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 run(params, config, capture, detector, reid):
win_name = 'Multi camera tracking'
frame_number = 0
avg_latency = AverageEstimator()
output_detections = [[] for _ in range(capture.get_num_sources())]
key = -1
if config['normalizer_config']['enabled']:
capture.add_transform(
NormalizerCLAHE(
config['normalizer_config']['clip_limit'],
config['normalizer_config']['tile_size'],
))
tracker = MultiCameraTracker(capture.get_num_sources(),
reid,
config['sct_config'],
**config['mct_config'],
visual_analyze=config['analyzer'])
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):
frame_size, fps = capture.get_source_parameters()
target_width, target_height = get_target_size(
frame_size, None, **config['visualization_config'])
video_output_size = (target_width, target_height)
fourcc = cv.VideoWriter_fourcc(*'XVID')
output_video = cv.VideoWriter(params.output_video, fourcc, min(fps),
video_output_size)
else:
output_video = None
prev_frames = thread_body.frames_queue.get()
detector.run_async(prev_frames, frame_number)
presenter = monitors.Presenter(params.utilization_monitors, 0)
while thread_body.process:
if not params.no_show:
key = check_pressed_keys(key)
if key == 27:
break
presenter.handleKey(key)
start = time.perf_counter()
try:
frames = thread_body.frames_queue.get_nowait()
except queue.Empty:
frames = None
if frames is None:
continue
all_detections = detector.wait_and_grab()
if params.save_detections:
update_detections(output_detections, all_detections, frame_number)
frame_number += 1
detector.run_async(frames, frame_number)
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(prev_frames, all_detections, all_masks)
tracked_objects = tracker.get_tracked_objects()
latency = max(time.perf_counter() - start, sys.float_info.epsilon)
avg_latency.update(latency)
fps = round(1. / latency, 1)
vis = visualize_multicam_detections(prev_frames, tracked_objects, fps,
**config['visualization_config'])
presenter.drawGraphs(vis)
if not params.no_show:
cv.imshow(win_name, vis)
if output_video:
output_video.write(cv.resize(vis, video_output_size))
print('\rProcessing frame: {}, fps = {} (avg_fps = {:.3})'.format(
frame_number, fps, 1. / avg_latency.get()),
end="")
prev_frames, frames = frames, prev_frames
print(presenter.reportMeans())
print('')
thread_body.process = False
frames_thread.join()
if len(params.history_file):
save_json_file(params.history_file,
tracker.get_all_tracks_history(),
description='History file')
if len(params.save_detections):
save_json_file(params.save_detections,
output_detections,
description='Detections')
if len(config['embeddings']['save_path']):
save_embeddings(tracker.scts, **config['embeddings'])
def main():
"""Visualize the results of the multi camera multi person tracker demo"""
parser = argparse.ArgumentParser(description='Multi camera multi person \
tracking visualization demo script')
parser.add_argument('-i', type=str, nargs='+',
help='Input videos')
parser.add_argument('--history_file', type=str, default='', required=True,
help='File with tracker history')
parser.add_argument('--output_video', type=str, default='', required=False,
help='Output video file')
parser.add_argument('--gt_files', type=str, nargs='+', required=False,
help='Files with ground truth annotation')
parser.add_argument('--timeline', type=str, default='',
help='Plot and save timeline')
parser.add_argument('--match_gt_ids', default=False, action='store_true',
help='Match GT ids to ids from history')
parser.add_argument('--merge_outputs', default=False, action='store_true',
help='Merge GT and history tracks into one frame')
args = parser.parse_args()
capture = MulticamCapture(args.i)
with open(args.history_file) as hist_f:
history = json.load(hist_f)
assert len(history) == capture.get_num_sources()
# Configure output video files
output_video = None
output_video_gt = None
frame_size, fps_source = capture.get_source_parameters()
if len(args.output_video):
video_output_size, fps = calc_output_video_params(frame_size, fps_source, args.gt_files, args.merge_outputs)
fourcc = cv.VideoWriter_fourcc(*'XVID')
output_video = cv.VideoWriter(args.output_video, fourcc, fps, video_output_size)
if args.gt_files and not args.merge_outputs:
