def read_img_seq(path, require_mod_crop=False, scale=1):
"""Read a sequence of images from a given folder path.
Args:
path (list[str] | str): List of image paths or image folder path.
require_mod_crop (bool): Require mod crop for each image.
Default: False.
scale (int): Scale factor for mod_crop. Default: 1.
Returns:
Tensor: size (t, c, h, w), RGB, [0, 1].
"""
if isinstance(path, list):
img_paths = path
else:
img_paths = sorted([osp.join(path, v) for v in mmcv.scandir(path)])
imgs = [mmcv.imread(v).astype(np.float32) / 255. for v in img_paths]
# imgs = []
# for v in img_paths:
# imm = mmcv.imread(v).astype(np.float32) / 255.
# w = v[:-4]+'_40.png'
# imm_3d = mmcv.imread(w).astype(np.float32) / 255.
# imm_concat_3d = np.concatenate((imm,imm_3d),axis=2)
# print(imm_concat_3d.shape)
# print(imm_3d.shape)
# imgs.append(imm_concat_3d)
if require_mod_crop:
imgs = [mod_crop(img, scale) for img in imgs]
imgs = totensor(imgs, bgr2rgb=True, float32=True)
imgs = torch.stack(imgs, dim=0)
return imgs
def calculate_lpips(img1, img2, crop_border, input_order='HWC'):
"""Calculate LPIPS metric.
We use the official params estimated from the pristine dataset.
We use the recommended block size (96, 96) without overlaps.
Args:
img (ndarray): Input image whose quality needs to be computed.
The input image must be in range [0, 255] with float/int type.
The input_order of image can be 'HW' or 'HWC' or 'CHW'. (BGR order)
If the input order is 'HWC' or 'CHW', it will be converted to gray
or Y (of YCbCr) image according to the ``convert_to`` argument.
crop_border (int): Cropped pixels in each edge of an image. These
pixels are not involved in the metric calculation.
input_order (str): Whether the input order is 'HW', 'HWC' or 'CHW'.
Default: 'HWC'.
Returns:
float: LPIPS result.
"""
assert img1.shape == img2.shape, (
f'Image shapes are differnet: {img1.shape}, {img2.shape}.')
if input_order not in ['HWC', 'CHW']:
raise ValueError(
f'Wrong input_order {input_order}. Supported input_orders are '
'"HWC" and "CHW"')
img1 = reorder_image(img1, input_order=input_order)
img2 = reorder_image(img2, input_order=input_order)
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
if crop_border != 0:
img1 = img1[crop_border:-crop_border, crop_border:-crop_border, ...]
img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...]
img1 = img1.astype(np.float32)
img2 = img2.astype(np.float32)
img1, img2 = totensor([img1, img2], bgr2rgb=False, float32=True)
img1 = img1.unsqueeze(0)
img2 = img2.unsqueeze(0)
# image should be RGB, IMPORTANT: normalized to [-1,1]
img1 = (img1 / 255. - 0.5) * 2
img2 = (img2 / 255. - 0.5) * 2
loss_fn_alex = lpips.LPIPS(net='alex',
verbose=False) # best forward scores
metric = loss_fn_alex(img1, img2).squeeze(0).float().detach().cpu().numpy()
return metric.mean()
def __getitem__(self, index):
if self.file_client is None:
self.file_client = FileClient(self.io_backend_opt.pop('type'),
**self.io_backend_opt)
# random reverse
if self.random_reverse and random.random() < 0.5:
self.neighbor_list.reverse()
scale = self.opt['scale']
gt_size = self.opt['gt_size']
key = self.keys[index]
clip, seq = key.split('/') # key example: 00001/0001
# get the GT frame (im4.png)
if self.is_lmdb:
img_gt_path = f'{key}/im4'
else:
img_gt_path = self.gt_root / clip / seq / 'im4.png'
img_bytes = self.file_client.get(img_gt_path, 'gt')
img_gt = mmcv.imfrombytes(img_bytes).astype(np.float32) / 255.
