DigitClassfication/test_model.py at master · sterben04/DigitClassfication · GitHub

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import numpy as np
import cv2
import tensorflow as tf
############
width = 640
height = 480
threshold = 0.65 # MINIMUM PROBABILITY TO CLASSIFY

###########
export_path = 'model'
model = tf.keras.models.load_model(export_path)

cap = cv2.VideoCapture(0)
cap.set(3,width)
cap.set(4,height)


def preProcessing(img):
    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    img = cv2.equalizeHist(img)
    img = img / 255
    return img


while True:
    success, imgOriginal = cap.read()
    # print(success)
    img = np.asarray(imgOriginal)
    img = cv2.resize(img,(32,32))
    img = preProcessing(img)
    cv2.imshow("pro",img)
    img = img.reshape(1,32,32,1)

    classIndex = int(model.predict_classes(img))
    # print(classIndex)
    predictions = model.predict(img)
    # print(predictions)
    probVal = np.amax(predictions)
    print(classIndex, probVal)

    if probVal > threshold:
        cv2.putText(imgOriginal, str(classIndex) + "   " + str(probVal),
                    (50, 50), cv2.FONT_HERSHEY_COMPLEX,
                    1, (0, 0, 255), 1)
    cv2.imshow("Original Image",imgOriginal)

    if cv2.waitKey(1) & 0xff == ord('q'):
        break