forked from 170010011/fr
647 lines
25 KiB
Python
647 lines
25 KiB
Python
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import numpy as np
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from scipy.ndimage import gaussian_filter, gaussian_laplace
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import math
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from math import sqrt, log
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from scipy import spatial
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from ..util import img_as_float
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from .peak import peak_local_max
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from ._hessian_det_appx import _hessian_matrix_det
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from ..transform import integral_image
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from .._shared.utils import check_nD
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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: https://en.wikipedia.org/wiki/Blob_detection (04.04.2013)
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def _compute_disk_overlap(d, r1, r2):
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"""
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Compute fraction of surface overlap between two disks of radii
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``r1`` and ``r2``, with centers separated by a distance ``d``.
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Parameters
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----------
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d : float
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Distance between centers.
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r1 : float
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Radius of the first disk.
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r2 : float
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Radius of the second disk.
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Returns
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-------
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fraction: float
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Fraction of area of the overlap between the two disks.
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"""
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ratio1 = (d ** 2 + r1 ** 2 - r2 ** 2) / (2 * d * r1)
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ratio1 = np.clip(ratio1, -1, 1)
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acos1 = math.acos(ratio1)
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ratio2 = (d ** 2 + r2 ** 2 - r1 ** 2) / (2 * d * r2)
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ratio2 = np.clip(ratio2, -1, 1)
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acos2 = math.acos(ratio2)
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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 -
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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 _compute_sphere_overlap(d, r1, r2):
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"""
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Compute volume overlap fraction between two spheres of radii
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``r1`` and ``r2``, with centers separated by a distance ``d``.
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Parameters
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----------
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d : float
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Distance between centers.
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r1 : float
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Radius of the first sphere.
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r2 : float
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Radius of the second sphere.
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Returns
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-------
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fraction: float
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Fraction of volume of the overlap between the two spheres.
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Notes
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-----
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See for example http://mathworld.wolfram.com/Sphere-SphereIntersection.html
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for more details.
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"""
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vol = (math.pi / (12 * d) * (r1 + r2 - d)**2 *
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(d**2 + 2 * d * (r1 + r2) - 3 * (r1**2 + r2**2) + 6 * r1 * r2))
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return vol / (4./3 * math.pi * min(r1, r2) ** 3)
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def _blob_overlap(blob1, blob2, *, sigma_dim=1):
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"""Finds the overlapping area fraction between two blobs.
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Returns a float representing fraction of overlapped area. Note that 0.0
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is *always* returned for dimension greater than 3.
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Parameters
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----------
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blob1 : sequence of arrays
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A sequence of ``(row, col, sigma)`` or ``(pln, row, col, sigma)``,
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where ``row, col`` (or ``(pln, row, col)``) are coordinates
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of blob and ``sigma`` is the standard deviation of the Gaussian kernel
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which detected the blob.
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blob2 : sequence of arrays
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A sequence of ``(row, col, sigma)`` or ``(pln, row, col, sigma)``,
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where ``row, col`` (or ``(pln, row, col)``) are coordinates
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of blob and ``sigma`` is the standard deviation of the Gaussian kernel
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which detected the blob.
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sigma_dim : int, optional
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The dimensionality of the sigma value. Can be 1 or the same as the
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dimensionality of the blob space (2 or 3).
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Returns
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-------
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f : float
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Fraction of overlapped area (or volume in 3D).
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"""
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ndim = len(blob1) - sigma_dim
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if ndim > 3:
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return 0.0
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root_ndim = sqrt(ndim)
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# we divide coordinates by sigma * sqrt(ndim) to rescale space to isotropy,
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# giving spheres of radius = 1 or < 1.
