def main():
args = build_argparser().parse_args()
cap = open_images_capture(args.input, args.loop)
target_bgr = open_images_capture(args.target_bgr,
loop=True) if args.target_bgr else None
if args.adapter == 'openvino':
plugin_config = get_user_config(args.device, args.num_streams,
args.num_threads)
model_adapter = OpenvinoAdapter(
create_core(),
args.model,
device=args.device,
plugin_config=plugin_config,
max_num_requests=args.num_infer_requests,
model_parameters={'input_layouts': args.layout})
elif args.adapter == 'ovms':
model_adapter = OVMSAdapter(args.model)
labels = ['__background__', 'person'
] if args.labels is None else load_labels(args.labels)
assert len(labels), 'The file with class labels is empty'
configuration = {
'confidence_threshold': args.prob_threshold,
'resize_type': args.resize_type
}
model, need_bgr_input = get_model(model_adapter, configuration, args)
input_bgr = open_images_capture(args.background,
False).read() if need_bgr_input else None
person_id = -1
for i, label in enumerate(labels):
if label == 'person':
person_id = i
break
assert person_id >= 0, 'Person class did not find in labels list.'
model.log_layers_info()
pipeline = AsyncPipeline(model)
next_frame_id = 0
next_frame_id_to_show = 0
metrics = PerformanceMetrics()
render_metrics = PerformanceMetrics()
presenter = None
output_transform = None
video_writer = cv2.VideoWriter()
while True:
if pipeline.is_ready():
# Get new image/frame
start_time = perf_counter()
frame = cap.read()
bgr = target_bgr.read() if target_bgr is not None else None
if frame is None:
if next_frame_id == 0:
raise ValueError("Can't read an image from the input")
break
if next_frame_id == 0:
output_transform = OutputTransform(frame.shape[:2],
args.output_resolution)
if args.output_resolution:
output_resolution = output_transform.new_resolution
else:
output_resolution = (frame.shape[1], frame.shape[0])
presenter = monitors.Presenter(
args.utilization_monitors, 55,
(round(output_resolution[0] / 4),
round(output_resolution[1] / 8)))
if args.output and not video_writer.open(
args.output, cv2.VideoWriter_fourcc(*'MJPG'),
cap.fps(), tuple(output_resolution)):
raise RuntimeError("Can't open video writer")
# Submit for inference
data = {
'src': frame,
'bgr': input_bgr
} if input_bgr is not None else frame
pipeline.submit_data(data, next_frame_id, {
'frame': frame,
'start_time': start_time
})
next_frame_id += 1
else:
# Wait for empty request
pipeline.await_any()
if pipeline.callback_exceptions:
raise pipeline.callback_exceptions[0]
# Process all completed requests
results = pipeline.get_result(next_frame_id_to_show)
if results:
objects, frame_meta = results
if args.raw_output_message:
print_raw_results(objects, next_frame_id_to_show)
frame = frame_meta['frame']
start_time = frame_meta['start_time']
rendering_start_time = perf_counter()
frame = render_results(frame, objects, output_resolution, bgr,
person_id, args.blur_bgr,
args.show_with_original_frame)
render_metrics.update(rendering_start_time)
presenter.drawGraphs(frame)
metrics.update(start_time, frame)
if video_writer.isOpened() and (
args.output_limit <= 0
or next_frame_id_to_show <= args.output_limit - 1):
video_writer.write(frame)
next_frame_id_to_show += 1
if not args.no_show:
cv2.imshow('Background subtraction results', frame)
key = cv2.waitKey(1)
if key == 27 or key == 'q' or key == 'Q':
break
presenter.handleKey(key)
pipeline.await_all()
if pipeline.callback_exceptions:
raise pipeline.callback_exceptions[0]
# Process completed requests
for next_frame_id_to_show in range(next_frame_id_to_show, next_frame_id):
results = pipeline.get_result(next_frame_id_to_show)
objects, frame_meta = results
if args.raw_output_message:
print_raw_results(objects, next_frame_id_to_show, model.labels)
frame = frame_meta['frame']
start_time = frame_meta['start_time']
rendering_start_time = perf_counter()
frame = render_results(frame, objects, output_resolution, bgr,
person_id, args.blur_bgr,
args.show_with_original_frame)
