- image_l = image.load()
- size = image.size
-
- matrix = [[0]*size[1] for _ in xrange(size[0])]
-
- dt = self.dt
- initial_angle = self.initial_angle
-
- for x in xrange(size[0]):
- for y in xrange(size[1]):
- if image_l[x, y]:
- # for every angle:
- for a in xrange(size[1]):
- # find the distance:
- # distance is the dot product of vector (x, y) - centerpoint
- # and a unit vector orthogonal to the angle
- distance = (((x - (size[0] / 2)) * sin((dt * a) + initial_angle)) +
- ((y - (size[1] / 2)) * -cos((dt * a) + initial_angle)) +
- size[0] / 2)
- # column of the matrix closest to the distance
- column = int(round(distance))
- if column >= 0 and column < size[0]:
- matrix[column][a] += 1
-
- new_image = Image.new('L', size)
- new_image_l = new_image.load()
-
- minimum = min([min(m) for m in matrix])
-
- maximum = max([max(m) for m in matrix]) - minimum
-
- for y in xrange(size[1]):
- for x in xrange(size[0]):
- new_image_l[x, y] = (float(matrix[x][y] - minimum) / maximum) * 255
-
- return new_image
+ image_s = pcf.hough(image.size, image.tostring(), self.initial_angle, self.dt)
+ image = Image.fromstring('L', image.size, image_s)
+ return image