if __name__ == '__main__':
# parsing
parser = make_parser()
parsed_args = parser.parse_args()
# experiment config
exp_cfg_path = osp.realpath(parsed_args.config)
root_cfg.merge_from_file(exp_cfg_path)
logger.info("Load experiment configuration at: %s" % exp_cfg_path)
# resolve config
root_cfg = complete_path_wt_root_in_cfg(root_cfg, ROOT_PATH)
root_cfg = root_cfg.test
task, task_cfg_origin = specify_task(root_cfg)
# hpo config
with open(parsed_args.hpo_config, "r") as f:
hpo_cfg = yaml.safe_load(f)
hpo_cfg = hpo_cfg["test"][task]
hpo_schedules = hpo.parse_hp_path_and_range(hpo_cfg)
csv_file = osp.join(hpo_cfg["exp_save"],
"hpo_{}.csv".format(task_cfg_origin["exp_name"]))
while True:
task_cfg = deepcopy(task_cfg_origin)
hpo_exp_dict = hpo.sample_and_update_hps(task_cfg, hpo_schedules)
if task == "track":
testers = build_siamfcpp_tester(task_cfg)
exit(0)
if __name__ == '__main__':
# parsing
parser = make_parser()
parsed_args = parser.parse_args()
# experiment config
exp_cfg_path = osp.realpath(parsed_args.config)
root_cfg.merge_from_file(exp_cfg_path)
logger.info("Load experiment configuration at: %s" % exp_cfg_path)
logger.info(
"Merged with root_cfg imported from videoanalyst.config.config.cfg")
# resolve config
root_cfg = root_cfg.train
task, task_cfg = specify_task(root_cfg)
task_cfg.data.num_workers = 2
task_cfg.data.sampler.submodules.dataset.GOT10kDataset.check_integrity = False
task_cfg.freeze()
if parsed_args.target == "dataloader":
logger.info("visualize for dataloader")
with Timer(name="Dataloader building", verbose=True):
dataloader = dataloader_builder.build(task, task_cfg.data)
for batch_training_data in dataloader:
keys = list(batch_training_data.keys())
batch_size = len(batch_training_data[keys[0]])
training_samples = [{
k: v[[idx]]
for k, v in batch_training_data.items()
default="cpu",
type=str,
help='torch.device')
return parser
parser = make_parser()
parsed_args = parser.parse_args()
exp_cfg_path = parsed_args.config
root_cfg.merge_from_file(exp_cfg_path)
logger.info("Load experiment configuration at: %s" % exp_cfg_path)
# resolve config
test_cfg = root_cfg.test
task, task_cfg = specify_task(test_cfg)
task_cfg.freeze()
# build model
model = model_builder.build(task, task_cfg.model)
# build pipeline
pipeline = pipeline_builder.build(task, task_cfg.pipeline, model)
# build dataset
datasets = dataset_buidler.build(
task, root_cfg.train.track.data.sampler.submodules.dataset)
dataset = datasets[0]
dev = torch.device(parsed_args.device)
pipeline.set_device(dev)
if __name__ == "__main__":
if __name__ == '__main__':
# parsing
parser = make_parser()
parsed_args = parser.parse_args()
# experiment config
exp_cfg_path = osp.realpath(parsed_args.config)
# from IPython import embed;embed()
root_cfg.merge_from_file(exp_cfg_path)
logger.info("Load experiment configuration at: %s" % exp_cfg_path)
# resolve config
root_cfg = complete_path_wt_root_in_cfg(root_cfg, ROOT_PATH)
root_cfg = root_cfg.test
task, task_cfg_origin = specify_task(root_cfg)
# hpo config
with open(parsed_args.hpo_config, "r") as f:
hpo_cfg = yaml.safe_load(f)
hpo_cfg = hpo_cfg["test"]
_, hpo_cfg = specify_task(hpo_cfg)
hpo_schedules = hpo.parse_hp_path_and_range(hpo_cfg)
# results = [hpo.sample_and_update_hps(task_cfg, hpo_schedules) for _ in range(5)]
# merged_result = hpo.merge_result_dict(results)
csv_file = osp.join(hpo_cfg["exp_save"],
"hpo_{}.csv".format(task_cfg_origin["exp_name"]))
# from IPython import embed;embed();exit(0)
def main(args):
root_cfg = cfg
root_cfg.merge_from_file(args.config)
logger.info("Load experiment configuration at: %s" % args.config)
# resolve config
root_cfg = complete_path_wt_root_in_cfg(root_cfg, ROOT_PATH)
root_cfg = root_cfg.test
task, task_cfg = specify_task(root_cfg)
task_cfg.freeze()
window_name = task_cfg.exp_name
# build model
model = model_builder.build(task, task_cfg.model)
# build pipeline
pipeline = pipeline_builder.build(task, task_cfg.pipeline, model)
dev = torch.device(args.device)
pipeline.to_device(dev)
init_box = None
template = None
vw = None
if args.video == "webcam":
logger.info("[INFO] starting video stream...")
