1. PCL (Point Cloud Library)

主要功能：支持2D/3D圖像和點雲處理
PCL Android資源
主要功能模塊
PCL教材
PCL依賴庫

庫名	最小版本	描述
Eigen	3.0	高層次的C ++庫，有效支持線性代數，矩陣和矢量運算，數值分析及其相關的算法，功能列表： Core：Matrix和Array類，基礎的線性代數運算和數組操作 Geometry：旋轉、平移、縮放、2D/3D旋轉(Quanternion, AngleAxis) LU：求逆、行列式和LU分解 Cholesky：LLT和LDLT Cholesky分解 SVD：SVD分解（JacobiSVD） QR：QR分解 Eigenvalues：求特徵值和特徵向量
FLANN	1.7.1	Fast Library for Approximate Nearest Neighbors (近似近鄰快速庫) 一個在高維空間中執行快速近似近鄰搜索的庫最適合最近鄰搜索的算法，以及一個根據數據集自動選擇最佳算法和最佳參數的系統
Boost	1.40 (without OpenNI) 1.47 (with OpenNI)	爲C++語言標準庫提供擴展的一些C++程序庫的總稱 Boost庫是一個可移植、提供源代碼的C++庫，作爲標準庫的後備，是C++標準化進程的開發引擎之一現有的 Boost 包含大約150種不同的函數庫
VTK	5.6	PCL使用的可視化工具 (Visualization Toolkit) 支持3D渲染、3D交互

PCL編譯

# flann庫
sudo apt-get install libflann1.8 libflann-dev 
# eigen庫
sudo apt-get install libeigen3-dev
# boost庫  
sudo apt-get install libboost-all-dev 
# VTK 
sudo apt-get install libvtk5.10-qt4 libvtk5.10 libvtk5-dev

# 下載PCL
git clone https://github.com/PointCloudLibrary/pcl.git
#編譯
cd pcl
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=/usr -DBUILD_apps=ON ..
make -j4
# install
sudo make install

# test
pcl_viewer model.pcd

1.1 基於pcl::visualization::PCLVisualizer 實時顯示點雲流的代碼

include <pcl/point_types.h>
#include <pcl/visualization/cloud_viewer.h>



boost::mutex updateModelMutex;

boost::shared_ptr<pcl::visualization::PCLVisualizer> simpleVis (pcl::PointCloud<pcl::PointXYZ>::ConstPtr cloud)
{
        // --------------------------------------------
        // -----Open 3D viewer and add point cloud-----
        // --------------------------------------------
        boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer (new pcl::visualization::PCLVisualizer ("3D Viewer"));
        viewer->setCameraPosition(0, 0, 0, 0, 0, 0, 0, 0, -1);
        viewer->setBackgroundColor(0,0,0);
        viewer->addPointCloud<pcl::PointXYZ> (cloud, "sample cloud");
        viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "sample cloud");
        viewer->initCameraParameters ();
        return (viewer);
}

void viewerRunner(boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer)
{
        while (!viewer->wasStopped ())
        {
               viewer->spinOnce (100);
               boost::this_thread::sleep (boost::posix_time::microseconds (100));
        }
}

void main()
{      
	    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_ptr (new pcl::PointCloud<pcl::PointXYZ>);
        pcl::PointCloud<pcl::PointXYZ> &pcloud1 = *cloud_ptr;
        boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer (new pcl::visualization::PCLVisualizer ("3D Viewer"));
        
        pcloud1.points.push_back( pcl::PointXYZ(10, 10, 80) );
        pcloud1.width = cloud_ptr->size();
        pcloud1.height = 1;
        pcloud1.is_dense = true;
        viewer = simpleVis(cloud_ptr);

        boost::thread vthread(&viewerRunner,viewer);
       
        while(1)//循環抓取深度數據
        {
              pcloud1.clear();
              for ( int _row = 0; _row < disp.rows; _row++ )
              {
                   for ( int _col = 0; _col < disp.cols; _col++ )
                   {
                       float x, y, z;
                       pcl::PointXYZ ptemp(x, y, z);
                       pcloud1.points.push_back( ptemp );
                    }
               }
               pcloud1.width = cloud_ptr->size();
               pcloud1.height = 1;
               pcloud1.is_dense = true;
               boost::mutex::scoped_lock updateLock(updateModelMutex);
               viewer->updatePointCloud<pcl::PointXYZ>(cloud_ptr,"sample cloud");
               updateLock.unlock();
               boost::this_thread::sleep (boost::posix_time::microseconds (100));
       }
       vthread.joint();
}

int main()
{
	pcl::visualization::PCLVisualizer viewer(“Cloud”);
	pcl::PointCloud::Ptr cloud(new pcl::PointCloud);
	int frame=0;
	while(1)
	{
		char buf[233];
		if (frame != 0)
		{
			sprintf(buf, “frame%d”, frame);
			viewer.removePointCloud(buf);
		}
		sprintf(buf, “frame%d”, ++frame);
		viewer.addPointCloud(cloud, buf);
		viewer.spinOnce();
	}
	return 0;
}

