def main():
args = build_argparser().parse_args()
cap = open_images_capture(args.input, args.loop)
# Plugin initialization for specified device and load extensions library if specified
log.info('OpenVINO Inference Engine')
log.info('\tbuild: {}'.format(get_version()))
core = Core()
# Read IR
log.info('Reading Proposal model {}'.format(args.model_pnet))
p_net = core.read_model(args.model_pnet)
if len(p_net.inputs) != 1:
raise RuntimeError("Pnet supports only single input topologies")
if len(p_net.outputs) != 2:
raise RuntimeError("Pnet supports two output topologies")
log.info('Reading Refine model {}'.format(args.model_rnet))
r_net = core.read_model(args.model_rnet)
if len(r_net.inputs) != 1:
raise RuntimeError("Rnet supports only single input topologies")
if len(r_net.outputs) != 2:
raise RuntimeError("Rnet supports two output topologies")
log.info('Reading Output model {}'.format(args.model_onet))
o_net = core.read_model(args.model_onet)
if len(o_net.inputs) != 1:
raise RuntimeError("Onet supports only single input topologies")
if len(o_net.outputs) != 3:
raise RuntimeError("Onet supports three output topologies")
pnet_input_tensor_name = p_net.inputs[0].get_any_name()
rnet_input_tensor_name = r_net.inputs[0].get_any_name()
onet_input_tensor_name = o_net.inputs[0].get_any_name()
for node in p_net.outputs:
if node.shape[1] == 2:
pnet_cls_name = node.get_any_name()
elif node.shape[1] == 4:
pnet_roi_name = node.get_any_name()
else:
raise RuntimeError("Unsupported output layer for Pnet")
for node in r_net.outputs:
if node.shape[1] == 2:
rnet_cls_name = node.get_any_name()
elif node.shape[1] == 4:
rnet_roi_name = node.get_any_name()
else:
raise RuntimeError("Unsupported output layer for Rnet")
for node in o_net.outputs:
if node.shape[1] == 2:
onet_cls_name = node.get_any_name()
elif node.shape[1] == 4:
onet_roi_name = node.get_any_name()
elif node.shape[1] == 10:
onet_pts_name = node.get_any_name()
else:
raise RuntimeError("Unsupported output layer for Onet")
next_frame_id = 0
metrics = PerformanceMetrics()
presenter = None
video_writer = cv2.VideoWriter()
is_loaded_before = False
while True:
start_time = perf_counter()
origin_image = cap.read()
if origin_image is None:
if next_frame_id == 0:
raise ValueError("Can't read an image from the input")
break
if next_frame_id == 0:
presenter = monitors.Presenter(args.utilization_monitors, 55,
(round(origin_image.shape[1] / 4), round(origin_image.shape[0] / 8)))
if args.output and not video_writer.open(args.output, cv2.VideoWriter_fourcc(*'MJPG'),
cap.fps(), (origin_image.shape[1], origin_image.shape[0])):
raise RuntimeError("Can't open video writer")
next_frame_id += 1
rgb_image = cv2.cvtColor(origin_image, cv2.COLOR_BGR2RGB)
oh, ow, _ = rgb_image.shape
scales = utils.calculate_scales(rgb_image)
# *************************************
# Pnet stage
# *************************************
pnet_res = []
for i, scale in enumerate(scales):
hs = int(oh*scale)
ws = int(ow*scale)
image = preprocess_image(rgb_image, ws, hs)
p_net.reshape({pnet_input_tensor_name: PartialShape([1, 3, ws, hs])}) # Change weidth and height of input blob
compiled_pnet = core.compile_model(p_net, args.device)
infer_request_pnet = compiled_pnet.create_infer_request()
if i == 0 and not is_loaded_before:
log.info("The Proposal model {} is loaded to {}".format(args.model_pnet, args.device))
infer_request_pnet.infer(inputs={pnet_input_tensor_name: image})
p_res = {name: infer_request_pnet.get_tensor(name).data[:] for name in {pnet_roi_name, pnet_cls_name}}
pnet_res.append(p_res)
image_num = len(scales)
rectangles = []
for i in range(image_num):
roi = pnet_res[i][pnet_roi_name]
cls = pnet_res[i][pnet_cls_name]
_, _, out_h, out_w = cls.shape
out_side = max(out_h, out_w)
rectangle = utils.detect_face_12net(cls[0][1], roi[0], out_side, 1/scales[i], ow, oh,
score_threshold[0], iou_threshold[0])
rectangles.extend(rectangle)
rectangles = utils.NMS(rectangles, iou_threshold[1], 'iou')
# Rnet stage
if len(rectangles) > 0:
r_net.reshape({rnet_input_tensor_name: PartialShape([len(rectangles), 3, 24, 24])}) # Change batch size of input blob
compiled_rnet = core.compile_model(r_net, args.device)
infer_request_rnet = compiled_rnet.create_infer_request()
if not is_loaded_before:
log.info("The Refine model {} is loaded to {}".format(args.model_rnet, args.device))
