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basic_registration.py
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basic_registration.py
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#!/usr/bin/python3
# SPDX-FileCopyrightText: Copyright 2024 Kenji Koide
# SPDX-License-Identifier: MIT
import numpy
from scipy.spatial.transform import Rotation
import small_gicp
# Basic registation example with numpy arrays
def example_numpy1(target_raw_numpy : numpy.ndarray, source_raw_numpy : numpy.ndarray):
print('*** example_numpy1 ***')
# Example A : Perform registration with numpy arrays
# Arguments
# - target_points : Nx4 or Nx3 numpy array of the target point cloud
# - source_points : Nx4 or Nx3 numpy array of the source point cloud
# Optional arguments
# - init_T_target_source : Initial guess of the transformation matrix (4x4 numpy array)
# - registration_type : Registration type ("ICP", "PLANE_ICP", "GICP", "VGICP")
# - voxel_resolution : Voxel resolution for VGICP
# - downsampling_resolution : Downsampling resolution
# - max_correspondence_distance : Maximum correspondence distance
# - num_threads : Number of threads
result = small_gicp.align(target_raw_numpy, source_raw_numpy, downsampling_resolution=0.25)
print('--- registration result ---')
print(result)
return result.T_target_source
# Example to perform preprocessing and registration separately
def example_numpy2(target_raw_numpy : numpy.ndarray, source_raw_numpy : numpy.ndarray):
print('*** example_numpy2 ***')
# Example B : Perform preprocessing and registration separately
# Preprocess point clouds
# Arguments
# - points : Nx4 or Nx3 numpy array of the target point cloud
# Optional arguments
# - downsampling_resolution : Downsampling resolution
# - num_neighbors : Number of neighbors for normal and covariance estimation
# - num_threads : Number of threads
target, target_tree = small_gicp.preprocess_points(target_raw_numpy, downsampling_resolution=0.25)
source, source_tree = small_gicp.preprocess_points(source_raw_numpy, downsampling_resolution=0.25)
print('preprocessed target=', target)
print('preprocessed source=', source)
# Align point clouds
# Arguments
# - target : Target point cloud (small_gicp.PointCloud)
# - source : Source point cloud (small_gicp.PointCloud)
# - target_tree : KD-tree of the target point cloud
# Optional arguments
# - init_T_target_source : Initial guess of the transformation matrix (4x4 numpy array)
# - max_correspondence_distance : Maximum correspondence distance
# - num_threads : Number of threads
result = small_gicp.align(target, source, target_tree)
print('--- registration result ---')
print(result)
return result.T_target_source
# Basic registation example with small_gicp.PointCloud
def example_small1(target_raw_numpy : numpy.ndarray, source_raw_numpy : numpy.ndarray):
print('*** example_small1 ***')
# Convert numpy arrays (Nx3 or Nx4) to small_gicp.PointCloud
target_raw = small_gicp.PointCloud(target_raw_numpy)
source_raw = small_gicp.PointCloud(source_raw_numpy)
# Preprocess point clouds
target, target_tree = small_gicp.preprocess_points(target_raw, downsampling_resolution=0.25)
source, source_tree = small_gicp.preprocess_points(source_raw, downsampling_resolution=0.25)
print('preprocessed target=', target)
print('preprocessed source=', source)
result = small_gicp.align(target, source, target_tree)
print('--- registration result ---')
print(result)
return result.T_target_source
# Example to perform each preprocessing and registration separately
def example_small2(target_raw_numpy : numpy.ndarray, source_raw_numpy : numpy.ndarray):
print('*** example_small2 ***')
# Convert numpy arrays (Nx3 or Nx4) to small_gicp.PointCloud
target_raw = small_gicp.PointCloud(target_raw_numpy)
source_raw = small_gicp.PointCloud(source_raw_numpy)
# Downsampling
target = small_gicp.voxelgrid_sampling(target_raw, 0.25)
source = small_gicp.voxelgrid_sampling(source_raw, 0.25)
# KdTree construction
target_tree = small_gicp.KdTree(target)
source_tree = small_gicp.KdTree(source)
# Estimate covariances
small_gicp.estimate_covariances(target, target_tree)
small_gicp.estimate_covariances(source, source_tree)
print('preprocessed target=', target)
print('preprocessed source=', source)
# Align point clouds
result = small_gicp.align(target, source, target_tree)
print('--- registration result ---')
print(result)
return result.T_target_source
### Following functions are for testing ###
# Verity the estimated transformation matrix (for testing)
def verify_result(T_target_source, gt_T_target_source):
error = numpy.linalg.inv(T_target_source) @ gt_T_target_source
error_trans = numpy.linalg.norm(error[:3, 3])
error_rot = Rotation.from_matrix(error[:3, :3]).magnitude()
assert error_trans < 0.05
assert error_rot < 0.05
import pytest
# Load the point clouds and the ground truth transformation matrix
@pytest.fixture(scope='module', autouse=True)
def load_points():
gt_T_target_source = numpy.loadtxt('data/T_target_source.txt') # Load the ground truth transformation matrix
print('--- gt_T_target_source ---')
print(gt_T_target_source)
target_raw = small_gicp.read_ply(('data/target.ply')) # Read the target point cloud (small_gicp.PointCloud)
source_raw = small_gicp.read_ply(('data/source.ply')) # Read the source point cloud (small_gicp.PointCloud)
target_raw_numpy = target_raw.points() # Nx4 numpy array of the target point cloud
source_raw_numpy = source_raw.points() # Nx4 numpy array of the source point cloud
yield (gt_T_target_source, target_raw_numpy, source_raw_numpy)
# Check if the point clouds are loaded correctly
def test_load_points(load_points):
gt_T_target_source, target_raw_numpy, source_raw_numpy = load_points
assert gt_T_target_source.shape[0] == 4 and gt_T_target_source.shape[1] == 4
assert len(target_raw_numpy) > 0
assert len(source_raw_numpy) > 0
def test_example_numpy1(load_points):
gt_T_target_source, target_raw_numpy, source_raw_numpy = load_points
T_target_source = example_numpy1(target_raw_numpy, source_raw_numpy)
verify_result(T_target_source, gt_T_target_source)
def test_example_numpy2(load_points):
gt_T_target_source, target_raw_numpy, source_raw_numpy = load_points
T_target_source = example_numpy2(target_raw_numpy, source_raw_numpy)
verify_result(T_target_source, gt_T_target_source)
def test_example_small1(load_points):
gt_T_target_source, target_raw_numpy, source_raw_numpy = load_points
T_target_source = example_small1(target_raw_numpy, source_raw_numpy)
verify_result(T_target_source, gt_T_target_source)
def test_example_small2(load_points):
gt_T_target_source, target_raw_numpy, source_raw_numpy = load_points
T_target_source = example_small2(target_raw_numpy, source_raw_numpy)
verify_result(T_target_source, gt_T_target_source)
if __name__ == "__main__":
target_raw = small_gicp.read_ply(('data/target.ply')) # Read the target point cloud (small_gicp.PointCloud)
source_raw = small_gicp.read_ply(('data/source.ply')) # Read the source point cloud (small_gicp.PointCloud)
target_raw_numpy = target_raw.points() # Nx4 numpy array of the target point cloud
source_raw_numpy = source_raw.points() # Nx4 numpy array of the source point cloud
T_target_source = example_numpy1(target_raw_numpy, source_raw_numpy)
T_target_source = example_numpy2(target_raw_numpy, source_raw_numpy)
T_target_source = example_small1(target_raw_numpy, source_raw_numpy)
T_target_source = example_small2(target_raw_numpy, source_raw_numpy)