def main(args):
for model_result in os.listdir(args.detection_dir):
detection_path = os.path.join(args.detection_dir, model_result)
outdir = os.path.join(args.output_dir, model_result)
if not os.path.exists(outdir):
os.makedirs(outdir)
dpaths = glob.glob(detection_path + '/*.txt')
total_time = 0
total_frames = 0
for dpath in dpaths:
detections = load_viva(dpath)
name = os.path.basename(dpath)
print("Processing %s." % (name[:-4]))
start = time()
tracks = track_iou(detections,
args.sigma_l,
args.sigma_h,
args.sigma_iou,
args.t_min,
start_idx=0)
end = time()
total_time += end - start
total_frames += len(detections)
save_to_csv(os.path.join(outdir, name), tracks)
print("Model: %s Total Tracking took: %.3f for %d frames or %.1f FPS" %
(model_result, total_time, total_frames,
total_frames / total_time))
def main(args):
detections = load_mot(args.detection_path)
start = time()
tracks = track_iou(detections, args.sigma_l, args.sigma_h, args.sigma_iou, args.t_min)
end = time()
num_frames = len(detections)
print("finished at " + str(int(num_frames / (end - start))) + " fps!")
save_to_csv(args.output_path, tracks)
def main(args):
detections = load_mot(args.detection_path)
start = time()
tracks = track_iou(detections, args.sigma_l, args.sigma_h, args.sigma_iou,
args.t_min)
end = time()
num_frames = len(detections)
print("finished at " + str(int(num_frames / (end - start))) + " fps!")
save_to_csv(args.output_path, tracks)
def main(args):
formats = ["motchallenge", "visdrone"]
assert (
args.format
in formats), "format '{}' unknown supported formats are: {}".format(
args.format, formats)
with_classes = False
if args.format == "visdrone":
with_classes = True
detections = load_mot(
args.detection_path,
nms_overlap_thresh=args.nms,
with_classes=with_classes,
min_short=args.min_short,
max_short=args.max_short,
max_long=args.max_long,
)
if args.visual:
tracks = track_viou(
args.frames_path,
detections,
args.sigma_l,
args.sigma_h,
args.sigma_iou,
args.t_min,
args.ttl,
args.visual,
args.keep_upper_height_ratio,
)
else:
if with_classes:
# track_viou can also be used without visual tracking, but note that the speed will be much slower compared
# to track_iou. However, this way supports the optimal LAP solving and the handling of multiple object classes:
tracks = track_viou(
args.frames_path,
detections,
args.sigma_l,
args.sigma_h,
args.sigma_iou,
args.t_min,
args.ttl,
"NONE",
args.keep_upper_height_ratio,
)
else:
tracks = track_iou(detections, args.sigma_l, args.sigma_h,
args.sigma_iou, args.t_min)
save_to_csv(args.output_path, tracks, fmt=args.format)
def _save(stock, date):
data = util.collect_detail(stock, date, pause=1)
if data is None:
pfile = CONF.DATAFILENAME
if not os.path.exists(pfile):
os.mknod(pfile)
util.append_to_pkl((stock, date), pfile)
return
if len(data) < 10:
return
data = util.data_adapter(date, data)
filename = stock + ".csv"
tmpfile = os.path.join(CONF.TMPFILEDIR, filename)
util.save_to_csv(data, tmpfile)
def main(datasetPath):
if 'Fluo-N2DL-HeLa' in datasetPath:
mode = 'Fluo-N2DL-HeLa'
elif 'PhC-C2DL-PSC' in datasetPath:
mode = 'PhC-C2DL-PSC'
else:
mode = 'DIC-C2DH-HeLa'
detPath = datasetPath.replace(r'/', '_') + '.txt'
detAbsPath = os.path.join('det', detPath)
genDets(datasetPath, mode)
print('loading detections...')
detections = load_mot(detAbsPath,
nms_overlap_thresh=cfg.nms,
with_classes=False)
print('tracking...')
