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Use predefined header files from Cython

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This commit is contained in:
Johannes Schönberger
2012-08-21 15:15:27 +02:00
parent 3b227e226d
commit a08779e06a
4 changed files with 25 additions and 48 deletions
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skimage/transform/_hough_transform.pyx
+19 -26
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@@ -2,27 +2,20 @@ cimport cython
import numpy as np
cimport numpy as np
from random import randint
from libc.math cimport abs, fabs, sqrt, ceil, floor, round
from libc.stdlib cimport rand
np.import_array()
cdef extern from "stdlib.h":
int rand()
cdef extern from "math.h":
int abs(int)
double fabs(double)
double sqrt(double)
double ceil(double)
double floor(double)
cdef double round(double val):
return floor(val + 0.5);
cdef double PI_2 = 1.5707963267948966
cdef double NEG_PI_2 = -PI_2
@cython.boundscheck(False)
def _hough(np.ndarray img, np.ndarray[ndim=1, dtype=np.double_t] theta=None):
if img.ndim != 2:
raise ValueError('The input image must be 2D.')
@@ -31,7 +24,7 @@ def _hough(np.ndarray img, np.ndarray[ndim=1, dtype=np.double_t] theta=None):
cdef np.ndarray[ndim=1, dtype=np.double_t] stheta
if theta is None:
theta = np.linspace(PI_2, NEG_PI_2, 180)
theta = np.linspace(PI_2, NEG_PI_2, 180)
ctheta = np.cos(theta)
stheta = np.sin(theta)
@@ -39,14 +32,14 @@ def _hough(np.ndarray img, np.ndarray[ndim=1, dtype=np.double_t] theta=None):
# compute the bins and allocate the accumulator array
cdef np.ndarray[ndim=2, dtype=np.uint64_t] accum
cdef np.ndarray[ndim=1, dtype=np.double_t] bins
cdef int max_distance, offset
cdef int max_distance, offset
max_distance = 2 * <int>ceil((sqrt(img.shape[0] * img.shape[0] +
max_distance = 2 * <int>ceil((sqrt(img.shape[0] * img.shape[0] +
img.shape[1] * img.shape[1])))
accum = np.zeros((max_distance, theta.shape[0]), dtype=np.uint64)
bins = np.linspace(-max_distance / 2.0, max_distance / 2.0, max_distance)
offset = max_distance / 2
# compute the nonzero indexes
cdef np.ndarray[ndim=1, dtype=np.npy_intp] x_idxs, y_idxs
y_idxs, x_idxs = np.PyArray_Nonzero(img)
@@ -58,7 +51,7 @@ def _hough(np.ndarray img, np.ndarray[ndim=1, dtype=np.double_t] theta=None):
nthetas = theta.shape[0]
for i in range(nidxs):
x = x_idxs[i]
y = y_idxs[i]
y = y_idxs[i]
for j in range(nthetas):
accum_idx = <int>round((ctheta[j] * x + stheta[j] * y)) + offset
accum[accum_idx, j] += 1
@@ -94,7 +87,7 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \
# maximum line number cutoff
cdef int lines_max = 2 ** 15
cdef int xflag, x0, y0, dx0, dy0, dx, dy, gap, x1, y1, good_line, count
max_distance = 2 * <int>ceil((sqrt(img.shape[0] * img.shape[0] +
max_distance = 2 * <int>ceil((sqrt(img.shape[0] * img.shape[0] +
img.shape[1] * img.shape[1])))
accum = np.zeros((max_distance, theta.shape[0]), dtype=np.int64)
offset = max_distance / 2
@@ -114,11 +107,11 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \
# select random non-zero point
count = len(points)
if count == 0:
break
break
index = rand() % (count)
x = points[index][0]
y = points[index][1]
del points[index]
del points[index]
# if previously eliminated, skip
if not mask[y, x]:
continue
@@ -147,7 +140,7 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \
dx0 = 1
else:
dx0 = -1
dy0 = <int>round(b * (1 << shift) / fabs(a))
dy0 = <int>round(b * (1 << shift) / fabs(a))
y0 = (y0 << shift) + (1 << (shift - 1))
else:
if b > 0:
@@ -156,7 +149,7 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \
dy0 = -1
dx0 = <int>round(a * (1 << shift) / fabs(b))
x0 = (x0 << shift) + (1 << (shift - 1))
# pass 1: walk the line, merging lines less than specified gap length
for k in range(2):
gap = 0
@@ -208,9 +201,9 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \
x1 = px >> shift
y1 = py
# if non-zero point found, continue the line
if mask[y1, x1]:
if good_line:
accum_idx = <int>round((ctheta[j] * x1 + stheta[j] * y1)) + offset
if mask[y1, x1]:
if good_line:
accum_idx = <int>round((ctheta[j] * x1 + stheta[j] * y1)) + offset
accum[accum_idx, max_theta] -= 1
mask[y1, x1] = 0
# exit when the point is the line end