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{ "__type": "DefListItem", "__tag": 4037, "dt": { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Strong", "__tag": 4048, "children": [ { "__type": "Text", "__tag": 4046, "value": "perimeter" } ] } ] }, "dd": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Perimeter of the region which approximates the contour as a line through the centers of border pixels using a 4-connectivity." } ] } ] }, { "__type": "DefListItem", "__tag": 4037, "dt": { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Strong", "__tag": 4048, "children": [ { "__type": "Text", "__tag": 4046, "value": "perimeter_crofton" } ] } ] }, "dd": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Perimeter of the region approximated by the Crofton formula in 4 directions." } ] } ] }, { "__type": "DefListItem", "__tag": 4037, "dt": { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Strong", "__tag": 4048, "children": [ { "__type": "Text", "__tag": 4046, "value": "slice" } ] } ] }, "dd": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "A slice to extract the region from the input image." } ] } ] }, { "__type": "DefListItem", "__tag": 4037, "dt": { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Strong", "__tag": 4048, "children": [ { "__type": "Text", "__tag": 4046, "value": "solidity" } ] } ] }, "dd": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Ratio of pixels in the region to pixels of the convex hull image." } ] } ] } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "CrossRef", "__tag": 4002, "value": "properties", "reference": { "__type": "RefInfo", "__tag": 4000, "module": null, "version": null, "kind": "local", "path": "properties" }, "kind": "local" }, { "__type": "Text", "__tag": 4046, "value": " also supports iteration, so that you can do " } ] }, { "__type": "Code", "__tag": 4050, "value": "for region in properties:\n print(region, properties[region])", "execution_status": null, "out": "" } ], "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": "properties", "annotation": "list of RegionProperties", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Each item of the list corresponds to one labeled image region, and can be accessed using the attributes listed below." } ] } ] } ] } ], "title": [], "level": 0, "target": null }, "Summary": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Measure properties of labeled image regions." } ] } ], "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": "label_image", "annotation": "(M, N[, P]) ndarray", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Label image. Labels with value 0 are ignored." } ] }, { "__type": "Admonition", "__tag": 4056, "kind": "versionchanged", "base_type": "neutral", "children": [ { "__type": "AdmonitionTitle", "__tag": 4055, "children": [ { "__type": "Text", "__tag": 4046, "value": "versionchanged 0.14.1" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Previously, " }, { "__type": "InlineCode", "__tag": 4051, "value": "label_image" }, { "__type": "Text", "__tag": 4046, "value": " was processed by " }, { "__type": "InlineCode", "__tag": 4051, "value": "numpy.squeeze" }, { "__type": "Text", "__tag": 4046, "value": " and so any number of singleton dimensions was allowed. This resulted in inconsistent handling of images with singleton dimensions. To recover the old behaviour, use " }, { "__type": "InlineCode", "__tag": 4051, "value": "regionprops(np.squeeze(label_image), ...)" }, { "__type": "Text", "__tag": 4046, "value": "." } ] } ] } ] }, { "__type": "DocParam", "__tag": 4016, "name": "intensity_image", "annotation": "(M, N[, P][, C]) ndarray, optional", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Intensity (input) image of same shape as label image, plus optionally an extra dimension for multichannel data. Currently, this extra channel dimension, if present, must be the last axis. Default is None." } ] }, { "__type": "Admonition", "__tag": 4056, "kind": "versionchanged", "base_type": "neutral", "children": [ { "__type": "AdmonitionTitle", "__tag": 4055, "children": [ { "__type": "Text", "__tag": 4046, "value": "versionchanged 0.18.0" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "The ability to provide an extra dimension for channels was added." } ] } ] } ] }, { "__type": "DocParam", "__tag": 4016, "name": "cache", "annotation": "bool, optional", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Determine whether to cache calculated properties. The computation is much faster for cached properties, whereas the memory consumption increases." } ] } ] }, { "__type": "DocParam", "__tag": 4016, "name": "extra_properties", "annotation": "iterable of callables", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Add extra property computation functions that are not included with skimage. The name of the property is derived from the function name and its dtype is inferred by calling the function on a small sample. If the name of an extra property clashes with the name of an existing property, the extra property will not be visible and a UserWarning will be issued. A property computation function must take " }, { "__type": "ParamRef", "__tag": 4071, "name": "label_image" }, { "__type": "Text", "__tag": 4046, "value": " as its first argument. If the property requires an intensity