tracks = track_iou(detections, cfg.sigma_l, cfg.sigma_h, cfg.sigma_iou,
cfg.t_min, cfg.max_missing)
save_to_csv(os.path.join(cfg.output_path, detPath), tracks, fmt=cfg.format)
def main(args):
with open(args.seqmap) as fd:
seqs = [line.rstrip('\n') for line in fd]
for idx, seq in enumerate(seqs):
if seq == "name" or seq == "":
continue
else:
if "DPM" in seq:
sigma_l = -0.5
sigma_h = 0.5
sigma_iou = 0.4
t_min = 4
elif "FRCNN" in seq:
sigma_l = 0.0
sigma_h = 0.9
sigma_iou = 0.3
t_min = 3
elif "SDP" in seq:
sigma_l = 0.4
sigma_h = 0.5
sigma_iou = 0.2
t_min = 2
else:
print("No detector name found, this could happen with the "
"wrong seqmap seqmap file. "
"Please use c10-train.txt, c10-test.txt or c10-all.txt")
exit()
# Take the pre-computed detections.
det_path = args.benchmark_dir + "/" + seq + "/det/det.txt"
out_path = args.res_dir + "/" + seq + ".txt"
detections = load_mot(det_path)
start = time()
tracks = track_iou(detections, sigma_l, sigma_h, sigma_iou, t_min)
end = time()
num_frames = len(detections)
print("finished " + seq + " at " +
str(int(num_frames / (end - start))) + " fps!")
save_to_csv(out_path, tracks)
def main(args):
with open(args.seqmap) as fd:
seqs = [line.rstrip('\n') for line in fd]
for idx, seq in enumerate(seqs):
if seq == "name" or seq == "":
continue
else:
if "DPM" in seq:
sigma_l = -0.5
sigma_h = 0.5
sigma_iou = 0.4
t_min = 4
elif "FRCNN" in seq:
sigma_l = 0.0
sigma_h = 0.9
sigma_iou = 0.3
t_min = 3
elif "SDP" in seq:
sigma_l = 0.4
sigma_h = 0.5
sigma_iou = 0.2
t_min = 2
else:
print("No detector name found, this could happen with the wrong seqmap seqmap file. "
"Please use c10-train.txt, c10-test.txt or c10-all.txt")
exit()
det_path = args.benchmark_dir + "/" + seq + "/det/det.txt"
out_path = args.res_dir + "/" + seq + ".txt"
detections = load_mot(det_path)
start = time()
tracks = track_iou(detections, sigma_l, sigma_h, sigma_iou, t_min)
end = time()
num_frames = len(detections)
print("finished " + seq + " at " + str(int(num_frames / (end - start))) + " fps!")
save_to_csv(out_path, tracks)
def main(args):
print('loading detections...')
det_path = os.path.expanduser('~/') + args.detection_path
detections = load_mot(det_path)
print('tracking...')
start = time()
tracks = track_iou(detections, args.sigma_l, args.sigma_h, args.sigma_iou,
args.t_min)
end = time()
print("Finished Tracking", tracks)
num_frames = len(detections)
print("finished at " + str(int(num_frames / (end - start))) + " fps!")
orig_out_path = os.path.expanduser('~/') + args.output_path
save_to_csv(orig_out_path, tracks)
out_path = os.path.expanduser('~/') + args.output_path + 'ordredfrm'
if args.frameorder:
#reading in all the data again for re-formatting
idorder_frameorder(orig_out_path, out_path)
def main(args):
with open(args.seqmap) as fd:
seqs = [line.rstrip('\n') for line in fd]
for idx, seq in enumerate(seqs):
if seq == "name" or seq == "":
continue
else:
det_path = args.benchmark_dir + "/" + seq + "/det/det.txt"
out_path = args.res_dir + "/" + seq + ".txt"
detections = load_mot(det_path, with_classes=False)
start = time()
tracks = track_iou(detections, args.sigma_l, args.sigma_h,
args.sigma_iou, args.t_min)
end = time()
num_frames = len(detections)
print("finished " + seq + " at " +
str(int(num_frames / (end - start))) + " fps!")
save_to_csv(out_path, tracks)
def extractAnsTime(c, DIR, MODE, VERSION):
if not os.path.exists(DIR + 'posts'):
os.makedirs(DIR + 'posts')
if MODE == 'FIRST':
ans_time_first = query.firstAnsTime(c)
util.save_to_csv(ans_time_first, header['ans_time_first'], 'posts/ans_time_first.csv')
elif MODE == 'ACCEPTED':
ans_time_accepted = pd.DataFrame(query.acceptAnsTime(c), columns=header['ans_time_accepted'])
ans_time_accepted.to_csv(DIR + 'posts/ans_time_accepted.csv', index=False)
elif MODE == 'UPVOTED':
ans_time_upvoted = query.upvotedAnsTime(c)
util.save_to_csv(ans_time_upvoted, header['ans_time_upvoted'], 'ans_time_upvoted.csv')
if VERSION == 'EXTENDED':
ans_time_upvoted_ex = query.upvotedAnsTime1(c)
util.save_to_csv(ans_time_upvoted_ex, header['ans_time_upvoted_ex'], 'ans_time_upvoted_ex.csv')
def run(self):
self.vc = cv2.VideoCapture(self.mot_video_dir)
