def __init__(self,
args,
is_cropped,
root='/path/to/frames/only/folder',
iext='png',
replicates=1):
self.args = args
self.is_cropped = is_cropped
self.crop_size = args.crop_size
self.render_size = args.inference_size
self.replicates = replicates
images = sorted(glob(join(root, '*.' + iext)))
self.image_list = []
for i in range(len(images) - 1):
im1 = images[i]
im2 = images[i + 1]
self.image_list += [[im1, im2]]
self.size = len(self.image_list)
self.frame_size = frame_utils.read_gen(self.image_list[0][0]).shape
if (self.render_size[0] < 0) or (self.render_size[1] < 0) or (
self.frame_size[0] % 64) or (self.frame_size[1] % 64):
self.render_size[0] = ((self.frame_size[0]) // 64) * 64
self.render_size[1] = ((self.frame_size[1]) // 64) * 64
args.inference_size = self.render_size
def __init__(self,
train_val,
args,
is_cropped,
txt_file,
root='/path/to/FlyingChairs_release/data',
replicates=1):
self.args = args
self.is_cropped = is_cropped
self.crop_size = args.crop_size
self.render_size = args.inference_size
self.replicates = replicates
images = sorted(glob(join(root, '*.ppm')))
flow_list = sorted(glob(join(root, '*.flo')))
assert (len(images) // 2 == len(flow_list))
image_list = []
for i in range(len(flow_list)):
im1 = images[2 * i]
im2 = images[2 * i + 1]
image_list += [[im1, im2]]
assert len(image_list) == len(flow_list)
if train_val == 'train':
intindex = np.array(read_txt_to_index(txt_file))
image_list = np.array(image_list)
image_list = image_list[intindex == 1]
image_list = image_list.tolist()
flow_list = np.array(flow_list)
flow_list = flow_list[intindex == 1]
flow_list = flow_list.tolist()
assert len(image_list) == len(flow_list)
elif train_val == 'val':
intindex = np.array(read_txt_to_index(txt_file))
image_list = np.array(image_list)
image_list = image_list[intindex == 2]
image_list = image_list.tolist()
flow_list = np.array(flow_list)
flow_list = flow_list[intindex == 2]
flow_list = flow_list.tolist()
assert len(image_list) == len(flow_list)
else:
raise ValueError(
'FlyingChairs_train_val.txt not found for txt_file ......')
self.flow_list = flow_list
self.image_list = image_list
self.size = len(self.image_list)
self.frame_size = frame_utils.read_gen(self.image_list[0][0]).shape
if (self.render_size[0] < 0) or (self.render_size[1] < 0) or (
self.frame_size[0] % 64) or (self.frame_size[1] % 64):
self.render_size[0] = ((self.frame_size[0]) // 64) * 64
self.render_size[1] = ((self.frame_size[1]) // 64) * 64
args.inference_size = self.render_size
def __getitem__(self, index):
index = index % self.size
img1 = frame_utils.read_gen(self.image_list[index][0])
img2 = frame_utils.read_gen(self.image_list[index][1])
images = [img1, img2]
image_size = img1.shape[:2]
if self.is_cropped:
cropper = StaticRandomCrop(image_size, self.crop_size)
else:
cropper = StaticCenterCrop(image_size, self.render_size)
images = list(map(cropper, images))
images = np.array(images).transpose(3, 0, 1, 2)
return [images
], [np.zeros(images.size()[0:1] + (2, ) + images.size()[-2:])]
def __getitem__(self, index):
index = index % self.size
img1 = frame_utils.read_gen(self.image_list[index][0])
img2 = frame_utils.read_gen(self.image_list[index][1])
flow = frame_utils.read_gen(self.flow_list[index])
images = [img1, img2]
image_size = img1.shape[:2]
if self.is_cropped:
cropper = StaticRandomCrop(image_size, self.crop_size)
else:
cropper = StaticCenterCrop(image_size, self.render_size)
images = list(map(cropper, images))
flow = cropper(flow)
images = np.array(images).transpose(3, 0, 1, 2)
flow = flow.transpose(2, 0, 1)
return [images], [flow]
