Vslam14講(複習五)

相機與圖像

主要目標：

• 理解針孔相機的模型、內參與徑向畸變係數。
• 理解一個空間點是如何投影到相機成像平面的。
• 掌握Opencv的圖像存儲方式
• 學會基本的攝像頭標定方法

前面幾個博客講了“相機如何表達自身位置的問題”，這個博客的重點是“機器人如何觀測外部世界”。

這部分我在第二個博客中已經寫了相機模型的筆記。

5.2 圖像

5.3 實踐：計算機中的圖像

安裝opencv有很多教程。。。。以往沒少重裝，這裏就略過了。

#include <iostream>
#include <chrono>

using namespace std;

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>

int main(int argc, char **argv) {
  // 讀取argv[1]指定的圖像
  cv::Mat image;
  image = cv::imread(argv[1]); //cv::imread函數讀取指定路徑下的圖像

  // 判斷圖像文件是否正確讀取
  if (image.data == nullptr) { //數據不存在,可能是文件不存在
    cerr << "文件" << argv[1] << "不存在." << endl;
    return 0;
  }

  // 文件順利讀取, 首先輸出一些基本信息
  cout << "圖像寬爲" << image.cols << ",高爲" << image.rows << ",通道數爲" << image.channels() << endl;
  cv::imshow("image", image);      // 用cv::imshow顯示圖像
  cv::waitKey(0);                  // 暫停程序,等待一個按鍵輸入

  // 判斷image的類型
  if (image.type() != CV_8UC1 && image.type() != CV_8UC3) {
    // 圖像類型不符合要求
    cout << "請輸入一張彩色圖或灰度圖." << endl;
    return 0;
  }

  // 遍歷圖像, 請注意以下遍歷方式亦可使用於隨機像素訪問
  // 使用 std::chrono 來給算法計時
  chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
  for (size_t y = 0; y < image.rows; y++) {
    // 用cv::Mat::ptr獲得圖像的行指針
    unsigned char *row_ptr = image.ptr<unsigned char>(y);  // row_ptr是第y行的頭指針
    for (size_t x = 0; x < image.cols; x++) {
      // 訪問位於 x,y 處的像素
      unsigned char *data_ptr = &row_ptr[x * image.channels()]; // data_ptr 指向待訪問的像素數據
      // 輸出該像素的每個通道,如果是灰度圖就只有一個通道
      for (int c = 0; c != image.channels(); c++) {
        unsigned char data = data_ptr[c]; // data爲I(x,y)第c個通道的值
      }
    }
  }
  chrono::steady_clock::time_point t2 = chrono::steady_clock::now();
  chrono::duration<double> time_used = chrono::duration_cast < chrono::duration < double >> (t2 - t1);
  cout << "遍歷圖像用時：" << time_used.count() << " 秒。" << endl;

  // 關於 cv::Mat 的拷貝
  // 直接賦值並不會拷貝數據
  cv::Mat image_another = image;
  // 修改 image_another 會導致 image 發生變化
  image_another(cv::Rect(0, 0, 100, 100)).setTo(0); // 將左上角100*100的塊置零
  cv::imshow("image", image);
  cv::waitKey(0);

  // 使用clone函數來拷貝數據
  cv::Mat image_clone = image.clone();
  image_clone(cv::Rect(0, 0, 100, 100)).setTo(255);
  cv::imshow("image", image);
  cv::imshow("image_clone", image_clone);
  cv::waitKey(0);

  // 對於圖像還有很多基本的操作,如剪切,旋轉,縮放等,限於篇幅就不一一介紹了,請參看OpenCV官方文檔查詢每個函數的調用方法.
  cv::destroyAllWindows();
  return 0;
}

#include <opencv2/opencv.hpp>
#include <string>

using namespace std;

string image_file = "./distorted.png";   // 請確保路徑正確

int main(int argc, char **argv) {

  // 本程序實現去畸變部分的代碼。儘管我們可以調用OpenCV的去畸變，但自己實現一遍有助於理解。
  // 畸變參數
  double k1 = -0.28340811, k2 = 0.07395907, p1 = 0.00019359, p2 = 1.76187114e-05;
  // 內參
  double fx = 458.654, fy = 457.296, cx = 367.215, cy = 248.375;

  cv::Mat image = cv::imread(image_file, 0);   // 圖像是灰度圖，CV_8UC1
  int rows = image.rows, cols = image.cols;
  cv::Mat image_undistort = cv::Mat(rows, cols, CV_8UC1);   // 去畸變以後的圖

