解码格雷码模式教程

目标

在本教程中，您将学习如何使用 GrayCodePattern 类来

• 解码先前获取的格雷码图案。
• 生成视差图。
• 生成点云。

代码

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#include <iostream>
#include <opencv2/core.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/calib3d.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/structured_light.hpp>
#include <opencv2/opencv_modules.hpp>
 
// (如果您没有构建 opencv_viz 模块，您将只能看到视差图像)
#ifdef HAVE_OPENCV_VIZ
#include <opencv2/viz.hpp>
#endif
 
using namespace std;
using namespace cv;
 
static const char* keys =
{ "{@images_list | | Image list where the captured pattern images are saved}"
    "{@calib_param_path | | Calibration_parameters }"
    "{@proj_width | | The projector width used to acquire the pattern }"
    "{@proj_height | | The projector height used to acquire the pattern}"
    "{@white_thresh | | The white threshold height (optional)}"
    "{@black_thresh | | The black threshold (optional)}" };
 
static void help()
{
cout << "\nThis example shows how to use the \"Structured Light module\" to decode a previously acquired gray code pattern, generating a pointcloud"
        "\nCall:\n"
        "./example_structured_light_pointcloud <images_list> <calib_param_path> <proj_width> <proj_height> <white_thresh> <black_thresh>\n"
<< endl;
}
 
static bool readStringList( const string& filename, vector<string>& l )
{
l.resize( 0 );
  FileStorage fs( filename, FileStorage::READ );
  if( !fs.isOpened() )
  {
cerr << "failed to open " << filename << endl;
    return false;
  }
  FileNode n = fs.getFirstTopLevelNode();
  if( n.type() != FileNode::SEQ )
  {
cerr << "cam 1 images are not a sequence! FAIL" << endl;
    return false;
  }
 
  FileNodeIterator it = n.begin(), it_end = n.end();
  for( ; it != it_end; ++it )
  {
l.push_back( ( string ) *it );
  }
 
n = fs["cam2"];
  if( n.type() != FileNode::SEQ )
  {
cerr << "cam 2 images are not a sequence! FAIL" << endl;
    return false;
  }
 
it = n.begin(), it_end = n.end();
  for( ; it != it_end; ++it )
  {
l.push_back( ( string ) *it );
  }
 
  if( l.size() % 2 != 0 )
  {
cout << "Error: the image list contains odd (non-even) number of elements\n";
    return false;
  }
  return true;
}
 
int main( int argc, char** argv )
{
  structured_light::GrayCodePattern::Params params;
  CommandLineParser parser( argc, argv, keys );
  String images_file = parser.get<String>( 0 );
  String calib_file = parser.get<String>( 1 );
 
  params.width = parser.get<int>( 2 );
  params.height = parser.get<int>( 3 );
 
  if( images_file.empty() || calib_file.empty() || params.width < 1 || params.height < 1 || argc < 5 || argc > 7 )
  {
help();
    return -1;
  }
 
  // Set up GraycodePattern with params
  Ptr<structured_light::GrayCodePattern> graycode = structured_light::GrayCodePattern::create( params );
  size_t white_thresh = 0;
  size_t black_thresh = 0;
 
  if( argc == 7 )
  {
    // If passed, setting the white and black threshold, otherwise using default values
white_thresh = parser.get<unsigned>( 4 );
black_thresh = parser.get<unsigned>( 5 );
 
graycode->setWhiteThreshold( white_thresh );
graycode->setBlackThreshold( black_thresh );
  }
 
vector<string> imagelist;
  bool ok = readStringList( images_file, imagelist );
  if( !ok || imagelist.empty() )
  {
cout << "can not open " << images_file << " or the string list is empty" << endl;
help();
    return -1;
  }
 
  FileStorage fs( calib_file, FileStorage::READ );
  if( !fs.isOpened() )
  {
cout << "Failed to open Calibration Data File." << endl;
help();
    return -1;
  }
 
  // Loading calibration parameters
  Mat cam1intrinsics, cam1distCoeffs, cam2intrinsics, cam2distCoeffs, R, T;
fs["cam1_intrinsics"] >> cam1intrinsics;
fs["cam2_intrinsics"] >> cam2intrinsics;
fs["cam1_distorsion"] >> cam1distCoeffs;
fs["cam2_distorsion"] >> cam2distCoeffs;
fs["R"] >> R;
fs["T"] >> T;
 
cout << "cam1intrinsics" << endl << cam1intrinsics << endl;
cout << "cam1distCoeffs" << endl << cam1distCoeffs << endl;
cout << "cam2intrinsics" << endl << cam2intrinsics << endl;
cout << "cam2distCoeffs" << endl << cam2distCoeffs << endl;
cout << "T" << endl << T << endl << "R" << endl << R << endl;
 
  if( (!R.data) || (!T.data) || (!cam1intrinsics.data) || (!cam2intrinsics.data) || (!cam1distCoeffs.data) || (!cam2distCoeffs.data) )
  {
cout << "Failed to load cameras calibration parameters" << endl;
help();
    return -1;
  }
 
  size_t numberOfPatternImages = graycode->getNumberOfPatternImages();
vector<vector<Mat> > captured_pattern;
captured_pattern.resize( 2 );
captured_pattern[0].resize( numberOfPatternImages );
captured_pattern[1].resize( numberOfPatternImages );
 
