TODO: 1. to float with scale from 0 to 1 2. resize to (64, 128) as Market1501 dataset did 3. concatenate to a numpy array 3. to torch Tensor 4. normalize
(self, im_crops)
| 122 | ]) |
| 123 | |
| 124 | def _preprocess(self, im_crops): |
| 125 | """ |
| 126 | TODO: |
| 127 | 1. to float with scale from 0 to 1 |
| 128 | 2. resize to (64, 128) as Market1501 dataset did |
| 129 | 3. concatenate to a numpy array |
| 130 | 3. to torch Tensor |
| 131 | 4. normalize |
| 132 | """ |
| 133 | def _resize(im, size): |
| 134 | return cv2.resize(im.astype(np.float32)/255., size) |
| 135 | |
| 136 | im_batch = torch.cat([self.norm(_resize(im, self.size)).unsqueeze( |
| 137 | 0) for im in im_crops], dim=0).float() |
| 138 | return im_batch |
| 139 | |
| 140 | def __call__(self, im_crops): |
| 141 | im_batch = self._preprocess(im_crops) |