+"""Imago grid-fitting module"""
+
import multiprocessing
+import itertools
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
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(args):
- X, Y, im_l, a, b, c, d, s, v1, 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) for y in range(2 * k)]
-
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)]
+ im_l, v1, v2, h1, h2, x, y, dv, dh, size = args
+ v1 = (v1[0] + x * dv, v1[1] + x)
+ v2 = (v2[0] + y * dv, v2[1] + y)
+ return (distance(im_l,
+ get_grid([v1, v2], [h1, h2], size),
+ size), x, y)
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 error_surface(im_l, a, b, c, d, hough, size, v1):
- import matplotlib.pyplot as plt
- from matplotlib import cm
- import time
- import sys
- import pickle
-
- X = []
- Y = []
- Z = []
- s = 0.001
- k = 250
- for i in range(-k, k):
- X.append(range(-k, k))
- Y.append(2*k*[i])
-
- tasks = [(X[x], Y[x], im_l, a, b, c, d, s, v1, k, hough, size) for x in xrange(0, 2 * k)]
- #everything is passed by value here; can it somehow be passed by reference?
-
- pool = multiprocessing.Pool(None)
-
- start = time.time()
- Z = pool.map(job, tasks, 1)
- print time.time() - start
-
- s_file = open('surface' + str(k), 'w')
- pickle.dump((X, Y, Z), s_file)
- s_file.close()
- plt.imshow(Z, cmap=cm.jet, interpolation='bicubic',
- origin='upper', extent=(-k, k, -k, k), aspect='equal')
- plt.colorbar()
-
- plt.show()
-
- sys.exit()
-
-def find(lines, size, l1, l2, bounds, hough, do_something):
- a, b, c, d = [V(*a) for a in bounds]
+ im_l, v1, v2, h1, h2, x, y, dv, dh, size = args
+ h1 = (h1[0] + x * dh, h1[1] + x)
+ h2 = (h2[0] + y * dh, h2[1] + y)
+ return (distance(im_l,
+ get_grid([v1, v2], [h1, h2], size),
+ size), x, y)
+
+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])
+ 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))
- #GaussianBlur is undocumented class, may not work in future versions of PIL
- im_l = im_l.tostring()
- #error_surface(im_l, a, b, c, d, hough, size, v1)
+ im_l = im_l.filter(MyGaussianBlur(radius=5))
+ #GaussianBlur is undocumented class, may not work in future versions of PIL
+ im_l_s = im_l.tostring()
- 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])
-
- tasks = [(X[x], Y[x], im_l, a, b, c, d, s, v1, v2, k, hough, size) for x in xrange(0, 2 * k)]
+ k = 30
pool = multiprocessing.Pool(None)
-
- #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]
- an, bn = tpl[1], tpl[2]
- d1 = 0
- for lst in opt_cd:
- for tpl in lst:
- if tpl[0] > d1:
- d1 = tpl[0]
- cn, dn = tpl[1], tpl[2]
- #print time.time() - start
- grid = get_grid(an, bn, cn, dn, hough, size)
- grid_lines = [[l2ad(l, size) for l in grid[0]], [l2ad(l, size) for l in grid[1]]]
- return grid, grid_lines
-
- #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'))
+ tasks = [(im_l_s, v1, v2, h1, h2, x, y, delta_v, delta_h, size) for (x, y) in
+ itertools.product(xrange(-k, k), xrange(-k, k))]
- 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-')
+ opt_v = pool.map(job_br1, tasks, 8)
+ opt_h = pool.map(job_br2, tasks, 8)
+ _, x_v, y_v = max(opt_v)
+ _, x_h, y_h = max(opt_h)
- plt.show()
- ###MNTR
+ 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)
- print "---"
+ 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]]]
- 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
+ pool.terminate()
+ pool.join()
+
+### 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")
+###
- 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])
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
+ distance_d = pcf.combine(im_l, im_g.tostring())
+ return distance_d
projekty / imago.git / blobdiff