def __init__(self,
video_path,
list_path,
patch_size,
rotate=10,
scale=1.2,
is_train=True,
moreaug=True):
super(VidListv1, self).__init__()
self.data_dir = video_path
self.list_path = list_path
normalize = transforms.Normalize(mean=(128, 128, 128),
std=(128, 128, 128))
t = []
if rotate > 0:
t.append(transforms.RandomRotate(rotate))
if scale > 0:
t.append(transforms.RandomScale(scale))
t.extend([
transforms.RandomCrop(patch_size, seperate=moreaug),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(), normalize
])
self.transforms = transforms.Compose(t)
self.is_train = is_train
self.read_list()
def __init__(self,
video_path,
list_path,
patch_size,
rotate=10,
scale=1.2,
is_train=True,
moreaug=True):
super(VidListv1, self).__init__()
csv_path = "/raid/codes/CorrFlows/functional/feeder/dataset/oxuva.csv"
filenames = open(csv_path).readlines()
frame_all = [filename.split(',')[0].strip() for filename in filenames]
nframes = [
int(filename.split(',')[1].strip()) for filename in filenames
]
self.data_dir = video_path
self.list = frame_all
self.nframes = nframes
normalize = transforms.Normalize(mean=(128, 128, 128),
std=(128, 128, 128))
t = []
if rotate > 0:
t.append(transforms.RandomRotate(rotate))
if scale > 0:
t.append(transforms.RandomScale(scale))
t.extend([
transforms.RandomCrop(patch_size, seperate=moreaug),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(), normalize
])
self.transforms = transforms.Compose(t)
self.is_train = is_train
def __init__(self,
video_path,
list_path,
patch_size,
rotate=10,
scale=1.2,
is_train=True,
moreaug=True):
super(VidListv1, self).__init__()
csv_path = "/raid/codes/UVC/libs/GOT-new.csv"
filenames = open(csv_path).readlines()
frame_all = [filename.split(',')[0].strip() for filename in filenames]
nframes = [
int(filename.split(',')[1].strip()) for filename in filenames
]
self.data_dir = video_path
self.list = frame_all
self.nframes = nframes
normalize = transforms.Normalize(mean=(128, 128, 128),
std=(128, 128, 128))
t = []
if rotate > 0:
t.append(transforms.RandomRotate(rotate))
if scale > 0:
t.append(transforms.RandomScale(scale))
t.extend([
transforms.RandomCrop(patch_size, seperate=moreaug),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(), normalize
])
self.transforms = transforms.Compose(t)
self.is_train = is_train
self.video_list = []
for filename in filenames:
record = VideoRecord()
record.path = os.path.join(video_path,
filename.split(',')[0].strip())
record.num_frames = int(filename.split(',')[1].strip(
)) # len(glob.glob(os.path.join(video_path, '*.jpg')))
record.label = filename.split(',')[0].strip()
self.video_list.append(record)