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()))
ie = IECore()
if args.cpu_extension and 'CPU' in args.device:
ie.add_extension(args.cpu_extension, 'CPU')
# Read IR
log.info('Reading Mask-RCNN model {}'.format(args.mask_rcnn_model))
mask_rcnn_net = ie.read_network(args.mask_rcnn_model)
model_required_inputs = {'image'}
if set(mask_rcnn_net.input_info) == model_required_inputs:
required_output_keys = {'boxes', 'labels', 'masks', 'text_features.0'}
n, c, h, w = mask_rcnn_net.input_info['image'].input_data.shape
assert n == 1, 'Only batch 1 is supported by the demo application'
else:
raise RuntimeError(
'Demo supports only topologies with the following input keys: '
f'{model_required_inputs}.')
assert required_output_keys.issubset(mask_rcnn_net.outputs.keys()), \
f'Demo supports only topologies with the following output keys: {required_output_keys}' \
f'Found: {mask_rcnn_net.outputs.keys()}.'
log.info('Reading Text Recognition Encoder model {}'.format(
args.text_enc_model))
text_enc_net = ie.read_network(args.text_enc_model)
log.info('Reading Text Recognition Decoder model {}'.format(
args.text_dec_model))
text_dec_net = ie.read_network(args.text_dec_model)
mask_rcnn_exec_net = ie.load_network(network=mask_rcnn_net,
device_name=args.device,
num_requests=2)
log.info('The Mask-RCNN model {} is loaded to {}'.format(
args.mask_rcnn_model, args.device))
text_enc_exec_net = ie.load_network(network=text_enc_net,
device_name=args.device)
log.info('The Text Recognition Encoder model {} is loaded to {}'.format(
args.text_enc_model, args.device))
text_dec_exec_net = ie.load_network(network=text_dec_net,
device_name=args.device)
log.info('The Text Recognition Decoder model {} is loaded to {}'.format(
args.text_dec_model, args.device))
hidden_shape = text_dec_net.input_info[
args.trd_input_prev_hidden].input_data.shape
del mask_rcnn_net
del text_enc_net
del text_dec_net
if args.no_track:
tracker = None
else:
tracker = StaticIOUTracker()
if args.delay:
delay = args.delay
else:
delay = int(cap.get_type() in ('VIDEO', 'CAMERA'))
visualizer = Visualizer(['__background__', 'text'],
show_boxes=args.show_boxes,
show_scores=args.show_scores)
frames_processed = 0
metrics = PerformanceMetrics()
video_writer = cv2.VideoWriter()
start_time = perf_counter()
frame = cap.read()
if frame is None:
raise RuntimeError("Can't read an image from the input")
presenter = monitors.Presenter(args.utilization_monitors, 45,
(frame.shape[1] // 4, frame.shape[0] // 8))
if args.output and not video_writer.open(
args.output, cv2.VideoWriter_fourcc(*'MJPG'), cap.fps(),
(frame.shape[1], frame.shape[0])):
raise RuntimeError("Can't open video writer")
while frame is not None:
if not args.keep_aspect_ratio:
# Resize the image to a target size.
scale_x = w / frame.shape[1]
scale_y = h / frame.shape[0]
input_image = cv2.resize(frame, (w, h))
else:
# Resize the image to keep the same aspect ratio and to fit it to a window of a target size.
scale_x = scale_y = min(h / frame.shape[0], w / frame.shape[1])
input_image = cv2.resize(frame, None, fx=scale_x, fy=scale_y)
input_image_size = input_image.shape[:2]
input_image = np.pad(input_image,
((0, h - input_image_size[0]),
(0, w - input_image_size[1]), (0, 0)),
mode='constant',
constant_values=0)
# Change data layout from HWC to CHW.
input_image = input_image.transpose((2, 0, 1))
input_image = input_image.reshape((n, c, h, w)).astype(np.float32)
# Run the net.
outputs = mask_rcnn_exec_net.infer({'image': input_image})
# Parse detection results of the current request
boxes = outputs['boxes'][:, :4]
scores = outputs['boxes'][:, 4]
classes = outputs['labels'].astype(np.uint32)
raw_masks = outputs['masks']
text_features = outputs['text_features.0']
