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iradon_sart's cython exts: Use typed memoryviews, not ndarrays.

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Jostein Bø Fløystad
2013-06-10 13:22:55 +02:00
parent c977c5974d
commit 91aeb658dd
1 changed files with 30 additions and 29 deletions
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skimage/transform/_radon_transform.pyx
+30 -29
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@@ -9,8 +9,8 @@ cimport cython
from libc.math cimport cos, sin, floor, ceil, sqrt, abs, M_PI
cpdef bilinear_ray_sum(cnp.ndarray[cnp.double_t, ndim=2] image, double theta,
double ray_position):
cpdef bilinear_ray_sum(cnp.double_t[:, :] image, cnp.double_t theta,
cnp.double_t ray_position):
"""
Compute the projection of an image along a ray.
@@ -32,18 +32,18 @@ cpdef bilinear_ray_sum(cnp.ndarray[cnp.double_t, ndim=2] image, double theta,
circle was
"""
theta = theta / 180. * M_PI
cdef double radius = image.shape[0] // 2 - 1
cdef double projection_center = image.shape[0] // 2 - 1
cdef double rotation_center = image.shape[0] // 2
cdef cnp.double_t radius = image.shape[0] // 2 - 1
cdef cnp.double_t projection_center = image.shape[0] // 2 - 1
cdef cnp.double_t rotation_center = image.shape[0] // 2
# (s, t) is the (x, y) system rotated by theta
cdef double t = ray_position - projection_center
cdef cnp.double_t t = ray_position - projection_center
# s0 is the half-length of the ray's path in the reconstruction circle
cdef double s0
cdef cnp.double_t s0
s0 = sqrt(radius**2 - t**2) if radius**2 >= t**2 else 0.
cdef Py_ssize_t Ns = 2 * (<Py_ssize_t> ceil(2 * s0)) # number of steps along the ray
cdef double ray_sum = 0.
cdef double weight_norm = 0.
cdef double ds, dx, dy, x0, y0, x, y, di, dj, index_i, index_j
cdef cnp.double_t ray_sum = 0.
cdef cnp.double_t weight_norm = 0.
cdef cnp.double_t ds, dx, dy, x0, y0, x, y, di, dj, index_i, index_j
cdef Py_ssize_t k, i, j
if Ns > 0:
# step length between samples
@@ -79,9 +79,10 @@ cpdef bilinear_ray_sum(cnp.ndarray[cnp.double_t, ndim=2] image, double theta,
return ray_sum, weight_norm
cpdef bilinear_ray_update(cnp.ndarray[cnp.double_t, ndim=2] image,
cnp.ndarray[cnp.double_t, ndim=2] image_update,
double theta, double ray_position, double projected_value):
cpdef bilinear_ray_update(cnp.double_t[:, :] image,
cnp.double_t[:, :] image_update,
cnp.double_t theta, cnp.double_t ray_position,
cnp.double_t projected_value):
"""
Compute the update along a ray using bilinear interpolation.
@@ -103,26 +104,26 @@ cpdef bilinear_ray_update(cnp.ndarray[cnp.double_t, ndim=2] image,
deviation :
Deviation before updating the image
"""
cdef double ray_sum, weight_norm, deviation
cdef cnp.double_t ray_sum, weight_norm, deviation
ray_sum, weight_norm = bilinear_ray_sum(image, theta, ray_position)
if weight_norm > 0.:
deviation = -(ray_sum - projected_value) / weight_norm
else:
deviation = 0.
theta = theta / 180. * M_PI
cdef double radius = image.shape[0] // 2 - 1
cdef double projection_center = image.shape[0] // 2 - 1
cdef double rotation_center = image.shape[0] // 2
cdef cnp.double_t radius = image.shape[0] // 2 - 1
cdef cnp.double_t projection_center = image.shape[0] // 2 - 1
cdef cnp.double_t rotation_center = image.shape[0] // 2
# (s, t) is the (x, y) system rotated by theta
cdef double t = ray_position - projection_center
cdef cnp.double_t t = ray_position - projection_center
# s0 is the half-length of the ray's path in the reconstruction circle
cdef double s0
cdef cnp.double_t s0
s0 = sqrt(radius*radius - t*t) if radius**2 >= t**2 else 0.
cdef Py_ssize_t Ns = 2 * (<Py_ssize_t> ceil(2 * s0))
cdef double hamming_beta = 0.46164 # beta for equiripple Hamming window
cdef cnp.double_t hamming_beta = 0.46164 # beta for equiripple Hamming window
cdef double ds, dx, dy, x0, y0, x, y, di, dj, index_i, index_j
cdef double hamming_window
cdef cnp.double_t ds, dx, dy, x0, y0, x, y, di, dj, index_i, index_j
cdef cnp.double_t hamming_window
cdef Py_ssize_t k, i, j
if Ns > 0:
# Step length between samples
@@ -159,10 +160,10 @@ cpdef bilinear_ray_update(cnp.ndarray[cnp.double_t, ndim=2] image,
@cython.boundscheck(True)
def sart_projection_update(cnp.ndarray[cnp.double_t, ndim=2] image not None,
double theta,
cnp.ndarray[cnp.double_t, ndim=1] projection not None,
double projection_shift=0.):
def sart_projection_update(cnp.double_t[:, :] image not None,
cnp.double_t theta,
cnp.double_t[:] projection not None,
cnp.double_t projection_shift=0.):
"""
Compute update to a reconstruction estimate from a single projection
using bilinear interpolation.
@@ -185,11 +186,11 @@ def sart_projection_update(cnp.ndarray[cnp.double_t, ndim=2] image not None,
Array of same shape as ``image`` containing updates that should be
added to ``image`` to improve the reconstruction estimate
"""
cdef cnp.ndarray[cnp.double_t, ndim=2] image_update = np.zeros_like(image)
cdef double ray_position
cdef cnp.double_t[:, :] image_update = np.zeros_like(image)
cdef cnp.double_t ray_position
cdef Py_ssize_t i
for i in range(projection.shape[0]):
ray_position = i + projection_shift
bilinear_ray_update(image, image_update, theta, ray_position,
projection[i])
return image_update
return np.asarray(image_update)