  Mat color = imread( imagelist[numberOfPatternImages], IMREAD_COLOR );
  Size imagesSize = color.size();
 
  // Stereo rectify
cout << "Rectifying images..." << endl;
  Mat R1, R2, P1, P2, Q;
  Rect validRoi[2];
  stereoRectify( cam1intrinsics, cam1distCoeffs, cam2intrinsics, cam2distCoeffs, imagesSize, R, T, R1, R2, P1, P2, Q, 0,
-1, imagesSize, &validRoi[0], &validRoi[1] );
 
  Mat map1x, map1y, map2x, map2y;
  initUndistortRectifyMap( cam1intrinsics, cam1distCoeffs, R1, P1, imagesSize, CV_32FC1, map1x, map1y );
  initUndistortRectifyMap( cam2intrinsics, cam2distCoeffs, R2, P2, imagesSize, CV_32FC1, map2x, map2y );
 
  // Loading pattern images
  for( size_t i = 0; i < numberOfPatternImages; i++ )
  {
captured_pattern[0][i] = imread( imagelist[i], IMREAD_GRAYSCALE );
captured_pattern[1][i] = imread( imagelist[i + numberOfPatternImages + 2], IMREAD_GRAYSCALE );
 
    if( (!captured_pattern[0][i].data) || (!captured_pattern[1][i].data) )
    {
cout << "Empty images" << endl;
help();
      return -1;
    }
 
    remap( captured_pattern[1][i], captured_pattern[1][i], map1x, map1y, INTER_NEAREST, BORDER_CONSTANT, Scalar() );
    remap( captured_pattern[0][i], captured_pattern[0][i], map2x, map2y, INTER_NEAREST, BORDER_CONSTANT, Scalar() );
 
  }
cout << "done" << endl;
 
vector<Mat> blackImages;
vector<Mat> whiteImages;
 
blackImages.resize( 2 );
whiteImages.resize( 2 );
 
  // Loading images (all white + all black) needed for shadows computation
  cvtColor( color, whiteImages[0], COLOR_RGB2GRAY );
 
whiteImages[1] = imread( imagelist[2 * numberOfPatternImages + 2], IMREAD_GRAYSCALE );
blackImages[0] = imread( imagelist[numberOfPatternImages + 1], IMREAD_GRAYSCALE );
blackImages[1] = imread( imagelist[2 * numberOfPatternImages + 2 + 1], IMREAD_GRAYSCALE );
 
  remap( color, color, map2x, map2y, INTER_NEAREST, BORDER_CONSTANT, Scalar() );
 
  remap( whiteImages[0], whiteImages[0], map2x, map2y, INTER_NEAREST, BORDER_CONSTANT, Scalar() );
  remap( whiteImages[1], whiteImages[1], map1x, map1y, INTER_NEAREST, BORDER_CONSTANT, Scalar() );
 
  remap( blackImages[0], blackImages[0], map2x, map2y, INTER_NEAREST, BORDER_CONSTANT, Scalar() );
  remap( blackImages[1], blackImages[1], map1x, map1y, INTER_NEAREST, BORDER_CONSTANT, Scalar() );
 
cout << endl << "Decoding pattern ..." << endl;
  Mat disparityMap;
  bool decoded = graycode->decode( captured_pattern, disparityMap, blackImages, whiteImages,
structured_light::DECODE_3D_UNDERWORLD );
  if( decoded )
  {
cout << endl << "pattern decoded" << endl;
 
    // 为了更好地可视化结果，将颜色映射应用于计算出的视差
    double min;
    double max;
    minMaxIdx(disparityMap, &min, &max);
    Mat cm_disp, scaledDisparityMap;
cout << "disp min " << min << endl << "disp max " << max << endl;
    convertScaleAbs( disparityMap, scaledDisparityMap, 255 / ( max - min ) );
    applyColorMap( scaledDisparityMap, cm_disp, COLORMAP_JET );
 
    // 显示结果
    resize( cm_disp, cm_disp, Size( 640, 480 ), 0, 0, INTER_LINEAR_EXACT );
    imshow( "cm disparity m", cm_disp );
 
    // 计算点云
    Mat pointcloud;
disparityMap.convertTo( disparityMap, CV_32FC1 );
    reprojectImageTo3D( disparityMap, pointcloud, Q, true, -1 );
 