vs = cv2.VideoCapture(0)
vs.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'))
else:
vs = cv2.VideoCapture(args.video)
if args.output:
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
width, height = vs.get(3), vs.get(4)
vw = cv2.VideoWriter(args.output, fourcc, 25,
(int(width), int(height)))
while vs.isOpened():
ret, frame = vs.read()
if ret:
if init_box is not None:
time_a = time.time()
rect_pred = pipeline.update(frame)
show_frame = frame.copy()
time_cost = time.time() - time_a
bbox_pred = xywh2xyxy(rect_pred)
bbox_pred = tuple(map(int, bbox_pred))
cv2.putText(show_frame,
"track cost: {:.4f} s".format(time_cost),
(128, 20), cv2.FONT_HERSHEY_COMPLEX, font_size,
(0, 0, 255), font_width)
cv2.rectangle(show_frame, bbox_pred[:2], bbox_pred[2:],
(0, 255, 0))
if template is not None:
show_frame[:128, :128] = template
else:
show_frame = frame
cv2.imshow(window_name, show_frame)
if vw is not None:
vw.write(show_frame)
key = cv2.waitKey(30) & 0xFF
if key == ord("q"):
break
# if the 's' key is selected, we are going to "select" a bounding
# box to track
elif key == ord("s"):
# select the bounding box of the object we want to track (make
# sure you press ENTER or SPACE after selecting the ROI)
box = cv2.selectROI(window_name,
frame,
fromCenter=False,
showCrosshair=True)
if box[2] > 0 and box[3] > 0:
init_box = box
template = cv2.resize(
frame[box[1]:box[1] + box[3], box[0]:box[0] + box[2]],
(128, 128))
pipeline.init(frame, init_box)
elif key == ord("c"):
init_box = None
template = None
vs.release()
if vw is not None:
vw.release()
cv2.destroyAllWindows()
def main(args):
root_cfg = cfg
root_cfg.merge_from_file(args.config)
logger.info("Load experiment configuration at: %s" % args.config)
# resolve config
root_cfg = complete_path_wt_root_in_cfg(root_cfg, ROOT_PATH)
root_cfg = root_cfg.test
task, task_cfg = specify_task(root_cfg)
task_cfg.freeze()
window_name = task_cfg.exp_name
# build model
model = model_builder.build(task, task_cfg.model)
# build pipeline
pipeline = pipeline_builder.build(task, task_cfg.pipeline, model)
dev = torch.device(args.device)
pipeline.set_device(dev)
init_box = None
template = None
if len(args.init_bbox) == 4:
init_box = args.init_bbox
video_name = "untitled"
vw = None
resize_ratio = args.resize
dump_only = args.dump_only
# create video stream
# from webcam
if args.video == "webcam":
logger.info("Starting video stream...")
vs = cv2.VideoCapture(0)
vs.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'))
formated_time_str = time.strftime(r"%Y%m%d-%H%M%S", time.localtime())
video_name = "webcam-{}".format(formated_time_str)
# from image files
elif not osp.isfile(args.video):
logger.info("Starting from video frame image files...")
