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load_plys.py
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load_plys.py
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import os
from tqdm import tqdm
import cv2
import numpy as np
import open3d as o3d
import matplotlib.pyplot as plt
import plotly.graph_objs as go
import copy
ply_folder = 'Treasure_Chest/plys-2v2'
ply_files = os.listdir(ply_folder)
ply_files = [f for f in ply_files if f.endswith('.ply') and f == 'merged.ply']
# ply_folder = 'Treasure_Chest/plys'
# ply_files = ['merged.ply']
# Load all ply files
plys = []
# Generate random colors for each ply file
# colors = np.random.rand(len(ply_files), 3)
for i in tqdm(range(len(ply_files))):
if i==1:
continue
ply = o3d.io.read_point_cloud(os.path.join(ply_folder, ply_files[i]))
# tetra_mesh, pt_map = o3d.geometry.TetraMesh.create_from_point_cloud(ply)
# mesh = o3d.geometry.TriangleMesh.create_from_point_cloud_alpha_shape(ply, 0.001, tetra_mesh, pt_map)
# mesh = o3d.geometry.TriangleMesh.create_from_point_cloud_alpha_shape(ply, 0.01)
# mesh_2 = o3d.geometry.TriangleMesh.create_from_point_cloud_ball_pivoting(
# ply, o3d.utility.DoubleVector([0.005, 0.01, 0.02, 0.04]))
# ply.paint_uniform_color(colors[i])
# with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Debug) as cm:
# labels = np.array(ply.cluster_dbscan(eps=0.0002, min_points=5, print_progress=True))
# max_label = labels.max()
# print(f"point cloud has {max_label + 1} clusters")
# colors = plt.get_cmap("tab20")(labels / (max_label if max_label > 0 else 1))
# ply.colors = o3d.utility.Vector3dVector(colors[:, :3])
# o3d.visualization.draw_geometries([ply])
# o3d.visualization.draw_geometries([ply, mesh])
# o3d.visualization.draw_geometries([ply, mesh_2])
plys.append(ply)
# # Visualize all ply files
o3d.visualization.draw_geometries(plys)
#
# def preprocess_point_cloud_2(pcd, voxel_size):
# print(":: Downsample with a voxel size %.3f." % voxel_size)
# pcd_down = pcd.voxel_down_sample(voxel_size)
# radius_normal = voxel_size * 150
# pcd_down.estimate_normals(
# o3d.geometry.KDTreeSearchParamHybrid(radius=radius_normal, max_nn=50))
# radius_feature = voxel_size * 200
# pcd_fpfh = o3d.pipelines.registration.compute_fpfh_feature(
# pcd_down,
# o3d.geometry.KDTreeSearchParamHybrid(radius=radius_feature, max_nn=100))
# return pcd_down, pcd_fpfh
#
#
# def execute_global_registration(source_down, target_down, source_fpfh,
# target_fpfh, voxel_size, voxel_multiple, iterations):
# distance_threshold = voxel_size * voxel_multiple
# print(":: RANSAC registration on downsampled point clouds.")
# print(" Since the downsampling voxel size is %.3f," % voxel_size)
# print(" we use a liberal distance threshold %.3f." % distance_threshold)
# result = o3d.pipelines.registration.registration_ransac_based_on_feature_matching(
# source_down, target_down, source_fpfh, target_fpfh, True,
# distance_threshold,
# o3d.pipelines.registration.TransformationEstimationPointToPoint(False),
# 4, # RANSAC n points
# [o3d.pipelines.registration.CorrespondenceCheckerBasedOnEdgeLength(0.9),
# o3d.pipelines.registration.CorrespondenceCheckerBasedOnDistance(distance_threshold)],
# o3d.pipelines.registration.RANSACConvergenceCriteria(iterations, confidence=1))
# return result
#
#
# def pairwise_registration(source, target, result_matrix):
# max_correspondence_distance_coarse = 100
# max_correspondence_distance_fine = 15
# print("Apply point-to-plane ICP")
# try:
# icp_coarse = o3d.pipelines.registration.registration_icp(
# source, target, max_correspondence_distance_coarse, result_matrix.transformation,
# o3d.pipelines.registration.TransformationEstimationPointToPlane())
# except:
# icp_coarse = o3d.pipelines.registration.registration_icp(
# source, target, max_correspondence_distance_coarse, result_matrix,
# o3d.pipelines.registration.TransformationEstimationPointToPlane(),
# o3d.pipelines.registration.ICPConvergenceCriteria(max_iteration=10000,
# relative_rmse=1e-6,
# relative_fitness=1e-6))
#
# icp_fine = o3d.pipelines.registration.registration_icp(
# source, target, max_correspondence_distance_fine,
# icp_coarse.transformation,
# o3d.pipelines.registration.TransformationEstimationPointToPlane())
# transformation_icp = icp_fine.transformation
# information_icp = o3d.pipelines.registration.get_information_matrix_from_point_clouds(
