.depth_mask is not None, "Depth mask must be set with set_depth_mask method. Use this method only with RGBD cameras" #TODO: ENV_DEPENDENCE: the second value depends on the distance from the camera to the maximum depth where it can be found in a scale of 0-255 - mask = cv2.inRange(self.depth_mask,1,109) + mask = cv2.inRange(self.depth_mask,1,float(self.depth_threshold)) + mask = self.depth_mask_to_image(mask) # Kernel matrices for morphological transformation kernel_square = np.ones((11, 11), np.uint8) kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) @@ -545,6 +548,14 @@ def create_hands_mask(self, image, mode=None): # _, mask = cv2.threshold(mask, 100, 255, cv2.THRESH_BINARY) return mask + def depth_mask_to_image(self, depth): + depth_min = np.min(depth) + depth_max = np.max(depth) + depth = np.interp(depth, [depth_min, depth_max], [0.0, 255.0], right=255, left=0) + depth = np.array(depth, dtype=np.uint8) + depth = depth.reshape(480, 640, 1) + return depth + def compute(self): while self.capture.isOpened(): From 4e42e5652b7145c18bd8341381d7c2b85dba3689 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Thu, 26 Jul 2018 20:33:11 +0200 Subject: [PATCH 29/54] Little fix for the depth mask mode. --- HandDetection.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index e6ce469..10d8a0f 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -494,9 +494,8 @@ def create_contours_and_mask(self, frame, roi_mask=None): def set_mask_mode(self, mode): self.mask_mode = mode - def set_depth_mask(self, depth_mask, threshold=600): + def set_depth_mask(self, depth_mask): self.depth_mask = depth_mask - self.depth_threshold = threshold def set_depth_threshold(self, threshold): self.depth_threshold = threshold @@ -531,8 +530,10 @@ def create_hands_mask(self, image, mode=None): print "Mode depth" assert self.depth_mask is not None, "Depth mask must be set with set_depth_mask method. Use this method only with RGBD cameras" #TODO: ENV_DEPENDENCE: the second value depends on the distance from the camera to the maximum depth where it can be found in a scale of 0-255 - mask = cv2.inRange(self.depth_mask,1,float(self.depth_threshold)) + mask = self.depth_mask + mask[mask>self.depth_threshold]= 0 mask = self.depth_mask_to_image(mask) + # Kernel matrices for morphological transformation kernel_square = np.ones((11, 11), np.uint8) kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) @@ -551,7 +552,9 @@ def create_hands_mask(self, image, mode=None): def depth_mask_to_image(self, depth): depth_min = np.min(depth) depth_max = np.max(depth) - depth = np.interp(depth, [depth_min, depth_max], [0.0, 255.0], right=255, left=0) + if depth_max!= depth_min and depth_max>0: + depth = np.interp(depth, [depth_min, depth_max], [0.0, 255.0], right=255, left=0) + depth = np.array(depth, dtype=np.uint8) depth = depth.reshape(480, 640, 1) return depth From b098dfe4705fe7b6a1f47400d04288566b90dd50 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Mon, 3 Sep 2018 19:14:25 +0200 Subject: [PATCH 30/54] Fixed the truth value for a hand calculated frame by frame. Added some DEBUG info to the windows. --- HandDetection.py | 41 +++++++++++++++++++++++------------------ 1 file changed, 23 insertions(+), 18 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index 10d8a0f..9de2457 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -118,13 +118,14 @@ def update_truth_value_by_frame(self): self.tracking_fails += 1 tracking_adition = -1 * UNTRACKING_TRUTH_FACTOR * one_frame_truth_subtraction new_truth_value = self.truth_value - one_frame_truth_subtraction + detection_adition + tracking_adition - if new_truth_value <= 100: + if new_truth_value <= MAX_TRUTH_VALUE: self.truth_value = new_truth_value else: - self.truth_value = 100 + self.truth_value = MAX_TRUTH_VALUE self.frame_count += 1 def update_truth_value_by_frame2(self): + substraction = 0 one_frame_truth_subtraction = MAX_TRUTH_VALUE / MAX_UNDETECTED_FRAMES if not self.detected: self.detection_fails += 1 @@ -133,7 +134,11 @@ def update_truth_value_by_frame2(self): if not self.detected and not self.tracked: substraction = -1 * UNDETECTION_TRUTH_FACTOR * UNTRACKING_TRUTH_FACTOR * one_frame_truth_subtraction else: - substraction = 0 + if self.tracked: + substraction = substraction + UNTRACKING_TRUTH_FACTOR * one_frame_truth_subtraction + if self.detected: + substraction = substraction + UNDETECTION_TRUTH_FACTOR * one_frame_truth_subtraction + new_truth_value = self.truth_value + substraction if new_truth_value <= 100: self.truth_value = new_truth_value @@ -355,7 +360,7 @@ def add_hand(self, frame): # cv2.imshow("realtime hand mask", realtime_handmask) diff = cv2.absdiff(hand_template_roi_image.astype(np.uint8), frame_mask_roi_image.astype(np.uint8)) if self.debug: - cv2.imshow("diff", diff) + cv2.imshow("DEBUG: HandDetection_lib: diff", diff) result = cv2.matchShapes(frame_mask_roi_image_contour, hand_template_contour, 1, 0) print result if frame_mask_roi_image_contour is not None: @@ -467,7 +472,7 @@ def create_contours_and_mask(self, frame, roi_mask=None): if hands_mask is None: return ([], []) if self.debug: - cv2.imshow("create_contours_and_mask (Frame Mask)", hands_mask) + cv2.imshow("DEBUG: HandDetection_lib: create_contours_and_mask (Frame Mask)", hands_mask) if roi_mask is not None: current_roi_mask = np.zeros(hands_mask.shape, dtype='uint8') @@ -483,7 +488,7 @@ def create_contours_and_mask(self, frame, roi_mask=None): # cv2.putText(to_show, (str(x)+", "+str(y)), (x-10, y-10), self.font, 0.3, [255, 255, 255], 1) cv2.rectangle(to_show, (x, y), (x + w, y + h), [255, 255, 255]) if self.debug: - cv2.imshow("create_contours_and_mask (ROIed Mask)", to_show) + cv2.imshow("DEBUG: HandDetection_lib: create_contours_and_mask (ROIed Mask)", to_show) ret, thresh = cv2.threshold(hands_mask, 127, 255, 0) @@ -521,8 +526,8 @@ def create_hands_mask(self, image, mode=None): if diff_mask is not None and color_mask is not None: mask = cv2.bitwise_and(diff_mask, color_mask) if self.debug: - cv2.imshow("diff_mask", diff_mask) - cv2.imshow("color_mask", color_mask) + cv2.imshow("DEBUG: HandDetection_lib: diff_mask", diff_mask) + cv2.imshow("DEBUG: HandDetection_lib: color_mask", color_mask) elif mode == "movement_buffer": # Absolutly unusefull mask = self.get_movement_buffer_mask(image) @@ -580,7 +585,7 @@ def compute(self): hand.bounding_rect = hand.tracking_window else: print "_____________No updated information" - hand.update_truth_value_by_frame() + hand.update_truth_value_by_frame2() if hand.truth_value <= 0: print "removing hand" self.hands.remove(hand) @@ -592,7 +597,7 @@ def compute(self): ##### Show final image ######## if self.debug: - cv2.imshow('Detection', overlayed_frame) + cv2.imshow('DEBUG: HandDetection_lib: Detection', overlayed_frame) ############################### # Print execution time # print time.time()-start_time @@ -623,7 +628,7 @@ def compute2(self): ##### Show final image ######## if self.debug: - cv2.imshow('Detection', overlayed_frame) + cv2.imshow('DEBUG: HandDetection_lib: Detection', overlayed_frame) ############################### # Print execution time # print time.time()-start_time @@ -1000,7 +1005,7 @@ def follow(self, frame, mask, bounding_rect, method="camshift"): # mask = cv2.inRange(hsv_roi, np.array((0., 60., 32.)), np.array((180., 255., 255.))) roi_mask = mask[y:y + h, x:x + w] if self.debug: - cv2.imshow("follow (ROI extracted mask)", roi_mask) + cv2.imshow("DEBUG: HandDetection_lib: follow (ROI extracted mask)", roi_mask) roi_hist = cv2.calcHist([hsv_roi], [0], roi_mask, [180], [0, 180]) cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX) # Setup the termination criteria, either 10 iteration or move by atleast 1 pt @@ -1045,7 +1050,7 @@ def get_movement_buffer_mask(self, image): # print "diff" gray_diff = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY) if self.debug: - cv2.imshow("diff", gray_diff) + cv2.imshow("DEBUG: HandDetection_lib: diff", gray_diff) # TODO: ENV_DEPENDENCE: it could depend on the lighting and environment _, mask = cv2.threshold(gray_diff, 40, 255, cv2.THRESH_BINARY) return mask @@ -1066,7 +1071,7 @@ def get_simple_diff_mask(self, image): # print "diff" gray_diff = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY) if self.debug: - cv2.imshow("diff", gray_diff) + cv2.imshow("DEBUG: HandDetection_lib: diff", gray_diff) # TODO: ENV_DEPENDENCE: it could depend on the lighting and environment _, mask = cv2.threshold(gray_diff, 40, 255, cv2.THRESH_BINARY) return mask @@ -1158,7 +1163,7 @@ def detect_hands_in_frame(self, frame, roi=None): cv2.drawContours(to_show, contours, -1, (122, 122, 0), 1) if roi is not None: cv2.rectangle(to_show, (roi[0], roi[1]), (roi[0] + roi[2], roi[1] + roi[3]), (122, 122, 255)) - cv2.imshow("detect_hands_in_frame (Detected Contours)", to_show) + cv2.imshow("DEBUG: HandDetection_lib: detect_hands_in_frame (Detected Contours)", to_show) k = cv2.waitKey(1) if len(contours) > 0: @@ -1373,7 +1378,7 @@ def detect_fist(self, frame, roi): if hand_contour is not None: cv2.drawContours(to_show, [hand_contour], -1, (0, 255, 255), 1) if self.debug: - cv2.imshow("detect_fist (Hand with contour)", to_show) + cv2.imshow("DEBUG: HandDetection_lib: detect_fist (Hand with contour)", to_show) hull = cv2.convexHull(hand_contour, returnPoints=False) new_contour = [] # for index in hull: @@ -1419,7 +1424,7 @@ def detect_fist(self, frame, roi): for point in max_group: cv2.circle(to_show, tuple(point), 5, (0, 255, 255), 2) if self.debug: - cv2.imshow("detect_fist (Fist_ring)", to_show) + cv2.imshow("DEBUG: HandDetection_lib: detect_fist (Fist_ring)", to_show) new_contour = extract_contour_inside_circle(hand_contour, (center, radius)) return cv2.boundingRect(new_contour), new_contour else: @@ -1471,7 +1476,7 @@ def update_hand_attributes(self, frame, hand, strict=True): if self.debug: to_show = frame.copy() cv2.drawContours(to_show, contours, -1, 255) - cv2.imshow("update_hand_charasteristics (New Contour)", to_show) + cv2.imshow("DEBUG: HandDetection_lib: update_hand_charasteristics (New Contour)", to_show) updated_hand = self.update_hand_with_contour(frame, hand_contour, updated_hand) else: return None From c41420e1f4d4dc96efcccbec6763f6c1f2d9cba9 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Mon, 10 Dec 2018 10:22:30 +0100 Subject: [PATCH 31/54] Changed some method names for better comprenhension porpose. --- HandDetection.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index 9de2457..c092dca 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -564,7 +564,7 @@ def depth_mask_to_image(self, depth): depth = depth.reshape(480, 640, 1) return depth - def compute(self): + def capture_and_compute(self): while self.capture.isOpened(): # Measure execution time @@ -609,7 +609,7 @@ def compute(self): if k == 27: break - def compute2(self): + def capture_and_compute2(self): while self.capture.isOpened(): # Measure execution time @@ -1508,7 +1508,7 @@ def main(): hand_detector = HandDetector() hand_detector.debug = True # hand_detector = HandDetector('./resources/testing_hand_video2.mp4') - hand_detector.compute2() + hand_detector.capture_and_compute2() hand_detector.exit() From be7efad4e3fb8a4ef55d0a45248b25476ddeda60 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Fri, 1 Feb 2019 12:33:29 +0100 Subject: [PATCH 32/54] Added debug flag for print --- HandDetection.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/HandDetection.py b/HandDetection.py index c092dca..84fc92b 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -532,7 +532,8 @@ def create_hands_mask(self, image, mode=None): # Absolutly unusefull mask = self.get_movement_buffer_mask(image) elif mode == "depth": - print "Mode depth" + if self.debug: + print "Mode depth" assert self.depth_mask is not None, "Depth mask must be set with set_depth_mask method. Use this method only with RGBD cameras" #TODO: ENV_DEPENDENCE: the second value depends on the distance from the camera to the maximum depth where it can be found in a scale of 0-255 mask = self.depth_mask From 9a5953a1b10bc5ee4f4a3664d0d74d89b53def10 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Tue, 2 Apr 2019 14:29:59 +0200 Subject: [PATCH 33/54] (no branch): Added some comments and removed unneded code for debug only --- HandDetection.py | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index 84fc92b..7e71cad 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -1362,7 +1362,6 @@ def update_detection_and_tracking(self, frame): self.hands.remove(existing_hand) def detect_fist(self, frame, roi): - to_show = frame.copy() contours, _ = self.create_contours_and_mask(frame, roi) hand_contour = None if len(contours) > 0: @@ -1377,8 +1376,9 @@ def detect_fist(self, frame, roi): hand_contour = contours[i] if hand_contour is not None: - cv2.drawContours(to_show, [hand_contour], -1, (0, 255, 255), 1) if self.debug: + to_show = frame.copy() + cv2.drawContours(to_show, [hand_contour], -1, (0, 255, 255), 1) cv2.imshow("DEBUG: HandDetection_lib: detect_fist (Hand with contour)", to_show) hull = cv2.convexHull(hand_contour, returnPoints=False) new_contour = [] @@ -1400,6 +1400,8 @@ def detect_fist(self, frame, roi): # define criteria and apply kmeans() criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0) new_contour_2d_points = np.float32(np.array(new_contour).reshape(len(new_contour), 2)) + + #Try to separate point into 2 groups. ret, label, center = cv2.kmeans(new_contour_2d_points, 2, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS) # Now separate the data, Note the ravel() @@ -1407,7 +1409,6 @@ def detect_fist(self, frame, roi): max_group = None for label_number in range(2): - group = new_contour_2d_points[label.ravel() == label_number] rand_color = get_random_color() for point in group: @@ -1416,16 +1417,16 @@ def detect_fist(self, frame, roi): max_group_len = len(group) max_group = group + # get the circle enclosing the bigger group of points in the contour of the fist (x, y), radius = cv2.minEnclosingCircle(max_group) center = (int(x), int(y)) radius = int(radius) + 20 - - cv2.circle(to_show, center, radius, (122, 122, 0), 1) - - for point in max_group: - cv2.circle(to_show, tuple(point), 5, (0, 255, 255), 2) if self.debug: + cv2.circle(to_show, center, radius, (122, 122, 0), 1) + for point in max_group: + cv2.circle(to_show, tuple(point), 5, (0, 255, 255), 2) cv2.imshow("DEBUG: HandDetection_lib: detect_fist (Fist_ring)", to_show) + # create the countour with only the points inside that circle new_contour = extract_contour_inside_circle(hand_contour, (center, radius)) return cv2.boundingRect(new_contour), new_contour else: From e9ab7efa8150afbcb2153c29af55c9d039d4caa5 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Thu, 18 Jul 2019 11:52:06 +0200 Subject: [PATCH 34/54] Check if device is open before capturing --- HandDetection.py | 53 +++++++++++++++++++++++++----------------------- 1 file changed, 28 insertions(+), 25 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index 7e71cad..73c0f11 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -611,38 +611,41 @@ def capture_and_compute(self): break def capture_and_compute2(self): - while self.capture.isOpened(): + if self.capture.isOpened(): + while self.capture.isOpened(): - # Measure execution time - # start_time = time.time() + # Measure execution time + # start_time = time.time() - # Capture frames from the camera - ret, frame = self.capture.read() + # Capture frames from the camera + ret, frame = self.capture.read() - if ret is True: - if len(self.hands) != 1: - self.add_hand2(frame) + if ret is True: + if len(self.hands) != 1: + self.add_hand2(frame) - self.update_detection_and_tracking(frame) + self.update_detection_and_tracking(frame) - overlayed_frame = self.compute_overlayed_frame(frame) + overlayed_frame = self.compute_overlayed_frame(frame) - ##### Show final image ######## - if self.debug: - cv2.imshow('DEBUG: HandDetection_lib: Detection', overlayed_frame) - ############################### - # Print execution time - # print time.time()-start_time - else: - print "No video detected" + ##### Show final image ######## + if self.debug: + cv2.imshow('DEBUG: HandDetection_lib: Detection', overlayed_frame) + ############################### + # Print execution time + # print time.time()-start_time + else: + print "No video detected" - # close the output video by pressing 'ESC' - if self.debug and len(self.hands) > 0: - k = cv2.waitKey(0) & 0xFF - else: - k = cv2.waitKey(5) & 0xFF - if k == 27: - break + # close the output video by pressing 'ESC' + if self.debug and len(self.hands) > 0: + k = cv2.waitKey(0) & 0xFF + else: + k = cv2.waitKey(5) & 0xFF + if k == 27: + break + else: + print("Capture device is not oppened.") def compute_overlayed_frame(self, frame): overlayed_frame = frame.copy() From 09d5523b9afeaff2204f83600363aadf191feab5 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Mon, 22 Jul 2019 18:56:14 +0200 Subject: [PATCH 35/54] Adding documentation and refactoring code Created new roi class Created new Hand class --- Hand.py | 651 +++++++++++++++++++++++++++++++++++++++++++++ HandDetection.py | 680 +++++++---------------------------------------- roi.py | 74 ++++++ 3 files changed, 827 insertions(+), 578 deletions(-) create mode 100644 Hand.py create mode 100644 roi.py diff --git a/Hand.py b/Hand.py new file mode 100644 index 0000000..1427f1f --- /dev/null +++ b/Hand.py @@ -0,0 +1,651 @@ +import math +from datetime import datetime + +import numpy as np +import random + +import cv2 + +from HandDetection.roi import Roi + +MAX_UNDETECTED_FRAMES = 30 * 10 # 30 FPS * 10 seconds +MAX_UNDETECTED_SECONDS = 10 +DETECTION_TRUTH_FACTOR = 2. # points of life to recover if detected in one iteration +TRACKING_TRUTH_FACTOR = .1 # points of life to recover if tracked in one iteration +UNDETECTION_TRUTH_FACTOR = 3. # points of life to recover if detected in one iteration +UNTRACKING_TRUTH_FACTOR = 2 # points of life to recover if tracked in one iteration +MAX_TRUTH_VALUE = 100. + + + +class MASKMODES: + COLOR=0 + MOG2=1 + DIFF=2 + MIXED=3 + MOVEMENT_BUFFER=4 + DEPTH=5 + +def get_random_color(n=1): + """ + Generate a randonm RGB color with values between 0-255 for R & G & B + + :param n: increment over the ranadome + :return: return an array with 3 int representing the R & G & B values + """ + ret = [] + r = int(random.random() * 256) + g = int(random.random() * 256) + b = int(random.random() * 256) + step = 256 / n + for i in range(n): + r += step + g += step + b += step + r = int(r) % 256 + g = int(g) % 256 + b = int(b) % 256 + ret = [r, g, b] + return ret + +def clean_mask_noise(mask, blur=5): + """ + Given an image mask it perfoms a clean up of it with a series of erodes and dilate + + :param mask: mask to be cleaned + :param blur: blur to be applied to the mask after clean up + :return: mask cleaned + """ + # Kernel matrices for morphological transformation + kernel_square = np.ones((11, 11), np.uint8) + kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + + # Perform morphological transformations to filter out the background noise + # Dilation increase skin color area + # Erosion increase skin color area + dilation = cv2.dilate(mask, kernel_ellipse, iterations=1) + erosion = cv2.erode(dilation, kernel_square, iterations=1) + dilation2 = cv2.dilate(erosion, kernel_ellipse, iterations=1) + # filtered = cv2.medianBlur(dilation2, 5) + # kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8, 8)) + # dilation2 = cv2.dilate(filtered, kernel_ellipse, iterations=1) + # kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + # dilation3 = cv2.dilate(filtered, kernel_ellipse, iterations=1) + median = cv2.medianBlur(dilation2, blur) + return median + +def get_color_mask(image, color_from=[2, 50, 50], color_to=[15, 255, 255]): + """ + Create a mask for an image with the colors between color_from to color_to + :param image: Image to get the mask from it + :param color_from: HSV values from where the colors will be got + :param color_to: HSV values to where the colors will be got + :return: mask for the matching colors on the image + """ + # Blur the image + blur_radius = 5 + blurred = cv2.GaussianBlur(image, (blur_radius, blur_radius), 0) + # Convert to HSV color space + hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV) + mask = cv2.inRange(hsv, np.array(color_from), np.array(color_to)) + return mask + + +#TODO: save a roi for each hand that we want to track. This roi would be the initial where we want to look for the hand an the one we could use if the hand is lost. + +class Hand(object): + """ + This contains all the usefull information for a detected hand. + + """ + def __init__(self): + """ + Hand class attributes values. + + """ + self._id = None + self._fingertips = [] + self._intertips = [] + self._center_of_mass = None + self._finger_distances = [] + self._average_defect_distance = [] + self._contour = None + self._bounding_rect = None + self._tracking_fails = 0 + self._detection_fails = 0 + self._frame_count = 0 + self._tracking_window = None + self._tracked = False + self._detected = True + self._position_history = [] + self._color = get_random_color() + self._truth_value = 100 + self._initial_roi = Roi() + self._detected_roi = Roi() + self._bounding_rect = Roi() + self._mask_mode = MASKMODES.COLOR + self._debug = False + self._depth_threshold = -1 + + @property + def initial_roi(self): + return self._initial_roi + + @property + def depth_threshold(self): + return self._depth_threshold + + @initial_roi.setter + def initial_roi(self, roi): + + self._initial_roi = roi + + @depth_threshold.setter + def depth_threshold(self, depth_threshold): + self._depth_threshold = depth_threshold + + + #TODO: Check if we need a deep copy of the data. + def update_attributes_from_detected(self, other_hand): + """ + update current hand with the values of other hand + + :param other_hand: the hand where the values are going to be copied + :return: None + """ + self._fingertips = other_hand.fingertips + self._intertips = other_hand.intertips + self._center_of_mass = other_hand.center_of_mass + self._finger_distances = other_hand.finger_distances + self._average_defect_distance = other_hand.average_defect_distance + self._contour = other_hand.contour + self._bounding_rect = other_hand.bounding_rect + self._detected = True + + def update_truth_value_by_time(self): + """ + Update the truth value of the hand based on the time elapsed between two calls + and if the hand is detected and tracked + + :return: None + """ + if self.last_time_update is not None: + elapsed_time = datetime.now() - self.last_time_update + elapsed_miliseconds = int(elapsed_time.total_seconds() * 1000) + + # Calculate how much we would substract if the hand is undetected + truth_subtraction = elapsed_miliseconds * MAX_TRUTH_VALUE / MAX_UNDETECTED_SECONDS * 1000 + + # Calculate how much we should increment if the hand has been detected + detection_adition = DETECTION_TRUTH_FACTOR if self._detected is True else 0 + + # Calculate how much we should increment if the is tracked + tracking_adition = TRACKING_TRUTH_FACTOR if self._tracked is True else 0 + + # update of the truth value + self._truth_value = self._truth_value - truth_subtraction + detection_adition + tracking_adition + self.last_time_update = datetime.now() + + + # Deprecated: using update_truth_value_by_frame2 + def update_truth_value_by_frame(self): + """ + Update the truth value of the hand based on the frames elapsed between two calls + and if the hand is detected and tracked + + :return: None + """ + one_frame_truth_subtraction = MAX_TRUTH_VALUE / MAX_UNDETECTED_FRAMES + detection_adition = 0 + if self._detected: + detection_adition = DETECTION_TRUTH_FACTOR * one_frame_truth_subtraction + else: + self._detection_fails += 1 + detection_adition = -1 * UNDETECTION_TRUTH_FACTOR * one_frame_truth_subtraction + tracking_adition = 0 + if self._tracked: + tracking_adition = TRACKING_TRUTH_FACTOR * one_frame_truth_subtraction + else: + self._tracking_fails += 1 + tracking_adition = -1 * UNTRACKING_TRUTH_FACTOR * one_frame_truth_subtraction + new_truth_value = self._truth_value - one_frame_truth_subtraction + detection_adition + tracking_adition + if new_truth_value <= MAX_TRUTH_VALUE: + self._truth_value = new_truth_value + else: + self._truth_value = MAX_TRUTH_VALUE + self._frame_count += 1 + + def update_truth_value_by_frame2(self): + substraction = 0 + one_frame_truth_subtraction = MAX_TRUTH_VALUE / MAX_UNDETECTED_FRAMES + if not self._detected: + self._detection_fails += 1 + if not self._tracked: + self._tracking_fails += 1 + if not self._detected and not self._tracked: + substraction = -1 * UNDETECTION_TRUTH_FACTOR * UNTRACKING_TRUTH_FACTOR * one_frame_truth_subtraction + else: + if self._tracked: + substraction = substraction + UNTRACKING_TRUTH_FACTOR * one_frame_truth_subtraction + if self._detected: + substraction = substraction + UNDETECTION_TRUTH_FACTOR * one_frame_truth_subtraction + + new_truth_value = self._truth_value + substraction + if new_truth_value <= 100: + self._truth_value = new_truth_value + else: + self._truth_value = 100 + self._frame_count += 1 + + + def copy_main_attributes(self): + """ + Return a new hand with the main attributes of this copied into it + + :return: New Hand with the main attributes copied into it + """ + updated_hand = Hand() + updated_hand._id = self._id + updated_hand._fingertips = [] + updated_hand._intertips = [] + updated_hand._center_of_mass = None + updated_hand._finger_distances = [] + updated_hand._average_defect_distance = [] + updated_hand._contour = None + updated_hand._bounding_rect = self._bounding_rect + updated_hand._tracking_fails = self._tracking_fails + updated_hand._position_history = self._position_history + updated_hand._color = self._color + return updated_hand + + def create_contours_and_mask(self, frame, roi=None): + # Create a binary image with where white will be skin colors and rest is black + hands_mask = self.create_hand_mask(frame) + if hands_mask is None: + return ([], []) + if self._debug: + cv2.imshow("DEBUG: HandDetection_lib: create_contours_and_mask (Frame Mask)", hands_mask) + + if roi is not None: + current_roi_mask = np.zeros(hands_mask.shape, dtype='uint8') + x, y, w, h = roi.params + + current_roi_mask[y:y + h, x:x + w] = 255 + hands_mask = cv2.bitwise_and(hands_mask, current_roi_mask) + to_show = cv2.resize(hands_mask, None, fx=.3, fy=.3, interpolation=cv2.INTER_CUBIC) + to_show = hands_mask.copy() + # cv2.putText(to_show, (str(w)), (x + w, y), self.font, 0.3, [255, 255, 255], 1) + # cv2.putText(to_show, (str(h)), (x + w, y + h), self.font, 0.3, [100, 255, 255], 1) + # cv2.putText(to_show, (str(w * h)), (x + w / 2, y + h / 2), self.font, 0.3, [100, 100, 255], 1) + # cv2.putText(to_show, (str(x)+", "+str(y)), (x-10, y-10), self.font, 0.3, [255, 255, 255], 1) + cv2.rectangle(to_show, (x, y), (x + w, y + h), [255, 255, 255]) + if self._debug: + cv2.imshow("DEBUG: HandDetection_lib: create_contours_and_mask (ROIed Mask)", to_show) + + ret, thresh = cv2.threshold(hands_mask, 127, 255, 0) + + # Find contours of the filtered frame + _, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + return (contours, hands_mask) + + def create_hand_mask(self, image, mode=None): + if mode is None: + mode = self._mask_mode + # print "create_hands_mask %s" % mode + mask = None + if mode == MASKMODES.COLOR: + mask = get_color_mask(image) + elif mode == MASKMODES.MOG2: + mask = self.get_MOG2_mask(image) + elif mode == MASKMODES.DIFF: + mask = self.get_simple_diff_mask2(image) + elif mode == MASKMODES.MIXED: + diff_mask = self.get_simple_diff_mask(image) + + color_mask = get_color_mask(image) + color_mask = clean_mask_noise(color_mask) + + if diff_mask is not None and color_mask is not None: + mask = cv2.bitwise_and(diff_mask, color_mask) + if self._debug: + cv2.imshow("DEBUG: HandDetection_lib: diff_mask", diff_mask) + cv2.imshow("DEBUG: HandDetection_lib: color_mask", color_mask) + elif mode == MASKMODES.MOVEMENT_BUFFER: + # Absolutly unusefull + mask = self.get_movement_buffer_mask(image) + elif mode == MASKMODES.DEPTH: + if self._debug: + print("Mode depth") + assert self._depth_threshold != -1, "Depth threshold must be set with set_depth_mask method. Use this method only with RGBD cameras" + assert image.shape[2] == 1, "Depth image should have only one channel and it have %d"%image.shape[2] + #TODO: ENV_DEPENDENCE: the second value depends on the distance from the camera to the maximum depth where it can be found in a scale of 0-255 + # TODO: We could remove self.depth_mask and get it as if an image/frame on the input of this method? + mask = image + mask[mask>self._depth_threshold]= 0 + mask = self.depth_mask_to_image(mask) + + # Kernel matrices for morphological transformation + kernel_square = np.ones((11, 11), np.uint8) + kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + dilation = cv2.dilate(mask, kernel_ellipse, iterations=1) + erosion = cv2.erode(dilation, kernel_square, iterations=1) + # dilation2 = cv2.dilate(erosion, kernel_ellipse, iterations=1) + # filtered = cv2.medianBlur(dilation2, 5) + # kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8, 8)) + # dilation2 = cv2.dilate(filtered, kernel_ellipse, iterations=1) + # kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + # dilation3 = cv2.dilate(filtered, kernel_ellipse, iterations=1) + mask = cv2.medianBlur(erosion, 3) + # _, mask = cv2.threshold(mask, 100, 255, cv2.THRESH_BINARY) + return mask + + def depth_mask_to_image(self, depth): + depth_min = np.min(depth) + depth_max = np.max(depth) + if depth_max!= depth_min and depth_max>0: + image = np.interp(depth, [depth_min, depth_max], [0.0, 255.0], right=255, left=0) + + image = np.array(image, dtype=np.uint8) + image = image.reshape(480, 640, 1) + return image + + def detect_in_frame(self, frame): + if self._detected_roi: + search_roi = self._detected_roi + else: + # If no roi is given an square of 200 x 200 is taken on the center + if self._initial_roi is None: + search_roi = (frame.shape[1] / 2 - 100, frame.shape[0] / 2 - 100, 200, 200) + else: + search_roi = self._initial_roi + + template_x, template_y, template_w, template_h = search_roi + + # Create contours and mask + self._frame_contours, self._frame_mask = self.create_contours_and_mask(frame, search_roi) + masked_frame = np.zeros(frame.shape, dtype="uint8") + masked_frame[::] = 255 + + # get the maximum contour + if len(self._frame_contours) > 0 and len(self._frame_mask) > 0: + # Get the maximum area contour + min_area = 100 + hand_contour = None + for i in range(len(self._frame_contours)): + cnt = self._frame_contours[i] + area = cv2.contourArea(cnt) + if area > min_area: + min_area = area + hand_contour = self._frame_contours[i] + + if hand_contour is not None: + # cv2.drawContours(frame, [hand_contour], -1, (0, 255, 255), 2) + detected_hand_bounding_rect = cv2.boundingRect(hand_contour) + detected_hand_x, detected_hand_y, detected_hand_w, detected_hand_h = detected_hand_bounding_rect + frame_mask_roi_image = self._frame_mask[template_y:template_y + template_h, + template_x:template_x + template_w] + frame_mask_roi_image_contour, _, _ = self.calculate_max_contour(frame_mask_roi_image, to_binary=False) + self.update_hand_with_contour(frame, hand_contour) + else: + + return -1 + else: + return -2 + return 1 + + + def update_hand_with_contour(self, hand_contour): + """ + Attributes of the hand are calculated from the hand contour. + A score of 100 is the maximum value for the hand truth. + This value is calculated like this: + A hand is expoected to have 5 finger tips, 4 intertips, a center of mass + + :param hand_contour: + :return: + """ + hull2 = cv2.convexHull(hand_contour, returnPoints=False) + # Get defect points + defects = cv2.convexityDefects(hand_contour, hull2) + + if defects is not None: + estimated_fingertips_coords, \ + estimated_fingertips_indexes, \ + estimated_intertips_coords, \ + estimated_intertips_indexes = self._calculate_fingertips(hand_contour, defects) + + is_hand = self.is_hand(estimated_fingertips_coords, estimated_intertips_coords, strict=True) + if is_hand: + self._fingertips = estimated_fingertips_coords + self._intertips = estimated_intertips_coords + + if len(estimated_fingertips_coords) == 5: + fingers_contour = np.take(hand_contour, + estimated_fingertips_indexes + estimated_intertips_indexes, + axis=0, + mode="wrap") + bounding_rect, hand_circle, self._contour = self.get_hand_bounding_rect_from_fingers( + hand_contour, + fingers_contour) + self._bounding_rect.params = bounding_rect + else: + return + else: + return + # Find moments of the largest contour + moments = cv2.moments(hand_contour) + center_of_mass = None + finger_distances = [] + average_defect_distance = None + # Central mass of first order moments + if moments['m00'] != 0: + cx = int(moments['m10'] / moments['m00']) # cx = M10/M00 + cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 + center_of_mass = (cx, cy) + self._center_of_mass = center_of_mass + self._position_history.append(center_of_mass) + + if center_of_mass is not None and len(estimated_intertips_coords) > 0: + # Distance from each finger defect(finger webbing) to the center mass + distance_between_defects_to_center = [] + for far in estimated_intertips_coords: + x = np.array(far) + center_mass_array = np.array(center_of_mass) + distance = np.sqrt( + np.power(x[0] - center_mass_array[0], + 2) + np.power(x[1] - center_mass_array[1], 2) + ) + distance_between_defects_to_center.append(distance) + + # Get an average of three shortest distances from finger webbing to center mass + sorted_defects_distances = sorted(distance_between_defects_to_center) + average_defect_distance = np.mean(sorted_defects_distances[0:2]) + self._average_defect_distance = average_defect_distance + # # Get fingertip points from contour hull + # # If points are in proximity of 80 pixels, consider as a single point in the group + # finger = [] + # for i in range(0, len(hull) - 1): + # if (np.absolute(hull[i][0][0] - hull[i + 1][0][0]) > 10) or ( + # np.absolute(hull[i][0][1] - hull[i + 1][0][1]) > 10): + # if hull[i][0][1] < 500: + # finger.append(hull[i][0]) + # + # + # # The fingertip points are 5 hull points with largest y coordinates + # finger = sorted(finger, key=lambda x: x[1]) + # fingers = finger[0:5] + if center_of_mass is not None and len(estimated_fingertips_coords) > 0: + # Calculate distance of each finger tip to the center mass + finger_distances = [] + for i in range(0, len(estimated_fingertips_coords)): + distance = np.sqrt( + np.power(estimated_fingertips_coords[i][0] - center_of_mass[0], 2) + np.power( + estimated_fingertips_coords[i][1] - center_of_mass[0], 2)) + finger_distances.append(distance) + self._finger_distances = finger_distances + else: + return + + + def _calculate_fingertips(self, hand_contour, defects): + intertips_coords = [] + intertips_indexes = [] + far_defect = [] + fingertips_coords = [] + fingertips_indexes = [] + defect_indices = [] + for defect_index in range(defects.shape[0]): + s, e, f, d = defects[defect_index, 0] + start = tuple(hand_contour[s][0]) + end = tuple(hand_contour[e][0]) + far = tuple(hand_contour[f][0]) + far_defect.append(far) + # cv2.line(frame, start, end, [0, 255, 0], 1) + a = math.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2) + b = math.sqrt((far[0] - start[0]) ** 2 + (far[1] - start[1]) ** 2) + c = math.sqrt((end[0] - far[0]) ** 2 + (end[1] - far[1]) ** 2) + angle = math.acos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c)) # cosine theorem + # cv2.circle(frame, far, 8, [211, 84, 125], -1) + # cv2.circle(frame, start, 8, [0, 84, 125], -1) + # cv2.circle(frame, end, 8, [0, 84, 125], -1) + # Get tips and intertips coordinates + # TODO: ENV_DEPENDENCE: this angle > 90degrees determinate if two points are considered fingertips or not and 90 make thumb to fail in some occasions + intertips_max_angle = math.pi / 1.7 + if angle <= intertips_max_angle: # angle less than 90 degree, treat as fingers + defect_indices.append(defect_index) + # cv2.circle(frame, far, 8, [211, 84, 0], -1) + intertips_coords.append(far) + intertips_indexes.append(f) + # cv2.putText(frame, str(s), start, self.font, 0.7, (255, 255, 255), 1) + # cv2.putText(frame, str(e), end, self.font, 0.7, (255, 255, 200), 1) + if len(fingertips_coords) > 0: + from scipy.spatial import distance + # calculate distances from start and end to the already known tips + start_distance, end_distance = tuple( + distance.cdist(fingertips_coords, [start, end]).min(axis=0)) + # TODO: ENV_DEPENDENCE: it determinate the pixels distance to consider two points the same. It depends on camera resolution and distance from the hand to the camera + same_fingertip_radius = 10 + if start_distance > same_fingertip_radius: + fingertips_coords.append(start) + fingertips_indexes.append(s) + + # cv2.circle(frame, start, 10, [255, 100, 255], 3) + if end_distance > same_fingertip_radius: + fingertips_coords.append(end) + fingertips_indexes.append(e) + + # cv2.circle(frame, end, 10, [255, 100, 255], 3) + else: + fingertips_coords.append(start) + fingertips_indexes.append(s) + + # cv2.circle(frame, start, 10, [255, 100, 255], 3) + fingertips_coords.append(end) + fingertips_indexes.append(e) + + # cv2.circle(frame, end, 10, [255, 100, 255], 3) + + # cv2.circle(frame, far, 10, [100, 255, 255], 3) + return fingertips_coords, fingertips_indexes, intertips_coords, intertips_indexes + + def is_hand(self, fingertips, intertips, strict=True): + if strict: + return len(fingertips) == 5 and len(intertips) > 2 + else: + return 5 >= len(fingertips) > 2 + + + def detect_and_track(self, frame): + # detect + # if detected + # update information + # set roi _detected_roi to bounding rect of detection + # else + # track + # if ret and tracking_window + + result = self.detect_in_frame(frame) + + if result < 0: + # not detected + self._detected = False + self._tracked = False + ret, tracking_window = self._track_in_frame(frame, self._detected_roi) + if ret and tracking_window is not None: + self._tracking_window = tracking_window + self._tracked = True + # detecction by intersection with hands found in frame + extended_roi = self._bounding_rect.upscaled(frame.shape, 60) + # if self._detected: + # extended_roi = upscale_bounding_rect(self._bounding_rect, frame.shape, 60) + # else: + # extended_roi = None + detected_hands = self.detect_hands_in_frame(frame, extended_roi) + self._detected = False + for detected_hand in detected_hands: + intersection_value, _ = self.calculate_bounding_rects_intersection( + detected_hand._bounding_rect, + self._bounding_rect) + if intersection_value > 0.1: + self._update_attributes_from_detected(detected_hand) + self._position_history.extend(detected_hand._position_history) + self._detected = True + break + + if self._detected is False: + extended_roi = extended_roi if extended_roi is not None else self._bounding_rect + fist_bounding_rect, new_contour = self.detect_fist(frame, extended_roi) + if fist_bounding_rect is not None and new_contour is not None: + self._contour = new_contour + self._bounding_rect = upscale_bounding_rect(fist_bounding_rect, frame.shape, 50) + updated_hand = self.update_hand_attributes(frame, existing_hand, strict=False) + if updated_hand is not None: + self._update_attributes_from_detected(updated_hand) + self._position_history = updated_hand.position_history + self._detected = False + self._tracked = True + else: + self._tracked = False + # if self._tracked: + else: + self._tracked = False + # self._bounding_rect = self._tracking_window + # updated_hand = self.update_hand_attributes(frame, existing_hand, strict=False) + # if updated_hand is not None: + # self._update_attributes_from_detected(updated_hand) + # self._position_history = updated_hand.position_history + # self._detected = False + # else: + # print "_____________No updated information" + self._update_truth_value_by_frame2() + + def _track_in_frame(self, frame, bounding_rect, method="camshift"): + mask = self.create_hand_mask(frame) + x, y, w, h = bounding_rect + track_window = bounding_rect + # set up the ROI for tracking + roi = frame[y:y + h, x:x + w] + hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV) + # mask = cv2.inRange(hsv_roi, np.array((0., 60., 32.)), np.array((180., 255., 255.))) + roi_mask = mask[y:y + h, x:x + w] + if self.debug: + cv2.imshow("DEBUG: HandDetection_lib: follow (ROI extracted mask)", roi_mask) + roi_hist = cv2.calcHist([hsv_roi], [0], roi_mask, [180], [0, 180]) + cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX) + # Setup the termination criteria, either 10 iteration or move by atleast 1 pt + term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1) + hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) + dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1) + # apply meanshift to get the new location + if method == "meanshift": + ret, track_window = cv2.meanShift(dst, track_window, term_crit) + else: + ret, track_window = cv2.CamShift(dst, track_window, term_crit) + return ret, track_window + # return ret, (max(newx-30,0), max(newy-30,0), min(neww+30,frame.shape[1]), min(newh+30,frame.shape[0])) + +if __name__ == '__main__': + hand = Hand() + hand.initial_roi = 27 + hand.depth_threshold = 600 + print(hand.initial_roi, hand.depth_threshold) \ No newline at end of file diff --git a/HandDetection.py b/HandDetection.py index 73c0f11..df924ca 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -1,35 +1,24 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- import copy -import math + import os -import random from collections import deque -from datetime import datetime +from Hand import Hand import cv2 import numpy as np -def get_random_color(n=1): - ret = [] - r = int(random.random() * 256) - g = int(random.random() * 256) - b = int(random.random() * 256) - step = 256 / n - for i in range(n): - r += step - g += step - b += step - r = int(r) % 256 - g = int(g) % 256 - b = int(b) % 256 - ret.append((r, g, b)) - return ret - - # Function to find angle between two vectors def calculate_angle(v1, v2): + """ + Calculate an return the angle between two verctors in degrees + + :param v1: input vector 1 + :param v2: input vector + :return: Degree angle between the two vectors + """ dot = np.dot(v1, v2) x_modulus = np.sqrt((v1 * v1).sum()) y_modulus = np.sqrt((v2 * v2).sum()) @@ -38,201 +27,16 @@ def calculate_angle(v1, v2): return angle -# Function to find distance between two points in a list of lists -def find_distance(point_a, point_b): - return np.sqrt(np.power((point_a[0][0] - point_b[0][0]), 2) + np.power((point_a[0][1] - point_b[0][1]), 2)) - - -# # Creating a window for HSV track bars -# cv2.namedWindow('HSV_TrackBar') -# -# # Starting with 100's to prevent error while masking -# h, s, v = 100, 100, 100 -# -# # Creating track bar -# cv2.createTrackbar('h', 'HSV_TrackBar', 0, 179, nothing) -# cv2.createTrackbar('s', 'HSV_TrackBar', 0, 255, nothing) -# cv2.createTrackbar('v', 'HSV_TrackBar', 0, 255, nothing) - -MAX_UNDETECTED_FRAMES = 30 * 10 # 30 FPS * 10 seconds -MAX_UNDETECTED_SECONDS = 10 -DETECTION_TRUTH_FACTOR = 2. # points of life to recover if detected in one iteration -TRACKING_TRUTH_FACTOR = .1 # points of life to recover if tracked in one iteration -UNDETECTION_TRUTH_FACTOR = 3. # points of life to recover if detected in one iteration -UNTRACKING_TRUTH_FACTOR = 2 # points of life to recover if tracked in one iteration -MAX_TRUTH_VALUE = 100. - - -class Hand: - def __init__(self): - self.id = None - self.fingertips = [] - self.intertips = [] - self.center_of_mass = None - self.finger_distances = [] - self.average_defect_distance = [] - self.contour = None - self.bounding_rect = None - self.tracking_fails = 0 - self.detection_fails = 0 - self.frame_count = 0 - self.tracking_window = None - self.tracked = False - self.detected = True - self.position_history = [] - self.color = get_random_color()[0] - self.truth_value = 100 - - def update_attributes_from_detected(self, other_hand): - self.fingertips = other_hand.fingertips - self.intertips = other_hand.intertips - self.center_of_mass = other_hand.center_of_mass - self.finger_distances = other_hand.finger_distances - self.average_defect_distance = other_hand.average_defect_distance - self.contour = other_hand.contour - self.bounding_rect = other_hand.bounding_rect - self.detected = True - - def update_truth_value_by_time(self): - if self.last_time_update is not None: - elapsed_time = datetime.now() - self.last_time_update - elapsed_miliseconds = int(elapsed_time.total_seconds() * 1000) - truth_subtraction = elapsed_miliseconds * MAX_TRUTH_VALUE / MAX_UNDETECTED_SECONDS * 1000 - detection_adition = DETECTION_TRUTH_FACTOR if self.detected is True else 0 - tracking_adition = TRACKING_TRUTH_FACTOR if self.tracked is True else 0 - self.truth_value = self.truth_value - truth_subtraction + detection_adition + tracking_adition - self.last_time_update = datetime.now() - - def update_truth_value_by_frame(self): - one_frame_truth_subtraction = MAX_TRUTH_VALUE / MAX_UNDETECTED_FRAMES - detection_adition = 0 - if self.detected: - detection_adition = DETECTION_TRUTH_FACTOR * one_frame_truth_subtraction - else: - self.detection_fails += 1 - detection_adition = -1 * UNDETECTION_TRUTH_FACTOR * one_frame_truth_subtraction - tracking_adition = 0 - if self.tracked: - tracking_adition = TRACKING_TRUTH_FACTOR * one_frame_truth_subtraction - else: - self.tracking_fails += 1 - tracking_adition = -1 * UNTRACKING_TRUTH_FACTOR * one_frame_truth_subtraction - new_truth_value = self.truth_value - one_frame_truth_subtraction + detection_adition + tracking_adition - if new_truth_value <= MAX_TRUTH_VALUE: - self.truth_value = new_truth_value - else: - self.truth_value = MAX_TRUTH_VALUE - self.frame_count += 1 - - def update_truth_value_by_frame2(self): - substraction = 0 - one_frame_truth_subtraction = MAX_TRUTH_VALUE / MAX_UNDETECTED_FRAMES - if not self.detected: - self.detection_fails += 1 - if not self.tracked: - self.tracking_fails += 1 - if not self.detected and not self.tracked: - substraction = -1 * UNDETECTION_TRUTH_FACTOR * UNTRACKING_TRUTH_FACTOR * one_frame_truth_subtraction - else: - if self.tracked: - substraction = substraction + UNTRACKING_TRUTH_FACTOR * one_frame_truth_subtraction - if self.detected: - substraction = substraction + UNDETECTION_TRUTH_FACTOR * one_frame_truth_subtraction - - new_truth_value = self.truth_value + substraction - if new_truth_value <= 100: - self.truth_value = new_truth_value - else: - self.truth_value = 100 - self.frame_count += 1 - - def copy_main_attributes(self): - updated_hand = Hand() - updated_hand.id = self.id - updated_hand.fingertips = [] - updated_hand.intertips = [] - updated_hand.center_of_mass = None - updated_hand.finger_distances = [] - updated_hand.average_defect_distance = [] - updated_hand.contour = None - updated_hand.bounding_rect = self.bounding_rect - updated_hand.tracking_fails = self.tracking_fails - updated_hand.position_history = self.position_history - updated_hand.color = self.color - return updated_hand - - -def clean_mask_noise(mask, blur=5): - # Kernel matrices for morphological transformation - kernel_square = np.ones((11, 11), np.uint8) - kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) - - # Perform morphological transformations to filter out the background noise - # Dilation increase skin color area - # Erosion increase skin color area - dilation = cv2.dilate(mask, kernel_ellipse, iterations=1) - erosion = cv2.erode(dilation, kernel_square, iterations=1) - dilation2 = cv2.dilate(erosion, kernel_ellipse, iterations=1) - # filtered = cv2.medianBlur(dilation2, 5) - # kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8, 8)) - # dilation2 = cv2.dilate(filtered, kernel_ellipse, iterations=1) - # kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) - # dilation3 = cv2.dilate(filtered, kernel_ellipse, iterations=1) - median = cv2.medianBlur(dilation2, blur) - return median - - -def get_color_mask(image): - # Blur the image - blur_radius = 5 - blurred = cv2.GaussianBlur(image, (blur_radius, blur_radius), 0) - # Convert to HSV color space - hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV) - mask = cv2.inRange(hsv, np.array([2, 50, 50]), np.array([15, 255, 255])) - return mask - - -def upscale_bounding_rect(bounding_rect, frame_shape, upscaled_pixels): - x, y, w, h = bounding_rect - new_x = max(x - int(upscaled_pixels / 2), 0) - - new_y = max(y - int(upscaled_pixels / 2), 0) - - if x + w + upscaled_pixels < frame_shape[1]: - new_w = w + upscaled_pixels - else: - exceded_pixels = x + w + upscaled_pixels - frame_shape[1] - new_w = w + exceded_pixels - - if y + h + upscaled_pixels < frame_shape[0]: - new_h = h + upscaled_pixels - else: - exceded_pixels = y + h + upscaled_pixels - frame_shape[0] - new_h = h + exceded_pixels - upscaled_bounding_rect = (new_x, new_y, new_w, new_h) - return upscaled_bounding_rect - - -def downscale_bounding_rect(bounding_rect, frame_shape, downscaled_pixels): - x, y, w, h = bounding_rect - new_x = min(x + int(downscaled_pixels / 2), frame_shape[1]) - - new_y = min(y + int(downscaled_pixels / 2), frame_shape[0]) - - if w - downscaled_pixels > 0: - new_w = w - downscaled_pixels - else: - new_w = 0 - - if h - downscaled_pixels > 0: - new_h = h + downscaled_pixels - else: - new_h = 0 - downscaled_bounding_rect = (new_x, new_y, new_w, new_h) - return downscaled_bounding_rect def extract_contour_inside_circle(full_contour, circle): + """ + Get the intersection of a contour and a circle + + :param full_contour: Contour to be intersected + :param circle: circle to be intersected with the contour + :return: contour that is inside the given circle + """ center, radius = circle new_contour = [] for point in full_contour: @@ -242,6 +46,13 @@ def extract_contour_inside_circle(full_contour, circle): def extract_contour_inside_rect(full_contour, rect): + """ + Get the intersection of a contour and a rectangle + + :param full_contour: Contour to be intersected + :param rect: rectangle to be intersected with the contour + :return: ontour that is inside the given rectangle + """ x1, y1, w, h = rect x2 = x1 + w y2 = y1 + h @@ -252,17 +63,25 @@ def extract_contour_inside_rect(full_contour, rect): return np.array(new_contour) + + + class HandDetector: + """ + Class to detect hands on a image + + """ def __init__(self, source=0): + # Open Camera object - # self.capture = cv2.VideoCapture(0) + # TODO: For testing only if source != -1: self.capture = cv2.VideoCapture(source) else: self.capture = None - self.hands = [] # [{"fingers":None, "center_of_mass":None}] + self.hands = [] self.font = cv2.FONT_HERSHEY_SIMPLEX self.first_frame = None self.next_hand_id = 0 @@ -271,58 +90,68 @@ def __init__(self, source=0): self.last_frames = deque(maxlen=self.discarded_frames) self.debug = False self.mask_mode = "rgbd" + # Only used with RGBD cameras to create the mask. - self.depth_mask = None - self.depth_threshold = 600 + self.depth_threshold = -1 + # Decrease frame size # self.capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1000) # self.capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 600) + + # TODO: Extend to return mutiple hands? + # TODO: Propability of been a hand could be calculated depending contour points, area, number of fingertips detected + def hand_from_frame(self, frame, roi = None): + """ + Get a Hand class from a roi in a frame + + :param frame: frame where we want to look for the hand + :param roi: roi in frame to look for the hand + :return: Hand if found on frame, None if not + """ + hand = Hand() + + + hand.initial_roi = roi + #TODO: only to test. Replace + hand.depth_threshold = 600 + return hand + # hand.depth_threshold = self.depth_threshold + + + hand.update_with_frame(frame) + + + # TODO: extract from here a method to get a hand from a frame, and this would only try and append. def add_hand2(self, frame, roi = None): - if roi is None: - search_roi = (frame.shape[1] / 2 - 100, frame.shape[0] / 2 - 100, 200, 200) - else: - search_roi = roi - template_x, template_y, template_w, template_h = search_roi + """ - frame_contours, frame_mask = self.create_contours_and_mask(frame, search_roi) - masked_frame = np.zeros(frame.shape, dtype="uint8") - masked_frame[::] = 255 - if len(frame_contours) > 0 and len(frame_mask) > 0: - # Get the maximum area contour - min_area = 100 - hand_contour = None - for i in range(len(frame_contours)): - cnt = frame_contours[i] - area = cv2.contourArea(cnt) - if area > min_area: - min_area = area - hand_contour = frame_contours[i] - if hand_contour is not None: - # cv2.drawContours(frame, [hand_contour], -1, (0, 255, 255), 2) - detected_hand_bounding_rect = cv2.boundingRect(hand_contour) - detected_hand_x, detected_hand_y, detected_hand_w, detected_hand_h = detected_hand_bounding_rect - frame_mask_roi_image = frame_mask[template_y:template_y + template_h, - template_x:template_x + template_w] - frame_mask_roi_image_contour, _, _ = self.calculate_max_contour(frame_mask_roi_image, to_binary=False) - new_hand = self.contour_to_new_hand(frame, hand_contour) - if new_hand is not None: - new_hand.id = len(self.hands) - self.hands.append(new_hand) - cv2.putText(masked_frame, "HAND FOUND", - (template_x, template_y + template_h + 10), - self.font, 1, [0, 0, 0], 2) - else: - cv2.putText(masked_frame, "CENTER YOUR HAND", - (template_x, template_y + template_h + 10), - self.font, 1, [0, 0, 0], 2) + :param frame: + :param roi: + :return: + """ + + new_hand = self.hand_from_frame(frame, roi) + + if new_hand is not None: + new_hand.id = len(self.hands) + self.hands.append(new_hand) + # cv2.putText(masked_frame, "HAND FOUND", + # (template_x, template_y + template_h + 10), + # self.font, 1, [0, 0, 0], 2) + print("HAND FOUND") else: - cv2.putText(masked_frame, "PLEASE PUT YOUR HAND HERE", (template_x - 100, template_y + template_h + 10), - self.font, 1, [0, 0, 0], 2) - masked_frame = cv2.rectangle(masked_frame, (template_x, template_y), - (template_x + template_w, template_y + template_h), [0, 0, 0]) - return masked_frame + # cv2.putText(masked_frame, "CENTER YOUR HAND", + # (template_x, template_y + template_h + 10), + # self.font, 1, [0, 0, 0], 2) + print("CENTER THE HAND") + # if ret == -2: + # cv2.putText(masked_frame, "PLEASE PUT YOUR HAND HERE", (template_x - 100, template_y + template_h + 10), + # self.font, 1, [0, 0, 0], 2) + # masked_frame = cv2.rectangle(masked_frame, (template_x, template_y), + # (template_x + template_w, template_y + template_h), [0, 0, 0]) + return def add_hand(self, frame): # Load the hand template @@ -372,9 +201,9 @@ def add_hand(self, frame): if result < 0.05: print "Match!!! " + str(result) hand = Hand() - hand.id = len(self.hands) - hand.contour = hand_contour - hand.bounding_rect = detected_hand_bounding_rect + hand._id = len(self.hands) + hand._contour = hand_contour + hand._bounding_rect = detected_hand_bounding_rect self.hands.append(hand) cv2.putText(masked_frame, "HAND FOUND", (template_x, template_y + template_h + 10), @@ -466,105 +295,16 @@ def calculate_max_contour(self, image, to_binary=True): image_roi = mask[y:y + h, x:x + w] return cnts, bounding_rect, image_roi - def create_contours_and_mask(self, frame, roi_mask=None): - # Create a binary image with where white will be skin colors and rest is black - hands_mask = self.create_hands_mask(frame) - if hands_mask is None: - return ([], []) - if self.debug: - cv2.imshow("DEBUG: HandDetection_lib: create_contours_and_mask (Frame Mask)", hands_mask) - - if roi_mask is not None: - current_roi_mask = np.zeros(hands_mask.shape, dtype='uint8') - x, y, w, h = roi_mask - - current_roi_mask[y:y + h, x:x + w] = 255 - hands_mask = cv2.bitwise_and(hands_mask, current_roi_mask) - to_show = cv2.resize(hands_mask, None, fx=.3, fy=.3, interpolation=cv2.INTER_CUBIC) - to_show = hands_mask.copy() - # cv2.putText(to_show, (str(w)), (x + w, y), self.font, 0.3, [255, 255, 255], 1) - # cv2.putText(to_show, (str(h)), (x + w, y + h), self.font, 0.3, [100, 255, 255], 1) - # cv2.putText(to_show, (str(w * h)), (x + w / 2, y + h / 2), self.font, 0.3, [100, 100, 255], 1) - # cv2.putText(to_show, (str(x)+", "+str(y)), (x-10, y-10), self.font, 0.3, [255, 255, 255], 1) - cv2.rectangle(to_show, (x, y), (x + w, y + h), [255, 255, 255]) - if self.debug: - cv2.imshow("DEBUG: HandDetection_lib: create_contours_and_mask (ROIed Mask)", to_show) - - ret, thresh = cv2.threshold(hands_mask, 127, 255, 0) - - # Find contours of the filtered frame - _, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) - return (contours, hands_mask) def set_mask_mode(self, mode): self.mask_mode = mode - def set_depth_mask(self, depth_mask): - self.depth_mask = depth_mask def set_depth_threshold(self, threshold): self.depth_threshold = threshold + #TODO: Set to all available hands - def create_hands_mask(self, image, mode=None): - if mode is None: - mode = self.mask_mode - # print "create_hands_mask %s" % mode - mask = None - if mode == "color": - mask = get_color_mask(image) - elif mode == "MOG2": - mask = self.get_MOG2_mask(image) - elif mode == "diff": - mask = self.get_simple_diff_mask2(image) - elif mode == "mixed": - diff_mask = self.get_simple_diff_mask(image) - - color_mask = get_color_mask(image) - color_mask = clean_mask_noise(color_mask) - - if diff_mask is not None and color_mask is not None: - mask = cv2.bitwise_and(diff_mask, color_mask) - if self.debug: - cv2.imshow("DEBUG: HandDetection_lib: diff_mask", diff_mask) - cv2.imshow("DEBUG: HandDetection_lib: color_mask", color_mask) - elif mode == "movement_buffer": - # Absolutly unusefull - mask = self.get_movement_buffer_mask(image) - elif mode == "depth": - if self.debug: - print "Mode depth" - assert self.depth_mask is not None, "Depth mask must be set with set_depth_mask method. Use this method only with RGBD cameras" - #TODO: ENV_DEPENDENCE: the second value depends on the distance from the camera to the maximum depth where it can be found in a scale of 0-255 - mask = self.depth_mask - mask[mask>self.depth_threshold]= 0 - mask = self.depth_mask_to_image(mask) - - # Kernel matrices for morphological transformation - kernel_square = np.ones((11, 11), np.uint8) - kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) - dilation = cv2.dilate(mask, kernel_ellipse, iterations=1) - erosion = cv2.erode(dilation, kernel_square, iterations=1) - # dilation2 = cv2.dilate(erosion, kernel_ellipse, iterations=1) - # filtered = cv2.medianBlur(dilation2, 5) - # kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8, 8)) - # dilation2 = cv2.dilate(filtered, kernel_ellipse, iterations=1) - # kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) - # dilation3 = cv2.dilate(filtered, kernel_ellipse, iterations=1) - mask = cv2.medianBlur(erosion, 3) - # _, mask = cv2.threshold(mask, 100, 255, cv2.THRESH_BINARY) - return mask - - def depth_mask_to_image(self, depth): - depth_min = np.min(depth) - depth_max = np.max(depth) - if depth_max!= depth_min and depth_max>0: - depth = np.interp(depth, [depth_min, depth_max], [0.0, 255.0], right=255, left=0) - - depth = np.array(depth, dtype=np.uint8) - depth = depth.reshape(480, 640, 1) - return depth - def capture_and_compute(self): while self.capture.isOpened(): @@ -645,7 +385,7 @@ def capture_and_compute2(self): if k == 27: break else: - print("Capture device is not oppened.") + print("Capture device is not opened.") def compute_overlayed_frame(self, frame): overlayed_frame = frame.copy() @@ -653,89 +393,7 @@ def compute_overlayed_frame(self, frame): overlayed_frame = self.draw_hand_overlay(frame, hand) return overlayed_frame - def contour_to_new_hand(self, frame, hand_contour): - hand = Hand() - hull2 = cv2.convexHull(hand_contour, returnPoints=False) - defects = cv2.convexityDefects(hand_contour, hull2) - # Get defect points and draw them in the original image - if defects is not None: - fingertips_coords, \ - fingertips_indexes, \ - intertips_coords, \ - intertips_indexes = self.get_hand_fingertips(hand_contour, defects) - - is_hand = self.is_hand(fingertips_coords, intertips_coords, strict=True) - if is_hand: - hand.fingertips = fingertips_coords - hand.intertips = intertips_coords - - if len(fingertips_coords) == 5: - fingers_contour = np.take(hand_contour, - fingertips_indexes + intertips_indexes, - axis=0, - mode="wrap") - hand.bounding_rect, hand_circle, hand.contour = self.get_hand_bounding_rect_from_fingers( - hand_contour, - fingers_contour) - else: - return None - else: - return None - # Find moments of the largest contour - moments = cv2.moments(hand_contour) - center_of_mass = None - finger_distances = [] - average_defect_distance = None - # Central mass of first order moments - if moments['m00'] != 0: - cx = int(moments['m10'] / moments['m00']) # cx = M10/M00 - cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 - center_of_mass = (cx, cy) - hand.center_of_mass = center_of_mass - hand.position_history.append(center_of_mass) - - if center_of_mass is not None and len(intertips_coords) > 0: - # Distance from each finger defect(finger webbing) to the center mass - distance_between_defects_to_center = [] - for far in intertips_coords: - x = np.array(far) - center_mass_array = np.array(center_of_mass) - distance = np.sqrt( - np.power(x[0] - center_mass_array[0], - 2) + np.power(x[1] - center_mass_array[1], 2) - ) - distance_between_defects_to_center.append(distance) - - # Get an average of three shortest distances from finger webbing to center mass - sorted_defects_distances = sorted(distance_between_defects_to_center) - average_defect_distance = np.mean(sorted_defects_distances[0:2]) - hand.average_defect_distance = average_defect_distance - # # Get fingertip points from contour hull - # # If points are in proximity of 80 pixels, consider as a single point in the group - # finger = [] - # for i in range(0, len(hull) - 1): - # if (np.absolute(hull[i][0][0] - hull[i + 1][0][0]) > 10) or ( - # np.absolute(hull[i][0][1] - hull[i + 1][0][1]) > 10): - # if hull[i][0][1] < 500: - # finger.append(hull[i][0]) - # - # - # # The fingertip points are 5 hull points with largest y coordinates - # finger = sorted(finger, key=lambda x: x[1]) - # fingers = finger[0:5] - if center_of_mass is not None and len(fingertips_coords) > 0: - # Calculate distance of each finger tip to the center mass - finger_distances = [] - for i in range(0, len(fingertips_coords)): - distance = np.sqrt( - np.power(fingertips_coords[i][0] - center_of_mass[0], 2) + np.power( - fingertips_coords[i][1] - center_of_mass[0], 2)) - finger_distances.append(distance) - hand.finger_distances = finger_distances - else: - return None - return hand def update_hand_with_contour(self, frame, hand_contour, hand_to_update): hand = copy.deepcopy(hand_to_update) @@ -1000,29 +658,7 @@ def exit(self): self.capture.release() cv2.destroyAllWindows() - def follow(self, frame, mask, bounding_rect, method="camshift"): - x, y, w, h = bounding_rect - track_window = bounding_rect - # set up the ROI for tracking - roi = frame[y:y + h, x:x + w] - hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV) - # mask = cv2.inRange(hsv_roi, np.array((0., 60., 32.)), np.array((180., 255., 255.))) - roi_mask = mask[y:y + h, x:x + w] - if self.debug: - cv2.imshow("DEBUG: HandDetection_lib: follow (ROI extracted mask)", roi_mask) - roi_hist = cv2.calcHist([hsv_roi], [0], roi_mask, [180], [0, 180]) - cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX) - # Setup the termination criteria, either 10 iteration or move by atleast 1 pt - term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1) - hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) - dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1) - # apply meanshift to get the new location - if method == "meanshift": - ret, track_window = cv2.meanShift(dst, track_window, term_crit) - else: - ret, track_window = cv2.CamShift(dst, track_window, term_crit) - return ret, track_window - # return ret, (max(newx-30,0), max(newy-30,0), min(neww+30,frame.shape[1]), min(newh+30,frame.shape[0])) + def get_MOG2_mask(self, image): # TODO: not working (it considers everything shadows or moves (too dark background) @@ -1184,66 +820,6 @@ def detect_hands_in_frame(self, frame, roi=None): new_hands.append(hand) return new_hands - def get_hand_fingertips(self, hand_contour, defects): - intertips_coords = [] - intertips_indexes = [] - far_defect = [] - fingertips_coords = [] - fingertips_indexes = [] - defect_indices = [] - for defect_index in range(defects.shape[0]): - s, e, f, d = defects[defect_index, 0] - start = tuple(hand_contour[s][0]) - end = tuple(hand_contour[e][0]) - far = tuple(hand_contour[f][0]) - far_defect.append(far) - # cv2.line(frame, start, end, [0, 255, 0], 1) - a = math.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2) - b = math.sqrt((far[0] - start[0]) ** 2 + (far[1] - start[1]) ** 2) - c = math.sqrt((end[0] - far[0]) ** 2 + (end[1] - far[1]) ** 2) - angle = math.acos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c)) # cosine theorem - # cv2.circle(frame, far, 8, [211, 84, 125], -1) - # cv2.circle(frame, start, 8, [0, 84, 125], -1) - # cv2.circle(frame, end, 8, [0, 84, 125], -1) - # Get tips and intertips coordinates - # TODO: ENV_DEPENDENCE: this angle > 90ยบ determinate if two points are considered fingertips or not and 90 make thumb to fail in some occasions - intertips_max_angle = math.pi / 1.7 - if angle <= intertips_max_angle: # angle less than 90 degree, treat as fingers - defect_indices.append(defect_index) - # cv2.circle(frame, far, 8, [211, 84, 0], -1) - intertips_coords.append(far) - intertips_indexes.append(f) - # cv2.putText(frame, str(s), start, self.font, 0.7, (255, 255, 255), 1) - # cv2.putText(frame, str(e), end, self.font, 0.7, (255, 255, 200), 1) - if len(fingertips_coords) > 0: - from scipy.spatial import distance - # calculate distances from start and end to the already known tips - start_distance, end_distance = tuple( - distance.cdist(fingertips_coords, [start, end]).min(axis=0)) - # TODO: ENV_DEPENDENCE: it determinate the pixels distance to consider two points the same. It depends on camera resolution and distance from the hand to the camera - same_fingertip_radius = 10 - if start_distance > same_fingertip_radius: - fingertips_coords.append(start) - fingertips_indexes.append(s) - - # cv2.circle(frame, start, 10, [255, 100, 255], 3) - if end_distance > same_fingertip_radius: - fingertips_coords.append(end) - fingertips_indexes.append(e) - - # cv2.circle(frame, end, 10, [255, 100, 255], 3) - else: - fingertips_coords.append(start) - fingertips_indexes.append(s) - - # cv2.circle(frame, start, 10, [255, 100, 255], 3) - fingertips_coords.append(end) - fingertips_indexes.append(e) - - # cv2.circle(frame, end, 10, [255, 100, 255], 3) - - # cv2.circle(frame, far, 10, [100, 255, 255], 3) - return fingertips_coords, fingertips_indexes, intertips_coords, intertips_indexes def get_hand_bounding_rect_from_fingers(self, hand_contour, fingers_contour): (x, y), radius = cv2.minEnclosingCircle(fingers_contour) @@ -1267,11 +843,7 @@ def get_hand_bounding_rect_from_center_of_mass(self, hand_contour, center_of_mas hand_bounding_rect = cv2.boundingRect(hand_contour) return hand_bounding_rect, ((int(x), int(y)), radius), hand_contour - def is_hand(self, fingertips, intertips, strict=True): - if strict: - return len(fingertips) == 5 and len(intertips) > 2 - else: - return 5 >= len(fingertips) > 2 + def reset_background(self): # TODO test it better @@ -1287,21 +859,21 @@ def update_detection(self, frame): hand_exists = False for existing_hand_index, existing_hand in enumerate(self.hands): intersection_value, _ = self.calculate_bounding_rects_intersection( - detected_hand.bounding_rect, + detected_hand._bounding_rect, existing_hand.bounding_rect) if intersection_value > 0.1: hand_exists = True existing_hand.update_attributes_from_detected(detected_hand) break if not hand_exists: - detected_hand.id = str(self.next_hand_id) - detected_hand.detected = True + detected_hand._id = str(self.next_hand_id) + detected_hand._detected = True self.next_hand_id += 1 self.hands.append(detected_hand) print "New hand" else: - detected_hand.id = str(self.next_hand_id) - detected_hand.detected = True + detected_hand._id = str(self.next_hand_id) + detected_hand._detected = True self.next_hand_id += 1 self.hands.append(detected_hand) print "New hand" @@ -1310,56 +882,7 @@ def update_detection_and_tracking(self, frame): if len(self.hands) > 0: for index, existing_hand in enumerate(self.hands): - existing_hand.tracked = False - hands_mask = self.create_hands_mask(frame) - ret, tracking_window = self.follow(frame, hands_mask, existing_hand.bounding_rect) - if ret and tracking_window is not None: - existing_hand.tracking_window = tracking_window - existing_hand.tracked = True - # detecction by intersection with hands found in frame - extended_roi = upscale_bounding_rect(existing_hand.bounding_rect, frame.shape, 60) - # if existing_hand.detected: - # extended_roi = upscale_bounding_rect(existing_hand.bounding_rect, frame.shape, 60) - # else: - # extended_roi = None - detected_hands = self.detect_hands_in_frame(frame, extended_roi) - existing_hand.detected = False - for detected_hand in detected_hands: - intersection_value, _ = self.calculate_bounding_rects_intersection( - detected_hand.bounding_rect, - existing_hand.bounding_rect) - if intersection_value > 0.1: - existing_hand.update_attributes_from_detected(detected_hand) - existing_hand.position_history.extend(detected_hand.position_history) - existing_hand.detected = True - break - - if existing_hand.detected is False: - extended_roi = extended_roi if extended_roi is not None else existing_hand.bounding_rect - fist_bounding_rect, new_contour = self.detect_fist(frame, extended_roi) - if fist_bounding_rect is not None and new_contour is not None: - existing_hand.contour = new_contour - existing_hand.bounding_rect = upscale_bounding_rect(fist_bounding_rect, frame.shape, 50) - updated_hand = self.update_hand_attributes(frame, existing_hand, strict=False) - if updated_hand is not None: - existing_hand.update_attributes_from_detected(updated_hand) - existing_hand.position_history = updated_hand.position_history - existing_hand.detected = False - existing_hand.tracked=True - else: - existing_hand.tracked = False - # if existing_hand.tracked: - else: - existing_hand.tracked = False - # existing_hand.bounding_rect = existing_hand.tracking_window - # updated_hand = self.update_hand_attributes(frame, existing_hand, strict=False) - # if updated_hand is not None: - # existing_hand.update_attributes_from_detected(updated_hand) - # existing_hand.position_history = updated_hand.position_history - # existing_hand.detected = False - # else: - # print "_____________No updated information" - existing_hand.update_truth_value_by_frame2() + existing_hand.detect_and_track() if existing_hand.truth_value <= 0: print "removing hand" self.hands.remove(existing_hand) @@ -1415,7 +938,7 @@ def detect_fist(self, frame, roi): group = new_contour_2d_points[label.ravel() == label_number] rand_color = get_random_color() for point in group: - cv2.circle(to_show, tuple(point), 5, rand_color[0], 2) + cv2.circle(to_show, tuple(point), 5, rand_color, 2) if len(group) > max_group_len: max_group_len = len(group) max_group = group @@ -1511,8 +1034,9 @@ def update_tracking(self, frame): def main(): hand_detector = HandDetector() - hand_detector.debug = True # hand_detector = HandDetector('./resources/testing_hand_video2.mp4') + hand_detector.debug = True + hand_detector.capture_and_compute2() hand_detector.exit() diff --git a/roi.py b/roi.py new file mode 100644 index 0000000..961fb78 --- /dev/null +++ b/roi.py @@ -0,0 +1,74 @@ + + +class Roi(object): + def __init__(self): + self._x = 0 + self._y = 0 + self._width = 0 + self._height = 0 + + @property + def params(self): + return (self._x, self._y, self._width, self._height) + + @params.setter + def params(self, params): + self._x, self._y, self._width, self._height = (params[0], params[1], params[2], params[3]) + + + def upscaled(self, limiting_roi, upscaled_pixels): + """ + Create an upscaled version of an input ROI restricted to the size of a frame + + :param limiting_roi: frame that limit the possible