# get the neighboring LQ frames
img_lqs = []
for neighbor in self.neighbor_list:
if self.is_lmdb:
img_lq_path = f'{clip}/{seq}/im{neighbor}'
else:
img_lq_path = self.lq_root / clip / seq / f'im{neighbor}.png'
img_bytes = self.file_client.get(img_lq_path, 'lq')
img_lq = mmcv.imfrombytes(img_bytes).astype(np.float32) / 255.
img_lqs.append(img_lq)
# randomly crop
img_gt, img_lqs = paired_random_crop(img_gt, img_lqs, gt_size, scale,
img_gt_path)
# augmentation - flip, rotate
img_lqs.append(img_gt)
img_results = augment(img_lqs, self.opt['use_flip'],
self.opt['use_rot'])
img_results = totensor(img_results)
img_lqs = torch.stack(img_results[0:-1], dim=0)
img_gt = img_results[-1]
# img_lqs: (t, c, h, w)
# img_gt: (c, h, w)
# key: str
return {'lq': img_lqs, 'gt': img_gt, 'key': key}
def __getitem__(self, index):
if self.file_client is None:
self.file_client = FileClient(self.io_backend_opt.pop('type'),
**self.io_backend_opt)
# load lq image
lq_path = self.paths[index]
img_bytes = self.file_client.get(lq_path)
img_lq = mmcv.imfrombytes(img_bytes).astype(np.float32) / 255.
# TODO: color space transform
# BGR to RGB, HWC to CHW, numpy to tensor
img_lq = totensor(img_lq, bgr2rgb=True, float32=True)
return {'lq': img_lq, 'lq_path': lq_path}
def __getitem__(self, index):
if self.file_client is None:
self.file_client = FileClient(self.io_backend_opt.pop('type'),
**self.io_backend_opt)
lq_path = self.paths[index]['lq_path']
img_bytes = self.file_client.get(lq_path, 'lq')
img_lq = mmcv.imfrombytes(img_bytes).astype(np.float32) / 255.
# TODO: color space transform
# BGR to RGB, HWC to CHW, numpy to tensor
img_lq = totensor(img_lq, bgr2rgb=True, float32=True)
# normalize
if self.mean is not None or self.std is not None:
normalize(img_lq, self.mean, self.std, inplace=True)
return {'lq': img_lq, 'lq_path': lq_path}
def __getitem__(self, index):
if self.file_client is None:
self.file_client = FileClient(self.io_backend_opt.pop('type'),
**self.io_backend_opt)
# load gt image
gt_path = self.paths[index]
img_bytes = self.file_client.get(gt_path)
img_gt = mmcv.imfrombytes(img_bytes).astype(np.float32) / 255.
# random horizontal flip
img_gt = augment(img_gt, hflip=self.opt['use_hflip'], rotation=False)
# BGR to RGB, HWC to CHW, numpy to tensor
img_gt = totensor(img_gt, bgr2rgb=True, float32=True)
# normalize
normalize(img_gt, self.mean, self.std, inplace=True)
return {'gt': img_gt, 'gt_path': gt_path}
def __getitem__(self, index):
if self.file_client is None:
self.file_client = FileClient(
self.io_backend_opt.pop('type'), **self.io_backend_opt)
scale = self.opt['scale']
lq_map_type = self.opt['lq_map_type']
gt_map_type = self.opt['gt_map_type']