# source, target, max_correspondence_distance_fine,
# icp_fine.transformation)
# return transformation_icp, information_icp
#
#
# def full_registration(pcds, result_matrix):
# print(len(pcds))
# pose_graph = o3d.pipelines.registration.PoseGraph()
# odometry = np.identity(4)
# pose_graph.nodes.append(o3d.pipelines.registration.PoseGraphNode(odometry))
# n_pcds = len(pcds)
# for source_id in range(n_pcds):
# for target_id in range(source_id + 1, n_pcds):
# transformation_icp, information_icp = pairwise_registration(
# pcds[source_id], pcds[target_id], result_matrix)
# print("Build o3d.registration.PoseGraph")
# if target_id == source_id + 1: # odometry case
# print("Odometry")
# odometry = np.dot(transformation_icp, odometry)
# pose_graph.nodes.append(
# o3d.pipelines.registration.PoseGraphNode(np.linalg.inv(odometry)))
# pose_graph.edges.append(
# o3d.pipelines.registration.PoseGraphEdge(source_id,
# target_id,
# transformation_icp,
# information_icp,
# uncertain=False))
# else: # loop closure case
# pose_graph.edges.append(
# o3d.pipelines.registration.PoseGraphEdge(source_id,
# target_id,
# transformation_icp,
# information_icp,
# uncertain=True))
# return pose_graph
#
#
# def draw_registration_result(source, target, transformation):
# source_temp = copy.deepcopy(source)
# source_temp_2 = copy.deepcopy(source)
# target_temp = copy.deepcopy(target)
# source_temp.paint_uniform_color([1, 0.706, 0]) # Color: Orange
# target_temp.paint_uniform_color([0, 0.651, 0.929]) # Color: Blue
# source_temp_2.paint_uniform_color([0, 1, 0]) # Color: Green
# source_temp.transform(transformation.transformation)
# o3d.visualization.draw_geometries([source_temp, target_temp, source_temp_2])
# return None
#
#
# def draw_pcds(pcd_1, pcd_2):
# pcd_1.paint_uniform_color([1, 0.706, 0]) # Color: Orange
# # pcd_2.paint_uniform_color([0, 0.651, 0.929]) # Color: Blue
# o3d.visualization.draw_geometries([pcd_1, pcd_2])
# return None
#
#
# def process_point_clouds(pcds, voxel_size=0.001, threshold=100, voxel_multiple=150, iterations=12000000, bReg=False):
# # Downsample and extract features from the point clouds
# pcd_downsampled = []
# pcd_fpfh = []
# for pcd in pcds:
# down, fpfh = preprocess_point_cloud_2(pcd, voxel_size)
# pcd_downsampled.append(down)
# pcd_fpfh.append(fpfh)
#
# if bReg:
# # Global registration
# result = execute_global_registration(pcd_downsampled[0], pcd_downsampled[1], pcd_fpfh[0], pcd_fpfh[1],
# voxel_size,
# voxel_multiple, iterations)
#
# # Evaluation
# evaluation = o3d.pipelines.registration.evaluate_registration(
# pcds[0], pcds[1], threshold, result.transformation)
# print(evaluation)
#
# # Update the original point clouds
# pcds[0], pcds[1] = pcd_downsampled
#
# # Refinement using ICP
# registered_images = o3d.pipelines.registration.registration_icp(
# pcds[0], pcds[1], threshold, result.transformation,
# o3d.pipelines.registration.TransformationEstimationPointToPlane())
# # print(registered_images)
# print("[INFO] Transformation Matrix:")
# print(registered_images.transformation)
# draw_registration_result(pcds[0], pcds[1], registered_images)
#
# # Pose graph generation for multiway registration
# with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Debug) as cm:
# pose_graph = full_registration(pcds, result)
#
# else:
# pcds[0], pcds[1] = pcd_downsampled
# result = np.identity(4)
# # Pose graph generation for multiway registration
# with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Debug) as cm:
# pose_graph = full_registration(pcds, result)
#
# # Combine and visualize the transformed point clouds
# pcd_combined = o3d.geometry.PointCloud()
# for i, pcd in enumerate(pcds):
# pcd_combined += pcd.transform(pose_graph.nodes[i].pose)
# pcd_combined_down = pcd_combined.voxel_down_sample(voxel_size=0.03)
# o3d.visualization.draw_geometries([pcd_combined_down])
# print('Done!')
#
# return pcd_combined_down
#
#
# finito_pcds = []
# print(f"Processing point clouds...")
# for i in tqdm(range(1, len(plys))):
# # if i == 1:
# pcd_first = plys[i - 1]
# pcd_second = plys[i]
# # else:
# # pcd_first = finito_pcds[-1]
# # pcd_second = plys[i]
#
# draw_pcds(pcd_first, pcd_second)
#
# input_pcds = [pcd_first, pcd_second]
#
# final_cloud = process_point_clouds(input_pcds, voxel_size=0.001, threshold=100, voxel_multiple=150,
# iterations=12000000)
# finito_pcds.append(final_cloud)