+"""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
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):
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
projekty / imago.git / blobdiff