Initial commit: Speckle-Scanner 3D pipeline with setup README
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#include <iostream>
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#include <opencv2/core.hpp>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/highgui.hpp>
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#include <libsgm.h>
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#include "sample_common.h"
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static const std::string keys =
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"{ @left_img | <none> | path to input left image }"
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"{ @right_img | <none> | path to input right image }"
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"{ disp_size | 256 | maximum possible disparity value }"
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"{ P1 | 10 | penalty on the disparity change by plus or minus 1 between neighbor pixels }"
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"{ P2 | 120 | penalty on the disparity change by more than 1 between neighbor pixels }"
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"{ uniqueness | 0.80 | margin in ratio by which the best cost function value should be at least second one }"
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"{ num_paths | 8 | number of scanlines used in cost aggregation }"
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"{ min_disp | -160 | minimum disparity value }"
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"{ LR_max_diff | 1 | maximum allowed difference between left and right disparity }"
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"{ census_type | 1 | type of census transform (0:CENSUS_9x7 1:SYMMETRIC_CENSUS_9x7) }"
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"{ output_dir | . | directory to save disparity.xml and disparity_color.png }"
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"{ no_display | 0 | set to 1 to skip interactive display window (for pipeline/headless use) }"
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"{ help h | | display this help and exit }";
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int main(int argc, char* argv[])
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{
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double start_time = cv::getTickCount(); // Start total execution time
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cv::CommandLineParser parser(argc, argv, keys);
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if (parser.has("help")) {
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parser.printMessage();
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return 0;
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}
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double load_start = cv::getTickCount(); // Start loading time
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cv::Mat I1 = cv::imread(parser.get<cv::String>("@left_img"), cv::IMREAD_UNCHANGED);
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cv::Mat I2 = cv::imread(parser.get<cv::String>("@right_img"), cv::IMREAD_UNCHANGED);
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double load_end = cv::getTickCount();
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double load_time_s = (load_end - load_start) / cv::getTickFrequency(); // Seconds
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double load_time_ms = load_time_s * 1000.0; // Milliseconds
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std::cout << "Image Loading Time: " << load_time_s << " s (" << load_time_ms << " ms)" << std::endl;
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if (I1.channels() > 1) cv::cvtColor(I1, I1, cv::COLOR_BGR2GRAY);
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if (I2.channels() > 1) cv::cvtColor(I2, I2, cv::COLOR_BGR2GRAY);
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const int disp_size = parser.get<int>("disp_size");
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const int P1 = parser.get<int>("P1");
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const int P2 = parser.get<int>("P2");
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const float uniqueness = parser.get<float>("uniqueness");
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const int num_paths = parser.get<int>("num_paths");
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const int min_disp = parser.get<int>("min_disp");
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const int LR_max_diff = parser.get<int>("LR_max_diff");
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const auto census_type = static_cast<sgm::CensusType>(parser.get<int>("census_type"));
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if (!parser.check()) {
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parser.printErrors();
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parser.printMessage();
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std::exit(EXIT_FAILURE);
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}
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ASSERT_MSG(!I1.empty() && !I2.empty(), "imread failed.");
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ASSERT_MSG(I1.size() == I2.size() && I1.type() == I2.type(), "input images must be same size and type.");
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ASSERT_MSG(I1.type() == CV_8U || I1.type() == CV_16U, "input image format must be CV_8U or CV_16U.");
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ASSERT_MSG(disp_size == 64 || disp_size == 128 || disp_size == 256, "disparity size must be 64, 128 or 256.");
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ASSERT_MSG(num_paths == 4 || num_paths == 8, "number of scanlines must be 4 or 8.");
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ASSERT_MSG(census_type == sgm::CensusType::CENSUS_9x7 || census_type == sgm::CensusType::SYMMETRIC_CENSUS_9x7, "census type must be 0 or 1.");
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const int src_depth = I1.type() == CV_8U ? 8 : 16;
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const int dst_depth = 16;
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const sgm::PathType path_type = num_paths == 8 ? sgm::PathType::SCAN_8PATH : sgm::PathType::SCAN_4PATH;
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const sgm::StereoSGM::Parameters param(P1, P2, uniqueness, false, path_type, min_disp, LR_max_diff, census_type);
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sgm::StereoSGM ssgm(I1.cols, I1.rows, disp_size, src_depth, dst_depth, sgm::EXECUTE_INOUT_HOST2HOST, param);
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cv::Mat disparity(I1.size(), CV_16S);
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double disparity_start = cv::getTickCount(); // Start disparity computation time
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ssgm.execute(I1.data, I2.data, disparity.data);
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double disparity_end = cv::getTickCount();
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double disparity_time_s = (disparity_end - disparity_start) / cv::getTickFrequency(); // Seconds
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double disparity_time_ms = disparity_time_s * 1000.0; // Milliseconds
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std::cout << "Disparity Computation Time: " << disparity_time_s << " s (" << disparity_time_ms << " ms)" << std::endl;
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const std::string output_dir = parser.get<std::string>("output_dir");
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// Save disparity
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cv::FileStorage fs(output_dir + "/disparity.xml", cv::FileStorage::WRITE);
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fs << "disparity" << disparity;
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fs.release();
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// Convert disparity to 8-bit for visualization
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cv::Mat disparity_8u, disparity_color;
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disparity.convertTo(disparity_8u, CV_8U, 255.0 / disp_size);
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cv::applyColorMap(disparity_8u, disparity_color, cv::COLORMAP_TURBO);
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// Save colored disparity image
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cv::imwrite(output_dir + "/disparity_color.png", disparity_color);
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double total_end = cv::getTickCount();
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double total_time_s = (total_end - start_time) / cv::getTickFrequency(); // Seconds
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double total_time_ms = total_time_s * 1000.0; // Milliseconds
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std::cout << "Total Execution Time: " << total_time_s << " s (" << total_time_ms << " ms)" << std::endl;
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// Display images
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const std::vector<cv::Mat> images = { disparity_8u, disparity_color, I1 };
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const std::vector<std::string> titles = { "Disparity", "Colored Disparity", "Input Image" };
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if (!parser.get<int>("no_display")) {
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std::cout << "Hot keys:\n";
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std::cout << "\tESC - Quit the program\n";
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std::cout << "\ts - Switch display (Disparity | Colored Disparity | Input Image)\n";
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int mode = 0;
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while (true) {
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cv::setWindowTitle("Image", titles[mode]);
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cv::imshow("Image", images[mode]);
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const char c = cv::waitKey(0);
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if (c == 's') mode = (mode < 2 ? mode + 1 : 0);
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if (c == 27) break;
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}
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}
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return 0;
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}
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