# Load gt and lq images. Dimension order: HWC; channel order: RGGB;
# HDR image range: [0, +inf], float32.
gt_path = self.paths[index]['gt_path']
lq_path = self.paths[index]['lq_path']
img_gt = self.file_client.get(gt_path)
img_lq = self.file_client.get(lq_path)
# tone mapping
img_lq = self._tonemap(img_lq, type=lq_map_type)
img_gt = self._tonemap(img_gt, type=gt_map_type)
# expand dimension
img_gt = self._expand_dim(img_gt)
img_lq = self._expand_dim(img_lq)
# augmentation
if self.opt['phase'] == 'train':
gt_size = self.opt['gt_size']
# random crop
img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale,
gt_path)
# flip, rotation
img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_flip'],
self.opt['use_rot'])
# TODO: color space transform
# BGR to RGB, HWC to CHW, numpy to tensor
img_gt, img_lq = totensor([img_gt, img_lq], bgr2rgb=False, float32=True)
return {
'lq': img_lq,
'gt': img_gt,
'lq_path': lq_path,
'gt_path': gt_path
}
def read_img_seq(path, require_mod_crop=False, scale=1):
"""Read a sequence of images from a given folder path.
Args:
path (list[str] | str): List of image paths or image folder path.
require_mod_crop (bool): Require mod crop for each image.
Default: False.
scale (int): Scale factor for mod_crop. Default: 1.
Returns:
Tensor: size (t, c, h, w), RGB, [0, 1].
"""
if isinstance(path, list):
img_paths = path
else:
img_paths = sorted([osp.join(path, v) for v in mmcv.scandir(path)])
imgs = [mmcv.imread(v).astype(np.float32) / 255. for v in img_paths]
if require_mod_crop:
imgs = [mod_crop(img, scale) for img in imgs]
imgs = totensor(imgs, bgr2rgb=True, float32=True)
imgs = torch.stack(imgs, dim=0)
return imgs
def __getitem__(self, index):
if self.file_client is None:
self.file_client = FileClient(self.io_backend_opt.pop('type'),
**self.io_backend_opt)
scale = self.opt['scale']
# Load gt and lq images. Dimension order: HWC; channel order: BGR;
# image range: [0, 1], float32.
gt_path = self.paths[index]['gt_path']
img_bytes = self.file_client.get(gt_path, 'gt')
img_gt = mmcv.imfrombytes(img_bytes).astype(np.float32) / 255.
img_gt_h = img_gt.shape[0]
img_gt_w = img_gt.shape[1]
lq_path = self.paths[index]['lq_path']
img_bytes = self.file_client.get(lq_path, 'lq')
img_lq = np.copy(np.frombuffer(img_bytes, dtype='float32')).reshape(
img_gt_h // scale, img_gt_w // scale, -1)
# No augmentation for training
# if self.opt['phase'] == 'train':
# gt_size = self.opt['gt_size']
# # random crop
# img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale,
# gt_path)
# # flip, rotation
# img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_flip'],
# self.opt['use_rot'])
# TODO: color space transform
# BGR to RGB, HWC to CHW, numpy to tensor
img_gt, img_lq = totensor([img_gt, img_lq], bgr2rgb=True, float32=True)
return {
'lq': img_lq,
'gt': img_gt,
'lq_path': lq_path,
'gt_path': gt_path
}
def __getitem__(self, index):
if self.file_client is None:
self.file_client = FileClient(self.io_backend_opt.pop('type'),
**self.io_backend_opt)
scale = self.opt['scale']
gt_size = self.opt['gt_size']
key = self.keys[index]
clip_name, frame_name = key.split('/') # key example: 000/00000000
center_frame_idx = int(frame_name)
# determine the neighboring frames
interval = random.choice(self.interval_list)
# ensure not exceeding the borders
start_frame_idx = center_frame_idx - self.num_half_frames * interval
end_frame_idx = center_frame_idx + self.num_half_frames * interval
# each clip has 100 frames starting from 0 to 99
while (start_frame_idx < 0) or (end_frame_idx > 99):
center_frame_idx = random.randint(0, 99)
start_frame_idx = (center_frame_idx -
self.num_half_frames * interval)
end_frame_idx = center_frame_idx + self.num_half_frames * interval
frame_name = f'{center_frame_idx:08d}'
neighbor_list = list(
range(center_frame_idx - self.num_half_frames * interval,
center_frame_idx + self.num_half_frames * interval + 1,
interval))
# random reverse
if self.random_reverse and random.random() < 0.5:
neighbor_list.reverse()
assert len(neighbor_list) == self.num_frame, (
f'Wrong length of neighbor list: {len(neighbor_list)}')
# get the GT frame (as the center frame)
if self.is_lmdb:
img_gt_path = f'{clip_name}/{frame_name}'
else:
img_gt_path = self.gt_root / clip_name / f'{frame_name}.png'
img_bytes = self.file_client.get(img_gt_path, 'gt')
img_gt = mmcv.imfrombytes(img_bytes).astype(np.float32) / 255.
