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 read_img_seq(path, require_mod_crop=False, scale=1, return_imgname=False):
"""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.
return_imgname(bool): Whether return image names. Default False.
Returns:
Tensor: size (t, c, h, w), RGB, [0, 1].
list[str]: Returned image name list.
"""
if isinstance(path, list):
img_paths = path
else:
img_paths = sorted(list(scandir(path, full_path=True)))
imgs = [cv2.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 = img2tensor(imgs, bgr2rgb=True, float32=True)
imgs = torch.stack(imgs, dim=0)
if return_imgname:
imgnames = [osp.splitext(osp.basename(path))[0] for path in img_paths]
return imgs, imgnames
else:
return imgs
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