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if blob1[-1] == blob2[-1] == 0:
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return 0.0
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elif blob1[-1] > blob2[-1]:
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max_sigma = blob1[-sigma_dim:]
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r1 = 1
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r2 = blob2[-1] / blob1[-1]
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else:
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max_sigma = blob2[-sigma_dim:]
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r2 = 1
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r1 = blob1[-1] / blob2[-1]
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pos1 = blob1[:ndim] / (max_sigma * root_ndim)
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pos2 = blob2[:ndim] / (max_sigma * root_ndim)
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d = np.sqrt(np.sum((pos2 - pos1)**2))
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if d > r1 + r2: # centers farther than sum of radii, so no overlap
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return 0.0
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# one blob is inside the other
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if d <= abs(r1 - r2):
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return 1.0
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if ndim == 2:
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return _compute_disk_overlap(d, r1, r2)
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else: # ndim=3 http://mathworld.wolfram.com/Sphere-SphereIntersection.html
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return _compute_sphere_overlap(d, r1, r2)
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def _prune_blobs(blobs_array, overlap, *, sigma_dim=1):
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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 (or 4) values,
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``(row, col, sigma)`` or ``(pln, row, col, sigma)`` in 3D,
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where ``(row, col)`` (``(pln, row, col)``) are coordinates of the 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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This array must not have a dimension of size 0.
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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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sigma_dim : int, optional
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The number of columns in ``blobs_array`` corresponding to sigmas rather
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than positions.
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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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sigma = blobs_array[:, -sigma_dim:].max()
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distance = 2 * sigma * sqrt(blobs_array.shape[1] - sigma_dim)
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tree = spatial.cKDTree(blobs_array[:, :-sigma_dim])
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pairs = np.array(list(tree.query_pairs(distance)))
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if len(pairs) == 0:
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return blobs_array
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else:
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for (i, j) in pairs:
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blob1, blob2 = blobs_array[i], blobs_array[j]
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if _blob_overlap(blob1, blob2, sigma_dim=sigma_dim) > overlap:
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# note: this test works even in the anisotropic case because
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# all sigmas increase together.
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if blob1[-1] > blob2[-1]:
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blob2[-1] = 0
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else:
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blob1[-1] = 0
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return np.stack([b for b in blobs_array if b[-1] > 0])
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def _format_exclude_border(img_ndim, exclude_border):
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"""Format an ``exclude_border`` argument as a tuple of ints for calling
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``peak_local_max``.
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"""
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if isinstance(exclude_border, tuple):
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if len(exclude_border) != img_ndim:
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raise ValueError(
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"`exclude_border` should have the same length as the "
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"dimensionality of the image.")
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for exclude in exclude_border:
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if not isinstance(exclude, int):
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raise ValueError(
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"exclude border, when expressed as a tuple, must only "
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"contain ints.")
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return exclude_border
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elif isinstance(exclude_border, int):
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return (exclude_border,) * img_ndim + (0,)
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elif exclude_border is True:
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raise ValueError("exclude_border cannot be True")
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elif exclude_border is False:
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return (0,) * (img_ndim + 1)
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else:
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raise ValueError(
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f"Unsupported value ({exclude_border}) for exclude_border"
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)
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def blob_dog(image, min_sigma=1, max_sigma=50, sigma_ratio=1.6, threshold=2.0,
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overlap=.5, *, exclude_border=False):
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r"""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, the method returns its coordinates and the standard
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deviation of the Gaussian kernel that detected the blob.
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Parameters
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----------
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image : 2D or 3D 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 : scalar or sequence of scalars, optional
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The minimum standard deviation for Gaussian kernel. Keep this low to
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detect smaller blobs. The standard deviations of the Gaussian filter
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are given for each axis as a sequence, or as a single number, in
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which case it is equal for all axes.
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max_sigma : scalar or sequence of scalars, optional
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The maximum standard deviation for Gaussian kernel. Keep this high to
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detect larger blobs. The standard deviations of the Gaussian filter
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are given for each axis as a sequence, or as a single number, in
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which case it is equal for all axes.
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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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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 `threshold`, the smaller blob is eliminated.
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exclude_border : tuple of ints, int, or False, optional
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If tuple of ints, the length of the tuple must match the input array's
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dimensionality. Each element of the tuple will exclude peaks from
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within `exclude_border`-pixels of the border of the image along that
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dimension.
