PCL特徵點與配準-轉

原創 tony2278 2019-07-17 00:38

關於輸入一個具體的物體的點雲，從場景中找出與該物體點雲相匹配的，這種方法可以用來抓取指定的物體等等，具體的代碼的解釋如下，需要用到的一些基礎的知識，在之前的博客中都有提及，其中用到的一些方法可以翻閱前面的博客

[Code]

#include <pcl/io/pcd_io.h>
#include <pcl/point_cloud.h>     //點雲類型頭文件
#include <pcl/correspondence.h>   //對應表示兩個實體之間的匹配（例如，點，描述符等）。
#include <pcl/features/normal_3d_omp.h>   //法線
#include <pcl/features/shot_omp.h>    //描述子
#include <pcl/features/board.h>       
#include <pcl/filters/uniform_sampling.h>   //均勻採樣
#include <pcl/recognition/cg/hough_3d.h>    //hough算子
#include <pcl/recognition/cg/geometric_consistency.h>  //幾何一致性
#include <pcl/visualization/pcl_visualizer.h>   //可視化
#include <pcl/kdtree/kdtree_flann.h>             //配準方法
#include <pcl/kdtree/impl/kdtree_flann.hpp>      //
#include <pcl/common/transforms.h>             //轉換矩陣
#include <pcl/console/parse.h>

typedef pcl::PointXYZRGBA PointType;  //PointXYZRGBA數據結構
typedef pcl::Normal NormalType;       //法線結構
typedef pcl::ReferenceFrame RFType;    //參考幀
typedef pcl::SHOT352 DescriptorType;   //SHOT特徵的數據結構

std::string model_filename_;   //模型的文件名
std::string scene_filename_;

//Algorithm params
bool show_keypoints_ (false);
bool show_correspondences_ (false);
bool use_cloud_resolution_ (false);
bool use_hough_ (true);
float model_ss_ (0.01f);
float scene_ss_ (0.03f);
float rf_rad_ (0.015f);
float descr_rad_ (0.02f);
float cg_size_ (0.01f);
float cg_thresh_ (5.0f);

void
showHelp (char *filename)
{
  std::cout << std::endl;
  std::cout << "***************************************************************************" << std::endl;
  std::cout << "*                                                                         *" << std::endl;
  std::cout << "*             Correspondence Grouping Tutorial - Usage Guide              *" << std::endl;
  std::cout << "*                                                                         *" << std::endl;
  std::cout << "***************************************************************************" << std::endl << std::endl;
  std::cout << "Usage: " << filename << " model_filename.pcd scene_filename.pcd [Options]" << std::endl << std::endl;
  std::cout << "Options:" << std::endl;
  std::cout << "     -h:                     Show this help." << std::endl;
  std::cout << "     -k:                     Show used keypoints." << std::endl;
  std::cout << "     -c:                     Show used correspondences." << std::endl;
  std::cout << "     -r:                     Compute the model cloud resolution and multiply" << std::endl;
  std::cout << "                             each radius given by that value." << std::endl;
  std::cout << "     --algorithm (Hough|GC): Clustering algorithm used (default Hough)." << std::endl;
  std::cout << "     --model_ss val:         Model uniform sampling radius (default 0.01)" << std::endl;
  std::cout << "     --scene_ss val:         Scene uniform sampling radius (default 0.03)" << std::endl;
  std::cout << "     --rf_rad val:           Reference frame radius (default 0.015)" << std::endl;
  std::cout << "     --descr_rad val:        Descriptor radius (default 0.02)" << std::endl;
  std::cout << "     --cg_size val:          Cluster size (default 0.01)" << std::endl;
  std::cout << "     --cg_thresh val:        Clustering threshold (default 5)" << std::endl << std::endl;
}

void
parseCommandLine (int argc, char *argv[])
{
  //Show help
  if (pcl::console::find_switch (argc, argv, "-h"))
  {
    showHelp (argv[0]);
    exit (0);
  }

  //Model & scene filenames
  std::vector<int> filenames;
  filenames = pcl::console::parse_file_extension_argument (argc, argv, ".pcd");
  if (filenames.size () != 2)
  {
    std::cout << "Filenames missing.\n";
    showHelp (argv[0]);
    exit (-1);
  }

  model_filename_ = argv[filenames[0]];
  scene_filename_ = argv[filenames[1]];

