mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-31 12:41:20 +08:00
big clean-up!
This commit is contained in:
François Boulogne
@@ -1,3 +1,6 @@
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from ._hough_transform import hough_circle,
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hough_line,
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probabilistic_hough_line
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from .hough_transform import *
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from .radon_transform import *
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from .finite_radon_transform import *
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@@ -20,7 +20,7 @@ cdef inline Py_ssize_t round(double r):
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return <Py_ssize_t>((r + 0.5) if (r > 0.0) else (r - 0.5))
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def _hough_circle(cnp.ndarray img,
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def hough_circle(cnp.ndarray img,
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cnp.ndarray[ndim=1, dtype=cnp.intp_t] radius,
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char normalize=True):
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"""Perform a circular Hough transform.
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@@ -88,8 +88,51 @@ def _hough_circle(cnp.ndarray img,
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return acc
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def _hough(cnp.ndarray img, cnp.ndarray[ndim=1, dtype=cnp.double_t] theta=None):
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def hough_line(cnp.ndarray img, cnp.ndarray[ndim=1, dtype=cnp.double_t] theta=None):
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"""Perform a straight line Hough transform.
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Parameters
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----------
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img : (M, N) ndarray
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Input image with nonzero values representing edges.
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theta : 1D ndarray of double
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Angles at which to compute the transform, in radians.
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Defaults to -pi/2 .. pi/2
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Returns
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-------
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H : 2-D ndarray of uint64
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Hough transform accumulator.
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theta : ndarray
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Angles at which the transform was computed, in radians.
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distances : ndarray
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Distance values.
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Notes
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-----
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The origin is the top left corner of the original image.
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X and Y axis are horizontal and vertical edges respectively.
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The distance is the minimal algebraic distance from the origin to the detected line.
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Examples
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--------
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Generate a test image:
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>>> img = np.zeros((100, 150), dtype=bool)
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>>> img[30, :] = 1
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>>> img[:, 65] = 1
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>>> img[35:45, 35:50] = 1
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>>> for i in range(90):
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... img[i, i] = 1
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>>> img += np.random.random(img.shape) > 0.95
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Apply the Hough transform:
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>>> out, angles, d = hough_line(img)
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.. plot:: hough_tf.py
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"""
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if img.ndim != 2:
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raise ValueError('The input image must be 2D.')
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@@ -131,10 +174,38 @@ def _hough(cnp.ndarray img, cnp.ndarray[ndim=1, dtype=cnp.double_t] theta=None):
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return accum, theta, bins
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def _probabilistic_hough(cnp.ndarray img, int value_threshold,
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def probabilistic_hough_line(cnp.ndarray img, int value_threshold,
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int line_length, int line_gap,
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cnp.ndarray[ndim=1, dtype=cnp.double_t] theta=None):
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"""Return lines from a progressive probabilistic line Hough transform.
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Parameters
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----------
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img : (M, N) ndarray
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Input image with nonzero values representing edges.
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threshold : int
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Threshold
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line_length : int, optional (default 50)
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Minimum accepted length of detected lines.
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Increase the parameter to extract longer lines.
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line_gap : int, optional, (default 10)
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Maximum gap between pixels to still form a line.
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Increase the parameter to merge broken lines more aggresively.
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theta : 1D ndarray, dtype=double, optional, default (-pi/2 .. pi/2)
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Angles at which to compute the transform, in radians.
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Returns
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-------
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lines : list
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List of lines identified, lines in format ((x0, y0), (x1, y0)), indicating
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line start and end.
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References
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----------
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.. [1] C. Galamhos, J. Matas and J. Kittler, "Progressive probabilistic
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Hough transform for line detection", in IEEE Computer Society
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Conference on Computer Vision and Pattern Recognition, 1999.
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"""
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if img.ndim != 2:
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raise ValueError('The input image must be 2D.')
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@@ -1,179 +1,32 @@
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__all__ = ['hough', 'hough_line', 'hough_circle', 'hough_peaks', 'probabilistic_hough']
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__all__ = ['hough_peaks']
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from itertools import izip as zip
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import numpy as np
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from scipy import ndimage
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from ._hough_transform import _probabilistic_hough
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from skimage import measure, morphology
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def _hough(img, theta=None):
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if img.ndim != 2:
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raise ValueError('The input image must be 2-D')
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if theta is None:
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theta = np.linspace(-np.pi / 2, np.pi / 2, 180)
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# compute the vertical bins (the distances)
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d = np.ceil(np.hypot(*img.shape))
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nr_bins = 2 * d
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bins = np.linspace(-d, d, nr_bins)
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# allocate the output image
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out = np.zeros((nr_bins, len(theta)), dtype=np.uint64)
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# precompute the sin and cos of the angles
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cos_theta = np.cos(theta)
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sin_theta = np.sin(theta)
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# find the indices of the non-zero values in
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# the input image
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y, x = np.nonzero(img)
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# x and y can be large, so we can't just broadcast to 2D
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# arrays as we may run out of memory. Instead we process
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# one vertical slice at a time.
