gist detect

gistfile1.txt

if __name__ == '__main__':
    load_model()
    # Adding some arg to a parser so its easy to manage
    parser = argparse.ArgumentParser()
    parser.add_argument('-src', '--source', dest='video_source', type=int, default=0, help='Device index of the camera.')
    parser.add_argument('-wd', '--width', dest='width', type=int, default=320, help='Width of the frames in the video stream.')
    parser.add_argument('-ht', '--height', dest='height', type=int, default=360, help='Height of the frames in the video stream.')
    args = parser.parse_args()


    logger = multiprocessing.log_to_stderr()
    logger.setLevel(multiprocessing.SUBDEBUG)





    video_capture = cv2.VideoCapture(args.video_source)

    # in this way it always works, because your get the right "size"
    size = (int(video_capture.get(3)),
            int(video_capture.get(4)))



    fps = FPS().start()

    while True:
        ret, frame = video_capture.read()

        if frame is None:
            break


        if ret == True:
            boxes, scores, classes, num_detections, image = PersonDetection.GetHumans(frame)
            # humans = poseEstimator.inference(frame)

            # show, _, bboxes, _, _ = TfPoseEstimator.get_skeleton(frame, humans, imgcopy=False)

            # print(bboxes)



            t = time.time()

            # show = TfPoseEstimator.draw_humans(frame, humans, imgcopy=False)
            # imagetoShow = cv2.cvtColor(show, cv2.COLOR_RGB2BGR)

            cv2.imshow('Video', image)





            fps.update()

            print('[INFO] elapsed time: {:.2f}'.format(time.time() - t))

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



    fps.stop()
    print('[INFO] elapsed time (total): {:.2f}'.format(fps.elapsed()))
    print('[INFO] approx. FPS: {:.2f}'.format(fps.fps()))




    # cleaan up all
    # pool.terminate()
    out = None
    video_capture.stop()
    cv2.destroyAllWindows()

def detectObjects(image, sess, detection_graph):
    # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
    image_np_expanded = np.expand_dims(image, axis=0)
    image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')

    # Each box represents a part of the image where a particular object was detected.
    boxes = detection_graph.get_tensor_by_name('detection_boxes:0')

    # Each score represent how level of confidence for each of the objects.
    # Score is shown on the result image, together with the class label.
    scores = detection_graph.get_tensor_by_name('detection_scores:0')
    classes = detection_graph.get_tensor_by_name('detection_classes:0')
    num_detections = detection_graph.get_tensor_by_name('num_detections:0')

    # Actual detection.
    (boxes, scores, classes, num_detections) = sess.run(
        [boxes, scores, classes, num_detections],
        feed_dict={image_tensor: image_np_expanded})


    # Visualization of the results of a detection.
    vis_util.visualize_boxes_and_labels_on_image_array(
        image,
        np.squeeze(boxes),
        np.squeeze(classes).astype(np.int32),
        np.squeeze(scores),
        category_index,
        use_normalized_coordinates=True,
        line_thickness=1)

    return boxes, scores, classes, num_detections, image



def GetHumans(frame):
    detection_graph = tf.Graph()
    with detection_graph.as_default():
        od_graph_def = tf.GraphDef()
        with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:

            tfgraph = trt.create_inference_graph(
            input_graph_def=od_graph_def,
            outputs=[“your_output_node_names”],
            max_batch_size=your_batch_size,
            max_workspace_size_bytes=max_GPU_mem_size_for_TRT,
            precision_mode=”your_precision_mode”)

            serialized_graph = fid.read()
            od_graph_def.ParseFromString(serialized_graph)
            tf.import_graph_def(od_graph_def, name='')

        sess = tf.Session(graph=detection_graph)
        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

    return detectObjects(frame_rgb, sess, detection_graph)