rnet_input = []
for rectangle in rectangles:
crop_img = rgb_image[int(rectangle[1]):int(rectangle[3]), int(rectangle[0]):int(rectangle[2])]
crop_img = preprocess_image(crop_img, 24, 24)
rnet_input.extend(crop_img)
infer_request_rnet.infer(inputs={rnet_input_tensor_name: rnet_input})
rnet_res = {name: infer_request_rnet.get_tensor(name).data[:] for name in {rnet_roi_name, rnet_cls_name}}
roi = rnet_res[rnet_roi_name]
cls = rnet_res[rnet_cls_name]
rectangles = utils.filter_face_24net(cls, roi, rectangles, ow, oh, score_threshold[1], iou_threshold[2])
# Onet stage
if len(rectangles) > 0:
o_net.reshape({onet_input_tensor_name: PartialShape([len(rectangles), 3, 48, 48])}) # Change batch size of input blob
compiled_onet = core.compile_model(o_net, args.device)
infer_request_onet = compiled_onet.create_infer_request()
if not is_loaded_before:
log.info("The Output model {} is loaded to {}".format(args.model_onet, args.device))
is_loaded_before = True
onet_input = []
for rectangle in rectangles:
crop_img = rgb_image[int(rectangle[1]):int(rectangle[3]), int(rectangle[0]):int(rectangle[2])]
crop_img = preprocess_image(crop_img, 48, 48)
onet_input.extend(crop_img)
infer_request_onet.infer(inputs={onet_input_tensor_name: onet_input})
onet_res = {name: infer_request_onet.get_tensor(name).data[:] for name in {onet_roi_name, onet_cls_name, onet_pts_name}}
roi = onet_res[onet_roi_name]
cls = onet_res[onet_cls_name]
pts = onet_res[onet_pts_name]
rectangles = utils.filter_face_48net(cls, roi, pts, rectangles, ow, oh,
score_threshold[2], iou_threshold[3])
# display results
for rectangle in rectangles:
# Draw detected boxes
cv2.putText(origin_image, 'confidence: {:.2f}'.format(rectangle[4]),
(int(rectangle[0]), int(rectangle[1])), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0))
cv2.rectangle(origin_image, (int(rectangle[0]), int(rectangle[1])), (int(rectangle[2]), int(rectangle[3])),
(255, 0, 0), 1)
# Draw landmarks
for i in range(5, 15, 2):
cv2.circle(origin_image, (int(rectangle[i+0]), int(rectangle[i+1])), 2, (0, 255, 0))
metrics.update(start_time, origin_image)
if video_writer.isOpened() and (args.output_limit <= 0 or next_frame_id <= args.output_limit):
video_writer.write(origin_image)
if not args.no_show:
cv2.imshow('MTCNN Results', origin_image)
key = cv2.waitKey(1)
if key in {ord('q'), ord('Q'), 27}:
break
presenter.handleKey(key)
metrics.log_total()
def main():
metrics = PerformanceMetrics()
args = build_argparser().parse_args()
# Plugin initialization for specified device and load extensions library if specified
log.info("Creating Inference Engine")
ie = IECore()
# Read IR
log.info("Loading network files:\n\t{}".format(args.model_pnet))
p_net = ie.read_network(args.model_pnet)
assert len(p_net.input_info.keys()
) == 1, "Pnet supports only single input topologies"
assert len(p_net.outputs) == 2, "Pnet supports two output topologies"
log.info("Loading network files:\n\t{}".format(args.model_rnet))
r_net = ie.read_network(args.model_rnet)
assert len(r_net.input_info.keys()
) == 1, "Rnet supports only single input topologies"
assert len(r_net.outputs) == 2, "Rnet supports two output topologies"
log.info("Loading network files:\n\t{}".format(args.model_onet))
o_net = ie.read_network(args.model_onet)
assert len(o_net.input_info.keys()
) == 1, "Onet supports only single input topologies"
assert len(o_net.outputs) == 3, "Onet supports three output topologies"
log.info("Preparing input blobs")
pnet_input_blob = next(iter(p_net.input_info))
rnet_input_blob = next(iter(r_net.input_info))
onet_input_blob = next(iter(o_net.input_info))
log.info("Preparing output blobs")
for name, blob in p_net.outputs.items():
if blob.shape[1] == 2:
pnet_cls_name = name
elif blob.shape[1] == 4:
pnet_roi_name = name
else:
raise RuntimeError("Unsupported output layer for Pnet")
for name, blob in r_net.outputs.items():
if blob.shape[1] == 2:
rnet_cls_name = name
elif blob.shape[1] == 4:
rnet_roi_name = name
else:
raise RuntimeError("Unsupported output layer for Rnet")
for name, blob in o_net.outputs.items():
if blob.shape[1] == 2:
onet_cls_name = name
elif blob.shape[1] == 4:
onet_roi_name = name
elif blob.shape[1] == 10:
onet_pts_name = name
else:
raise RuntimeError("Unsupported output layer for Onet")
cap = open_images_capture(args.input, args.loop)
next_frame_id = 0
log.info('Starting inference...')
