def main():
""" Main function. """
log.basicConfig(format='[ %(levelname)s ] %(message)s',
level=log.INFO,
stream=sys.stdout)
args = build_argparser().parse_args()
class_map = load_class_map(args.class_map)
assert class_map is not None
ie_core = load_ie_core(args.device, args.cpu_extension)
person_detector = PersonDetector(args.detection_model,
args.device,
ie_core,
num_requests=2,
output_shape=DETECTOR_OUTPUT_SHAPE)
action_recognizer = ActionRecognizer(args.action_model,
args.device,
ie_core,
num_requests=2,
img_scale=ACTION_IMAGE_SCALE,
num_classes=len(class_map))
person_tracker = Tracker(person_detector, TRACKER_SCORE_THRESHOLD,
TRACKER_IOU_THRESHOLD)
video_stream = VideoStream(args.input, ACTION_NET_INPUT_FPS,
action_recognizer.input_length)
video_stream.start()
visualizer = Visualizer(VISUALIZER_TRG_FPS)
visualizer.register_window('Demo')
presenter = monitors.Presenter(args.utilization_monitors)
samples_library = None
if args.samples_dir is not None and os.path.exists(args.samples_dir):
visualizer.register_window('Gesture library')
visualizer.start()
library_queue = visualizer.get_queue('Gesture library')
samples_library = VideoLibrary(args.samples_dir,
SAMPLES_MAX_WINDOW_SIZE,
list(class_map.values()), library_queue,
SAMPLES_TRG_FPS)
samples_library.start()
else:
visualizer.start()
last_caption = None
active_object_id = -1
tracker_labels_map = dict()
tracker_labels = set()
start_time = time.perf_counter()
while True:
frame = video_stream.get_live_frame()
batch = video_stream.get_batch()
if frame is None or batch is None:
break
detections, tracker_labels_map = person_tracker.add_frame(
frame, len(OBJECT_IDS), tracker_labels_map)
if detections is None:
active_object_id = -1
last_caption = None
if len(detections) == 1:
active_object_id = 0
if active_object_id >= 0:
cur_det = [det for det in detections if det.id == active_object_id]
if len(cur_det) != 1:
active_object_id = -1
last_caption = None
continue
recognizer_result = action_recognizer(batch,
cur_det[0].roi.reshape(-1))
if recognizer_result is not None:
action_class_id = np.argmax(recognizer_result)
action_class_label = \
class_map[action_class_id] if class_map is not None else action_class_id
action_class_score = np.max(recognizer_result)
if action_class_score > args.action_threshold:
last_caption = 'Last gesture: {} '.format(
action_class_label)
end_time = time.perf_counter()
elapsed_time = end_time - start_time
start_time = end_time
presenter.drawGraphs(frame)
if active_object_id >= 0:
current_fps = 1.0 / elapsed_time
cv2.putText(frame, 'FPS: {:.2f}'.format(current_fps), (10, 40),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
if detections is not None:
tracker_labels = set(det.id for det in detections)
for det in detections:
roi_color = (0, 255,
0) if active_object_id == det.id else (128, 128,
128)
border_width = 2 if active_object_id == det.id else 1
person_roi = det.roi[0]
cv2.rectangle(frame, (person_roi[0], person_roi[1]),
(person_roi[2], person_roi[3]), roi_color,
border_width)
cv2.putText(frame, str(det.id),
(person_roi[0] + 10, person_roi[1] + 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, roi_color, 2)
if last_caption is not None:
cv2.putText(frame, last_caption, (10, frame.shape[0] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
if args.no_show:
continue
visualizer.put_queue(frame, 'Demo')
key = visualizer.get_key()
if key == 27: # esc
break
elif key == ord(' '): # space
active_object_id = -1
last_caption = None
elif key == 13: # enter
last_caption = None
elif key in OBJECT_IDS: # 0-9
local_bbox_id = int(chr(key))
if local_bbox_id in tracker_labels:
active_object_id = local_bbox_id
else:
presenter.handleKey(key)
if samples_library is not None:
if key == ord('f'): # forward
samples_library.next()
elif key == ord('b'): # backward
samples_library.prev()
if samples_library is not None:
samples_library.release()
visualizer.release()
video_stream.release()
print(presenter.reportMeans())
def main():
args = build_argparser().parse_args()
