peak-local-max

peak_local_max

skimage.feature.peak_local_max(image, min_distance=1, threshold_abs=None, threshold_rel=None, exclude_border=True, indices=True, num_peaks=inf, footprint=None, labels=None) [source]

Find peaks in an image as coordinate list or boolean mask.

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

If peaks are flat (i.e. multiple adjacent pixels have identical intensities), the coordinates of all such pixels are returned.

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

Parameters:

image : ndarray Input image. min_distance : int, optional Minimum number of pixels separating peaks in a region of 2 * min_distance + 1 (i.e. peaks are separated by at least min_distance). To find the maximum number of peaks, use min_distance=1. threshold_abs : float, optional Minimum intensity of peaks. By default, the absolute threshold is the minimum intensity of the image. threshold_rel : float, optional Minimum intensity of peaks, calculated as max(image) * threshold_rel. exclude_border : int, optional If nonzero, exclude_border excludes peaks from within exclude_border-pixels of the border of the image. indices : bool, optional If True, the output will be an array representing peak coordinates. If False, the output will be a boolean array shaped as image.shape with peaks present at True elements. num_peaks : int, optional Maximum number of peaks. When the number of peaks exceeds num_peaks, return num_peaks peaks based on highest peak intensity. footprint : ndarray of bools, optional If provided, footprint == 1 represents the local region within which to search for peaks at every point in image. Overrides min_distance (also for exclude_border). labels : ndarray of ints, optional If provided, each unique region labels == value represents a unique region to search for peaks. Zero is reserved for background.

Returns:

output : ndarray or ndarray of bools If indices = True : (row, column, ...) coordinates of peaks. If indices = False : Boolean array shaped like image, with peaks represented by True values.

Notes

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

Examples

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>>> img1 = np.zeros((7, 7)) >>> img1[3, 4] = 1 >>> img1[3, 2] = 1.5 >>> img1 array([[ 0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ],        [ 0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ],        [ 0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ],        [ 0. ,  0. ,  1.5,  0. ,  1. ,  0. ,  0. ],        [ 0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ],        [ 0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ],        [ 0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ]])

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>>> peak_local_max(img1, min_distance=1) array([[3, 2],        [3, 4]])

1 2	>>> peak_local_max(img1, min_distance=2) array([[3, 2]])

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>>> img2 = np.zeros((20, 20, 20)) >>> img2[10, 10, 10] = 1 >>> peak_local_max(img2, exclude_border=0) array([[10, 10, 10]])