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for i, (cT, sT) in enumerate(zip(cos_theta, sin_theta)):
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# compute the base distances
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distances = x * cT + y * sT
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# round the distances to the nearest integer
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# and shift them to a nonzero bin
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shifted = np.round(distances) - bins[0]
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# cast the shifted values to ints to use as indices
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indices = shifted.astype(np.int)
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# use bin count to accumulate the coefficients
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bincount = np.bincount(indices)
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# finally assign the proper values to the out array
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out[:len(bincount), i] = bincount
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return out, theta, bins
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_py_hough = _hough
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# try to import and use the faster Cython version if it exists
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try:
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from ._hough_transform import _hough
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except ImportError:
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pass
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def probabilistic_hough(img, threshold=10, line_length=50, line_gap=10,
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theta=None):
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"""Return lines from a progressive probabilistic line Hough transform.
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Parameters
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----------
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img : (M, N) ndarray
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Input image with nonzero values representing edges.
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threshold : int
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Threshold
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line_length : int, optional (default 50)
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Minimum accepted length of detected lines.
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Increase the parameter to extract longer lines.
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line_gap : int, optional, (default 10)
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Maximum gap between pixels to still form a line.
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Increase the parameter to merge broken lines more aggresively.
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theta : 1D ndarray, dtype=double, optional, default (-pi/2 .. pi/2)
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Angles at which to compute the transform, in radians.
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Returns
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-------
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lines : list
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List of lines identified, lines in format ((x0, y0), (x1, y0)), indicating
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line start and end.
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References
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----------
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.. [1] C. Galamhos, J. Matas and J. Kittler, "Progressive probabilistic
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Hough transform for line detection", in IEEE Computer Society
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Conference on Computer Vision and Pattern Recognition, 1999.
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"""
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return _probabilistic_hough(img, threshold, line_length, line_gap, theta)
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from ._hough_transform import hough_line, probabilistic_hough_line
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from skimage._shared.utils import deprecated
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@deprecated('hough_line')
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def hough(img, theta=None):
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return hough_line(img, theta)
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from ._hough_transform import _hough_circle
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def hough_line(img, theta=None):
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"""Perform a straight line Hough transform.
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Parameters
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----------
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img : (M, N) ndarray
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Input image with nonzero values representing edges.
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theta : 1D ndarray of double
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Angles at which to compute the transform, in radians.
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Defaults to -pi/2 .. pi/2
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Returns
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-------
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H : 2-D ndarray of uint64
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Hough transform accumulator.
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theta : ndarray
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Angles at which the transform was computed, in radians.
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distances : ndarray
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Distance values.
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Notes
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-----
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The origin is the top left corner of the original image.
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X and Y axis are horizontal and vertical edges respectively.
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The distance is the minimal algebraic distance from the origin to the detected line.
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Examples
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--------
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Generate a test image:
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>>> img = np.zeros((100, 150), dtype=bool)
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>>> img[30, :] = 1
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>>> img[:, 65] = 1
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>>> img[35:45, 35:50] = 1
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>>> for i in range(90):
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... img[i, i] = 1
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>>> img += np.random.random(img.shape) > 0.95
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Apply the Hough transform:
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>>> out, angles, d = hough_line(img)
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.. plot:: hough_tf.py
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"""
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return _hough(img, theta)
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def hough_circle(img, radius, normalize=True):
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"""Perform a circular Hough transform.
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Parameters
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----------
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img : (M, N) ndarray
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Input image with nonzero values representing edges.
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radius : ndarray
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Radii at which to compute the Hough transform.
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normalize : boolean, optional
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Normalize the accumulator with the number
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of pixels used to draw the radius
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Returns
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-------
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H : 3D ndarray (radius index, (M, N) ndarray)
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Hough transform accumulator for each radius
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"""
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return _hough_circle(img, radius.astype(np.intp), normalize)
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@deprecated('probabilistic_hough')
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def probabilistic_hough(img, threshold=10, line_length=50, line_gap=10,
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theta=None):
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return probabilistic_hough_line(img, threshold, line_length, line_gap, theta)
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@deprecated('hough_peaks')
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def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10,
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threshold=None, num_peaks=np.inf):
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return hough_line_peaks(hspace, angles, dists, min_distance, min_angle,
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threshold, num_peaks)
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def hough_line_peaks(hspace, angles, dists, min_distance=10, min_angle=10,
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threshold=None, num_peaks=np.inf):
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"""Return peaks in hough transform.
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Identifies most prominent lines separated by a certain angle and distance in
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@@ -187,6 +40,7 @@ def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10,
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Hough space returned by the `hough_line` function.
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angles : (M,) array
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Angles returned by the `hough_line` function. Assumed to be continuous.
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(`angles[-1] - angles[0] == PI`).
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dists : (N, ) array
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Distances returned by the `hough_line` function.
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min_distance : int
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@@ -41,15 +41,6 @@ def test_hough_line_angles():
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assert_equal(len(angles), 10)
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def test_py_hough():
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ht._hough, fast_hough = ht._py_hough, ht._hough
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yield append_desc(test_hough_line, '_python')
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yield append_desc(test_hough_line_angles, '_python')
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tf._hough = fast_hough
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def test_probabilistic_hough():
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# Generate a test image
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img = np.zeros((100, 100), dtype=int)
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