image, it must accept " }, { "__type": "ParamRef", "__tag": 4071, "name": "intensity_image" }, { "__type": "Text", "__tag": 4046, "value": " as the second argument." } ] } ] }, { "__type": "DocParam", "__tag": 4016, "name": "spacing", "annotation": "tuple of float, shape (ndim,)", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "The pixel spacing along each axis of the image." } ] } ] }, { "__type": "DocParam", "__tag": 4016, "name": "offset", "annotation": "array-like of int, shape `(label_image.ndim,)`, optional", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Coordinates of the origin (\"top-left\" corner) of the label image. Normally this is ([0, ]0, 0), but it might be different if one wants to obtain regionprops of subvolumes within a larger volume." } ] } ] } ] } ], "title": [], "level": 0, "target": null }, "Extended Summary": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Region properties are evaluated on demand and come in diverse types. If you want to do tabular data analysis of specific properties, consider using " }, { "__type": "CrossRef", "__tag": 4002, "value": "skimage.measure.regionprops_table", "reference": { "__type": "RefInfo", "__tag": 4000, "module": "skimage", "version": "?", "kind": "module", "path": "skimage.measure._regionprops:regionprops_table" }, "kind": "module" }, { "__type": "Text", "__tag": 4046, "value": " instead." } ] } ], "title": [], "level": 0, "target": null }, "Other Parameters": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null } }, "_ordered_sections": [ "Summary", "Extended Summary", "Parameters", "Attributes", "Methods", "Returns", "Yields", "Receives", "Other Parameters", "Raises", "Warns", "Warnings", "Notes" ], "item_file": "/_skimage2/measure/_regionprops.py", "item_line": 1164, "item_type": "function", "aliases": [ "skimage.measure.regionprops" ], "example_section_data": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Code", "__tag": 4050, "value": "import skimage as ski\nimg = ski.util.img_as_ubyte(ski.data.coins()) > 110\nlabel_img = ski.measure.label(img, connectivity=img.ndim)\nprops = ski.measure.regionprops(label_img)\n", "execution_status": "success", "out": "" }, { "__type": "Code", "__tag": 4050, "value": "props[0].centroid\nprops[0]['centroid']\n", "execution_status": "failure", "out": "(np.float64(22.72987986048314), np.float64(81.91228523446583))\n" }, { "__type": "Text", "__tag": 4046, "value": "\nAdd custom measurements by passing functions as ``extra_properties``:\n\n" }, { "__type": "Code", "__tag": 4050, "value": "import numpy as np\nimport skimage as ski\nimg = ski.util.img_as_ubyte(ski.data.coins()) > 110\nlabel_img = ski.measure.label(img, connectivity=img.ndim)\ndef pixelcount(regionmask):\n return np.sum(regionmask)\nprops = ski.measure.regionprops(label_img, extra_properties=(pixelcount,))\n", "execution_status": "success", "out": "" }, { "__type": "Code", "__tag": 4050, "value": "props[0].pixelcount\nprops[1]['pixelcount']\n", "execution_status": "failure", "out": "np.int64(42)\n" } ], "title": [], "level": 0, "target": null }, "see_also": [ { "__type": "SeeAlsoItem", "__tag": 4028, "name": { "__type": "CrossRef", "__tag": 4002, "value": "label", "reference": { "__type": "RefInfo", "__tag": 4000, "module": "current-module", "version": "current-version", "kind": "to-resolve", "path": "label" }, "kind": "module" }, "descriptions": [], "type": null } ], "signature": { "__type": "SignatureNode", "__tag": 4029, "kind": "function", "parameters": [ { "__type": "SigParam", "__tag": 4030, "name": "label_image", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": { "__type": "Empty", "__tag": 4031 } }, { "__type": "SigParam", "__tag": 4030, "name": "intensity_image", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "None" }, { "__type": "SigParam", "__tag": 4030, "name": "cache", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "True" }, { "__type": "SigParam", "__tag": 4030, "name": "extra_properties", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "KEYWORD_ONLY", "default": "None" }, { "__type": "SigParam", "__tag": 4030, "name": "spacing", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "KEYWORD_ONLY", "default": "None" }, { "__type": "SigParam", "__tag": 4030, "name": "offset", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "KEYWORD_ONLY", "default": "None" } ], "return_annotation": { "__type": "Empty", "__tag": 4031 }, "target_name": "regionprops" }, "references": [ ".. [1] Wilhelm Burger, Mark Burge. Principles of Digital Image Processing:", " Core Algorithms. Springer-Verlag, London, 2009.", ".. [2] B. Jähne. Digital Image Processing. Springer-Verlag,", " Berlin-Heidelberg, 6. edition, 2005.", ".. [3] T. H. Reiss. Recognizing Planar Objects Using Invariant Image", " Features, from Lecture notes in computer science, p. 676. Springer,", " Berlin, 1993.", ".. [4] https://en.wikipedia.org/wiki/Image_moment", ".. [5] W. Pabst, E. Gregorová. Characterization of particles and particle", " systems, pp. 27-28. ICT Prague, 2007.", " https://old.vscht.cz/sil/keramika/Characterization_of_particles/CPPS%20_English%20version_.pdf" ], "qa": "skimage.measure._regionprops:regionprops", "arbitrary": [], "local_refs": [ "cache", "extra_properties", "intensity_image", "label_image", "offset", "properties", "spacing" ] }