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import torch
from DeDoDe import dedode_detector_L, dedode_descriptor_G
from DeDoDe.matchers.dual_softmax_matcher import DualSoftMaxMatcher
from DeDoDe.utils import *
from PIL import Image
import cv2
import numpy as np
def draw_matches(im_A, kpts_A, im_B, kpts_B):
kpts_A = [cv2.KeyPoint(x,y,1.) for x,y in kpts_A.cpu().numpy()]
kpts_B = [cv2.KeyPoint(x,y,1.) for x,y in kpts_B.cpu().numpy()]
matches_A_to_B = [cv2.DMatch(idx, idx, 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
if __name__ == "__main__":
device = get_best_device()
detector = dedode_detector_L(weights = torch.load("dedode_detector_L.pth", map_location = device))
descriptor = dedode_descriptor_G(weights = torch.load("dedode_descriptor_G.pth", map_location = device))
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 = 0.01)#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)
Image.fromarray(draw_matches(im_A, matches_A, im_B, matches_B)).save("demo/matches.jpg") |