Image matching using the lightglue library for keypoint detection in images and matching of keypoints.

image_matcher.py

from pathlib import Path
import torch
import glob


from lightglue import LightGlue, SuperPoint, DISK, SIFT, ALIKED, DoGHardNet
from lightglue.utils import load_image, rbd
from lightglue import viz2d

torch.set_grad_enabled(False)

class ImageMatcher:
    def __init__(self, device):
        self.device = device
        self.extractor = SuperPoint(max_num_keypoints=2048).eval().to(self.device)
        self.matcher = LightGlue(features="superpoint").eval().to(self.device)

        # or DISK+LightGlue, ALIKED+LightGlue or SIFT+LightGlue
        # extractor = DISK(max_num_keypoints=2048).eval()  # load the extractor
        # matcher = LightGlue(features='disk').eval()  # load the matcher
    
    def compare_images(self, image_path1, image_path2):
        image0 = load_image(image_path1).to(self.device)
        image1 = load_image(image_path2).to(self.device)
        
        
        feats0 = self.extractor.extract(image0.to(self.device))
        feats1 = self.extractor.extract(image1.to(self.device))
        matches01 = self.matcher({"image0": feats0, "image1": feats1})
        feats0, feats1, matches01 = [
            rbd(x) for x in [feats0, feats1, matches01]
        ]  # remove batch dimension
        
        kpts0, kpts1, matches = feats0["keypoints"], feats1["keypoints"], matches01["matches"]
        m_kpts0, m_kpts1 = kpts0[matches[..., 0]], kpts1[matches[..., 1]]

        print(f"Number of matches; {len(matches)}; {image_path1} ; {image_path2} ")

        axes = viz2d.plot_images([image0, image1])
        viz2d.plot_matches(m_kpts0, m_kpts1, color="lime", lw=0.2)
        viz2d.add_text(0, f'Stop after {matches01["stop"]} layers', fs=20)

        kpc0, kpc1 = viz2d.cm_prune(matches01["prune0"]), viz2d.cm_prune(matches01["prune1"])
        viz2d.plot_images([image0, image1])
        viz2d.plot_keypoints([kpts0, kpts1], colors=[kpc0, kpc1], ps=10)

        return axes

matcher = ImageMatcher("cpu")

image_files = ['./' + f for f in glob.glob('*.jpg')]

for one in image_files:
    for two in image_files:
        if one != two:
            print(f"Comparing {one} and {two}")
            matcher.compare_images(Path(one), Path(two))

See here for installation instructions: https://github.com/cvg/LightGlue

Once dependencies are installed, run with python image_matcher.py in a folder with several *.jpg files. The jpg files do not need to be scaled beforehand. Execution on cpu is reasonably fast.

Both the matcher as the keypoint detector can be modified by using the following code:

self.extractor = SuperPoint(max_num_keypoints=2048).eval().to(self.device)
self.matcher = LightGlue(features="superpoint").eval().to(self.device)

# or DISK+LightGlue, ALIKED+LightGlue or SIFT+LightGlue
# self.extractor = DISK(max_num_keypoints=2048).eval()  # load the extractor
# self.matcher = LightGlue(features='disk').eval()  # load the matcher