class_map = load_class_map(args.class_map)
if class_map is None:
raise RuntimeError("Can't read {}".format(args.class_map))
core = load_core()
person_detector = PersonDetector(args.detection_model,
args.device,
core,
num_requests=2,
output_shape=DETECTOR_OUTPUT_SHAPE)
action_recognizer = ActionRecognizer(args.action_model,
args.device,
core,
num_requests=2,
img_scale=ACTION_IMAGE_SCALE,
num_classes=len(class_map))
person_tracker = Tracker(person_detector, TRACKER_SCORE_THRESHOLD,
TRACKER_IOU_THRESHOLD)
video_stream = VideoStream(args.input, ACTION_NET_INPUT_FPS,
action_recognizer.input_length)
video_stream.start()
metrics = PerformanceMetrics()
visualizer = Visualizer(VISUALIZER_TRG_FPS)
visualizer.register_window('Demo')
presenter = monitors.Presenter(args.utilization_monitors)
samples_library = None
if args.samples_dir is not None and os.path.exists(args.samples_dir):
visualizer.register_window('Gesture library')
visualizer.start()
library_queue = visualizer.get_queue('Gesture library')
samples_library = VideoLibrary(args.samples_dir,
SAMPLES_MAX_WINDOW_SIZE,
list(class_map.values()), library_queue,
SAMPLES_TRG_FPS)
samples_library.start()
else:
visualizer.start()
last_caption = None
active_object_id = -1
tracker_labels_map = {}
tracker_labels = set()
frames_processed = 0
while True:
start_time = perf_counter()
frame = video_stream.get_live_frame()
batch = video_stream.get_batch()
if frame is None or batch is None:
break
if frames_processed == 0:
video_writer = cv2.VideoWriter()
if args.output and not video_writer.open(
args.output, cv2.VideoWriter_fourcc(*'MJPG'),
video_stream.fps(), (frame.shape[1], frame.shape[0])):
raise RuntimeError("Can't open video writer")
detections, tracker_labels_map = person_tracker.add_frame(
frame, len(OBJECT_IDS), tracker_labels_map)
if detections is None:
active_object_id = -1
last_caption = None
if len(detections) == 1:
active_object_id = 0
if active_object_id >= 0:
cur_det = [det for det in detections if det.id == active_object_id]
if len(cur_det) != 1:
active_object_id = -1
last_caption = None
continue
recognizer_result = action_recognizer(batch,
cur_det[0].roi.reshape(-1))
if recognizer_result is not None:
action_class_id = np.argmax(recognizer_result)
action_class_label = \
class_map[action_class_id] if class_map is not None else action_class_id
action_class_score = np.max(recognizer_result)
if action_class_score > args.action_threshold:
last_caption = 'Last gesture: {} '.format(
action_class_label)
presenter.drawGraphs(frame)
if detections is not None:
tracker_labels = {det.id for det in detections}
for det in detections:
roi_color = (0, 255,
0) if active_object_id == det.id else (128, 128,
128)
border_width = 2 if active_object_id == det.id else 1
person_roi = det.roi[0]
cv2.rectangle(frame, (person_roi[0], person_roi[1]),
(person_roi[2], person_roi[3]), roi_color,
border_width)
cv2.putText(frame, str(det.id),
(person_roi[0] + 10, person_roi[1] + 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, roi_color, 2)
if last_caption is not None:
cv2.putText(frame, last_caption, (10, frame.shape[0] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
metrics.update(start_time, frame)
frames_processed += 1
if video_writer.isOpened() and (args.output_limit <= 0 or
frames_processed <= args.output_limit):
video_writer.write(frame)
if args.no_show:
continue
visualizer.put_queue(frame, 'Demo')
key = visualizer.get_key()
if key == 27: # esc
break
elif key == ord(' '): # space
active_object_id = -1
last_caption = None
elif key == 13: # enter
last_caption = None
elif key in OBJECT_IDS: # 0-9
local_bbox_id = int(chr(key))
if local_bbox_id in tracker_labels:
active_object_id = local_bbox_id
else:
presenter.handleKey(key)
if samples_library is not None:
if key == ord('f'): # forward
samples_library.next()
elif key == ord('b'): # backward
samples_library.prev()
if samples_library is not None:
samples_library.release()
visualizer.release()
video_stream.release()
metrics.log_total()
for rep in presenter.reportMeans():
log.info(rep)