vs = ImageFileVideoStream(args.video, init_counter=args.start_index)
video_name = osp.basename(osp.dirname(args.video))
# from video file
else:
logger.info("Starting from video file...")
vs = cv2.VideoCapture(args.video)
video_name = osp.splitext(osp.basename(args.video))[0]
# create video writer to output video
if args.output:
# save as image files
if not str(args.output).endswith(r".mp4"):
vw = ImageFileVideoWriter(osp.join(args.output, video_name))
# save as a single video file
else:
vw = VideoWriter(args.output, fps=20)
# loop over sequence
frame_idx = 0 # global frame index
while vs.isOpened():
key = 255
ret, frame = vs.read()
if ret:
logger.debug("frame: {}".format(frame_idx))
if template is not None:
time_a = time.time()
rect_pred = pipeline.update(frame)
logger.debug(rect_pred)
show_frame = frame.copy()
time_cost = time.time() - time_a
bbox_pred = xywh2xyxy(rect_pred)
bbox_pred = tuple(map(int, bbox_pred))
cv2.putText(show_frame,
"track cost: {:.4f} s".format(time_cost), (128, 20),
cv2.FONT_HERSHEY_COMPLEX, font_size, (0, 0, 255),
font_width)
cv2.rectangle(show_frame, bbox_pred[:2], bbox_pred[2:],
(0, 255, 0))
if template is not None:
show_frame[:128, :128] = template
else:
show_frame = frame
show_frame = cv2.resize(
show_frame, (int(show_frame.shape[1] * resize_ratio),
int(show_frame.shape[0] * resize_ratio))) # resize
if not dump_only:
cv2.imshow(window_name, show_frame)
if vw is not None:
vw.write(show_frame)
else:
break
# catch key if
if (init_box is None) or (vw is None):
logger.debug("Press key s to select object.")
if (frame_idx == 0):
wait_time = 5000
else:
wait_time = 30
key = cv2.waitKey(wait_time) & 0xFF
logger.debug("key: {}".format(key))
if key == ord("q"):
break
# if the 's' key is selected, we are going to "select" a bounding
# box to track
elif key == ord("s"):
# select the bounding box of the object we want to track (make
# sure you press ENTER or SPACE after selecting the ROI)
logger.debug("Select object to track")
box = cv2.selectROI(window_name,
frame,
fromCenter=False,
showCrosshair=True)
if box[2] > 0 and box[3] > 0:
init_box = box
elif key == ord("c"):
logger.debug(
"init_box/template released, press key s again to select object."
)
init_box = None
template = None
if (init_box is not None) and (template is None):
template = cv2.resize(
frame[int(init_box[1]):int(init_box[1] + init_box[3]),
int(init_box[0]):int(init_box[0] + init_box[2])],
(128, 128))
pipeline.init(frame, init_box)
logger.debug("pipeline initialized with bbox : {}".format(init_box))
frame_idx += 1
vs.release()
if vw is not None:
vw.release()
cv2.destroyAllWindows()
def main(args):
global polygon_points, lbt_flag, rbt_flag
root_cfg = cfg
root_cfg.merge_from_file(args.config)
logger.info("Load experiment configuration at: %s" % args.config)
# resolve config
root_cfg = root_cfg.test
task, task_cfg = specify_task(root_cfg)
task_cfg.freeze()
window_name = task_cfg.exp_name
cv2.namedWindow(window_name)
cv2.setMouseCallback(window_name, draw_polygon)
# build model
tracker_model = model_builder.build("track", task_cfg.tracker_model)
tracker = pipeline_builder.build("track",
task_cfg.tracker_pipeline,
model=tracker_model)
segmenter = model_builder.build('vos', task_cfg.segmenter)
# build pipeline
pipeline = pipeline_builder.build('vos',
task_cfg.pipeline,
segmenter=segmenter,
tracker=tracker)
dev = torch.device(args.device)
pipeline.set_device(dev)
init_mask = None
init_box = None
template = None
video_name = "untitled"
vw = None
resize_ratio = args.resize
dump_only = args.dump_only
# create video stream
# from webcam
if args.video == "webcam":
logger.info("Starting video stream...")