print(
"To close the application, press 'CTRL+C' here or switch to the output window and press ESC key"
)
presenter = None
video_writer = cv2.VideoWriter()
while True:
start_time = perf_counter()
origin_image = cap.read()
if origin_image is None:
if next_frame_id == 0:
raise ValueError("Can't read an image from the input")
break
if next_frame_id == 0:
presenter = monitors.Presenter(args.utilization_monitors, 55,
(round(origin_image.shape[1] / 4),
round(origin_image.shape[0] / 8)))
if args.output and not video_writer.open(
args.output, cv2.VideoWriter_fourcc(*'MJPG'), cap.fps(),
(origin_image.shape[1], origin_image.shape[0])):
raise RuntimeError("Can't open video writer")
next_frame_id += 1
rgb_image = cv2.cvtColor(origin_image, cv2.COLOR_BGR2RGB)
oh, ow, _ = rgb_image.shape
scales = utils.calculate_scales(rgb_image)
# *************************************
# Pnet stage
# *************************************
log.info("Loading Pnet model to the plugin")
t0 = cv2.getTickCount()
pnet_res = []
for scale in scales:
hs = int(oh * scale)
ws = int(ow * scale)
image = preprocess_image(rgb_image, ws, hs)
p_net.reshape({pnet_input_blob:
[1, 3, ws,
hs]}) # Change weidth and height of input blob
exec_pnet = ie.load_network(network=p_net, device_name=args.device)
p_res = exec_pnet.infer(inputs={pnet_input_blob: image})
pnet_res.append(p_res)
image_num = len(scales)
rectangles = []
for i in range(image_num):
roi = pnet_res[i][pnet_roi_name]
cls = pnet_res[i][pnet_cls_name]
_, _, out_h, out_w = cls.shape
out_side = max(out_h, out_w)
rectangle = utils.detect_face_12net(cls[0][1], roi[0], out_side,
1 / scales[i], ow, oh,
score_threshold[0],
iou_threshold[0])
rectangles.extend(rectangle)
rectangles = utils.NMS(rectangles, iou_threshold[1], 'iou')
# Rnet stage
if len(rectangles) > 0:
log.info("Loading Rnet model to the plugin")
r_net.reshape({rnet_input_blob:
[len(rectangles), 3, 24,
24]}) # Change batch size of input blob
exec_rnet = ie.load_network(network=r_net, device_name=args.device)
rnet_input = []
for rectangle in rectangles:
crop_img = rgb_image[int(rectangle[1]):int(rectangle[3]),
int(rectangle[0]):int(rectangle[2])]
crop_img = preprocess_image(crop_img, 24, 24)
rnet_input.extend(crop_img)
rnet_res = exec_rnet.infer(inputs={rnet_input_blob: rnet_input})
roi = rnet_res[rnet_roi_name]
cls = rnet_res[rnet_cls_name]
rectangles = utils.filter_face_24net(cls, roi, rectangles, ow, oh,
score_threshold[1],
iou_threshold[2])
# Onet stage
if len(rectangles) > 0:
log.info("Loading Onet model to the plugin")
o_net.reshape({onet_input_blob:
[len(rectangles), 3, 48,
48]}) # Change batch size of input blob
exec_onet = ie.load_network(network=o_net, device_name=args.device)
onet_input = []
for rectangle in rectangles:
crop_img = rgb_image[int(rectangle[1]):int(rectangle[3]),
int(rectangle[0]):int(rectangle[2])]
crop_img = preprocess_image(crop_img, 48, 48)
onet_input.extend(crop_img)
onet_res = exec_onet.infer(inputs={onet_input_blob: onet_input})
roi = onet_res[onet_roi_name]
cls = onet_res[onet_cls_name]
pts = onet_res[onet_pts_name]
rectangles = utils.filter_face_48net(cls, roi, pts, rectangles, ow,
oh, score_threshold[2],
iou_threshold[3])
# display results
for rectangle in rectangles:
# Draw detected boxes
cv2.putText(origin_image,
'confidence: {:.2f}'.format(rectangle[4]),
(int(rectangle[0]), int(rectangle[1])),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0))
cv2.rectangle(origin_image, (int(rectangle[0]), int(rectangle[1])),
(int(rectangle[2]), int(rectangle[3])), (255, 0, 0),
1)
# Draw landmarks
for i in range(5, 15, 2):
cv2.circle(origin_image,
(int(rectangle[i + 0]), int(rectangle[i + 1])), 2,
(0, 255, 0))
infer_time = (cv2.getTickCount() -
t0) / cv2.getTickFrequency() # Record infer time
cv2.putText(origin_image,
'summary: {:.1f} FPS'.format(1.0 / infer_time), (5, 15),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 200))
if video_writer.isOpened() and (args.output_limit <= 0 or next_frame_id
<= args.output_limit - 1):
video_writer.write(origin_image)
if not args.no_show:
cv2.imshow('MTCNN Results', origin_image)
key = cv2.waitKey(1)
if key in {ord('q'), ord('Q'), 27}:
break
presenter.handleKey(key)
metrics.update(start_time, origin_image)
metrics.print_total()