2. Open3D

Open3D是Intel繼OpenCV又一開源鉅作
官方文檔
主要特點
- 天生支持最愛的Python
- 採用Google Filament作爲渲染引擎，可在Android, Linux, MacOS和Windows上運行
- 聚焦可視化和渲染

2.1 編譯環境

Ubuntu 18.04+: GCC 5+, Clang 7+
macOS 10.14+: XCode 8.0+
Windows 10 (64-bit): Visual Studio 2019+

2.2 編譯過程 (Ubuntu 18.04)

下載代碼

git clone --recursive https://github.com/intel-isl/Open3D

安裝依賴

sudo apt install nasm  
util/scripts/install-deps-ubuntu.sh

2.3 設置Python綁定環境 (pybind11)

設置正確的Python執行文件

# Ubuntu 18.04默認安裝了 Python 3.6.9
# 檢查python版本
which python3
python3 -V
# 在CMake中增加
-DPYTHON_EXECUTABLE=/usr/bin/python3

支持Jupyter可視化

# 安裝npm
sudo apt install nodejs
sudo apt install npm
#check npm
node -v
npm -v

2.4 編譯過程

mkdir build; cd build

Ubuntu 和 MacOS

cmake -DCMAKE_INSTALL_PREFIX=/usr/local/open3d -DGLIBCXX_USE_CXX11_ABI=OFF -DPYTHON_EXECUTABLE=/usr/bin/python3 ..

Windows

cmake -DCMAKE_INSTALL_PREFIX=/usr/local/open3d \ 
      -DGLIBCXX_USE_CXX11_ABI=OFF \
      -DPYTHON_EXECUTABLE=/usr/bin/python3 \
	  -DBUILD_EIGEN3=ON  \
      -DBUILD_FLANN=ON   \
      -DBUILD_GLEW=ON    \
      -DBUILD_GLFW=ON    \
      -DBUILD_PNG=ON     \
      ..

執行編譯

make -j4

安裝和卸載C++庫

# 安裝
sudo make install
# 卸載
sudo make uninstall

安裝和卸載Python包

# Create Python package in build/lib/python_package
make python-package
# Create pip wheel in build/lib/python_package/pip_package
make pip-package
# Create conda package in build/lib/python_package/conda_package
make conda-package
# Install pip wheel
sudo make install-pip-package

測試導入Open3D

python -c "import open3d"

2.5 阻塞和非阻塞示例代碼

# -*-coding: utf-8 -*-
import open3d
import numpy as np
import cv2

# 繪製open3d座標系
axis_pcd = open3d.create_mesh_coordinate_frame(size=0.5, origin=[0, 0, 0])
# 在3D座標上繪製點：座標點[x,y,z]對應R，G，B顏色
points = np.array([[0.1, 0.1, 0.1], [1, 0, 0], [0, 1, 0], [0, 0, 1]])
colors = [[1, 1, 1], [1, 0, 0], [0, 1, 0], [0, 0, 1]]
 
test_pcd = open3d.geometry.PointCloud()  # 定義點雲
 
# 方法1（非阻塞顯示）
vis = open3d.Visualizer()
vis.create_window(window_name="Open3D1")
vis.get_render_option().point_size = 3
first_loop = True
# 先把點雲對象添加給Visualizer
vis.add_geometry(axis_pcd)
vis.add_geometry(test_pcd)
while True:
    # 給點雲添加顯示的數據
    points -= 0.001
    test_pcd.points = open3d.utility.Vector3dVector(points)  # 定義點雲座標位置
    test_pcd.colors = open3d.Vector3dVector(colors)  # 定義點雲的顏色
    # update_renderer顯示當前的數據
    vis.update_geometry()
    vis.poll_events()
    vis.update_renderer()
    cv2.waitKey(100)
 
# 方法2（阻塞顯示）：調用draw_geometries直接把需要顯示點雲數據
test_pcd.points = open3d.utility.Vector3dVector(points)  # 定義點雲座標位置
test_pcd.colors = open3d.Vector3dVector(colors)  # 定義點雲的顏色
open3d.visualization.draw_geometries([test_pcd] + [axis_pcd], window_name="Open3D2")

2.6 添加自定義的 draw_geometries 函數

import os
import numpy as np
from open3d import *
points = np.random.rand(10000, 3)
point_cloud = PointCloud()
point_cloud.points = Vector3dVector(points)
draw_geometries([point_cloud])