# get the neighboring LQ frames
img_lqs = []
for neighbor in neighbor_list:
if self.is_lmdb:
img_lq_path = f'{clip_name}/{neighbor:08d}'
else:
img_lq_path = self.lq_root / clip_name / f'{neighbor:08d}.png'
img_bytes = self.file_client.get(img_lq_path, 'lq')
img_lq = mmcv.imfrombytes(img_bytes).astype(np.float32) / 255.
img_lqs.append(img_lq)
# get flows
if self.flow_root is not None:
img_flows = []
# read previous flows
for i in range(self.num_half_frames, 0, -1):
if self.is_lmdb:
flow_path = f'{clip_name}/{frame_name}_p{i}'
else:
flow_path = (self.flow_root / clip_name /
f'{frame_name}_p{i}.png')
img_bytes = self.file_client.get(flow_path, 'flow')
cat_flow = mmcv.imfrombytes(
img_bytes, flag='grayscale') # uint8, [0, 255]
dx, dy = np.split(cat_flow, 2, axis=0)
flow = mmcv.video.dequantize_flow(
dx, dy, max_val=20,
denorm=False) # we use max_val 20 here.
img_flows.append(flow)
# read next flows
for i in range(1, self.num_half_frames + 1):
if self.is_lmdb:
flow_path = f'{clip_name}/{frame_name}_n{i}'
else:
flow_path = (self.flow_root / clip_name /
f'{frame_name}_n{i}.png')
img_bytes = self.file_client.get(flow_path, 'flow')
cat_flow = mmcv.imfrombytes(img_bytes, flag='grayscale')
dx, dy = np.split(cat_flow, 2, axis=0)
flow = mmcv.video.dequantize_flow(
dx, dy, max_val=20,
denorm=False) # we use max_val 20 here.
img_flows.append(flow)
# for random crop, here, img_flows and img_lqs have the same
# spatial size
img_lqs.extend(img_flows)
# randomly crop
img_gt, img_lqs = paired_random_crop(img_gt, img_lqs, gt_size, scale,
img_gt_path)
if self.flow_root is not None:
img_lqs, img_flows = img_lqs[:self.num_frame], img_lqs[self.
num_frame:]
# augmentation - flip, rotate
img_lqs.append(img_gt)
if self.flow_root is not None:
img_results, img_flows = augment(img_lqs, self.opt['use_flip'],
self.opt['use_rot'], img_flows)
else:
img_results = augment(img_lqs, self.opt['use_flip'],
self.opt['use_rot'])
img_results = totensor(img_results)
img_lqs = torch.stack(img_results[0:-1], dim=0)
img_gt = img_results[-1]
if self.flow_root is not None:
img_flows = totensor(img_flows)
# add the zero center flow
img_flows.insert(self.num_half_frames,
torch.zeros_like(img_flows[0]))
img_flows = torch.stack(img_flows, dim=0)
# img_lqs: (t, c, h, w)
# img_flows: (t, 2, h, w)
# img_gt: (c, h, w)
# key: str
if self.flow_root is not None:
return {'lq': img_lqs, 'flow': img_flows, 'gt': img_gt, 'key': key}
else:
return {'lq': img_lqs, 'gt': img_gt, 'key': key}
def __getitem__(self, index):
if self.file_client is None:
self.file_client = FileClient(self.io_backend_opt.pop('type'),
**self.io_backend_opt)
# random reverse
if self.random_reverse and random.random() < 0.5:
self.neighbor_list.reverse()
scale = self.opt['scale']
gt_size = self.opt['gt_size']
key = self.keys[index]
clip, seq = key.split('/') # key example: 00001/0001
# get the GT frame (im4.png)
if self.is_lmdb:
img_gt_path = f'{key}/im4'
else:
img_gt_path = self.gt_root / clip / seq / 'im4.png'
img_bytes = self.file_client.get(img_gt_path, 'gt')
img_gt = mmcv.imfrombytes(img_bytes).astype(np.float32) / 255.
