vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e
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#include "opencv2/xstereo.hpp"
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#include "opencv2/imgproc.hpp"
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#include "opencv2/highgui.hpp"
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#include <stdio.h>
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#include <string.h>
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#include <iostream>
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using namespace std;
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using namespace cv;
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using namespace cv::stereo;
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enum { STEREO_BINARY_BM, STEREO_BINARY_SGM };
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static bool parse_argument_values(int argc, char **argv, string &left, string &right, int &kernel_size, int &number_of_disparities,
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int &aggregation_window, int &P1, int &P2, float &scale, int &algo, int &binary_descriptor_type, int &success);
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int main(int argc, char** argv)
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{
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string left, right;
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int kernel_size = 0, number_of_disparities = 0, aggregation_window = 0, P1 = 0, P2 = 0;
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float scale = 4;
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int algo = STEREO_BINARY_BM;
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int binary_descriptor_type = 0;
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int success;
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// here we extract the values that were added as arguments
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// we also test to see if they are provided correcly
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if (!parse_argument_values(argc, argv, left, right,
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kernel_size,
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number_of_disparities,
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aggregation_window,
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P1, P2,
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scale,
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algo, binary_descriptor_type,success))
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{
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return 1;
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}
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// verify if the user inputs the correct number of parameters
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Mat image1, image2;
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// we read a pair of images from the disk
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image1 = imread(left, CV_8UC1);
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image2 = imread(right, CV_8UC1);
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// verify if they are loaded correctly
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if (image1.empty() || image2.empty())
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{
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cout << " --(!) Error reading images \n";
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return 1;
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}
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// we display the parsed parameters
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const char *b[7] = { "CV_DENSE_CENSUS", "CV_SPARSE_CENSUS", "CV_CS_CENSUS", "CV_MODIFIED_CS_CENSUS",
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"CV_MODIFIED_CENSUS_TRANSFORM", "CV_MEAN_VARIATION", "CV_STAR_KERNEL" };
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cout << "Program Name: " << argv[0];
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cout << "\nPath to left image " << left << " \n" << "Path to right image " << right << "\n";
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cout << "\nkernel size " << kernel_size << "\n"
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<< "numberOfDisparities " << number_of_disparities << "\n"
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<< "aggregationWindow " << aggregation_window << "\n"
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<< "scallingFactor " << scale << "\n" << "Descriptor name : " << b[binary_descriptor_type] << "\n";
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Mat imgDisparity16S2 = Mat(image1.rows, image1.cols, CV_16S);
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Mat imgDisparity8U2 = Mat(image1.rows, image1.cols, CV_8UC1);
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imshow("Original Left image", image1);
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if (algo == STEREO_BINARY_BM)
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{
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Ptr<StereoBinaryBM> sbm = StereoBinaryBM::create(number_of_disparities, kernel_size);
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// we set the corresponding parameters
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sbm->setPreFilterCap(31);
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sbm->setMinDisparity(0);
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sbm->setTextureThreshold(10);
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sbm->setUniquenessRatio(0);
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sbm->setSpeckleWindowSize(400); // speckle size
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sbm->setSpeckleRange(200);
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sbm->setDisp12MaxDiff(0);
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sbm->setScalleFactor((int)scale); // the scaling factor
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sbm->setBinaryKernelType(binary_descriptor_type); // binary descriptor kernel
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sbm->setAgregationWindowSize(aggregation_window);
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// the user can choose between the average speckle removal algorithm or
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// the classical version that was implemented in OpenCV
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sbm->setSpekleRemovalTechnique(CV_SPECKLE_REMOVAL_AVG_ALGORITHM);
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sbm->setUsePrefilter(false);
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//-- calculate the disparity image
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sbm->compute(image1, image2, imgDisparity8U2);
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imshow("Disparity", imgDisparity8U2);
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}
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else if (algo == STEREO_BINARY_SGM)
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{
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// we set the corresponding parameters
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Ptr<StereoBinarySGBM> sgbm = StereoBinarySGBM::create(0, number_of_disparities, kernel_size);
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// setting the penalties for sgbm
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sgbm->setP1(P1);
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sgbm->setP2(P2);
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sgbm->setMinDisparity(0);
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sgbm->setUniquenessRatio(5);
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sgbm->setSpeckleWindowSize(400);
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sgbm->setSpeckleRange(0);
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sgbm->setDisp12MaxDiff(1);
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sgbm->setBinaryKernelType(binary_descriptor_type);
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sgbm->setSpekleRemovalTechnique(CV_SPECKLE_REMOVAL_AVG_ALGORITHM);
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sgbm->setSubPixelInterpolationMethod(CV_SIMETRICV_INTERPOLATION);