# Filter out detections with low confidence.
detections_filter = scores > args.prob_threshold
scores = scores[detections_filter]
classes = classes[detections_filter]
boxes = boxes[detections_filter]
raw_masks = raw_masks[detections_filter]
text_features = text_features[detections_filter]
boxes[:, 0::2] /= scale_x
boxes[:, 1::2] /= scale_y
masks = []
for box, cls, raw_mask in zip(boxes, classes, raw_masks):
mask = segm_postprocess(box, raw_mask, frame.shape[0],
frame.shape[1])
masks.append(mask)
texts = []
for feature in text_features:
feature = text_enc_exec_net.infer({'input': feature})['output']
feature = np.reshape(feature,
(feature.shape[0], feature.shape[1], -1))
feature = np.transpose(feature, (0, 2, 1))
hidden = np.zeros(hidden_shape)
prev_symbol_index = np.ones((1, )) * SOS_INDEX
text = ''
text_confidence = 1.0
for i in range(MAX_SEQ_LEN):
decoder_output = text_dec_exec_net.infer({
args.trd_input_prev_symbol:
prev_symbol_index,
args.trd_input_prev_hidden:
hidden,
args.trd_input_encoder_outputs:
feature
})
symbols_distr = decoder_output[args.trd_output_symbols_distr]
symbols_distr_softmaxed = softmax(symbols_distr, axis=1)[0]
prev_symbol_index = int(np.argmax(symbols_distr, axis=1))
text_confidence *= symbols_distr_softmaxed[prev_symbol_index]
if prev_symbol_index == EOS_INDEX:
break
text += args.alphabet[prev_symbol_index]
hidden = decoder_output[args.trd_output_cur_hidden]
texts.append(text if text_confidence >= args.tr_threshold else '')
if len(boxes) and args.raw_output_message:
log.debug(
' -------------------------- Frame # {} -------------------------- '
.format(frames_processed))
log.debug(
' Class ID | Confidence | XMIN | YMIN | XMAX | YMAX '
)
for box, cls, score, mask in zip(boxes, classes, scores, masks):
log.debug(
'{:>10} | {:>10f} | {:>8.2f} | {:>8.2f} | {:>8.2f} | {:>8.2f} '
.format(cls, score, *box))
# Get instance track IDs.
masks_tracks_ids = None
if tracker is not None:
masks_tracks_ids = tracker(masks, classes)
presenter.drawGraphs(frame)
# Visualize masks.
frame = visualizer(frame, boxes, classes, scores, masks, texts,
masks_tracks_ids)
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 not args.no_show:
# Show resulting image.
cv2.imshow('Results', frame)
if not args.no_show:
key = cv2.waitKey(delay)
esc_code = 27
if key == esc_code:
break
presenter.handleKey(key)
start_time = perf_counter()
frame = cap.read()
metrics.log_total()
for rep in presenter.reportMeans():
log.info(rep)
cv2.destroyAllWindows()
def main():
log.basicConfig(format='[ %(levelname)s ] %(message)s',
level=log.INFO,
stream=sys.stdout)
args = build_argparser().parse_args()
mask_rcnn_model_xml = args.mask_rcnn_model
mask_rcnn_model_bin = os.path.splitext(mask_rcnn_model_xml)[0] + '.bin'
text_enc_model_xml = args.text_enc_model
text_enc_model_bin = os.path.splitext(text_enc_model_xml)[0] + '.bin'
text_dec_model_xml = args.text_dec_model
text_dec_model_bin = os.path.splitext(text_dec_model_xml)[0] + '.bin'
# Plugin initialization for specified device and load extensions library if specified.
log.info('Creating Inference Engine...')
