diff --git a/modules/datasets/CMakeLists.txt b/modules/datasets/CMakeLists.txt
index 47afccbb8..217acc335 100644
--- a/modules/datasets/CMakeLists.txt
+++ b/modules/datasets/CMakeLists.txt
@@ -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
diff --git a/modules/datasets/samples/fr_lfw_benchmark.cpp b/modules/datasets/samples/fr_lfw_benchmark.cpp
index 7ec939e98..07079c217 100644
--- a/modules/datasets/samples/fr_lfw_benchmark.cpp
+++ b/modules/datasets/samples/fr_lfw_benchmark.cpp
@@ -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)
{