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If nonzero int, `exclude_border` excludes peaks from within
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`exclude_border`-pixels of the border of the image.
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If zero or False, peaks are identified regardless of their
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distance from the border.
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Returns
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-------
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A : (n, image.ndim + sigma) ndarray
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A 2d array with each row representing 2 coordinate values for a 2D
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image, and 3 coordinate values for a 3D image, plus the sigma(s) used.
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When a single sigma is passed, outputs are:
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``(r, c, sigma)`` or ``(p, r, c, sigma)`` where ``(r, c)`` or
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``(p, r, c)`` are coordinates of the blob and ``sigma`` is the standard
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deviation of the Gaussian kernel which detected the blob. When an
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anisotropic gaussian is used (sigmas per dimension), the detected sigma
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is returned for each dimension.
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See also
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--------
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skimage.filters.difference_of_gaussians
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References
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----------
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.. [1] https://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(), threshold=.5, max_sigma=40)
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array([[120. , 272. , 16.777216],
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[193. , 213. , 16.777216],
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[263. , 245. , 16.777216],
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[185. , 347. , 16.777216],
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[128. , 154. , 10.48576 ],
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[198. , 155. , 10.48576 ],
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[124. , 337. , 10.48576 ],
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[ 45. , 336. , 16.777216],
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[195. , 102. , 16.777216],
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[125. , 45. , 16.777216],
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[261. , 173. , 16.777216],
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[194. , 277. , 16.777216],
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[127. , 102. , 10.48576 ],
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[125. , 208. , 10.48576 ],
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[267. , 115. , 10.48576 ],
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[263. , 302. , 16.777216],
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[196. , 43. , 10.48576 ],
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[260. , 46. , 16.777216],
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[267. , 359. , 16.777216],
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[ 54. , 276. , 10.48576 ],
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[ 58. , 100. , 10.48576 ],
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[ 52. , 155. , 16.777216],
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[ 52. , 216. , 16.777216],
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[ 54. , 42. , 16.777216]])
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Notes
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-----
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The radius of each blob is approximately :math:`\sqrt{2}\sigma` for
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a 2-D image and :math:`\sqrt{3}\sigma` for a 3-D image.
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"""
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image = img_as_float(image)
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# if both min and max sigma are scalar, function returns only one sigma
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scalar_sigma = np.isscalar(max_sigma) and np.isscalar(min_sigma)
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# Gaussian filter requires that sequence-type sigmas have same
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# dimensionality as image. This broadcasts scalar kernels
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if np.isscalar(max_sigma):
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max_sigma = np.full(image.ndim, max_sigma, dtype=float)
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if np.isscalar(min_sigma):
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min_sigma = np.full(image.ndim, min_sigma, dtype=float)
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# Convert sequence types to array
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min_sigma = np.asarray(min_sigma, dtype=float)
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max_sigma = np.asarray(max_sigma, dtype=float)
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# k such that min_sigma*(sigma_ratio**k) > max_sigma
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k = int(np.mean(np.log(max_sigma / min_sigma) / np.log(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 successive Gaussian blurred images
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# multiplying with average standard deviation provides scale invariance
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dog_images = [(gaussian_images[i] - gaussian_images[i + 1])
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* np.mean(sigma_list[i]) for i in range(k)]
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image_cube = np.stack(dog_images, axis=-1)
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exclude_border = _format_exclude_border(image.ndim, exclude_border)
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local_maxima = peak_local_max(
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image_cube,
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threshold_abs=threshold,
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footprint=np.ones((3,) * (image.ndim + 1)),
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threshold_rel=0.0,
|
||
|
|
exclude_border=exclude_border,
|
||
|
|
)
|
||
|
|
|
||
|
|
# Catch no peaks
|
||
|
|
if local_maxima.size == 0:
|
||
|
|
return np.empty((0, 3))
|
||
|
|
|
||
|
|
# Convert local_maxima to float64
|
||
|
|
lm = local_maxima.astype(np.float64)
|
||
|
|
|
||
|
|
# translate final column of lm, which contains the index of the
|
||
|
|
# sigma that produced the maximum intensity value, into the sigma
|
||
|
|
sigmas_of_peaks = sigma_list[local_maxima[:, -1]]
|
||
|
|
|
||
|
|
if scalar_sigma:
|
||
|
|
# select one sigma column, keeping dimension
|
||
|
|
sigmas_of_peaks = sigmas_of_peaks[:, 0:1]
|
||
|
|
|
||
|
|
# Remove sigma index and replace with sigmas
|
||
|
|
lm = np.hstack([lm[:, :-1], sigmas_of_peaks])
|
||
|
|
|
||
|
|
sigma_dim = sigmas_of_peaks.shape[1]
|
||
|
|
|
||
|
|
return _prune_blobs(lm, overlap, sigma_dim=sigma_dim)
|
||
|
|
|
||
|
|
|
||
|
|
def blob_log(image, min_sigma=1, max_sigma=50, num_sigma=10, threshold=.2,
|
||
|
|
overlap=.5, log_scale=False, *, exclude_border=False):
|
||
|
|
r"""Finds blobs in the given grayscale image.
