def __init__(self,
video_path,
list_path,
patch_size,
window_len,
rotate=10,
scale=1.2,
full_size=640,
is_train=True):
super(VidListv2, self).__init__()
self.data_dir = video_path
self.list_path = list_path
self.window_len = window_len
normalize = transforms.Normalize(mean=(128, 128, 128),
std=(128, 128, 128))
self.transforms1 = transforms.Compose([
transforms.RandomRotate(rotate),
# transforms.RandomScale(scale),
transforms.ResizeandPad(full_size),
transforms.RandomCrop(patch_size),
transforms.ToTensor(),
normalize
])
self.transforms2 = transforms.Compose([
transforms.ResizeandPad(full_size),
transforms.ToTensor(), normalize
])
self.is_train = is_train
self.read_list()
def __init__(self,
video_path,
patch_size,
window_len,
rotate=10,
scale=1.2,
full_size=640,
is_train=True):
super(VidListv2, self).__init__()
self.data_dir = video_path
self.window_len = window_len
normalize = transforms.Normalize(mean=(128, 128, 128),
std=(128, 128, 128))
self.transforms1 = transforms.Compose([
transforms.RandomRotate(rotate),
transforms.ResizeandPad(full_size),
transforms.RandomCrop(patch_size),
transforms.ToTensor(), normalize
])
self.transforms2 = transforms.Compose([
transforms.ResizeandPad(full_size),
transforms.ToTensor(), normalize
])
self.is_train = is_train
self.list = list_sequences(video_path, set_ids=list(
range(12))) # training sets: 0~11
def __init__(self, video_path, list_path, patch_size, window_len, rotate = 10, scale = 1.2, full_size = 640, is_train=True):
super(VidListv2, self).__init__()
csv_path = "/raid/codes/CorrFlows/GOT_10k.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.window_len = window_len
self.nframes = nframes
normalize = transforms.Normalize(mean = (128, 128, 128), std = (128, 128, 128))
self.transforms1 = transforms.Compose([
transforms.RandomRotate(rotate),
# transforms.RandomScale(scale),
transforms.ResizeandPad(full_size),
transforms.RandomCrop(patch_size),
transforms.ToTensor(),
normalize])
self.transforms2 = transforms.Compose([
transforms.ResizeandPad(full_size),
transforms.ToTensor(),
normalize])
self.is_train = is_train