| import torch | |
| from PIL import Image | |
| import numpy as np | |
| from DeDoDe import dedode_detector_L | |
| from DeDoDe.utils import tensor_to_pil | |
| detector = dedode_detector_L(weights=torch.load("dedode_detector_l.pth")) | |
| H, W = 768, 768 | |
| im_path = "assets/im_A.jpg" | |
| out = detector.detect_from_path(im_path, dense=True, H=H, W=W) | |
| logit_map = out["dense_keypoint_logits"].clone() | |
| min = logit_map.max() - 3 | |
| logit_map[logit_map < min] = min | |
| logit_map = (logit_map - min) / (logit_map.max() - min) | |
| logit_map = logit_map.cpu()[0].expand(3, H, W) | |
| im_A = torch.tensor(np.array(Image.open(im_path).resize((W, H))) / 255.0).permute( | |
| 2, 0, 1 | |
| ) | |
| tensor_to_pil(logit_map * logit_map + 0.15 * (1 - logit_map) * im_A).save( | |
| "demo/dense_logits.png" | |
| ) | |