def draw_bbox_open3d(cloud_points_array):
cloud = geometry.PointCloud()
cloud.points = utility.Vector3dVector(np.asarray(cloud_points_array))
minBound = cloud.get_min_bound()
maxBound = cloud.get_max_bound()
box_center = np.array([
maxBound[0] - minBound[0], maxBound[1] - minBound[1],
maxBound[2] - minBound[2]
])
points = [[minBound[0], maxBound[1], minBound[2]],
[maxBound[0], maxBound[1], minBound[2]],
[maxBound[0], minBound[1], minBound[2]],
[minBound[0], minBound[1], minBound[2]],
[minBound[0], minBound[1], maxBound[2]],
[maxBound[0], minBound[1], maxBound[2]],
[maxBound[0], maxBound[1], maxBound[2]],
[minBound[0], maxBound[1], maxBound[2]], [0, 0, 0],
[box_center[0], box_center[1], box_center[2]]]
lines = [[0, 1], [1, 2], [2, 3], [0, 3], [0, 7], [1, 6], [2, 5], [3, 4],
[4, 5], [4, 7], [5, 6], [6, 7]]
colors = [[0, 255, 0] for i in range(0, len(lines))]
line_set = geometry.LineSet()
line_set.points = utility.Vector3dVector(points)
line_set.lines = utility.Vector2iVector(lines)
line_set.colors = utility.Vector3dVector(colors)
return line_set
def load_point_cloud(self, normalize=False):
""" Method used for reading the off file that holds the pcd points and populate the point_cloud and normal points inside this object."""
try:
try:
file_extension = (str(self.path).rsplit("\\", 1)[1]).rsplit(".", 1)[1]
except Exception as e:
print('Exception at path split.', e)
if file_extension == "off" or file_extension == "OFF":
if normalize:
points_array, normals_array = read_off_file(self.path)
normalized_points = normalize_cloud(points_array)
self.point_cloud.points = utility.Vector3dVector(normalized_points)
else:
points_array, normals_array = read_off_file(self.path)
self.point_cloud.points = utility.Vector3dVector(points_array)
elif file_extension == 'pcd':
io.read_point_cloud(self.path, self.point_cloud)
elif file_extension == 'ply':
self.mesh = io.read_triangle_mesh(self.path)
self.point_cloud.points = utility.Vector3dVector(self.mesh.vertices)
except Exception as e:
print('Exception at reading off file.', e)
def translate_to_origin(self, point=None):
"""
Translates object into origin, based on its centroid
:return: [out] Sets the point cloud translated into origin.
"""
assert len(self.point_cloud.points) > 0
if point is None:
centroid = compute_centroid(self.point_cloud.points)
translated_points = translate_point_cloud(self.point_cloud.points, centroid, action="translate")
else:
translated_points = translate_point_cloud(self.point_cloud.xyz, [0, 0, 0], action="translate")
self.point_cloud.points = utility.Vector3dVector(translated_points)
def delaunay_triangulation(points_array):
import numpy as np
from scipy.spatial import Delaunay
from open3d.open3d import geometry, utility
u = [point[0] for point in points_array]
v = [point[1] for point in points_array]
tri = Delaunay(np.array([u, v]).T)
mesh = geometry.TriangleMesh()
mesh.vertices = utility.Vector3dVector(points_array)
mesh.triangles = utility.Vector3iVector(tri.simplices)
mesh.remove_duplicated_triangles()
mesh.remove_degenerate_triangles()
return mesh
def __init__(self, points):
self.point_cloud = geometry.PointCloud()
self.point_cloud.points = utility.Vector3dVector(points)
self.cloud_down = geometry.PointCloud()
self.cloud_fpfh = registration.RegistrationResult()
self.centroid = []
# DECLARING OBJECTS
source_primitive_cloud_path = PATHS.PATH_TO_PRIMITIVES.CAR.root + "CarPrimitive_15_500.off"
srcPrimitive = IoPrimitive(source_primitive_cloud_path)
srcPrimitive.load_primitive()
srcPrimitive.compute_normals(30)
srcPrimitive.point_cloud.normalize_normals()
srcPrimitive.point_cloud.paint_uniform_color([1, 0, 0])
frame = 8 # sample id from kitti dataset
final_clouds = []
lidar, labels = load_cloud_and_labels(frame)
img_dir = str(PATHS.KITTI.IMG_ROOT + '%06d.png' % frame)
pc_dir = str(PATHS.KITTI.PC_ROOT + '%06d.bin' % frame)
calib_dir = str(PATHS.KITTI.CALIB_ROOT + '%06d.txt' % frame)
full_cloud_points = align_img_and_pc(img_dir, pc_dir, calib_dir)
full_cloud.points = utility.Vector3dVector(
np.array(full_cloud_points[:, 0:3]))
full_cloud.colors = utility.Vector3dVector(
[[0, 0, 0] for i in range(0, len(full_cloud.points))])
# keep original labels, and work on original labels
lab_cnt = 0
for line in labels:
# Clear cloud to be repopulated
values = line.split(' ')
lab_cnt += 1
[
cls, obj_lv_image, occl_state, obs_angl, bb_l, bb_t, bb_r, bb_d, h,
w, l, y, z, x, rot
] = values
x = float(x)
y = float(y)
z = float(z)