blob_dog
-
skimage.feature.blob_dog(image, min_sigma=1, max_sigma=50, sigma_ratio=1.6, threshold=2.0, overlap=0.5)
[source] -
Finds blobs in the given grayscale image.
Blobs are found using the Difference of Gaussian (DoG) method [R126]. 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.
sigma_ratio : float, optional
The ratio between the standard deviation of Gaussian Kernels used for computing the Difference of Gaussians
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.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
[R126] (1, 2) http://en.wikipedia.org/wiki/Blob_detection#The_difference_of_Gaussians_approach Examples
>>> from skimage import data, feature >>> feature.blob_dog(data.coins(), threshold=.5, max_sigma=40) array([[ 45. , 336. , 16.777216], [ 52. , 155. , 16.777216], [ 52. , 216. , 16.777216], [ 54. , 42. , 16.777216], [ 54. , 276. , 10.48576 ], [ 58. , 100. , 10.48576 ], [ 120. , 272. , 16.777216], [ 124. , 337. , 10.48576 ], [ 125. , 45. , 16.777216], [ 125. , 208. , 10.48576 ], [ 127. , 102. , 10.48576 ], [ 128. , 154. , 10.48576 ], [ 185. , 347. , 16.777216], [ 193. , 213. , 16.777216], [ 194. , 277. , 16.777216], [ 195. , 102. , 16.777216], [ 196. , 43. , 10.48576 ], [ 198. , 155. , 10.48576 ], [ 260. , 46. , 16.777216], [ 261. , 173. , 16.777216], [ 263. , 245. , 16.777216], [ 263. , 302. , 16.777216], [ 267. , 115. , 10.48576 ], [ 267. , 359. , 16.777216]])
Please login to continue.