  //Program behavior
  if (pcl::console::find_switch (argc, argv, "-k"))
  {
    show_keypoints_ = true;
  }
  if (pcl::console::find_switch (argc, argv, "-c"))
  {
    show_correspondences_ = true;
  }
  if (pcl::console::find_switch (argc, argv, "-r")) //計算點雲的分辨率和多樣性
  {
    use_cloud_resolution_ = true;
  }

  std::string used_algorithm;
  if (pcl::console::parse_argument (argc, argv, "--algorithm", used_algorithm) != -1)
  {
    if (used_algorithm.compare ("Hough") == 0)
    {
      use_hough_ = true;
    }else if (used_algorithm.compare ("GC") == 0)
    {
      use_hough_ = false;
    }
    else
    {
      std::cout << "Wrong algorithm name.\n";
      showHelp (argv[0]);
      exit (-1);
    }
  }

  //General parameters
  pcl::console::parse_argument (argc, argv, "--model_ss", model_ss_);
  pcl::console::parse_argument (argc, argv, "--scene_ss", scene_ss_);
  pcl::console::parse_argument (argc, argv, "--rf_rad", rf_rad_);
  pcl::console::parse_argument (argc, argv, "--descr_rad", descr_rad_);
  pcl::console::parse_argument (argc, argv, "--cg_size", cg_size_);
  pcl::console::parse_argument (argc, argv, "--cg_thresh", cg_thresh_);
}

double
computeCloudResolution (const pcl::PointCloud<PointType>::ConstPtr &cloud)//計算點雲分辨率
{
  double res = 0.0;
  int n_points = 0;
  int nres;
  std::vector<int> indices (2);
  std::vector<float> sqr_distances (2);
  pcl::search::KdTree<PointType> tree;
  tree.setInputCloud (cloud);   //設置輸入點雲

  for (size_t i = 0; i < cloud->size (); ++i)
  {
    if (! pcl_isfinite ((*cloud)[i].x))
    {
      continue;
    }
    //Considering the second neighbor since the first is the point itself.
    //運算第二個臨近點，因爲第一個點是它本身
    nres = tree.nearestKSearch (i, 2, indices, sqr_distances);//return :number of neighbors found 
    if (nres == 2)
    {
      res += sqrt (sqr_distances[1]);
      ++n_points;
    }
  }
  if (n_points != 0)
  {
    res /= n_points;
  }
  return res;
}

int
main (int argc, char *argv[])
{
  parseCommandLine (argc, argv);

  pcl::PointCloud<PointType>::Ptr model (new pcl::PointCloud<PointType> ()); //模型點雲
  pcl::PointCloud<PointType>::Ptr model_keypoints (new pcl::PointCloud<PointType> ());//模型角點
  pcl::PointCloud<PointType>::Ptr scene (new pcl::PointCloud<PointType> ());  //目標點雲
  pcl::PointCloud<PointType>::Ptr scene_keypoints (new pcl::PointCloud<PointType> ());//目標角點
  pcl::PointCloud<NormalType>::Ptr model_normals (new pcl::PointCloud<NormalType> ()); //法線
  pcl::PointCloud<NormalType>::Ptr scene_normals (new pcl::PointCloud<NormalType> ());
  pcl::PointCloud<DescriptorType>::Ptr model_descriptors (new pcl::PointCloud<DescriptorType> ()); //描述子
  pcl::PointCloud<DescriptorType>::Ptr scene_descriptors (new pcl::PointCloud<DescriptorType> ());

  //載入文件
  if (pcl::io::loadPCDFile (model_filename_, *model) < 0)
  {
    std::cout << "Error loading model cloud." << std::endl;
    showHelp (argv[0]);
    return (-1);
  }
  if (pcl::io::loadPCDFile (scene_filename_, *scene) < 0)
  {
    std::cout << "Error loading scene cloud." << std::endl;
    showHelp (argv[0]);
    return (-1);
  }