vs = cv2.VideoCapture(0)
vs.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'))
formated_time_str = time.strftime(r"%Y%m%d-%H%M%S", time.localtime())
video_name = "webcam-{}".format(formated_time_str)
# from image files
elif not osp.isfile(args.video):
logger.info("Starting from video frame image files...")
vs = ImageFileVideoStream(args.video, init_counter=args.start_index)
video_name = osp.basename(osp.dirname(args.video))
# from video file
else:
logger.info("Starting from video file...")
vs = cv2.VideoCapture(args.video)
video_name = osp.splitext(osp.basename(args.video))[0]
# create video writer to output video
if args.output:
# save as image files
if not str(args.output).endswith(r".mp4"):
vw = ImageFileVideoWriter(osp.join(args.output, video_name))
# save as a single video file
else:
vw = VideoWriter(args.output, fps=20)
# loop over sequence
frame_idx = 0 # global frame index
while vs.isOpened():
key = 255
ret, frame = vs.read()
if ret:
if template is not None:
time_a = time.time()
score_map = pipeline.update(frame)
mask = (score_map > 0.5).astype(np.uint8) * 2
color_mask = mask_colorize(mask, 10, color_map)
color_mask = cv2.resize(color_mask,
(frame.shape[1], frame.shape[0]),
interpolation=cv2.INTER_NEAREST)
show_frame = cv2.addWeighted(frame, 0.6, color_mask, 0.4, 0)
time_cost = time.time() - time_a
cv2.putText(show_frame,
"track cost: {:.4f} s".format(time_cost),
(128, 20), cv2.FONT_HERSHEY_COMPLEX, font_size,
(0, 0, 255), font_width)
if template is not None:
show_frame[:128, :128] = template
else:
show_frame = frame
show_frame = cv2.resize(
show_frame,
(int(show_frame.shape[1] * resize_ratio),
int(show_frame.shape[0] * resize_ratio))) # resize
if not dump_only:
cv2.imshow(window_name, show_frame)
if vw is not None:
vw.write(show_frame)
else:
break
# catch key if
if (init_mask is None) or (vw is None):
if (frame_idx == 0):
wait_time = 5000
else:
wait_time = 30
key = cv2.waitKey(wait_time) & 0xFF
if key == ord("q"):
break
# if the 's' key is selected, we are going to "select" a bounding
# box to track
elif key == ord("s"):
# select the bounding box of the object we want to track (make
# sure you press ENTER or SPACE after selecting the ROI)
logger.debug(
"Select points object to track, left click for new pt, right click to finish"
)
polygon_points = []
while not rbt_flag:
if lbt_flag:
print(polygon_points[-1])
cv2.circle(show_frame, polygon_points[-1], 5, (0, 0, 255),
2)
if len(polygon_points) > 1:
cv2.line(show_frame, polygon_points[-2],
polygon_points[-1], (255, 0, 0), 2)
lbt_flag = False
cv2.imshow(window_name, show_frame)
key = cv2.waitKey(10) & 0xFF
if len(polygon_points) > 2:
np_pts = np.array(polygon_points)
init_box = cv2.boundingRect(np_pts)
zero_mask = np.zeros(
(show_frame.shape[0], show_frame.shape[1]), dtype=np.uint8)
init_mask = cv2.fillPoly(zero_mask, [np_pts], (1, ))
rbt_flag = False
elif key == ord("c"):
logger.debug(
"init_box/template released, press key s again to select object."
)
init_mask = None
init_box = None
template = None
if (init_mask is not None) and (template is None):
template = cv2.resize(
frame[int(init_box[1]):int(init_box[1] + init_box[3]),
int(init_box[0]):int(init_box[0] + init_box[2])],
(128, 128))
pipeline.init(frame, init_box, init_mask)
logger.debug(
"pipeline initialized with bbox : {}".format(init_box))
frame_idx += 1
vs.release()
if vw is not None:
vw.release()
cv2.destroyAllWindows()
