def __init__(self,
record_thresh,
base_name,
vid_fname,
return_thresh=None):
self.vid_fname = vid_fname
self.base_name = base_name
self.record_thresh = record_thresh
self.return_thresh = return_thresh
self.vin = swag.VideoCapture(self.vid_fname)
self.vid_data = get_video_data(self.base_name)
self.decode_time = 0.
def __init__(self, base_name, video_fname, csv_fname, objects, normalize,
model, model_dump_fname, **kwargs):
self.base_name = base_name
self.video_fname = video_fname
self.csv_fname = csv_fname
self.objects = objects
self.normalize = normalize
self.model = model
self.model_dump_fname = model_dump_fname
for key, value in kwargs.items():
setattr(self, key, value)
self.vid_data = get_video_data(base_name)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--data_path', default='/lfs/1/ddkang/blazeit/data/')
parser.add_argument('--out_dir', default='/lfs/1/ddkang/blazeit/data/resol-65/')
parser.add_argument('--base_name', required=True)
parser.add_argument('--date', required=True)
args = parser.parse_args()
DATA_PATH = args.data_path
base_name = args.base_name
date = args.date
RESOL = 65
OUT_DIR = args.out_dir
OUT_DIR = os.path.join(OUT_DIR, base_name)
os.makedirs(OUT_DIR, exist_ok=True)
OUT_FNAME = os.path.join(OUT_DIR, base_name + '-' + date + '.npy')
vd = get_video_data(base_name)
csv_fname = get_csv_fname(DATA_PATH, base_name, date)
video_fname = get_video_fname(DATA_PATH, base_name, date, load_video=True)
cap = swag.VideoCapture(video_fname)
nb_frames = cap.cum_frames[-1] - 1
data = np.zeros((nb_frames, 3, RESOL, RESOL), dtype=np.float32)
for i in tqdm.tqdm(range(nb_frames)):
ret, frame = cap.read()
if not ret:
print('uhoh')
break
# frame = vd.process_frame(frame)
frame = cv2.resize(frame, (RESOL, RESOL)).astype('float32')
frame /= 255.
frame[...,:] -= [0.485, 0.456, 0.406]
frame[...,:] /= [0.229, 0.224, 0.225]
frame = frame.transpose(2, 0, 1).copy()
data[i] = frame
np.save(OUT_FNAME, data)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--video_dir', required=True)
parser.add_argument('--index_fname', required=True)
parser.add_argument('--npy_fname', required=True)
parser.add_argument('--resol', type=int, required=True)
parser.add_argument('--base_name', default=None)
args = parser.parse_args()
vid_dir = args.video_dir
index_fname = args.index_fname
npy_fname = args.npy_fname
resol = args.resol
if args.base_name is None:
crop_and_exclude = False
else:
crop_and_exclude = True
from blazeit.data.video_data import get_video_data
video_data = get_video_data(args.base_name)
cap = VideoCapture(vid_dir, index_fname)
data = np.zeros((cap.cum_frames[-1], resol, resol, 3), dtype=np.float32)
for i in range(len(data)):
if i % 1000 == 0:
print('Processing frame', i)
ret, frame = cap.read()
if not ret:
print('Something really bad happened')
sys.exit(1)
if crop_and_exclude:
frame = video_data.process_frame(frame)
data[i] = cv2.resize(frame, (resol, resol))
data /= 255.
data[..., :] -= [0.485, 0.456, 0.406]
data[..., :] /= [0.229, 0.224, 0.225]
np.save(npy_fname, data)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--base_name', required=True)
parser.add_argument('--base_dir', default='/lfs/1/ddkang/blazeit/data/')
parser.add_argument('--type', default='ivid')
args = parser.parse_args()
base_name = args.base_name
base_dir = args.base_dir
vid_data = get_video_data(base_name)
OBJECTS = vid_data.classes
feather_dir = os.path.join(base_dir, 'feather', base_name)
csv_dir = os.path.join(base_dir, 'csv', base_name)
proc = functools.partial(f, classes=args.type, feather_dir=feather_dir,
csv_dir=csv_dir, OBJECTS=OBJECTS,
feather_to_csv=feather_to_csv)
pool = mp.Pool()
pool.map(proc, sorted(os.listdir(feather_dir)))
'''for fname in sorted(os.listdir(feather_dir)):
min_conf = 0.2
elif args.type == 'mask':
min_conf = 0.8
else:
raise NotImplementedError
base_name = args.base_name
base_dir = args.base_dir
seqnms_dir = os.path.join(base_dir, 'seqnms', base_name)
filtered_dir = os.path.join(base_dir, 'filtered', base_name)
'''proc = functools.partial(f, seqnms_dir=seqnms_dir, filtered_dir=filtered_dir)
pool = mp.Pool()
pool.map(proc, sorted(os.listdir(seqnms_dir)))'''
video_data = get_video_data(base_name)
_, json_fname = tempfile.mkstemp()
video_data.serialize(json_fname)
for fname in sorted(os.listdir(seqnms_dir)):
print('Processing:', fname)
csv_in_fname = os.path.join(seqnms_dir, fname)
csv_out_fname = os.path.join(filtered_dir, fname)
ret = subprocess.call([
'./filter_cc',
str(min_conf), json_fname, csv_in_fname, csv_out_fname
])
if ret != 0:
print(ret)
raise RuntimeError('Something very bad happened')
