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| from transformers import ViTFeatureExtractor | |
| import torchvision | |
| import torchvision.transforms.functional as fn | |
| import torch as th | |
| def video2image_from_path(video_path, feature_extractor_name): | |
| video = torchvision.io.read_video(video_path) | |
| return video2image(video[0], feature_extractor_name) | |
| def video2image(video, feature_extractor_name): | |
| feature_extractor = ViTFeatureExtractor.from_pretrained( | |
| feature_extractor_name | |
| ) | |
| vid = th.permute(video, (3, 0, 1, 2)) | |
| samp = th.linspace(0, vid.shape[1]-1, 49, dtype=th.long) | |
| vid = vid[:, samp, :, :] | |
| im_l = list() | |
| for i in range(vid.shape[1]): | |
| im_l.append(vid[:, i, :, :]) | |
| inputs = feature_extractor(im_l, return_tensors="pt") | |
| inputs = inputs['pixel_values'] | |
| im_h = list() | |
| for i in range(7): | |
| im_v = th.cat((inputs[0+i*7, :, :, :], | |
| inputs[1+i*7, :, :, :], | |
| inputs[2+i*7, :, :, :], | |
| inputs[3+i*7, :, :, :], | |
| inputs[4+i*7, :, :, :], | |
| inputs[5+i*7, :, :, :], | |
| inputs[6+i*7, :, :, :]), 2) | |
| im_h.append(im_v) | |
| resize = fn.resize(th.cat(im_h, 1), size=[224]) | |
| return resize | |