def main():
""" Main function. """
log.basicConfig(format='[ %(levelname)s ] %(message)s', level=log.INFO, stream=sys.stdout)
args = build_argparser().parse_args()
img_retrieval = ImageRetrieval(args.model, args.device, args.gallery, INPUT_SIZE,
args.cpu_extension)
frames = RoiDetectorOnVideo(args.i)
compute_embeddings_times = []
search_in_gallery_times = []
positions = []
for image, view_frame in frames:
position = None
sorted_indexes = []
if image is not None:
image = central_crop(image, divide_by=5, shift=1)
elapsed, probe_embedding = time_elapsed(img_retrieval.compute_embedding, image)
compute_embeddings_times.append(elapsed)
elapsed, (sorted_indexes, distances) = time_elapsed(img_retrieval.search_in_gallery,
probe_embedding)
search_in_gallery_times.append(elapsed)
sorted_classes = [img_retrieval.gallery_classes[i] for i in sorted_indexes]
if args.ground_truth is not None:
position = sorted_classes.index(
img_retrieval.text_label_to_class_id[args.ground_truth])
positions.append(position)
log.info("ROI detected, found: %d, postion of target: %d",
sorted_classes[0], position)
else:
log.info("ROI detected, found: %s", sorted_classes[0])
key = visualize(view_frame, position,
[img_retrieval.impaths[i] for i in sorted_indexes],
distances[sorted_indexes] if position is not None else None,
img_retrieval.input_size, np.mean(compute_embeddings_times),
np.mean(search_in_gallery_times), imshow_delay=3)
if key == 27:
break
if positions:
compute_metrics(positions)
def main():
log.basicConfig(format='[ %(levelname)s ] %(message)s',
level=log.INFO,
stream=sys.stdout)
args = build_argparser().parse_args()
img_retrieval = ImageRetrieval(args.model, args.device, args.gallery,
INPUT_SIZE, args.cpu_extension)
cap = open_images_capture(args.input, args.loop)
if cap.get_type() not in ('VIDEO', 'CAMERA'):
raise RuntimeError(
"The input should be a video file or a numeric camera ID")
frames = RoiDetectorOnVideo(cap)
compute_embeddings_times = []
search_in_gallery_times = []
positions = []
frames_processed = 0
presenter = monitors.Presenter(args.utilization_monitors, 0)
video_writer = cv2.VideoWriter()
for image, view_frame in frames:
position = None
sorted_indexes = []
if image is not None:
image = central_crop(image, divide_by=5, shift=1)
elapsed, probe_embedding = time_elapsed(
img_retrieval.compute_embedding, image)
compute_embeddings_times.append(elapsed)
elapsed, (sorted_indexes, distances) = time_elapsed(
img_retrieval.search_in_gallery, probe_embedding)
search_in_gallery_times.append(elapsed)
sorted_classes = [
img_retrieval.gallery_classes[i] for i in sorted_indexes
]
if args.ground_truth is not None:
position = sorted_classes.index(
img_retrieval.text_label_to_class_id[args.ground_truth])
positions.append(position)
log.info("ROI detected, found: %d, position of target: %d",
sorted_classes[0], position)
else:
log.info("ROI detected, found: %s", sorted_classes[0])
image, key = visualize(
view_frame,
position, [img_retrieval.impaths[i] for i in sorted_indexes],
distances[sorted_indexes] if position is not None else None,
img_retrieval.input_size,
np.mean(compute_embeddings_times),
np.mean(search_in_gallery_times),
imshow_delay=3,
presenter=presenter,
no_show=args.no_show)
if frames_processed == 0:
if args.output and not video_writer.open(
args.output, cv2.VideoWriter_fourcc(*'MJPG'), cap.fps(),
(image.shape[1], image.shape[0])):
raise RuntimeError("Can't open video writer")
frames_processed += 1
if video_writer.isOpened() and (args.output_limit <= 0 or
frames_processed <= args.output_limit):
video_writer.write(image)
if key == 27:
break
print(presenter.reportMeans())
if positions:
compute_metrics(positions)