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added python call and doc strings

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Vighnesh Birodkar
2014-04-09 10:27:53 +05:30
parent d58cab146f
commit cca0c74cf7
3 changed files with 115 additions and 6 deletions
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skimage/feature/__init__.py
+2 -1
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@@ -14,7 +14,7 @@ from .censure import CENSURE
from .orb import ORB
from .match import match_descriptors
from .util import plot_matches
from .blob import blob_dog, blob_log
from .blob import blob_dog, blob_log, blob_doh
__all__ = ['daisy',
@@ -43,4 +43,5 @@ __all__ = ['daisy',
'match_descriptors',
'plot_matches',
'blob_dog',
'blob_doh',
'blob_log']
skimage/feature/_hessian_det_appx.pyx
+4 -4
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@@ -1,6 +1,5 @@
import numpy as np
cimport numpy as np
from skimage.transform import integral_image, integrate
from skimage import util
@@ -113,8 +112,9 @@ def _hessian_det_appx(np.ndarray[np.int_t, ndim=2] image, float sigma):
cdef int r, c
cdef int s2 = (size - 1) / 2
cdef int s3 = size / 3
cdef int l = size / 3
cdef int l = size/3
cdef int w = size
cdef int b = (size - 1)/2
cdef int mid, side
zeros = np.zeros_like(img)
cdef np.ndarray[np.float_t, ndim = 2] out = zeros.astype(np.float)
@@ -141,8 +141,8 @@ def _hessian_det_appx(np.ndarray[np.int_t, ndim=2] image, float sigma):
dxx = mid - 3 * side
dxx = -dxx / w / w
mid = _integ(img, r - s2, c - s2 + 1, w, 2 * s3 - 1)
side = _integ(img, r - s3 / 2, c - s3 + 1, s3, 2 * s3 - 1) * 3
mid = _integ(img, r - s2, c - s3 + 1, w, 2 * s3 - 1)
side = _integ(img, r - s3 / 2, c - s3 + 1, s3, 2 * s3 - 1)
dyy = mid - 3 * side
dyy = -dyy / w / w
skimage/feature/blob.py
+109 -1
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@@ -4,8 +4,11 @@ import itertools as itt
import math
from math import sqrt, hypot, log
from numpy import arccos
from skimage.util import img_as_float
from skimage.util import img_as_float, img_as_ubyte
from .peak import peak_local_max
from ._hessian_det_appx import _hessian_det_appx
from skimage.transform import integral_image
# This basic blob detection algorithm is based on:
@@ -298,3 +301,108 @@ def blob_log(image, min_sigma=1, max_sigma=50, num_sigma=10, threshold=.2,
# Convert the last index to its corresponding scale value
local_maxima[:, 2] = sigma_list[local_maxima[:, 2]]
return _prune_blobs(local_maxima, overlap)
def blob_doh(image, min_sigma=1, max_sigma=30, num_sigma=10, threshold=500,
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 : 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 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] http://en.wikipedia.org/wiki/Blob_detection#The_Laplacian_of_Gaussian
.. [2] ftp://ftp.vision.ee.ethz.ch/publications/articles/eth_biwi_00517.pdf
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],
[124, 336, 11],
[126, 46, 11],
[126, 208, 11],
[127, 102, 11],
[128, 154, 11],
[185, 344, 17],
[194, 213, 17],
[194, 276, 17],
[197, 44, 11],
[198, 103, 11],
[198, 155, 11],
[260, 174, 17],
[263, 244, 17],
[263, 302, 17],
[266, 115, 11]])
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
mathods line :py:meth:`blob_dog` and :py:math:`blob_log` the computation
of Gaussians for larger `sigma` takes more time.
"""
if image.ndim != 2:
raise ValueError("'image' must be a grayscale ")
image = img_as_ubyte(image).astype(np.uint8)
image = integral_image(image).astype(np.int)
print 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_det_appx(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, 3, 3)),
threshold_rel=0.0,
exclude_border=False)
# Convert the last index to its corresponding scale value
local_maxima[:, 2] = sigma_list[local_maxima[:, 2]]
return _prune_blobs(local_maxima, overlap)