  // 計算去畸變後圖像的內容
  for (int v = 0; v < rows; v++) {
    for (int u = 0; u < cols; u++) {
      // 按照公式，計算點(u,v)對應到畸變圖像中的座標(u_distorted, v_distorted)
      double x = (u - cx) / fx, y = (v - cy) / fy;
      double r = sqrt(x * x + y * y);
      double x_distorted = x * (1 + k1 * r * r + k2 * r * r * r * r) + 2 * p1 * x * y + p2 * (r * r + 2 * x * x);
      double y_distorted = y * (1 + k1 * r * r + k2 * r * r * r * r) + p1 * (r * r + 2 * y * y) + 2 * p2 * x * y;
      double u_distorted = fx * x_distorted + cx;
      double v_distorted = fy * y_distorted + cy;

      // 賦值 (最近鄰插值)
      if (u_distorted >= 0 && v_distorted >= 0 && u_distorted < cols && v_distorted < rows) {
        image_undistort.at<uchar>(v, u) = image.at<uchar>((int) v_distorted, (int) u_distorted);
      } else {
        image_undistort.at<uchar>(v, u) = 0;
      }
    }
  }

  // 畫圖去畸變後圖像
  cv::imshow("distorted", image);
  cv::imshow("undistorted", image_undistort);
  cv::waitKey();
  return 0;
}

#include <opencv2/opencv.hpp>
#include <vector>
#include <string>
#include <Eigen/Core>
#include <pangolin/pangolin.h>
#include <unistd.h>

using namespace std;
using namespace Eigen;

// 文件路徑
string left_file = "./left.png";
string right_file = "./right.png";

// 在pangolin中畫圖，已寫好，無需調整
void showPointCloud(
    const vector<Vector4d, Eigen::aligned_allocator<Vector4d>> &pointcloud);

int main(int argc, char **argv) {

    // 內參
    double fx = 459.8806, fy = 460.0172, cx = 323.1796, cy = 265.2708;
    // 基線
    double b = 0.10;

    // 讀取圖像
    cv::Mat left = cv::imread(left_file, 0);
    cv::Mat right = cv::imread(right_file, 0);
    cv::Ptr<cv::StereoSGBM> sgbm = cv::StereoSGBM::create(
        0, 96, 9, 8 * 9 * 9, 32 * 9 * 9, 1, 63, 10, 100, 32);    // 神奇的參數
    cv::Mat disparity_sgbm, disparity;
    sgbm->compute(left, right, disparity_sgbm);
    disparity_sgbm.convertTo(disparity, CV_32F, 1.0 / 16.0f);

    // 生成點雲
    vector<Vector4d, Eigen::aligned_allocator<Vector4d>> pointcloud;

    // 如果你的機器慢，請把後面的v++和u++改成v+=2, u+=2
    for (int v = 0; v < left.rows; v++)
        for (int u = 0; u < left.cols; u++) {
            if (disparity.at<float>(v, u) <= 0.0 || disparity.at<float>(v, u) >= 96.0) continue;

            Vector4d point(0, 0, 0, left.at<uchar>(v, u) / 255.0); // 前三維爲xyz,第四維爲顏色

            // 根據雙目模型計算 point 的位置
            double x = (u - cx) / fx;
            double y = (v - cy) / fy;
            double depth = fx * b / (disparity.at<float>(v, u));
            point[0] = x * depth;
            point[1] = y * depth;
            point[2] = depth;

            pointcloud.push_back(point);
        }

    cv::imshow("disparity", disparity / 96.0);
    cv::waitKey(0);
    // 畫出點雲
    showPointCloud(pointcloud);
    return 0;
}

void showPointCloud(const vector<Vector4d, Eigen::aligned_allocator<Vector4d>> &pointcloud) {

    if (pointcloud.empty()) {
        cerr << "Point cloud is empty!" << endl;
        return;
    }

    pangolin::CreateWindowAndBind("Point Cloud Viewer", 1024, 768);
    glEnable(GL_DEPTH_TEST);
    glEnable(GL_BLEND);
    glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA);

    pangolin::OpenGlRenderState s_cam(
        pangolin::ProjectionMatrix(1024, 768, 500, 500, 512, 389, 0.1, 1000),
        pangolin::ModelViewLookAt(0, -0.1, -1.8, 0, 0, 0, 0.0, -1.0, 0.0)
    );

    pangolin::View &d_cam = pangolin::CreateDisplay()
        .SetBounds(0.0, 1.0, pangolin::Attach::Pix(175), 1.0, -1024.0f / 768.0f)
        .SetHandler(new pangolin::Handler3D(s_cam));

    while (pangolin::ShouldQuit() == false) {
        glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT);

        d_cam.Activate(s_cam);
        glClearColor(1.0f, 1.0f, 1.0f, 1.0f);

        glPointSize(2);
        glBegin(GL_POINTS);
        for (auto &p: pointcloud) {
            glColor3f(p[3], p[3], p[3]);
            glVertex3d(p[0], p[1], p[2]);
        }
        glEnd();
        pangolin::FinishFrame();
        usleep(5000);   // sleep 5 ms
    }
    return;
}