  // 設置分辨率
  if (use_cloud_resolution_)
  {
    float resolution = static_cast<float> (computeCloudResolution (model));
    if (resolution != 0.0f)
    {
      model_ss_   *= resolution;
      scene_ss_   *= resolution;
      rf_rad_     *= resolution;
      descr_rad_  *= resolution;
      cg_size_    *= resolution;
    }

    std::cout << "Model resolution:       " << resolution << std::endl;
    std::cout << "Model sampling size:    " << model_ss_ << std::endl;
    std::cout << "Scene sampling size:    " << scene_ss_ << std::endl;
    std::cout << "LRF support radius:     " << rf_rad_ << std::endl;
    std::cout << "SHOT descriptor radius: " << descr_rad_ << std::endl;
    std::cout << "Clustering bin size:    " << cg_size_ << std::endl << std::endl;
  }

  //計算法線 normalestimationomp估計局部表面特性在每個三個特點，分別表面的法向量和曲率，平行，使用OpenMP標準。//初始化調度程序並設置要使用的線程數（默認爲0）。
  pcl::NormalEstimationOMP<PointType, NormalType> norm_est; 
  norm_est.setKSearch (10);       //設置K鄰域搜索閥值爲10個點
  norm_est.setInputCloud (model);  //設置輸入點雲
  norm_est.compute (*model_normals);   //計算點雲法線

  norm_est.setInputCloud (scene);   
  norm_est.compute (*scene_normals);

  //均勻採樣點雲並提取關鍵點
  pcl::UniformSampling<PointType> uniform_sampling;
  uniform_sampling.setInputCloud (model);  //輸入點雲
  uniform_sampling.setRadiusSearch (model_ss_);   //設置半徑
  uniform_sampling.filter (*model_keypoints);   //濾波
  std::cout << "Model total points: " << model->size () << "; Selected Keypoints: " << model_keypoints->size () << std::endl;

  uniform_sampling.setInputCloud (scene);
  uniform_sampling.setRadiusSearch (scene_ss_);
  uniform_sampling.filter (*scene_keypoints);
  std::cout << "Scene total points: " << scene->size () << "; Selected Keypoints: " << scene_keypoints->size () << std::endl;


  //爲關鍵點計算描述子
  pcl::SHOTEstimationOMP<PointType, NormalType, DescriptorType> descr_est;
  descr_est.setRadiusSearch (descr_rad_);  //設置搜索半徑

  descr_est.setInputCloud (model_keypoints);  //輸入模型的關鍵點
  descr_est.setInputNormals (model_normals);  //輸入模型的法線
  descr_est.setSearchSurface (model);         //輸入的點雲
  descr_est.compute (*model_descriptors);     //計算描述子

  descr_est.setInputCloud (scene_keypoints);  //同理
  descr_est.setInputNormals (scene_normals);
  descr_est.setSearchSurface (scene);
  descr_est.compute (*scene_descriptors);

  //  使用Kdtree找出 Model-Scene 匹配點
  pcl::CorrespondencesPtr model_scene_corrs (new pcl::Correspondences ());

  pcl::KdTreeFLANN<DescriptorType> match_search;   //設置配準的方法
  match_search.setInputCloud (model_descriptors);  //輸入模板點雲的描述子

//每一個場景的關鍵點描述子都要找到模板中匹配的關鍵點描述子並將其添加到對應的匹配向量中。
  for (size_t i = 0; i < scene_descriptors->size (); ++i)
  {
    std::vector<int> neigh_indices (1);   //設置最近鄰點的索引
    std::vector<float> neigh_sqr_dists (1); //申明最近鄰平方距離值
    if (!pcl_isfinite (scene_descriptors->at (i).descriptor[0])) //忽略 NaNs點
    {
      continue;
    }
    int found_neighs = match_search.nearestKSearch (scene_descriptors->at (i), 1, neigh_indices, neigh_sqr_dists);
//scene_descriptors->at (i)是給定點雲 1是臨近點個數 ，neigh_indices臨近點的索引  neigh_sqr_dists是與臨近點的索引

    if(found_neighs == 1 && neigh_sqr_dists[0] < 0.25f) // 僅當描述子與臨近點的平方距離小於0.25（描述子與臨近的距離在一般在0到1之間）才添加匹配
    {
//neigh_indices[0]給定點，  i  是配準數  neigh_sqr_dists[0]與臨近點的平方距離
      pcl::Correspondence corr (neigh_indices[0], static_cast<int> (i), neigh_sqr_dists[0]);
      model_scene_corrs->push_back (corr);   //把配準的點存儲在容器中
    }
  }
  std::cout << "Correspondences found: " << model_scene_corrs->size () << std::endl;