    // 计算一个移除背景的掩码
    Mat dst, thresholded_disp;
    threshold( scaledDisparityMap, thresholded_disp, 0, 255, THRESH_OTSU + THRESH_BINARY );
    resize( thresholded_disp, dst, Size( 640, 480 ), 0, 0, INTER_LINEAR_EXACT );
    imshow( "threshold disp otsu", dst );
 
#ifdef HAVE_OPENCV_VIZ
    // 将掩码应用于点云
    Mat pointcloud_tresh, color_tresh;
pointcloud.copyTo( pointcloud_tresh, thresholded_disp );
color.copyTo( color_tresh, thresholded_disp );
 
    // 在 viz 上显示点云
    viz::Viz3d myWindow( "Point cloud with color" );
myWindow.setBackgroundMeshLab();
myWindow.showWidget( "coosys", viz::WCoordinateSystem() );
myWindow.showWidget( "pointcloud", viz::WCloud( pointcloud_tresh, color_tresh ) );
myWindow.showWidget( "text2d", viz::WText( "Point cloud", Point(20, 20), 20, viz::Color::green() ) );
myWindow.spin();
#endif // HAVE_OPENCV_VIZ
 
  }
 
  waitKey();
  return 0;
}

解释

首先，必须将所需参数传递给程序。第一个是先前获取的图案图像的名称列表，存储在 .yaml 文件中，组织方式如下：

%YAML:1.0
cam1
- "/data/pattern_cam1_im1.png"
- "/data/pattern_cam1_im2.png"
           ..............
- "/data/pattern_cam1_im42.png"
- "/data/pattern_cam1_im43.png"
- "/data/pattern_cam1_im44.png"
cam2
- "/data/pattern_cam2_im1.png"
- "/data/pattern_cam2_im2.png"
           ..............
- "/data/pattern_cam2_im42.png"
- "/data/pattern_cam2_im43.png"
- "/data/pattern_cam2_im44.png"

例如，本教程使用的数据集是使用分辨率为 1280x800 的投影仪获取的，因此使用两台相机捕获了 42 张图案图像（从 1 到 42 号）+ 1 张白色图像（43 号）和 1 张黑色图像（44 号）。

然后，必须将存储在另一个 .yml 文件中的相机校准参数，以及用于投射图案的投影仪的宽度和高度，以及（可选）白色和黑色阈值传递给教程程序。

这样，可以使用图案采集期间使用的投影仪的宽度和高度设置 GrayCodePattern 类的参数，并可以创建一个 GrayCodePattern 对象的指针。

structured_light::GrayCodePattern::Params params;
   ....
params.width = parser.get<int>( 2 );
params.height = parser.get<int>( 3 );
   ....
// Set up GraycodePattern with params
Ptr<structured_light::GrayCodePattern> graycode = structured_light::GrayCodePattern::create( params );

如果白色和黑色阈值作为参数传递（这些阈值会影响解码像素的数量），则可以设置其值，否则算法将使用默认值。

size_t white_thresh = 0;
size_t black_thresh = 0;
if( argc == 7 )
{
  // If passed, setting the white and black threshold, otherwise using default values
white_thresh = parser.get<size_t>( 4 );
black_thresh = parser.get<size_t>( 5 );
graycode->setWhiteThreshold( white_thresh );
graycode->setBlackThreshold( black_thresh );
}

此时，要使用 GrayCodePattern 类的 decode 方法，必须将获取的图案图像存储在 Mat 的向量的向量中。外部向量的大小为 2，因为有两台相机：第一个向量存储从左侧相机捕获的图案图像，第二个向量存储从右侧相机获取的图案图像。两台相机的图案图像数量显然相同，可以使用 getNumberOfPatternImages() 方法检索。

size_t numberOfPatternImages = graycode->getNumberOfPatternImages();
vector<vector<Mat> > captured_pattern;
captured_pattern.resize( 2 );
captured_pattern[0].resize( numberOfPatternImages );
captured_pattern[1].resize( numberOfPatternImages );
 
.....
 
for( size_t i = 0; i < numberOfPatternImages; i++ )
{
captured_pattern[0][i] = imread( imagelist[i], IMREAD_GRAYSCALE );
captured_pattern[1][i] = imread( imagelist[i + numberOfPatternImages + 2], IMREAD_GRAYSCALE );
   ......
}

至于黑白图像，它们必须存储在两个不同的 Mat 向量中：

vector<Mat> blackImages;
vector<Mat> whiteImages;
blackImages.resize( 2 );
whiteImages.resize( 2 );
// Loading images (all white + all black) needed for shadows computation
cvtColor( color, whiteImages[0], COLOR_RGB2GRAY );
whiteImages[1] = imread( imagelist[2 * numberOfPatternImages + 2], IMREAD_GRAYSCALE );
blackImages[0] = imread( imagelist[numberOfPatternImages + 1], IMREAD_GRAYSCALE );
blackImages[1] = imread( imagelist[2 * numberOfPatternImages + 2 + 1], IMREAD_GRAYSCALE );