upscale of the bounding rect + :param upscaled_pixels: Number of pixels to add to both the height and the with from the center + :return: + """ + x, y, w, h = self.params + new_x = max(x - int(upscaled_pixels / 2), 0) + + new_y = max(y - int(upscaled_pixels / 2), 0) + + # add the need pixels to the width and height checking the frame limits + if x + w + upscaled_pixels < limiting_roi[1]: + new_w = w + upscaled_pixels + else: + exceded_pixels = x + w + upscaled_pixels - limiting_roi[1] + new_w = w + exceded_pixels + + if y + h + upscaled_pixels < limiting_roi[0]: + new_h = h + upscaled_pixels + else: + exceded_pixels = y + h + upscaled_pixels - limiting_roi[0] + new_h = h + exceded_pixels + upscaled_bounding_rect = Roi() + upscaled_bounding_rect.params = (new_x, new_y, new_w, new_h) + + return upscaled_bounding_rect + + def downscaled(self, limiting_roi, downscaled_pixels): + """ + Create an downscaled version of an input bounding rect restricted to the size of a frame + + :param limiting_roi: frame that limit the possible downscale of the bounding rect + :param downscaled_pixels: Number of pixels to substracted to both the height and the with from the center + :return: + """ + x, y, w, h = self + new_x = min(x + int(downscaled_pixels / 2), limiting_roi[1]) + + new_y = min(y + int(downscaled_pixels / 2), limiting_roi[0]) + + if w - downscaled_pixels > 0: + new_w = w - downscaled_pixels + else: + new_w = 0 + + if h - downscaled_pixels > 0: + new_h = h + downscaled_pixels + else: + new_h = 0 + downscaled_bounding_rect = Roi() + downscaled_bounding_rect.params = (new_x, new_y, new_w, new_h) + return downscaled_bounding_rect + From 2cc990e2a17ab5885853ff343548fd1eb17d8f25 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Tue, 23 Jul 2019 11:40:24 +0200 Subject: [PATCH 36/54] Added from_frame static method and SIDE class to get roi from frames with a side and percentage. Added some test to main. --- roi.py | 78 ++++++++++++++++++++++++++++++++++++++++++++++++++++------ 1 file changed, 71 insertions(+), 7 deletions(-) diff --git a/roi.py b/roi.py index 961fb78..cd0a2da 100644 --- a/roi.py +++ b/roi.py @@ -1,5 +1,11 @@ +class SIDE: + RIGHT = 0 + LEFT = 1 + TOP = 2 + BOTTOM = 3 + class Roi(object): def __init__(self): self._x = 0 @@ -15,6 +21,33 @@ def params(self): def params(self, params): self._x, self._y, self._width, self._height = (params[0], params[1], params[2], params[3]) + @staticmethod + def from_frame(frame, side, percent): + new_roi = Roi() + assert isinstance(side, int) and 0 <= side < 4, "Side must be a value between 0 and 3 but %d given. Use the SIDE class to get valid values." % side + assert isinstance(percent, (int, float)) , "Percent must be must be Integer or Float but %r given" % percent + assert (0 < percent <= 100), "Percent must be a value between 0 and 100 but %d given" % percent + frame_width, frame_height = frame.shape[:2] + # calculating percentage of the frame width and height + frame_width_percent = frame_width * percent / 100 + frame_height_percent = frame_height * percent / 100 + if side == SIDE.TOP: + x, y = (0,0) + width, height = (frame_width, frame_height_percent) + elif side == SIDE.BOTTOM: + x, y = (0, frame_height-frame_height_percent) + width, height = (frame_width, frame_height_percent) + elif side == SIDE.RIGHT: + x, y = (frame_width-frame_width_percent, 0) + width, height = (frame_width_percent, frame_height) + elif side == SIDE.LEFT: + x, y = (0, 0) + width, height = (frame_width_percent, frame_height) + new_roi.params = (x, y, width, height) + return new_roi + + def __str__(self): + return str(self.params) def upscaled(self, limiting_roi, upscaled_pixels): """ @@ -41,10 +74,10 @@ def upscaled(self, limiting_roi, upscaled_pixels): else: exceded_pixels = y + h + upscaled_pixels - limiting_roi[0] new_h = h + exceded_pixels - upscaled_bounding_rect = Roi() - upscaled_bounding_rect.params = (new_x, new_y, new_w, new_h) + upscaled_roi = Roi() + upscaled_roi.params = (new_x, new_y, new_w, new_h) - return upscaled_bounding_rect + return upscaled_roi def downscaled(self, limiting_roi, downscaled_pixels): """ @@ -68,7 +101,38 @@ def downscaled(self, limiting_roi, downscaled_pixels): new_h = h + downscaled_pixels else: new_h = 0 - downscaled_bounding_rect = Roi() - downscaled_bounding_rect.params = (new_x, new_y, new_w, new_h) - return downscaled_bounding_rect - + downscaled_roi = Roi() + downscaled_roi.params = (new_x, new_y, new_w, new_h) + return downscaled_roi + + +if __name__ == '__main__': + class Frame: pass + frame = Frame + frame.shape = [640, 480, 3] + r = Roi.from_frame(frame, SIDE.RIGHT, 100) + print(r) + r = Roi.from_frame(frame, SIDE.LEFT, 100) + print(r) + r = Roi.from_frame(frame, SIDE.TOP, 100) + print(r) + r = Roi.from_frame(frame, SIDE.BOTTOM, 100) + print(r) + print("-----------") + r = Roi.from_frame(frame, SIDE.RIGHT, 90) + print(r) + r = Roi.from_frame(frame, SIDE.LEFT, 90) + print(r) + r = Roi.from_frame(frame, SIDE.TOP, 90) + print(r) + r = Roi.from_frame(frame, SIDE.BOTTOM, 90) + print(r) + print("-----------") + r = Roi.from_frame(frame, SIDE.RIGHT, 10) + print(r) + r = Roi.from_frame(frame, SIDE.LEFT, 10) + print(r) + r = Roi.from_frame(frame, SIDE.TOP, 10) + print(r) + r = Roi.from_frame(frame, SIDE.BOTTOM, 10) + print(r) \ No newline at end of file From 9311e417e1c0e33bfa23c335d1d2b330c4119276 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Fri, 26 Jul 2019 14:04:06 +0200 Subject: [PATCH 37/54] Added unit test files for roi and hand --- test_hand.py | 35 ++++++++++++++++++ test_roi.py | 100 +++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 135 insertions(+) create mode 100644 test_hand.py create mode 100644 test_roi.py diff --git a/test_hand.py b/test_hand.py new file mode 100644 index 0000000..5a0254e --- /dev/null +++ b/test_hand.py @@ -0,0 +1,35 @@ +import unittest + +import cv2 + +from HandDetection.Hand import Hand +from HandDetection.roi import Roi, SIDE + + +class Frame: pass +frame = Frame +frame.shape = [640, 480, 3] + +class TestRoi(unittest.TestCase): + def test_hand_detect(self): + """ + Test that check the creation of roi from frames + """ + hand = Hand() + hand.depth_threshold = 130 + frame = cv2.imread("/home/robolab/robocomp/components/robocomp-robolab/components/handDetection/src/images/depth_images/20190725130248.png",0 ) + hand.initial_roi = Roi.from_frame(frame,SIDE.CENTER, 50) + hand._detect_in_frame(frame) + hand._track_in_frame(frame, ) + self.assertGreater(hand._detected, 0) + + + + # def test_roi_upscale(self): + # """ + # Test that roi upscale right + # """ + # self.assertEqual(r, [0, 480 - 48, 640, 48]) + +if __name__ == '__main__': + unittest.main() \ No newline at end of file diff --git a/test_roi.py b/test_roi.py new file mode 100644 index 0000000..bd4bf24 --- /dev/null +++ b/test_roi.py @@ -0,0 +1,100 @@ +import unittest + +from roi import Roi, SIDE + + +class Frame: pass + + +frame = Frame +frame.shape = [640, 480, 3] + + +class TestRoi(unittest.TestCase): + + def test_roi_from_frame_top(self): + """ + Test that check the creation of roi from frames + """ + + + r = Roi.from_frame(frame, SIDE.TOP, 100) + self.assertEqual(r, [0, 0, 640, 480]) + r = Roi.from_frame(frame, SIDE.TOP, 90) + self.assertEqual(r, [0, 0, 640, 480-48]) + r = Roi.from_frame(frame, SIDE.TOP, 10) + self.assertEqual(r, [0, 0, 640, 48]) + + + def test_roi_from_frame_bottom(self): + + r = Roi.from_frame(frame, SIDE.BOTTOM, 100) + self.assertEqual(r, [0, 0, 640, 480]) + r = Roi.from_frame(frame, SIDE.BOTTOM, 90) + self.assertEqual(r, [0, 48, 640, 480-48]) + r = Roi.from_frame(frame, SIDE.BOTTOM, 10) + self.assertEqual(r, [0, 480-48, 640, 48]) + + + def test_roi_from_frame_left(self): + + r = Roi.from_frame(frame, SIDE.LEFT, 100) + self.assertEqual(r, [0, 0, 640, 480]) + r = Roi.from_frame(frame, SIDE.LEFT, 90) + self.assertEqual(r, [0, 0, 640-64, 480]) + r = Roi.from_frame(frame, SIDE.LEFT, 10) + self.assertEqual(r, [0, 0, 64, 480]) + + + def test_roi_from_frame_right(self): + + r = Roi.from_frame(frame, SIDE.RIGHT, 100) + self.assertEqual(r, [0, 0, 640, 480]) + + r = Roi.from_frame(frame, SIDE.RIGHT, 90) + self.assertEqual(r, [64, 0, 640 - 64, 480]) + + r = Roi.from_frame(frame, SIDE.RIGHT, 10) + self.assertEqual(r, [640 - 64, 0, 64, 480]) + + + def test_roi_from_frame_center(self): + + r = Roi.from_frame(frame, SIDE.CENTER, 100) + self.assertEqual(r, [0, 0, 640, 480]) + r = Roi.from_frame(frame, SIDE.CENTER, 90) + self.assertEqual(r, [(64/2), (48/2), 640 - 64, 480-48]) + r = Roi.from_frame(frame, SIDE.CENTER, 10) + self.assertEqual(r, [640/2 - 64/2, 480/2 - 48/2, 64, 48]) + + + def test_roi_upscale(self): + """ + Test that roi upscale right + """ + roi = Roi([0,0,640,480]) + limit = Roi([0,0, 640,480]) + a = roi.upscaled(limit, 10) + self.assertEqual(a, [0, 0, 640, 480]) + + roi = Roi([0, 0, 320, 240]) + a = roi.upscaled(limit, 10) + self.assertEqual(a, [0, 0, 330, 250]) + + roi = Roi([10, 10, 320, 240]) + a = roi.upscaled(limit, 10) + self.assertEqual(a, [5, 5, 330, 250]) + + roi = Roi([40, 40, 50, 50]) + limit = Roi([30, 30, 60, 60]) + a = roi.upscaled(limit, 10) + self.assertEqual(a, [35, 35, 60, 60]) + + # def test_roi_upscale(self): + # """ + # Test that roi upscale right + # """ + # self.assertEqual(r, [0, 480 - 48, 640, 48]) + +if __name__ == '__main__': + unittest.main() \ No newline at end of file From a5e6343dfb626208c602dae1fee4a32eaff3bae9 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Fri, 26 Jul 2019 14:04:28 +0200 Subject: [PATCH 38/54] Created new Roi class --- roi.py | 146 +++++++++++++++++++++++++++++++++++---------------------- 1 file changed, 90 insertions(+), 56 deletions(-) diff --git a/roi.py b/roi.py index cd0a2da..8f2bbe6 100644 --- a/roi.py +++ b/roi.py @@ -5,26 +5,60 @@ class SIDE: LEFT = 1 TOP = 2 BOTTOM = 3 + CENTER = 4 + +class Roi(list): + def __init__(self, in_list=None): + if in_list is not None: + assert isinstance(in_list, (list, tuple)), "in_list must be of the python list tupe. in_list is %s type" % type(in_list) + assert len(in_list) == 4, "in_list must 4 len. in_list is %d len" % len(in_list) + self.extend(list(in_list)) + else: + self.extend([0,0,0,0]) + # self._x = 0 + # self._y = 0 + # self._width = 0 + # self._height = 0 + + @property + def x(self): + return self[0] + + @x.setter + def x(self, x): + self[0]=x + + @property + def y(self): + return self[1] -class Roi(object): - def __init__(self): - self._x = 0 - self._y = 0 - self._width = 0 - self._height = 0 + @y.setter + def y(self, y): + self[1]=y @property - def params(self): - return (self._x, self._y, self._width, self._height) + def width(self): + return self[2] - @params.setter - def params(self, params): - self._x, self._y, self._width, self._height = (params[0], params[1], params[2], params[3]) + @width.setter + def width(self, width): + self[2]=width + + @property + def height(self): + return self[3] + + @height.setter + def height(self, height): + self[3]=height + + @property + def init_coords(self): + return (self[0], self[1]) @staticmethod - def from_frame(frame, side, percent): - new_roi = Roi() - assert isinstance(side, int) and 0 <= side < 4, "Side must be a value between 0 and 3 but %d given. Use the SIDE class to get valid values." % side + def from_frame(frame, side=SIDE.TOP, percent=100): + assert isinstance(side, int) and 0 <= side < 5, "Side must be a value between 0 and 3 but %d given. Use the SIDE class to get valid values." % side assert isinstance(percent, (int, float)) , "Percent must be must be Integer or Float but %r given" % percent assert (0 < percent <= 100), "Percent must be a value between 0 and 100 but %d given" % percent frame_width, frame_height = frame.shape[:2] @@ -43,11 +77,15 @@ def from_frame(frame, side, percent): elif side == SIDE.LEFT: x, y = (0, 0) width, height = (frame_width_percent, frame_height) - new_roi.params = (x, y, width, height) + elif side == SIDE.CENTER: + # TODO: End implementation + x = int(frame_width/2) -int(frame_width_percent/2) + y = int(frame_height/2) - int(frame_height_percent/2) + width, height = (frame_width_percent, frame_height_percent) + new_roi = Roi([x, y, width, height]) return new_roi - def __str__(self): - return str(self.params) + def upscaled(self, limiting_roi, upscaled_pixels): """ @@ -57,59 +95,55 @@ def upscaled(self, limiting_roi, upscaled_pixels): :param upscaled_pixels: Number of pixels to add to both the height and the with from the center :return: """ - x, y, w, h = self.params - new_x = max(x - int(upscaled_pixels / 2), 0) + x, y, w, h = self - new_y = max(y - int(upscaled_pixels / 2), 0) + new_x = max(max(x - int(upscaled_pixels / 2), limiting_roi.x), 0) + new_y = max(max(y - int(upscaled_pixels / 2), limiting_roi.y), 0) - # add the need pixels to the width and height checking the frame limits - if x + w + upscaled_pixels < limiting_roi[1]: + # add the needed pixels to the width and height checking the frame limits + if x + w + upscaled_pixels < limiting_roi.x+limiting_roi.width: new_w = w + upscaled_pixels else: - exceded_pixels = x + w + upscaled_pixels - limiting_roi[1] - new_w = w + exceded_pixels + new_w = limiting_roi.width - if y + h + upscaled_pixels < limiting_roi[0]: + if y + h + upscaled_pixels < limiting_roi.height: new_h = h + upscaled_pixels else: - exceded_pixels = y + h + upscaled_pixels - limiting_roi[0] - new_h = h + exceded_pixels - upscaled_roi = Roi() - upscaled_roi.params = (new_x, new_y, new_w, new_h) - + new_h = limiting_roi.height + upscaled_roi = Roi([new_x, new_y, new_w, new_h]) return upscaled_roi - def downscaled(self, limiting_roi, downscaled_pixels): - """ - Create an downscaled version of an input bounding rect restricted to the size of a frame - - :param limiting_roi: frame that limit the possible downscale of the bounding rect - :param downscaled_pixels: Number of pixels to substracted to both the height and the with from the center - :return: - """ - x, y, w, h = self - new_x = min(x + int(downscaled_pixels / 2), limiting_roi[1]) - - new_y = min(y + int(downscaled_pixels / 2), limiting_roi[0]) - - if w - downscaled_pixels > 0: - new_w = w - downscaled_pixels - else: - new_w = 0 - - if h - downscaled_pixels > 0: - new_h = h + downscaled_pixels - else: - new_h = 0 - downscaled_roi = Roi() - downscaled_roi.params = (new_x, new_y, new_w, new_h) - return downscaled_roi + # def downscaled(self, limiting_roi, downscaled_pixels): + # """ + # Create an downscaled version of an input bounding rect restricted to the size of a frame + # + # :param limiting_roi: frame that limit the possible downscale of the bounding rect + # :param downscaled_pixels: Number of pixels to substracted to both the height and the with from the center + # :return: + # """ + # x, y, w, h = self + # new_x = min(x + int(downscaled_pixels / 2), limiting_roi[1]) + # + # new_y = min(y + int(downscaled_pixels / 2), limiting_roi[0]) + # + # if w - downscaled_pixels > 0: + # new_w = w - downscaled_pixels + # else: + # new_w = 0 + # + # if h - downscaled_pixels > 0: + # new_h = h + downscaled_pixels + # else: + # new_h = 0 + # downscaled_roi = Roi() + # downscaled_roi = Roi([new_x, new_y, new_w, new_h]) + # return downscaled_roi if __name__ == '__main__': class Frame: pass frame = Frame - frame.shape = [640, 480, 3] + frame.shape =[640, 480, 3] r = Roi.from_frame(frame, SIDE.RIGHT, 100) print(r) r = Roi.from_frame(frame, SIDE.LEFT, 100) From c64eb7ace9da60e4e42934ac8f1bf80798fd9984 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Fri, 26 Jul 2019 14:05:24 +0200 Subject: [PATCH 39/54] Moved most of the detection code to the Hand class and refactoring to simplify some methods. --- Hand.py | 477 ++++++++++++++++++++++++++++++++--------------- HandDetection.py | 144 +++----------- 2 files changed, 351 insertions(+), 270 deletions(-) diff --git a/Hand.py b/Hand.py index 1427f1f..a670c3d 100644 --- a/Hand.py +++ b/Hand.py @@ -6,7 +6,7 @@ import cv2 -from HandDetection.roi import Roi +from roi import Roi, SIDE MAX_UNDETECTED_FRAMES = 30 * 10 # 30 FPS * 10 seconds MAX_UNDETECTED_SECONDS = 10 @@ -15,6 +15,7 @@ UNDETECTION_TRUTH_FACTOR = 3. # points of life to recover if detected in one iteration UNTRACKING_TRUTH_FACTOR = 2 # points of life to recover if tracked in one iteration MAX_TRUTH_VALUE = 100. +FONT = cv2.FONT_HERSHEY_SIMPLEX @@ -110,22 +111,34 @@ def __init__(self): self._finger_distances = [] self._average_defect_distance = [] self._contour = None - self._bounding_rect = None - self._tracking_fails = 0 - self._detection_fails = 0 + self._consecutive_tracking_fails = 0 + self._consecutive_detection_fails = 0 self._frame_count = 0 - self._tracking_window = None + self._color = get_random_color() + self._confidence = 0 self._tracked = False - self._detected = True + self._detected = False self._position_history = [] - self._color = get_random_color() - self._truth_value = 100 + + + # The region of the image where the hand is expected to be located when initialized or lost self._initial_roi = Roi() - self._detected_roi = Roi() - self._bounding_rect = Roi() - self._mask_mode = MASKMODES.COLOR - self._debug = False + # The region where the hand have been detected the last time + self._detection_roi = Roi() + # The region where the hand was tracked the last time + self._tracking_roi = Roi() + # Region extended from tracking_roi to a maximum of initial_roi to look for the hand + self._extended_roi = Roi() + + self._mask_mode = MASKMODES.DEPTH + self._debug = True self._depth_threshold = -1 + self._last_frame = None + + +##################################################################### +########## Properties and setters ########## +##################################################################### @property def initial_roi(self): @@ -136,20 +149,32 @@ def depth_threshold(self): return self._depth_threshold @initial_roi.setter - def initial_roi(self, roi): + def initial_roi(self, value): - self._initial_roi = roi + self._initial_roi = value @depth_threshold.setter - def depth_threshold(self, depth_threshold): - self._depth_threshold = depth_threshold + def depth_threshold(self, value): + self._depth_threshold = value + + @property + def confidence(self): + return self._confidence + + @confidence.setter + def confidence(self, value): + self._confidence = value + + @property + def valid(self): + return (self._detected > 0 or self._tracked or self._confidence > 0) #TODO: Check if we need a deep copy of the data. def update_attributes_from_detected(self, other_hand): """ update current hand with the values of other hand - + TODO: need to be checked. :param other_hand: the hand where the values are going to be copied :return: None """ @@ -159,7 +184,7 @@ def update_attributes_from_detected(self, other_hand): self._finger_distances = other_hand.finger_distances self._average_defect_distance = other_hand.average_defect_distance self._contour = other_hand.contour - self._bounding_rect = other_hand.bounding_rect + self._detection_roi = other_hand._detection_roi self._detected = True def update_truth_value_by_time(self): @@ -183,7 +208,7 @@ def update_truth_value_by_time(self): tracking_adition = TRACKING_TRUTH_FACTOR if self._tracked is True else 0 # update of the truth value - self._truth_value = self._truth_value - truth_subtraction + detection_adition + tracking_adition + self._confidence = self._confidence - truth_subtraction + detection_adition + tracking_adition self.last_time_update = datetime.now() @@ -200,28 +225,28 @@ def update_truth_value_by_frame(self): if self._detected: detection_adition = DETECTION_TRUTH_FACTOR * one_frame_truth_subtraction else: - self._detection_fails += 1 + self._consecutive_detection_fails += 1 detection_adition = -1 * UNDETECTION_TRUTH_FACTOR * one_frame_truth_subtraction tracking_adition = 0 if self._tracked: tracking_adition = TRACKING_TRUTH_FACTOR * one_frame_truth_subtraction else: - self._tracking_fails += 1 + self._consecutive_tracking_fails += 1 tracking_adition = -1 * UNTRACKING_TRUTH_FACTOR * one_frame_truth_subtraction - new_truth_value = self._truth_value - one_frame_truth_subtraction + detection_adition + tracking_adition + new_truth_value = self._confidence - one_frame_truth_subtraction + detection_adition + tracking_adition if new_truth_value <= MAX_TRUTH_VALUE: - self._truth_value = new_truth_value + self._confidence = new_truth_value else: - self._truth_value = MAX_TRUTH_VALUE + self._confidence = MAX_TRUTH_VALUE self._frame_count += 1 def update_truth_value_by_frame2(self): substraction = 0 one_frame_truth_subtraction = MAX_TRUTH_VALUE / MAX_UNDETECTED_FRAMES if not self._detected: - self._detection_fails += 1 + self._consecutive_detection_fails += 1 if not self._tracked: - self._tracking_fails += 1 + self._consecutive_tracking_fails += 1 if not self._detected and not self._tracked: substraction = -1 * UNDETECTION_TRUTH_FACTOR * UNTRACKING_TRUTH_FACTOR * one_frame_truth_subtraction else: @@ -230,11 +255,11 @@ def update_truth_value_by_frame2(self): if self._detected: substraction = substraction + UNDETECTION_TRUTH_FACTOR * one_frame_truth_subtraction - new_truth_value = self._truth_value + substraction + new_truth_value = self._confidence + substraction if new_truth_value <= 100: - self._truth_value = new_truth_value + self._confidence = new_truth_value else: - self._truth_value = 100 + self._confidence = 100 self._frame_count += 1 @@ -252,42 +277,45 @@ def copy_main_attributes(self): updated_hand._finger_distances = [] updated_hand._average_defect_distance = [] updated_hand._contour = None - updated_hand._bounding_rect = self._bounding_rect - updated_hand._tracking_fails = self._tracking_fails + updated_hand._detection_roi = self._detection_roi + updated_hand._consecutive_tracking_fails = self._consecutive_tracking_fails updated_hand._position_history = self._position_history updated_hand._color = self._color return updated_hand def create_contours_and_mask(self, frame, roi=None): - # Create a binary image with where white will be skin colors and rest is black + # Create a binary image where white will be skin colors and rest is black hands_mask = self.create_hand_mask(frame) if hands_mask is None: return ([], []) - if self._debug: - cv2.imshow("DEBUG: HandDetection_lib: create_contours_and_mask (Frame Mask)", hands_mask) if roi is not None: - current_roi_mask = np.zeros(hands_mask.shape, dtype='uint8') - x, y, w, h = roi.params - - current_roi_mask[y:y + h, x:x + w] = 255 - hands_mask = cv2.bitwise_and(hands_mask, current_roi_mask) - to_show = cv2.resize(hands_mask, None, fx=.3, fy=.3, interpolation=cv2.INTER_CUBIC) - to_show = hands_mask.copy() - # cv2.putText(to_show, (str(w)), (x + w, y), self.font, 0.3, [255, 255, 255], 1) - # cv2.putText(to_show, (str(h)), (x + w, y + h), self.font, 0.3, [100, 255, 255], 1) - # cv2.putText(to_show, (str(w * h)), (x + w / 2, y + h / 2), self.font, 0.3, [100, 100, 255], 1) - # cv2.putText(to_show, (str(x)+", "+str(y)), (x-10, y-10), self.font, 0.3, [255, 255, 255], 1) + x, y, w, h = roi + else: + x, y, w, h = self._initial_roi + + current_roi_mask = np.zeros(hands_mask.shape, dtype='uint8') + current_roi_mask[y:y + h, x:x + w] = 255 + roied_hands_mask = cv2.bitwise_and(hands_mask, current_roi_mask) + if self._debug: + cv2.imshow("DEBUG: HandDetection_lib: create_contours_and_mask (Frame Mask)", hands_mask) + # to_show = cv2.resize(hands_mask, None, fx=.3, fy=.3, interpolation=cv2.INTER_CUBIC) + to_show = roied_hands_mask.copy() + # cv2.putText(to_show, (str(w)), (x + w, y), FONT, 0.3, [255, 255, 255], 1) + # cv2.putText(to_show, (str(h)), (x + w, y + h), FONT, 0.3, [100, 255, 255], 1) + # cv2.putText(to_show, (str(w * h)), (x + w / 2, y + h / 2), FONT, 0.3, [100, 100, 255], 1) + # cv2.putText(to_show, (str(x)+", "+str(y)), (x-10, y-10), FONT, 0.3, [255, 255, 255], 1) cv2.rectangle(to_show, (x, y), (x + w, y + h), [255, 255, 255]) - if self._debug: - cv2.imshow("DEBUG: HandDetection_lib: create_contours_and_mask (ROIed Mask)", to_show) + cv2.imshow("DEBUG: HandDetection_lib: create_contours_and_mask (current_roi_mask)", current_roi_mask) + cv2.imshow("DEBUG: HandDetection_lib: create_contours_and_mask (ROIed