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sgbm->compute(image1, image2, imgDisparity16S2);
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/*Alternative for scalling
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imgDisparity16S2.convertTo(imgDisparity8U2, CV_8UC1, scale);
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*/
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double minVal; double maxVal;
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minMaxLoc(imgDisparity16S2, &minVal, &maxVal);
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imgDisparity16S2.convertTo(imgDisparity8U2, CV_8UC1, 255 / (maxVal - minVal));
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//show the disparity image
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imshow("Windowsgm", imgDisparity8U2);
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}
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waitKey(0);
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return 0;
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}
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static bool parse_argument_values(int argc, char **argv, string &left, string &right, int &kernel_size, int &number_of_disparities,
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int &aggregation_window, int &P1, int &P2, float &scale, int &algo, int &binary_descriptor_type, int &success)
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{
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static const char* keys =
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"{ @left | | }"
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"{ @right | | }"
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"{ k kernel_size | 9 | }"
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"{ d disparity | 128 | }"
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"{ w aggregation_window | 9 | }"
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"{ P1 | 100 | }"
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"{ P2 | 1000 | }"
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"{ b binary_descriptor | 4 | Index of the descriptor type:\n 0 - CV_DENSE_CENSUS,\n 1 - CV_SPARSE_CENSUS,\n 2 - CV_CS_CENSUS,\n 3 - CV_MODIFIED_CS_CENSUS,\n 4 - CV_MODIFIED_CENSUS_TRANSFORM,\n 5 - CV_MEAN_VARIATION,\n 6 - CV_STAR_KERNEL}"
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"{ s scale | 1.01593 | }"
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"{ a algorithm | sgm | }"
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;
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cv::CommandLineParser parser( argc, argv, keys );
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left = parser.get<string>(0);
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right = parser.get<string>(1);
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kernel_size = parser.get<int>("kernel_size");
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number_of_disparities = parser.get<int>("disparity");
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aggregation_window = parser.get<int>("aggregation_window");
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P1 = parser.get<int>("P1");
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P2 = parser.get<int>("P2");
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binary_descriptor_type = parser.get<int>("binary_descriptor");
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scale = parser.get<float>("scale");
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algo = parser.get<string>("algorithm") == "sgm" ? STEREO_BINARY_SGM : STEREO_BINARY_BM;
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parser.about("\nDemo stereo matching converting L and R images into disparity images using BM and SGBM\n");
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success = 1;
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//TEST if the provided parameters are correct
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if(binary_descriptor_type == CV_DENSE_CENSUS && kernel_size > 5)
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{
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cout << "For the dense census transform the maximum kernel size should be 5\n";
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success = 0;
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}
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if((binary_descriptor_type == CV_MEAN_VARIATION || binary_descriptor_type == CV_MODIFIED_CENSUS_TRANSFORM || binary_descriptor_type == CV_STAR_KERNEL) && kernel_size != 9)
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{
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cout <<" For Mean variation and the modified census transform the kernel size should be equal to 9\n";
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success = 0;
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}
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if((binary_descriptor_type == CV_CS_CENSUS || binary_descriptor_type == CV_MODIFIED_CS_CENSUS) && kernel_size > 7)
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{
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cout << " The kernel size should be smaller or equal to 7 for the CS census and modified center symetric census\n";
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success = 0;
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}
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if(binary_descriptor_type == CV_SPARSE_CENSUS && kernel_size > 11)
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{
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cout << "The kernel size for the sparse census must be smaller or equal to 11\n";
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success = 0;
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}
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if(number_of_disparities < 10)
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{
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cout << "Number of disparities should be greater than 10\n";
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success = 0;
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}
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if(aggregation_window < 3)
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{
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cout << "Aggregation window should be > 3";
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success = 0;
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}
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if(scale < 1)
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{
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cout << "The scale should be a positive number \n";
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success = 0;
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}
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if(P1 != 0)
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{
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if(P2 / P1 < 2)
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{
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cout << "You should probably choose a greater P2 penalty\n";
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success = 0;
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}
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}
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else
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{
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cout << " Penalties should be greater than 0\n";
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success = 0;
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}
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if (!parser.check() || !success)
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{
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parser.printMessage();
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return false;
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}
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return true;
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}
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