  //  實際的配準方法的實現
  std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > rototranslations;
  std::vector<pcl::Correspondences> clustered_corrs;

  //  使用 Hough3D算法尋找匹配點
  if (use_hough_)
  {
    //
    //  Compute (Keypoints) Reference Frames only for Hough
    //計算參考幀的Hough（也就是關鍵點）
    pcl::PointCloud<RFType>::Ptr model_rf (new pcl::PointCloud<RFType> ());
    pcl::PointCloud<RFType>::Ptr scene_rf (new pcl::PointCloud<RFType> ());
    //特徵估計的方法（點雲，法線，參考幀）
    pcl::BOARDLocalReferenceFrameEstimation<PointType, NormalType, RFType> rf_est;
    rf_est.setFindHoles (true);
    rf_est.setRadiusSearch (rf_rad_);   //設置搜索半徑

    rf_est.setInputCloud (model_keypoints);  //模型關鍵點
    rf_est.setInputNormals (model_normals); //模型法線
    rf_est.setSearchSurface (model);    //模型
    rf_est.compute (*model_rf);      //模型的參考幀   

    rf_est.setInputCloud (scene_keypoints);  //同理
    rf_est.setInputNormals (scene_normals);
    rf_est.setSearchSurface (scene);
    rf_est.compute (*scene_rf);

    //  Clustering聚類的方法

    //對輸入點與的聚類，以區分不同的實例的場景中的模型 
    pcl::Hough3DGrouping<PointType, PointType, RFType, RFType> clusterer;
    clusterer.setHoughBinSize (cg_size_);//霍夫空間設置每個bin的大小
    clusterer.setHoughThreshold (cg_thresh_);//閥值
    clusterer.setUseInterpolation (true);
    clusterer.setUseDistanceWeight (false);

    clusterer.setInputCloud (model_keypoints);
    clusterer.setInputRf (model_rf);   //設置輸入的參考幀
    clusterer.setSceneCloud (scene_keypoints);
    clusterer.setSceneRf (scene_rf);
    clusterer.setModelSceneCorrespondences (model_scene_corrs);//model_scene_corrs存儲配準的點

    //clusterer.cluster (clustered_corrs);辨認出聚類的對象
    clusterer.recognize (rototranslations, clustered_corrs);
  }
  else // Using GeometricConsistency  或者使用幾何一致性性質
  {
    pcl::GeometricConsistencyGrouping<PointType, PointType> gc_clusterer;
    gc_clusterer.setGCSize (cg_size_);   //設置幾何一致性的大小
    gc_clusterer.setGCThreshold (cg_thresh_); //閥值

    gc_clusterer.setInputCloud (model_keypoints);
    gc_clusterer.setSceneCloud (scene_keypoints);
    gc_clusterer.setModelSceneCorrespondences (model_scene_corrs);

    //gc_clusterer.cluster (clustered_corrs);
    gc_clusterer.recognize (rototranslations, clustered_corrs);
  }

  //輸出的結果  找出輸入模型是否在場景中出現
  std::cout << "Model instances found: " << rototranslations.size () << std::endl;
  for (size_t i = 0; i < rototranslations.size (); ++i)
  {
    std::cout << "\n    Instance " << i + 1 << ":" << std::endl;
    std::cout << "        Correspondences belonging to this instance: " << clustered_corrs[i].size () << std::endl;

    // 打印處相對於輸入模型的旋轉矩陣與平移矩陣
    Eigen::Matrix3f rotation = rototranslations[i].block<3,3>(0, 0);
    Eigen::Vector3f translation = rototranslations[i].block<3,1>(0, 3);

    printf ("\n");
    printf ("            | %6.3f %6.3f %6.3f | \n", rotation (0,0), rotation (0,1), rotation (0,2));
    printf ("        R = | %6.3f %6.3f %6.3f | \n", rotation (1,0), rotation (1,1), rotation (1,2));
    printf ("            | %6.3f %6.3f %6.3f | \n", rotation (2,0), rotation (2,1), rotation (2,2));
    printf ("\n");
    printf ("        t = < %0.3f, %0.3f, %0.3f >\n", translation (0), translation (1), translation (2));
  }

