From: Tomas Musil Date: Fri, 6 Apr 2012 20:52:02 +0000 (+0200) Subject: finding the grid X-Git-Url: http://git.tomasm.cz/imago.git/commitdiff_plain/8d05ccd11445ae069ae14dfb95d566907546e989?ds=sidebyside finding the grid Works fine on the smaller image, worse on the bigger one. --- diff --git a/commons.py b/commons.py index 787a19d..fcfe469 100644 --- a/commons.py +++ b/commons.py @@ -3,5 +3,5 @@ import os def clear(): if os.name == 'posix': os.system('clear') - elif os.name == ('ce', 'nt', 'dos'): + elif os.name in ('ce', 'nt', 'dos'): os.system('cls') diff --git a/hough.py b/hough.py index 7ac3aac..5fca93f 100644 --- a/hough.py +++ b/hough.py @@ -2,42 +2,89 @@ from PIL import Image from math import sin, cos, pi from commons import clear -def transform(image): +class Hough: + def __init__(self, size): + self.size = size + self.dt = pi / size[1] + self.initial_angle = (pi / 4) + (self.dt / 2) - image_l = image.load() - size = image.size - - dt = pi / size[1] - initial_angle = (pi / 4) + (dt / 2) + def transform(self, image): + image_l = image.load() + size = image.size + + matrix = [[0]*size[1] for _ in xrange(size[0])] - matrix = [[0]*size[1] for _ in xrange(size[0])] + dt = self.dt + initial_angle = self.initial_angle - for x in xrange(size[0]): - clear() - print "hough transform: {0}/{1}".format(x + 1, 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 = int(round(distance)) # column of the matrix closest to the 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 + 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: + 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 = int(round(distance)) # column of the matrix closest to the 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 + return new_image + + def all_lines(self, image): + im_l = image.load() + lines = [] + for x in xrange(image.size[0]): + for y in xrange(image.size[1]): + if im_l[x, y]: + 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) + diff --git a/imago.py b/imago.py index bfa4c7a..6ab51a1 100755 --- a/imago.py +++ b/imago.py @@ -2,10 +2,11 @@ """Usage: imago.py file""" import sys -import Image +import math +import Image, ImageDraw import im_debug import filter -import hough +from hough import Hough class UsageError(Exception): def __init__(self, msg): @@ -13,38 +14,122 @@ class UsageError(Exception): def main(*argv): """Main function of the program.""" + + show_all = False + try: if argv is (): argv = sys.argv[1:] if argv == []: raise UsageError('Missing filename') if "--help" in argv: - print __doc__ - return 0 + print __doc__ + return 0 + if "--debug" in argv: + show_all = True except UsageError, err: print >>sys.stderr, err.msg, "(\"imago.py --help\" for help)" return 2 - #TODO exception on file error - image = Image.open(argv[0]) - #im_debug.show(image, "original image") + try: + image = Image.open(argv[0]) + except IOError, msg: + print >>sys.stderr, msg + return 1 + if show_all: + im_debug.show(image, "original image") im_l = image.convert('L') - #im_debug.show(im_l, "ITU-R 601-2 luma transform") + if show_all: + im_debug.show(im_l, "ITU-R 601-2 luma transform") im_edges = filter.edge_detection(im_l) - #im_debug.show(im_edges, "edge detection") - - im_h = filter.high_pass(im_edges, 80) - #im_debug.show(im_h, "high pass filter") + if show_all: + im_debug.show(im_edges, "edge detection") - im_hough = hough.transform(im_h) - #im_debug.show(im_hough, "hough transform") + im_h = filter.high_pass(im_edges, 100) + if show_all: + im_debug.show(im_h, "high pass filter") + + hough1 = Hough(im_h.size) + im_hough = hough1.transform(im_h) + if show_all: + im_debug.show(im_hough, "hough transform") im_h2 = filter.high_pass(im_hough, 120) - im_debug.show(im_h2, "high pass filter") + if show_all: + im_debug.show(im_h2, "second high pass filter") + + hough2 = Hough(im_h2.size) + im_hough2 = hough2.transform(im_h2) + if show_all: + im_debug.show(im_hough2, "second hough transform") + + im_h3 = filter.high_pass(im_hough2, 120) + if show_all: + im_debug.show(im_h3, "third high pass filter") + + lines = hough2.find_angle_distance(im_h3) + + im_lines = Image.new('L', im_h2.size) + + draw = ImageDraw.Draw(im_lines) + + for line in lines: + draw.line(line_from_angl_dist(line, im_h2.size), fill=255) + if show_all: + im_debug.show(im_lines, "lines") + + im_c = combine(im_h2, im_lines) + if show_all: + im_debug.show(im_c, "first hough x lines") + + collapse(im_c) + if show_all: + im_debug.show(im_c, "optimalised hough") + + lines = hough1.all_lines(im_c) + draw = ImageDraw.Draw(image) + for line in lines: + draw.line(line_from_angl_dist(line, image.size), fill=(120, 255, 120)) + + im_debug.show(image, "the grid") return 0 +def collapse(image): + #HACK + im_l = image.load() + last = False + for y in xrange(image.size[1]): + for x in xrange(image.size[0]): + if im_l[x,y] and last: + im_l[x, y] = 0 + last = False + elif im_l[x, y]: + last = True + elif last: + last = False + +def combine(image1, image2): + im_l1 = image1.load() + im_l2 = image2.load() + + im_n = Image.new('L', image1.size) + im_nl = im_n.load() + + for x in xrange(image1.size[0]): + for y in xrange(image1.size[1]): + if im_l1[x, y] and im_l2[x, y]: + im_nl[x, y] = 255 + return im_n + +def line_from_angl_dist((angle, distance), size): + x1 = - size[0] / 2 + y1 = int(round((x1 * math.sin(angle) - distance)/math.cos(angle))) + size[1] / 2 + x2 = size[0] / 2 + y2 = int(round((x2 * math.sin(angle) - distance)/math.cos(angle))) + size[1] / 2 + return [(0, y1), (size[0] - 1, y2)] + if __name__ == '__main__': sys.exit(main())