Unexpected results when passing webcam frames to object detection

Background

I have a well-trained ssd320x320 tensorflow model from tensorflow-zoo. The reports are pretty good as the train log indicates a low loss and the eval log indicates that 7 out of 9 test images were detected successfully. The model was trained with GPU and was saved as ckpt3.

The goal is to detect when a person "likes" with their hand.

Problem

Loading a model from its last checkpoint works well, and I achieved detection with the following function:

    def test1(self):
        # Works great
        for img_path in glob.glob("test_dir\*.jpg"):
            plt.figure()
            plt.imshow(self.get_image_np_with_detections(self._load_image_into_numpy_array(img_path)))
            plt.show()

# Note that get get_image_np_with_detections() is the detection @tf.function() 
# as it is written in tensorflow documentation, with no changes.
# _load_image() function simply returns np.array(Image.open(path))

Object detection in image was successfuly achieved in test1.
Problem is that I failed to detect an object in webcam frames.

From another function, which opens my webcam, I call the same detection function for each frame. This function is failing, as not even one green detection box appears on the screen.

    def open_webcam(self):
        # Doesn't show detection green boxes at all
        cap = cv2.VideoCapture(0)
        while cap.isOpened():
            ret, image_np = cap.read()
            im_detected = self.get_image_np_with_detections(image_np)
            cv2.imshow('object detection', cv2.resize(im_detected, (800, 600)))
        # release, destroy...

Where is the problem

During my debug, I have saved screenshots from my webcam, while running the open_webcam() function (took a screenshot every 1-2 seconds). The screenshots were saved into test_dir, and then were processed to test1. The test was successful as all screenshots were marked with a green detection box (hand-like-sign).

This test indicates that the problem regards the way I pass frames to the function, as all the frames were successfully detected in the test1 approach, but not in real-time. To summarize:

• I failed to detect a like-sign in a webcam frame (real-time).
• I saved the frame inside test_dir, with a unique-id.
• I managed to detect a like-sign after opening the jpg, in test1() (9/10 screenshots).

I have tried to…

• pass frames as numpy array with no luck.
• Expand the dimensions as mentioned in tf-documentation, again, with no luck.

Note that…

• I have only 1 label, which is Like (hand-sign).
• I used around 25 train images, and 9 test images.
• As mentioned, the model works great when opening saved jpg files. Eval report looks good.
• PY is 3.7, TF is 2.7, CV is 4.5.5.

Thanks in advance!

>Solution :

TensorFlow model is most likely to be trained on RGB images, while cv2 works with BGR. Try

image_np  = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)

Also, model may be trained on normalized images, so, if changing BGR to RGB doesn’t help, try

image_np = image_np / 255.