grid = None
for (line1, line2, c) in cen_lin:
diag1 = Line(line1)
diag1.points = ransac.filter_near(data, diag1, 2)
diag2 = Line(line2)
diag2.points = ransac.filter_near(data, diag2, 2)
grids = list(gen_corners(diag1, diag2, min(size) / 3))
try:
new_sc, new_grid = min(map(lambda g: (score(sum(g, []), data), g), grids))
if new_sc < sc:
sc, grid = new_sc, new_grid
except ValueError:
pass
if grid:
break
else:
raise GridFittingFailedError
grid_lines = [[l2ad(l, size) for l in grid[0]],
[l2ad(l, size) for l in grid[1]]]
grid_lines[0].sort(key=lambda l: l[1])
grid_lines[1].sort(key=lambda l: l[1])
if grid_lines[0][0][0] > grid_lines[1][0][0]:
grid_lines = grid_lines[1], grid_lines[0]
return grid, grid_lines
def find(lines, size, l1, l2, bounds, hough, show_all, do_something, logger):
logger("finding the grid")
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=3))
#GaussianBlur is undocumented class, may not work in future versions of PIL
im_l_s = im_l.tostring()
#import time
#start = time.time()
f_dist = partial(job_4,
im_l=im_l_s,
v1=v1,
v2=v2,
h1=h1,
h2=h2,
dv=delta_v,
dh=delta_h,
size=size)
x_v, y_v, x_h, y_h = Optimizer.optimize(4, 30, f_dist, 128, 512, 1)
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)
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]]]
#print time.time() - start
### 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)
###
if show_all:
### 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")
#
###
return grid, grid_lines
corners = corners[1:]
corners.append(np)
(x, y) = corners[-1]
draw.line((x-2, y, x + 2, y), fill=color)
draw.line((x, y+2, x, y-2), fill=color)
if len(corners) == 4:
im = im_orig.copy()
draw = ImageDraw.Draw(im)
try:
l_vert, l_hor = lines(corners)
except Exception:
corners = corners[:-1]
for l in l_vert:
draw.line(l, fill=color, width=line_width)
for l in l_hor:
draw.line(l, fill=color, width=line_width)
# TODO sort by distance
#l_vert.sort()
#l_hor.sort()
#for i in [3, 9, 15]:
# for j in [3, 9, 15]:
# hoshi(intersection(line(l_vert[i][0], l_vert[i][1]),
# line(l_hor[j][0], l_hor[j][1])))
lines_r = [[l2ad(l, im.size) for l in l_vert],
[l2ad(l, im.size) for l in l_hor]]
screen.display_picture(im)
clock.tick(15)
corners = corners[1:]
corners.append(np)
(x, y) = corners[-1]
draw.line((x - 2, y, x + 2, y), fill=color)
draw.line((x, y + 2, x, y - 2), fill=color)
if len(corners) == 4:
im = im_orig.copy()
draw = ImageDraw.Draw(im)
try:
l_vert, l_hor = lines(corners)
except Exception as e:
print "exception!", e
corners = corners[:-1]
continue
for l in l_vert:
draw.line(l, fill=color, width=line_width)
for l in l_hor:
draw.line(l, fill=color, width=line_width)
# TODO sort by distance
#l_vert.sort()
#l_hor.sort()
#for i in [3, 9, 15]:
# for j in [3, 9, 15]:
# hoshi(intersection(line(l_vert[i][0], l_vert[i][1]),
# line(l_hor[j][0], l_hor[j][1])))
lines_r = [[l2ad(l, im.size) for l in l_vert],
[l2ad(l, im.size) for l in l_hor]]
screen.display_picture(im)
clock.tick(15)
def find(lines, size, l1, l2, bounds, hough, show_all, do_something, logger):
logger("finding the grid")
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=3))
#GaussianBlur is undocumented class, may not work in future versions of PIL
im_l_s = im_l.tostring()
#import time
#start = time.time()
f_dist = partial(job_4, im_l=im_l_s, v1=v1, v2=v2, h1=h1, h2=h2,
dv=delta_v, dh=delta_h, size=size)
x_v, y_v, x_h, y_h = Optimizer.optimize(4, 30, f_dist, 128, 512, 1)
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)
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]]]
#print time.time() - start
### 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)
###
if show_all:
### 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")
#
###
return grid, grid_lines
