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Restructure cdef declarations

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This commit is contained in:
Johannes Schönberger
2012-10-06 08:32:08 +02:00
parent f9ecd140d8
commit cec7b6055d
1 changed files with 48 additions and 37 deletions
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skimage/filter/_denoise.pyx
+48 -37
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@@ -15,22 +15,30 @@ cdef inline double _gaussian_weight(double sigma, double value):
cdef double* _compute_color_lut(int bins, double sigma, double max_value):
cdef double* color_lut = <double*>malloc(bins * sizeof(double))
cdef Py_ssize_t b
cdef:
double* color_lut = <double*>malloc(bins * sizeof(double))
Py_ssize_t b
for b in range(bins):
color_lut[b] = _gaussian_weight(sigma, b * max_value / bins)
return color_lut
cdef double* _compute_range_lut(int win_size, double sigma):
cdef double* range_lut = <double*>malloc(win_size**2 * sizeof(double))
cdef Py_ssize_t kr, kc
cdef Py_ssize_t window_ext = (win_size - 1) / 2
cdef double dist
cdef:
double* range_lut = <double*>malloc(win_size**2 * sizeof(double))
Py_ssize_t kr, kc
Py_ssize_t window_ext = (win_size - 1) / 2
double dist
for kr in range(win_size):
for kc in range(win_size):
dist = sqrt((kr - window_ext)**2 + (kc - window_ext)**2)
range_lut[kr * win_size + kc] = _gaussian_weight(sigma, dist)
return range_lut
@@ -39,24 +47,25 @@ def _denoise_bilateral2d(cnp.ndarray[dtype=cnp.double_t, ndim=2, mode='c'] image
double sigma_range, int bins, char mode,
double cval):
cdef Py_ssize_t rows = image.shape[0]
cdef Py_ssize_t cols = image.shape[1]
cdef Py_ssize_t window_ext = (win_size - 1) / 2
cdef double max_value = image.max()
cdef:
Py_ssize_t rows = image.shape[0]
Py_ssize_t cols = image.shape[1]
Py_ssize_t window_ext = (win_size - 1) / 2
double max_value = image.max()
cdef cnp.ndarray[dtype=cnp.double_t, ndim=2, mode='c'] out = \
np.zeros((rows, cols), dtype=np.double)
cnp.ndarray[dtype=cnp.double_t, ndim=2, mode='c'] out = \
np.zeros((rows, cols), dtype=np.double)
cdef double* image_data = <double*>image.data
cdef double* out_data = <double*>out.data
double* image_data = <double*>image.data
double* out_data = <double*>out.data
cdef double* color_lut = _compute_color_lut(bins, sigma_color, max_value)
cdef double* range_lut = _compute_range_lut(win_size, sigma_range)
double* color_lut = _compute_color_lut(bins, sigma_color, max_value)
double* range_lut = _compute_range_lut(win_size, sigma_range)
cdef Py_ssize_t r, c, wr, wc, kr, kc, rr, cc, pixel_addr
cdef double centre, value, weight, total_value, total_weight, \
color_weight, range_weight, diff
cdef double dist_scale = bins / max_value
Py_ssize_t r, c, wr, wc, kr, kc, rr, cc, pixel_addr
double centre, value, weight, total_value, total_weight, \
color_weight, range_weight, diff
double dist_scale = bins / max_value
for r in range(rows):
for c in range(cols):
@@ -95,27 +104,29 @@ def _denoise_bilateral3d(cnp.ndarray[dtype=cnp.double_t, ndim=3, mode='c'] image
double sigma_range, int bins, char mode,
double cval):
cdef Py_ssize_t rows = image.shape[0]
cdef Py_ssize_t cols = image.shape[1]
cdef Py_ssize_t dims = image.shape[2]
cdef Py_ssize_t window_ext = (win_size - 1) / 2
cdef double max_value = image.max()
cdef:
Py_ssize_t rows = image.shape[0]
Py_ssize_t cols = image.shape[1]
Py_ssize_t dims = image.shape[2]
Py_ssize_t window_ext = (win_size - 1) / 2
cdef cnp.ndarray[dtype=cnp.double_t, ndim=3, mode='c'] out = \
np.zeros((rows, cols, dims), dtype=np.double)
double max_value = image.max()
cdef double* image_data = <double*>image.data
cdef double* out_data = <double*>out.data
cnp.ndarray[dtype=cnp.double_t, ndim=3, mode='c'] out = \
np.zeros((rows, cols, dims), dtype=np.double)
cdef double* color_lut = _compute_color_lut(bins, sigma_color, max_value)
cdef double* range_lut = _compute_range_lut(win_size, sigma_range)
double* image_data = <double*>image.data
double* out_data = <double*>out.data
cdef Py_ssize_t r, c, d, wr, wc, kr, kc, rr, cc, pixel_addr
cdef double value, weight, dist, total_weight, color_weight, range_weight
cdef double dist_scale = bins / dims / max_value
cdef double* values = <double*>malloc(dims * sizeof(double))
cdef double* centres = <double*>malloc(dims * sizeof(double))
cdef double* total_values = <double*>malloc(dims * sizeof(double))
double* color_lut = _compute_color_lut(bins, sigma_color, max_value)
double* range_lut = _compute_range_lut(win_size, sigma_range)
Py_ssize_t r, c, d, wr, wc, kr, kc, rr, cc, pixel_addr
double value, weight, dist, total_weight, color_weight, range_weight
double dist_scale = bins / dims / max_value
double* values = <double*>malloc(dims * sizeof(double))
double* centres = <double*>malloc(dims * sizeof(double))
double* total_values = <double*>malloc(dims * sizeof(double))
for r in range(rows):
for c in range(cols):