hough.py

   1 from PIL import Image
   2 from math import sin, cos, pi
   3 from commons import clear
   4
   5 class Hough:
   6     def __init__(self, size):
   7         self.size = size
   8         self.dt = pi / size[1]
   9         self.initial_angle = (pi / 4) + (self.dt / 2)
  10
  11     def transform(self, image):
  12         image_l = image.load()
  13         size = image.size
  14
  15         matrix = [[0]*size[1] for _ in xrange(size[0])]
  16
  17         dt = self.dt
  18         initial_angle = self.initial_angle
  19
  20         for x in xrange(size[0]):
  21             clear()
  22             print "hough transform: {0:>3}/{1}".format(x + 1, size[0])
  23             for y in xrange(size[1]):
  24                 if image_l[x, y]:
  25                     # for every angle:
  26                     for a in xrange(size[1]):
  27                         # find the distance:
  28                         # distance is the dot product of vector (x, y) - centerpoint
  29                         # and a unit vector orthogonal to the angle
  30                         distance = (((x - (size[0] / 2)) * sin((dt * a) + initial_angle)) +
  31                                     ((y - (size[1] / 2)) * -cos((dt * a) + initial_angle)) +
  32                                     size[0] / 2)
  33                         column = int(round(distance)) # column of the matrix closest to the distance
  34                         if column >= 0 and column < size[0]:
  35                             matrix[column][a] += 1
  36
  37         new_image = Image.new('L', size)
  38         new_image_l = new_image.load()
  39
  40         minimum = min([min(m) for m in matrix])
  41
  42         maximum = max([max(m) for m in matrix]) - minimum
  43
  44         for y in xrange(size[1]):
  45             for x in xrange(size[0]):
  46                 new_image_l[x, y] = (float(matrix[x][y] - minimum) / maximum) * 255
  47
  48         return new_image
  49
  50     def all_lines(self, image):
  51         im_l = image.load()
  52         lines = []
  53         for x in xrange(image.size[0]):
  54             for y in xrange(image.size[1]):
  55                 if im_l[x, y]:
  56                     lines.append(self.angle_distance((x, y)))
  57         return lines
  58
  59     def find_angle_distance(self, image):
  60         image_l = image.load()
  61
  62         points = []
  63
  64         count = 0
  65         point_x = 0
  66         point_y = 0
  67         for x in xrange(image.size[0] / 2):
  68             for y in xrange(image.size[1] / 2, image.size[1]):
  69                 if image_l[x, y]:
  70                     count += 1
  71                     point_x += x
  72                     point_y += y
  73         points.append((float(point_x) / count, float(point_y) / count))
  74
  75         count = 0
  76         point_x = 0
  77         point_y = 0
  78         for x in xrange(image.size[0] / 2, image.size[0]):
  79             for y in xrange(image.size[1] / 2, image.size[1]):
  80                 if image_l[x, y]:
  81                     count += 1
  82                     point_x += x
  83                     point_y += y
  84         points.append((float(point_x) / count, float(point_y) / count))
  85
  86         return [self.angle_distance(p) for p in points]
  87
  88     def angle_distance(self, point):
  89         return (self.dt * point[1] + self.initial_angle, point[0] - self.size[0] / 2)
  90