# or
def custom_draw_geometry_with_key_callback(pcd):
    def change_background_to_black(vis):
        opt = vis.get_render_option()
        opt.background_color = np.asarray([0, 0, 0])
        return False
    def load_render_option(vis):
        vis.get_render_option().load_from_json(
                "../../TestData/renderoption.json")
        return False
    def capture_depth(vis):
        depth = vis.capture_depth_float_buffer()
        plt.imshow(np.asarray(depth))
        plt.show()
        return False
    def capture_image(vis):
        image = vis.capture_screen_float_buffer()
        plt.imshow(np.asarray(image))
        plt.show()
        return False
    key_to_callback = {}
    key_to_callback[ord("K")] = change_background_to_black
    key_to_callback[ord("R")] = load_render_option
    key_to_callback[ord(",")] = capture_depth
    key_to_callback[ord(".")] = capture_image
    draw_geometries_with_key_callbacks([pcd], key_to_callback)

custom_draw_geometry_with_key_callback(point_cloud)

2.7 體素下采樣 (Voxel downsampling)

print("Downsample the point cloud with a voxel of 0.05")
downpcd = pcd.voxel_down_sample(voxel_size=0.05)
o3d.visualization.draw_geometries([downpcd], zoom=0.3412,
                                  front=[0.4257, -0.2125, -0.8795],
                                  lookat=[2.6172, 2.0475, 1.532],
                                  up=[-0.0694, -0.9768, 0.2024])

2.8 平面分割 (Plane segmentation)

使用RANSAC進行平面分割

pcd = o3d.io.read_point_cloud("../../TestData/fragment.pcd")
plane_model, inliers = pcd.segment_plane(distance_threshold=0.01,
                                         ransac_n=3,
                                         num_iterations=1000)
[a, b, c, d] = plane_model
print(f"Plane equation: {a:.2f}x + {b:.2f}y + {c:.2f}z + {d:.2f} = 0")

inlier_cloud = pcd.select_by_index(inliers)
inlier_cloud.paint_uniform_color([1.0, 0, 0])
outlier_cloud = pcd.select_by_index(inliers, invert=True)
o3d.visualization.draw_geometries([inlier_cloud, outlier_cloud], zoom=0.8,
                                  front=[-0.4999, -0.1659, -0.8499],
                                  lookat=[2.1813, 2.0619, 2.0999],
                                  up=[0.1204, -0.9852, 0.1215])

2.9 刪除隱藏點

print("Convert mesh to a point cloud and estimate dimensions")
pcd = o3dtut.get_armadillo_mesh().sample_points_poisson_disk(5000)
diameter = np.linalg.norm(np.asarray(pcd.get_max_bound()) - np.asarray(pcd.get_min_bound()))
o3d.visualization.draw_geometries([pcd])

print("Define parameters used for hidden_point_removal")
camera = [0, 0, diameter]
radius = diameter * 100

print("Get all points that are visible from given view point")
_, pt_map = pcd.hidden_point_removal(camera, radius)

print("Visualize result")
pcd = pcd.select_by_index(pt_map)
o3d.visualization.draw_geometries([pcd])

3. 安裝cmake 3.12.2或更高版本

# 刪除已經安裝的CMake舊版本（非必需項）：
apt-get autoremove cmake1
#下載cmake-3.12.2-Linux-x86_64.tar.gz安裝包：
wget https://cmake.org/files/v3.12/cmake-3.12.2-Linux-x86_64.tar.gz
# 解壓請運行以下命令：
tar zxvf cmake-3.12.2-Linux-x86_64.tar.gz
# 創建軟鏈接
#   注: 文件路徑是可以指定的，一般選擇在/opt或者是/usr路徑下，這裏選擇/opt：
mv cmake-3.12.2-Linux-x86_64 /opt/cmake-3.12.2
ln -sf /opt/cmake-3.12.2/bin/*  /usr/bin/
# 然後執行命令檢查一下：
cmake --version

# cmake gui
cmake-gui

深度圖+點雲常用工具彙總

1. PCL (Point Cloud Library)

1.1 基於pcl::visualization::PCLVisualizer 實時顯示點雲流的代碼

2. Open3D

2.1 編譯環境

2.2 編譯過程 (Ubuntu 18.04)

2.3 設置Python綁定環境 (pybind11)

2.4 編譯過程

2.5 阻塞和非阻塞示例代碼

2.6 添加自定義的 draw_geometries 函數

2.7 體素下采樣 (Voxel downsampling)

2.8 平面分割 (Plane segmentation)

2.9 刪除隱藏點

3. 安裝cmake 3.12.2或更高版本

認知提升的方法

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