def build_siamfcpp_tester(task_cfg):
# build model
model = model_builder.build("track", task_cfg.model)
# build pipeline
pipeline = pipeline_builder.build("track", task_cfg.pipeline, model)
# build tester
testers = tester_builder("track", task_cfg.tester, "tester", pipeline)
return testers
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
vw = None
resize_ratio = args.resize
dump_only = args.dump_only
# create video stream
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'))
elif not osp.isfile(args.video):
logger.info("Starting from video frame image files...")
vs = ImageFileVideoStream(args.video, init_counter=args.start_index)
else:
logger.info("Starting from video file...")
vs = cv2.VideoCapture(args.video)
# create video writer to output video
if args.output:
if osp.isdir(args.output):
vw = ImageFileVideoWriter(args.output)
else:
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
width, height = vs.get(3), vs.get(4)
vw = cv2.VideoWriter(
args.output, fourcc, 25,
(int(width * resize_ratio), int(height * resize_ratio)))
# loop over sequence
while vs.isOpened():
key = 255
ret, frame = vs.read()
logger.debug("frame: {}".format(ret))
if ret:
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)
# catch key if
if (init_box is None) or (vw is None):
logger.debug("Press key s to select object.")
key = cv2.waitKey(30) & 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 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))
vs.release()
if vw is not None:
vw.release()
cv2.destroyAllWindows()
if args.model_path:
root_cfg.test.track.model.task_model.SiamTrack.pretrain_model_path = args.model_path
#print(root_cfg.test.track.model.task_model.SiamTrack.pretrain_model_path)
logger.info("Load experiment configuration at: %s" % exp_cfg_path)
ROOT_PATH = os.getcwd()
root_cfg = complete_path_wt_root_in_cfg(root_cfg,
ROOT_PATH) #把cfg中的相对路径,变成了绝对路径
root_cfg = root_cfg.test #获取test的yaml root_cfg['test']
#root_cfg['track']
#root_cfg['vos']
task, task_cfg = specify_task(root_cfg) #获取任务 track or vos ,
task_cfg.freeze()
torch.multiprocessing.set_start_method('spawn', force=True)
# build_siamfcpp_tester
model = model_builder.build("track", task_cfg.model)
# build pipeline
pipeline = pipeline_builder.build("track", task_cfg.pipeline,
model) #配置超参数
# build tester
testers = tester_builder("track", task_cfg.tester, "tester", pipeline)
for tester in testers:
tester.test()