mirrors/opencv_contrib
1
0
Fork 0
Code Pull Requests Packages Releases Activity

vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e

Browse Source
This commit is contained in:
Gitea Mirror Bot
2026-08-22 00:11:13 +08:00
commit 12022378a3
3872 changed files with 2513409 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/datasets/samples/fr_lfw_benchmark.cpp
+171
View File
@@ -0,0 +1,171 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2014, Itseez Inc, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Itseez Inc or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/datasets/fr_lfw.hpp"
#include <iostream>
#include <cstdio>
#include <string>
#include <vector>
using namespace std;
using namespace cv;
using namespace cv::datasets;
int main(int argc, const char *argv[])
{
const char *keys =
"{ help h usage ? | | show this message }"
"{ path p |true| path to dataset (lfw2 folder) }"
"{ train t |dev | train method: 'dev'(pairsDevTrain.txt) or 'split'(pairs.txt) }";
CommandLineParser parser(argc, argv, keys);
string path(parser.get<string>("path"));
if (parser.has("help") || path=="true")
{
parser.printMessage();
return -1;
}
string trainMethod(parser.get<string>("train"));
// our trained threshold for "same":
double threshold = 0;
// load dataset
Ptr<FR_lfw> dataset = FR_lfw::create();
dataset->load(path);
unsigned int numSplits = dataset->getNumSplits();
printf("splits number: %u\n", numSplits);
if (trainMethod == "dev")
printf("train size: %u\n", (unsigned int)dataset->getTrain().size());
else
printf("train size: %u\n", (numSplits-1) * (unsigned int)dataset->getTest().size());
printf("test size: %u\n", (unsigned int)dataset->getTest().size());
if (trainMethod == "dev") // train on personsDevTrain.txt
{
// collect average same-distances:
double avg = 0;
int count = 0;
for (unsigned int i=0; i<dataset->getTrain().size(); ++i)
{
FR_lfwObj *example = static_cast<FR_lfwObj *>(dataset->getTrain()[i].get());
Mat a = imread(path+example->image1, IMREAD_GRAYSCALE);
Mat b = imread(path+example->image2, IMREAD_GRAYSCALE);
double dist = norm(a,b);
if (example->same)
{
avg += dist;
count ++;
}
}
threshold = avg / count;
}
vector<double> p;
for (unsigned int j=0; j<numSplits; ++j)
{
if (trainMethod == "split") // train on the remaining 9 splits from pairs.txt
{
double avg = 0;
int count = 0;
for (unsigned int j2=0; j2<numSplits; ++j2)
{
if (j==j2) continue; // skip test split for training
vector < Ptr<Object> > &curr = dataset->getTest(j2);
for (unsigned int i=0; i<curr.size(); ++i)
{
FR_lfwObj *example = static_cast<FR_lfwObj *>(curr[i].get());
Mat a = imread(path+example->image1, IMREAD_GRAYSCALE);
Mat b = imread(path+example->image2, IMREAD_GRAYSCALE);
double dist = norm(a,b);
if (example->same)
{
avg += dist;
count ++;
}
}
}
threshold = avg / count;
}
unsigned int incorrect = 0, correct = 0;
vector < Ptr<Object> > &curr = dataset->getTest(j);
for (unsigned int i=0; i<curr.size(); ++i)
{
FR_lfwObj *example = static_cast<FR_lfwObj *>(curr[i].get());
Mat a = imread(path+example->image1, IMREAD_GRAYSCALE);
Mat b = imread(path+example->image2, IMREAD_GRAYSCALE);
bool same = (norm(a,b) <= threshold);
if (same == example->same)
correct++;
else
incorrect++;
}
p.push_back(1.0*correct/(correct+incorrect));
printf("correct: %u, from: %u -> %f\n", correct, correct+incorrect, p.back());
}
double mu = 0.0;
for (vector<double>::iterator it=p.begin(); it!=p.end(); ++it)
{
mu += *it;
}
mu /= p.size();
double sigma = 0.0;
for (vector<double>::iterator it=p.begin(); it!=p.end(); ++it)
{
sigma += (*it - mu)*(*it - mu);
}
sigma = sqrt(sigma/p.size());
double se = sigma/sqrt(double(p.size()));
printf("estimated mean accuracy: %f and the standard error of the mean: %f\n", mu, se);
return 0;
}