def run(params, capture, detector, reid):
win_name = 'Multi camera tracking'
config = {}
if len(params.config):
config = read_py_config(params.config)
tracker = MultiCameraTracker(capture.get_num_sources(), reid, **config)
thread_body = FramesThreadBody(capture,
max_queue_length=len(capture.captures) * 2)
frames_thread = Thread(target=thread_body)
frames_thread.start()
if len(params.output_video):
video_output_size = (1920 // capture.get_num_sources(), 1080)
fourcc = cv.VideoWriter_fourcc(*'XVID')
output_video = cv.VideoWriter(params.output_video, fourcc, 24.0,
video_output_size)
else:
output_video = None
while thread_body.process:
start = time.time()
try:
frames = thread_body.frames_queue.get_nowait()
except queue.Empty:
frames = None
if frames is None:
continue
all_detections = detector.get_detections(frames)
all_masks = [[] for _ in range(len(all_detections))]
for i, detections in enumerate(all_detections):
all_detections[i] = [det[0] for det in detections]
all_masks[i] = [det[2] for det in detections if len(det) == 3]
tracker.process(frames, all_detections, all_masks)
tracked_objects = tracker.get_tracked_objects()
fps = round(1 / (time.time() - start), 1)
vis = visualize_multicam_detections(frames, tracked_objects, fps)
if not params.no_show:
cv.imshow(win_name, vis)
if cv.waitKey(1) == 27:
break
if output_video:
output_video.write(cv.resize(vis, video_output_size))
thread_body.process = False
frames_thread.join()
if len(params.history_file):
history = tracker.get_all_tracks_history()
with open(params.history_file, 'w') as outfile:
json.dump(history, outfile)
def main():
current_dir = os.path.dirname(os.path.abspath(__file__))
"""Prepares data for the person recognition demo"""
parser = argparse.ArgumentParser(description='Multi camera multi person \
tracking live demo script')
parser.add_argument('-i',
type=str,
nargs='+',
help='Input sources (indexes \
of cameras or paths to video files)',
required=True)
parser.add_argument('--config',
type=str,
default=os.path.join(current_dir, 'config.py'),
required=False,
help='Configuration file')
parser.add_argument('--detections',
type=str,
help='JSON file with bounding boxes')
parser.add_argument('-m',
'--m_detector',
type=str,
required=False,
help='Path to the person detection model')
parser.add_argument('--t_detector',
type=float,
default=0.6,
help='Threshold for the person detection model')
parser.add_argument('--m_segmentation',
type=str,
required=False,
help='Path to the person instance segmentation model')
parser.add_argument(
'--t_segmentation',
type=float,
default=0.6,
help='Threshold for person instance segmentation model')
parser.add_argument('--m_reid',
type=str,
required=True,
help='Path to the person re-identification model')
parser.add_argument('--output_video',
type=str,
default='',
required=False,
help='Optional. Path to output video')
parser.add_argument(
'--history_file',
type=str,
default='',
required=False,
help='Optional. Path to file in JSON format to save results of the demo'
)
parser.add_argument(
'--save_detections',
type=str,
default='',
required=False,
help='Optional. Path to file in JSON format to save bounding boxes')
parser.add_argument("--no_show",
help="Optional. Don't show output",
action='store_true')
parser.add_argument('-d', '--device', type=str, default='CPU')
parser.add_argument('-l',
'--cpu_extension',
help='MKLDNN (CPU)-targeted custom layers.Absolute \
path to a shared library with the kernels impl.',
type=str,
default=None)
parser.add_argument('-u',
'--utilization_monitors',
default='',
type=str,
help='Optional. List of monitors to show initially.')