|
||
|
|
|
||
|
|
Blobs are found using the Laplacian of Gaussian (LoG) method [1]_.
|
||
|
|
For each blob found, the method returns its coordinates and the standard
|
||
|
|
deviation of the Gaussian kernel that detected the blob.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
image : 2D or 3D ndarray
|
||
|
|
Input grayscale image, blobs are assumed to be light on dark
|
||
|
|
background (white on black).
|
||
|
|
min_sigma : scalar or sequence of scalars, optional
|
||
|
|
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.
|
||
|
|
max_sigma : scalar or sequence of scalars, optional
|
||
|
|
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.
|
||
|
|
num_sigma : int, optional
|
||
|
|
The number of intermediate values of standard deviations to consider
|
||
|
|
between `min_sigma` and `max_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.
|
||
|
|
exclude_border : tuple of ints, int, or False, optional
|
||
|
|
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 `exclude_border`-pixels of the border of the image along that
|
||
|
|
dimension.
|
||
|
|
If nonzero int, `exclude_border` excludes peaks from within
|
||
|
|
`exclude_border`-pixels of the border of the image.
|
||
|
|
If zero or False, peaks are identified regardless of their
|
||
|
|
distance from the border.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
A : (n, image.ndim + sigma) ndarray
|
||
|
|
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.
|
||
|
|
|
||
|
|
References
|
||
|
|
----------
|
||
|
|
.. [1] https://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([[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. ]])
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
The radius of each blob is approximately :math:`\sqrt{2}\sigma` for
|
||
|
|
a 2-D image and :math:`\sqrt{3}\sigma` for a 3-D image.
|
||
|
|
"""
|
||
|
|
image = img_as_float(image)
|
||
|
|
|
||
|
|
# if both min and max sigma are scalar, function returns only one sigma
|
||
|
|
scalar_sigma = (
|
||
|
|
True if np.isscalar(max_sigma) and np.isscalar(min_sigma) else False
|
||
|
|
)
|
||
|
|
|
||
|
|
# Gaussian filter requires that sequence-type sigmas have same
|
||
|
|
# dimensionality as image. This broadcasts scalar kernels
|
||
|
|
if np.isscalar(max_sigma):
|
||
|
|
max_sigma = np.full(image.ndim, max_sigma, dtype=float)
|
||
|
|
if np.isscalar(min_sigma):
|
||
|
|
min_sigma = np.full(image.ndim, min_sigma, dtype=float)
|
||
|
|
|
||
|
|
# Convert sequence types to array
|
||
|
|
min_sigma = np.asarray(min_sigma, dtype=float)
|
||
|
|
max_sigma = np.asarray(max_sigma, dtype=float)
|
||
|
|
|
||
|
|
if log_scale:
|
||
|
|
# for anisotropic data, we use the "highest resolution/variance" axis
|
||
|
|
standard_axis = np.argmax(min_sigma)
|
||
|
|
start = np.log10(min_sigma[standard_axis])
|
||
|
|
stop = np.log10(max_sigma[standard_axis])