ie = IECore()
if args.cpu_extension and 'CPU' in args.device:
ie.add_extension(args.cpu_extension, 'CPU')
# Read IR
log.info('Loading network files:\n\t{}\n\t{}'.format(
mask_rcnn_model_xml, mask_rcnn_model_bin))
mask_rcnn_net = IENetwork(model=mask_rcnn_model_xml,
weights=mask_rcnn_model_bin)
log.info('Loading network files:\n\t{}\n\t{}'.format(
text_enc_model_xml, text_enc_model_bin))
text_enc_net = IENetwork(model=text_enc_model_xml,
weights=text_enc_model_bin)
log.info('Loading network files:\n\t{}\n\t{}'.format(
text_dec_model_xml, text_dec_model_bin))
text_dec_net = IENetwork(model=text_dec_model_xml,
weights=text_dec_model_bin)
if 'CPU' in args.device:
supported_layers = ie.query_network(mask_rcnn_net, 'CPU')
not_supported_layers = [
l for l in mask_rcnn_net.layers.keys() if l not in supported_layers
]
if len(not_supported_layers) != 0:
log.error(
'Following layers are not supported by the plugin for specified device {}:\n {}'
.format(args.device, ', '.join(not_supported_layers)))
log.error(
"Please try to specify cpu extensions library path in sample's command line parameters using -l "
"or --cpu_extension command line argument")
sys.exit(1)
required_input_keys = {'im_data', 'im_info'}
assert required_input_keys == set(mask_rcnn_net.inputs.keys()), \
'Demo supports only topologies with the following input keys: {}'.format(', '.join(required_input_keys))
required_output_keys = {
'boxes', 'scores', 'classes', 'raw_masks', 'text_features'
}
assert required_output_keys.issubset(mask_rcnn_net.outputs.keys()), \
'Demo supports only topologies with the following output keys: {}'.format(', '.join(required_output_keys))
n, c, h, w = mask_rcnn_net.inputs['im_data'].shape
assert n == 1, 'Only batch 1 is supported by the demo application'
log.info('Loading IR to the plugin...')
mask_rcnn_exec_net = ie.load_network(network=mask_rcnn_net,
device_name=args.device,
num_requests=2)
text_enc_exec_net = ie.load_network(network=text_enc_net,
device_name=args.device)
text_dec_exec_net = ie.load_network(network=text_dec_net,
device_name=args.device)
hidden_shape = text_dec_net.inputs[args.trd_input_prev_hidden].shape
del mask_rcnn_net
del text_enc_net
del text_dec_net
try:
input_source = int(args.input_source)
except ValueError:
input_source = args.input_source
if os.path.isdir(input_source):
cap = FolderCapture(input_source)
else:
cap = cv2.VideoCapture(input_source)
if not cap.isOpened():
log.error('Failed to open "{}"'.format(args.input_source))
if isinstance(cap, cv2.VideoCapture):
cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
if args.no_track:
tracker = None
else:
tracker = StaticIOUTracker()
visualizer = Visualizer(['__background__', 'text'],
show_boxes=args.show_boxes,
show_scores=args.show_scores)
render_time = 0
log.info('Starting inference...')
print(
"To close the application, press 'CTRL+C' here or switch to the output window and press ESC key"
)
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
if not args.keep_aspect_ratio:
# Resize the image to a target size.
scale_x = w / frame.shape[1]
scale_y = h / frame.shape[0]
input_image = cv2.resize(frame, (w, h))
else:
# Resize the image to keep the same aspect ratio and to fit it to a window of a target size.
scale_x = scale_y = min(h / frame.shape[0], w / frame.shape[1])
input_image = cv2.resize(frame, None, fx=scale_x, fy=scale_y)
input_image_size = input_image.shape[:2]
input_image = np.pad(input_image,
((0, h - input_image_size[0]),
(0, w - input_image_size[1]), (0, 0)),
mode='constant',
constant_values=0)
# Change data layout from HWC to CHW.
input_image = input_image.transpose((2, 0, 1))
input_image = input_image.reshape((n, c, h, w)).astype(np.float32)
input_image_info = np.asarray(
[[input_image_size[0], input_image_size[1], 1]], dtype=np.float32)
# Run the net.
inf_start = time.time()
outputs = mask_rcnn_exec_net.infer({
'im_data': input_image,
'im_info': input_image_info
})
# Parse detection results of the current request
boxes = outputs['boxes']
scores = outputs['scores']
classes = outputs['classes'].astype(np.uint32)
raw_masks = outputs['raw_masks']
text_features = outputs['text_features']
# Filter out detections with low confidence.
detections_filter = scores > args.prob_threshold
scores = scores[detections_filter]
classes = classes[detections_filter]
boxes = boxes[detections_filter]
raw_masks = raw_masks[detections_filter]
text_features = text_features[detections_filter]
boxes[:, 0::2] /= scale_x
boxes[:, 1::2] /= scale_y
masks = []
for box, cls, raw_mask in zip(boxes, classes, raw_masks):
raw_cls_mask = raw_mask[cls, ...]
