X-Git-Url: http://git.tomasm.cz/imago.git/blobdiff_plain/b91b0e43b25e64b3516b9156ca42e7309665628b..577a280086d400b3ab83caed89424b6f5e064be9:/gridf.py?ds=inline diff --git a/gridf.py b/gridf.py index 6f044f1..58229a4 100644 --- a/gridf.py +++ b/gridf.py @@ -1,12 +1,16 @@ +"""Imago grid-fitting module""" + import multiprocessing +from functools import partial import Image, ImageDraw, ImageFilter -from geometry import V -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 +import pso class GridFittingFailedError(Exception): pass @@ -19,144 +23,84 @@ class MyGaussianBlur(ImageFilter.Filter): def filter(self, image): return image.gaussian_blur(self.radius) -def projection(point, line, vector): - return V(*intersection(g_line(point, point + vector.normal), g_line(*line))) - -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 job_4(x, y, w, z, im_l, v1, v2, h1, h2, dv, dh, size): + v1 = (v1[0] + x * dv, v1[1] + x) + v2 = (v2[0] + y * dv, v2[1] + y) + h1 = (h1[0] + w * dh, h1[1] + w) + h2 = (h2[0] + z * dh, h2[1] + z) + return (distance(im_l, get_grid([v1, v2], [h1, h2], size), size)) 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) v1 = V(*l1[0]) - V(*l1[1]) v2 = V(*l2[0]) - V(*l2[1]) + a, b, c, d = [V(*a) for a in bounds] a = projection(a, l1, v1) b = projection(b, l1, v1) c = projection(c, l2, v2) d = projection(d, l2, v2) + + v1, v2 = hough.lines_from_list([a, b]) + h1, h2 = hough.lines_from_list([c, d]) + + delta_v = ((l1[1][1] - l1[0][1]) * hough.dt) / l1[1][0] + delta_h = ((l2[1][1] - l2[0][1]) * hough.dt) / l2[1][0] 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=30)) + + im_l = im_l.filter(MyGaussianBlur(radius=5)) #GaussianBlur is undocumented class, may not work in future versions of PIL - #im_l = im_l.tostring() - im_l = im_h.tostring() # hocus pocus + im_l_s = im_l.tostring() - #from gridf_analyzer import error_surface - #error_surface(im_l, a, b, c, d, hough, size, v1 ,v2) + import time + start = time.time() - grid = get_grid(a, b, c, d, hough, size) - dist = distance(im_l, grid, size) + f_dist = partial(job_4, im_l=im_l_s, v1=v1, v2=v2, h1=h1, h2=h2, + dv=delta_v, dh=delta_h, size=size) + + x_v, y_v, x_h, y_h = pso.optimize(4, 30, f_dist, 32, 1028) + + v1 = (v1[0] + x_v * delta_v, v1[1] + x_v) + v2 = (v2[0] + y_v * delta_v, v2[1] + y_v) + h1 = (h1[0] + x_h * delta_h, h1[1] + x_h) + h2 = (h2[0] + y_h * delta_h, h2[1] + y_h) + + grid = get_grid([v1, v2], [h1, h2], size) + 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, a, b, c, d, s, v1, v2, k, hough, size) for x in xrange(0, 2 * k)] - - #start = time.time() - opt_ab = pool.map(job_br1, tasks, 1) - opt_cd = pool.map(job_br2, tasks, 1) - an, bn, cn, dn = 4 * [0] - 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]]] - return grid, grid_lines + print time.time() - start + +### Show error surface +# +# from gridf_analyzer import error_surface +# error_surface(k, im_l_s, v1_i, v2_i, h1_i, h2_i, +# delta_v, delta_h, x_v, y_v, x_h, y_h, size) +### + +### Show grid over lines +# + 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") +### - #old optimization experiments: - print dist - - path = [(0,0)] #MNTR - s = 0.01 - for _ in range(10): - ts1 = [(s, 0), (0, s), (-s, 0), (0, -s)] - grids = [(get_grid(a + t[0] * v1, b + t[1] * v1, - c, d, hough, size), t) for t in ts1] - distances = [distance(im_l, grid, size) for (grid, t) in grids] - gradient = [(di - dist) for di in distances] - gradient = [gradient[0] - gradient[2], gradient[1] - gradient[3]] - norm = (gradient[0] ** 2 + gradient[1] ** 2) ** 0.5 - gradient = [g / (100 * norm) for g in gradient] - path.append(gradient) - a, b = a + gradient[0] * v1, b + gradient[1] * v1 - dist = distance(im_l, grid, size) - print dist - - ###MNTR - import matplotlib.pyplot as plt - from matplotlib import cm - import pickle - - X, Y, Z = pickle.load(open('surface250')) - - plt.imshow(Z, cmap=cm.jet, interpolation='none', - origin='upper', extent=(-0.250, 0.250, -0.250, 0.250), aspect='equal') - plt.colorbar() - plt.plot([y for (x, y) in path], [x for (x, y) in path], 'go-') - - plt.show() - ###MNTR - - 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(im_l, 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]]] return grid, grid_lines -def get_grid(a, b, c, d, hough, size): - l1 = hough.lines_from_list([a, b]) - l2 = hough.lines_from_list([c, d]) +def get_grid(l1, l2, size): 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]) @@ -183,24 +127,5 @@ def distance(im_l, grid, size): #im_g = im_g.filter(MyGaussianBlur(radius=3)) #GaussianBlur is undocumented class, may not work in future versions of PIL #im_d, distance = combine(im_l, im_g) - distance = pcf.combine(im_l, im_g.tostring()) - 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 - return None, float(score)/area + distance_d = pcf.combine(im_l, im_g.tostring()) + return distance_d