Mask)", to_show) - ret, thresh = cv2.threshold(hands_mask, 127, 255, 0) + ret, thresh = cv2.threshold(roied_hands_mask, 127, 255, 0) # Find contours of the filtered frame _, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) return (contours, hands_mask) + def create_hand_mask(self, image, mode=None): if mode is None: mode = self._mask_mode @@ -317,49 +345,76 @@ def create_hand_mask(self, image, mode=None): if self._debug: print("Mode depth") assert self._depth_threshold != -1, "Depth threshold must be set with set_depth_mask method. Use this method only with RGBD cameras" - assert image.shape[2] == 1, "Depth image should have only one channel and it have %d"%image.shape[2] + assert len(image.shape) == 2 or image.shape[2] == 1, "Depth image should have only one channel and it have %d" % image.shape[2] #TODO: ENV_DEPENDENCE: the second value depends on the distance from the camera to the maximum depth where it can be found in a scale of 0-255 - # TODO: We could remove self.depth_mask and get it as if an image/frame on the input of this method? + mask = image mask[mask>self._depth_threshold]= 0 mask = self.depth_mask_to_image(mask) # Kernel matrices for morphological transformation - kernel_square = np.ones((11, 11), np.uint8) - kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) - dilation = cv2.dilate(mask, kernel_ellipse, iterations=1) - erosion = cv2.erode(dilation, kernel_square, iterations=1) + kernel_square = np.ones((5, 5), np.uint8) + kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)) + + # cv2.imwrite("/home/robolab/robocomp/components/robocomp-robolab/components/handDetection/src/images/"+str(datetime.now().strftime("%Y%m%d%H%M%S"))+".png", mask) + # + # dilation = cv2.dilate(mask, kernel_ellipse, iterations=1) + # erosion = cv2.erode(dilation, kernel_square, iterations=1) + mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel_square) + mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel_square) + # dilation2 = cv2.dilate(erosion, kernel_ellipse, iterations=1) # filtered = cv2.medianBlur(dilation2, 5) # kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8, 8)) # dilation2 = cv2.dilate(filtered, kernel_ellipse, iterations=1) # kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) # dilation3 = cv2.dilate(filtered, kernel_ellipse, iterations=1) - mask = cv2.medianBlur(erosion, 3) + mask = cv2.medianBlur(mask, 3) # _, mask = cv2.threshold(mask, 100, 255, cv2.THRESH_BINARY) + return mask - def depth_mask_to_image(self, depth): + def get_hand_bounding_rect_from_fingers(self, hand_contour, fingers_contour): + (x, y), radius = cv2.minEnclosingCircle(fingers_contour) + center = (int(x), int(y)) + radius = int(radius) + 10 + new_hand_contour = extract_contour_inside_circle(hand_contour, (center, radius)) + hand_bounding_rect = cv2.boundingRect(new_hand_contour) + return hand_bounding_rect, ((int(x), int(y)), radius), new_hand_contour + + def get_hand_bounding_rect_from_rect(self, hand_contour, bounding_rect): + hand_contour = extract_contour_inside_rect(hand_contour, bounding_rect) + hand_bounding_rect = cv2.boundingRect(hand_contour) + return hand_bounding_rect, hand_contour + + def get_hand_bounding_rect_from_center_of_mass(self, hand_contour, center_of_mass, average_distance): + (x, y) = center_of_mass + radius = average_distance + center = (int(x), int(y)) + radius = int(radius) + 10 + hand_contour = extract_contour_inside_circle(hand_contour, (center, radius)) + hand_bounding_rect = cv2.boundingRect(hand_contour) + return hand_bounding_rect, ((int(x), int(y)), radius), hand_contour + + + + # TODO: Move to Utils file + @staticmethod + def depth_mask_to_image(depth): depth_min = np.min(depth) depth_max = np.max(depth) if depth_max!= depth_min and depth_max>0: image = np.interp(depth, [depth_min, depth_max], [0.0, 255.0], right=255, left=0) + else: + image = np.zeros(depth.shape, dtype=np.uint8) image = np.array(image, dtype=np.uint8) image = image.reshape(480, 640, 1) return image - def detect_in_frame(self, frame): - if self._detected_roi: - search_roi = self._detected_roi - else: - # If no roi is given an square of 200 x 200 is taken on the center - if self._initial_roi is None: - search_roi = (frame.shape[1] / 2 - 100, frame.shape[0] / 2 - 100, 200, 200) - else: - search_roi = self._initial_roi - - template_x, template_y, template_w, template_h = search_roi + def _detect_in_frame(self, frame): + self._last_frame = frame + search_roi = self.get_roi_to_use(frame) # Create contours and mask self._frame_contours, self._frame_mask = self.create_contours_and_mask(frame, search_roi) @@ -382,27 +437,77 @@ def detect_in_frame(self, frame): # cv2.drawContours(frame, [hand_contour], -1, (0, 255, 255), 2) detected_hand_bounding_rect = cv2.boundingRect(hand_contour) detected_hand_x, detected_hand_y, detected_hand_w, detected_hand_h = detected_hand_bounding_rect - frame_mask_roi_image = self._frame_mask[template_y:template_y + template_h, - template_x:template_x + template_w] + frame_mask_roi_image = self._frame_mask[search_roi.y:search_roi.y+ search_roi.height, + search_roi.x:search_roi.x + search_roi.width] frame_mask_roi_image_contour, _, _ = self.calculate_max_contour(frame_mask_roi_image, to_binary=False) - self.update_hand_with_contour(frame, hand_contour) + # self._detected is updated inside + self.update_hand_with_contour(hand_contour) else: + self._detected = -1 + else: + self._detected = -2 + - return -1 + def calculate_max_contour(self, image, to_binary=True): + if self._debug: + cv2.imshow("Hand: calculate_max_contour, image", image) + bounding_rect = None + image_roi = None + if to_binary: + gray_diff = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + _, mask = cv2.threshold(gray_diff, 40, 255, cv2.THRESH_BINARY) else: - return -2 - return 1 + mask = image + # kernel_square = np.ones((11, 11), np.uint8) + # kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + # + # # Perform morphological transformations to filter out the background noise + # # Dilation increase skin color area + # # Erosion increase skin color area + # dilation = cv2.dilate(mask, kernel_ellipse, iterations=1) + # erosion = cv2.erode(dilation, kernel_square, iterations=1) + # dilation2 = cv2.dilate(erosion, kernel_ellipse, iterations=1) + # filtered = cv2.medianBlur(dilation2.astype(np.uint8), 5) + # kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8, 8)) + # dilation2 = cv2.dilate(filtered, kernel_ellipse, iterations=1) + # kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + # dilation3 = cv2.dilate(filtered, kernel_ellipse, iterations=1) + # median = cv2.medianBlur(dilation2, 5) + # if self._debug: + # cv2.imshow("Hand: calculate_max_contour, median", median) + ret, thresh = cv2.threshold(mask, 127, 255, 0) + if self._debug: + cv2.imshow("Hand: calculate_max_contour, thresh", thresh) + cnts = None + max_area = 100 + ci = 0 + # Find contours of the filtered frame + _, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + if contours: + for i in range(len(contours)): + cnt = contours[i] + area = cv2.contourArea(cnt) + if area > max_area: + max_area = area + ci = i + cnts = contours[ci] + bounding_rect = cv2.boundingRect(cnts) + x, y, w, h = bounding_rect + image_roi = mask[y:y + h, x:x + w] + return cnts, bounding_rect, image_roi + def update_hand_with_contour(self, hand_contour): """ Attributes of the hand are calculated from the hand contour. + TODO: calculate a truth value A score of 100 is the maximum value for the hand truth. This value is calculated like this: - A hand is expoected to have 5 finger tips, 4 intertips, a center of mass + A hand is expected to have 5 finger tips, 4 intertips, a center of mass - :param hand_contour: - :return: + :param hand_contour: calculated contour that is expected to describe a hand + :return: None """ hull2 = cv2.convexHull(hand_contour, returnPoints=False) # Get defect points @@ -427,10 +532,31 @@ def update_hand_with_contour(self, hand_contour): bounding_rect, hand_circle, self._contour = self.get_hand_bounding_rect_from_fingers( hand_contour, fingers_contour) - self._bounding_rect.params = bounding_rect + # detection roi is set to the bounding rect of the fingers upscaled 20 pixels + # self._detection_roi = Roi(bounding_rect) + self._detection_roi = Roi(bounding_rect).upscaled(Roi.from_frame(self._last_frame, SIDE.CENTER, 100), 10) + if self._debug: + to_show = self._last_frame.copy() + cv2.drawContours(to_show, [hand_contour], -1, (255, 255, 255), 2) + cv2.drawContours(to_show, [fingers_contour], -1, (200, 200, 200), 2) + cv2.rectangle(to_show, (self._detection_roi.x, self._detection_roi.y), ( + self._detection_roi.x + self._detection_roi.width, + self._detection_roi.y + self._detection_roi.height), [255, 255, 255]) + # cv2.rectangle(to_show, (self._detection_roi.y, self._detection_roi.x), (self._detection_roi.y + self._detection_roi.height, self._detection_roi.x + self._detection_roi.width), [255, 255, 0]) + # (x, y, w, h) = cv2.boundingRect(hand_contour) + # cv2.rectangle(to_show, (self._detection_roi.y, self._detection_roi.x), (self._detection_roi.x + self._detection_roi.height, self._detection_roi.x + self._detection_roi.width), [255, 255, 0]) + cv2.imshow("update_hand_with_contour", to_show) + + + self._detected = 1 + self._confidence = 100 else: + self._detected = -1 + self._confidence = 0 return else: + self._detected = -1 + self._confidence = 0 return # Find moments of the largest contour moments = cv2.moments(hand_contour) @@ -484,6 +610,8 @@ def update_hand_with_contour(self, hand_contour): finger_distances.append(distance) self._finger_distances = finger_distances else: + self._detected = -2 + self._confidence = 0 return @@ -516,8 +644,8 @@ def _calculate_fingertips(self, hand_contour, defects): # cv2.circle(frame, far, 8, [211, 84, 0], -1) intertips_coords.append(far) intertips_indexes.append(f) - # cv2.putText(frame, str(s), start, self.font, 0.7, (255, 255, 255), 1) - # cv2.putText(frame, str(e), end, self.font, 0.7, (255, 255, 200), 1) + # cv2.putText(frame, str(s), start, FONT, 0.7, (255, 255, 255), 1) + # cv2.putText(frame, str(e), end, FONT, 0.7, (255, 255, 200), 1) if len(fingertips_coords) > 0: from scipy.spatial import distance # calculate distances from start and end to the already known tips @@ -556,96 +684,141 @@ def is_hand(self, fingertips, intertips, strict=True): def detect_and_track(self, frame): - # detect - # if detected - # update information - # set roi _detected_roi to bounding rect of detection - # else - # track - # if ret and tracking_window - - result = self.detect_in_frame(frame) - - if result < 0: - # not detected - self._detected = False - self._tracked = False - ret, tracking_window = self._track_in_frame(frame, self._detected_roi) - if ret and tracking_window is not None: - self._tracking_window = tracking_window - self._tracked = True - # detecction by intersection with hands found in frame - extended_roi = self._bounding_rect.upscaled(frame.shape, 60) - # if self._detected: - # extended_roi = upscale_bounding_rect(self._bounding_rect, frame.shape, 60) - # else: - # extended_roi = None - detected_hands = self.detect_hands_in_frame(frame, extended_roi) - self._detected = False - for detected_hand in detected_hands: - intersection_value, _ = self.calculate_bounding_rects_intersection( - detected_hand._bounding_rect, - self._bounding_rect) - if intersection_value > 0.1: - self._update_attributes_from_detected(detected_hand) - self._position_history.extend(detected_hand._position_history) - self._detected = True - break - - if self._detected is False: - extended_roi = extended_roi if extended_roi is not None else self._bounding_rect - fist_bounding_rect, new_contour = self.detect_fist(frame, extended_roi) - if fist_bounding_rect is not None and new_contour is not None: - self._contour = new_contour - self._bounding_rect = upscale_bounding_rect(fist_bounding_rect, frame.shape, 50) - updated_hand = self.update_hand_attributes(frame, existing_hand, strict=False) - if updated_hand is not None: - self._update_attributes_from_detected(updated_hand) - self._position_history = updated_hand.position_history - self._detected = False - self._tracked = True + """ + Try to detect and track the hand on the given frame + + If the hand is not detected the _extended_roi is updated which will be used in the next detection + :param frame: + :return: + """ + self._detect_in_frame(frame) + self._track_in_frame(frame) + print(self._detected>0, self._tracked) + if self._detected > 0: + self._consecutive_detection_fails = 0 + else: + self._consecutive_detection_fails += 1 + # if it's the first time we don't detect on a row... + if self._consecutive_detection_fails == 1: + # if we have a tracking roi we use it + if self._tracked: + self._extended_roi = self._tracking_roi else: - self._tracked = False - # if self._tracked: + # if we don't, we use the last detected roi + self._extended_roi = self._detection_roi else: - self._tracked = False - # self._bounding_rect = self._tracking_window - # updated_hand = self.update_hand_attributes(frame, existing_hand, strict=False) - # if updated_hand is not None: - # self._update_attributes_from_detected(updated_hand) - # self._position_history = updated_hand.position_history - # self._detected = False - # else: - # print "_____________No updated information" - self._update_truth_value_by_frame2() - - def _track_in_frame(self, frame, bounding_rect, method="camshift"): + # if it's not the first time we don't detect we just extend the extended roi. + self._extended_roi = self._extended_roi.upscaled(Roi.from_frame(frame), 10) + + if self._tracked: + self._consecutive_tracking_fails = 0 + else: + self._consecutive_tracking_fails += 1 + + # self._update_truth_value_by_frame2() + + def get_roi_to_use(self, frame): + """ + Calculate the roi to be used depending on the situation of the hand (initial, detected, tracked) + :param frame: + :return: + """ + current_roi = None + if self._detected > 0: + current_roi = self._detection_roi + else: + # if we already have failed to detect we use the extended_roi + if self._consecutive_detection_fails > 0: + current_roi = self._extended_roi + else: + # Not detected and not consecutive fails on detection. + # It's probably the first time we try to detect. + # If no initial_roi is given an square of 200 x 200 is taken on the center + if self._initial_roi is not None: + current_roi = self._initial_roi + else: + current_roi = Roi.from_frame(frame, SIDE.CENTER, 50) + assert current_roi != Roi(), "hand can't be detected on a %s roi of the frame" % str(current_roi) + return current_roi + + def _track_in_frame(self, frame, method="camshift"): + self._last_frame = frame + roi_for_tracking = self.get_roi_to_use(frame) + mask = self.create_hand_mask(frame) - x, y, w, h = bounding_rect - track_window = bounding_rect + x, y, w, h = roi_for_tracking + track_window = tuple(roi_for_tracking) # set up the ROI for tracking roi = frame[y:y + h, x:x + w] - hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV) + + if self._debug: + print roi_for_tracking + cv2.imshow("DEBUG: HandDetection_lib: _track_in_frame (frame_roied)", roi) + + # fi masked frame is only 1 channel + if len(frame.shape) == 2 or (len(frame.shape) == 3 and frame.shape[2] == 1): + hsv_roi = cv2.cvtColor(roi, cv2.COLOR_GRAY2RGB) + hsv_roi = cv2.cvtColor(hsv_roi, cv2.COLOR_RGB2HSV) + hsv = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR) + hsv = cv2.cvtColor(hsv, cv2.COLOR_BGR2HSV) + else: + hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV) + hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) # mask = cv2.inRange(hsv_roi, np.array((0., 60., 32.)), np.array((180., 255., 255.))) roi_mask = mask[y:y + h, x:x + w] - if self.debug: + if self._debug: cv2.imshow("DEBUG: HandDetection_lib: follow (ROI extracted mask)", roi_mask) roi_hist = cv2.calcHist([hsv_roi], [0], roi_mask, [180], [0, 180]) cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX) # Setup the termination criteria, either 10 iteration or move by atleast 1 pt term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1) - hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) + dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1) # apply meanshift to get the new location if method == "meanshift": - ret, track_window = cv2.meanShift(dst, track_window, term_crit) + self._tracked, track_window = cv2.meanShift(dst, track_window, term_crit) else: - ret, track_window = cv2.CamShift(dst, track_window, term_crit) - return ret, track_window - # return ret, (max(newx-30,0), max(newy-30,0), min(neww+30,frame.shape[1]), min(newh+30,frame.shape[0])) + rotated_rect, track_window = cv2.CamShift(dst, track_window, term_crit) + if len(rotated_rect)>0: + self._tracked = True + else: + self._tracked = False + self._tracking_roi = Roi(track_window) + + +def extract_contour_inside_circle(full_contour, circle): + """ + Get the intersection of a contour and a circle + + :param full_contour: Contour to be intersected + :param circle: circle to be intersected with the contour + :return: contour that is inside the given circle + """ + center, radius = circle + new_contour = [] + for point in full_contour: + if (point[0][0] - center[0]) ** 2 + (point[0][1] - center[1]) ** 2 < radius ** 2: + new_contour.append(point) + return np.array(new_contour) + + +def extract_contour_inside_rect(full_contour, rect): + """ + Get the intersection of a contour and a rectangle + + :param full_contour: Contour to be intersected + :param rect: rectangle to be intersected with the contour + :return: ontour that is inside the given rectangle + """ + x1, y1, w, h = rect + x2 = x1 + w + y2 = y1 + h + new_contour = [] + for point in full_contour: + if x1 < point[0][0] < x2 and y1 < point[0][1] < y2: + new_contour.append(point) + return np.array(new_contour) if __name__ == '__main__': hand = Hand() - hand.initial_roi = 27 - hand.depth_threshold = 600 print(hand.initial_roi, hand.depth_threshold) \ No newline at end of file diff --git a/HandDetection.py b/HandDetection.py index df924ca..0f0896d 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -29,41 +29,6 @@ def calculate_angle(v1, v2): -def extract_contour_inside_circle(full_contour, circle): - """ - Get the intersection of a contour and a circle - - :param full_contour: Contour to be intersected - :param circle: circle to be intersected with the contour - :return: contour that is inside the given circle - """ - center, radius = circle - new_contour = [] - for point in full_contour: - if (point[0][0] - center[0]) ** 2 + (point[0][1] - center[1]) ** 2 < radius ** 2: - new_contour.append(point) - return np.array(new_contour) - - -def extract_contour_inside_rect(full_contour, rect): - """ - Get the intersection of a contour and a rectangle - - :param full_contour: Contour to be intersected - :param rect: rectangle to be intersected with the contour - :return: ontour that is inside the given rectangle - """ - x1, y1, w, h = rect - x2 = x1 + w - y2 = y1 + h - new_contour = [] - for point in full_contour: - if x1 < point[0][0] < x2 and y1 < point[0][1] < y2: - new_contour.append(point) - return np.array(new_contour) - - - class HandDetector: @@ -82,7 +47,6 @@ def __init__(self, source=0): self.capture = None self.hands = [] - self.font = cv2.FONT_HERSHEY_SIMPLEX self.first_frame = None self.next_hand_id = 0 # TODO: ENV_DEPENDENCE: depending on the environment and camera it would be more or less frames to discard @@ -100,7 +64,7 @@ def __init__(self, source=0): # TODO: Extend to return mutiple hands? - # TODO: Propability of been a hand could be calculated depending contour points, area, number of fingertips detected + # TODO: create a static method on Hand to return all the hands detected on a frame def hand_from_frame(self, frame, roi = None): """ Get a Hand class from a roi in a frame @@ -114,15 +78,31 @@ def hand_from_frame(self, frame, roi = None): hand.initial_roi = roi #TODO: only to test. Replace - hand.depth_threshold = 600 + hand.depth_threshold = self.depth_threshold + hand._detect_in_frame(frame) return hand # hand.depth_threshold = self.depth_threshold - hand.update_with_frame(frame) + + def add_new_expected_hand(self, frame_roi): + hand = Hand() + hand.initial_roi = frame_roi + # TODO: add only mode is Depth + hand.depth_threshold = self.depth_threshold + self.hands.append(hand) + + def update_expected_hands(self, frame): + if len(self.hands) > 0: + for existing_hand in self.hands: + existing_hand.detect_and_track(frame) + # TODO: determine if hands are automatically removed or must be removed by the user + # if existing_hand.confidence <= 0: + # print "removing hand" + # self.hands.remove(existing_hand) + - # TODO: extract from here a method to get a hand from a frame, and this would only try and append. def add_hand2(self, frame, roi = None): """ @@ -133,14 +113,15 @@ def add_hand2(self, frame, roi = None): """ new_hand = self.hand_from_frame(frame, roi) + new_hand._track_in_frame(frame) - if new_hand is not None: + if new_hand.valid: new_hand.id = len(self.hands) self.hands.append(new_hand) # cv2.putText(masked_frame, "HAND FOUND", # (template_x, template_y + template_h + 10), # self.font, 1, [0, 0, 0], 2) - print("HAND FOUND") + # print("add_hand2:", "HAND FOUND") else: # cv2.putText(masked_frame, "CENTER YOUR HAND", # (template_x, template_y + template_h + 10), @@ -203,7 +184,7 @@ def add_hand(self, frame): hand = Hand() hand._id = len(self.hands) hand._contour = hand_contour - hand._bounding_rect = detected_hand_bounding_rect + hand._detection_roi = detected_hand_bounding_rect self.hands.append(hand) cv2.putText(masked_frame, "HAND FOUND", (template_x, template_y + template_h + 10), @@ -253,48 +234,6 @@ def calculate_bounding_rects_intersection(self, bounding_rect_1, bounding_rect_2 # return the intersection over union value return iou, (x_left, y_top, x_right, y_bottom) - def calculate_max_contour(self, image, to_binary=True): - bounding_rect = None - image_roi = None - if to_binary: - gray_diff = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) - _, mask = cv2.threshold(gray_diff, 40, 255, cv2.THRESH_BINARY) - else: - mask = image - kernel_square = np.ones((11, 11), np.uint8) - kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) - - # Perform morphological transformations to filter out the background noise - # Dilation increase skin color area - # Erosion increase skin color area - dilation = cv2.dilate(mask, kernel_ellipse, iterations=1) - erosion = cv2.erode(dilation, kernel_square, iterations=1) - dilation2 = cv2.dilate(erosion, kernel_ellipse, iterations=1) - filtered = cv2.medianBlur(dilation2.astype(np.uint8), 5) - kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8, 8)) - dilation2 = cv2.dilate(filtered, kernel_ellipse, iterations=1) - kernel_ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) - dilation3 = cv2.dilate(filtered, kernel_ellipse, iterations=1) - median = cv2.medianBlur(dilation2, 5) - ret, thresh = cv2.threshold(median, 127, 255, 0) - cnts = None - max_area = 100 - ci = 0 - # Find contours of the filtered frame - _, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) - if contours: - for i in range(len(contours)): - cnt = contours[i] - area = cv2.contourArea(cnt) - if area > max_area: - max_area = area - ci = i - cnts = contours[ci] - bounding_rect = cv2.boundingRect(cnts) - x, y, w, h = bounding_rect - image_roi = mask[y:y + h, x:x + w] - return cnts, bounding_rect, image_roi - def set_mask_mode(self, mode): self.mask_mode = mode @@ -327,7 +266,7 @@ def capture_and_compute(self): else: print "_____________No updated information" hand.update_truth_value_by_frame2() - if hand.truth_value <= 0: + if hand.confidence <= 0: print "removing hand" self.hands.remove(hand) @@ -610,7 +549,7 @@ def draw_hand_overlay(self, frame, hand): cv2.putText(frame, 'Center', tuple(hand.center_of_mass), self.font, 0.5, (255, 255, 255), 1) hand_string = "hand %d %s: D=%s|T=%s|L=%s|F=%d" % ( - hand.id, str(hand.center_of_mass), str(hand.detected), str(hand.tracked), str(hand.truth_value), + hand.id, str(hand.center_of_mass), str(hand.detected), str(hand.tracked), str(hand.confidence), hand.frame_count) cv2.putText(frame, hand_string, (10, 