{ "__type": "IngestedDoc", "__tag": 4010, "_content": { "Notes": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "This function implements a watershed algorithm " }, { "__type": "FootnoteReference", "__tag": 4066, "label": "2" }, { "__type": "Text", "__tag": 4046, "value": " " }, { "__type": "FootnoteReference", "__tag": 4066, "label": "3" }, { "__type": "Text", "__tag": 4046, "value": " that apportions pixels into marked basins. The algorithm uses a priority queue to hold the pixels with the metric for the priority queue being pixel value, then the time of entry into the queue -- this settles ties in favor of the closest marker." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Some ideas are taken from " }, { "__type": "FootnoteReference", "__tag": 4066, "label": "4" }, { "__type": "Text", "__tag": 4046, "value": ". The most important insight in the paper is that entry time onto the queue solves two problems: a pixel should be assigned to the neighbor with the largest gradient or, if there is no gradient, pixels on a plateau should be split between markers on opposite sides." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "This implementation converts all arguments to specific, lowest common denominator types, then passes these to a C algorithm." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Markers can be determined manually, or automatically using for example the local minima of the gradient of the image, or the local maxima of the distance function to the background for separating overlapping objects (see example)." } ] } ], "title": [], "level": 0, "target": null }, "Warns": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Raises": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Yields": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Methods": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Returns": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Parameters", "__tag": 4026, "children": [ { "__type": "DocParam", "__tag": 4016, "name": "out", "annotation": "ndarray", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "A labeled matrix of the same type and shape as " }, { "__type": "ParamRef", "__tag": 4071, "name": "markers" }, { "__type": "Text", "__tag": 4046, "value": "." } ] } ] } ] } ], "title": [], "level": 0, "target": null }, "Summary": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Find watershed basins in an image flooded from given markers." } ] } ], "title": [], "level": 0, "target": null }, "Receives": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Warnings": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Attributes": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Parameters": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Parameters", "__tag": 4026, "children": [ { "__type": "DocParam", "__tag": 4016, "name": "image", "annotation": "(M, N[, ...]) ndarray", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Data array where the lowest value points are labeled first." } ] } ] }, { "__type": "DocParam", "__tag": 4016, "name": "markers", "annotation": "int, or (M, N[, ...]) ndarray of int, optional", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "The desired number of basins, or an array marking the basins with the values to be assigned in the label matrix. 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We generate markers at the\nmaxima of the distance to the background:\n\n" }, { "__type": "Code", "__tag": 4050, "value": "from scipy import ndimage as ndi\ndistance = ndi.distance_transform_edt(image)\nfrom skimage.feature import peak_local_max\nmax_coords = peak_local_max(distance, labels=image,\n footprint=np.ones((3, 3)))\nlocal_maxima = np.zeros_like(image, dtype=bool)\nlocal_maxima[tuple(max_coords.T)] = True\nmarkers = ndi.label(local_maxima)[0]\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\nFinally, we run the watershed on the image and markers:\n\n" }, { "__type": "Code", "__tag": 4050, "value": "labels = watershed(-distance, markers, mask=image)\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\nThe algorithm works also for 3D images, and can be used for example to\nseparate overlapping spheres." } ], "title": [], "level": 0, "target": null }, "see_also": [ { "__type": "SeeAlsoItem", "__tag": 4028, "name": { "__type": "CrossRef", "__tag": 4002, "value": "skimage.segmentation.random_walker", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "skimage.segmentation.random_walker_segmentation:random_walker" }, "kind": "module" }, "descriptions": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "A segmentation algorithm based on anisotropic diffusion, usually slower than the watershed but with good results on noisy data and boundaries with holes." } ] } ], "type": null } ], "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": "markers", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "None" }, { "__type": "SigParam", "__tag": 4030, "name": "connectivity", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "1" }, { "__type": "SigParam", "__tag": 4030, "name": "offset", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "None" }, { "__type": "SigParam", "__tag": 4030, "name": "mask", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "None" }, { "__type": "SigParam", "__tag": 4030, "name": "compactness", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "0" }, { "__type": "SigParam", "__tag": 4030, "name": "watershed_line", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "False" } ], "return_annotation": { "__type": "Empty", "__tag": 4031 }, "target_name": "watershed" }, "references": [ ".. [1] P. Neubert and P. Protzel, \"Compact Watershed and Preemptive SLIC:", " On Improving Trade-offs of Superpixel Segmentation Algorithms,\"", " 2014 22nd International Conference on Pattern Recognition,", " Stockholm, Sweden, 2014, pp. 996-1001, :DOI:`10.1109/ICPR.2014.181`", " https://www.tu-chemnitz.de/etit/proaut/publications/cws_pSLIC_ICPR.pdf", "", ".. [2] https://en.wikipedia.org/wiki/Watershed_%28image_processing%29", "", ".. [3] https://web.archive.org/web/20180702213110/http://cmm.ensmp.fr/~beucher/wtshed.html", "", ".. [4] P. J. Soille and M. M. Ansoult, \"Automated basin delineation from", " digital elevation models using mathematical morphology,\" Signal", " Processing, 20(2):171-182, :DOI:`10.1016/0165-1684(90)90127-K`" ], "qa": "skimage.segmentation._watershed:watershed", "arbitrary": [], "local_refs": [ "compactness", "connectivity", "image", "markers", "mask", "offset", "out", "watershed_line" ] }