177 lines
7.1 KiB
C++
177 lines
7.1 KiB
C++
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#include <sstream>
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#include <iostream>
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#include "opencv2/quality.hpp"
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#include "opencv2/quality/quality_utils.hpp"
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#include "opencv2/imgcodecs.hpp"
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#include "opencv2/ml.hpp"
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/*
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BRISQUE Trainer using LIVE DB R2
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http://live.ece.utexas.edu/research/Quality/subjective.htm
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H.R. Sheikh, Z.Wang, L. Cormack and A.C. Bovik, "LIVE Image Quality Assessment Database Release 2", http://live.ece.utexas.edu/research/quality .
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H.R. Sheikh, M.F. Sabir and A.C. Bovik, "A statistical evaluation of recent full reference image quality assessment algorithms", IEEE Transactions on Image Processing, vol. 15, no. 11, pp. 3440-3451, Nov. 2006.
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Z. Wang, A.C. Bovik, H.R. Sheikh and E.P. Simoncelli, "Image quality assessment: from error visibility to structural similarity," IEEE Transactions on Image Processing , vol.13, no.4, pp. 600- 612, April 2004.
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*/
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/*
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Copyright (c) 2011 The University of Texas at Austin
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All rights reserved.
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Permission is hereby granted, without written agreement and without license or royalty fees, to use, copy,
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modify, and distribute this code (the source files) and its documentation for
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any purpose, provided that the copyright notice in its entirety appear in all copies of this code, and the
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original source of this code, Laboratory for Image and Video Engineering (LIVE, http://live.ece.utexas.edu)
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and Center for Perceptual Systems (CPS, http://www.cps.utexas.edu) at the University of Texas at Austin (UT Austin,
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http://www.utexas.edu), is acknowledged in any publication that reports research using this code. The research
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is to be cited in the bibliography as:
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1) A. Mittal, A. K. Moorthy and A. C. Bovik, "BRISQUE Software Release",
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URL: http://live.ece.utexas.edu/research/quality/BRISQUE_release.zip, 2011
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2) A. Mittal, A. K. Moorthy and A. C. Bovik, "No Reference Image Quality Assessment in the Spatial Domain"
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submitted
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IN NO EVENT SHALL THE UNIVERSITY OF TEXAS AT AUSTIN BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL,
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OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OF THIS DATABASE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY OF TEXAS
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AT AUSTIN HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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THE UNIVERSITY OF TEXAS AT AUSTIN SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE DATABASE PROVIDED HEREUNDER IS ON AN "AS IS" BASIS,
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AND THE UNIVERSITY OF TEXAS AT AUSTIN HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
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*/
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/* Original Paper: @cite Mittal2 and Original Implementation: @cite Mittal2_software */
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namespace {
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#define CATEGORIES 5
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#define IMAGENUM 982
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#define JP2KNUM 227
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#define JPEGNUM 233
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#define WNNUM 174
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#define GBLURNUM 174
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#define FFNUM 174
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// collects training data from LIVE R2 database
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// returns {features, responses}, 1 row per image
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std::pair<cv::Mat, cv::Mat> collect_data_live_r2(const std::string& foldername)
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{
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FILE* fid = nullptr;
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//----------------------------------------------------
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// class is the distortion category, there are 982 images in LIVE database
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std::vector<std::string> distortionlabels;
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distortionlabels.push_back("jp2k");
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distortionlabels.push_back("jpeg");
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distortionlabels.push_back("wn");
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distortionlabels.push_back("gblur");
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distortionlabels.push_back("fastfading");
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int imnumber[5] = { 0,227,460,634,808 };
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std::vector<int>categorylabels;
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categorylabels.insert(categorylabels.end(), JP2KNUM, 0);
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categorylabels.insert(categorylabels.end(), JPEGNUM, 1);
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categorylabels.insert(categorylabels.end(), WNNUM, 2);
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categorylabels.insert(categorylabels.end(), GBLURNUM, 3);
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categorylabels.insert(categorylabels.end(), FFNUM, 4);
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int iforg[IMAGENUM];
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fid = fopen((foldername + "orgs.txt").c_str(), "r");
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for (int itr = 0; itr < IMAGENUM; itr++)
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CV_Assert( fscanf(fid, "%d", iforg + itr) > 0);
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fclose(fid);
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float dmosscores[IMAGENUM];
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fid = fopen((foldername + "dmos.txt").c_str(), "r");
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for (int itr = 0; itr < IMAGENUM; itr++)
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CV_Assert( fscanf(fid, "%f", dmosscores + itr) > 0 );
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fclose(fid);
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// features vector, 1 row per image
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cv::Mat features(0, 0, CV_32FC1);
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// response vector, 1 row per image
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cv::Mat responses(0, 1, CV_32FC1);
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for (int itr = 0; itr < IMAGENUM; itr++)
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{
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//Dont compute features for original images
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if (iforg[itr])
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continue;
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// append dmos score
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float score = dmosscores[itr];
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responses.push_back(cv::Mat(1, 1, CV_32FC1, (void*)&score));
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// load image, calc features
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std::string imname = "";
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imname.append(foldername);
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imname.append("/");
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imname.append(distortionlabels[categorylabels[itr]].c_str());
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imname.append("/img");
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imname += std::to_string((itr - imnumber[categorylabels[itr]] + 1));
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imname.append(".bmp");
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cv::Mat im_features;
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cv::quality::QualityBRISQUE::computeFeatures(cv::imread(imname), im_features); // outputs a row vector
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features.push_back(im_features.row(0)); // append row vector
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}
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return std::make_pair(std::move(features), std::move(responses));
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} // collect_data_live_r2
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}
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inline void printHelp()
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{
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using namespace std;
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cout << " Demo of training BRISQUE quality assessment model using LIVE R2 database." << endl;
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cout << " A. Mittal, A. K. Moorthy and A. C. Bovik, 'No Reference Image Quality Assessment in the Spatial Domain'" << std::endl << std::endl;
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cout << " Usage: program <live_r2_db_path> <output_model_path> <output_range_path>" << endl << endl;
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}
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int main(int argc, const char * argv[])
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{
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using namespace cv::ml;
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if (argc != 4)
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{
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printHelp();
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exit(1);
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}
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std::cout << "Training BRISQUE on database at " << argv[1] << "..." << std::endl;
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// collect data from the data set
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auto data = collect_data_live_r2( std::string( argv[1] ) + "/" );
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// extract column ranges for features
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const auto range = cv::quality::quality_utils::get_column_range(data.first);
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// scale all features from -1 to 1
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cv::quality::quality_utils::scale<float>(data.first, range, -1.f, 1.f);
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// do training, output train file
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// libsvm call from original BRISQUE impl: svm-train -s 3 -g 0.05 -c 1024 -b 1 -q train_scale allmodel
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auto svm = SVM::create();
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svm->setType(SVM::Types::EPS_SVR);
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svm->setKernel(SVM::KernelTypes::RBF);
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svm->setGamma(0.05);
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svm->setC(1024.);
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svm->setTermCriteria(cv::TermCriteria(cv::TermCriteria::Type::EPS, 1000, 0.001));
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svm->setP(.1);// default p (epsilon) from libsvm
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svm->train(data.first, cv::ml::ROW_SAMPLE, data.second);
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svm->save( argv[2] ); // save to location specified in argv[2]
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// output scale file to argv[3]
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cv::Mat range_mat(range);
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cv::FileStorage fs(argv[3], cv::FileStorage::WRITE );
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fs << "range" << range_mat;
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return 0;
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}
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