third_party/DeDoDe/demo/demo_match.py

import torch
from DeDoDe import dedode_detector_L, dedode_descriptor_B
from DeDoDe.matchers.dual_softmax_matcher import DualSoftMaxMatcher
from DeDoDe.utils import *
from PIL import Image
import cv2


def draw_matches(im_A, kpts_A, im_B, kpts_B):
    kpts_A = [cv2.KeyPoint(x, y, 1.0) for x, y in kpts_A.cpu().numpy()]
    kpts_B = [cv2.KeyPoint(x, y, 1.0) for x, y in kpts_B.cpu().numpy()]
    matches_A_to_B = [cv2.DMatch(idx, idx, 0.0) for idx in range(len(kpts_A))]
    im_A, im_B = np.array(im_A), np.array(im_B)
    ret = cv2.drawMatches(im_A, kpts_A, im_B, kpts_B, matches_A_to_B, None)
    return ret


detector = dedode_detector_L(weights=torch.load("dedode_detector_L.pth"))
descriptor = dedode_descriptor_B(weights=torch.load("dedode_descriptor_B.pth"))
matcher = DualSoftMaxMatcher()

im_A_path = "assets/im_A.jpg"
im_B_path = "assets/im_B.jpg"
im_A = Image.open(im_A_path)
im_B = Image.open(im_B_path)
W_A, H_A = im_A.size
W_B, H_B = im_B.size


detections_A = detector.detect_from_path(im_A_path, num_keypoints=10_000)
keypoints_A, P_A = detections_A["keypoints"], detections_A["confidence"]
detections_B = detector.detect_from_path(im_B_path, num_keypoints=10_000)
keypoints_B, P_B = detections_B["keypoints"], detections_B["confidence"]
description_A = descriptor.describe_keypoints_from_path(im_A_path, keypoints_A)[
    "descriptions"
]
description_B = descriptor.describe_keypoints_from_path(im_B_path, keypoints_B)[
    "descriptions"
]
matches_A, matches_B, batch_ids = matcher.match(
    keypoints_A,
    description_A,
    keypoints_B,
    description_B,
    P_A=P_A,
    P_B=P_B,
    normalize=True,
    inv_temp=20,
    threshold=0.1,
)  # Increasing threshold -> fewer matches, fewer outliers

matches_A, matches_B = matcher.to_pixel_coords(matches_A, matches_B, H_A, W_A, H_B, W_B)

import cv2
import numpy as np

Image.fromarray(draw_matches(im_A, matches_A[::5], im_B, matches_B[::5])).save(
    "demo/matches.png"
)