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Testing different accumulator clearing on good lines.

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This commit is contained in:
Pieter Holtzhausen
2011-08-22 15:31:31 +02:00
parent a79dbc7e2b
commit 24e3ee7e1b
2 changed files with 48 additions and 33 deletions
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scikits/image/transform/_hough_transform.pyx
+47 -32
View File
@@ -7,6 +7,7 @@ np.import_array()
cdef extern from "math.h":
int abs(int)
double fabs(double)
double sqrt(double)
double ceil(double)
@@ -35,14 +36,14 @@ def _hough(np.ndarray img, np.ndarray[ndim=1, dtype=np.double_t] theta=None):
ctheta = np.cos(theta)
stheta = np.sin(theta)
# compute the bins and allocate the output array
cdef np.ndarray[ndim=2, dtype=np.uint64_t] out
# 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
max_distance = 2 * <int>ceil((sqrt(img.shape[0] * img.shape[0] +
img.shape[1] * img.shape[1])))
out = np.zeros((max_distance, theta.shape[0]), dtype=np.uint64)
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
@@ -52,16 +53,16 @@ def _hough(np.ndarray img, np.ndarray[ndim=1, dtype=np.double_t] theta=None):
# finally, run the transform
cdef int nidxs, nthetas, i, j, x, y, out_idx
cdef int nidxs, nthetas, i, j, x, y, accum_idx
nidxs = y_idxs.shape[0] # x and y are the same shape
nthetas = theta.shape[0]
for i in range(nidxs):
x = x_idxs[i]
y = y_idxs[i]
for j in range(nthetas):
out_idx = <int>round((ctheta[j] * x + stheta[j] * y)) + offset
out[out_idx, j] += 1
return out, theta, bins
accum_idx = <int>round((ctheta[j] * x + stheta[j] * y)) + offset
accum[accum_idx, j] += 1
return accum, theta, bins
@cython.boundscheck(False)
@@ -79,38 +80,45 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, i
stheta = np.sin(theta)
cdef int height = img.shape[0]
cdef int width = img.shape[1]
# compute the bins and allocate the output array
cdef np.ndarray[ndim=2, dtype=np.uint64_t] out
# compute the bins and allocate the accumulator array
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.uint32_t] line_end = np.zeros((2, 2), dtype=np.uint32)
cdef np.ndarray[ndim=1, dtype=np.double_t] bins
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 double a, b
cdef int nidxs, nthetas, i, j, x, y, px, py, out_idx, value, max_value, max_theta
cdef int nidxs, nthetas, i, j, x, y, px, py, accum_idx, value, max_value, max_theta
cdef int shift = 16
# maximum line number cutoff
cdef int lines_max = 2 ** 15
cdef int xflag, x0, y0, dx0, dy0, dx, dy, gap, x1, y1, good_line
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] +
img.shape[1] * img.shape[1])))
out = np.zeros((max_distance, theta.shape[0]), dtype=np.uint64)
bins = np.linspace(-max_distance / 2.0, max_distance / 2.0, max_distance)
accum = np.zeros((max_distance, theta.shape[0]), dtype=np.int64)
offset = max_distance / 2
# find the nonzero indexes
cdef np.ndarray[ndim=1, dtype=np.int_t] x_idxs, y_idxs
y_idxs, x_idxs = np.PyArray_Nonzero(img)
y_idxs, x_idxs = np.nonzero(img)
num_indexes = y_idxs.shape[0] # x and y are the same shape
nthetas = theta.shape[0]
points = []
for i in range(num_indexes):
points.append((x_idxs[i], y_idxs[i]))
lines = []
# create mask of all non-zero indexes
for i in range(num_indexes):
mask[y_idxs[i], x_idxs[i]] = 1
for i in range(num_indexes):
while 1:
#for i in range(num_indexes):
# select random non-zero point
index = randint(0, num_indexes-1)
x = x_idxs[i]
y = y_idxs[i]
count = len(points)
if count == 0:
break
index = randint(0, count-1)
x = points[index][0]
y = points[index][1]
del points[index]
# if previously eliminated, skip
if not mask[y, x]:
continue
@@ -119,9 +127,9 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, i
max_theta = 0
# apply hough transform on point
for j in range(nthetas):
out_idx = <int>round((ctheta[j] * x + stheta[j] * y)) + offset
out[out_idx, j] += 1
value = out[out_idx, j]
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:
max_value = value
max_theta = j
@@ -181,8 +189,8 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, i
px += dx
py += dy
# confirm line length is sufficient
good_line = fabs(line_end[1, 1] - line_end[0, 1]) >= line_length or \
fabs(line_end[1, 0] - line_end[0, 0]) >= line_length
good_line = abs(line_end[1, 1] - line_end[0, 1]) >= line_length or \
abs(line_end[1, 0] - line_end[0, 0]) >= line_length
# pass 2: walk the line again and reset accumulator and mask
for k in range(2):
px = x0
@@ -200,15 +208,22 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, i
x1 = px >> shift
y1 = py
# if non-zero point found, continue the line
if mask[y1, x1]:
if good_line:
for j in range(nthetas):
out_idx = <int>round((ctheta[j] * x1 + stheta[j] * y1)) + offset
out[out_idx, j] -= 1
mask[y1, x1] = 0
if 1:
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
else:
if mask[y1, x1]:
if good_line:
for j in range(nthetas):
accum_idx = <int>round((ctheta[j] * x1 + stheta[j] * y1)) + offset
accum[accum_idx, j] -= 1
mask[y1, x1] = 0
# exit when the point is the line end
if x1 == line_end[k, 0] and y1 == line_end[k, 1]:
break;
break
px += dx
py += dy
scikits/image/transform/hough_transform.py
+1 -1
View File
@@ -59,7 +59,7 @@ except ImportError:
pass
def probabilistic_hough(img, value_threshold=50, line_length=50, line_gap=10, theta=None):
def probabilistic_hough(img, value_threshold=10, line_length=50, line_gap=10, theta=None):
"""Performs a progressive probabilistic line Hough transform and returns the detected lines.
Parameters