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            {
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              "name": "tf",
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                      "value": "An instance of the requested transformation if the estimation Otherwise, we return a special "
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                      "value": "FailedEstimation"
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                    {
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                      "value": " object to signal a failed estimation. Testing the truth value of the failed estimation object will return "
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                  "value": "tf = estimate_transform(...)\nif not tf:\n    raise RuntimeError(f\"Failed estimation: {tf}\")",
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              "value": "Estimate 2D geometric transformation parameters."
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              "annotation": "{'euclidean', similarity', 'affine', 'piecewise-affine',              'projective', 'polynomial'}",
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                      "value": "Function parameters (src, dst, n, angle)      "
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                  "value": "NAME / TTYPE        FUNCTION PARAMETERS\n'euclidean'         `src, `dst`\n'similarity'        `src, `dst`\n'affine'            `src, `dst`\n'piecewise-affine'  `src, `dst`\n'projective'        `src, `dst`\n'polynomial'        `src, `dst`, `order` (polynomial order,\n                                          default order is 2)",
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              "value": "You can determine the over-, well- and under-determined parameters with the total least-squares method."
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  "item_file": "/dev/scikit-image/src/skimage/transform/_geometric.py",
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        "value": "src = np.array([0, 0, 10, 10]).reshape((2, 2))\ndst = np.array([12, 14, 1, -20]).reshape((2, 2))\n",
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        "value": "tform = ski.transform.estimate_transform('similarity', src, dst)\n",
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        "value": "np.allclose(tform.inverse(tform(src)), src)\n",
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        "value": "tform2 = ski.transform.SimilarityTransform(scale=1.1, rotation=1,\n    translation=(10, 20))\n",
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        "value": "tform3 = tform + tform2\nnp.allclose(tform3(src), tform2(tform(src)))\n",
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        "value": "\nThe estimation can fail - for example, if all the input or output points\nare the same.  If this happens, you will get a transform that is not\n\"truthy\" - meaning that ``bool(tform)`` is ``False``:\n\n"
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        "value": "if tform:\n    print(\"Estimation succeeded.\")\nbad_src = np.ones((2, 2))\nbad_tform = ski.transform.estimate_transform('similarity',\n                                             bad_src, dst)\nif not bad_tform:\n    print(\"Estimation failed.\")\n",
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        "value": "\nTrying to use this failed estimation transform result will give a suitable\nerror:\n\n"
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        "value": "bad_tform.params  # doctest: +IGNORE_EXCEPTION_DETAIL\n",
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