third_party/DeDoDe/demo/demo_scoremap.py

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.).permute(2,0,1)
tensor_to_pil(logit_map * logit_map  +  0.15 * (1-logit_map) * im_A).save("demo/dense_logits.png")