import filters
import k_means
import output
+import linef
def dst(line):
"""Return normalized line."""
"""Return mean luma of the *cluster* of points."""
return sum(c[0][0] for c in cluster) / float(len(cluster))
+def to_general(line, size):
+ # TODO comment
+ (x1, y1), (x2, y2) = linef.line_from_angl_dist(line, size)
+ return (y2 - y1, x1 - x2, x2 * y1 - x1 * y2)
+
+def intersection(l1, l2):
+ a1, b1, c1 = l1
+ a2, b2, c2 = l2
+ delim = float(a1 * b2 - b1 * a2)
+ x = (b1 * c2 - c1 * b2) / delim
+ y = (c1 * a2 - a1 * c2) / delim
+ return x, y
+
def intersections_from_angl_dist(lines, size, get_all=True):
"""Take grid-lines and size of the image. Return intersections."""
+ lines1 = map(lambda l: to_general(l, size), lines[1])
+ lines0 = map(lambda l: to_general(l, size), lines[0])
intersections = []
- for (angl1, dist1) in lines[1]:
+ for l1 in lines1:
line = []
- for (angl2, dist2) in lines[0]:
- if abs(angl1 - angl2) > 0.4:
- i_x = (- ((dist2 / cos(angl2)) - (dist1 / cos(angl1)))
- / (tan(angl1) - tan(angl2)))
- i_y = (tan(angl1) * i_x) - (dist1 / cos(angl1))
- if get_all or (-size[0] / 2 < i_x < size[0] / 2 and
- -size[1] / 2 < i_y < size[1] / 2):
- line.append((int(i_x + size[0] / 2),
- int(i_y + size[1] / 2)))
+ for l2 in lines0:
+ line.append(intersection(l1, l2))
intersections.append(line)
return intersections
pass
norm = float(len(points))
if norm == 0:
- return 0, 0, 0 #TODO trow exception here
+ return 0, 0, (0, 0, 0) #TODO trow exception here
color = (sum(p[0] for p in points) / norm,
sum(p[1] for p in points) / norm,
sum(p[2] for p in points) / norm)