mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 17:37:20 +08:00
Stefan van der Walt
520c54f5ec
Conflicts: scikits/image/setup.py scikits/image/transform/hough_transform.py scikits/image/transform/tests/test_hough_transform.py
64 lines
1.8 KiB
Cython
64 lines
1.8 KiB
Cython
cimport cython
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import numpy as np
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cimport numpy as np
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np.import_array()
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cdef extern from "math.h":
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double sqrt(double)
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double ceil(double)
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double round(double)
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cdef double PI_2 = 1.5707963267948966
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cdef double NEG_PI_2 = -PI_2
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@cython.boundscheck(False)
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def _hough(np.ndarray img, np.ndarray[ndim=1, dtype=np.double_t] theta=None):
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if img.ndim != 2:
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raise ValueError('The input image must be 2D.')
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# Compute the array of angles and their sine and cosine
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cdef np.ndarray[ndim=1, dtype=np.double_t] ctheta
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cdef np.ndarray[ndim=1, dtype=np.double_t] stheta
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if theta is None:
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theta = np.linspace(PI_2, NEG_PI_2, 180)
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ctheta = np.cos(theta)
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stheta = np.sin(theta)
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# compute the bins and allocate the output array
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cdef np.ndarray[ndim=2, dtype=np.uint64_t] out
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cdef np.ndarray[ndim=1, dtype=np.double_t] bins
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cdef int max_distance, offset
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max_distance = 2 * <int>ceil((sqrt(img.shape[0] * img.shape[0] +
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img.shape[1] * img.shape[1])))
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out = np.zeros((max_distance, theta.shape[0]), dtype=np.uint64)
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bins = np.linspace(-max_distance / 2.0, max_distance / 2.0, max_distance)
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offset = max_distance / 2
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# compute the nonzero indexes
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cdef np.ndarray[ndim=1, dtype=np.int32_t] x_idxs, y_idxs
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y_idxs, x_idxs = np.PyArray_Nonzero(img)
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# finally, run the transform
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cdef int nidxs, nthetas, i, j, x, y, out_idx
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nidxs = y_idxs.shape[0] # x and y are the same shape
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nthetas = theta.shape[0]
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for i in range(nidxs):
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x = x_idxs[i]
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y = y_idxs[i]
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for j in range(nthetas):
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out_idx = <int>round((ctheta[j] * x + stheta[j] * y)) + offset
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out[out_idx, j] += 1
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return out, theta, bins
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