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))
+
+ #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 = im_l.tostring()
- im_l = im_h.tostring() # hocus pocus
+ 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)
- dist = distance(im_l, grid, size)
+ dist = distance(im_l_s, grid, size)
#let's try the bruteforce aproach:
s = 0.001
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)]
+ 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)]
- #start = time.time()
+ import time
+ 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]
if tpl[0] > d1:
d1 = tpl[0]
c, d = tpl[1], tpl[2]
- #print time.time() - start
+ 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
+
+ ###
- #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
+ 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):
#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