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changed params and functionality of get_local_maxima

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This commit is contained in:
Vighnesh Birodkar
2014-03-05 21:08:37 +05:30
parent e449ce9870
commit 7681598380
1 changed files with 14 additions and 13 deletions
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skimage/feature/blob.py
+14 -13
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@@ -16,23 +16,24 @@ from skimage.util import img_as_float
# https://github.com/adonath/blob_detection/tree/master/blob_detection
def get_local_maxima_3d(array, threshold, connectivity=3):
"""Finds local maxima in a 3d array.
def get_local_maxima(ar, threshold, connectivity=3):
"""Finds local maxima in an array.
A pixel is considered to be a maximum if it is greater than or equal to all
its neighbors in the 3d cube.
A point is considered to be a maximum if it is greater than or equal to all
its neighbors.
Parameters
----------
array : ndarray
The 3d array whose local maximas are sought.
ar : ndarray
The array whose local maximas are sought.
thresh : float
Local maximas lesser than `thresh` are ignored.
connectivity : float, optional
Elements up to a squared distance of `connectivity` from a point are
considered neighbors. If `connectivity` is 1, 6 neighbors are
considered, if `connectivity` is 2, 18 neighbors are considered and if
`connectivity` is 3, all 26 neighbors are considered.
considered neighbors. For example in a 3 Dimensional array, if
`connectivity` is 1, 6 neighbors are considered, if `connectivity` is
2, 18 neighbors are considered and if `connectivity` is 3, all 26
neighbors are considered.
Returns
-------
@@ -42,9 +43,9 @@ def get_local_maxima_3d(array, threshold, connectivity=3):
"""
# computing max filter using all neighbors in cube
fp = generate_binary_structure(3, connectivity)
max_array = maximum_filter(array, footprint=fp)
peaks = (max_array == array) & (array > threshold)
fp = generate_binary_structure(ar.ndim, connectivity)
max_ar = maximum_filter(ar, footprint=fp)
peaks = (max_ar == ar) & (ar > threshold)
return np.argwhere(peaks)
@@ -219,7 +220,7 @@ def blob_dog(image, min_sigma=1, max_sigma=50, sigma_ratio=1.6, threshold=2.0,
* sigma_list[i] for i in range(k)]
image_cube = np.dstack(dog_images)
local_maxima = get_local_maxima_3d(image_cube, threshold)
local_maxima = get_local_maxima(image_cube, threshold)
# Convert the last index to its corresponding scale value
local_maxima[:, 2] = sigma_list[local_maxima[:, 2]]