mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-03 02:00:26 +08:00
added blob_dog
This commit is contained in:
Vighnesh Birodkar
@@ -14,6 +14,7 @@ from .censure import CENSURE
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from .orb import ORB
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from .match import match_descriptors
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from .util import plot_matches
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from .blob import blob_dog
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__all__ = ['daisy',
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@@ -40,4 +41,5 @@ __all__ = ['daisy',
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'CENSURE',
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'ORB',
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'match_descriptors',
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'plot_matches']
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'plot_matches',
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'blob_dog']
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@@ -0,0 +1,233 @@
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import numpy as np
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from scipy.ndimage.filters import gaussian_filter, maximum_filter
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import itertools as itt
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import math
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from math import sqrt, hypot, log
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from numpy import arccos
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from skimage.util import img_as_float
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# This basic blob detection algorithm is based on:
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# http://www.cs.utah.edu/~jfishbau/advimproc/project1/ (04.04.2013)
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# Theory behind: http://en.wikipedia.org/wiki/Blob_detection (04.04.2013)
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# A lot of this code is borrowed from here
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# https://github.com/adonath/blob_detection/tree/master/blob_detection
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def _get_local_maxima_3d(array, threshold):
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"""Finds local maxima in a 3d array.
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A pixel is considered to be a maximum if it is greater than or equal to all
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its 28 neighbors in the 3d cube.
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Parameters
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----------
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array : ndarray
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The 3d array whose local maximas are sought.
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thresh : float
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Local maximas lesser than thresh are ignored.
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Returns
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-------
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A : (n, 3) ndarray
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A 2d array in which each row contains 3 values, the indices of local
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maxima.
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"""
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# computing max filter using all neighbors in cube
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fp = np.ones((3, 3, 3))
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max_array = maximum_filter(array, footprint=fp)
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peaks = (max_array == array) & (array > threshold)
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return np.argwhere(peaks)
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def _blob_overlap(blob1, blob2):
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"""Finds the overlapping area fraction between two blobs.
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Returns a float representing fraction of overlapped area.
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Parameters
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----------
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blob1 : sequence
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A sequence of ``(y,x,sigma)``, where ``x,y`` are coordinates of blob
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and sigma is the standard deviation of the Gaussian kernel which
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detected the blob.
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blob2 : sequence
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A sequence of ``(y,x,sigma)``, where ``x,y`` are coordinates of blob
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and sigma is the standard deviation of the Gaussian kernel which
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detected the blob.
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Returns
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-------
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f : float
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Fraction of overlapped area.
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"""
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root2 = sqrt(2)
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# extent of the blob is given by sqrt(2)*scale
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r1 = blob1[2] * root2
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r2 = blob2[2] * root2
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d = hypot(blob1[0] - blob2[0], blob1[1] - blob2[1])
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if d > r1 + r2:
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return 0
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# one blob is inside the other, the smaller blob must die
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if d <= abs(r1 - r2):
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return 1
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acos1 = arccos((d ** 2 + r1 ** 2 - r2 ** 2) / (2 * d * r1))
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acos2 = arccos((d ** 2 + r2 ** 2 - r1 ** 2) / (2 * d * r2))
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a = -d + r2 + r1
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b = d - r2 + r1
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c = d + r2 - r1
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d = d + r2 + r1
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area = r1 ** 2 * acos1 + r2 ** 2 * acos2 - 0.5 * sqrt(abs(a * b * c * d))
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return area / (math.pi * (min(r1, r2) ** 2))
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def _prune_blobs(blobs_array, overlap):
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"""Eliminated blobs with area overlap.
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Parameters
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----------
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blobs_array : ndarray
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a 2d array with each row representing 3 values, the ``(y,x,sigma)``
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where ``(y,x)`` are coordinates of the blob and sigma is the standard
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deviation of the Gaussian kernel which detected the blob.
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overlap : float
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A value between 0 and 1. If the fraction of area overlapping for 2
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blobs is greater than `overlap` the smaller blob is eliminated.
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Returns
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-------
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A : ndarray
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`array` with overlapping blobs removed.
