Initial commit: Speckle-Scanner 3D pipeline with setup README
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/*
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Copyright 2016 Fixstars Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http ://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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*/
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#include <iostream>
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#include <iomanip>
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#include <chrono>
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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 | 128 | maximum possible disparity value }"
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"{ out_depth | 8 | disparity image's bits per pixel }"
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"{ subpixel | | enable subpixel estimation }"
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"{ num_paths | 8 | number of scanlines used in cost aggregation }"
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"{ census_type | 1 | type of census transform (0:CENSUS_9x7 1:SYMMETRIC_CENSUS_9x7) }"
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"{ iterations | 100 | number of iterations for measuring performance }"
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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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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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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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const int disp_size = parser.get<int>("disp_size");
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const int dst_depth = parser.get<int>("out_depth");
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const bool subpixel = parser.has("subpixel");
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const int num_paths = parser.get<int>("num_paths");
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const auto census_type = static_cast<sgm::CensusType>(parser.get<int>("census_type"));
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const int iterations = parser.get<int>("iterations");
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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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ASSERT_MSG(dst_depth == 8 || dst_depth == 16, "output depth bits must be 8 or 16");
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if (subpixel)
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ASSERT_MSG(dst_depth == 16, "output depth bits must be 16 if subpixel option is enabled.");
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const int width = I1.cols;
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const int height = I1.rows;
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const int src_depth = I1.type() == CV_8U ? 8 : 16;
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const int src_bytes = src_depth * width * height / 8;
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const int dst_bytes = dst_depth * width * height / 8;
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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(10, 120, 0.95f, subpixel, path_type, 0, 1, census_type);
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sgm::StereoSGM sgm(width, height, disp_size, src_depth, dst_depth, sgm::EXECUTE_INOUT_CUDA2CUDA, param);
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device_buffer d_I1(src_bytes), d_I2(src_bytes), d_disparity(dst_bytes);
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cv::Mat disparity(height, width, dst_depth == 8 ? CV_8S : CV_16S);
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d_I1.upload(I1.data);
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d_I2.upload(I2.data);
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cudaDeviceProp prop;
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int version;
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cudaGetDeviceProperties(&prop, 0);
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cudaRuntimeGetVersion(&version);
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// show settings
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std::cout << "# Settings" << std::endl;
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std::cout << "device name : " << prop.name << std::endl;
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std::cout << "CUDA runtime version: " << version << std::endl;
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std::cout << "image size : " << I1.size() << std::endl;
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std::cout << "disparity size : " << disp_size << std::endl;
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std::cout << "output depth : " << dst_depth << std::endl;
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std::cout << "subpixel option : " << (subpixel ? "true" : "false") << std::endl;
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std::cout << "sgm path : " << num_paths << " path" << std::endl;
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std::cout << "census type : " << (census_type == sgm::CensusType::CENSUS_9x7 ? "CENSUS_9x7" : "SYMMETRIC_CENSUS_9x7") << std::endl;
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std::cout << "iterations : " << iterations << std::endl;
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std::cout << std::endl;
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// run benchmark
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std::cout << "Running benchmark..." << std::endl;
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uint64_t sum = 0;
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for (int i = 0; i <= iterations; i++) {
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const auto t1 = std::chrono::system_clock::now();
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sgm.execute(d_I1.data, d_I2.data, d_disparity.data);
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cudaDeviceSynchronize();
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const auto t2 = std::chrono::system_clock::now();
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if (i > 0)
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sum += std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count();
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}
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std::cout << "Done." << std::endl << std::endl;
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// show results
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const double time_millisec = 1e-3 * sum / iterations;
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const double fps = 1e3 / time_millisec;
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std::cout << "# Results" << std::endl;
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std::cout.setf(std::ios::fixed);
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std::cout << std::setprecision(1) << "Processing Time[Milliseconds]: " << time_millisec << std::endl;
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std::cout << std::setprecision(1) << "FPS : " << fps << std::endl;
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std::cout << std::endl;
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// save disparity image
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const int disp_scale = subpixel ? sgm::StereoSGM::SUBPIXEL_SCALE : 1;
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d_disparity.download(disparity.data);
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colorize_disparity(disparity, disparity, disp_scale * disp_size, disparity == sgm.get_invalid_disparity());
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cv::imwrite("disparity.png", disparity);
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return 0;
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}
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