def DualUnitBall(L):
#produces the dual unit ball for an element of the MCG
pts = WalkOnList(L)
newpts = np.asarray(pts)
hull = ConvexHull(newpts)
poly = MultiPoint(pts).convex_hull
MD = []
for i in hull.vertices:
vertex = np.asarray(pts[i])
neighbours = [vertex + (0,1), vertex + (1,1), vertex + (1,0), vertex + (1,-1), vertex + (0,-1), vertex + (-1,-1), vertex + (-1,0), vertex + (-1,1)]
a1 = Point(tuple(neighbours[0]))
a2 = Point(tuple(neighbours[1]))
a3 = Point(tuple(neighbours[2]))
a4 = Point(tuple(neighbours[3]))
a5 = Point(tuple(neighbours[4]))
a6 = Point(tuple(neighbours[5]))
a7 = Point(tuple(neighbours[6]))
a8 = Point(tuple(neighbours[7]))
if poly.intersects(a1):
if poly.intersects(a2) and poly.intersects(a3):
MD.append(vertex + (0.5, 0.5))
if poly.intersects(a7) and poly.intersects(a8):
MD.append(vertex + (-0.5, 0.5))
if poly.intersects(a5):
if poly.intersects(a3) and poly.intersects(a4):
MD.append(vertex + (0.5, -0.5))
if poly.intersects(a6) and poly.intersects(a7):
MD.append(vertex + (-0.5, -0.5))
return(MD)
class Region(object):
@classmethod
def from_dict(cls, data):
A = np.array(data['A'])
B = np.array([data['B']]).T
vertices = np.array(data['points'])
new_reg = cls(A=A, B=B, vertices=vertices, region_id=data['region_id'])
return new_reg
@classmethod
def from_vertices(cls, vertices, region_id):
A, B = compute_hull(vertices)
return cls(A, B, vertices, region_id)
def __init__(self, A, B, vertices, region_id):
# Vertices are arrays N x 2
self.A = A
self.B = B
self.vertices = vertices
#self.iris_region = iris_region
self.region_id = region_id
self.poly = MultiPoint(vertices).convex_hull
def intersects(self, other_region):
return self.poly.intersects(other_region.poly)
def contains_point(self, point):
return self.poly.intersects(Point(point))
def draw(self, ax=None, **kwargs):
if ax is None:
ax = plt.gca()
hull = scipy.spatial.ConvexHull(self.vertices)
kwargs.setdefault("facecolor", "none")
return [
ax.add_patch(plt.Polygon(xy=self.vertices[hull.vertices],
**kwargs))
]
def __repr__(self):
return "<Region {}>".format(self.region_id)
# def __eq__(self, other):
# if isinstance(other, self.__class__):
# return check_equal_vertices(self.vertices, other.vertices, tol=1e-1)
# return NotImplemented
# def __ne__(self, other):
# """Define a non-equality test"""
# if isinstance(other, self.__class__):
# return not self.__eq__(other)
# return NotImplemented
def to_dict(self):
ir = {}
ir['points'] = self.vertices.tolist() # (xi,yi) each line
ir['A'] = self.A.tolist() # row by row
ir['B'] = self.B[:, 0].tolist() # transpose of B
return ir
def post_process_coords(
corner_coords: List, imsize: Tuple[int, int] = (1600, 900)
) -> Union[Tuple[float, float, float, float], None]:
"""
Get the intersection of the convex hull of the reprojected bbox corners and the image canvas, return None if no
intersection.
:param corner_coords: Corner coordinates of reprojected bounding box.
:param imsize: Size of the image canvas.
:return: Intersection of the convex hull of the 2D box corners and the image canvas.
"""
polygon_from_2d_box = MultiPoint(corner_coords).convex_hull
img_canvas = box(0, 0, imsize[0], imsize[1])
if polygon_from_2d_box.intersects(img_canvas):
img_intersection = polygon_from_2d_box.intersection(img_canvas)
intersection_coords = np.array(
[coord for coord in img_intersection.exterior.coords])
min_x = min(intersection_coords[:, 0])
min_y = min(intersection_coords[:, 1])
max_x = max(intersection_coords[:, 0])
max_y = max(intersection_coords[:, 1])
return min_x, min_y, max_x, max_y
else:
return None
def project_camera( corners, cam_model ):
""" -------------------------------------------------------------------------------------------------------------
Project a 3D bounding box into camera (image) space (the origin of the camera space is top left corner).