mask = segm_postprocess(box, raw_cls_mask, frame.shape[0],
frame.shape[1])
masks.append(mask)
texts = []
for feature in text_features:
feature = text_enc_exec_net.infer({'input': feature})['output']
feature = np.reshape(feature,
(feature.shape[0], feature.shape[1], -1))
feature = np.transpose(feature, (0, 2, 1))
hidden = np.zeros(hidden_shape)
prev_symbol_index = np.ones((1, )) * SOS_INDEX
text = ''
for i in range(MAX_SEQ_LEN):
decoder_output = text_dec_exec_net.infer({
args.trd_input_prev_symbol:
prev_symbol_index,
args.trd_input_prev_hidden:
hidden,
args.trd_input_encoder_outputs:
feature
})
symbols_distr = decoder_output[args.trd_output_symbols_distr]
prev_symbol_index = int(np.argmax(symbols_distr, axis=1))
if prev_symbol_index == EOS_INDEX:
break
text += args.alphabet[prev_symbol_index]
hidden = decoder_output[args.trd_output_cur_hidden]
texts.append(text)
inf_end = time.time()
inf_time = inf_end - inf_start
render_start = time.time()
if len(boxes) and args.raw_output_message:
log.info('Detected boxes:')
log.info(
' Class ID | Confidence | XMIN | YMIN | XMAX | YMAX '
)
for box, cls, score, mask in zip(boxes, classes, scores, masks):
log.info(
'{:>10} | {:>10f} | {:>8.2f} | {:>8.2f} | {:>8.2f} | {:>8.2f} '
.format(cls, score, *box))
# Get instance track IDs.
masks_tracks_ids = None
if tracker is not None:
masks_tracks_ids = tracker(masks, classes)
# Visualize masks.
frame = visualizer(frame, boxes, classes, scores, masks, texts,
masks_tracks_ids)
# Draw performance stats.
inf_time_message = 'Inference and post-processing time: {:.3f} ms'.format(
inf_time * 1000)
render_time_message = 'OpenCV rendering time: {:.3f} ms'.format(
render_time * 1000)
cv2.putText(frame, inf_time_message, (15, 15),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (200, 10, 10), 1)
cv2.putText(frame, render_time_message, (15, 30),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 10, 200), 1)
# Print performance counters.
if args.perf_counts:
perf_counts = mask_rcnn_exec_net.requests[0].get_perf_counts()
log.info('Performance counters:')
print('{:<70} {:<15} {:<15} {:<15} {:<10}'.format(
'name', 'layer_type', 'exet_type', 'status', 'real_time, us'))
for layer, stats in perf_counts.items():
print('{:<70} {:<15} {:<15} {:<15} {:<10}'.format(
layer, stats['layer_type'], stats['exec_type'],
stats['status'], stats['real_time']))
if not args.no_show:
# Show resulting image.
cv2.imshow('Results', frame)
render_end = time.time()
render_time = render_end - render_start
if not args.no_show:
key = cv2.waitKey(args.delay)
esc_code = 27
if key == esc_code:
break
cv2.destroyAllWindows()
cap.release()
def main():
log.basicConfig(format='[ %(levelname)s ] %(message)s',
level=log.INFO,
stream=sys.stdout)
args = build_argparser().parse_args()
# Plugin initialization for specified device and load extensions library if specified.
log.info('Creating Inference Engine...')
