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))) / 255.0).permute(
    2, 0, 1
)
tensor_to_pil(logit_map * logit_map + 0.15 * (1 - logit_map) * im_A).save(
    "demo/dense_logits.png"
)