X-Git-Url: http://git.tomasm.cz/imago.git/blobdiff_plain/129886d4580041098317fa70be6f03cc731b26a0..577a280086d400b3ab83caed89424b6f5e064be9:/hough.py diff --git a/hough.py b/hough.py index 9d44bcb..ec8058c 100644 --- a/hough.py +++ b/hough.py @@ -2,6 +2,8 @@ from math import sin, cos, pi from PIL import Image +import pcf + class Hough: def __init__(self, size): self.size = size @@ -9,42 +11,9 @@ class Hough: self.initial_angle = (pi / 4) + (self.dt / 2) def transform(self, image): - 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 def lines_from_list(self, p_list): lines = []