from PIL import Image
-from commons import clear
-
class Hough:
def __init__(self, size):
self.size = size
initial_angle = self.initial_angle
for x in xrange(size[0]):
- clear()
- print "hough transform: {0:>3}/{1}".format(x + 1, size[0])
for y in xrange(size[1]):
if image_l[x, y]:
# for every angle:
size[0] / 2)
# column of the matrix closest to the distance
column = int(round(distance))
- if column >= 0 and column < size[0]:
+ if 0 <= column < size[0]:
matrix[column][a] += 1
new_image = Image.new('L', size)
return new_image
+ def lines_from_list(self, p_list):
+ lines = []
+ for p in p_list:
+ lines.append(self.angle_distance(p))
+ return lines
+
+ def all_lines_h(self, image):
+ im_l = image.load()
+ lines1 = []
+ for x in xrange(image.size[0] / 2):
+ for y in xrange(image.size[1]):
+ if im_l[x, y]:
+ lines1.append(self.angle_distance((x, y)))
+ lines2 = []
+ for x in xrange(image.size[0] / 2, image.size[0]):
+ for y in xrange(image.size[1]):
+ if im_l[x, y]:
+ lines2.append(self.angle_distance((x, y)))
+ return [lines1, lines2]
+
def all_lines(self, image):
im_l = image.load()
lines = []
lines.append(self.angle_distance((x, y)))
return lines
- def find_angle_distance(self, image):
- image_l = image.load()
-
- points = []
-
- count = 0
- point_x = 0
- point_y = 0
- for x in xrange(image.size[0] / 2):
- for y in xrange(image.size[1] / 2, image.size[1]):
- if image_l[x, y]:
- count += 1
- point_x += x
- point_y += y
- points.append((float(point_x) / count, float(point_y) / count))
-
- count = 0
- point_x = 0
- point_y = 0
- for x in xrange(image.size[0] / 2, image.size[0]):
- for y in xrange(image.size[1] / 2, image.size[1]):
- if image_l[x, y]:
- count += 1
- point_x += x
- point_y += y
- points.append((float(point_x) / count, float(point_y) / count))
-
- return [self.angle_distance(p) for p in points]
-
def angle_distance(self, point):
return (self.dt * point[1] + self.initial_angle, point[0] - self.size[0] / 2)