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datasets: remove face dependancy

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pull/1037/head
berak 8 years ago
parent ab63d35db2
commit cd345ea87f
2 changed files with 32 additions and 71 deletions
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  1. 2
      modules/datasets/CMakeLists.txt
  2. 101
      modules/datasets/samples/fr_lfw_benchmark.cpp

2
modules/datasets/CMakeLists.txt
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@ -1,4 +1,4 @@
set(the_description "datasets framework")
ocv_define_module(datasets opencv_core opencv_face opencv_ml opencv_flann opencv_text WRAP python)
ocv_define_module(datasets opencv_core opencv_ml opencv_flann opencv_text WRAP python)
ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4267) # flann, Win64

101
modules/datasets/samples/fr_lfw_benchmark.cpp
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@ -41,38 +41,17 @@
#include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/face.hpp"
#include "opencv2/datasets/fr_lfw.hpp"
#include <iostream>
#include <cstdio>
#include <string>
#include <vector>
#include <map>
using namespace std;
using namespace cv;
using namespace cv::datasets;
using namespace cv::face;
map<string, int> people;
int getLabel(const string &imagePath);
int getLabel(const string &imagePath)
{
size_t pos = imagePath.find('/');
string curr = imagePath.substr(0, pos);
map<string, int>::iterator it = people.find(curr);
if (people.end() == it)
{
people.insert(make_pair(curr, (int)people.size()));
it = people.find(curr);
}
return (*it).second;
}
int main(int argc, const char *argv[])
{
@ -90,9 +69,8 @@ int main(int argc, const char *argv[])
}
string trainMethod(parser.get<string>("train"));
// These vectors hold the images and corresponding labels.
vector<Mat> images;
vector<int> labels;
// our trained threshold for "same":
double threshold = 0;
// load dataset
Ptr<FR_lfw> dataset = FR_lfw::create();
@ -106,33 +84,26 @@ int main(int argc, const char *argv[])
printf("train size: %u\n", (numSplits-1) * (unsigned int)dataset->getTest().size());
printf("test size: %u\n", (unsigned int)dataset->getTest().size());
// 2200 pairsDevTrain, first split: correct: 373, from: 600 -> 62.1667%
Ptr<FaceRecognizer> model = createLBPHFaceRecognizer();
// 2200 pairsDevTrain, first split: correct: correct: 369, from: 600 -> 61.5%
//Ptr<FaceRecognizer> model = createEigenFaceRecognizer();
// 2200 pairsDevTrain, first split: correct: 372, from: 600 -> 62%
//Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
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());
int currNum = getLabel(example->image1);
Mat img = imread(path+example->image1, IMREAD_GRAYSCALE);
images.push_back(img);
labels.push_back(currNum);
currNum = getLabel(example->image2);
img = imread(path+example->image2, IMREAD_GRAYSCALE);
images.push_back(img);
labels.push_back(currNum);
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 ++;
}
}
model->train(images, labels);
//string saveModelPath = "face-rec-model.txt";
//cout << "Saving the trained model to " << saveModelPath << endl;
//model->save(saveModelPath);
threshold = avg / count;
}
vector<double> p;
@ -140,8 +111,8 @@ int main(int argc, const char *argv[])
{
if (trainMethod == "split") // train on the remaining 9 splits from pairs.txt
{
images.clear();
labels.clear();
double avg = 0;
int count = 0;
for (unsigned int j2=0; j2<numSplits; ++j2)
{
if (j==j2) continue; // skip test split for training
@ -150,19 +121,17 @@ int main(int argc, const char *argv[])
for (unsigned int i=0; i<curr.size(); ++i)
{
FR_lfwObj *example = static_cast<FR_lfwObj *>(curr[i].get());
int currNum = getLabel(example->image1);
Mat img = imread(path+example->image1, IMREAD_GRAYSCALE);
images.push_back(img);
labels.push_back(currNum);
currNum = getLabel(example->image2);
img = imread(path+example->image2, IMREAD_GRAYSCALE);
images.push_back(img);
labels.push_back(currNum);
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 ++;
}
}
}
model->train(images, labels);
threshold = avg / count;
}
unsigned int incorrect = 0, correct = 0;
@ -171,26 +140,18 @@ int main(int argc, const char *argv[])
{
FR_lfwObj *example = static_cast<FR_lfwObj *>(curr[i].get());
//int currNum = getLabel(example->image1);
Mat img = imread(path+example->image1, IMREAD_GRAYSCALE);
int predictedLabel1 = model->predict(img);
//currNum = getLabel(example->image2);
img = imread(path+example->image2, IMREAD_GRAYSCALE);
int predictedLabel2 = model->predict(img);
if ((predictedLabel1 == predictedLabel2 && example->same) ||
(predictedLabel1 != predictedLabel2 && !example->same))
{
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
{
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)
{

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