wassname/scikit-image
1
0
Fork 0
mirror of https://github.com/wassname/scikit-image.git synced 2026-07-01 12:01:51 +08:00
Code Issues Packages Projects Releases Wiki Activity

Merge

Browse Source
This commit is contained in:
Pieter Holtzhausen
2011-08-22 15:35:57 +02:00
parent e5ddf85907
commit 9cd072d925
2 changed files with 18 additions and 29 deletions
Download Patch File Download Diff File Expand all files Collapse all files
scikits/image/transform/_hough_transform.pyx
+2 -18
View File
@@ -86,7 +86,7 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \
cdef np.ndarray[ndim=2, dtype=np.int64_t] accum
cdef np.ndarray[ndim=2, dtype=np.uint8_t] mask = np.zeros((height, width), dtype=np.uint8)
cdef np.ndarray[ndim=2, dtype=np.int32_t] line_end = np.zeros((2, 2), dtype=np.int32)
cdef int max_distance, offset, num_indexes, index, other_max, settle
cdef int max_distance, offset, num_indexes, index
cdef double a, b
cdef int nidxs, nthetas, i, j, x, y, px, py, accum_idx, value, max_value, max_theta
cdef int shift = 16
@@ -111,8 +111,6 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \
# create mask of all non-zero indexes
for i in range(num_indexes):
mask[y_idxs[i], x_idxs[i]] = 1
settle = 0
print
while 1:
# select random non-zero point
count = len(points)
@@ -125,33 +123,19 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \
# if previously eliminated, skip
if not mask[y, x]:
continue
print "select", y, x
value = 0
max_value = value_threshold-1
max_theta = -1
other_max = 0
# apply hough transform on point
for j in range(nthetas):
accum_idx = <int>round((ctheta[j] * x + stheta[j] * y)) + offset
accum[accum_idx, j] += 1
value = accum[accum_idx, j]
if value > max_value:
print "max", j, value
max_value = value
max_theta = j
# if two bordering angles have max values, extend the threshold for a while
if j != max_theta and value == max_value:
if abs(j - max_theta) <= 2:
if settle > 100:
other_max = 0
else:
other_max = 1
if j == 134 or j == 135:
print j, value, accum_idx, ctheta[j], stheta[j]
if max_value < value_threshold or other_max:
if max_value < value_threshold:
continue
settle = 0
print "THETA", max_theta, max_value
# from the random point walk in opposite directions and find line beginning and end
a = -stheta[max_theta]
b = ctheta[max_theta]
scikits/image/transform/tests/test_hough_transform.py
+16 -11
View File
@@ -13,6 +13,9 @@ def append_desc(func, description):
return func
from scikits.image.transform import *
import math
def test_hough():
# Generate a test image
img = np.zeros((100, 100), dtype=int)
@@ -28,6 +31,7 @@ def test_hough():
assert_equal(dist > 70, dist < 72)
assert_equal(theta > 0.78, theta < 0.79)
def test_hough_angles():
img = np.zeros((10, 10))
img[0, 0] = 1
@@ -50,17 +54,18 @@ def test_probabilistic_hough():
for i in range(25, 75):
img[100 - i, i] = 100
img[i, i] = 100
# test the line extraction a few times
for i in range(100):
lines = probabilistic_hough(img, threshold=10, line_length=10, line_gap=1)
# sort the lines according to the x-axis
sorted_lines = []
for line in lines:
line = list(line)
line.sort(lambda x,y: cmp(x[0], y[0]))
sorted_lines.append(line)
assert([(25, 75), (74, 26)] in sorted_lines)
assert([(25, 25), (74, 74)] in sorted_lines)
# decrease default theta sampling because similar orientations may confuse
# as mentioned in article of Galambos et al
theta=np.linspace(0, math.pi, 45)
lines = probabilistic_hough(img, theta=theta, threshold=10, line_length=10, line_gap=1)
# sort the lines according to the x-axis
sorted_lines = []
for line in lines:
line = list(line)
line.sort(lambda x,y: cmp(x[0], y[0]))
sorted_lines.append(line)
assert([(25, 75), (74, 26)] in sorted_lines)
assert([(25, 25), (74, 74)] in sorted_lines)
if __name__ == "__main__":