-from manual import lines as g_grid, l2ad
+import multiprocessing
+
+import Image, ImageDraw, ImageFilter
+
+from geometry import V
+from manual import lines as g_grid, l2ad, intersection, line as g_line
from intrsc import intersections_from_angl_dist
+from linef import line_from_angl_dist
+import pcf
+
+class GridFittingFailedError(Exception):
+ pass
+
+class MyGaussianBlur(ImageFilter.Filter):
+ name = "GaussianBlur"
+
+ def __init__(self, radius=2):
+ self.radius = radius
+ 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 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_l.filter(MyGaussianBlur(radius=30))
+ #GaussianBlur is undocumented class, may not work in future versions of PIL
+ #im_l = im_l.tostring()
+ im_l = im_h.tostring() # hocus pocus
+
+ #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(im_l, grid, size)
+
+ #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
-def find(lines, size, l1, l2):
- c = intersections_from_angl_dist(lines, size)
- corners = [c[0][0], c[0][-1], c[-1][0], c[-1][-1]]
+ #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])
+ 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
+ raise GridFittingFailedError
grid = g_grid(corners)
return grid
+
+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, 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
projekty / imago.git / blobdiff