render_metrics.update(rendering_start_time)
presenter.drawGraphs(frame)
metrics.update(start_time, frame)
if video_writer.isOpened() and (
args.output_limit <= 0
or next_frame_id_to_show <= args.output_limit - 1):
video_writer.write(frame)
if not args.no_show:
cv2.imshow('Background subtraction results', frame)
cv2.waitKey(1)
metrics.log_total()
log_latency_per_stage(cap.reader_metrics.get_latency(),
pipeline.preprocess_metrics.get_latency(),
pipeline.inference_metrics.get_latency(),
pipeline.postprocess_metrics.get_latency(),
render_metrics.get_latency())
for rep in presenter.reportMeans():
log.info(rep)
def main():
args = build_argparser().parse_args()
cap = open_images_capture(args.input, args.loop)
if args.adapter == 'openvino':
plugin_config = get_user_config(args.device, args.num_streams,
args.num_threads)
model_adapter = OpenvinoAdapter(
create_core(),
args.model,
device=args.device,
plugin_config=plugin_config,
max_num_requests=args.num_infer_requests,
model_parameters={'input_layouts': args.layout})
elif args.adapter == 'ovms':
model_adapter = OVMSAdapter(args.model)
model = MonoDepthModel(model_adapter)
model.log_layers_info()
pipeline = AsyncPipeline(model)
next_frame_id = 0
next_frame_id_to_show = 0
metrics = PerformanceMetrics()
presenter = None
output_transform = None
video_writer = cv2.VideoWriter()
while True:
if pipeline.is_ready():
# Get new image/frame
start_time = perf_counter()
frame = cap.read()
if frame is None:
if next_frame_id == 0:
raise ValueError("Can't read an image from the input")
break
if next_frame_id == 0:
output_transform = OutputTransform(frame.shape[:2],
args.output_resolution)
if args.output_resolution:
output_resolution = output_transform.new_resolution
else:
output_resolution = (frame.shape[1], frame.shape[0])
presenter = monitors.Presenter(
args.utilization_monitors, 55,
(round(output_resolution[0] / 4),
round(output_resolution[1] / 8)))
if args.output and not video_writer.open(
args.output, cv2.VideoWriter_fourcc(*'MJPG'),
cap.fps(), output_resolution):
raise RuntimeError("Can't open video writer")
# Submit for inference
pipeline.submit_data(frame, next_frame_id,
{'start_time': start_time})
next_frame_id += 1
else:
# Wait for empty request
pipeline.await_any()
if pipeline.callback_exceptions:
raise pipeline.callback_exceptions[0]
# Process all completed requests
results = pipeline.get_result(next_frame_id_to_show)
if results:
depth_map, frame_meta = results
depth_map = apply_color_map(depth_map, output_transform)
start_time = frame_meta['start_time']
presenter.drawGraphs(depth_map)
metrics.update(start_time, depth_map)
if video_writer.isOpened() and (
args.output_limit <= 0
or next_frame_id_to_show <= args.output_limit - 1):
video_writer.write(depth_map)
next_frame_id_to_show += 1
if not args.no_show:
cv2.imshow(DEMO_NAME, depth_map)
key = cv2.waitKey(1)
if key == 27 or key == 'q' or key == 'Q':
break
presenter.handleKey(key)
pipeline.await_all()
if pipeline.callback_exceptions:
raise pipeline.callback_exceptions[0]
# Process completed requests
for next_frame_id_to_show in range(next_frame_id_to_show, next_frame_id):
results = pipeline.get_result(next_frame_id_to_show)
depth_map, frame_meta = results
depth_map = apply_color_map(depth_map, output_transform)
start_time = frame_meta['start_time']
presenter.drawGraphs(depth_map)
metrics.update(start_time, depth_map)
if video_writer.isOpened() and (
args.output_limit <= 0
or next_frame_id_to_show <= args.output_limit - 1):
video_writer.write(depth_map)
if not args.no_show:
cv2.imshow(DEMO_NAME, depth_map)
key = cv2.waitKey(1)
if key == 27 or key == 'q' or key == 'Q':
break
presenter.handleKey(key)
metrics.log_total()
for rep in presenter.reportMeans():
log.info(rep)
def main():
args = build_argparser().parse_args()
cap = open_images_capture(args.input, args.loop)
next_frame_id = 1
next_frame_id_to_show = 0
metrics = PerformanceMetrics()
render_metrics = PerformanceMetrics()
video_writer = cv2.VideoWriter()
