mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-17 11:32:45 +08:00
Modified name of image dtype.
This commit is contained in:
emmanuelle
@@ -4,14 +4,14 @@ cimport numpy as np
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cimport cython
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from libc.math cimport exp
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ctypedef np.float32_t DTYPE_t
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ctypedef np.float32_t IMGDTYPE
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cdef float DISTANCE_CUTOFF = 5.
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@cython.boundscheck(False)
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cdef inline float patch_distance_2d(DTYPE_t [:, :] p1,
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DTYPE_t [:, :] p2,
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DTYPE_t [:, ::] w, int s):
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cdef inline float patch_distance_2d(IMGDTYPE [:, :] p1,
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IMGDTYPE [:, :] p2,
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IMGDTYPE [:, ::] w, int s):
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"""
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Compute a Gaussian distance between two image patches.
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@@ -57,9 +57,9 @@ cdef inline float patch_distance_2d(DTYPE_t [:, :] p1,
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@cython.boundscheck(False)
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cdef inline float patch_distance_2drgb(DTYPE_t [:, :, :] p1,
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DTYPE_t [:, :, :] p2,
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DTYPE_t [:, ::] w, int s):
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cdef inline float patch_distance_2drgb(IMGDTYPE [:, :, :] p1,
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IMGDTYPE [:, :, :] p2,
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IMGDTYPE [:, ::] w, int s):
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"""
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Compute a Gaussian distance between two image patches.
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@@ -103,9 +103,9 @@ cdef inline float patch_distance_2drgb(DTYPE_t [:, :, :] p1,
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@cython.boundscheck(False)
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cdef inline float patch_distance_3d(DTYPE_t [:, :, :] p1,
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DTYPE_t [:, :, :] p2,
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DTYPE_t [:, :, ::] w, int s):
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cdef inline float patch_distance_3d(IMGDTYPE [:, :, :] p1,
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IMGDTYPE [:, :, :] p2,
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IMGDTYPE [:, :, ::] w, int s):
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"""
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Compute a Gaussian distance between two image patches.
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@@ -177,16 +177,16 @@ def _nl_means_denoising_2d(image, int s=7, int d=13, float h=0.1):
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cdef int row, col, i, j, color
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cdef int row_start, row_end, col_start, col_end
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cdef int row_start_i, row_end_i, col_start_j, col_end_j
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cdef DTYPE_t [::1] new_values = np.zeros(n_ch).astype(np.float32)
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cdef DTYPE_t [:, :, ::1] padded = np.ascontiguousarray(util.pad(image,
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cdef IMGDTYPE [::1] new_values = np.zeros(n_ch).astype(np.float32)
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cdef IMGDTYPE [:, :, ::1] padded = np.ascontiguousarray(util.pad(image,
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((offset, offset), (offset, offset), (0, 0)),
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mode='reflect').astype(np.float32))
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cdef DTYPE_t [:, :, ::1] result = padded.copy()
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cdef IMGDTYPE [:, :, ::1] result = padded.copy()
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cdef float A = ((s - 1.) / 4.)
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cdef float new_value
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cdef float weight_sum, weight
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xg_row, xg_col = np.mgrid[-offset:offset + 1, -offset:offset + 1]
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cdef DTYPE_t [:, ::1] w = np.ascontiguousarray(np.exp(
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cdef IMGDTYPE [:, ::1] w = np.ascontiguousarray(np.exp(
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-(xg_row ** 2 + xg_col ** 2) / (2 * A ** 2)).
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astype(np.float32))
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cdef float distance
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@@ -262,19 +262,19 @@ def _nl_means_denoising_3d(image, int s=7,
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n_pln, n_row, n_col = image.shape
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cdef int offset = s / 2
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# padd the image so that boundaries are denoised as well
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cdef DTYPE_t [:, :, ::1] padded = np.ascontiguousarray(util.pad(
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cdef IMGDTYPE [:, :, ::1] padded = np.ascontiguousarray(util.pad(
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image.astype(np.float32),
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offset, mode='reflect'))
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cdef DTYPE_t [:, :, ::1] result = padded.copy()
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cdef IMGDTYPE [:, :, ::1] result = padded.copy()
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cdef float A = ((s - 1.) / 4.)