|
||
|
|
scale = np.logspace(start, stop, num_sigma)[:, np.newaxis]
|
||
|
|
sigma_list = scale * min_sigma / np.max(min_sigma)
|
||
|
|
else:
|
||
|
|
scale = np.linspace(0, 1, num_sigma)[:, np.newaxis]
|
||
|
|
sigma_list = scale * (max_sigma - min_sigma) + min_sigma
|
||
|
|
|
||
|
|
# computing gaussian laplace
|
||
|
|
# average s**2 provides scale invariance
|
||
|
|
gl_images = [-gaussian_laplace(image, s) * np.mean(s) ** 2
|
||
|
|
for s in sigma_list]
|
||
|
|
|
||
|
|
image_cube = np.stack(gl_images, axis=-1)
|
||
|
|
|
||
|
|
exclude_border = _format_exclude_border(image.ndim, exclude_border)
|
||
|
|
local_maxima = peak_local_max(
|
||
|
|
image_cube,
|
||
|
|
threshold_abs=threshold,
|
||
|
|
footprint=np.ones((3,) * (image.ndim + 1)),
|
||
|
|
threshold_rel=0.0,
|
||
|
|
exclude_border=exclude_border,
|
||
|
|
)
|
||
|
|
|
||
|
|
# Catch no peaks
|
||
|
|
if local_maxima.size == 0:
|
||
|
|
return np.empty((0, 3))
|
||
|
|
|
||
|
|
# Convert local_maxima to float64
|
||
|
|
lm = local_maxima.astype(np.float64)
|
||
|
|
|
||
|
|
# translate final column of lm, which contains the index of the
|
||
|
|
# sigma that produced the maximum intensity value, into the sigma
|
||
|
|
sigmas_of_peaks = sigma_list[local_maxima[:, -1]]
|
||
|
|
|
||
|
|
if scalar_sigma:
|
||
|
|
# select one sigma column, keeping dimension
|
||
|
|
sigmas_of_peaks = sigmas_of_peaks[:, 0:1]
|
||
|
|
|
||
|
|
# Remove sigma index and replace with sigmas
|
||
|
|
lm = np.hstack([lm[:, :-1], sigmas_of_peaks])
|
||
|
|
|
||
|
|
sigma_dim = sigmas_of_peaks.shape[1]
|
||
|
|
|
||
|
|
return _prune_blobs(lm, overlap, sigma_dim=sigma_dim)
|
||
|
|
|
||
|
|
|
||
|
|
def blob_doh(image, min_sigma=1, max_sigma=30, num_sigma=10, threshold=0.01,
|
||
|
|
overlap=.5, log_scale=False):
|
||
|
|
"""Finds blobs in the given grayscale image.
|
||
|
|
|
||
|
|
Blobs are found using the Determinant of Hessian method [1]_. For each blob
|
||
|
|
found, the method returns its coordinates and the standard deviation
|
||
|
|
of the Gaussian Kernel used for the Hessian matrix whose determinant
|
||
|
|
detected the blob. Determinant of Hessians is approximated using [2]_.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
image : 2D ndarray
|
||
|
|
Input grayscale image.Blobs can either be light on dark or vice versa.
|
||
|
|
min_sigma : float, optional
|
||
|
|
The minimum standard deviation for Gaussian Kernel used to compute
|
||
|
|
Hessian matrix. Keep this low to detect smaller blobs.
|
||
|
|
max_sigma : float, optional
|
||
|
|
The maximum standard deviation for Gaussian Kernel used to compute
|
||
|
|
Hessian matrix. Keep this high to detect larger blobs.
|
||
|
|
num_sigma : int, optional
|
||
|
|
The number of intermediate values of standard deviations to consider
|
||
|
|
between `min_sigma` and `max_sigma`.
|
||
|
|
threshold : float, optional.
