blob_log
-
skimage.feature.blob_log(image, min_sigma=1, max_sigma=50, num_sigma=10, threshold=0.2, overlap=0.5, log_scale=False)
[source] -
Finds blobs in the given grayscale image.
Blobs are found using the Laplacian of Gaussian (LoG) method [R129]. For each blob found, the method returns its coordinates and the standard deviation of the Gaussian kernel that detected the blob.
Parameters: image : ndarray
Input grayscale image, blobs are assumed to be light on dark background (white on black).
min_sigma : float, optional
The minimum standard deviation for Gaussian Kernel. Keep this low to detect smaller blobs.
max_sigma : float, optional
The maximum standard deviation for Gaussian Kernel. Keep this high to detect larger blobs.
num_sigma : int, optional
The number of intermediate values of standard deviations to consider between
min_sigma
andmax_sigma
.threshold : float, optional.
The absolute lower bound for scale space maxima. Local maxima smaller than thresh are ignored. Reduce this to detect blobs with less intensities.
overlap : float, optional
A value between 0 and 1. If the area of two blobs overlaps by a fraction greater than
threshold
, the smaller blob is eliminated.log_scale : bool, optional
If set intermediate values of standard deviations are interpolated using a logarithmic scale to the base
10
. If not, linear interpolation is used.Returns: A : (n, 3) ndarray
A 2d array with each row representing 3 values,
(y,x,sigma)
where(y,x)
are coordinates of the blob andsigma
is the standard deviation of the Gaussian kernel which detected the blob.Notes
The radius of each blob is approximately .
References
[R129] (1, 2) http://en.wikipedia.org/wiki/Blob_detection#The_Laplacian_of_Gaussian Examples
>>> from skimage import data, feature, exposure >>> img = data.coins() >>> img = exposure.equalize_hist(img) # improves detection >>> feature.blob_log(img, threshold = .3) array([[ 113. , 323. , 1. ], [ 121. , 272. , 17.33333333], [ 124. , 336. , 11.88888889], [ 126. , 46. , 11.88888889], [ 126. , 208. , 11.88888889], [ 127. , 102. , 11.88888889], [ 128. , 154. , 11.88888889], [ 185. , 344. , 17.33333333], [ 194. , 213. , 17.33333333], [ 194. , 276. , 17.33333333], [ 197. , 44. , 11.88888889], [ 198. , 103. , 11.88888889], [ 198. , 155. , 11.88888889], [ 260. , 174. , 17.33333333], [ 263. , 244. , 17.33333333], [ 263. , 302. , 17.33333333], [ 266. , 115. , 11.88888889]])
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