ie = IECore()
if args.cpu_extension and 'CPU' in args.device:
ie.add_extension(args.cpu_extension, 'CPU')
# Read IR
log.info('Loading Mask-RCNN network')
mask_rcnn_net = ie.read_network(
args.mask_rcnn_model,
os.path.splitext(args.mask_rcnn_model)[0] + '.bin')
log.info('Loading encoder part of text recognition network')
text_enc_net = ie.read_network(
args.text_enc_model,
os.path.splitext(args.text_enc_model)[0] + '.bin')
log.info('Loading decoder part of text recognition network')
text_dec_net = ie.read_network(
args.text_dec_model,
os.path.splitext(args.text_dec_model)[0] + '.bin')
model_required_inputs = {'image'}
old_model_required_inputs = {'im_data', 'im_info'}
if set(mask_rcnn_net.input_info) == model_required_inputs:
old_model = False
required_output_keys = {'boxes', 'labels', 'masks', 'text_features.0'}
n, c, h, w = mask_rcnn_net.input_info['image'].input_data.shape
elif set(mask_rcnn_net.input_info) == old_model_required_inputs:
old_model = True
required_output_keys = {
'boxes', 'scores', 'classes', 'raw_masks', 'text_features'
}
n, c, h, w = mask_rcnn_net.input_info['im_data'].input_data.shape
args.alphabet = ' 0123456789abcdefghijklmnopqrstuvwxyz'
args.tr_threshold = 0
else:
raise RuntimeError(
'Demo supports only topologies with the following input keys: '
f'{model_required_inputs} or {old_model_required_inputs}.')
assert required_output_keys.issubset(mask_rcnn_net.outputs.keys()), \
f'Demo supports only topologies with the following output keys: {required_output_keys}' \
f'Found: {mask_rcnn_net.outputs.keys()}.'
assert n == 1, 'Only batch 1 is supported by the demo application'
log.info('Loading IR to the plugin...')
mask_rcnn_exec_net = ie.load_network(network=mask_rcnn_net,
device_name=args.device,
num_requests=2)
text_enc_exec_net = ie.load_network(network=text_enc_net,
device_name=args.device)
text_dec_exec_net = ie.load_network(network=text_dec_net,
device_name=args.device)
hidden_shape = text_dec_net.input_info[
args.trd_input_prev_hidden].input_data.shape
del mask_rcnn_net
del text_enc_net
del text_dec_net
input_source = args.input_source
if os.path.isdir(input_source):
cap = FolderCapture(input_source)
else:
try:
input_source = int(args.input_source)
cap = cv2.VideoCapture(input_source)
cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
except ValueError:
cap = cv2.VideoCapture(input_source)
if not cap.isOpened():
raise RuntimeError('Failed to open "{}"'.format(input_source))
ret, frame = cap.read()
if not ret:
raise RuntimeError("Can't read an image from the input")
if args.no_track:
tracker = None
else:
tracker = StaticIOUTracker()
visualizer = Visualizer(['__background__', 'text'],
show_boxes=args.show_boxes,
show_scores=args.show_scores)
render_time = 0
presenter = monitors.Presenter(args.utilization_monitors, 45,
(frame.shape[1] // 4, frame.shape[0] // 8))
log.info('Starting inference...')
print(
"To close the application, press 'CTRL+C' here or switch to the output window and press ESC key"
)
while ret:
if not args.keep_aspect_ratio:
# Resize the image to a target size.
scale_x = w / frame.shape[1]
scale_y = h / frame.shape[0]
input_image = cv2.resize(frame, (w, h))
else:
# Resize the image to keep the same aspect ratio and to fit it to a window of a target size.
scale_x = scale_y = min(h / frame.shape[0], w / frame.shape[1])
input_image = cv2.resize(frame, None, fx=scale_x, fy=scale_y)
input_image_size = input_image.shape[:2]
input_image = np.pad(input_image,
((0, h - input_image_size[0]),
(0, w - input_image_size[1]), (0, 0)),
mode='constant',
constant_values=0)
# Change data layout from HWC to CHW.
input_image = input_image.transpose((2, 0, 1))
input_image = input_image.reshape((n, c, h, w)).astype(np.float32)
input_image_info = np.asarray(
[[input_image_size[0], input_image_size[1], 1]], dtype=np.float32)
# Run the net.
inf_start = time.time()
if old_model:
outputs = mask_rcnn_exec_net.infer({
'im_data': input_image,
'im_info': input_image_info
})
else:
outputs = mask_rcnn_exec_net.infer({'image': input_image})
# Parse detection results of the current request
if old_model:
boxes = outputs['boxes']
scores = outputs['scores']
classes = outputs['classes'].astype(np.uint32)
raw_masks = outputs['raw_masks']
text_features = outputs['text_features']
else:
boxes = outputs['boxes'][:, :4]
scores = outputs['boxes'][:, 4]
classes = outputs['labels'].astype(np.uint32)
raw_masks = outputs['masks']
text_features = outputs['text_features.0']