### get 160
img_160_path = self.lq_root / clip / seq / 'im4_hr.png'
img_bytes_160 = self.file_client.get(img_160_path, 'gt')
img_160 = mmcv.imfrombytes(img_bytes_160).astype(np.float32) / 255.
# get the neighboring LQ frames
img_gt_160 = []
img_gt_160.append(img_gt)
img_gt_160.append(img_160)
# ###visualization
# path='/home/wei/exp/EDVR/visualization'
# number = 1
# ###visualization
img_lqs = []
for neighbor in self.neighbor_list:
if self.is_lmdb:
img_lq_path = f'{clip}/{seq}/im{neighbor}'
else:
img_lq_path = self.lq_root / clip / seq / f'im{neighbor}.png'
img_bytes = self.file_client.get(img_lq_path, 'lq')
img_lq = mmcv.imfrombytes(img_bytes).astype(np.float32) / 255.
# img_con_3d = np.vstack((img_lq, img_3d))
# ##visualization
# number +=1
# visual_lq = img_con_3d[:,:,:3]
# visual_lq = Image.fromarray((visual_lq).astype(np.uint8)).convert("RGB")
# visual_lq.save(path+'/'+str(number)+'_lq.png')
# visual_3d = img_con_3d[:,:,3:]
# visual_3d = Image.fromarray((visual_3d).astype(np.uint8)).convert("RGB")
# visual_3d.save(path+'/'+str(number)+'_3d.png')
# ##visualization
img_lqs.append(img_lq)
# randomly crop
img_gt, img_lqs = paired_random_crop(img_gt_160, img_lqs, gt_size,
scale, img_gt_path)
img_160_input = img_gt[1]
img_gt = img_gt[0]
# augmentation - flip, rotate
img_lqs.append(img_160_input)
img_lqs.append(img_gt)
img_results = augment(img_lqs, self.opt['use_flip'],
self.opt['use_rot'])
img_results = totensor(img_results)
hr_3d = img_results[-2]
img_lqs = torch.stack(img_results[0:-2], dim=0)
img_gt = img_results[-1]
# img_lqs: (t, c, h, w)
# img_gt: (c, h, w)
# key: str
return {'lq': img_lqs, 'gt': img_gt, 'hr_3d': hr_3d, 'key': key}
def __getitem__(self, index):
if self.file_client is None:
self.file_client = FileClient(self.io_backend_opt.pop('type'),
**self.io_backend_opt)
# random reverse
if self.random_reverse and random.random() < 0.5:
self.neighbor_list.reverse()
scale = self.opt['scale']
gt_size = self.opt['gt_size']
key = self.keys[index]
clip, seq = key.split('/') # key example: 00001/0001
# get the GT frame (im4.png)
if self.is_lmdb:
img_gt_path = f'{key}/im4'
else:
img_gt_path = self.gt_root / clip / seq / 'im4.png'
img_bytes = self.file_client.get(img_gt_path, 'gt')
img_gt = mmcv.imfrombytes(img_bytes).astype(np.float32) / 255.
