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Zero
Running
on
Zero
| import numbers | |
| import cv2 | |
| import numpy as np | |
| import PIL | |
| import torch | |
| def _is_tensor_clip(clip): | |
| return torch.is_tensor(clip) and clip.ndimension() == 4 | |
| def crop_clip(clip, min_h, min_w, h, w): | |
| if isinstance(clip[0], np.ndarray): | |
| cropped = [img[min_h:min_h + h, min_w:min_w + w, :] for img in clip] | |
| elif isinstance(clip[0], PIL.Image.Image): | |
| cropped = [ | |
| img.crop((min_w, min_h, min_w + w, min_h + h)) for img in clip | |
| ] | |
| else: | |
| raise TypeError('Expected numpy.ndarray or PIL.Image' + | |
| 'but got list of {0}'.format(type(clip[0]))) | |
| return cropped | |
| def resize_clip(clip, size, interpolation='bilinear'): | |
| if isinstance(clip[0], np.ndarray): | |
| if isinstance(size, numbers.Number): | |
| im_h, im_w, im_c = clip[0].shape | |
| # Min spatial dim already matches minimal size | |
| if (im_w <= im_h and im_w == size) or (im_h <= im_w | |
| and im_h == size): | |
| return clip | |
| new_h, new_w = get_resize_sizes(im_h, im_w, size) | |
| size = (new_w, new_h) | |
| else: | |
| size = size[0], size[1] | |
| if interpolation == 'bilinear': | |
| np_inter = cv2.INTER_LINEAR | |
| else: | |
| np_inter = cv2.INTER_NEAREST | |
| scaled = [ | |
| cv2.resize(img, size, interpolation=np_inter) for img in clip | |
| ] | |
| elif isinstance(clip[0], PIL.Image.Image): | |
| if isinstance(size, numbers.Number): | |
| im_w, im_h = clip[0].size | |
| # Min spatial dim already matches minimal size | |
| if (im_w <= im_h and im_w == size) or (im_h <= im_w | |
| and im_h == size): | |
| return clip | |
| new_h, new_w = get_resize_sizes(im_h, im_w, size) | |
| size = (new_w, new_h) | |
| else: | |
| size = size[1], size[0] | |
| if interpolation == 'bilinear': | |
| pil_inter = PIL.Image.BILINEAR | |
| else: | |
| pil_inter = PIL.Image.NEAREST | |
| scaled = [img.resize(size, pil_inter) for img in clip] | |
| else: | |
| raise TypeError('Expected numpy.ndarray or PIL.Image' + | |
| 'but got list of {0}'.format(type(clip[0]))) | |
| return scaled | |
| def get_resize_sizes(im_h, im_w, size): | |
| if im_w < im_h: | |
| ow = size | |
| oh = int(size * im_h / im_w) | |
| else: | |
| oh = size | |
| ow = int(size * im_w / im_h) | |
| return oh, ow | |
| def normalize(clip, mean, std, inplace=False): | |
| if not _is_tensor_clip(clip): | |
| raise TypeError('tensor is not a torch clip.') | |
| if not inplace: | |
| clip = clip.clone() | |
| dtype = clip.dtype | |
| mean = torch.as_tensor(mean, dtype=dtype, device=clip.device) | |
| std = torch.as_tensor(std, dtype=dtype, device=clip.device) | |
| clip.sub_(mean[:, None, None, None]).div_(std[:, None, None, None]) | |
| return clip | |