`skimage.measure._regionprops:regionprops`

source: /dev/scikit-image/src/skimage/measure/_regionprops.py :1164

Signature

   def     regionprops (    label_image    ,    intensity_image    =  None   ,    cache    =  True   ,  * ,     extra_properties    =  None   ,    spacing    =  None   ,    offset    =  None     )

Summary

Measure properties of labeled image regions.

Extended Summary

Region properties are evaluated on demand and come in diverse types. If you want to do tabular data analysis of specific properties, consider using skimage.measure.regionprops_table instead.

Parameters

label_image : (M, N[, P]) ndarray: Label image. Labels with value 0 are ignored.
versionchanged 0.14.1
Previously, label_image was processed by numpy.squeeze 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 regionprops(np.squeeze(label_image), ...).
intensity_image : (M, N[, P][, C]) ndarray, optional: 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.
versionchanged 0.18.0
The ability to provide an extra dimension for channels was added.
cache : bool, optional: Determine whether to cache calculated properties. The computation is much faster for cached properties, whereas the memory consumption increases.
extra_properties : iterable of callables: 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 label_image as its first argument. If the property requires an intensity image, it must accept intensity_image as the second argument.
spacing : tuple of float, shape (ndim,): The pixel spacing along each axis of the image.
offset : array-like of int, shape `(label_image.ndim,)`, optional: 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.

Returns

properties : list of RegionProperties: Each item of the list corresponds to one labeled image region, and can be accessed using the attributes listed below.

Notes

The following properties can be accessed as attributes or keys:

area: area
area_bbox: area_bbox
area_convex: area_convex
area_filled: area_filled
axis_major_length: axis_major_length
axis_minor_length: axis_minor_length
bbox: bbox
centroid: centroid
centroid_local: centroid_local
centroid_weighted: centroid_weighted
centroid_weighted_local: centroid_weighted_local
coords_scaled: coords_scaled
coords: coords
eccentricity: eccentricity
equivalent_diameter_area: equivalent_diameter_area
euler_number: euler_number
extent: extent
feret_diameter_max: feret_diameter_max
image: image
image_convex: image_convex
image_filled: image_filled
image_intensity: image_intensity
inertia_tensor: inertia_tensor
inertia_tensor_eigvals: inertia_tensor_eigvals
intensity_max: intensity_max
intensity_mean: intensity_mean
intensity_median: intensity_median
intensity_min: intensity_min
intensity_std: intensity_std
label: label
moments: moments
moments_central: moments_central
moments_hu: moments_hu
moments_normalized: moments_normalized
moments_weighted: moments_weighted
moments_weighted_central: moments_weighted_central
moments_weighted_hu: moments_weighted_hu
moments_weighted_normalized: moments_weighted_normalized
num_pixels: num_pixels
orientation: orientation
perimeter: perimeter
perimeter_crofton: perimeter_crofton
slice: slice
solidity: solidity

properties also supports iteration, so that you can do

for region in properties:
    print(region, properties[region])

Examples

import skimage as ski
img = ski.util.img_as_ubyte(ski.data.coins()) > 110
label_img = ski.measure.label(img, connectivity=img.ndim)
props = ski.measure.regionprops(label_img)

✓

props[0].centroid
props[0]['centroid']

✗

Add custom measurements by passing functions as ``extra_properties``:

import numpy as np
import skimage as ski
img = ski.util.img_as_ubyte(ski.data.coins()) > 110
label_img = ski.measure.label(img, connectivity=img.ndim)
def pixelcount(regionmask):
    return np.sum(regionmask)
props = ski.measure.regionprops(label_img, extra_properties=(pixelcount,))

✓

props[0].pixelcount
props[1]['pixelcount']

`skimage.measure._regionprops:regionprops`

Signature

Summary

Extended Summary

Parameters

Returns

Notes

Examples

See also

Aliases

Referenced by

This package