X-Git-Url: http://git.tomasm.cz/imago.git/blobdiff_plain/a4df2b5c27591a0a2c2d6b33d58f54b59759504f..f16fe1e775f2159741146264a6494e63b0e2d618:/gridf.py?ds=inline diff --git a/gridf.py b/gridf.py index 51b0266..d39bdd5 100644 --- a/gridf.py +++ b/gridf.py @@ -1,6 +1,11 @@ +"""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 @@ -16,175 +21,100 @@ class MyGaussianBlur(ImageFilter.Filter): def filter(self, image): return image.gaussian_blur(self.radius) -class V(object): - 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 __len__(self): - return 2; - - def __getitem__(self, key): - if key == 0: - return self.x - elif key == 1: - return self.y - elif type(key) != int: - raise TypeError("V indices must be integers") - else: - raise KeyError("V index ({}) out of range".format(key)) - - def __iter__(self): - yield self.x - yield self.y - - @property - def normal(self): - return V(-self.y, self.x) - -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 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 = projection(a, l1, v1) + 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) -def error_surface(lines, a, b, c, d, hough, size, v1): - import matplotlib.pyplot as plt - from matplotlib import cm - import threading - import Queue - import time - import sys - import pickle - - class Worker(threading.Thread): - def __init__(self, q_in, q_out, job): - threading.Thread.__init__(self) - self.q_in = q_in - self.q_out = q_out - self.job = job - - def run(self): - while True: - x = self.q_in.get() - try: - self.q_out.put((x, self.job(x))) - except Exception: - pass - print x - self.q_in.task_done() - - X = [] - Y = [] - Z = [] + #let's try the bruteforce aproach: s = 0.001 - k = 200 + k = 50 + X, Y = [], [] for i in range(-k, k): X.append(range(-k, k)) Y.append(2*k*[i]) - job = lambda x: [distance(lines, get_grid(a + X[x][y] * s * v1, - b + Y[x][y] * s * v1, - c, d, hough, size), - size) for y in range(0,2 * k)] - - q_in = Queue.Queue() - q_out = Queue.Queue() - for i in range(4): - t = Worker(q_in, q_out, job) - t.daemon = True - t.start() - - start = time.time() - for x in range(0, 2*k): - q_in.put(x) + pool = multiprocessing.Pool(None) - q_in.join() + 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]]] + + ### - while True: - try: - Z.append(q_out.get_nowait()) - except Queue.Empty: - break + 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) - Z.sort() - Z = [t for (x, t) in Z] + #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)) - s_file = open('surface' + str(k), 'w') - pickle.dump((X, Y, Z), s_file) - s_file.close() - plt.imshow(Z, cmap=cm.gnuplot2, interpolation='bicubic', - origin='upper', extent=(-k, k, -k, k), aspect='equal') - plt.colorbar() + #do_something(im_t, "lines and grid") - plt.show() - sys.exit() + ### -def find(lines, size, l1, l2, bounds, hough, do_something): - 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 = projection(a, l1, v1) - b = projection(b, l1, v1) - c = projection(c, l2, v2) - d = projection(d, l2, v2) - - #error_surface(lines, a, b, c, d, hough, size, v1) - - 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]]] return grid, grid_lines def get_grid(a, b, c, d, hough, size): @@ -199,37 +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=15)) - #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)) + #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.tostring(), 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