sigma_l = 0.4
sigma_h = 0.5
sigma_iou = 0.2
t_min = 2
# load bounding boxes.
detections = load_mot(self.mot_det_dir)
tracks_finished = []
# set the color of the object randomly.
color_for_boundingbox = [(13 * i % 255, (255 - 5 * i) % 255, (240 + 10 * i) % 255) for i in range(0, 51)]
# run algorithm.
for frame_num, detections_frame in enumerate(detections, start=1):
# apply low threshold to detections
dets = [det for det in detections_frame if det['score'] >= sigma_l]
updated_tracks = []
for track in self.tracks_active:
if len(dets) > 0:
# get det with highest iou
best_match = max(dets, key=lambda x: iou(track['bboxes'][-1], x['bbox']))
if iou(track['bboxes'][-1], best_match['bbox']) >= sigma_iou:
track['bboxes'].append(best_match['bbox'])
track['max_score'] = max(track['max_score'], best_match['score'])
updated_tracks.append(track)
# remove from best matching detection from detections
del dets[dets.index(best_match)]
# if track was not updated
if len(updated_tracks) == 0 or track is not updated_tracks[-1]:
# finish track when the conditions are met
if track['max_score'] >= sigma_h and len(track['bboxes']) >= t_min:
tracks_finished.append(track)
# create new tracks
new_tracks = [{'bboxes': [det['bbox']], 'max_score': det['score'], 'start_frame': frame_num,
'color': color_for_boundingbox[(len(self.tracks_active) + random.randint(0, 51)) % 51]}
for i, det in enumerate(dets)]
self.tracks_active = updated_tracks + new_tracks
self.retval, current_frame = self.vc.read()
labeled_frame = show_tracking_results(current_frame, self.tracks_active)
rgb_frame = convert_cvimage_to_qimage(labeled_frame)
self.mot_signal.emit(rgb_frame)
# finish all remaining active tracks
tracks_finished += [track for track in self.tracks_active
if track['max_score'] >= sigma_h and len(track['bboxes']) >= t_min]
output_path = os.path.dirname(self.mot_video_dir)
save_to_csv(output_path, tracks_finished)
self.vc.release()
def main(args):
time_iou = 0
time_ext = 0
time_merge = 0
time_ttl = 0
with open(args.seqmap) as fd:
seqs = [line.rstrip('\n') for line in fd]
for idx, seq in enumerate(seqs):
print(seq)
if seq == "name" or seq == "":
continue
else:
if "DPM" in seq:
sigma_l = -0.5
sigma_h = 0.5
sigma_iou = 0.5
t_min = 4
elif "FRCNN" in seq:
sigma_l = 0.0
sigma_h = 0.9
sigma_iou = 0.4
t_min = 3
elif "SDP" in seq:
sigma_l = 0.4
sigma_h = 0.5
sigma_iou = 0.3
t_min = 2
else:
sigma_l = -0.5 # default -0.5
sigma_h = 0.5 # default 0.5
sigma_iou = 0.4 # default 0.4
t_min = 3 # default 4
ttl_vtracking = 8 # maximum length of visual track (amount of framess)
sigma_iou_merge = 0.4
# uncomment line below if f4k2013 data is used
# det_path = os.path.join(args.benchmark_dir,seq+"det.txt")
# motchallenge data used
det_path = os.path.join(args.benchmark_dir, seq, "det", "det.txt")
img_path = os.path.join(args.benchmark_dir, seq, "img1")
out_path = os.path.join(args.res_dir, seq + ".txt")
detections = load_mot(det_path)
# test preprocessing detections
if args.prep_detections:
detections = detections_preprocessing(detections,
iou_filter=0.90)
start = time()
tracks_iou = track_iou(detections, sigma_l, sigma_h, sigma_iou,
t_min)
end_iou = time()
tracks_iou_ext, tracks_kcf_front, tracks_kcf_rear = track_kcf_2(
tracks_iou, img_path, ttl_vtracking)
end_ext = time()
tracks_merged = merge(tracks_iou_ext, tracks_kcf_front,
tracks_kcf_rear, sigma_iou_merge)
end = time()
num_frames = len(detections)
print("finished " + seq + " at " +
str(int(num_frames / (end - start))) + " fps!")
save_to_csv(out_path, tracks_merged)
time_iou += (end_iou - start)
time_ext += (end_ext - end_iou)
time_merge += (end - end_ext)
time_ttl += (end - start)
print('Time IOU: ' + str(time_iou))
print('Time VOT: ' + str(time_ext))
print('Time Merge:' + str(time_merge))
print('Time total: ' + str(time_ttl))