# get the neighboring LQ frames
img_lqs = []
for neighbor in self.neighbor_list:
if self.is_lmdb:
img_lq_path = f'{clip}/{seq}/im{neighbor}'
else:
img_lq_path = self.lq_root / clip / seq / f'im{neighbor}.png'
img_bytes = self.file_client.get(img_lq_path, 'lq')
img_lq = mmcv.imfrombytes(img_bytes).astype(np.float32) / 255.
img_lqs.append(img_lq)
# randomly crop
img_gt, img_lqs = paired_random_crop(img_gt, img_lqs, gt_size, scale,
img_gt_path)
# augmentation - flip, rotate
img_lqs.append(img_gt)
img_results = augment(img_lqs, self.opt['use_flip'],
self.opt['use_rot'])
img_results = totensor(img_results)
img_lqs = torch.stack(img_results[0:-1], dim=0)
img_gt = img_results[-1]
# img_lqs: (t, c, h, w)
# img_gt: (c, h, w)
# key: str
### get 18
# ztm = np.load(path_flow,allow_pickle=True)
# result_7 = []
# for test in ztm:
# test = np.transpose(test, [2,1,0])
# width = test.shape[1]
# height = test.shape[2]
# ndarray=np.pad(test,((0,0),(1,1),(1,1)),'constant', constant_values=0)
# result=[]
# for i in range(0,3):
# for j in range(0,3):
# result.append(ndarray[:,i:i+448,j:j+448])
# result = np.array(result).reshape(18,448,448)
# #result = np.repeat(result,8,axis=0)
# result_7.append(np.array(result))
# save_path = path_flow.replace('flow.npy','flow_7.npy')
# np.save(save_path,np.array(result_7))
### get18
#return np.array(result_7)
return {'lq': img_lqs, 'gt': img_gt, 'key': key}
def __getitem__(self, index):
if self.file_client is None:
self.file_client = FileClient(self.io_backend_opt.pop('type'),
**self.io_backend_opt)
scale = self.opt['scale']
lq_map_type = self.opt['lq_map_type']
gt_map_type = self.opt['gt_map_type']
crop_scale = self.opt.get('crop_scale', None)
# Load gt and lq images. Dimension order: HWC; channel order: RGGB;
# HDR image range: [0, +inf], float32.
gt_path = self.paths[index]['gt_path']
lq_path = self.paths[index]['lq_path']
psf_path = self.paths[index]['psf_path']
img_gt = self.file_client.get(gt_path)
img_lq = self.file_client.get(lq_path)
psf_code = self.file_client.get(psf_path)
# tone mapping
img_lq = self._tonemap(img_lq, type=lq_map_type)
img_gt = self._tonemap(img_gt, type=gt_map_type)
# expand dimension
img_gt = self._expand_dim(img_gt)
img_lq = self._expand_dim(img_lq)
# Rescale for random crop
if crop_scale != None:
h, w, _ = img_lq.shape
img_lq = cv2.resize(img_lq,
(int(w * crop_scale), int(h * crop_scale)),
interpolation=cv2.INTER_LINEAR)
img_gt = cv2.resize(img_gt,
(int(w * crop_scale), int(h * crop_scale)),
interpolation=cv2.INTER_LINEAR)
# augmentation
if self.opt['phase'] == 'train':
gt_size = self.opt['gt_size']
# random crop
img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale,
gt_path)
# flip, rotation
img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_flip'],
self.opt['use_rot'])
# TODO: color space transform
# BGR to RGB, HWC to CHW, numpy to tensor
img_gt, img_lq = totensor([img_gt, img_lq],
bgr2rgb=False,
float32=True)
psf_code = torch.from_numpy(psf_code)[..., None, None]
return {
'lq': img_lq,
'gt': img_gt,
'psf_code': psf_code,
'lq_path': lq_path,
'gt_path': gt_path,
'psf_path': psf_path,
}
