(img)
| 199 | return seg_img |
| 200 | |
| 201 | def pad_img(img): |
| 202 | h, w, _ = img.shape |
| 203 | l = max(w,h) |
| 204 | pad = np.zeros((l,l,3), dtype=np.uint8) |
| 205 | if h > w: |
| 206 | pad[:,(h-w)//2:(h-w)//2 + w, :] = img |
| 207 | else: |
| 208 | pad[(w-h)//2:(w-h)//2 + h, :, :] = img |
| 209 | return pad |
| 210 | |
| 211 | def filter(keep: torch.Tensor, masks_result) -> None: |
| 212 | keep = keep.int().cpu().numpy() |