How can I use keras to predict on a pretrained model from the VideoCapture of OpenCV?

My code is

model = ResNet50(weights='imagenet')

def read_cam(video_capture):
    if video_capture.isOpened():
        windowName = "yolo"
        cv2.namedWindow(windowName, cv2.WINDOW_NORMAL)
        cv2.resizeWindow(windowName, 1280, 720)
        cv2.moveWindow(windowName, 0, 0)
        cv2.setWindowTitle(windowName, "Yolo Object Detection")
        while True:
            # Check to see if the user closed the window
            if cv2.getWindowProperty(windowName, 0) < 0:
                break

            ret_val, frame = video_capture.read()
            print(frame)
            frame = np.expand_dims(frame, axis=0)
            frame = preprocess_input(frame)

            preds = model.predict(frame)
            # print(preds)
            print('Predicted:', decode_predictions(preds, top=3)[0])

However, this causes a few errors. First, apparently, it's expecting a different sized array:

ValueError: Error when checking input: expected input_1 to have shape (224, 224, 3) but got array with shape (720, 1280, 3)

Solution

Use the cv2. resize function to change the frame before calling model.predict as the pretrained model you are using accepts only image of the size 224x224.

 frame=cv2.resize(frame,(224,224))