plugin_config = get_user_config(args.device, args.num_streams,
args.num_threads)
model_adapter = OpenvinoAdapter(create_core(),
args.model,
device=args.device,
plugin_config=plugin_config,
max_num_requests=args.num_infer_requests)
start_time = perf_counter()
frame = cap.read()
if frame is None:
raise RuntimeError("Can't read an image from the input")
config = {
'target_size': args.tsize,
'aspect_ratio': frame.shape[1] / frame.shape[0],
'confidence_threshold': args.prob_threshold,
'padding_mode': 'center' if args.architecture_type == 'higherhrnet'
else None, # the 'higherhrnet' and 'ae' specific
'delta':
0.5 if 'higherhrnet' else None, # the 'higherhrnet' and 'ae' specific
}
model = ImageModel.create_model(ARCHITECTURES[args.architecture_type],
model_adapter, config)
model.log_layers_info()
hpe_pipeline = AsyncPipeline(model)
hpe_pipeline.submit_data(frame, 0, {
'frame': frame,
'start_time': start_time
})
output_transform = OutputTransform(frame.shape[:2], args.output_resolution)
if args.output_resolution:
output_resolution = output_transform.new_resolution
else:
output_resolution = (frame.shape[1], frame.shape[0])
presenter = monitors.Presenter(
args.utilization_monitors, 55,
(round(output_resolution[0] / 4), round(output_resolution[1] / 8)))
if args.output and not video_writer.open(args.output,
cv2.VideoWriter_fourcc(*'MJPG'),
cap.fps(), output_resolution):
raise RuntimeError("Can't open video writer")
while True:
if hpe_pipeline.callback_exceptions:
raise hpe_pipeline.callback_exceptions[0]
# Process all completed requests
results = hpe_pipeline.get_result(next_frame_id_to_show)
if results:
(poses, scores), frame_meta = results
frame = frame_meta['frame']
start_time = frame_meta['start_time']
if len(poses) and args.raw_output_message:
print_raw_results(poses, scores, next_frame_id_to_show)
presenter.drawGraphs(frame)
rendering_start_time = perf_counter()
frame = draw_poses(frame, poses, args.prob_threshold,
output_transform)
render_metrics.update(rendering_start_time)
metrics.update(start_time, frame)
if video_writer.isOpened() and (
args.output_limit <= 0
or next_frame_id_to_show <= args.output_limit - 1):
video_writer.write(frame)
next_frame_id_to_show += 1
if not args.no_show:
cv2.imshow('Pose estimation results', frame)
key = cv2.waitKey(1)
ESC_KEY = 27
# Quit.
if key in {ord('q'), ord('Q'), ESC_KEY}:
break
presenter.handleKey(key)
continue
if hpe_pipeline.is_ready():
# Get new image/frame
start_time = perf_counter()
frame = cap.read()
if frame is None:
break
# Submit for inference
hpe_pipeline.submit_data(frame, next_frame_id, {
'frame': frame,
'start_time': start_time
})
next_frame_id += 1
else:
# Wait for empty request
hpe_pipeline.await_any()
hpe_pipeline.await_all()
# Process completed requests
for next_frame_id_to_show in range(next_frame_id_to_show, next_frame_id):
results = hpe_pipeline.get_result(next_frame_id_to_show)
while results is None:
results = hpe_pipeline.get_result(next_frame_id_to_show)
(poses, scores), frame_meta = results
frame = frame_meta['frame']
start_time = frame_meta['start_time']
if len(poses) and args.raw_output_message:
print_raw_results(poses, scores, next_frame_id_to_show)
presenter.drawGraphs(frame)
rendering_start_time = perf_counter()
frame = draw_poses(frame, poses, args.prob_threshold, output_transform)
render_metrics.update(rendering_start_time)
metrics.update(start_time, frame)
if video_writer.isOpened() and (
args.output_limit <= 0
or next_frame_id_to_show <= args.output_limit - 1):
video_writer.write(frame)
if not args.no_show:
cv2.imshow('Pose estimation results', frame)
key = cv2.waitKey(1)
ESC_KEY = 27
# Quit.
if key in {ord('q'), ord('Q'), ESC_KEY}:
break
presenter.handleKey(key)
metrics.log_total()
log_latency_per_stage(cap.reader_metrics.get_latency(),
hpe_pipeline.preprocess_metrics.get_latency(),
hpe_pipeline.inference_metrics.get_latency(),
hpe_pipeline.postprocess_metrics.get_latency(),
render_metrics.get_latency())
for rep in presenter.reportMeans():
log.info(rep)