#include <iostream>
#include <fstream>
#include <opencv2/opencv.hpp>
#include <boost/format.hpp>  // for formating strings
#include <sophus/se3.hpp>
#include <pangolin/pangolin.h>

using namespace std;
typedef vector<Sophus::SE3d, Eigen::aligned_allocator<Sophus::SE3d>> TrajectoryType;
typedef Eigen::Matrix<double, 6, 1> Vector6d;

// 在pangolin中畫圖，已寫好，無需調整
void showPointCloud(
    const vector<Vector6d, Eigen::aligned_allocator<Vector6d>> &pointcloud);

int main(int argc, char **argv) {
    vector<cv::Mat> colorImgs, depthImgs;    // 彩色圖和深度圖
    TrajectoryType poses;         // 相機位姿

    ifstream fin("./pose.txt");
    if (!fin) {
        cerr << "請在有pose.txt的目錄下運行此程序" << endl;
        return 1;
    }

    for (int i = 0; i < 5; i++) {
        boost::format fmt("./%s/%d.%s"); //圖像文件格式
        colorImgs.push_back(cv::imread((fmt % "color" % (i + 1) % "png").str()));
        depthImgs.push_back(cv::imread((fmt % "depth" % (i + 1) % "pgm").str(), -1)); // 使用-1讀取原始圖像

        double data[7] = {0};
        for (auto &d:data)
            fin >> d;
        Sophus::SE3d pose(Eigen::Quaterniond(data[6], data[3], data[4], data[5]),
                          Eigen::Vector3d(data[0], data[1], data[2]));
        poses.push_back(pose);
    }

    // 計算點雲並拼接
    // 相機內參 
    double cx = 325.5;
    double cy = 253.5;
    double fx = 518.0;
    double fy = 519.0;
    double depthScale = 1000.0;
    vector<Vector6d, Eigen::aligned_allocator<Vector6d>> pointcloud;
    pointcloud.reserve(1000000);

    for (int i = 0; i < 5; i++) {
        cout << "轉換圖像中: " << i + 1 << endl;
        cv::Mat color = colorImgs[i];
        cv::Mat depth = depthImgs[i];
        Sophus::SE3d T = poses[i];
        for (int v = 0; v < color.rows; v++)
            for (int u = 0; u < color.cols; u++) {
                unsigned int d = depth.ptr<unsigned short>(v)[u]; // 深度值
                if (d == 0) continue; // 爲0表示沒有測量到
                Eigen::Vector3d point;
                point[2] = double(d) / depthScale;
                point[0] = (u - cx) * point[2] / fx;
                point[1] = (v - cy) * point[2] / fy;
                Eigen::Vector3d pointWorld = T * point;

                Vector6d p;
                p.head<3>() = pointWorld;
                p[5] = color.data[v * color.step + u * color.channels()];   // blue
                p[4] = color.data[v * color.step + u * color.channels() + 1]; // green
                p[3] = color.data[v * color.step + u * color.channels() + 2]; // red
                pointcloud.push_back(p);
            }
    }

    cout << "點雲共有" << pointcloud.size() << "個點." << endl;
    showPointCloud(pointcloud);
    return 0;
}

void showPointCloud(const vector<Vector6d, Eigen::aligned_allocator<Vector6d>> &pointcloud) {

    if (pointcloud.empty()) {
        cerr << "Point cloud is empty!" << endl;
        return;
    }

    pangolin::CreateWindowAndBind("Point Cloud Viewer", 1024, 768);
    glEnable(GL_DEPTH_TEST);
    glEnable(GL_BLEND);
    glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA);

    pangolin::OpenGlRenderState s_cam(
        pangolin::ProjectionMatrix(1024, 768, 500, 500, 512, 389, 0.1, 1000),
        pangolin::ModelViewLookAt(0, -0.1, -1.8, 0, 0, 0, 0.0, -1.0, 0.0)
    );

    pangolin::View &d_cam = pangolin::CreateDisplay()
        .SetBounds(0.0, 1.0, pangolin::Attach::Pix(175), 1.0, -1024.0f / 768.0f)
        .SetHandler(new pangolin::Handler3D(s_cam));

    while (pangolin::ShouldQuit() == false) {
        glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT);

        d_cam.Activate(s_cam);
        glClearColor(1.0f, 1.0f, 1.0f, 1.0f);

        glPointSize(2);
        glBegin(GL_POINTS);
        for (auto &p: pointcloud) {
            glColor3d(p[3] / 255.0, p[4] / 255.0, p[5] / 255.0);
            glVertex3d(p[0], p[1], p[2]);
        }
        glEnd();
        pangolin::FinishFrame();
        usleep(5000);   // sleep 5 ms
    }
    return;
}

課後題：

https://blog.csdn.net/qq_17032807/article/details/84971560