blob_log(image, min_sigma=1, max_sigma=50, num_sigma=10, threshold=0.2, overlap=0.5, log_scale=False, *, exclude_border=False)
Blobs are found using the Laplacian of Gaussian (LoG) method . For each blob found, the method returns its coordinates and the standard deviation of the Gaussian kernel that detected the blob.
The radius of each blob is approximately $\sqrt{2}\sigma$ for a 2-D image and $\sqrt{3}\sigma$ for a 3-D image.
Input grayscale image, blobs are assumed to be light on dark background (white on black).
the minimum standard deviation for Gaussian kernel. Keep this low to detect smaller blobs. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes.
The maximum standard deviation for Gaussian kernel. Keep this high to detect larger blobs. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes.
The number of intermediate values of standard deviations to consider between :None:None:`min_sigma` and :None:None:`max_sigma`.
The absolute lower bound for scale space maxima. Local maxima smaller than thresh are ignored. Reduce this to detect blobs with less intensities.
A value between 0 and 1. If the area of two blobs overlaps by a fraction greater than threshold
, the smaller blob is eliminated.
If set intermediate values of standard deviations are interpolated using a logarithmic scale to the base :None:None:`10`. If not, linear interpolation is used.
If tuple of ints, the length of the tuple must match the input array's dimensionality. Each element of the tuple will exclude peaks from within :None:None:`exclude_border`-pixels of the border of the image along that dimension. If nonzero int, :None:None:`exclude_border` excludes peaks from within :None:None:`exclude_border`-pixels of the border of the image. If zero or False, peaks are identified regardless of their distance from the border.
A 2d array with each row representing 2 coordinate values for a 2D image, and 3 coordinate values for a 3D image, plus the sigma(s) used. When a single sigma is passed, outputs are: (r, c, sigma)
or (p, r, c, sigma)
where (r, c)
or (p, r, c)
are coordinates of the blob and sigma
is the standard deviation of the Gaussian kernel which detected the blob. When an anisotropic gaussian is used (sigmas per dimension), the detected sigma is returned for each dimension.
Finds blobs in the given grayscale image.
>>> from skimage import data, feature, exposureSee :
... img = data.coins()
... img = exposure.equalize_hist(img) # improves detection
... feature.blob_log(img, threshold = .3) array([[124. , 336. , 11.88888889], [198. , 155. , 11.88888889], [194. , 213. , 17.33333333], [121. , 272. , 17.33333333], [263. , 244. , 17.33333333], [194. , 276. , 17.33333333], [266. , 115. , 11.88888889], [128. , 154. , 11.88888889], [260. , 174. , 17.33333333], [198. , 103. , 11.88888889], [126. , 208. , 11.88888889], [127. , 102. , 11.88888889], [263. , 302. , 17.33333333], [197. , 44. , 11.88888889], [185. , 344. , 17.33333333], [126. , 46. , 11.88888889], [113. , 323. , 1. ]])
The following pages refer to to this document either explicitly or contain code examples using this.
skimage.feature.blob.blob_log
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them