//可視化
  pcl::visualization::PCLVisualizer viewer ("Correspondence Grouping");
  viewer.addPointCloud (scene, "scene_cloud");//可視化場景點雲

  pcl::PointCloud<PointType>::Ptr off_scene_model (new pcl::PointCloud<PointType> ());
  pcl::PointCloud<PointType>::Ptr off_scene_model_keypoints (new pcl::PointCloud<PointType> ());

  if (show_correspondences_ || show_keypoints_)  //可視化配準點
  {
    //  We are translating the model so that it doesn't end in the middle of the scene representation
    //就是要對輸入的模型進行旋轉與平移，使其在可視化界面的中間位置
    pcl::transformPointCloud (*model, *off_scene_model, Eigen::Vector3f (-1,0,0), Eigen::Quaternionf (1, 0, 0, 0));
    pcl::transformPointCloud (*model_keypoints, *off_scene_model_keypoints, Eigen::Vector3f (-1,0,0), Eigen::Quaternionf (1, 0, 0, 0));  //因爲模型的位置變化了，所以要對特徵點進行變化

    pcl::visualization::PointCloudColorHandlerCustom<PointType> off_scene_model_color_handler (off_scene_model, 255, 255, 128);  //對模型點雲上顏色
    viewer.addPointCloud (off_scene_model, off_scene_model_color_handler, "off_scene_model");
  }

  if (show_keypoints_)   //可視化關鍵點
  {
    pcl::visualization::PointCloudColorHandlerCustom<PointType> scene_keypoints_color_handler (scene_keypoints, 0, 0, 255);  //對場景中的點雲上爲綠色
    viewer.addPointCloud (scene_keypoints, scene_keypoints_color_handler, "scene_keypoints");
    viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 5, "scene_keypoints");

    pcl::visualization::PointCloudColorHandlerCustom<PointType> off_scene_model_keypoints_color_handler (off_scene_model_keypoints, 0, 0, 255);
    viewer.addPointCloud (off_scene_model_keypoints, off_scene_model_keypoints_color_handler, "off_scene_model_keypoints");
    viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 5, "off_scene_model_keypoints");
  }

  for (size_t i = 0; i < rototranslations.size (); ++i)
  {
    pcl::PointCloud<PointType>::Ptr rotated_model (new pcl::PointCloud<PointType> ());
    pcl::transformPointCloud (*model, *rotated_model, rototranslations[i]);//把model轉化爲rotated_model
//    <Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> >   rototranslations是射影變換矩陣
    std::stringstream ss_cloud; 
    ss_cloud << "instance" << i;

    pcl::visualization::PointCloudColorHandlerCustom<PointType> rotated_model_color_handler (rotated_model, 255, 0, 0);
    viewer.addPointCloud (rotated_model, rotated_model_color_handler, ss_cloud.str ());

    if (show_correspondences_)   //顯示配準連接
    {
      for (size_t j = 0; j < clustered_corrs[i].size (); ++j)
      {
        std::stringstream ss_line;
        ss_line << "correspondence_line" << i << "_" << j;
        PointType& model_point = off_scene_model_keypoints->at (clustered_corrs[i][j].index_query);
        PointType& scene_point = scene_keypoints->at (clustered_corrs[i][j].index_match);

        //  We are drawing a line for each pair of clustered correspondences found between the model and the scene
        viewer.addLine<PointType, PointType> (model_point, scene_point, 0, 255, 0, ss_line.str ());
      }
    }
  }

  while (!viewer.wasStopped ())
  {
    viewer.spinOnce ();
  }

  return (0);
}

 

From: PCL特徵點與配準（1）

發表評論
所有評論
還沒有人評論，想成為第一個評論的人麼? 請在上方評論欄輸入並且點擊發布.
相關文章

python可視化點雲文件

今天想要打開一個點雲文件看看，但是好像沒有怎麼搜到簡單查看點雲的基於python的方法，於是找了半天，找到了下面這個方法，非常簡單，也非常快，使用python就可以查看非常方便 使用的是Open3D這個python庫 文檔地址： http

qq_36265860 2020-07-08 03:40:49

[pcl::PLYReader::read] problem parsing header!