# Filter out detections with low confidence.
detections_filter = scores > args.prob_threshold
scores = scores[detections_filter]
classes = classes[detections_filter]
boxes = boxes[detections_filter]
raw_masks = raw_masks[detections_filter]
text_features = text_features[detections_filter]
boxes[:, 0::2] /= scale_x
boxes[:, 1::2] /= scale_y
masks = []
for box, cls, raw_mask in zip(boxes, classes, raw_masks):
if old_model:
raw_mask = raw_mask[cls, ...]
mask = segm_postprocess(box, raw_mask, frame.shape[0],
frame.shape[1])
masks.append(mask)
texts = []
for feature in text_features:
feature = text_enc_exec_net.infer({'input': feature})['output']
feature = np.reshape(feature,
(feature.shape[0], feature.shape[1], -1))
feature = np.transpose(feature, (0, 2, 1))
hidden = np.zeros(hidden_shape)
prev_symbol_index = np.ones((1, )) * SOS_INDEX
text = ''
text_confidence = 1.0
for i in range(MAX_SEQ_LEN):
decoder_output = text_dec_exec_net.infer({
args.trd_input_prev_symbol:
prev_symbol_index,
args.trd_input_prev_hidden:
hidden,
args.trd_input_encoder_outputs:
feature
})
symbols_distr = decoder_output[args.trd_output_symbols_distr]
symbols_distr_softmaxed = softmax(symbols_distr, axis=1)[0]
prev_symbol_index = int(np.argmax(symbols_distr, axis=1))
text_confidence *= symbols_distr_softmaxed[prev_symbol_index]
if prev_symbol_index == EOS_INDEX:
break
text += args.alphabet[prev_symbol_index]
hidden = decoder_output[args.trd_output_cur_hidden]
texts.append(text if text_confidence >= args.tr_threshold else '')
inf_end = time.time()
inf_time = inf_end - inf_start
render_start = time.time()
if len(boxes) and args.raw_output_message:
log.info('Detected boxes:')
log.info(
' Class ID | Confidence | XMIN | YMIN | XMAX | YMAX '
)
for box, cls, score, mask in zip(boxes, classes, scores, masks):
log.info(
'{:>10} | {:>10f} | {:>8.2f} | {:>8.2f} | {:>8.2f} | {:>8.2f} '
.format(cls, score, *box))
# Get instance track IDs.
masks_tracks_ids = None
if tracker is not None:
masks_tracks_ids = tracker(masks, classes)
presenter.drawGraphs(frame)
# Visualize masks.
frame = visualizer(frame, boxes, classes, scores, masks, texts,
masks_tracks_ids)
# Draw performance stats.
inf_time_message = 'Inference and post-processing time: {:.3f} ms'.format(
inf_time * 1000)
render_time_message = 'OpenCV rendering time: {:.3f} ms'.format(
render_time * 1000)
cv2.putText(frame, inf_time_message, (15, 15),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (200, 10, 10), 1)
cv2.putText(frame, render_time_message, (15, 30),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 10, 200), 1)
# Print performance counters.
if args.perf_counts:
perf_counts = mask_rcnn_exec_net.requests[0].get_perf_counts()
log.info('Performance counters:')
print('{:<70} {:<15} {:<15} {:<15} {:<10}'.format(
'name', 'layer_type', 'exet_type', 'status', 'real_time, us'))
for layer, stats in perf_counts.items():
print('{:<70} {:<15} {:<15} {:<15} {:<10}'.format(
layer, stats['layer_type'], stats['exec_type'],
stats['status'], stats['real_time']))
if not args.no_show:
# Show resulting image.
cv2.imshow('Results', frame)
render_end = time.time()
render_time = render_end - render_start
if not args.no_show:
key = cv2.waitKey(args.delay)
esc_code = 27
if key == esc_code:
break
presenter.handleKey(key)
ret, frame = cap.read()
print(presenter.reportMeans())
cv2.destroyAllWindows()
cap.release()