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cdef float new_value
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cdef float weight_sum, weight
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xg_pln, xg_row, xg_col = np.mgrid[-offset: offset + 1,
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-offset: offset + 1,
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-offset: offset + 1]
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cdef DTYPE_t [:, :, ::1] w = np.ascontiguousarray(np.exp(
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cdef IMGDTYPE [:, :, ::1] w = np.ascontiguousarray(np.exp(
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-(xg_pln ** 2 + xg_row ** 2 + xg_col ** 2) /
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(2 * A ** 2)).astype(np.float32))
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(2 * A ** 2)).astype(np.float32))
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cdef float distance
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cdef int pln, row, col, i, j, k
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cdef int pln_start, pln_end, row_start, row_end, col_start, col_end
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@@ -325,7 +325,7 @@ def _nl_means_denoising_3d(image, int s=7,
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@cython.cdivision(True)
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@cython.boundscheck(False)
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cdef inline float _integral_to_distance_2d(DTYPE_t [:, ::] integral,
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cdef inline float _integral_to_distance_2d(IMGDTYPE [:, ::] integral,
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int row, int col, int offset, float h2s2):
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"""
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References
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@@ -346,7 +346,7 @@ cdef inline float _integral_to_distance_2d(DTYPE_t [:, ::] integral,
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@cython.cdivision(True)
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@cython.boundscheck(False)
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cdef inline float _integral_to_distance_3d(DTYPE_t [:, :, ::] integral,
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cdef inline float _integral_to_distance_3d(IMGDTYPE [:, :, ::] integral,
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int pln, int row, int col, int offset,
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float s_cube_h_square):
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"""
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@@ -372,8 +372,8 @@ cdef inline float _integral_to_distance_3d(DTYPE_t [:, :, ::] integral,
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@cython.cdivision(True)
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@cython.boundscheck(False)
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cdef inline _integral_image_2d(DTYPE_t [:, :, ::] padded,
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DTYPE_t [:, ::] integral, int t_row,
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cdef inline _integral_image_2d(IMGDTYPE [:, :, ::] padded,
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IMGDTYPE [:, ::] integral, int t_row,
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int t_col, int n_row, int n_col, int n_ch):
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"""
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Computes the integral of the squared difference between an image ``padded``
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@@ -422,8 +422,8 @@ cdef inline _integral_image_2d(DTYPE_t [:, :, ::] padded,
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@cython.cdivision(True)
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@cython.boundscheck(False)
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cdef inline _integral_image_3d(DTYPE_t [:, :, ::] padded,
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DTYPE_t [:, :, ::] integral, int t_pln,
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cdef inline _integral_image_3d(IMGDTYPE [:, :, ::] padded,
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IMGDTYPE [:, :, ::] integral, int t_pln,
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int t_row, int t_col, int n_pln, int n_row,
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int n_col):
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"""
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@@ -501,12 +501,12 @@ def _fast_nl_means_denoising_2d(image, int s=7, int d=13, float h=0.1):
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# Image padding: we need to account for patch size, possible shift,
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# + 1 for the boundary effects in finite differences
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cdef int pad_size = offset + d + 1
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cdef DTYPE_t [:, :, ::1] padded = np.ascontiguousarray(util.pad(image,
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cdef IMGDTYPE [:, :, ::1] padded = np.ascontiguousarray(util.pad(image,
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((pad_size, pad_size), (pad_size, pad_size), (0, 0)),
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mode='reflect').astype(np.float32))
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cdef DTYPE_t [:, :, ::1] result = np.zeros_like(padded)
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cdef DTYPE_t [:, ::1] weights = np.zeros_like(padded[..., 0], order='C')
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cdef DTYPE_t [:, ::1] integral = np.zeros_like(padded[..., 0], order='C')
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cdef IMGDTYPE [:, :, ::1] result = np.zeros_like(padded)
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cdef IMGDTYPE [:, ::1] weights = np.zeros_like(padded[..., 0], order='C')
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cdef IMGDTYPE [:, ::1] integral = np.zeros_like(padded[..., 0], order='C')
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cdef int n_row, n_col, n_ch, t_row, t_col, row, col
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cdef float weight, distance
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cdef float alpha
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@@ -586,11 +586,11 @@ def _fast_nl_means_denoising_3d(image, int s=5, int d=7, float h=0.1):
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# Image padding: we need to account for patch size, possible shift,
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# + 1 for the boundary effects in finite differences
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cdef int pad_size = offset + d + 1
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cdef DTYPE_t [:, :, ::1] padded = np.ascontiguousarray(util.pad(image,
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cdef IMGDTYPE [:, :, ::1] padded = np.ascontiguousarray(util.pad(image,
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pad_size, mode='reflect').astype(np.float32))
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cdef DTYPE_t [:, :, ::1] result = np.zeros_like(padded)
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cdef DTYPE_t [:, :, ::1] weights = np.zeros_like(padded)
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cdef DTYPE_t [:, :, ::1] integral = np.zeros_like(padded)
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cdef IMGDTYPE [:, :, ::1] result = np.zeros_like(padded)
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cdef IMGDTYPE [:, :, ::1] weights = np.zeros_like(padded)
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cdef IMGDTYPE [:, :, ::1] integral = np.zeros_like(padded)
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cdef int n_pln, n_row, n_col, t_pln, t_row, t_col, \
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pln, row, col
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cdef int pln_dist_min, pln_dist_max, row_dist_min, row_dist_max, \
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