一、問題描述 RealSense保存的ply文件，用PCL可視化時，會報錯，是因爲RealSense保存的ply文件格式有問題。 二、問題分析 RealSense保存的ply文件格式是這樣的： ply format ascii

万俟淋曦 2020-07-07 07:13:11

meshlab常用功能

meshlab常用功能1.查看box corner2.修改座標系3.蒙特卡諾濾波 注： meshlab2016.12版 1.查看box corner render–》box corner 2.修改座標系 繞xyz旋轉和平移都有

做人嘛最重要的是开心啦 2020-07-06 00:40:12

【三維激光掃描】第三章：點雲數據採集

第一節  野外掃描方案 爲了獲取高精度完整的點雲數據，工作過程-般包括項目計劃制訂、 外業數據採集和內業數據處理三個環節。《規程)中指出地面三維激光掃描總體工作流程應包括技術準備與技術設計、控制測量、數據採集、數據預處理、成果製作、質量控

刘一哥GIS 2020-07-05 07:24:11

【三維激光掃描】第四章：點雲數據處理

第一節   點雲數據處理流程 由於外業獲取點雲數據時的多種因素影響，點雲數據質量直接影響到三維建模等方面的應用，點雲數據處理環節非常重要。本章主要介紹數據處理流程，數據的配準:濾波、縮減、分割、分類，最後介紹點雲數據應用。 5.1 數據處

刘一哥GIS 2020-07-05 07:24:11

CVPR2020：點雲三維目標跟蹤的點對盒網絡（P2B）

CVPR2020：點雲三維目標跟蹤的點對盒網絡（P2B） P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds 代碼：https://github.com

wujianming_110117 2020-07-04 18:50:02

CVPR2020：Grid-GCN用於快速和可擴展的點雲學習

CVPR2020：Grid-GCN用於快速和可擴展的點雲學習 Grid-GCN for Fast and Scalable Point Cloud Learning 論文地址： https://openaccess.thecvf.

wujianming_110117 2020-07-04 18:50:02

CVPR2020：基於自適應採樣的非局部神經網絡魯棒點雲處理（PointASNL）

CVPR2020：基於自適應採樣的非局部神經網絡魯棒點雲處理（PointASNL） PointASNL: Robust Point Clouds Processing Using Nonlocal Neural Networks

wujianming_110117 2020-07-04 18:50:02

CVPR2020：三維點雲無監督表示學習的全局局部雙向推理

CVPR2020：三維點雲無監督表示學習的全局局部雙向推理 Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D P

wujianming_110117 2020-07-04 18:50:02

CVPR2020：點雲分類的自動放大框架PointAugment

CVPR2020：點雲分類的自動放大框架PointAugment PointAugment: An Auto-Augmentation Framework for Point Cloud Classification 論文地址：

wujianming_110117 2020-07-04 18:50:02

TI voxelsdk點雲ros publisher

利用TI voxelsdk的python接口實時採集點雲數據並通過ros發佈 import Voxel import numpy as np import rospy import sensor_msgs.point_cloud2

zhoudekuai 2020-07-01 21:54:55

MeshLab怎麼換背景顏色？

tools->options-> 就可以調meshlab的背景顏色了 簡單但是不知道，爲什麼網上關於meshlab的使用東西這麼少呢，有大佬可以給我留言

科研小白 2020-06-27 22:19:53

cmake 找不到cuda Could Not find CUDA(missing:CUDA_INCLUDE_DIRS CUDA_CUDART_LIBRARY)

報錯如圖 在網上找了很多辦法 比如 Try adding -DCUDA_CUDART_LIBRARY=/usr/local/cuda/lib64/libcudart.so when you run cmake command. 參考的是

SY_qqq 2020-06-26 18:18:33

LAS點雲屬性

共同交流，歡迎提出問題。 Field Type Description Chinese Description Remark X Double X coordinate X The X, Y and

累了就要打游戏 2020-06-26 15:09:44

C++：OTSU確定標線點雲分割閾值

端午節快樂呀。 閒來無事，想着分割一下標線點雲，但是閾值怎麼確定呢？感覺OTSU應該可以。 一、OTSU簡介 OTSU（大津法、最大類間方差法）算法是由日本學者OTSU於1979年提出的一種對圖像進行二值化的高效算法。 1.1 原理 利用

累了就要打游戏 2020-06-26 15:09:42