This function is taken from post_process_coords() in
nuscenes-devkit/python-sdk/nuscenes/scripts/export_2d_annotations_as_json.py
corners: [np.array] the 8 corners of the 3D box
cam_model: ???
return: [tuple of int] ??? INT OR FLOAT
coordinates of the 2D box, None if the object is outside the camera frame
------------------------------------------------------------------------------------------------------------- """
front = np.argwhere( corners[ 2, :] > 0 ) # check which corners are in front of the camera
corners = corners[ :, front.flatten() ] # and take those only
corners_p = view_points( corners, cam_model, True ) # project the 3D corners in camera space
corners_p = corners_p.T[ :, : 2 ] # take only X and Y in camera space
poly = MultiPoint( corners_p.tolist() ).convex_hull
img = box( 0, 0, img_size[ 0 ], img_size[ 1 ] )
if not poly.intersects( img ):
return None # return None if the projection is out of the camera frame
inters = poly.intersection( img )
coords = np.array( [ c for c in inters.exterior.coords ] )
min_x = min( coords[ :, 0 ] )
min_y = min( coords[ :, 1 ] )
max_x = max( coords[ :, 0 ] )
max_y = max( coords[ :, 1 ] )
return min_x, min_y, max_x, max_y
def detectObstacle(self, rrt, path, newObstacle):
'''
Function to detect whether obstacle is actually bloocking the path or not
Parameters
----------
rrt : object of RRT()
Object which stores information of the tree expanded by RRT*.
path : list(int)
List of indices of the nodes in the tree which form the path traversed
by the robot.
newObstacle : Polygon
The dynamic obstacle in question.
Returns
-------
bool
A boolean which determines whetehr obstacle intersects the path (TRUE)
or not (FALSE).
'''
points = []
points = rrt.nodes[path,0:2]
points_shapely = MultiPoint(points)
if points_shapely.intersects(newObstacle):
return True
else:
return False
def _anno_to_2d_bbox(self, anno, pc_file, cam_front, lidar_top,
ego_pose_cam, ego_pose_lidar, cam_intrinsic):
# Make pixel indexes 0-based
dists = []
nusc_box = self.nusc.get_box(anno['token'])
# Move them to the ego-pose frame.
nusc_box.translate(-np.array(ego_pose_cam['translation']))
nusc_box.rotate(Quaternion(ego_pose_cam['rotation']).inverse)
# Move them to the calibrated sensor frame.
nusc_box.translate(-np.array(cam_front['translation']))
nusc_box.rotate(Quaternion(cam_front['rotation']).inverse)
dists.append(np.linalg.norm(nusc_box.center))
# Filter out the corners that are not in front of the calibrated sensor.
#Corners is a 3x8 matrix, first four corners are the ones facing forward, last 4 are ons facing backward
#(0,1) top, forward
#(2,3) bottom, forward
#(4,5) top, backward
#(6,7) bottom, backward
corners_3d = nusc_box.corners()
#Getting first 4 values of Z
dists.append(np.mean(corners_3d[2, :4]))
# z is height of object for ego pose or lidar
# y is height of object for camera frame
#TODO: Discover why this is taking the Z axis
in_front = np.argwhere(corners_3d[2, :] > 0).flatten()
corners_3d = corners_3d[:, in_front]
#print(corners_3d)
#above = np.argwhere(corners_3d[2, :] > 0).flatten()
#corners_3d = corners_3d[:, above]
# Project 3d box to 2d.
corner_coords = view_points(corners_3d, cam_intrinsic,
True).T[:, :2].tolist()
#print(corner_coords)
