def run_ransac(image):
# TODO comment
# TODO vizualize this
image_l = image.load()
width, height = image.size
data = []
for y in xrange(0, height):
for x in xrange(0, width):
if image_l[x, y] > 128:
data.append((x, y))
if y < 30:
data.append((width - x, y + height))
dist = 3
[(line, points), (line2, points2)] = ransac.ransac_multi(2, data, dist, 250)
line_to_points = lambda (a, b, c), x: (x, (a*x + c) / (- b))
# TODO width should not be here vvv
# TODO refactor gridf to use standard equations instead of points
line = [line_to_points(line, 0), line_to_points(line, width - 1)]
line2 = [line_to_points(line2, 0), line_to_points(line2, width - 1)]
return [sorted(points), sorted(points2)], line, line2
def run_ransac(image):
# TODO comment
# TODO vizualize this
image_l = image.load()
width, height = image.size
data = []
for y in xrange(0, height):
for x in xrange(0, width):
if image_l[x, y] > 128:
data.append((x, y))
if y < 30:
data.append((width - x, y + height))
dist = 3
[(line, points), (line2, points2)] = ransac.ransac_multi(2, data, dist, 250)
line_to_points = lambda (a, b, c), x: (x, (a*x + c) / (- b))
# TODO width should not be here vvv
# TODO refactor gridf to use standard equations instead of points
line = [line_to_points(line, 0), line_to_points(line, width - 1)]
line2 = [line_to_points(line2, 0), line_to_points(line2, width - 1)]
return [sorted(points), sorted(points2)], line, line2
def find(lines, size, l1, l2, bounds, hough, show_all, do_something, logger):
"""Find the best grid given the *lines* and *size* of the image.
Last three parameters serves for debugging, *l1*, *l2*, *bounds* and *hough*
are here for compatibility with older version of gridf, so they can be
easily exchanged, tested and compared.
"""
new_lines1 = map(lambda l: Line.from_ad(l, size), lines[0])
new_lines2 = map(lambda l: Line.from_ad(l, size), lines[1])
for l1 in new_lines1:
for l2 in new_lines2:
p = Point(intersection(l1, l2))
p.l1 = l1
p.l2 = l2
l1.points.append(p)
l2.points.append(p)
points = [l.points for l in new_lines1]
def dst_p(x, y):
x = x - size[0] / 2
y = y - size[1] / 2
return sqrt(x * x + y * y)
for n_tries in xrange(3):
logger("finding the diagonals")
model = Diagonal_model(points)
diag_lines = ransac.ransac_multi(6, points, 2,
params.ransac_diagonal_iter, model=model)
diag_lines = [l[0] for l in diag_lines]
centers = []
cen_lin = []
for i in xrange(len(diag_lines)):
line1 = diag_lines[i]
for line2 in diag_lines[i+1:]:
c = intersection(line1, line2)
if c and dst_p(*c) < min(size) / 2:
cen_lin.append((line1, line2, c))
centers.append(c)
if show_all:
import matplotlib.pyplot as pyplot
from PIL import Image
def plot_line_g((a, b, c), max_x):
find_y = lambda x: - (c + a * x) / b
pyplot.plot([0, max_x], [find_y(0), find_y(max_x)], color='b')
fig = pyplot.figure(figsize=(8, 6))
for l in diag_lines:
plot_line_g(l, size[0])
pyplot.scatter(*zip(*sum(points, [])))
if len(centers) >= 1:
pyplot.scatter([c[0] for c in centers], [c[1] for c in centers], color='r')
pyplot.xlim(0, size[0])
pyplot.ylim(0, size[1])
pyplot.gca().invert_yaxis()
fig.canvas.draw()
size_f = fig.canvas.get_width_height()
buff = fig.canvas.tostring_rgb()
image_p = Image.fromstring('RGB', size_f, buff, 'raw')
do_something(image_p, "finding diagonals")
logger("finding the grid")
data = sum(points, [])
# TODO what if lines are missing?
sc = float("inf")
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
