Deploy.py

# import the necessary packages
from keras.preprocessing.image import img_to_array
from keras.models import load_model
import numpy as np
import cv2, time, sys, imutils, argparse
import RPi.GPIO as GPIO
from picamera.array import PiRGBArray
from picamera import PiCamera
import motor_control as mc


GPIO.setmode(GPIO.BCM)
#choose the GPIO pins for mptor control
fwd1 = 23  # pin 16
bwd1 = 24 #pin 18
fwd2 = 16 # pin 36
bwd2 = 20 # pin 38
# declare selected pin as output pin
GPIO.setup(fwd1, GPIO.OUT)
GPIO.setup(bwd1, GPIO.OUT)
GPIO.setup(fwd2, GPIO.OUT)
GPIO.setup(bwd2, GPIO.OUT)



#function to control direction of robot based on prediction from CNN
def control_robot(image):
  prediction = np.argmax(model.predict(image))
  if prediction == 0:
    print("forward")
    mc.forward()
  elif prediction == 2:
    print("left")
    mc.left()
  else:
    print("right")
    mc.right()
    
    
model = load_model("model")
if __name__ == "__main__":
  try:
    mc.stop()
    # initialize the camera and grab a reference to the raw camera capture
    camera = PiCamera()
    camera.resolution = (640, 480)
    camera.framerate = 32
    rawCapture = PiRGBArray(camera, size=(640, 480))
    # allow the camera to warmup
    time.sleep(0.1)
    # capture frames from the camera
    for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
     # grab the raw NumPy array representing the image, then initialize the timestamp
     # and occupied/unoccupied text
     image = frame.array
     # show the frame
     key = cv2.waitKey(1) & 0xFF
     image = cv2.resize(image, (28, 28))
     image = img_to_array(image)
     image = np.array(image, dtype="float") / 255.0
     image = image.reshape(-1, 28, 28, 3)
     #cv2.imshow("Frame", image[0])
     control_robot(image)
     # clear the stream in preparation for the next frame
     rawCapture.truncate(0)
  except KeyboardInterrupt:
    mc.stop()
    GPIO.cleanup()
    sys.exit()