def get_external_contour(points, resolution=None):
""" takes a list of `points` defining a linear ring, which can be
self-intersecting, and returns an approximation to the external contour """
if resolution is None:
# determine resolution from minimal distance of consecutive points
dist_min = np.inf
for p1, p2 in itertools.izip(np.roll(points, 1, axis=0), points):
dist = curves.point_distance(p1, p2)
if dist > 0:
dist_min = min(dist_min, dist)
resolution = 0.5*dist_min
# limit the resolution such that there are at most 2048 points
dim_max = np.max(np.ptp(points, axis=0)) #< longest dimension
resolution = max(resolution, dim_max/2048)
# build a linear ring with integer coordinates
ps_int = np.array(np.asarray(points)/resolution, np.int)
ring = geometry.LinearRing(ps_int)
# get the image of the linear ring by plotting it into a mask
x_min, y_min, x_max, y_max = ring.bounds
shape = ((y_max - y_min) + 3, (x_max - x_min) + 3)
x_off, y_off = int(x_min - 1), int(y_min - 1)
mask = np.zeros(shape, np.uint8)
cv2.fillPoly(mask, [ps_int], 255, offset=(-x_off, -y_off))
# find the contour of this mask to recover the exterior contour
contours = cv2.findContours(mask, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE,
offset=(x_off, y_off))[1]
return np.array(np.squeeze(contours))*resolution
def get_external_contour(points, resolution=None):
""" takes a list of `points` defining a linear ring, which can be
self-intersecting, and returns an approximation to the external contour """
if resolution is None:
# determine resolution from minimal distance of consecutive points
dist_min = np.inf
for p1, p2 in itertools.izip(np.roll(points, 1, axis=0), points):
dist = curves.point_distance(p1, p2)
if dist > 0:
dist_min = min(dist_min, dist)
resolution = 0.5 * dist_min
# limit the resolution such that there are at most 2048 points
dim_max = np.max(np.ptp(points, axis=0)) #< longest dimension
resolution = max(resolution, dim_max / 2048)
# build a linear ring with integer coordinates
ps_int = np.array(np.asarray(points) / resolution, np.int)
ring = geometry.LinearRing(ps_int)
# get the image of the linear ring by plotting it into a mask
x_min, y_min, x_max, y_max = ring.bounds
shape = ((y_max - y_min) + 3, (x_max - x_min) + 3)
x_off, y_off = int(x_min - 1), int(y_min - 1)
mask = np.zeros(shape, np.uint8)
cv2.fillPoly(mask, [ps_int], 255, offset=(-x_off, -y_off))
# find the contour of this mask to recover the exterior contour
contours = cv2.findContours(mask,
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE,
offset=(x_off, y_off))[1]
return np.array(np.squeeze(contours)) * resolution
def get_ray_hitpoint(point_anchor, point_far, line_string, ret_dist=False):
""" returns the point where a ray anchored at point_anchor hits the polygon
given by line_string. The ray extends out to point_far, which should be a
point beyond the polygon.
If ret_dist is True, the distance to the hit point is also returned.
"""
# define the ray
ray = geometry.LineString((point_anchor, point_far))
# find the intersections between the ray and the burrow contour
try:
inter = line_string.intersection(ray)
except geos.TopologicalError:
inter = None
# process the result
if isinstance(inter, geometry.Point):
if ret_dist:
# also return the distance
dist = curves.point_distance(inter.coords[0], point_anchor)
return inter.coords[0], dist
else:
return inter.coords[0]
elif inter is not None and not inter.is_empty:
# find closest intersection if there are many points
dists = [
curves.point_distance(p.coords[0], point_anchor) for p in inter
]
k_min = np.argmin(dists)
if ret_dist:
return inter[k_min].coords[0], dists[k_min]
else:
return inter[k_min].coords[0]
else:
# return empty result
if ret_dist:
return None, np.nan
else:
return None
def get_ray_hitpoint(point_anchor, point_far, line_string, ret_dist=False):
""" returns the point where a ray anchored at point_anchor hits the polygon
given by line_string. The ray extends out to point_far, which should be a
point beyond the polygon.
If ret_dist is True, the distance to the hit point is also returned.
"""
# define the ray
ray = geometry.LineString((point_anchor, point_far))
# find the intersections between the ray and the burrow contour
try:
inter = line_string.intersection(ray)
except geos.TopologicalError:
inter = None
# process the result
if isinstance(inter, geometry.Point):
if ret_dist:
# also return the distance
dist = curves.point_distance(inter.coords[0], point_anchor)
return inter.coords[0], dist
else:
return inter.coords[0]
elif inter is not None and not inter.is_empty:
# find closest intersection if there are many points
dists = [curves.point_distance(p.coords[0], point_anchor) for p in inter]
k_min = np.argmin(dists)
if ret_dist:
return inter[k_min].coords[0], dists[k_min]
else:
return inter[k_min].coords[0]
else:
# return empty result
if ret_dist:
return None, np.nan
else:
return None
def get_closest_node(self, point):
""" get the node that is closest to a given point.