30 + 15 * int(hand.id)), self.font, 0.5, (255, 255, 255), 1) return frame @@ -821,29 +760,6 @@ def detect_hands_in_frame(self, frame, roi=None): return new_hands - def get_hand_bounding_rect_from_fingers(self, hand_contour, fingers_contour): - (x, y), radius = cv2.minEnclosingCircle(fingers_contour) - center = (int(x), int(y)) - radius = int(radius) + 10 - hand_contour = extract_contour_inside_circle(hand_contour, (center, radius)) - hand_bounding_rect = cv2.boundingRect(hand_contour) - return hand_bounding_rect, ((int(x), int(y)), radius), hand_contour - - def get_hand_bounding_rect_from_rect(self, hand_contour, bounding_rect): - hand_contour = extract_contour_inside_rect(hand_contour, bounding_rect) - hand_bounding_rect = cv2.boundingRect(hand_contour) - return hand_bounding_rect, hand_contour - - def get_hand_bounding_rect_from_center_of_mass(self, hand_contour, center_of_mass, average_distance): - (x, y) = center_of_mass - radius = average_distance - center = (int(x), int(y)) - radius = int(radius) + 10 - hand_contour = extract_contour_inside_circle(hand_contour, (center, radius)) - hand_bounding_rect = cv2.boundingRect(hand_contour) - return hand_bounding_rect, ((int(x), int(y)), radius), hand_contour - - def reset_background(self): # TODO test it better @@ -878,14 +794,6 @@ def update_detection(self, frame): self.hands.append(detected_hand) print "New hand" - def update_detection_and_tracking(self, frame): - - if len(self.hands) > 0: - for index, existing_hand in enumerate(self.hands): - existing_hand.detect_and_track() - if existing_hand.truth_value <= 0: - print "removing hand" - self.hands.remove(existing_hand) def detect_fist(self, frame, roi): contours, _ = self.create_contours_and_mask(frame, roi) From 51d4aee13f8d9e7669bbb926dcb6d4364ccc5597 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Tue, 30 Jul 2019 09:03:36 +0200 Subject: [PATCH 40/54] Added new test cases for hand class --- test_hand.py | 39 ++++++++++++++++++++++++++++++++++----- 1 file changed, 34 insertions(+), 5 deletions(-) diff --git a/test_hand.py b/test_hand.py index 5a0254e..3904041 100644 --- a/test_hand.py +++ b/test_hand.py @@ -1,3 +1,4 @@ +import os import unittest import cv2 @@ -17,14 +18,42 @@ def test_hand_detect(self): """ hand = Hand() hand.depth_threshold = 130 - frame = cv2.imread("/home/robolab/robocomp/components/robocomp-robolab/components/handDetection/src/images/depth_images/20190725130248.png",0 ) - hand.initial_roi = Roi.from_frame(frame,SIDE.CENTER, 50) - hand._detect_in_frame(frame) - hand._track_in_frame(frame, ) - self.assertGreater(hand._detected, 0) + expected_results = { + "20190725130248.png": [False, False], + "20190725130249.png": [False, False], + "20190725130250.png": [True, True], + "20190725130251.png": [False, True], # fingers too close and it's considered not a hand + "20190725130252.png": [True, True], + "20190725130253.png": [True, True], + "20190725130254.png": [False, True], + "20190725130255.png": [False, True], + "20190725130256.png": [False, True], + "20190725130257.png": [False, True], + "20190725130258.png": [False, True] + } + full_path = "/home/robolab/robocomp/components/robocomp-robolab/components/handDetection/src/images/depth_images" + for file in sorted(os.listdir(full_path)): + if file.endswith(".png") and file in expected_results: + frame = cv2.imread(os.path.join(full_path, file),0) + hand.initial_roi = Roi.from_frame(frame, SIDE.CENTER, 50) + hand.detect_and_track(frame) + print("testing file %s"%file) + self.assertEqual((hand._detected > 0), expected_results[file][0]) + self.assertEqual(hand._tracked, expected_results[file][1]) + frame = self.draw_in_frame(hand, frame) + cv2.imshow("final", frame) + cv2.waitKey(1000) + + def draw_in_frame(self, hand, frame): + frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB) + frame = hand._initial_roi.draw_on_frame(frame,[0,0,255]) + frame = hand._detection_roi.draw_on_frame(frame, [0, 255, 0]) + frame = hand._tracking_roi.draw_on_frame(frame, [255, 0, 0]) + # frame = hand._extended_roi.draw_on_frame(frame, [0, 0, 255]) + return frame # def test_roi_upscale(self): # """ # Test that roi upscale right From 7aa30fc6ee6b85324db5749f155e9f6a82c77a0e Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Tue, 30 Jul 2019 09:05:32 +0200 Subject: [PATCH 41/54] Fixed a bug when trying to track a never detected hand. Fixed a bug when extending the roi. --- Hand.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/Hand.py b/Hand.py index a670c3d..a5a2ac3 100644 --- a/Hand.py +++ b/Hand.py @@ -134,6 +134,7 @@ def __init__(self): self._debug = True self._depth_threshold = -1 self._last_frame = None + self._ever_detected = False ##################################################################### @@ -150,8 +151,8 @@ def depth_threshold(self): @initial_roi.setter def initial_roi(self, value): - self._initial_roi = value + self._extended_roi = value @depth_threshold.setter def depth_threshold(self, value): @@ -549,6 +550,7 @@ def update_hand_with_contour(self, hand_contour): self._detected = 1 + self._ever_detected = True self._confidence = 100 else: self._detected = -1 @@ -708,7 +710,7 @@ def detect_and_track(self, frame): self._extended_roi = self._detection_roi else: # if it's not the first time we don't detect we just extend the extended roi. - self._extended_roi = self._extended_roi.upscaled(Roi.from_frame(frame), 10) + self._extended_roi = self._extended_roi.upscaled(self._initial_roi, 10) if self._tracked: self._consecutive_tracking_fails = 0 @@ -743,6 +745,7 @@ def get_roi_to_use(self, frame): def _track_in_frame(self, frame, method="camshift"): self._last_frame = frame + if self._ever_detected: roi_for_tracking = self.get_roi_to_use(frame) mask = self.create_hand_mask(frame) @@ -779,11 +782,17 @@ def _track_in_frame(self, frame, method="camshift"): self._tracked, track_window = cv2.meanShift(dst, track_window, term_crit) else: rotated_rect, track_window = cv2.CamShift(dst, track_window, term_crit) - if len(rotated_rect)>0: + track_window = cv2.minAreaRect(rotated_rect) + + if roi_for_tracking.intersection_rate(Roi(track_window)) > 0.4 and roi_for_tracking != Roi(track_window): self._tracked = True else: self._tracked = False self._tracking_roi = Roi(track_window) + else: + self._tracked = False + + def extract_contour_inside_circle(full_contour, circle): From 1a16dcedb5fa1f7f804f89cd1439861d9c9c1329 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Tue, 30 Jul 2019 09:08:44 +0200 Subject: [PATCH 42/54] Fixed a bug on Roi for the width and height of the frame. Added method to Roi to apply a mask on a frame. Added method to Roi to draw a it on a frame Added method to Roi to extract it from a frame --- Hand.py | 90 +++++++++++++++++++++++++++------------------------------ roi.py | 21 +++++++++++++- 2 files changed, 62 insertions(+), 49 deletions(-) diff --git a/Hand.py b/Hand.py index a5a2ac3..80e7715 100644 --- a/Hand.py +++ b/Hand.py @@ -295,9 +295,7 @@ def create_contours_and_mask(self, frame, roi=None): else: x, y, w, h = self._initial_roi - current_roi_mask = np.zeros(hands_mask.shape, dtype='uint8') - current_roi_mask[y:y + h, x:x + w] = 255 - roied_hands_mask = cv2.bitwise_and(hands_mask, current_roi_mask) + roied_hands_mask = roi.apply_to_frame_as_mask(hands_mask) if self._debug: cv2.imshow("DEBUG: HandDetection_lib: create_contours_and_mask (Frame Mask)", hands_mask) # to_show = cv2.resize(hands_mask, None, fx=.3, fy=.3, interpolation=cv2.INTER_CUBIC) @@ -306,8 +304,8 @@ def create_contours_and_mask(self, frame, roi=None): # cv2.putText(to_show, (str(h)), (x + w, y + h), FONT, 0.3, [100, 255, 255], 1) # cv2.putText(to_show, (str(w * h)), (x + w / 2, y + h / 2), FONT, 0.3, [100, 100, 255], 1) # cv2.putText(to_show, (str(x)+", "+str(y)), (x-10, y-10), FONT, 0.3, [255, 255, 255], 1) - cv2.rectangle(to_show, (x, y), (x + w, y + h), [255, 255, 255]) - cv2.imshow("DEBUG: HandDetection_lib: create_contours_and_mask (current_roi_mask)", current_roi_mask) + to_show = roi.draw_on_frame(to_show) + cv2.imshow("DEBUG: HandDetection_lib: create_contours_and_mask (current_roi_mask)", roi.extract_from_frame(frame)) cv2.imshow("DEBUG: HandDetection_lib: create_contours_and_mask (ROIed Mask)", to_show) ret, thresh = cv2.threshold(roied_hands_mask, 127, 255, 0) @@ -419,8 +417,6 @@ def _detect_in_frame(self, frame): # Create contours and mask self._frame_contours, self._frame_mask = self.create_contours_and_mask(frame, search_roi) - masked_frame = np.zeros(frame.shape, dtype="uint8") - masked_frame[::] = 255 # get the maximum contour if len(self._frame_contours) > 0 and len(self._frame_mask) > 0: @@ -540,9 +536,7 @@ def update_hand_with_contour(self, hand_contour): to_show = self._last_frame.copy() cv2.drawContours(to_show, [hand_contour], -1, (255, 255, 255), 2) cv2.drawContours(to_show, [fingers_contour], -1, (200, 200, 200), 2) - cv2.rectangle(to_show, (self._detection_roi.x, self._detection_roi.y), ( - self._detection_roi.x + self._detection_roi.width, - self._detection_roi.y + self._detection_roi.height), [255, 255, 255]) + to_show = self._detection_roi.draw_on_frame(to_show) # cv2.rectangle(to_show, (self._detection_roi.y, self._detection_roi.x), (self._detection_roi.y + self._detection_roi.height, self._detection_roi.x + self._detection_roi.width), [255, 255, 0]) # (x, y, w, h) = cv2.boundingRect(hand_contour) # cv2.rectangle(to_show, (self._detection_roi.y, self._detection_roi.x), (self._detection_roi.x + self._detection_roi.height, self._detection_roi.x + self._detection_roi.width), [255, 255, 0]) @@ -746,49 +740,49 @@ def get_roi_to_use(self, frame): def _track_in_frame(self, frame, method="camshift"): self._last_frame = frame if self._ever_detected: - roi_for_tracking = self.get_roi_to_use(frame) + roi_for_tracking = self.get_roi_to_use(frame) - mask = self.create_hand_mask(frame) - x, y, w, h = roi_for_tracking - track_window = tuple(roi_for_tracking) - # set up the ROI for tracking - roi = frame[y:y + h, x:x + w] + mask = self.create_hand_mask(frame) + x, y, w, h = roi_for_tracking + track_window = tuple(roi_for_tracking) + # set up the ROI for tracking + roi = roi_for_tracking.extract_from_frame(frame) - if self._debug: - print roi_for_tracking - cv2.imshow("DEBUG: HandDetection_lib: _track_in_frame (frame_roied)", roi) - - # fi masked frame is only 1 channel - if len(frame.shape) == 2 or (len(frame.shape) == 3 and frame.shape[2] == 1): - hsv_roi = cv2.cvtColor(roi, cv2.COLOR_GRAY2RGB) - hsv_roi = cv2.cvtColor(hsv_roi, cv2.COLOR_RGB2HSV) - hsv = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR) - hsv = cv2.cvtColor(hsv, cv2.COLOR_BGR2HSV) - else: - hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV) - hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) - # mask = cv2.inRange(hsv_roi, np.array((0., 60., 32.)), np.array((180., 255., 255.))) - roi_mask = mask[y:y + h, x:x + w] - if self._debug: - cv2.imshow("DEBUG: HandDetection_lib: follow (ROI extracted mask)", roi_mask) - roi_hist = cv2.calcHist([hsv_roi], [0], roi_mask, [180], [0, 180]) - cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX) - # Setup the termination criteria, either 10 iteration or move by atleast 1 pt - term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1) - - dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1) - # apply meanshift to get the new location - if method == "meanshift": - self._tracked, track_window = cv2.meanShift(dst, track_window, term_crit) - else: - rotated_rect, track_window = cv2.CamShift(dst, track_window, term_crit) + if self._debug: + print roi_for_tracking + cv2.imshow("DEBUG: HandDetection_lib: _track_in_frame (frame_roied)", roi) + + # fi masked frame is only 1 channel + if len(frame.shape) == 2 or (len(frame.shape) == 3 and frame.shape[2] == 1): + hsv_roi = cv2.cvtColor(roi, cv2.COLOR_GRAY2RGB) + hsv_roi = cv2.cvtColor(hsv_roi, cv2.COLOR_RGB2HSV) + hsv = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR) + hsv = cv2.cvtColor(hsv, cv2.COLOR_BGR2HSV) + else: + hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV) + hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) + # mask = cv2.inRange(hsv_roi, np.array((0., 60., 32.)), np.array((180., 255., 255.))) + roi_mask = mask[y:y + h, x:x + w] + if self._debug: + cv2.imshow("DEBUG: HandDetection_lib: follow (ROI extracted mask)", roi_mask) + roi_hist = cv2.calcHist([hsv_roi], [0], roi_mask, [180], [0, 180]) + cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX) + # Setup the termination criteria, either 10 iteration or move by atleast 1 pt + term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1) + + dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1) + # apply meanshift to get the new location + if method == "meanshift": + self._tracked, track_window = cv2.meanShift(dst, track_window, term_crit) + else: + rotated_rect, track_window = cv2.CamShift(dst, track_window, term_crit) track_window = cv2.minAreaRect(rotated_rect) if roi_for_tracking.intersection_rate(Roi(track_window)) > 0.4 and roi_for_tracking != Roi(track_window): - self._tracked = True - else: - self._tracked = False - self._tracking_roi = Roi(track_window) + self._tracked = True + else: + self._tracked = False + self._tracking_roi = Roi(track_window) else: self._tracked = False diff --git a/roi.py b/roi.py index 8f2bbe6..462d3fb 100644 --- a/roi.py +++ b/roi.py @@ -1,3 +1,6 @@ +import numpy as np + +import cv2 class SIDE: @@ -61,7 +64,7 @@ def from_frame(frame, side=SIDE.TOP, percent=100): assert isinstance(side, int) and 0 <= side < 5, "Side must be a value between 0 and 3 but %d given. Use the SIDE class to get valid values." % side assert isinstance(percent, (int, float)) , "Percent must be must be Integer or Float but %r given" % percent assert (0 < percent <= 100), "Percent must be a value between 0 and 100 but %d given" % percent - frame_width, frame_height = frame.shape[:2] + frame_height, frame_width = frame.shape[:2] # calculating percentage of the frame width and height frame_width_percent = frame_width * percent / 100 frame_height_percent = frame_height * percent / 100 @@ -85,7 +88,23 @@ def from_frame(frame, side=SIDE.TOP, percent=100): new_roi = Roi([x, y, width, height]) return new_roi + def apply_to_frame_as_mask(self, frame): + # TODO: if a copy of the frame is needed. + mask = np.zeros(frame.shape, dtype='uint8') + mask[self.y:self.y + self.height, self.x:self.x + self.width] = 255 + roied_frame = cv2.bitwise_and(frame, mask) + return roied_frame + + def extract_from_frame(self, frame): + return frame[self.y:self.y + self.height, self.x:self.x + self.width] + def draw_on_frame(self, frame, color = [255, 255, 255], copy = True): + if copy: + new_frame = frame.copy() + else: + new_frame = frame + cv2.rectangle(new_frame, self.top_left, self.bottom_right, color) + return new_frame def upscaled(self, limiting_roi, upscaled_pixels): """ From 8721d6f0beb7d7c6c2eab3c8b079993dc9fddc37 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Tue, 30 Jul 2019 09:10:02 +0200 Subject: [PATCH 43/54] Added properties to get the points of the corners of the roi top_left, top_right, bottom_left, bottom_right. Added method to get a rate of the intersection of two rois. --- roi.py | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) diff --git a/roi.py b/roi.py index 462d3fb..8138d3a 100644 --- a/roi.py +++ b/roi.py @@ -106,6 +106,38 @@ def draw_on_frame(self, frame, color = [255, 255, 255], copy = True): cv2.rectangle(new_frame, self.top_left, self.bottom_right, color) return new_frame + + @property + def top_left(self): + return (self.x, self.y) + + @property + def top_right(self): + return (self.x, self.x+ self.width) + + @property + def bottom_left(self): + return (self.x, self.y + self.height) + + @property + def bottom_right(self): + return (self.x + self.width, self.y + self.height) + + def intersection_rate(self, s2): + + x1, y1 = self.top_left + x2, y2 = self.bottom_right + s_1 = np.array([[x1, y1], [x2, y1], [x2, y2], [x1, y2]]) + + x1, y1 = s2.top_left + x2, y2 = s2.bottom_right + s_2 = np.array([[x1, y1], [x2, y1], [x2, y2], [x1, y2]]) + + area, _intersection = cv2.intersectConvexConvex(s_1, s_2) + return 2 * area / (cv2.contourArea(s_1) + cv2.contourArea(s_2)) + + + def upscaled(self, limiting_roi, upscaled_pixels): """ Create an upscaled version of an input ROI restricted to the size of a frame From 9c2ac77c2630a182fce655eb373dc1f85d8b16ec Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Tue, 30 Jul 2019 13:53:41 +0200 Subject: [PATCH 44/54] Moved self._detected to Boolean and created a self._detection_status --- Hand.py | 24 ++++++++++++++++-------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/Hand.py b/Hand.py index 80e7715..5ba4b53 100644 --- a/Hand.py +++ b/Hand.py @@ -118,6 +118,7 @@ def __init__(self): self._confidence = 0 self._tracked = False self._detected = False + self._detection_status = 0 self._position_history = [] @@ -440,9 +441,11 @@ def _detect_in_frame(self, frame): # self._detected is updated inside self.update_hand_with_contour(hand_contour) else: - self._detected = -1 + self._detected = False + self._detection_status = -1 else: - self._detected = -2 + self._detected = False + self._detection_status = -2 def calculate_max_contour(self, image, to_binary=True): @@ -543,15 +546,18 @@ def update_hand_with_contour(self, hand_contour): cv2.imshow("update_hand_with_contour", to_show) - self._detected = 1 + self._detected = True + self._detection_status = 1 self._ever_detected = True self._confidence = 100 else: - self._detected = -1 + self._detection_status = -1 + self._detected = False self._confidence = 0 return else: - self._detected = -1 + self._detection_status = -1 + self._detected = False self._confidence = 0 return # Find moments of the largest contour @@ -606,7 +612,8 @@ def update_hand_with_contour(self, hand_contour): finger_distances.append(distance) self._finger_distances = finger_distances else: - self._detected = -2 + self._detection_status = -2 + self._detected = False self._confidence = 0 return @@ -688,13 +695,14 @@ def detect_and_track(self, frame): :return: """ self._detect_in_frame(frame) - self._track_in_frame(frame) + if self._detected: print(self._detected>0, self._tracked) if self._detected > 0: self._consecutive_detection_fails = 0 else: self._consecutive_detection_fails += 1 # if it's the first time we don't detect on a row... + print(self._detected, self._tracked) if self._consecutive_detection_fails == 1: # if we have a tracking roi we use it if self._tracked: @@ -720,7 +728,7 @@ def get_roi_to_use(self, frame): :return: """ current_roi = None - if self._detected > 0: + if self._detected: current_roi = self._detection_roi else: # if we already have failed to detect we use the extended_roi From bae9ffc1d867f1bafb62e2df4a06e81974a73679 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Tue, 30 Jul 2019 13:56:26 +0200 Subject: [PATCH 45/54] Added new properties and setter for initial_roi, detection_roi, tracking_roi and extended_roi. --- Hand.py | 146 ++++++++++++++++++++++++++++++++++++++++++++------------ 1 file changed, 115 insertions(+), 31 deletions(-) diff --git a/Hand.py b/Hand.py index 5ba4b53..7083437 100644 --- a/Hand.py +++ b/Hand.py @@ -146,15 +146,69 @@ def __init__(self): def initial_roi(self): return self._initial_roi + @initial_roi.setter + def initial_roi(self, value): + assert all(isinstance(n, (int, float)) for n in value) or isinstance(value, + Roi), "initial_roi must be of the type Roi" + if isinstance(value, Roi): + self._initial_roi = value + else: + self._initial_roi = Roi(value) + + self.extended_roi = self._initial_roi + + + @property + def tracking_roi(self): + return self._tracking_roi + + @tracking_roi.setter + def tracking_roi(self, value): + assert all(isinstance(n, (int, float)) for n in value) or isinstance(value, Roi), "tracking_roi must be of the type Roi" + if isinstance(value, Roi): + self._tracking_roi = value + else: + self._tracking_roi = Roi(value) + # Tracking_roi must be limited to the initial_roi + self._tracking_roi.limit_to_roi(self.initial_roi) + + @property + def detection_roi(self): + return self._detection_roi + + @detection_roi.setter + def detection_roi(self, value): + assert all(isinstance(n, (int, float)) for n in value) or isinstance(value, + Roi), "detection_roi must be of the type Roi" + if isinstance(value, Roi): + self._detection_roi = value + else: + self._detection_roi = Roi(value) + # Detection_roi must be limited to the initial_roi + self._detection_roi.limit_to_roi(self.initial_roi) + + @property + def extended_roi(self): + return self._extended_roi + + @extended_roi.setter + def extended_roi(self, value): + assert all(isinstance(n, (int, float)) for n in value) or isinstance(value, + Roi), "extended_roi must be of the type Roi" + if isinstance(value, Roi): + self._extended_roi = value + else: + self._extended_roi = Roi(value) + # Extended_roi must be limited to the initial_roi + self._extended_roi.limit_to_roi(self.initial_roi) + + + + @property def depth_threshold(self): return self._depth_threshold - @initial_roi.setter - def initial_roi(self, value): - self._initial_roi = value - self._extended_roi = value - @depth_threshold.setter def depth_threshold(self, value): self._depth_threshold = value @@ -169,8 +223,28 @@ def confidence(self, value): @property def valid(self): - return (self._detected > 0 or self._tracked or self._confidence > 0) + return (self.detected or self.tracked or self._confidence > 0) + + + @property + def detected(self): + return self._detected + + @detected.setter + def detected(self, value): + self._detected = value + @property + def tracked(self): + return self._tracked + + @tracked.setter + def tracked(self, value): + self._tracked = value + +##################################################################### +########## Probably deprecated methods # TODO: check ########## +##################################################################### #TODO: Check if we need a deep copy of the data. def update_attributes_from_detected(self, other_hand): @@ -186,7 +260,7 @@ def update_attributes_from_detected(self, other_hand): self._finger_distances = other_hand.finger_distances self._average_defect_distance = other_hand.average_defect_distance self._contour = other_hand.contour - self._detection_roi = other_hand._detection_roi + self.detection_roi = other_hand.detection_roi self._detected = True def update_truth_value_by_time(self): @@ -279,12 +353,16 @@ def copy_main_attributes(self): updated_hand._finger_distances = [] updated_hand._average_defect_distance = [] updated_hand._contour = None - updated_hand._detection_roi = self._detection_roi + updated_hand.detection_roi = self.detection_roi updated_hand._consecutive_tracking_fails = self._consecutive_tracking_fails updated_hand._position_history = self._position_history updated_hand._color = self._color return updated_hand +##################################################################### +########## Currently used methods ########## +##################################################################### + def create_contours_and_mask(self, frame, roi=None): # Create a binary image where white will be skin colors and rest is black hands_mask = self.create_hand_mask(frame) @@ -294,7 +372,7 @@ def create_contours_and_mask(self, frame, roi=None): if roi is not None: x, y, w, h = roi else: - x, y, w, h = self._initial_roi + x, y, w, h = self.initial_roi roied_hands_mask = roi.apply_to_frame_as_mask(hands_mask) if self._debug: @@ -533,16 +611,16 @@ def update_hand_with_contour(self, hand_contour): hand_contour, fingers_contour) # detection roi is set to the bounding rect of the fingers upscaled 20 pixels - # self._detection_roi = Roi(bounding_rect) - self._detection_roi = Roi(bounding_rect).upscaled(Roi.from_frame(self._last_frame, SIDE.CENTER, 100), 10) + # self.detection_roi = Roi(bounding_rect) + self.detection_roi = Roi(bounding_rect).upscaled(Roi.from_frame(self._last_frame, SIDE.CENTER, 100), 10) if self._debug: to_show = self._last_frame.copy() cv2.drawContours(to_show, [hand_contour], -1, (255, 255, 255), 2) cv2.drawContours(to_show, [fingers_contour], -1, (200, 200, 200), 2) - to_show = self._detection_roi.draw_on_frame(to_show) - # cv2.rectangle(to_show, (self._detection_roi.y, self._detection_roi.x), (self._detection_roi.y + self._detection_roi.height, self._detection_roi.x + self._detection_roi.width), [255, 255, 0]) + to_show = self.detection_roi.draw_on_frame(to_show) + # cv2.rectangle(to_show, (self.detection_roi.y, self.detection_roi.x), (self.detection_roi.y + self.detection_roi.height, self.detection_roi.x + self.detection_roi.width), [255, 255, 0]) # (x, y, w, h) = cv2.boundingRect(hand_contour) - # cv2.rectangle(to_show, (self._detection_roi.y, self._detection_roi.x), (self._detection_roi.x + self._detection_roi.height, self._detection_roi.x + self._detection_roi.width), [255, 255, 0]) + # cv2.rectangle(to_show, (self.detection_roi.y, self.detection_roi.x), (self.detection_roi.x + self.detection_roi.height, self.detection_roi.x + self.detection_roi.width), [255, 255, 0]) cv2.imshow("update_hand_with_contour", to_show) @@ -679,6 +757,7 @@ def _calculate_fingertips(self, hand_contour, defects): # cv2.circle(frame, far, 10, [100, 255, 255], 3) return fingertips_coords, fingertips_indexes, intertips_coords, intertips_indexes + # TODO: modify to use a calculated confidence def is_hand(self, fingertips, intertips, strict=True): if strict: return len(fingertips) == 5 and len(intertips) > 2 @@ -690,29 +769,31 @@ def detect_and_track(self, frame): """ Try to detect and track the hand on the given frame - If the hand is not detected the _extended_roi is updated which will be used in the next detection + If the hand is not detected the extended_roi is updated which will be used in the next detection :param frame: :return: """ self._detect_in_frame(frame) if self._detected: - print(self._detected>0, self._tracked) - if self._detected > 0: self._consecutive_detection_fails = 0 else: self._consecutive_detection_fails += 1 - # if it's the first time we don't detect on a row... + + self._track_in_frame(frame) print(self._detected, self._tracked) - if self._consecutive_detection_fails == 1: - # if we have a tracking roi we use it - if self._tracked: - self._extended_roi = self._tracking_roi - else: - # if we don't, we use the last detected roi - self._extended_roi = self._detection_roi + + # if it's the first time we don't detect in a row... + if self._consecutive_detection_fails == 1: + # if we have a tracking roi we use it + if self._tracked: + self.extended_roi = self.tracking_roi else: - # if it's not the first time we don't detect we just extend the extended roi. - self._extended_roi = self._extended_roi.upscaled(self._initial_roi, 10) + # if we don't, we use the last detected roi + self.extended_roi = self.detection_roi + elif self._consecutive_detection_fails > 1: + # if it's not the first time we don't detect we just extend the extended roi. + # it's autolimited to the initial Roi + self.extended_roi = self.extended_roi.upscaled(self.initial_roi, 10) if self._tracked: self._consecutive_tracking_fails = 0 @@ -729,17 +810,20 @@ def get_roi_to_use(self, frame): """ current_roi = None if self._detected: - current_roi = self._detection_roi + current_roi = self.detection_roi else: # if we already have failed to detect we use the extended_roi if self._consecutive_detection_fails > 0: - current_roi = self._extended_roi + if self._tracked: + current_roi = self.tracking_roi + else: + current_roi = self.extended_roi else: # Not detected and not consecutive fails on detection. # It's probably the first time we try to detect. # If no initial_roi is given an square of 200 x 200 is taken on the center - if self._initial_roi is not None: - current_roi = self._initial_roi + if self.initial_roi is not None and self.initial_roi != Roi(): + current_roi = self.initial_roi else: current_roi = Roi.from_frame(frame, SIDE.CENTER, 50) assert current_roi != Roi(), "hand can't be detected on a %s roi of the frame" % str(current_roi) From b56c84e604a27e73a3097be1f6ae57e808f0dd70 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Tue, 30 Jul 2019 13:59:35 +0200 Subject: [PATCH 46/54] Testing the tracking to look for a problem with the update of the tracking window. --- Hand.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/Hand.py b/Hand.py index 7083437..704f90e 100644 --- a/Hand.py +++ b/Hand.py @@ -865,22 +865,24 @@ def _track_in_frame(self, frame, method="camshift"): dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1) # apply meanshift to get the new location if method == "meanshift": - self._tracked, track_window = cv2.meanShift(dst, track_window, term_crit) + tracked, new_track_window = cv2.meanShift(dst, track_window, term_crit) + self._tracked = (tracked != 0) else: - rotated_rect, track_window = cv2.CamShift(dst, track_window, term_crit) - track_window = cv2.minAreaRect(rotated_rect) + rotated_rect, new_track_window = cv2.CamShift(dst, track_window, term_crit) - if roi_for_tracking.intersection_rate(Roi(track_window)) > 0.4 and roi_for_tracking != Roi(track_window): + intersection_rate = roi_for_tracking.intersection_rate(Roi(new_track_window)) + if intersection_rate and roi_for_tracking != Roi(new_track_window): self._tracked = True else: self._tracked = False - self._tracking_roi = Roi(track_window) + if self._tracked: + self.tracking_roi = Roi(new_track_window) else: self._tracked = False - +# TODO: move to a utils file def extract_contour_inside_circle(full_contour, circle): """ Get the intersection of a contour and a circle @@ -896,7 +898,7 @@ def extract_contour_inside_circle(full_contour, circle): new_contour.append(point) return np.array(new_contour) - +# TODO: move to a utils file def extract_contour_inside_rect(full_contour, rect): """ Get the intersection of a contour and a rectangle From 5ea71ba8dd0a73d945524cd4205b36b1ddfe75a1 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Tue, 30 Jul 2019 14:00:45 +0200 Subject: [PATCH 47/54] Added new properties and setter for x1, x2, y1, y2 coords and p1 and p2 points --- roi.py | 58 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 57 insertions(+), 1 deletion(-) diff --git a/roi.py b/roi.py index 8138d3a..ce25df0 100644 --- a/roi.py +++ b/roi.py @@ -55,9 +55,65 @@ def height(self): def height(self, height): self[3]=height + @property + def x1(self): + return self.x + + @x1.setter + def x1(self, value): + self.x = value + + @property + def x2(self): + return self.x+self.width + + @x2.setter + def x2(self, value): + width = value-self.x + assert width > 0, "Not a valid x2 value %d. It results in a width value of %d" % (value, width) + self.width = width + + @property + def y1(self): + return self.y + + @y1.setter + def y1(self, value): + self.y = value + + @property + def y2(self): + return self.y +self.height + + @y2.setter + def y2(self, value): + height = value - self.y + assert height > 0, "Not a valid y2 value %d. It results in a height value of %d" % (value, height) + self.height = height + + @property + def p1(self): + return (self.x, self.y) + + @p1.setter + def p1(self, value): + assert isinstance(value, (tuple, list)), "p1 need to be a tuple or list. type is %s" % (type(value)) + assert len(value)==2, "p1 need to have a len of 2. %r have a len of %d" % (value, len(value)) + self.x, self.y = value + + @property + def p2(self): + return (self.x2, self.y2) + + @p2.setter + def p2(self, value): + assert isinstance(value, (tuple, list)), "p1 need to be a tuple or list. type is %s" % (type(value)) + assert len(value) == 2, "p1 need to have a len of 2. %r have a len of %d" % (value, len(value)) + self.x2, self.y2 = value + @property def init_coords(self): - return (self[0], self[1]) + return self.p1 @staticmethod def from_frame(frame, side=SIDE.TOP, percent=100): From 173c77d3a1d84ec387213d97db7dc6949b22ac1b Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Tue, 30 Jul 2019 14:01:09 +0200 Subject: [PATCH 48/54] Added method to limit a roi to another given --- roi.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/roi.py b/roi.py index ce25df0..d3c3451 100644 --- a/roi.py +++ b/roi.py @@ -144,6 +144,20 @@ def from_frame(frame, side=SIDE.TOP, percent=100): new_roi = Roi([x, y, width, height]) return new_roi + def limit_to_roi(self, other): + a, b = self, other + x1 = max(min(a.x1, a.x2), min(b.x1, b.x2)) + y1 = max(min(a.y1, a.y2), min(b.y1, b.y2)) + x2 = min(max(a.x1, a.x2), max(b.x1, b.x2)) + y2 = min(max(a.y1, a.y2), max(b.y1, b.y2)) + if x1 < x2 and y1 < y2: + width = abs(x2 - x1) + height = abs(y2 - y1) + self.x = x1 + self.y = y1 + self.width = width + self.height = height + def apply_to_frame_as_mask(self, frame): # TODO: if a copy of the frame is needed. mask = np.zeros(frame.shape, dtype='uint8') From 0d09e3e69d498e84c0e15d591c5df67e338c6d00 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Tue, 30 Jul 2019 14:01:45 +0200 Subject: [PATCH 49/54] Added code to draw the coords of the initial point when drawing roi on frame --- roi.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/roi.py b/roi.py index d3c3451..51c2828 100644 --- a/roi.py +++ b/roi.py @@ -174,6 +174,16 @@ def draw_on_frame(self, frame, color = [255, 255, 255], copy = True): else: new_frame = frame cv2.rectangle(new_frame, self.top_left, self.bottom_right, color) + cv2.circle(new_frame, self.top_left,3,color,3,1) + font = cv2.FONT_HERSHEY_SIMPLEX + fontScale = 0.3 + lineType = 1 + cv2.putText(new_frame, str(self.top_left), + self.top_left, + font, + fontScale, + color, + lineType) return new_frame From 3064fb55259f90f19bfec103de844ebbf95c384c Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Tue, 30 Jul 2019 14:02:40 +0200 Subject: [PATCH 50/54] Added code to be able to pause test with the letter p and start again with the same letter. --- test_hand.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/test_hand.py b/test_hand.py index 3904041..7f8b796 100644 --- a/test_hand.py +++ b/test_hand.py @@ -40,18 +40,21 @@ def test_hand_detect(self): hand.initial_roi = Roi.from_frame(frame, SIDE.CENTER, 50) hand.detect_and_track(frame) print("testing file %s"%file) - self.assertEqual((hand._detected > 0), expected_results[file][0]) - self.assertEqual(hand._tracked, expected_results[file][1]) + self.assertEqual(hand.detected , expected_results[file][0]) + self.assertEqual(hand.tracked, expected_results[file][1]) frame = self.draw_in_frame(hand, frame) cv2.imshow("final", frame) - cv2.waitKey(1000) + key = cv2.waitKey(5000) + if key == 112: + while cv2.waitKey(1000) != 112: + pass def draw_in_frame(self, hand, frame): frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB) - frame = hand._initial_roi.draw_on_frame(frame,[0,0,255]) - frame = hand._detection_roi.draw_on_frame(frame, [0, 255, 0]) - frame = hand._tracking_roi.draw_on_frame(frame, [255, 0, 0]) + frame = hand.initial_roi.draw_on_frame(frame,[0,0,255]) + frame = hand.detection_roi.draw_on_frame(frame, [0, 255, 0]) + frame = hand.tracking_roi.draw_on_frame(frame, [255, 0, 0]) # frame = hand._extended_roi.draw_on_frame(frame, [0, 0, 255]) return frame # def test_roi_upscale(self): From e775669c53322321305f85e364912b243ab871a1 Mon Sep 17 00:00:00 2001 From: Esteban Martinena Guerrero Date: Wed, 31 Jul 2019 12:27:25 +0200 Subject: [PATCH 51/54] Beginning the implementation of RGBDFrame. --- rgbdframe.py | 20 ++++++++++++++++++++ test_hand.py | 3 ++- 2 files changed, 22 insertions(+), 1 deletion(-) create mode 100644 rgbdframe.py diff --git a/rgbdframe.py b/rgbdframe.py new file mode 100644 index 0000000..c07372f --- /dev/null +++ b/rgbdframe.py @@ -0,0 +1,20 @@ +import numpy as np + + + +class RGBDFrame(np.ndarray): + def __init__(self, *args, **kwargs): + super(RGBDFrame, self).__init__(*args, **kwargs) + self.__depth = None + + def __new__(cls, a): + obj = np.asarray(a).view(cls) + return obj + + @property + def depth(self): + return self.__depth + + @depth.setter + def depth(self, value): + self.__depth = value \ No newline at end of file diff --git a/test_hand.py b/test_hand.py index 7f8b796..2539b29 100644 --- a/test_hand.py +++ b/test_hand.py @@ -4,6 +4,7 @@ import cv2 from HandDetection.Hand import Hand +from HandDetection.rgbdframe import RGBDFrame from HandDetection.roi import Roi, SIDE @@ -36,7 +37,7 @@ def test_hand_detect(self): full_path = "/home/robolab/robocomp/components/robocomp-robolab/components/handDetection/src/images/depth_images" for file in sorted(os.listdir(full_path)): if file.endswith(".png") and file in expected_results: - frame = cv2.imread(os.path.join(full_path, file),0) + frame = RGBDFrame(cv2.imread(os.path.join(full_path, file),0)) hand.initial_roi = Roi.from_frame(frame, SIDE.CENTER, 50) hand.detect_and_track(frame) print("testing file %s"%file) From 841869b7e3bb115849cdc17735c93f75f25d751e Mon Sep 17 00:00:00 2001 From: orensbruli Date: Thu, 1 Aug 2019 13:47:16 +0200 Subject: [PATCH 52/54] Adding internal documentation, removing deprecated code. Added some comments and TODOs. --- HandDetection.py | 414 +++++++++-------------------------------------- 1 file changed, 80 insertions(+), 334 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index 0f0896d..57dffb8 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -10,10 +10,11 @@ import numpy as np -# Function to find angle between two vectors + def calculate_angle(v1, v2): """ - Calculate an return the angle between two verctors in degrees + Calculate an return the angle between two vectors in degrees + TODO: move to a utils file. :param v1: input vector 1 :param v2: input vector @@ -33,7 +34,7 @@ def calculate_angle(v1, v2): class HandDetector: """ - Class to detect hands on a image + Class to detect hands on a image. It keeps an array with the expected hands. """ def __init__(self, source=0): @@ -63,12 +64,12 @@ def __init__(self, source=0): # self.capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 600) - # TODO: Extend to return mutiple hands? + # TODO: Extend to return multiple hands? # TODO: create a static method on Hand to return all the hands detected on a frame def hand_from_frame(self, frame, roi = None): """ Get a Hand class from a roi in a frame - + TODO: Check if it can be removed. It have been replaced by update_expected_hands :param frame: frame where we want to look for the hand :param roi: roi in frame to look for the hand :return: Hand if found on frame, None if not @@ -86,13 +87,23 @@ def hand_from_frame(self, frame, roi = None): def add_new_expected_hand(self, frame_roi): + """ + Add a new expected hand to the list of the detector. + :param frame_roi: initial roi where the hand is expected to be find and that will limit the area of the frame where it's expected to be found + :return: + """ hand = Hand() hand.initial_roi = frame_roi - # TODO: add only mode is Depth + # TODO: add only if mode is Depth hand.depth_threshold = self.depth_threshold self.hands.append(hand) def update_expected_hands(self, frame): + """ + Update with a frame the list of expected hands. + :param frame: Frame to update the hands. + :return: + """ if len(self.hands) > 0: for existing_hand in self.hands: existing_hand.detect_and_track(frame) @@ -101,12 +112,13 @@ def update_expected_hands(self, frame): # print "removing hand" # self.hands.remove(existing_hand) + # TODO: add a new method to know if we are tracking with overlaping rois. def add_hand2(self, frame, roi = None): """ - - + # TODO: Deprecated. Check if it can be removed. + Add a new hand to the system to be tracked. :param frame: :param roi: :return: @@ -134,72 +146,6 @@ def add_hand2(self, frame, roi = None): # (template_x + template_w, template_y + template_h), [0, 0, 0]) return - def add_hand(self, frame): - # Load the hand template - template_path = os.path.join(os.path.dirname(__file__), './resources/right_hand_mask.png') - hand_template = cv2.imread(template_path) - hand_template_contour, hand_template_roi, hand_template_roi_image = self.calculate_max_contour(hand_template) - - template_x, template_y, template_w, template_h = hand_template_roi - # Get the contour and mask of the frame - frame_contours, frame_mask = self.create_contours_and_mask(frame, hand_template_roi) - masked_frame = frame.copy() - - if len(frame_contours) > 0 and len(frame_mask) > 0: - # Get the maximum area contour - min_area = 100 - hand_contour = None - for i in range(len(frame_contours)): - cnt = frame_contours[i] - area = cv2.contourArea(cnt) - if area > min_area: - min_area = area - hand_contour = frame_contours[i] - - if hand_contour is not None: - # cv2.drawContours(frame, [hand_contour], -1, (0, 255, 255), 2) - detected_hand_bounding_rect = cv2.boundingRect(hand_contour) - detected_hand_x, detected_hand_y, detected_hand_w, detected_hand_h = detected_hand_bounding_rect - frame_mask_roi_image = frame_mask[template_y:template_y + template_h, - template_x:template_x + template_w] - frame_mask_roi_image_contour, _, _ = self.calculate_max_contour(frame_mask_roi_image, to_binary=False) - - if frame_mask_roi_image is not None: - # realtime_handmask = np.zeros((frame_mask_roi_image.shape)) - # cv2.fillPoly(realtime_handmask, pts=[hand_contour], color=(255, 255, 255)) - # cv2.imshow("realtime hand mask", realtime_handmask) - diff = cv2.absdiff(hand_template_roi_image.astype(np.uint8), frame_mask_roi_image.astype(np.uint8)) - if self.debug: - cv2.imshow("DEBUG: HandDetection_lib: diff", diff) - result = cv2.matchShapes(frame_mask_roi_image_contour, hand_template_contour, 1, 0) - print result - if frame_mask_roi_image_contour is not None: - aux_detected_cnt = frame_mask_roi_image_contour - # Put in the original X and Y - aux_detected_cnt[:, :, 0] += hand_template_roi[0] - aux_detected_cnt[:, :, 1] += hand_template_roi[1] - cv2.drawContours(masked_frame, [aux_detected_cnt], -1, (0, 255, 0), 2) - if result < 0.05: - print "Match!!! " + str(result) - hand = Hand() - hand._id = len(self.hands) - hand._contour = hand_contour - hand._detection_roi = detected_hand_bounding_rect - self.hands.append(hand) - cv2.putText(masked_frame, "HAND FOUND", - (template_x, template_y + template_h + 10), - self.font, 1, [255, 255, 255], 2) - if len(self.hands) < 1: - cv2.putText(masked_frame, "CENTER YOUR HAND", - (template_x, template_y + template_h + 10), - self.font, 1, [255, 255, 255], 2) - else: - cv2.putText(masked_frame, "PLEASE PUT YOUR HAND HERE", (template_x - 100, template_y + template_h + 10), - self.font, 1, [255, 255, 255], 2) - - masked_frame = cv2.addWeighted(masked_frame, 0.7, hand_template, 0.3, 0) - # cv2.imshow('masked_frame', masked_frame) - return masked_frame def calculate_bounding_rects_intersection(self, bounding_rect_1, bounding_rect_2): b1_x_left = bounding_rect_1[0] @@ -244,52 +190,12 @@ def set_depth_threshold(self, threshold): #TODO: Set to all available hands - def capture_and_compute(self): - while self.capture.isOpened(): - - # Measure execution time - # start_time = time.time() - - # Capture frames from the camera - ret, frame = self.capture.read() - - if ret is True: - self.update_detection(frame) - - self.update_tracking(frame) - - for index, hand in enumerate(self.hands): - if hand.detected is False: - hand.detection_fail += 1 - if hand.tracked: - hand.bounding_rect = hand.tracking_window - else: - print "_____________No updated information" - hand.update_truth_value_by_frame2() - if hand.confidence <= 0: - print "removing hand" - self.hands.remove(hand) - - overlayed_frame = frame.copy() - for hand in self.hands: - overlayed_frame = self.draw_hand_overlay(frame, hand) - pass - - ##### Show final image ######## - if self.debug: - cv2.imshow('DEBUG: HandDetection_lib: Detection', overlayed_frame) - ############################### - # Print execution time - # print time.time()-start_time - else: - print "No video detected" - - # close the output video by pressing 'ESC' - k = cv2.waitKey(50) & 0xFF - if k == 27: - break - def capture_and_compute2(self): + """ + Method to run the lib independently capturing frames from camera an computing. + # TODO: Need to be refactored. Most of the current functionality have been moved to the Hand class... + :return: + """ if self.capture.isOpened(): while self.capture.isOpened(): @@ -305,7 +211,7 @@ def capture_and_compute2(self): self.update_detection_and_tracking(frame) - overlayed_frame = self.compute_overlayed_frame(frame) + overlayed_frame = self.draw_hands_on_frame(frame) ##### Show final image ######## if self.debug: @@ -326,195 +232,26 @@ def capture_and_compute2(self): else: print("Capture device is not opened.") - def compute_overlayed_frame(self, frame): + + def draw_hands_on_frame(self, frame): + """ + Draw the detected / tracked hands on the frame + :param frame: frame to be overlayed with hands + :return: overlayed frame + """ overlayed_frame = frame.copy() for hand in self.hands: overlayed_frame = self.draw_hand_overlay(frame, hand) return overlayed_frame - - def update_hand_with_contour(self, frame, hand_contour, hand_to_update): - hand = copy.deepcopy(hand_to_update) - hand.contour = hand_contour - hand.bounding_rect = cv2.boundingRect(hand_contour) - hull2 = cv2.convexHull(hand_contour, returnPoints=False) - defects = cv2.convexityDefects(hand_contour, hull2) - - # Get defect points and draw them in the original image - if defects is not None: - fingertips_coords, \ - fingertips_indexes, \ - intertips_coords, \ - intertips_indexes = self.get_hand_fingertips(hand_contour, defects) - - is_hand = self.is_hand(fingertips_coords, intertips_coords, strict=False) - if is_hand: - hand.fingertips = fingertips_coords - hand.intertips = intertips_coords - - if len(fingertips_coords) == 5: - fingers_contour = np.take(hand_contour, - fingertips_indexes + intertips_indexes, - axis=0, - mode="wrap") - hand.bounding_rect, hand_circle, hand.contour = self.get_hand_bounding_rect_from_fingers( - hand_contour, - fingers_contour) - - # Find moments of the largest contour - moments = cv2.moments(hand_contour) - center_of_mass = None - finger_distances = [] - average_defect_distance = None - # Central mass of first order moments - if moments['m00'] != 0: - cx = int(moments['m10'] / moments['m00']) # cx = M10/M00 - cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 - center_of_mass = (cx, cy) - hand.center_of_mass = center_of_mass - hand.position_history.append(center_of_mass) - - if center_of_mass is not None and len(intertips_coords) > 0: - # Distance from each finger defect(finger webbing) to the center mass - distance_between_defects_to_center = [] - for far in intertips_coords: - x = np.array(far) - center_mass_array = np.array(center_of_mass) - distance = np.sqrt( - np.power(x[0] - center_mass_array[0], - 2) + np.power(x[1] - center_mass_array[1], 2) - ) - distance_between_defects_to_center.append(distance) - - # Get an average of three shortest distances from finger webbing to center mass - sorted_defects_distances = sorted(distance_between_defects_to_center) - average_defect_distance = np.mean(sorted_defects_distances[0:2]) - hand.average_defect_distance = average_defect_distance - # # Get fingertip points from contour hull - # # If points are in proximity of 80 pixels, consider as a single point in the group - # finger = [] - # for i in range(0, len(hull) - 1): - # if (np.absolute(hull[i][0][0] - hull[i + 1][0][0]) > 10) or ( - # np.absolute(hull[i][0][1] - hull[i + 1][0][1]) > 10): - # if hull[i][0][1] < 500: - # finger.append(hull[i][0]) - # - # - # # The fingertip points are 5 hull points with largest y coordinates - # finger = sorted(finger, key=lambda x: x[1]) - # fingers = finger[0:5] - if center_of_mass is not None and len(fingertips_coords) > 0: - # Calculate distance of each finger tip to the center mass - finger_distances = [] - for i in range(0, len(fingertips_coords)): - distance = np.sqrt( - np.power(fingertips_coords[i][0] - center_of_mass[0], 2) + np.power( - fingertips_coords[i][1] - center_of_mass[0], 2)) - finger_distances.append(distance) - hand.finger_distances = finger_distances - return hand - - # - # def contour_to_hand(self, frame, hand_contour, hand_to_update=None): - # initial = (hand_to_update == None) - # if not initial: - # hand = copy.deepcopy(hand_to_update) - # updated_hand.contour = hand_contour - # hand_bounding_rect = cv2.boundingRect(hand_contour) - # else: - # updated_hand = Hand() - # - # hull2 = cv2.convexHull(hand_contour, returnPoints=False) - # defects = cv2.convexityDefects(hand_contour, hull2) - # - # # Get defect points and draw them in the original image - # if defects is not None: - # fingertips_coords, \ - # fingertips_indexes, \ - # intertips_coords, \ - # intertips_indexes = self.get_hand_fingertips(hand_contour, defects) - # - # is_hand = self.is_hand(fingertips_coords, intertips_coords, strict=initial) - # if is_hand: - # updated_hand.fingertips = fingertips_coords - # updated_hand.intertips = intertips_coords - # - # if len(fingertips_coords) == 5: - # fingers_contour = np.take(hand_contour, - # fingertips_indexes + intertips_indexes, - # axis=0, - # mode="wrap") - # hand_bounding_rect, hand_circle, hand_contour = self.get_hand_bounding_rect_from_fingers(hand_contour, - # fingers_contour) - # # elif hand_to_update is not None and hand_to_update.center_of_mass is not None and len(hand_to_update.finger_distances)>0: - # # hand_bounding_rect, hand_circle, hand_contour = \ - # # self.get_hand_bounding_rect_from_center_of_mass( - # # hand_contour, - # # hand_to_update.center_of_mass, - # # max(hand_to_update.finger_distances)) - # - # if not initial or is_hand: - # updated_hand.contour = hand_contour - # updated_hand.bounding_rect = hand_bounding_rect - # # Find moments of the largest contour - # moments = cv2.moments(hand_contour) - # center_of_mass = None - # finger_distances = [] - # average_defect_distance = None - # # Central mass of first order moments - # if moments['m00'] != 0: - # cx = int(moments['m10'] / moments['m00']) # cx = M10/M00 - # cy = int(moments['m01'] / moments['m00']) # cy = M01/M00 - # center_of_mass = (cx, cy) - # updated_hand.center_of_mass = center_of_mass - # updated_hand.position_history.append(center_of_mass) - # - # if center_of_mass is not None and len(intertips_coords) > 0: - # # Distance from each finger defect(finger webbing) to the center mass - # distance_between_defects_to_center = [] - # for far in intertips_coords: - # x = np.array(far) - # center_mass_array = np.array(center_of_mass) - # distance = np.sqrt( - # np.power(x[0] - center_mass_array[0], - # 2) + np.power(x[1] - center_mass_array[1], 2) - # ) - # distance_between_defects_to_center.append(distance) - # - # # Get an average of three shortest distances from finger webbing to center mass - # sorted_defects_distances = sorted(distance_between_defects_to_center) - # average_defect_distance = np.mean(sorted_defects_distances[0:2]) - # updated_hand.average_defect_distance = average_defect_distance - # # # Get fingertip points from contour hull - # # # If points are in proximity of 80 pixels, consider as a single point in the group - # # finger = [] - # # for i in range(0, len(hull) - 1): - # # if (np.absolute(hull[i][0][0] - hull[i + 1][0][0]) > 10) or ( - # # np.absolute(hull[i][0][1] - hull[i + 1][0][1]) > 10): - # # if hull[i][0][1] < 500: - # # finger.append(hull[i][0]) - # # - # # - # # # The fingertip points are 5 hull points with largest y coordinates - # # finger = sorted(finger, key=lambda x: x[1]) - # # fingers = finger[0:5] - # if center_of_mass is not None and len(fingertips_coords) > 0: - # # Calculate distance of each finger tip to the center mass - # finger_distances = [] - # for i in range(0, len(fingertips_coords)): - # distance = np.sqrt( - # np.power(fingertips_coords[i][0] - center_of_mass[0], 2) + np.power( - # fingertips_coords[i][1] - center_of_mass[0], 2)) - # finger_distances.append(distance) - # updated_hand.finger_distances = finger_distances - # else: - # return None - # elif initial: - # return None - # return updated_hand - def draw_hand_overlay(self, frame, hand): + """ + TODO: Move to hand class. It could have an static + :param frame: frame to be overlayed with hand information + :param hand: hand to be drawn on the frame. + :return: + """ if hand.detected: for finger_number, fingertip in enumerate(hand.fingertips): cv2.circle(frame, tuple(fingertip), 10, [255, 100, 255], 3) @@ -555,6 +292,14 @@ def draw_hand_overlay(self, frame, hand): return frame def draw_contour_features(self, to_show, hand_contour): + """ + Draw features of the hand on the frame + TODO: Move to to Hand class. + TODO: Refactor to get the info from the Hand class and not calculate it again + :param to_show: frame to be overlayed with hand information + :param hand_contour: hand contour to get the features from it + :return: TODO: return frame with overlayed information + """ perimeter = cv2.arcLength(hand_contour, True) # print perimeter hull = cv2.convexHull(hand_contour, returnPoints=False) @@ -594,12 +339,19 @@ def draw_contour_features(self, to_show, hand_contour): cv2.imshow("Hand with contour", to_show) def exit(self): + """ + Method for the self execution of the lib to end the opencv windows. + :return: + """ self.capture.release() cv2.destroyAllWindows() - - def get_MOG2_mask(self, image): + """ + TODO: Move to Hand class + :param image: + :return: + """ # TODO: not working (it considers everything shadows or moves (too dark background) fgbg = cv2.createBackgroundSubtractorMOG2(detectShadows=False) # fgbg= cv2.BackgroundSubtractorMOG2(0, 50) @@ -614,7 +366,13 @@ def get_MOG2_mask(self, image): mask = fgbg.apply(blurred) return mask + def get_movement_buffer_mask(self, image): + """ + TODO: Move to hand Class + :param image: + :return: + """ mask = None self.last_frames.append(image) mean_image = np.zeros(image.shape, dtype=np.float32) @@ -635,6 +393,11 @@ def get_movement_buffer_mask(self, image): return mask def get_simple_diff_mask(self, image): + """ + TODO: Move to Hand class + :param image: + :return: + """ mask = None if len(self.last_frames) < self.discarded_frames: self.last_frames.append(image) @@ -656,6 +419,11 @@ def get_simple_diff_mask(self, image): return mask def get_simple_diff_mask2(self, image): + """ + TODO: Move to Hand class + :param image: + :return: + """ mask = None # TODO: ENV_DEPENDENCE: it could depend on the lighting and environment blur_radius = 5 @@ -700,38 +468,16 @@ def get_simple_diff_mask2(self, image): self.last_frames.append(image) return mask - # - # # scale_up = 20 - # # current_hand_roi_mask[max(y - scale_up, 0):min(y + h + scale_up, frame.shape[0]), - # # max(x - scale_up, 0):min(x + w + scale_up, frame.shape[1])] = 255 - - # def calculate_hand_interst_points(self, frame, cnts): - # # convexity defect - # - # drawing = copy.deepcopy(frame) - # hull = cv2.convexHull(cnts, returnPoints=False) - # if len(hull) > 3: - # defects = cv2.convexityDefects(cnts, hull) - # if type(defects) != type(None): # avoid crashing. (BUG not found) - # - # cnt = 0 - # for i in range(defects.shape[0]): # calculate the angle - # s, e, f, d = defects[i][0] - # start = tuple(cnts[s][0]) - # end = tuple(cnts[e][0]) - # far = tuple(cnts[f][0]) - # a = math.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2) - # b = math.sqrt((far[0] - start[0]) ** 2 + (far[1] - start[1]) ** 2) - # c = math.sqrt((end[0] - far[0]) ** 2 + (end[1] - far[1]) ** 2) - # angle = math.acos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c)) # cosine theorem - # if angle <= math.pi / 2: # angle less than 90 degree, treat as fingers - # cnt += 1 - # cv2.circle(drawing, far, 8, [211, 84, 0], -1) - # interdigs.append(far) - # cv2.imshow("detect_fingers", drawing) - # return interdigs def detect_hands_in_frame(self, frame, roi=None): + """ + Detect multiple hands on a frame and return an array with these. + + TODO: move to hand class as an staticmethod + :param frame: frame to be processed to find the hands on it. + :param roi: region to look for the hands. + :return: list of hands found as Class + """ new_hands = [] contours, _ = self.create_contours_and_mask(frame, roi) From 9a26fdb8e22f0d3b7ea3aa54ad68a32dd3296627 Mon Sep 17 00:00:00 2001 From: orensbruli Date: Mon, 5 Aug 2019 13:07:02 +0200 Subject: [PATCH 53/54] Finishing to add documentation to existing methods. --- HandDetection.py | 111 ++++++++++++++--------------------------------- 1 file changed, 33 insertions(+), 78 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index 57dffb8..8e81e21 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -296,6 +296,7 @@ def draw_contour_features(self, to_show, hand_contour): Draw features of the hand on the frame TODO: Move to to Hand class. TODO: Refactor to get the info from the Hand class and not calculate it again + :param to_show: frame to be overlayed with hand information :param hand_contour: hand contour to get the features from it :return: TODO: return frame with overlayed information @@ -369,9 +370,10 @@ def get_MOG2_mask(self, image): def get_movement_buffer_mask(self, image): """ + Calculate a mask for the image based on the movement respect the previous frames. TODO: Move to hand Class - :param image: - :return: + :param image: image to get the mask from. + :return: mask for the frame mased on the movement of this image related with previous ones. """ mask = None self.last_frames.append(image) @@ -394,9 +396,10 @@ def get_movement_buffer_mask(self, image): def get_simple_diff_mask(self, image): """ + Calculate a mask for the image based on the difference with previous ones. TODO: Move to Hand class - :param image: - :return: + :param image: image to get the mask + :return: mask based on the difference with previous frames """ mask = None if len(self.last_frames) < self.discarded_frames: @@ -508,11 +511,26 @@ def detect_hands_in_frame(self, frame, roi=None): def reset_background(self): - # TODO test it better + """ + Method usefull only for the get_simple_diff_mask2. + TODO: If it's going to be used probably would be moved to a buffer class with the related methods for the background. + TODO: test it better + :return: None + """ self.last_frames = deque(maxlen=self.discarded_frames) self.first_frame = None def update_detection(self, frame): + """ + Method to update the detection of the existing hands. + This method look for all new hands detected on the frame. Then it looks for the intersection with the existing ones. + + TODO: Deprecated. Check if it would be usefull to adapt to the new way of detecting tracking hands. + + + :param frame: Image used to look for hands. + :return: None + """ for hand in self.hands: hand.detected = False detected_hands = self.detect_hands_in_frame(frame) @@ -538,10 +556,19 @@ def update_detection(self, frame): detected_hand._detected = True self.next_hand_id += 1 self.hands.append(detected_hand) - print "New hand" + print("New hand") def detect_fist(self, frame, roi): + """ + Method to detect fist. + TODO: Deprecated. Move to Hand class and review if it's working. + TODO: There's too much code similar to detect hand. It' would probably be unified or common code extracted. + + :param frame: Image to be processed looking for a fist. + :param roi: Roi of the image to be used to look for the fist. + :return: None + """ contours, _ = self.create_contours_and_mask(frame, roi) hand_contour = None if len(contours) > 0: @@ -612,78 +639,6 @@ def detect_fist(self, frame, roi): else: return None, None - def update_detection2(self, frame): - for existing_hand in self.hands: - existing_hand.detected = False - # new_bounding_rect = upscale_bounding_rec(existing_hand.bounding_rect,frame.shape, 100) - updated_hand = self.update_hand_attributes(frame, existing_hand) - if updated_hand is not None: - existing_hand.update_attributes_from_detected(updated_hand) - - def update_hand_attributes(self, frame, hand, strict=True): - updated_hand = copy.deepcopy(hand) - # if hand.detected or (not hand.detected and not hand.tracked): - # extended_bounding_rect = hand.bounding_rect - # else : - # extended_bounding_rect = upscale_bounding_rect(hand.tracking_window, frame.shape, 20) - # if hand.bounding_rect[2] > extended_bounding_rect[2] and hand.bounding_rect[3] > extended_bounding_rect[3]: - # extended_bounding_rect = (extended_bounding_rect[0], extended_bounding_rect[1],hand.bounding_rect[2], hand.bounding_rect[3]) - # if hand.detected or (not hand.detected and not hand.tracked): - contours, _ = self.create_contours_and_mask(frame, hand.bounding_rect) - # else: - # # upscaled_tracking_window = upscale_bounding_rect(hand.bounding_rect, frame.shape, 100) - # new_tracking_window = (hand.tracking_window[0], hand.tracking_window[1], hand.bounding_rect[2], hand.bounding_rect[3]) - # contours, _ = self.create_contours_and_mask(frame, new_tracking_window) - - if len(contours) > 0: - # Find Max contour area (Assume that hand is in the frame) - # TODO: ENV_DEPENDENCE: depends on the camera resolution, distance to the background, noisy areas sizes - max_area = 0 - hand_contour = None - for contour_index in range(len(contours)): - cnt = contours[contour_index] - area = cv2.contourArea(cnt) - if area >= max_area: - max_area = area - # Largest area contour - hand_contour = contours[contour_index] - - # Find convex hull - # hull = cv2.convexHull(hand_contour) - # for point in hull: - # cv2.circle(frame, tuple(point[0]), 8, [255, 100, 10], -1) - - # Find convex defects - if hand_contour is not None: - if self.debug: - to_show = frame.copy() - cv2.drawContours(to_show, contours, -1, 255) - cv2.imshow("DEBUG: HandDetection_lib: update_hand_charasteristics (New Contour)", to_show) - updated_hand = self.update_hand_with_contour(frame, hand_contour, updated_hand) - else: - return None - else: - return None - return updated_hand - - def update_tracking(self, frame): - if len(self.hands) > 0: - for index, hand in enumerate(self.hands): - hand.tracked = False - hands_mask = self.create_hands_mask(frame) - ret, tracking_window = self.follow(frame, hands_mask, hand.bounding_rect) - if ret and tracking_window is not None: - updated_hand = self.update_hand_attributes(frame, hand) - if updated_hand is not None: - updated_hand.detected = hand.detected - updated_hand.tracking_window = tracking_window - updated_hand.detection_fails = hand.detection_fail - updated_hand.tracked = True - self.hands[index] = updated_hand - else: - hand.tracked = False - else: - hand.tracked = False def main(): From 339dee0ccd4eeb966ade7a8d4fb3eb7f1dbded07 Mon Sep 17 00:00:00 2001 From: orensbruli Date: Mon, 5 Aug 2019 13:49:22 +0200 Subject: [PATCH 54/54] Adding internal documentation to the Roi class. --- HandDetection.py | 2 +- roi.py | 173 ++++++++++++++++++++++++++++++++++++++++------- 2 files changed, 150 insertions(+), 25 deletions(-) diff --git a/HandDetection.py b/HandDetection.py index 8e81e21..7a15ecc 100644 --- a/HandDetection.py +++ b/HandDetection.py @@ -296,7 +296,7 @@ def draw_contour_features(self, to_show, hand_contour): Draw features of the hand on the frame TODO: Move to to Hand class. TODO: Refactor to get the info from the Hand class and not calculate it again - + :param to_show: frame to be overlayed with hand information :param hand_contour: hand contour to get the features from it :return: TODO: return frame with overlayed information diff --git a/roi.py b/roi.py index 51c2828..f698d47 100644 --- a/roi.py +++ b/roi.py @@ -1,9 +1,13 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- import numpy as np - import cv2 class SIDE: + """ + Class containing the possible options of sides of a roi to be selected as relative areas of a frame. + """ RIGHT = 0 LEFT = 1 TOP = 2 @@ -11,7 +15,18 @@ class SIDE: CENTER = 4 class Roi(list): + """ + Class containing the attributes and methods needed to keep the information of a ROI + The Roi is expected to be created based on the initial point (top right) and the width and height. + This class can create Roi with a size related to a frame. + Multiple methods are provided to do operations with the ROI like upscaling, intersect or draw on a frame. + Also convenience properties are implemented to return points of the ROI. + """ def __init__(self, in_list=None): + """ + Roi class is based on a list of 4 int or floats. It can be initialized with the initial point and width and height. + :param in_list: initialization values. Tuple or List is expected to be received with (x, y, width, height) + """ if in_list is not None: assert isinstance(in_list, (list, tuple)), "in_list must be of the python list tupe. in_list is %s type" % type(in_list) assert len(in_list) == 4, "in_list must 4 len. in_list is %d len" % len(in_list) @@ -57,66 +72,164 @@ def height(self, height): @property def x1(self): + """ + Convenience property to return the initial point x coord. + :return: initial point x coord + """ return self.x @x1.setter def x1(self, value): + """ + Convenience property to set the initial point x coord. + :param value: value init x coord + :return: None + """ self.x = value @property def x2(self): + """ + Convenience property to return the end point x coord. + :return: end point x coord + """ return self.x+self.width @x2.setter def x2(self, value): + """ + Convenience property to set the end point x coord. + :param value: valueend x coord + :return: + """ width = value-self.x assert width > 0, "Not a valid x2 value %d. It results in a width value of %d" % (value, width) self.width = width @property def y1(self): + """ + Convenience property to return the initial point y coord. + :return: initial point y coord + """ return self.y @y1.setter def y1(self, value): + """ + Convenience property to set the initial point y coord. + :param value: value init y coord + :return: None + """ self.y = value @property def y2(self): + """ + Convenience property to return the end point y coord. + :return: end point y coord + """ return self.y +self.height @y2.setter def y2(self, value): + """ + Convenience property to set the end point y coord. + :param value: valueend y coord + :return: + """ height = value - self.y assert height > 0, "Not a valid y2 value %d. It results in a height value of %d" % (value, height) self.height = height @property def p1(self): + """ + Convenience property to return the initial point + :return: initial point + """ return (self.x, self.y) @p1.setter def p1(self, value): + """ + Convenience setter to set the initial point of the Roi + :param value: List or tupple with the coords of the initial point + :return: None + """ assert isinstance(value, (tuple, list)), "p1 need to be a tuple or list. type is %s" % (type(value)) assert len(value)==2, "p1 need to have a len of 2. %r have a len of %d" % (value, len(value)) self.x, self.y = value @property def p2(self): + """ + Convenience property to return the end point + :return: end point + """ return (self.x2, self.y2) @p2.setter def p2(self, value): + """ + Convenience setter to set the end point of the Roi + :param value: List or tupple with the coords of the end point + :return: None + """ assert isinstance(value, (tuple, list)), "p1 need to be a tuple or list. type is %s" % (type(value)) assert len(value) == 2, "p1 need to have a len of 2. %r have a len of %d" % (value, len(value)) self.x2, self.y2 = value @property def init_coords(self): + """ + Convenience property to get initial point. + :return: initial point + """ return self.p1 + @property + def top_left(self): + """ + Convenience property to return the top left point of the ROI + :return: top left point of the ROI + """ + return (self.x, self.y) + + @property + def top_right(self): + """ + Convenience property to return the top right point of the ROI + :return: top right point of the ROI + """ + return (self.x, self.x + self.width) + + @property + def bottom_left(self): + """ + Convenience property to return the bottom left point of the ROI + :return: bottom left point of the ROI + """ + return (self.x, self.y + self.height) + + @property + def bottom_right(self): + """ + Convenience property to return the bottom right point of the ROI + :return: bottom right point of the ROI + """ + return (self.x + self.width, self.y + self.height) + @staticmethod def from_frame(frame, side=SIDE.TOP, percent=100): + """ + Calculate a Roi based on a percentage of a source frame. + The user can set the side of the frame where the ROI is set. + :param frame: source frame to get the size of the ROI. Opencv frame is expected. + :param side: Predefined Side form the Side class specifying the side of the frame. + :param percent: Percentage of the frame that the ROI will cover. + :return: ROI based on the size of the frame + """ assert isinstance(side, int) and 0 <= side < 5, "Side must be a value between 0 and 3 but %d given. Use the SIDE class to get valid values." % side assert isinstance(percent, (int, float)) , "Percent must be must be Integer or Float but %r given" % percent assert (0 < percent <= 100), "Percent must be a value between 0 and 100 but %d given" % percent @@ -145,6 +258,11 @@ def from_frame(frame, side=SIDE.TOP, percent=100): return new_roi def limit_to_roi(self, other): + """ + Method to limit the current ROI to another given + :param other: ROI setting the limits for the current one. + :return: None + """ a, b = self, other x1 = max(min(a.x1, a.x2), min(b.x1, b.x2)) y1 = max(min(a.y1, a.y2), min(b.y1, b.y2)) @@ -159,16 +277,33 @@ def limit_to_roi(self, other): self.height = height def apply_to_frame_as_mask(self, frame): - # TODO: if a copy of the frame is needed. + """ + + :param frame: Apply the current ROI to the frame as a mask setting to black all the information outside the ROI. + :return: + """ + # TODO: check if a copy of the frame is needed. mask = np.zeros(frame.shape, dtype='uint8') mask[self.y:self.y + self.height, self.x:self.x + self.width] = 255 roied_frame = cv2.bitwise_and(frame, mask) return roied_frame def extract_from_frame(self, frame): + """ + Extract form the imput frame the image correspoding with the current ROI + :param frame: source frame to extract the ROI + :return: Part of the frame containded on the current ROI + """ return frame[self.y:self.y + self.height, self.x:self.x + self.width] def draw_on_frame(self, frame, color = [255, 255, 255], copy = True): + """ + Draw a retangle on the input frame with the color specified and anotate the initial coords of the frame. + :param frame: frame to be overlayed by the drawn ROI + :param color: Color to be set for the drawn ROI rectangle + :param copy: Determine if a copy of the frame is needed or not. + :return: frame with the ROI drawn on it. + """ if copy: new_frame = frame.copy() else: @@ -186,31 +321,21 @@ def draw_on_frame(self, frame, color = [255, 255, 255], copy = True): lineType) return new_frame + def intersection_rate(self, roi2): + """ + Return a float value representing the intersaction rate for this ROI with roi2 - @property - def top_left(self): - return (self.x, self.y) - - @property - def top_right(self): - return (self.x, self.x+ self.width) - - @property - def bottom_left(self): - return (self.x, self.y + self.height) - - @property - def bottom_right(self): - return (self.x + self.width, self.y + self.height) - - def intersection_rate(self, s2): + TODO: Check the value returned for a full inetersection and none + :param roi2: input roi to calculate the intersection ROI + :return: intersection rate float value + """ x1, y1 = self.top_left x2, y2 = self.bottom_right s_1 = np.array([[x1, y1], [x2, y1], [x2, y2], [x1, y2]]) - x1, y1 = s2.top_left - x2, y2 = s2.bottom_right + x1, y1 = roi2.top_left + x2, y2 = roi2.bottom_right s_2 = np.array([[x1, y1], [x2, y1], [x2, y2], [x1, y2]]) area, _intersection = cv2.intersectConvexConvex(s_1, s_2) @@ -220,11 +345,11 @@ def intersection_rate(self, s2): def upscaled(self, limiting_roi, upscaled_pixels): """ - Create an upscaled version of an input ROI restricted to the size of a frame + Create an upscaled version of an input ROI restricted to the size of limiting roi - :param limiting_roi: frame that limit the possible upscale of the bounding rect + :param limiting_roi: Roi that limit the possible upscale of the bounding rect :param upscaled_pixels: Number of pixels to add to both the height and the with from the center - :return: + :return: newroi upscaled """ x, y, w, h = self