`skimage2.feature`#

Warning

This module is part of the experimental skimage2 namespace and is subject to change without notice. Do not use it in production code. See the migration guide for more details.

Feature detection and extraction, for example, texture analysis, corners, etc.

`canny`	Edge filter an image using the Canny algorithm.
`peak_local_max`	Find peaks in an image as coordinate list.

skimage2.feature.canny(image, *, sigma=1.0, low_threshold=None, high_threshold=None, mask=None, use_quantiles=False, mode='nearest', cval=0.0)[source]#

Edge filter an image using the Canny algorithm.

Parameters:

imagendarray of shape (M, N): Grayscale input image to detect edges on; can be of any dtype.
sigmafloat, optional: Standard deviation of the Gaussian filter.
low_thresholdfloat, optional: Lower bound for hysteresis thresholding (linking edges). If None, low_threshold is set to 10% of dtype’s max.
high_thresholdfloat, optional: Upper bound for hysteresis thresholding (linking edges). If None, high_threshold is set to 20% of dtype’s max.
maskndarray of dtype bool, optional: Mask to limit the application of Canny to a certain area.
use_quantilesbool, optional: If True then treat low_threshold and high_threshold as quantiles of the edge magnitude image, rather than absolute edge magnitude values. If True then the thresholds must be in the range [0, 1].
mode{‘reflect’, ‘constant’, ‘nearest’, ‘mirror’, ‘wrap’}, optional: The mode parameter determines how the array borders are handled during Gaussian filtering, where cval is the value when mode is equal to ‘constant’.
cvalfloat, optional: Value to fill past edges of input image if mode is ‘constant’.

Returns:

outputndarray of shape (M, N): The binary edge map.

See also

skimage2.filters.sobel

Notes

The steps of the algorithm are as follows:

Smooth the image using a Gaussian with width sigma.
Apply the horizontal and vertical Sobel operators to get the gradients within the image. The edge strength is the norm of the gradient.
Thin potential edges to 1-pixel wide curves. First, find the normal to the edge at each point. This is done by looking at the signs and the relative magnitude of the X-Sobel and Y-Sobel to sort the points into 4 categories: horizontal, vertical, diagonal and antidiagonal. Then look in the normal and reverse directions to see if the values in either of those directions are greater than the point in question. Use interpolation to get a mix of points instead of picking the one that’s the closest to the normal.
Perform a hysteresis thresholding: first label all points above the high threshold as edges. Then recursively label any point above the low threshold that is 8-connected to a labeled point as an edge.

References

[1]

Canny, J., A Computational Approach To Edge Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, 8:679-714, 1986 DOI:10.1109/TPAMI.1986.4767851

[2]

William Green’s Canny tutorial https://web.archive.org/web/20110224083140/http://www.pages.drexel.edu/~weg22/can_tut.html

Examples

>>> import _skimage2 as ski2
>>> import numpy as np
>>> rng = np.random.default_rng()
>>> # Generate noisy image of a square
>>> im = np.zeros((256, 256))
>>> im[64:-64, 64:-64] = 1
>>> im += 0.2 * rng.random(im.shape)
>>> # First trial with the Canny filter, with the default smoothing
>>> edges1 = ski2.feature.canny(im)
>>> # Increase the smoothing for better results
>>> edges2 = ski2.feature.canny(im, sigma=3)

skimage2.feature.peak_local_max(image, *, min_distance=1, threshold_abs=None, threshold_rel=None, exclude_border=1, num_peaks=None, footprint=None, labels=None, num_peaks_per_label=None, p_norm=2.0)[source]#

Find peaks in an image as coordinate list.

Peaks are the local maxima in a region of floor(2 * min_distance + 1) (i.e. peaks are separated by at least min_distance).

If both threshold_abs and threshold_rel are provided, the maximum of the two is chosen as the minimum intensity threshold of peaks.

Parameters:

imagendarray

Input image.

min_distancefloat, optional

The minimal allowed distance separating peaks. To find the maximum number of peaks, use min_distance=1. See also p_norm.

threshold_absfloat, optional

Minimum intensity of peaks. By default, the absolute threshold is the minimum intensity of the image.

threshold_relfloat, optional

Minimum intensity of peaks, calculated as max(image) * threshold_rel.

exclude_borderint or tuple of (int, …), optional

Control peak detection close to the border of image. By default, only peaks exactly on the border are excluded.

0: Distance to border has no effect, all peaks are identified.
positive integer: Exclude peaks that are within this distance of the border.
tuple of positive integers: Same as for a single integer but with different distances for each respective dimension.

The value of p_norm has no impact on this border distance.

num_peaksint, optional

If given, maximum number of allowed peaks. When the number of peaks exceeds num_peaks, return num_peaks peaks based on highest peak intensity.

footprintndarray of dtype bool, optional

Binary mask that determines the neighborhood (where True) in which a peak must be a local maximum (see Notes). If not given, defaults to an array of ones of size floor(2 * min_distance + 1).

labelsndarray of dtype int, optional

If provided, each unique region labels == value represents a unique region to search for peaks. Zero labels are reserved for background.

num_peaks_per_labelint, optional

If given, maximum number of peaks for each label.

p_normfloat, optional

Which Minkowski p-norm to use. Should be in the range [1, inf]. A finite large p may cause a ValueError if overflow can occur. inf corresponds to the Chebyshev distance and 2 to the Euclidean distance. See also numpy.linalg.norm().

Returns:

outputndarray of shape (N, D): The coordinates of the peaks. N denotes the number of peaks and D corresponds to the number of dimensions in image.

skimage2.feature#

`skimage2.feature`#