def get_centerline_optimized(self,
alpha=1e3,
beta=1e6,
gamma=0.01,
spacing=20,
max_iterations=1000,
endpoints=None):
""" determines the center line of the polygon using an active contour
algorithm """
# use an active contour algorithm to find centerline
ac = ActiveContour(blur_radius=1,
alpha=alpha,
beta=beta,
gamma=gamma,
closed_loop=False)
ac.max_iterations = max_iterations
# set the potential from the distance map
mask, offset = self.get_mask(1, ret_offset=True)
potential = cv2.distanceTransform(mask, cv2.DIST_L2, 5)
ac.set_potential(potential)
# initialize the centerline from the estimate
points = self.get_centerline_estimate(endpoints)
points = curves.make_curve_equidistant(points, spacing=spacing)
points = curves.translate_points(points, -offset[0], -offset[1])
# anchor the end points
anchor = np.zeros(len(points), np.bool)
anchor[0] = anchor[-1] = True
# find the best contour
points = ac.find_contour(points, anchor, anchor)
points = curves.make_curve_equidistant(points, spacing=spacing)
return curves.translate_points(points, *offset)
def get_centerline_optimized(self, alpha=1e3, beta=1e6, gamma=0.01,
spacing=20, max_iterations=1000,
endpoints=None):
""" determines the center line of the polygon using an active contour
algorithm """
# use an active contour algorithm to find centerline
ac = ActiveContour(blur_radius=1, alpha=alpha, beta=beta,
gamma=gamma, closed_loop=False)
ac.max_iterations = max_iterations
# set the potential from the distance map
mask, offset = self.get_mask(1, ret_offset=True)
potential = cv2.distanceTransform(mask, cv2.DIST_L2, 5)
ac.set_potential(potential)
# initialize the centerline from the estimate
points = self.get_centerline_estimate(endpoints)
points = curves.make_curve_equidistant(points, spacing=spacing)
points = curves.translate_points(points, -offset[0], -offset[1])
# anchor the end points
anchor = np.zeros(len(points), np.bool)
anchor[0] = anchor[-1] = True
# find the best contour
points = ac.find_contour(points, anchor, anchor)
points = curves.make_curve_equidistant(points, spacing=spacing)
return curves.translate_points(points, *offset)
def _find_point_connection(p1, p2=None, maximize_distance=False):
""" estimate centerline between the one or two points """
mask, offset = self.get_mask(margin=2, dtype=np.int32,
ret_offset=True)
p1 = (p1[0] - offset[0], p1[1] - offset[1])
if maximize_distance or p2 is None:
dist_prev = 0 if maximize_distance else np.inf
# iterate until second point is found
while True:
# make distance map starting from point p1
distance_map = mask.copy()
regions.make_distance_map(distance_map, start_points=(p1,))
# find point farthest point away from p1
idx_max = np.unravel_index(distance_map.argmax(),
distance_map.shape)
dist = distance_map[idx_max]
p2 = idx_max[1], idx_max[0]
if dist <= dist_prev:
break
dist_prev = dist
# take farthest point as new start point
p1 = p2
else:
# locate the centerline between the two given points
p2 = (p2[0] - offset[0], p2[1] - offset[1])
distance_map = mask
regions.make_distance_map(distance_map,
start_points=(p1,), end_points=(p2,))
# find path between p1 and p2
path = regions.shortest_path_in_distance_map(distance_map, p2)
return curves.translate_points(path, *offset)
def _find_point_connection(p1, p2=None, maximize_distance=False):
""" estimate centerline between the one or two points """
mask, offset = self.get_mask(margin=2, dtype=np.int32,
ret_offset=True)
p1 = (p1[0] - offset[0], p1[1] - offset[1])
if maximize_distance or p2 is None:
dist_prev = 0 if maximize_distance else np.inf
# iterate until second point is found
while True:
# make distance map starting from point p1
distance_map = mask.copy()
regions.make_distance_map(distance_map, start_points=(p1,))
# find point farthest point away from p1
idx_max = np.unravel_index(distance_map.argmax(),
distance_map.shape)
dist = distance_map[idx_max]
p2 = idx_max[1], idx_max[0]
if dist <= dist_prev:
break
dist_prev = dist
# take farthest point as new start point
p1 = p2
else:
# locate the centerline between the two given points
p2 = (p2[0] - offset[0], p2[1] - offset[1])
distance_map = mask
regions.make_distance_map(distance_map,
start_points=(p1,), end_points=(p2,))
# find path between p1 and p2
path = regions.shortest_path_in_distance_map(distance_map, p2)
return curves.translate_points(path, *offset)
