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| import torch as th | |
| from torch.utils.data import Dataset | |
| import pandas as pd | |
| import os | |
| import numpy as np | |
| import ffmpeg | |
| class VideoLoader(Dataset): | |
| def __init__( | |
| self, | |
| fileList = [], | |
| framerate=1, | |
| size=112, | |
| centercrop=False, | |
| ): | |
| #self.csv = pd.read_csv(csv) | |
| self.fileList = fileList | |
| self.centercrop = centercrop | |
| self.size = size | |
| self.framerate = framerate | |
| def __len__(self): | |
| return len(self.fileList) | |
| def _get_video_dim(self, video_path): | |
| probe = ffmpeg.probe(video_path) | |
| video_stream = next((stream for stream in probe['streams'] | |
| if stream['codec_type'] == 'video'), None) | |
| width = int(video_stream['width']) | |
| height = int(video_stream['height']) | |
| return height, width | |
| def _get_output_dim(self, h, w): | |
| if isinstance(self.size, tuple) and len(self.size) == 2: | |
| return self.size | |
| elif h >= w: | |
| return int(h * self.size / w), self.size | |
| else: | |
| return self.size, int(w * self.size / h) | |
| def __getitem__(self, idx): | |
| video_path = self.fileList[idx] | |
| output_file = video_path[:video_path.rfind(".")] + ".npy" | |
| #video_path = self.csv['video_path'].values[idx] | |
| #output_file = self.csv['feature_path'].values[idx] | |
| if not(os.path.isfile(output_file)) and os.path.isfile(video_path): | |
| print('Decoding video: {}'.format(video_path)) | |
| try: | |
| h, w = self._get_video_dim(video_path) | |
| except: | |
| print('ffprobe failed at: {}'.format(video_path)) | |
| return {'video': th.zeros(1), 'input': video_path, | |
| 'output': output_file} | |
| height, width = self._get_output_dim(h, w) | |
| cmd = ( | |
| ffmpeg | |
| .input(video_path) | |
| .filter('fps', fps=self.framerate) | |
| .filter('scale', width, height) | |
| ) | |
| if self.centercrop: | |
| x = int((width - self.size) / 2.0) | |
| y = int((height - self.size) / 2.0) | |
| cmd = cmd.crop(x, y, self.size, self.size) | |
| out, _ = ( | |
| cmd.output('pipe:', format='rawvideo', pix_fmt='rgb24') | |
| .run(capture_stdout=True, quiet=True) | |
| ) | |
| if self.centercrop and isinstance(self.size, int): | |
| height, width = self.size, self.size | |
| video = np.frombuffer(out, np.uint8).reshape([-1, height, width, 3]) | |
| video = th.from_numpy(video.astype('float32')) | |
| video = video.permute(0, 3, 1, 2) | |
| else: | |
| video = th.zeros(1) | |
| return {'video': video, 'input': video_path, 'output': output_file} | |