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Added a settle count for similar angles to resolve themselves

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This commit is contained in:
Pieter Holtzhausen
2011-08-22 15:33:26 +02:00
parent 06f91af36a
commit 9ed82321c0
2 changed files with 28 additions and 17 deletions
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scikits/image/transform/_hough_transform.pyx
+17 -8
View File
@@ -77,8 +77,8 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \
# calculate thetas if none specified
if theta is None:
theta = np.linspace(math.pi/2, -math.pi/2, 180)
#p_2 = math.pi/2
#theta = p_2-np.arange(180)/180.0*p_2*2
p_2 = math.pi/2
theta = p_2-np.arange(180)/180.0*p_2*2
ctheta = np.cos(theta)
stheta = np.sin(theta)
cdef int height = img.shape[0]
@@ -87,7 +87,7 @@ 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
cdef int max_distance, offset, num_indexes, index, other_max, settle
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
@@ -112,6 +112,7 @@ 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
while 1:
# select random non-zero point
count = len(points)
@@ -127,17 +128,25 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \
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]
value = accum[accum_idx, j]
if value > max_value:
max_value = value
max_theta = j
if max_value < value_threshold:
# if two max angles are very similar, extend the threshold for a while
if j != max_theta and value == max_value and abs(j - max_theta) == 1:
settle += 1
if settle > 100:
other_max = 0
else:
other_max = 1
if max_value < value_threshold or other_max:
continue
settle = 0
# from the random point walk in opposite directions and find line beginning and end
a = -stheta[max_theta]
b = ctheta[max_theta]
@@ -151,14 +160,14 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \
else:
dx0 = -1
dy0 = <int>round(b * (1 << shift) / fabs(a))
y0 = (y0 << shift) + (1 << (shift - 1))
y0 = (y0 << shift) #+ (1 << (shift - 1))
else:
if b > 0:
dy0 = 1
else:
dy0 = -1
dx0 = <int>round(a * (1 << shift) / fabs(b))
x0 = (x0 << shift) + (1 << (shift - 1))
x0 = (x0 << shift) #+ (1 << (shift - 1))
# pass 1: walk the line, merging lines less than specified gap length
for k in range(2):
scikits/image/transform/tests/test_hough_transform.py
+11 -9
View File
@@ -50,15 +50,17 @@ def test_probabilistic_hough():
for i in range(25, 75):
img[100 - i, i] = 100
img[i, i] = 100
lines = probabilistic_hough(img, 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)
# 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)
if __name__ == "__main__":