X-Git-Url: http://git.tomasm.cz/imago.git/blobdiff_plain/10466a9c920f1d67bf85d85af671bcb8e5fbd533..fb4d31c62744561af4bc3941d1cb9da430c5c64a:/gridf.py?ds=sidebyside diff --git a/gridf.py b/gridf.py index 17be372..03e3014 100644 --- a/gridf.py +++ b/gridf.py @@ -1,8 +1,16 @@ +"""Imago grid-fitting module""" + +import multiprocessing +from functools import partial + import Image, ImageDraw, ImageFilter -from manual import lines as g_grid, l2ad +from geometry import V, projection, l2ad +from manual import lines as g_grid from intrsc import intersections_from_angl_dist from linef import line_from_angl_dist +import pcf +import cs as Optimizer class GridFittingFailedError(Exception): pass @@ -15,78 +23,86 @@ 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 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 __rmul__(self, other): - return V(other * self.x, other * self.y) - - def t(self): - return (self.x, self.y) - -def find(lines, size, l1, l2, bounds, hough, do_something): - a, b, c, d = [V(*a) for a in bounds] +def find(lines, size, l1, l2, bounds, hough, do_something, im_h): 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]) - grid = get_grid(a, b, c, d, hough, size) - dist = distance(lines, grid, size) - print dist + 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=3)) + #GaussianBlur is undocumented class, may not work in future versions of PIL + im_l_s = im_l.tostring() + + import time + start = time.time() + + 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 = Optimizer.optimize(4, 30, f_dist, 128, 256) + + 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]]] + + print time.time() - start - 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]]] +### 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") +### + 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()]) +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]) if len(corners) < 4: print l1, l2, c @@ -94,34 +110,22 @@ 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) - 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] - score += bg_l[x, y] - area += 1 - - return res, float(score)/area + if line_out(line, size): + return 0 + #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_d = pcf.combine(im_l, im_g.tostring()) + return distance_d