File size: 3,759 Bytes
97aa5af | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | import open3d as o3
import numpy as np
import copy
# Set random seed for reproducibility for all libraries
np.random.seed(42)
def apply_noise(pcd, noise_level):
'''
This function adds gaussian noise to the point cloud by adding random values to the x, y, and z coordinates of the points.
Based on https://github.com/MIT-SPARK/TEASER-plusplus/blob/master/examples/teaser_python_ply/teaser_python_ply.py#L7
Args:
pcd: Open3D point cloud
noise_level (float): level of noise to add
Returns:
pcloud: Open3D point cloud with added noise
'''
pcloud = copy.deepcopy(pcd)
pcloud_points = np.transpose(np.asarray(pcloud.points))
N = pcloud_points.shape[1]
noise = (np.random.rand(3, N) - 0.5) * 2 * noise_level # gaussian noise with mean 0 and std = noise_level
pcloud_points += noise
pcloud.points = o3.utility.Vector3dVector(pcloud_points.T)
return pcloud
def add_outliers(pcd, outlier_level, outlier_lowerbound, outlier_upperbound):
'''
This function adds points to a given point cloud that have no counterpart in the other point cloud (outliers).
The outliers are generated by adding random values between the lower and upper bound to randomly chosen points in the original point cloud.
Based on https://github.com/MIT-SPARK/TEASER-plusplus/blob/master/examples/teaser_python_ply/teaser_python_ply.py#L7
Args:
pcd: Open3D point cloud
outlier_level: level of outliers to add
outlier_lowerbound: lower bound for the random values to add to the original points
outlier_upperbound: upper bound for the random values to add to the original points
Returns:
pcloud: Open3D point cloud with added outliers
'''
pcloud = copy.deepcopy(pcd)
pcloud_points = np.asarray(pcloud.points) # (number_of_points, 3)
n_outliers = int(outlier_level/50 * pcloud_points.shape[0])
outliers_amounts = outlier_lowerbound + np.random.rand(n_outliers) * (outlier_upperbound - outlier_lowerbound) # (outlier_upperbound - outlier_lowerbound) = the distance of the outliers from the original points.
outliers_amounts = outliers_amounts[:, np.newaxis] # reshape to (n_outliers, 1)
original_indices = np.random.randint(0, pcloud_points.shape[0], size=n_outliers) # (n_outliers,)
outlier_points = pcloud_points[original_indices] + outliers_amounts
new_pcd = np.concatenate((pcloud_points, outlier_points))
pcloud.points = o3.utility.Vector3dVector(new_pcd)
return pcloud
def apply_occlusion(pc, radius_factor):
'''
This function removes points from the point cloud to simulate occlusion.
The points are removed based on the distance from the camera, not randomly.
Source: https://towardsdatascience.com/3d-data-processing-with-open3d-c3062aadc72e
Args:
pc (open3d.geometry.PointCloud): Point cloud
radius_factor (float): Factor to determine the radius for hidden point removal
Returns:
modified_pc (open3d.geometry.PointCloud): Point cloud with occlusion
pt_map (np.array): Indices of the points that are not hidden
'''
# Get the diameter of the target point cloud (max bound - min bound)
diameter = np.linalg.norm(np.asarray(pc.get_min_bound()) - np.asarray(pc.get_max_bound()))
# Parameters for hidden point removal
camera = [0, 0, diameter]
radius = diameter * radius_factor # bigger radius removes fewer points
_, pt_map = pc.hidden_point_removal(camera, radius) # returns a tuple of the point cloud and the indices of the points that are not hidden
# Applly the hidden point removal to the target point cloud
modified_pc = pc.select_by_index(pt_map)
return modified_pc, pt_map
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