zakirangwala

def detect_fn(image):
    detection_model = load_model()
    image, shapes = detection_model.preprocess(image)
    prediction_dict = detection_model.predict(image, shapes)
    detections = detection_model.postprocess(prediction_dict, shapes)
    return detections

zakirangwala

def check(image):
    image_np = cv2.imread(image)
    input_tensor = tf.convert_to_tensor(
        np.expand_dims(image_np, 0), dtype=tf.float32)

    detections = detect_fn(input_tensor)
    category_index = label_map_util.create_category_index_from_labelmap(
        ANNOTATION_PATH+'/label_map.pbtxt')

    num_detections = int(detections.pop('num_detections'))

zakirangwala

# 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: