244 lines
7.9 KiB
C++
244 lines
7.9 KiB
C++
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#include <fstream>
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#include "opencv2/quality.hpp"
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#include "opencv2/imgproc.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 evaluator using TID2008
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TID2008:
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http://www.ponomarenko.info/tid2008.htm
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[1] N. Ponomarenko, V. Lukin, A. Zelensky, K. Egiazarian, M. Carli,
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F. Battisti, "TID2008 - A Database for Evaluation of Full-Reference
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Visual Quality Assessment Metrics", Advances of Modern
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Radioelectronics, Vol. 10, pp. 30-45, 2009.
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[2] N. Ponomarenko, F. Battisti, K. Egiazarian, J. Astola, V. Lukin
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"Metrics performance comparison for color image database", Fourth
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international workshop on video processing and quality metrics
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for consumer electronics, Scottsdale, Arizona, USA. Jan. 14-16, 2009, 6 p.
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*/
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namespace {
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// get ordinal ranks of data, fractional ranks assigned for ties. O(n^2) time complexity
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// optional binary predicate used for rank ordering of data elements, equality evaluation
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template <typename T, typename PrEqual = std::equal_to<T>, typename PrLess = std::less<T>>
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std::vector<float> rank_ordinal(const T* data, std::size_t sz, PrEqual&& eq = {}, PrLess&& lt = {})
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{
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std::vector<float> result{};
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result.resize(sz, -1);// set all ranks to -1, indicating not yet done
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int rank = 0;
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while (rank < (int)sz)
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{
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std::vector<int> els = {};
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for (int i = 0; i < (int)sz; ++i)
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{
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if (result[i] < 0)//not yet done
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{
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if (!els.empty())// already found something
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{
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if (lt(data[i], data[els[0]]))//found a smaller item, replace existing
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{
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els.clear();
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els.emplace_back(i);
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}
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else if (eq(data[i], data[els[0]]))// found a tie, add to vector
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els.emplace_back(i);
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}
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else//els.empty==no current item, add it
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els.emplace_back(i);
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}
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}
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CV_Assert(!els.empty());
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// compute, assign arithmetic mean
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const auto assigned_rank = (double)rank + (double)(els.size() - 1) / 2.;
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for (auto el : els)
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result[el] = (float)assigned_rank;
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rank += (int)els.size();
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}
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return result;
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}
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template <typename T>
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double pearson(const T* x, const T* y, std::size_t sz)
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{
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// based on https://www.geeksforgeeks.org/program-spearmans-rank-correlation/
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double sigma_x = {}, sigma_y = {}, sigma_xy = {}, sigma_xsq = {}, sigma_ysq = {};
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for (unsigned i = 0; i < sz; ++i)
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{
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sigma_x += x[i];
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sigma_y += y[i];
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sigma_xy += x[i] * y[i];
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sigma_xsq += x[i] * x[i];
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sigma_ysq += y[i] * y[i];
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}
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const double
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num = (sz * sigma_xy - sigma_x * sigma_y)
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, den = std::sqrt(((double)sz*sigma_xsq - sigma_x * sigma_x) * ((double)sz*sigma_ysq - sigma_y * sigma_y))
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;
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return num / den;
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}
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// https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient
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template <typename T>
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double spearman(const T* x, const T* y, std::size_t sz)
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{
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// convert x, y to ranked integral vectors
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const auto
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x_rank = rank_ordinal(x, sz)
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, y_rank = rank_ordinal(y, sz)
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;
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return pearson(x_rank.data(), y_rank.data(), sz);
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}
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// returns cv::Mat of columns: { Distortion Type ID, MOS_Score, Brisque_Score }
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cv::Mat tid2008_eval(const std::string& root, cv::quality::QualityBRISQUE& alg)
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{
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const std::string
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mos_with_names_path = root + "mos_with_names.txt"
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, dist_imgs_root = root + "distorted_images/"
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;
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cv::Mat result(0, 3, CV_32FC1);
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// distortion types we care about
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static const std::vector<int> distortion_types = {
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10 // jpeg compression
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, 11 // jp2k compression
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, 1 // additive gaussian noise
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, 8 // gaussian blur
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};
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static const int
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num_images = 25 // [I01_ - I25_], file names
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, num_distortions = 4 // num distortions per image
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;
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// load mos_with_names. format: { mos, fname }
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std::vector<std::pair<float, std::string>> mos_with_names = {};
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std::ifstream mos_file(mos_with_names_path, std::ios::in);
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while (true)
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{
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std::string line;
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std::getline(mos_file, line);
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if (!line.empty())
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{
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const auto space_pos = line.find(' ');
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CV_Assert(space_pos != line.npos);
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mos_with_names.emplace_back(std::make_pair(
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(float)std::atof(line.substr(0, space_pos).c_str())
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, line.substr(space_pos + 1)
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));
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}
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if (mos_file.peek() == EOF)
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break;
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};
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// foreach image
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// foreach distortion type
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// foreach distortion level
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// distortion type id, mos value, brisque value
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for (int i = 0; i < num_images; ++i)
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{
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for (int ty = 0; ty < (int)distortion_types.size(); ++ty)
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{
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for (int dist = 1; dist <= num_distortions; ++dist)
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{
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float mos_val = 0.f;
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const std::string img_name = std::string("i")
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+ (((i + 1) < 10) ? "0" : "")
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+ std::to_string(i + 1)
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+ "_"
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+ ((distortion_types[ty] < 10) ? "0" : "")
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+ std::to_string(distortion_types[ty])
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+ "_"
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+ std::to_string(dist)
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+ ".bmp";
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// find mos
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bool found = false;
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for (const auto& val : mos_with_names)
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{
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if (val.second == img_name)
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{
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found = true;
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mos_val = val.first;
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break;
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}
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}
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CV_Assert(found);
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// do brisque
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auto img = cv::imread(dist_imgs_root + img_name);
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// typeid, mos, brisque
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cv::Mat row(1, 3, CV_32FC1);
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row.at<float>(0) = (float)distortion_types[ty];
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row.at<float>(1) = mos_val;
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row.at<float>(2) = (float)alg.compute(img)[0];
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result.push_back(row);
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}// dist
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}//ty
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}//i
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return result;
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}
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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 comparing BRISQUE quality assessment model against TID2008 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 <tid2008_path> <brisque_model_path> <brisque_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 << "Evaluating database at " << argv[1] << "..." << std::endl;
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const auto ptr = cv::quality::QualityBRISQUE::create(argv[2], argv[3]);
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const auto data = tid2008_eval( std::string( argv[1] ) + "/", *ptr );
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// create contiguous mats
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const auto mos = data.col(1).clone();
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const auto brisque = data.col(2).clone();
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// calc srocc
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const auto cc = spearman((const float*)mos.data, (const float*)brisque.data, data.rows);
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std::cout << "SROCC: " << cc << std::endl;
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return 0;
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}
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