ext = args.output_video.split('.')[-1]
output_path = args.output_video[:len(args.output_video) - len(ext) - 1] + '_gt.' + ext
output_video_gt = cv.VideoWriter(output_path, fourcc, fps, video_output_size)
# Read GT tracks if necessary
if args.gt_files:
assert len(args.gt_files) == capture.get_num_sources()
gt_tracks, _ = read_gt_tracks(args.gt_files)
accs = [mm.MOTAccumulator(auto_id=True) for _ in args.gt_files]
else:
gt_tracks = None
# If we need for matching GT IDs, accumulate metrics
if gt_tracks and args.match_gt_ids:
accumulate_mot_metrics(accs, gt_tracks, history)
match_gt_indices(gt_tracks, history, accs)
metrics_accumulated = True
else:
metrics_accumulated = False
# Process frames
win_name = 'Multi camera tracking history visualizer'
time = 0
key = -1
while True:
print('\rVisualizing frame: {}'.format(time), end="")
key = check_pressed_keys(key)
if key == 27:
break
has_frames, frames = capture.get_frames()
if not has_frames:
break
if gt_tracks:
gt_detections = get_detections_from_tracks(gt_tracks, time)
vis_gt = visualize_multicam_detections(copy.deepcopy(frames), gt_detections, fps='Ground truth')
else:
vis_gt = None
active_detections = get_detections_from_tracks(history, time)
vis = visualize_multicam_detections(frames, active_detections)
if vis_gt is not None:
if args.merge_outputs:
vis = np.hstack([vis, vis_gt])
cv.imshow(win_name, vis)
else:
cv.imshow('GT', vis_gt)
cv.imshow(win_name, vis)
else:
cv.imshow(win_name, vis)
time += 1
if output_video:
output_video.write(cv.resize(vis, video_output_size))
if vis_gt is not None and output_video_gt is not None:
output_video_gt.write(cv.resize(vis_gt, video_output_size))
if len(args.timeline):
for i in range(len(history)):
log.info('Source_{}: drawing timeline...'.format(i))
plot_timeline(i, time, history[i], save_path=args.timeline, name='SCT')
if gt_tracks:
for i in range(len(gt_tracks)):
log.info('GT_{}: drawing timeline...'.format(i))
plot_timeline(i, time, gt_tracks[i], save_path=args.timeline, name='GT')
if gt_tracks:
if not metrics_accumulated:
accumulate_mot_metrics(accs, gt_tracks, history)
mh = mm.metrics.create()
summary = mh.compute_many(accs,
metrics=mm.metrics.motchallenge_metrics,
generate_overall=True,
names=['video ' + str(i) for i in range(len(accs))])
strsummary = mm.io.render_summary(summary,
formatters=mh.formatters,
namemap=mm.io.motchallenge_metric_names)
print(strsummary)
def run(params, config, capture, detector, reid):
win_name = 'Multi camera tracking'
frame_number = 0
output_detections = [[] for _ in range(capture.get_num_sources())]
key = -1
if config.normalizer_config.enabled:
capture.add_transform(
NormalizerCLAHE(
config.normalizer_config.clip_limit,
config.normalizer_config.tile_size,
))
tracker = MultiCameraTracker(capture.get_num_sources(),
reid,
config.sct_config,
**vars(config.mct_config),
visual_analyze=config.analyzer)
thread_body = FramesThreadBody(capture,
max_queue_length=len(capture.captures) * 2)
frames_thread = Thread(target=thread_body)
frames_thread.start()
frames_read = False
set_output_params = False
prev_frames = thread_body.frames_queue.get()
detector.run_async(prev_frames, frame_number)
metrics = PerformanceMetrics()
presenter = monitors.Presenter(params.utilization_monitors, 0)
while thread_body.process:
if not params.no_show:
key = check_pressed_keys(key)
if key == 27:
break
presenter.handleKey(key)
start_time = time.perf_counter()
try:
frames = thread_body.frames_queue.get_nowait()
frames_read = True
except queue.Empty:
frames = None
if frames is None:
continue
all_detections = detector.wait_and_grab()
if params.save_detections:
update_detections(output_detections, all_detections, frame_number)
frame_number += 1
detector.run_async(frames, frame_number)
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(prev_frames, all_detections, all_masks)
tracked_objects = tracker.get_tracked_objects()
vis = visualize_multicam_detections(
prev_frames, tracked_objects, **vars(config.visualization_config))
metrics.update(start_time, vis)
presenter.drawGraphs(vis)
if not params.no_show:
cv.imshow(win_name, vis)
if frames_read and not set_output_params:
set_output_params = True
if len(params.output_video):