args = parser.parse_args()
if check_detectors(args) != 1:
sys.exit(1)
if len(args.config):
log.info('Reading configuration file {}'.format(args.config))
config = read_py_config(args.config)
else:
log.error(
'No configuration file specified. Please specify parameter \'--config\''
)
sys.exit(1)
random.seed(config['random_seed'])
capture = MulticamCapture(args.i)
log.info("Creating Inference Engine")
ie = IECore()
if args.detections:
person_detector = DetectionsFromFileReader(args.detections,
args.t_detector)
elif args.m_segmentation:
person_detector = MaskRCNN(ie, args.m_segmentation,
args.t_segmentation,
args.device, args.cpu_extension,
capture.get_num_sources())
else:
person_detector = Detector(ie, args.m_detector, args.t_detector,
args.device, args.cpu_extension,
capture.get_num_sources())
if args.m_reid:
person_recognizer = VectorCNN(ie, args.m_reid, args.device,
args.cpu_extension)
else:
person_recognizer = None
run(args, config, capture, person_detector, person_recognizer)
log.info('Demo finished successfully')
def run(params, capture, detector, reid, jot): #params : args 임
win_name = 'TEAM_KOTLIN'
config = {}
if len(params.config):
config = read_py_config(params.config)
tracker = MultiCameraTracker(capture.get_num_sources(), reid, **config)
thread_body = FramesThreadBody(capture,
max_queue_length=len(capture.captures) * 2)
frames_thread = Thread(target=thread_body)
frames_thread.start()
if len(params.output_video):
video_output_size = (1920 // capture.get_num_sources(), 1080)
fourcc = cv.VideoWriter_fourcc(*'XVID')
output_video = cv.VideoWriter(params.output_video, fourcc, 24.0,
video_output_size)
else:
output_video = None
print("##################################################################")
while thread_body.process:
start = time.time()
try:
frames = thread_body.frames_queue.get_nowait()
except queue.Empty:
frames = None
if frames is None:
continue
all_detections = detector.get_detections(frames)
#all_detections 좌표 성분 : ((left, right, top, bot), confidence) 값들의 리스트
# 1번 영상의 디텍션 위치는 앞에 2번 영상의 디텍션 위치는 뒤에 표시됨
# all_detextions : [[1번영상 사람들의 좌표, 컨디션], [2번 영상 사람들의 좌표, 컨디션]]
all_masks = [[] for _ in range(len(all_detections))
] # 디텍션 갯수만큼 비어있는 마스크 리스트 만듬
for i, detections in enumerate(all_detections):
all_detections[i] = [det[0] for det in detections]
all_masks[i] = [det[2] for det in detections if len(det) == 3]
feature_data = tracker.process(frames, all_detections, all_masks)
tracked_objects = tracker.get_tracked_objects()
#20200511 추가
track_data = copy.deepcopy(feature_data)
for track in track_data:
del track['features']
del track['boxes']
del track['timestamps']
del track['cam_id']
#jottable 넘기는 함수 콜
jot.check_jot(tracked_objects, frames, track_data)
fps = round(1 / (time.time() - start), 1)
#시간 처리
dates = datetime.now()
vis = visualize_multicam_detections(frames, tracked_objects, fps,
dates)
if not params.no_show:
cv.imshow(win_name, vis)
if cv.waitKey(1) == 27:
break
if output_video:
output_video.write(cv.resize(vis, video_output_size))
thread_body.process = False
frames_thread.join()
if len(params.history_file):
history = tracker.get_all_tracks_history()
with open(params.history_file, 'w') as outfile:
json.dump(history, outfile)
print("##################################################################")
def main():
current_dir = os.path.dirname(os.path.abspath(__file__))
"""Prepares data for the object tracking demo"""
parser = argparse.ArgumentParser(description='Multi camera multi object \
tracking live demo script')
parser.add_argument(
'-i',
'--input',
required=True,
nargs='+',
help=
'Required. Input sources (indexes of cameras or paths to video files)')
parser.add_argument('--loop',
default=False,
action='store_true',
help='Optional. Enable reading the input in a loop')
parser.add_argument('--config',
type=str,
default=os.path.join(current_dir, 'configs/person.py'),
required=False,
help='Configuration file')
parser.add_argument('--detections',
type=str,
help='JSON file with bounding boxes')
parser.add_argument('-m',
'--m_detector',
type=str,
required=False,
help='Path to the object detection model')
parser.add_argument('--t_detector',
type=float,
default=0.6,
help='Threshold for the object detection model')
parser.add_argument('--m_segmentation',
type=str,
required=False,
help='Path to the object instance segmentation model')
parser.add_argument(
'--t_segmentation',
type=float,
default=0.6,
help='Threshold for object instance segmentation model')
parser.add_argument(
'--m_reid',
type=str,
required=True,
help='Required. Path to the object re-identification model')
parser.add_argument('--output_video',
type=str,
default='',
required=False,
help='Optional. Path to output video')
parser.add_argument(
'--history_file',
type=str,
default='',
required=False,
help='Optional. Path to file in JSON format to save results of the demo'
)
parser.add_argument(
'--save_detections',
type=str,
default='',
required=False,
help='Optional. Path to file in JSON format to save bounding boxes')
parser.add_argument("--no_show",
help="Optional. Don't show output",
action='store_true')
parser.add_argument('-d', '--device', type=str, default='CPU')
parser.add_argument('-u',
'--utilization_monitors',
default='',
type=str,
help='Optional. List of monitors to show initially.')
args = parser.parse_args()
if check_detectors(args) != 1:
sys.exit(1)
if len(args.config):
log.debug('Reading config from {}'.format(args.config))
config = read_py_config(args.config)
else:
log.error(
'No configuration file specified. Please specify parameter \'--config\''
)
sys.exit(1)
random.seed(config.random_seed)
capture = MulticamCapture(args.input, args.loop)
log.info('OpenVINO Runtime')
log.info('\tbuild: {}'.format(get_version()))
core = Core()
if args.detections:
object_detector = DetectionsFromFileReader(args.detections,
args.t_detector)
elif args.m_segmentation:
object_detector = MaskRCNN(core, args.m_segmentation,
config.obj_segm.trg_classes,
args.t_segmentation, args.device,
capture.get_num_sources())
else:
object_detector = Detector(core, args.m_detector,
config.obj_det.trg_classes, args.t_detector,
args.device, capture.get_num_sources())
if args.m_reid:
object_recognizer = VectorCNN(core, args.m_reid, args.device)
else:
object_recognizer = None
run(args, config, capture, object_detector, object_recognizer)