需要强调的是，所有图像，包括图案图像、黑白图像，在传递给 decode 方法之前，都必须加载为灰度图像并进行校正。

// Stereo rectify
cout << "Rectifying images..." << endl;
Mat R1, R2, P1, P2, Q;
Rect validRoi[2];
stereoRectify( cam1intrinsics, cam1distCoeffs, cam2intrinsics, cam2distCoeffs, imagesSize, R, T, R1, R2, P1, P2, Q, 0,
-1, imagesSize, &validRoi[0], &validRoi[1] );
Mat map1x, map1y, map2x, map2y;
initUndistortRectifyMap( cam1intrinsics, cam1distCoeffs, R1, P1, imagesSize, CV_32FC1, map1x, map1y );
initUndistortRectifyMap( cam2intrinsics, cam2distCoeffs, R2, P2, imagesSize, CV_32FC1, map2x, map2y );
       ........
for( size_t i = 0; i < numberOfPatternImages; i++ )
{
       ........
remap( captured_pattern[1][i], captured_pattern[1][i], map1x, map1y, INTER_NEAREST, BORDER_CONSTANT, Scalar() );
remap( captured_pattern[0][i], captured_pattern[0][i], map2x, map2y, INTER_NEAREST, BORDER_CONSTANT, Scalar() );
}
       ........
remap( color, color, map2x, map2y, INTER_NEAREST, BORDER_CONSTANT, Scalar() );
remap( whiteImages[0], whiteImages[0], map2x, map2y, INTER_NEAREST, BORDER_CONSTANT, Scalar() );
remap( whiteImages[1], whiteImages[1], map1x, map1y, INTER_NEAREST, BORDER_CONSTANT, Scalar() );
remap( blackImages[0], blackImages[0], map2x, map2y, INTER_NEAREST, BORDER_CONSTANT, Scalar() );
remap( blackImages[1], blackImages[1], map1x, map1y, INTER_NEAREST, BORDER_CONSTANT, Scalar() );

通过这种方式，可以调用 decode 方法来解码图案并生成在第一台相机（左侧）上计算的相应视差图。

Mat disparityMap;
bool decoded = graycode->decode(captured_pattern, disparityMap, blackImages, whiteImages,
structured_light::DECODE_3D_UNDERWORLD);

为了更好地可视化结果，将颜色映射应用于计算出的视差。

double min;
double max;
minMaxIdx(disparityMap, &min, &max);
Mat cm_disp, scaledDisparityMap;
cout << "disp min " << min << endl << "disp max " << max << endl;
convertScaleAbs( disparityMap, scaledDisparityMap, 255 / ( max - min ) );
applyColorMap( scaledDisparityMap, cm_disp, COLORMAP_JET );
// 显示结果
resize( cm_disp, cm_disp, Size( 640, 480 ) );
imshow( "cm disparity m", cm_disp )

此时，可以使用 reprojectImageTo3D 方法生成点云，注意将计算出的视差转换为 CV_32FC1 Mat（decode 方法计算的是 CV_64FC1 视差图）。

Mat pointcloud;
disparityMap.convertTo( disparityMap, CV_32FC1 );
reprojectImageTo3D( disparityMap, pointcloud, Q, true, -1 );

然后计算一个用于移除背景的掩码。

Mat dst, thresholded_disp;
threshold( scaledDisparityMap, thresholded_disp, 0, 255, THRESH_OTSU + THRESH_BINARY );
resize( thresholded_disp, dst, Size( 640, 480 ) );
imshow( "threshold disp otsu", dst );

cam1 的白色图像之前也作为彩色图像加载，以便将对象的颜色映射到其重建的点云上。

Mat color = imread( imagelist[numberOfPatternImages], IMREAD_COLOR );

背景移除掩码因此应用于点云和彩色图像。

Mat pointcloud_tresh, color_tresh;
pointcloud.copyTo(pointcloud_tresh, thresholded_disp);
color.copyTo(color_tresh, thresholded_disp);

最后，可以在 viz 上可视化扫描对象的计算点云。

viz::Viz3d myWindow( "Point cloud with color");
myWindow.setBackgroundMeshLab();
myWindow.showWidget( "coosys", viz::WCoordinateSystem());
myWindow.showWidget( "pointcloud", viz::WCloud( pointcloud_tresh, color_tresh ) );
myWindow.showWidget( "text2d", viz::WText( "Point cloud", Point(20, 20), 20, viz::Color::green() ) );
myWindow.spin();