mirror of
https://github.com/wassname/scikit-image.git
synced 2026-08-07 11:28:14 +08:00
79 lines
2.3 KiB
Python
79 lines
2.3 KiB
Python
import numpy as np
|
|
from scipy import ndimage
|
|
|
|
|
|
def peak_local_max(image, min_distance=10, threshold=0.1):
|
|
"""Return coordinates of peaks in an image.
|
|
|
|
Peaks are the local maxima in a region of `2 * min_distance + 1`
|
|
(i.e. peaks are separated by at least `min_distance`).
|
|
|
|
Parameters
|
|
----------
|
|
image: ndarray of floats
|
|
Input image.
|
|
|
|
min_distance: int, optional
|
|
Minimum number of pixels separating peaks and image boundary.
|
|
|
|
threshold: float, optional
|
|
Candidate peaks are calculated as `max(image) * threshold`.
|
|
|
|
Returns
|
|
-------
|
|
coordinates : (N, 2) array
|
|
(row, column) coordinates of peaks.
|
|
|
|
Notes
|
|
-----
|
|
The peak local maximum function returns the coordinates of local peaks (maxima)
|
|
in a image. A maximum filter is used for finding local maxima. This operation
|
|
dilates the original image. After comparison between dilated and original image,
|
|
peak_local_max function returns the coordinates of peaks where
|
|
dilated image = original.
|
|
|
|
Examples
|
|
--------
|
|
>>> im = np.zeros((7, 7))
|
|
>>> im[3, 4] = 1
|
|
>>> im[3, 2] = 1.5
|
|
>>> im
|
|
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. ]])
|
|
|
|
>>> peak_local_max(im, min_distance=1)
|
|
array([[3, 2],
|
|
[3, 4]])
|
|
|
|
>>> peak_local_max(im, min_distance=2)
|
|
array([[3, 2]])
|
|
|
|
"""
|
|
image = image.copy()
|
|
# Non maximum filter
|
|
size = 2 * min_distance + 1
|
|
image_max = ndimage.maximum_filter(image, size=size, mode='constant')
|
|
mask = (image == image_max)
|
|
image *= mask
|
|
|
|
# Remove the image borders
|
|
image[:min_distance] = 0
|
|
image[-min_distance:] = 0
|
|
image[:, :min_distance] = 0
|
|
image[:, -min_distance:] = 0
|
|
|
|
# find top corner candidates above a threshold
|
|
corner_threshold = np.max(image.ravel()) * threshold
|
|
image_t = (image >= corner_threshold) * 1
|
|
|
|
# get coordinates of peaks
|
|
coordinates = np.transpose(image_t.nonzero())
|
|
|
|
return coordinates
|
|
|