optimize preprocessor
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32
utils/pts.py
32
utils/pts.py
@@ -1,6 +1,7 @@
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import numpy as np
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import open3d as o3d
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import torch
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from scipy.spatial import cKDTree
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class PtsUtil:
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@@ -56,17 +57,36 @@ class PtsUtil:
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return overlapping_points
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@staticmethod
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def filter_points(points, normals, cam_pose, theta=75, require_idx=False):
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def new_filter_points(points, normals, cam_pose, theta=75, require_idx=False):
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camera_axis = -cam_pose[:3, 2]
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normals_normalized = normals / np.linalg.norm(normals, axis=1, keepdims=True)
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cos_theta = np.dot(normals_normalized, camera_axis)
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theta_rad = np.deg2rad(theta)
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idx = cos_theta > np.cos(theta_rad)
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print(cos_theta, theta_rad)
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filtered_points= points[idx]
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# ------ Debug Start ------
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import ipdb;ipdb.set_trace()
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# ------ Debug End ------
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if require_idx:
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return filtered_points, idx
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return filtered_points
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return filtered_points
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@staticmethod
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def filter_points(points, points_normals, cam_pose, voxel_size=0.002, theta=45, z_range=(0.2, 0.45)):
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""" filter with z range """
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points_cam = PtsUtil.transform_point_cloud(points, np.linalg.inv(cam_pose))
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idx = (points_cam[:, 2] > z_range[0]) & (points_cam[:, 2] < z_range[1])
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z_filtered_points = points[idx]
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""" filter with normal """
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sampled_points = PtsUtil.voxel_downsample_point_cloud(z_filtered_points, voxel_size)
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kdtree = cKDTree(points_normals[:,:3])
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_, indices = kdtree.query(sampled_points)
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nearest_points = points_normals[indices]
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normals = nearest_points[:, 3:]
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camera_axis = -cam_pose[:3, 2]
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normals_normalized = normals / np.linalg.norm(normals, axis=1, keepdims=True)
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cos_theta = np.dot(normals_normalized, camera_axis)
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theta_rad = np.deg2rad(theta)
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idx = cos_theta > np.cos(theta_rad)
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filtered_sampled_points= sampled_points[idx]
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return filtered_sampled_points[:, :3]
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