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"""
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# iterating again might eliminate more blobs, but one iteration suffices
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# for most cases
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for blob1, blob2 in itt.combinations(blobs_array, 2):
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if _blob_overlap(blob1, blob2) > overlap:
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if blob1[2] > blob2[2]:
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blob2[2] = -1
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else:
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blob1[2] = -1
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# return blobs_array[blobs_array[:, 2] > 0]
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return np.array([b for b in blobs_array if b[2] > 0])
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def blob_dog(image, min_sigma=1, max_sigma=25, sigma_ratio=1.6, threshold=2.0,
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overlap=.5,):
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"""Finds blobs in the given grayscale image.
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Blobs are found using the Difference of Gaussian (DoG) method[1]_.
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For each blob found, its coordinates and area are returned.
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Parameters
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----------
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image : ndarray
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Input grayscale image, blobs are assumed to be light on dark
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background (white on black).
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min_sigma : float, optional
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The minimum standard deviation for Gaussian Kernel. Keep this low to
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detect smaller blobs.
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max_sigma : float, optional
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The maximum standard deviation for Gaussian Kernel. Keep this high to
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detect larger blobs.
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sigma_ratio : float, optional
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The ratio between the standard deviation of Gaussian Kernels used for
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computing the Difference of Gaussians
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`max_sigma`
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threshold : float, optional.
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The absolute lower bound for scale space maxima. Local maxima smaller
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than thresh are ignored. Reduce this to detect blobs with less
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intensities.
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overlap : float, optional
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A value between 0 and 1. If the area of two blobs overlaps by a
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fraction greater than `thresh`, the smaller blob is eliminated.
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log_scale : boolean, optional
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If set to True, the standard deviations of Gaussian Kernels are
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interpolated using a logarithmic scale. This is useful when finding
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blobs with a large variation in size. If set, scales are interpolated
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with log to the base 10.
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Returns
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-------
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A : (n, 3) ndarray
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A 2d array with each row containing the Y-Coordinate , the
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X-Coordinate and the estimated area of the blob respectively.
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References
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----------
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.. [1] http://en.wikipedia.org/wiki/Blob_detection#The_difference_of_Gaussians_approach
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Examples
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--------
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>>> from skimage import data,feature
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>>> feature.blob_dog(data.coins())
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array([[ 46, 336, 2513],
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[ 53, 156, 2035],
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[ 53, 217, 1608],
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[ 54, 276, 1231],
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[ 55, 42, 1608],
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[ 57, 100, 1231],
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[ 121, 272, 2035],
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[ 124, 337, 1413],
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[ 125, 45, 1815],
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[ 125, 207, 1608],
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[ 126, 102, 1231],
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[ 128, 154, 1231],
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[ 185, 347, 2513],
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[ 194, 213, 1815],
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[ 194, 277, 1608],
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[ 196, 42, 1231],
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[ 196, 101, 1608],
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[ 197, 155, 1231],
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[ 260, 46, 2513],
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[ 261, 174, 2035],
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[ 263, 245, 2035],
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[ 263, 302, 2035],
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[ 266, 114, 1608],
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[ 268, 358, 1608]])
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"""
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if image.ndim != 2:
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raise ValueError("'image' must be a grayscale ")
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image = img_as_float(image)
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# k such that min_sigma*(sigma_ratio**k) > max_sigma
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k = int(log(float(max_sigma) / min_sigma, sigma_ratio)) + 1
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# a geometric progression of standard deviations for gaussian kernels
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sigma_list = np.array([min_sigma * (sigma_ratio ** i)
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for i in range(k + 1)])
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gaussian_images = [gaussian_filter(image, s) for s in sigma_list]
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# computing difference between two succesive gaussian blurred images
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# multipying with square of standard deviation provides scale invariance
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dog_images = [(gaussian_images[i] - gaussian_images[i + 1])
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* sigma_list[i] ** 2 for i in range(k)]
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image_cube = np.dstack(dog_images)
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local_maxima = _get_local_maxima_3d(image_cube, threshold)
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# Convert the last index to its corresponding scale value
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local_maxima[:, 2] = sigma_list[local_maxima[:, 2]]
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ret_val = _prune_blobs(local_maxima, overlap)
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if len(ret_val) > 0:
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ret_val[:, 2] = math.pi * \
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((ret_val[:, 2] * math.sqrt(2)) ** 2).astype(int)
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return ret_val
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else:
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return []
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