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Also see https://scikit-image.org/docs/dev/user_guide/data_types.html" } ] } ] } ] } ], "title": [], "level": 0, "target": null }, "Extended Summary": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Other Parameters": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null } }, "_ordered_sections": [ "Summary", "Extended Summary", "Parameters", "Returns", "Yields", "Receives", "Raises", "Warns", "Other Parameters", "Attributes", "Methods", "Notes", "Warnings" ], "item_file": "/dev/scikit-image/src/skimage/transform/_warps.py", "item_line": 780, "item_type": "function", "aliases": [ "skimage.transform.warp" ], "example_section_data": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Code", "__tag": 4050, "value": "from skimage.transform import warp\nfrom skimage import data\nimage = data.camera()\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\nThe following image warps are all equal but differ substantially in\nexecution time. The image is shifted to the bottom.\n\nUse a geometric transform to warp an image (fast):\n\n" }, { "__type": "Code", "__tag": 4050, "value": "from skimage.transform import SimilarityTransform\ntform = SimilarityTransform(translation=(0, -10))\nwarped = warp(image, tform)\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\nUse a callable (slow):\n\n" }, { "__type": "Code", "__tag": 4050, "value": "def shift_down(xy):\n xy[:, 1] -= 10\n return xy\nwarped = warp(image, shift_down)\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\nUse a transformation matrix to warp an image (fast):\n\n" }, { "__type": "Code", "__tag": 4050, "value": "matrix = np.array([[1, 0, 0], [0, 1, -10], [0, 0, 1]])\nwarped = warp(image, matrix)\nfrom skimage.transform import ProjectiveTransform\nwarped = warp(image, ProjectiveTransform(matrix=matrix))\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\nYou can also use the inverse of a geometric transformation (fast):\n\n" }, { "__type": "Code", "__tag": 4050, "value": "warped = warp(image, tform.inverse)\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\nFor N-D images you can pass a coordinate array, that specifies the\ncoordinates in the input image for every element in the output image. E.g.\nif you want to rescale a 3-D cube, you can do:\n\n" }, { "__type": "Code", "__tag": 4050, "value": "cube_shape = np.array([30, 30, 30])\nrng = np.random.default_rng()\ncube = rng.random(cube_shape)\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\nSetup the coordinate array, that defines the scaling:\n\n" }, { "__type": "Code", "__tag": 4050, "value": "scale = 0.1\noutput_shape = (scale * cube_shape).astype(int)\ncoords0, coords1, coords2 = np.mgrid[:output_shape[0],\n :output_shape[1], :output_shape[2]]\ncoords = np.array([coords0, coords1, coords2])\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\nAssume that the cube contains spatial data, where the first array element\ncenter is at coordinate (0.5, 0.5, 0.5) in real space, i.e. we have to\naccount for this extra offset when scaling the image:\n\n" }, { "__type": "Code", "__tag": 4050, "value": "coords = (coords + 0.5) / scale - 0.5\nwarped = warp(cube, coords)\n", "execution_status": "success" } ], "title": [], "level": 0, "target": null }, "see_also": [], "signature": { "__type": "SignatureNode", "__tag": 4029, "kind": "function", "parameters": [ { "__type": "SigParam", "__tag": 4030, "name": "image", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": { "__type": "Empty", "__tag": 4031 } }, { "__type": "SigParam", "__tag": 4030, "name": "inverse_map", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": { "__type": "Empty", "__tag": 4031 } }, { "__type": "SigParam", "__tag": 4030, "name": "map_args", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "None" }, { "__type": "SigParam", "__tag": 4030, "name": "output_shape", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "None" }, { "__type": "SigParam", "__tag": 4030, "name": "order", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "None" }, { "__type": "SigParam", "__tag": 4030, "name": "mode", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "constant" }, { "__type": "SigParam", "__tag": 4030, "name": "cval", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "0.0" }, { "__type": "SigParam", "__tag": 4030, "name": "clip", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "True" }, { "__type": "SigParam", "__tag": 4030, "name": "preserve_range", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "False" } ], "return_annotation": { "__type": "Empty", "__tag": 4031 }, "target_name": "warp" }, "references": null, "qa": "skimage.transform._warps:warp", "arbitrary": [], "local_refs": [ "clip", "cval", "image", "inverse_map", "map_args", "mode", "order", "output_shape", "preserve_range", "warped" ] }