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              "name": "keypoints",
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                      "value": " according to the Harris corner response if more than "
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  "item_file": "/dev/scikit-image/src/skimage/feature/orb.py",
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        "value": "from skimage.feature import ORB, match_descriptors\nimg1 = np.zeros((100, 100))\nimg2 = np.zeros_like(img1)\nrng = np.random.default_rng(19481137)  # do not copy this value\nsquare = rng.random((20, 20))\nimg1[40:60, 40:60] = square\nimg2[53:73, 53:73] = square\ndetector_extractor1 = ORB(n_keypoints=5)\ndetector_extractor2 = ORB(n_keypoints=5)\ndetector_extractor1.detect_and_extract(img1)\ndetector_extractor2.detect_and_extract(img2)\nmatches = match_descriptors(detector_extractor1.descriptors,\n                            detector_extractor2.descriptors)\nmatches\ndetector_extractor1.keypoints[matches[:, 0]]\ndetector_extractor2.keypoints[matches[:, 1]]\n",
        "execution_status": "success"
      }
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  "signature": {
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    ],
    "return_annotation": {
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    "target_name": "ORB"
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  "references": [
    ".. [1] Ethan Rublee, Vincent Rabaud, Kurt Konolige and Gary Bradski",
    "      \"ORB: An efficient alternative to SIFT and SURF\"",
    "      http://www.vision.cs.chubu.ac.jp/CV-R/pdf/Rublee_iccv2011.pdf"
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