def __init__(self,
args,
is_cropped=False,
root='',
dstype='clean',
replicates=1):
self.args = args
self.is_cropped = is_cropped
self.crop_size = args.crop_size
self.render_size = args.inference_size
self.replicates = replicates
flow_root = join(root, 'flow')
image_root = join(root, dstype)
file_list = sorted(glob(join(flow_root, '*/*.flo')))
self.flow_list = []
self.image_list = []
for file in file_list:
if 'test' in file:
# print file
continue
fbase = file[len(flow_root) + 1:]
fprefix = fbase[:-8]
fnum = int(fbase[-8:-4])
img1 = join(image_root, fprefix + "%04d" % (fnum + 0) + '.png')
img2 = join(image_root, fprefix + "%04d" % (fnum + 1) + '.png')
if not isfile(img1) or not isfile(img2) or not isfile(file):
continue
self.image_list += [[img1, img2]]
self.flow_list += [file]
self.size = len(self.image_list)
self.frame_size = frame_utils.read_gen(self.image_list[0][0]).shape
if (self.render_size[0] < 0) or (self.render_size[1] < 0) or (
self.frame_size[0] % 64) or (self.frame_size[1] % 64):
self.render_size[0] = ((self.frame_size[0]) // 64) * 64
self.render_size[1] = ((self.frame_size[1]) // 64) * 64
args.inference_size = self.render_size
assert (len(self.image_list) == len(self.flow_list))
def __init__(self,
args,
is_cropped,
root='/path/to/flyingthings3d',
dstype='frames_cleanpass',
replicates=1):
self.args = args
self.is_cropped = is_cropped
self.crop_size = args.crop_size
self.render_size = args.inference_size
self.replicates = replicates
image_dirs = sorted(glob(join(root, dstype, 'TRAIN/*/*')))
image_dirs = sorted([join(f, 'left') for f in image_dirs] +
[join(f, 'right') for f in image_dirs])
flow_dirs = sorted(
glob(join(root, 'optical_flow_flo_format/TRAIN/*/*')))
flow_dirs = sorted([join(f, 'into_future/left') for f in flow_dirs] +
[join(f, 'into_future/right') for f in flow_dirs])
assert (len(image_dirs) == len(flow_dirs))
self.image_list = []
self.flow_list = []
for idir, fdir in zip(image_dirs, flow_dirs):
images = sorted(glob(join(idir, '*.png')))
flows = sorted(glob(join(fdir, '*.flo')))
for i in range(len(flows)):
self.image_list += [[images[i], images[i + 1]]]
self.flow_list += [flows[i]]
assert len(self.image_list) == len(self.flow_list)
self.size = len(self.image_list)
self.frame_size = frame_utils.read_gen(self.image_list[0][0]).shape
if (self.render_size[0] < 0) or (self.render_size[1] < 0) or (
self.frame_size[0] % 64) or (self.frame_size[1] % 64):
self.render_size[0] = ((self.frame_size[0]) // 64) * 64
self.render_size[1] = ((self.frame_size[1]) // 64) * 64
args.inference_size = self.render_size
def __init__(self,
args,
is_cropped,
root='/path/to/chairssdhom/data',
dstype='train',
replicates=1):
self.args = args
self.is_cropped = is_cropped
self.crop_size = args.crop_size
self.render_size = args.inference_size
self.replicates = replicates
image1 = sorted(glob(join(root, dstype, 't0/*.png')))
image2 = sorted(glob(join(root, dstype, 't1/*.png')))
self.flow_list = sorted(glob(join(root, dstype, 'flow/*.flo')))
assert (len(image1) == len(self.flow_list))
self.image_list = []
for i in range(len(self.flow_list)):
im1 = image1[i]
im2 = image2[i]
self.image_list += [[im1, im2]]
assert len(self.image_list) == len(self.flow_list)
self.size = len(self.image_list)
self.frame_size = frame_utils.read_gen(self.image_list[0][0]).shape
if (self.render_size[0] < 0) or (self.render_size[1] < 0) or (
self.frame_size[0] % 64) or (self.frame_size[1] % 64):
self.render_size[0] = ((self.frame_size[0]) // 64) * 64
self.render_size[1] = ((self.frame_size[1]) // 64) * 64
args.inference_size = self.render_size