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bundles / skimage 0.26.1rc0.dev0+git20260530.b607368ff / skimage / measure / _regionprops / regionprops_table

function

skimage.measure._regionprops:regionprops_table

source: /dev/scikit-image/src/skimage/measure/_regionprops.py :974

Signature

def   regionprops_table ( label_image ,  intensity_image = None ,  properties = ('label', 'bbox') ,  * ,  cache = True ,  separator = - ,  extra_properties = None ,  spacing = None )

Summary

Compute region properties and return them as a pandas-compatible table.

Extended Summary

The return value is a dictionary mapping property names to value arrays. This dictionary can be used as input to pandas.DataFrame to result in a "tidy" [1] table with one region per row and one property per column.

Use this function typically when you want to do downstream data analysis, or save region data to disk in a structured way. One downside of this function is that it breaks multi-dimensional properties into independent columns; for example, the region centroids of a 3D image end up in three different columns, one per dimension. If you need to do complex computations with the region properties, using skimage.measure.regionprops might be more fitting.

Parameters

label_image : (M, N[, P]) ndarray

Label image. Labels with value 0 are ignored.

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. The channel dimension, if present, must be the last axis. Default is None.

properties : tuple or list of str, optional

Properties that will be included in the resulting dictionary For a list of available properties, please see regionprops. Users should remember to add "label" to keep track of region identities.

cache : bool, optional

Determine whether to cache calculated properties. The computation is much faster for cached properties, whereas the memory consumption increases.

separator : str, optional

For non-scalar properties not listed in OBJECT_COLUMNS, each element will appear in its own column, with the index of that element separated from the property name by this separator. For example, the inertia tensor of a 2D region will appear in four columns: inertia_tensor-0-0, inertia_tensor-0-1, inertia_tensor-1-0, and inertia_tensor-1-1 (where the separator is -).

Object columns are those that cannot be split in this way because the number of columns would change depending on the object. For example, image and coords.

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, the 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 is issued. A property computation function must take a region mask as its first argument. If the property requires an intensity image, it must accept the intensity image as the second argument.

spacing : tuple of float, shape (ndim,)

The pixel spacing along each axis of the image.

Returns

out_dict : dict

Dictionary mapping property names to an array of values of that property, one value per region. This dictionary can be used as input to pandas DataFrame to map property names to columns in the frame and regions to rows. If the image has no regions, the arrays will have length 0, but the correct type.

Notes

Each column contains either a scalar property, an object property, or an element in a multidimensional array.

Properties with scalar values for each region, such as "eccentricity", will appear as a float or int array with that property name as key.

Multidimensional properties of fixed size for a given image dimension, such as "centroid" (every centroid will have three elements in a 3D image, no matter the region size), will be split into that many columns, with the name {property_name}{separator}{element_num} (for 1D properties), {property_name}{separator}{elem_num0}{separator}{elem_num1} (for 2D properties), and so on.

For multidimensional properties that don't have a fixed size, such as "image" (the image of a region varies in size depending on the region size), an object array will be used, with the corresponding property name as the key.

Examples

from skimage import data, util, measure
image = data.coins()
label_image = measure.label(image > 110, connectivity=image.ndim)
props = measure.regionprops_table(label_image, image,
                          properties=['label', 'inertia_tensor',
                                      'inertia_tensor_eigvals'])
✓
The resulting dictionary can be directly passed to pandas, if installed, to obtain a clean DataFrame: [5 rows x 7 columns] If we want to measure a feature that does not come as a built-in property, we can define custom functions and pass them as ``extra_properties``. For example, we can create a custom function that measures the intensity quartiles in a region: 
from skimage import data, util, measure
import numpy as np
def quartiles(regionmask, intensity):
    return np.percentile(intensity[regionmask], q=(25, 50, 75))
image = data.coins()
label_image = measure.label(image > 110, connectivity=image.ndim)
props = measure.regionprops_table(label_image, intensity_image=image,
                                  properties=('label',),
                                  extra_properties=(quartiles,))
✓

Aliases

  • skimage.measure.regionprops_table

Referenced by

This package