This function returns three values:
* the id of the closest node
* its coordinates
* the distance of this node to the given point
"""
node_min, coord_min, dist_min = None, None, np.inf
for node, data in self.nodes_iter(data=True):
dist = curves.point_distance(point, data['coords'])
if dist < dist_min:
node_min, coord_min, dist_min = node, data['coords'], dist
return node_min, coord_min, dist_min
def from_skeleton(cls, skeleton, copy=True, post_process=True):
""" determines the morphological graph from the image `skeleton`
`copy` determines whether the skeleton is copied before its modified
`post_process` determines whether some post processing is performed
that removes spurious edges and nodes
"""
if copy:
skeleton = skeleton.copy()
graph = cls()
# count how many neighbors each point has
kernel = np.ones((3, 3), np.uint8)
kernel[1, 1] = 0
neighbors = cv2.filter2D(skeleton, -1, kernel) * skeleton
# find an point with minimal neighbors to start iterating from
neighbors_min = neighbors[neighbors > 0].min()
ps = np.nonzero(neighbors == neighbors_min)
start_point = (ps[1][0], ps[0][0])
# initialize graph by adding first node
start_node = graph.add_node_point(start_point)
edge_seeds = {start_point: start_node}
# iterate over all edges
while edge_seeds:
# pick new point from edge_seeds and initialize the point list
p, start_node = edge_seeds.popitem()
points = [graph.node[start_node]['coords']]
# iterate along the edge
while True:
# handle the current point
points.append(p)
skeleton[p[1], p[0]] = 0
# look at the neighborhood of the current point
ps_n = skeleton[p[1]-1 : p[1]+2, p[0]-1 : p[0]+2]
neighbor_count = ps_n.sum()
#print neighbor_count
if neighbor_count == 1:
# find the next point along this edge
dy, dx = np.nonzero(ps_n)
p = (p[0] + dx[0] - 1, p[1] + dy[0] - 1)
else:
# the current point ends the edge
break
# check whether we are close to another edge seed
for p_seed in edge_seeds:
dist = curves.point_distance(p, p_seed)
if dist < 1.5:
# distance should be either 1 or sqrt(2)
if dist > 0:
points.append(p_seed)
node = edge_seeds.pop(p_seed)
points.append(graph.node[node]['coords'])
break
else:
# could not find a close edge seed => this is an end point
node = graph.add_node_point(p)
# check whether we have to branch off other edges
if neighbor_count > 0:
assert neighbor_count > 1
# current points is a crossing => branch off new edge seeds
# find all points from which we have to branch off
dps = np.transpose(np.nonzero(ps_n))
# initialize all edge seeds
seeds = set([(p[0] + dx - 1, p[1] + dy - 1)
for dy, dx in dps])
while seeds:
# check the neighbor hood of the seed point
p_seed = seeds.pop()
skeleton[p_seed[1], p_seed[0]] = 0
ps_n = skeleton[p_seed[1]-1 : p_seed[1]+2,
p_seed[0]-1 : p_seed[0]+2]
neighbor_count = ps_n.sum()
#print 'test_seed', p_seed, neighbor_count
if neighbor_count == 1:
edge_seeds[p_seed] = node
else:
# add more seeds
dps = np.transpose(np.nonzero(ps_n))
for dy, dx in dps:
p = (p_seed[0] + dx - 1, p_seed[1] + dy - 1)
if p not in seeds and p not in edge_seeds:
seeds.add(p)
# add the edge to the graph
graph.add_edge_line(start_node, node, points)
if post_process:
# remove small edges and self-loops
graph.remove_short_edges(4)
# remove nodes of degree 2
graph.simplify()
return graph
# Finding the max values of the contour i.e. the outline points of the mask.
# if hierarchy[0][1] != []:
c = cnts[0] #max(cnts , key = cv2.contourArea)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(img2, mask=thresh2)
# Defining the most bottom extreme point of the mask
bottom = tuple(c[c[:, :, 1].argmax()][0])
Botx.append(bottom[0])
Boty.append(bottom[1])
# Appending the most bottom point coordinates after each iteration and calculating the actual distance moved.
# Also recording the passing frames to compare against time.
Bottom_x.append(bottom[0])
Bottom_y.append(bottom[1])
bot_distance = 5 * 5.86 * point_distance(
(Bottom_x[0], Bottom_y[0]), bottom)
Bot_dist.append(bot_distance)
Frame_count.append(i)
#==============================================================================
#==============================================================================
#cv2.circle(gray,bottom,5,(255,255,255),-1)
#==============================================================================
#==============================================================================
# Fitting an eclipse around the contour points or the mask.
for c in cons:
# Fitting only starts if there are 5 or more outline points as opencv requires this amount for fitting. Also
# fitting starts only after the flash has passed.
