skimage2.feature.peak_local_max#

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.

See also

skimage2.feature.corner_peaks

Notes

The peak local maximum function returns the coordinates of local peaks (maxima) in an image. Internally, a maximum filter is used for finding local maxima. This operation dilates the original image. After comparison of the dilated and original images, this function returns the coordinates of the peaks where the dilated image equals the original image.

Examples

>>> import _skimage2 as ski2
>>> image = np.array(
...     [[1, 0, 0, 0, 0, 0, 0],
...      [0, 0, 0, 0, 0, 0, 0],
...      [0, 0, 0, 0, 1, 0, 0],
...      [0, 0, 3, 0, 2, 0, 0],
...      [0, 0, 2, 0, 0, 0, 0],
...      [0, 0, 0, 0, 0, 0, 0]]
... )

Find all peaks

>>> ski2.feature.peak_local_max(image)
array([[3, 2],
       [3, 4]])

Ensure peaks are at least 2 pixels apart

>>> ski2.feature.peak_local_max(image, min_distance=2)
array([[3, 2]])

Allow peaks on the image border

>>> ski2.feature.peak_local_max(image, exclude_border=0)
array([[3, 2],
       [3, 4],
       [0, 0]])