Real-Time Prediction - Mask Detection

detect.py

# Real Time Prediction -> Video Capture
def real_time_prediction():
    category_index = label_map_util.create_category_index_from_labelmap(
        ANNOTATION_PATH+'/label_map.pbtxt')
    cap = cv2.VideoCapture(0)
    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

    # Make detection
    while True:
        ret, frame = cap.read()
        image_np = np.array(frame)

        input_tensor = tf.convert_to_tensor(
            np.expand_dims(image_np, 0), dtype=tf.float32)
        detections = detect_fn(input_tensor)

        num_detections = int(detections.pop('num_detections'))
        detections = {key: value[0, :num_detections].numpy()
                      for key, value in detections.items()}
        detections['num_detections'] = num_detections
        detections['detection_classes'] = detections['detection_classes'].astype(
            np.int64)

        label_id_offset = 1
        image_np_with_detections = image_np.copy()

        viz_utils.visualize_boxes_and_labels_on_image_array(
            image_np_with_detections,
            detections['detection_boxes'],
            detections['detection_classes']+label_id_offset,
            detections['detection_scores'],
            category_index,
            use_normalized_coordinates=True,
            max_boxes_to_draw=1,
            min_score_thresh=.001,
            agnostic_mode=False)

        cv2.imshow('object detection',  cv2.resize(
            image_np_with_detections, (640, 480)))

        if cv2.waitKey(1) & 0xFF == ord('q'):
            cap.release()
            cv2.destroyAllWindows()
            break