|
||
|
|
The absolute lower bound for scale space maxima. Local maxima smaller
|
||
|
|
than thresh are ignored. Reduce this to detect less prominent blobs.
|
||
|
|
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 and ``sigma`` is the
|
||
|
|
standard deviation of the Gaussian kernel of the Hessian Matrix whose
|
||
|
|
determinant detected the blob.
|
||
|
|
|
||
|
|
References
|
||
|
|
----------
|
||
|
|
.. [1] https://en.wikipedia.org/wiki/Blob_detection#The_determinant_of_the_Hessian
|
||
|
|
|
||
|
|
.. [2] Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool,
|
||
|
|
"SURF: Speeded Up Robust Features"
|
||
|
|
ftp://ftp.vision.ee.ethz.ch/publications/articles/eth_biwi_00517.pdf
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from skimage import data, feature
|
||
|
|
>>> img = data.coins()
|
||
|
|
>>> feature.blob_doh(img)
|
||
|
|
array([[197. , 153. , 20.33333333],
|
||
|
|
[124. , 336. , 20.33333333],
|
||
|
|
[126. , 153. , 20.33333333],
|
||
|
|
[195. , 100. , 23.55555556],
|
||
|
|
[192. , 212. , 23.55555556],
|
||
|
|
[121. , 271. , 30. ],
|
||
|
|
[126. , 101. , 20.33333333],
|
||
|
|
[193. , 275. , 23.55555556],
|
||
|
|
[123. , 205. , 20.33333333],
|
||
|
|
[270. , 363. , 30. ],
|
||
|
|
[265. , 113. , 23.55555556],
|
||
|
|
[262. , 243. , 23.55555556],
|
||
|
|
[185. , 348. , 30. ],
|
||
|
|
[156. , 302. , 30. ],
|
||
|
|
[123. , 44. , 23.55555556],
|
||
|
|
[260. , 173. , 30. ],
|
||
|
|
[197. , 44. , 20.33333333]])
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
The radius of each blob is approximately `sigma`.
|
||
|
|
Computation of Determinant of Hessians is independent of the standard
|
||
|
|
deviation. Therefore detecting larger blobs won't take more time. In
|
||
|
|
methods line :py:meth:`blob_dog` and :py:meth:`blob_log` the computation
|
||
|
|
of Gaussians for larger `sigma` takes more time. The downside is that
|
||
|
|
this method can't be used for detecting blobs of radius less than `3px`
|
||
|
|
due to the box filters used in the approximation of Hessian Determinant.
|
||
|
|
"""
|
||
|
|
check_nD(image, 2)
|
||
|
|
|
||
|
|
image = img_as_float(image)
|
||
|
|
image = integral_image(image)
|
||
|
|
|
||
|
|
if log_scale:
|
||
|
|
start, stop = log(min_sigma, 10), log(max_sigma, 10)
|
||
|
|
sigma_list = np.logspace(start, stop, num_sigma)
|
||
|
|
else:
|
||
|
|
sigma_list = np.linspace(min_sigma, max_sigma, num_sigma)
|
||
|
|
|
||
|
|
hessian_images = [_hessian_matrix_det(image, s) for s in sigma_list]
|
||
|
|
image_cube = np.dstack(hessian_images)
|
||
|
|
|
||
|
|
local_maxima = peak_local_max(image_cube, threshold_abs=threshold,
|
||
|
|
footprint=np.ones((3,) * image_cube.ndim),
|
||
|
|
threshold_rel=0.0,
|
||
|
|
exclude_border=False)
|
||
|
|
|
||
|
|
# Catch no peaks
|
||
|
|
if local_maxima.size == 0:
|
||
|
|
return np.empty((0, 3))
|
||
|
|
# Convert local_maxima to float64
|
||
|
|
lm = local_maxima.astype(np.float64)
|
||
|
|
# Convert the last index to its corresponding scale value
|
||
|
|
lm[:, -1] = sigma_list[local_maxima[:, -1]]
|
||
|
|
return _prune_blobs(lm, overlap)
|