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@@ -0,0 +1,70 @@
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+import cv2
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+import numpy as np
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+from loguru import logger
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+
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+from superpoint_superglue_deployment import Matcher
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+
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+
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+def get_args():
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+ import argparse
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+
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+ parser = argparse.ArgumentParser("test matching two images")
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+ parser.add_argument("--query_path", "-q", type=str, required=True, help="path to query image")
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+ parser.add_argument("--ref_path", "-r", type=str, required=True, help="path to reference image")
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+ parser.add_argument("--use_gpu", action="store_true")
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+
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+ return parser.parse_args()
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+
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+
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+def main():
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+ args = get_args()
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+
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+ query_image = cv2.imread(args.query_path)
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+ ref_image = cv2.imread(args.ref_path)
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+
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+ query_gray = cv2.imread(args.query_path, 0)
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+ ref_gray = cv2.imread(args.ref_path, 0)
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+
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+ superglue_matcher = Matcher(
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+ {
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+ "superpoint": {
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+ "input_shape": (-1, -1),
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+ "keypoint_threshold": 0.005,
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+ },
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+ "superglue": {
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+ "match_threshold": 0.2,
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+ },
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+ "use_gpu": args.use_gpu,
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+ }
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+ )
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+ query_kpts, ref_kpts, _, _, matches = superglue_matcher.match(query_gray, ref_gray)
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+ logger.info(f"number of matches by superpoint+superglue: {len(matches)}")
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+ _, mask = cv2.findHomography(
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+ np.array([query_kpts[m.queryIdx].pt for m in matches], dtype=np.float64).reshape(-1, 1, 2),
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+ np.array([ref_kpts[m.trainIdx].pt for m in matches], dtype=np.float64).reshape(-1, 1, 2),
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+ method=cv2.USAC_MAGSAC,
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+ ransacReprojThreshold=5.0,
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+ maxIters=10000,
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+ confidence=0.95,
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+ )
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+ logger.info(f"number of inliers: {mask.sum()}")
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+ matches = np.array(matches)[np.all(mask > 0, axis=1)]
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+
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+ matches = sorted(matches, key=lambda match: match.distance)
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+ matched_image = cv2.drawMatches(
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+ query_image,
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+ query_kpts,
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+ ref_image,
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+ ref_kpts,
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+ matches[:100],
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+ None,
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+ flags=2,
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+ )
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+ cv2.imwrite("matched_image.jpg", matched_image)
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+ cv2.imshow("matched_image", matched_image)
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+ cv2.waitKey(0)
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+ cv2.destroyAllWindows()
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+
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+
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+if __name__ == "__main__":
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+ main()
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