# Keep only corners that fall within the image.
polygon_from_2d_box = MultiPoint(corner_coords).convex_hull
img_canvas = box(0, 0, self._imwidth - 1, self._imheight - 1)
if polygon_from_2d_box.intersects(img_canvas):
img_intersection = polygon_from_2d_box.intersection(img_canvas)
intersection_coords = np.array(
[coord for coord in img_intersection.exterior.coords])
min_x = min(intersection_coords[:, 0])
min_y = min(intersection_coords[:, 1])
max_x = max(intersection_coords[:, 0])
max_y = max(intersection_coords[:, 1])
#print('contained pts {}'.format(contained_points))
return [min_x, min_y, max_x, max_y], dists
else:
return None, dists
def check_on_boundaries(lon_min, lon_max, lat_min, lat_max, point_dict):
# core function to check if the data falls inside the user geographic boundaries
# create the geographic polygon containing the user input searching area:
data_longitudes = [x["Longitude"] for x in point_dict]
data_latitudes = [x["Latitude"] for x in point_dict]
data_lat_lon_coords = tuple(zip(data_latitudes, data_longitudes))
data_polygon_obj = MultiPoint(data_lat_lon_coords).convex_hull
# create the geographic polygon containing the current data
user_lat_lon_coords = tuple(
zip([lat_min, lat_min, lat_max, lat_max],
[lon_min, lon_max, lon_min, lon_max]))
user_polygon_obj = MultiPoint(user_lat_lon_coords).convex_hull
# check if the data polygon is at least partially intersected by the user polygon
boundaries_are_matching = user_polygon_obj.intersects(data_polygon_obj)
return boundaries_are_matching
def __findCrossedAirspaces(self):
flightPoints = []
for fix in self.igcFlight.fixes:
flightPoints.append((fix.lon, fix.lat))
trackPoints = MultiPoint(flightPoints)
crossedAirspacesDict = {}
for i, feature in enumerate(self.geojsonAirspace.features):
coords = np.array(feature.geometry.coordinates[0])
coords2 = np.empty((len(coords), ), dtype=object)
coords2[:] = [tuple(i) for i in coords]
polygon = Polygon(coords2.tolist())
inside = trackPoints.intersects(polygon)
if inside:
crossedAirspacesDict[i] = polygon
#print(f"Point crosses airspace (Horizontal only): {i} # {feature.properties.CLASS} # {feature.properties.NAME} # {feature.properties.CEILING} / {feature.properties.FLOOR}" )
airspaceCeiling = stringToMeter(feature.properties.CEILING)
airspaceFloor = stringToMeter(feature.properties.FLOOR)
return crossedAirspacesDict
def assign_label(self, tile_starts, tile_ends, regions, region_labels):
''' calculates overlap of tile with xml regions and creates dictionary based on unique labels '''
tile_box = [tile_starts[0],
tile_starts[1]], [tile_starts[0], tile_ends[1]
], [tile_ends[0], tile_starts[1]
], [tile_ends[0], tile_ends[1]]
tile_box = list(tile_box)
tile_box = MultiPoint(tile_box).convex_hull
tile_label = {}
# create a dictionary of label/value pairs: returns percent of tile containing unique
for label in set(region_labels):
# grab regions that correspond to this label
label_list = [i for i, e in enumerate(region_labels) if e == label]
labels = tuple(regions[i] for i in label_list)
# loop over every region associated with a given label, sum the overlap
box_label = False # initialize
ov = 0 # initialize
for reg in labels:
poly = Polygon(reg)
if poly.is_valid == False:
poly = poly.buffer(0)
poly_label = tile_box.intersects(poly)
if poly_label == True:
box_label = True
ov_reg = tile_box.intersection(poly)
ov += ov_reg.area / tile_box.area
if box_label == True:
tile_label[label] = ov
# p.s. if you are curious, you can plot the polygons by the following
# plt.plot(*poly.exterior.xy) and plt.plot(*tile_box.exterior.xy)
return tile_label
metrics1 = metrics1 / len(better_matches)
f1.write(str(metrics1) + '\n')
points = list()
for i in range(0, 4):
l = list()
l.append(dst[i][0][0])
l.append(dst[i][0][1])
points.append(l)
polygon = MultiPoint(points).convex_hull
error1 = list()
x2_1 = np.float32([x.pt for x in matched2_1])
for i in range(0, len(matched2_1)):
p = shapely.geometry.Point(x2_1[i])
k = polygon.intersects(p)
error1.append(k)
metrics2 = sum(error1) / len(error1)
f3.write(str(metrics2) + '\n')
else:
src_pts = np.float32([x.pt for x in matched1]).reshape(-1, 1, 2)
dst_pts = np.float32([x.pt for x in matched2]).reshape(-1, 1, 2)
M, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC, 6.0)
if M is None:
f1.write('findHomography error\n')
f3.write('findHomography error\n')
else:
matchesMask = mask.ravel().tolist()
pts = np.float32([[167, 290], [167, 1016], [719, 1016],
print(polygon3.area)
# 2 3多边形是否相交
# b = polygon2.contains(polygon3)
# print('2和3多边形的', b)
# c = polygon2.covers(polygon3)
# print('c', c)
# d = polygon3.crosses(polygon2)
# print('d', d)
#
# e = polygon2.disjoint(polygon3)
# print('e', e)
# f = polygon3.intersection(polygon2)
# print('f', f)
# g = polygon2.intersects(polygon3)
# print('g', g)
h = polygon3.intersects(polygon4)
print('h', h)
if (h):
u = cascaded_union([polygon3, polygon4])
print(u.area)
# print(u.boundary)
# print(u.centroid)
# df = pd.read_csv('../cu2/cu-1', header=None)
# df = df.iloc[:, 0:2]
# train_data = np.array(df) # np.ndarray()
# train_x_list = train_data.tolist() # list
# # print(train_x_list)
# # print(type(train_x_list))
# points = MultiPoint(train_x_list)
# py1 = points.convex_hull