frame_size = [frame.shape[::-1] for frame in frames]
fps = capture.get_fps()
target_width, target_height = get_target_size(
frame_size, None, **vars(config.visualization_config))
video_output_size = (target_width, target_height)
fourcc = cv.VideoWriter_fourcc(*'XVID')
output_video = cv.VideoWriter(params.output_video, fourcc,
min(fps), video_output_size)
else:
output_video = None
if set_output_params and output_video:
output_video.write(cv.resize(vis, video_output_size))
prev_frames, frames = frames, prev_frames
metrics.log_total()
for rep in presenter.reportMeans():
log.info(rep)
thread_body.process = False
frames_thread.join()
if len(params.history_file):
save_json_file(params.history_file,
tracker.get_all_tracks_history(),
description='History file')
if len(params.save_detections):
save_json_file(params.save_detections,
output_detections,
description='Detections')
if len(config.embeddings.save_path):
save_embeddings(tracker.scts, **vars(config.embeddings))
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 run(params, config, capture, detector, reid):
ix,iy = -1,-1
pts_src = np.array([[561,1022],[990,698],[486,273],[95,504]],dtype='float32')
pts_dest = np.array([[0,0],[0,400],[400,700],[0,700]],dtype='float32')
# calculate matrix H
h, status = cv.findHomography(pts_src,pts_dest)
win_name = 'Multi camera tracking'
frame_number = 0
avg_latency = AverageEstimator()
output_detections = [[] for _ in range(capture.get_num_sources())]
key = -1
refObj = []
if config['normalizer_config']['enabled']:
capture.add_transform(
NormalizerCLAHE(
config['normalizer_config']['clip_limit'],
config['normalizer_config']['tile_size'],
)
)
tracker = MultiCameraTracker(capture.get_num_sources(), reid, config['sct_config'], **config['mct_config'],
visual_analyze=config['analyzer'])
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):
frame_size, fps = capture.get_source_parameters()
target_width, target_height = get_target_size(frame_size, None, **config['visualization_config'])
video_output_size = (target_width, target_height)
fourcc = cv.VideoWriter_fourcc(*'XVID')
output_video = cv.VideoWriter(params.output_video,cv.VideoWriter_fourcc('M','J','P','G'),min(fps),video_output_size)
# output_video = cv.VideoWriter(params.output_video, fourcc, min(fps), video_output_size)
else:
output_video = None
prev_frames = thread_body.frames_queue.get()
detector.run_async(prev_frames, frame_number)
while thread_body.process:
if not params.no_show:
key = check_pressed_keys(key)
if key == 27:
break
start = time.time()
try:
frames = thread_body.frames_queue.get_nowait()
except queue.Empty:
frames = None
if frames is None:
continue
all_detections = detector.wait_and_grab()
for det in all_detections:
for obj in det:
print("Boxes:",obj[0])
print("Confidence:",obj[1])
if params.save_detections:
update_detections(output_detections, all_detections, frame_number)
frame_number += 1
detector.run_async(frames, frame_number)
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(prev_frames, all_detections, all_masks)
tracked_objects = tracker.get_tracked_objects()
latency = time.time() - start
avg_latency.update(latency)
fps = round(1. / latency, 1)
vis = visualize_multicam_detections(prev_frames, tracked_objects, fps, **config['visualization_config'],h=h)
if not params.no_show:
cv.setMouseCallback(win_name, getMousePointer)
if ix!=-1 and iy!=-1:
refObj.append((ix,iy))
ix=-1
iy=-1
print(len(refObj))
if len(refObj)==2:
print("Len 2 Rectangle Drawn.")
vis = cv.rectangle(vis, refObj[0], refObj[1],(255,0,0), 2)
refObj.clear()
cv.imshow(win_name, vis)
# cv.imwrite("refPicture.png",vis)
if output_video:
output_video.write(cv.resize(vis, video_output_size))
# print('\rProcessing frame: {}, fps = {} (avg_fps = {:.3})'.format(
# frame_number, fps, 1. / avg_latency.get()), end="")
prev_frames, frames = frames, prev_frames
# print('')
thread_body.process = False
frames_thread.join()
if len(params.history_file):
save_json_file(params.history_file, tracker.get_all_tracks_history(), description='History file')
if len(params.save_detections):
save_json_file(params.save_detections, output_detections, description='Detections')
if len(config['embeddings']['save_path']):
save_embeddings(tracker.scts, **config['embeddings'])