X-Git-Url: http://git.tomasm.cz/imago.git/blobdiff_plain/bad8c962c120db6ba0f6e38c9df875ab17b4bf8a..f16fe1e775f2159741146264a6494e63b0e2d618:/gridf.py diff --git a/gridf.py b/gridf.py index 83a9771..d39bdd5 100644 --- a/gridf.py +++ b/gridf.py @@ -1,8 +1,14 @@ +"""Imago grid-fitting module""" + +import multiprocessing + import Image, ImageDraw, ImageFilter -from manual import lines as g_grid, l2ad, intersection, line as g_line +from geometry import V, projection +from manual import lines as g_grid, l2ad from intrsc import intersections_from_angl_dist from linef import line_from_angl_dist +import pcf class GridFittingFailedError(Exception): pass @@ -15,33 +21,23 @@ class MyGaussianBlur(ImageFilter.Filter): def filter(self, image): return image.gaussian_blur(self.radius) -class V(): - def __init__(self, x, y): - self.x = x - self.y = y - - def __add__(self, other): - return V(self.x + other.x, self.y + other.y) - - def __sub__(self, other): - return V(self.x - other.x, self.y - other.y) - - def __rmul__(self, other): - return V(other * self.x, other * self.y) - - def t(self): - return (self.x, self.y) - - def normal(self): - return V(-self.y, self.x) - -def projection(point, line, vector): - n = vector.normal() - l2 = g_line(point.t(), (point + n).t()) - return V(*intersection(l2, g_line(*line))) - - -def find(lines, size, l1, l2, bounds, hough, do_something): +def job_br1(args): + X, Y, im_l, a, b, c, d, s, v1, v2, k, hough, size = args + return [(distance(im_l, + get_grid(a + X[y] * s * v1, + b + Y[y] * s * v1, + c, d, hough, size), + size), a + X[y] * s * v1, b + Y[y] * s * v1) for y in range(2 *k)] + +def job_br2(args): + X, Y, im_l, a, b, c, d, s, v1, v2, k, hough, size = args + return [(distance(im_l, + get_grid(a, b, c + X[y] * s * v2, + d + Y[y] * s * v2, + hough, size), + size), c + X[y] * s * v2, d + Y[y] * s * v2) for y in range(2 *k)] + +def find(lines, size, l1, l2, bounds, hough, do_something, im_h): a, b, c, d = [V(*a) for a in bounds] l1 = line_from_angl_dist(l1, size) l2 = line_from_angl_dist(l2, size) @@ -51,55 +47,81 @@ def find(lines, size, l1, l2, bounds, hough, do_something): b = projection(b, l1, v1) c = projection(c, l2, v2) d = projection(d, l2, v2) + + im_l = Image.new('L', size) + dr_l = ImageDraw.Draw(im_l) + for line in sum(lines, []): + dr_l.line(line_from_angl_dist(line, size), width=1, fill=255) + + #im_l = im_h #hocus pocus + im_l = im_l.filter(MyGaussianBlur(radius=2)) + #GaussianBlur is undocumented class, may not work in future versions of PIL + im_l_s = im_l.tostring() + + #from gridf_analyzer import error_surface + #error_surface(im_l, a, b, c, d, hough, size, v1 ,v2) + grid = get_grid(a, b, c, d, hough, size) - dist = distance(lines, grid, size) - print dist - s = 0.02 - while True: - ts1 = [(s, 0), (-s, 0), (s, s), (-s, -s), (-s, s), (s, -s), (0, s), (0, -s)] - grids = [(get_grid(a + t[0] * v1, b + t[1] * v1, - c, d, hough, size), t) for t in ts1] - distances = [(distance(lines, grid, size), - grid, t) for grid, t in grids] - distances.sort(reverse=True) - if distances[0][0] > dist: - dist = distances[0][0] - grid = distances[0][1] - t = distances[0][2] - a, b = a + t[0] * v1, b + t[1] * v1 - print dist - s *= 0.75 - else: - break - - print "---" - - s = 0.02 - while True: - ts1 = [(s, 0), (-s, 0), (s, s), (-s, -s), (-s, s), (s, -s), (0, s), (0, -s)] - grids = [(get_grid(a, b, - c + t[0] * v2, d + t[1] * v2, hough, size), t) for t in ts1] - distances = [(distance(lines, grid, size), - grid, t) for grid, t in grids] - distances.sort(reverse=True) - if distances[0][0] > dist: - dist = distances[0][0] - grid = distances[0][1] - t = distances[0][2] - c, d = c + t[0] * v2, d + t[1] * v2 - print dist - s *= 0.75 - else: - break - - grid_lines = [[l2ad(l, size) for l in grid[0]], [l2ad(l, size) for l in grid[1]]] + #let's try the bruteforce aproach: + s = 0.001 + k = 50 + X, Y = [], [] + for i in range(-k, k): + X.append(range(-k, k)) + Y.append(2*k*[i]) + + pool = multiprocessing.Pool(None) + + tasks = [(X[x], Y[x], im_l_s, a, b, c, d, s, + v1, v2, k, hough, size) for x in xrange(0, 2 * k)] + + import time + start = time.time() + opt_ab = pool.map(job_br1, tasks, 1) + opt_cd = pool.map(job_br2, tasks, 1) + d1 = 0 + for lst in opt_ab: + for tpl in lst: + if tpl[0] > d1: + d1 = tpl[0] + a, b = tpl[1], tpl[2] + d1 = 0 + for lst in opt_cd: + for tpl in lst: + if tpl[0] > d1: + d1 = tpl[0] + c, d = tpl[1], tpl[2] + print time.time() - start + grid = get_grid(a, b, c, d, hough, size) + grid_lines = [[l2ad(l, size) for l in grid[0]], + [l2ad(l, size) for l in grid[1]]] + + ### + + im_t = Image.new('RGB', im_l.size, None) + im_t_l = im_t.load() + im_l_l = im_l.load() + for x in xrange(im_t.size[0]): + for y in xrange(im_t.size[1]): + im_t_l[x, y] = (im_l_l[x, y], 0, 0) + + #im_t_d = ImageDraw.Draw(im_t) + #for l in grid[0] + grid[1]: + # im_t_d.line(l, width=1, fill=(0, 255, 0)) + + #do_something(im_t, "lines and grid") + + + ### + return grid, grid_lines def get_grid(a, b, c, d, hough, size): - l1 = hough.lines_from_list([a.t(), b.t()]) - l2 = hough.lines_from_list([c.t(), d.t()]) + l1 = hough.lines_from_list([a, b]) + l2 = hough.lines_from_list([c, d]) c = intersections_from_angl_dist([l1, l2], size, get_all=True) + #TODO do something when a corner is outside the image corners = (c[0] + c[1]) if len(corners) < 4: print l1, l2, c @@ -107,35 +129,22 @@ def get_grid(a, b, c, d, hough, size): grid = g_grid(corners) return grid -def distance(lines, grid, size): - im_l = Image.new('L', size) - dr_l = ImageDraw.Draw(im_l) - for line in sum(lines, []): - dr_l.line(line_from_angl_dist(line, size), width=1, fill=255) - im_l = im_l.filter(MyGaussianBlur(radius=3)) - # GaussianBlur is undocumented class, may not work in future versions of PIL +def line_out(line, size): + for p in line: + if p[0] < 0 or p[0] > size[0] or p[1] < 0 or p[1] > size[1]: + return True + else: + return False + +def distance(im_l, grid, size): im_g = Image.new('L', size) dr_g = ImageDraw.Draw(im_g) for line in grid[0] + grid[1]: dr_g.line(line, width=1, fill=255) + if line_out(line, size): + return 0 #im_g = im_g.filter(MyGaussianBlur(radius=3)) - im_d, distance = combine(im_l, im_g) - return distance - -def combine(bg, fg): - bg_l = bg.load() - fg_l = fg.load() - res = Image.new('L', fg.size) - res_l = res.load() - - score = 0 - area = 0 - - for x in xrange(fg.size[0]): - for y in xrange(fg.size[1]): - if fg_l[x, y]: - res_l[x, y] = bg_l[x, y] * fg_l[x, y] - score += bg_l[x, y] - area += 1 - - return res, float(score)/area + #GaussianBlur is undocumented class, may not work in future versions of PIL + #im_d, distance = combine(im_l, im_g) + distance_d = pcf.combine(im_l, im_g.tostring()) + return distance_d