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add a 'split' train method to datasets/lfw_benckmark

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This commit is contained in:
berak
2015-02-20 14:55:26 +01:00
parent 312c8fa7e1
commit 04dfaaf935
1 changed files with 57 additions and 22 deletions
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79
modules/datasets/samples/fr_lfw_benchmark.cpp
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@@ -78,7 +78,9 @@ int main(int argc, const char *argv[])
{
const char *keys =
"{ help h usage ? | | show this message }"
"{ path p |true| path to dataset (lfw2 folder) }";
"{ 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")
@@ -86,6 +88,7 @@ int main(int argc, const char *argv[])
parser.printMessage();
return -1;
}
string trainMethod(parser.get<string>("train"));
// These vectors hold the images and corresponding labels.
vector<Mat> images;
@@ -97,24 +100,12 @@ int main(int argc, const char *argv[])
unsigned int numSplits = dataset->getNumSplits();
printf("splits number: %u\n", numSplits);
printf("train size: %u\n", (unsigned int)dataset->getTrain().size());
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());
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);
}
// 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%
@@ -122,14 +113,58 @@ int main(int argc, const char *argv[])
// 2200 pairsDevTrain, first split: correct: 372, from: 600 -> 62%
//Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
model->train(images, labels);
//string saveModelPath = "face-rec-model.txt";
//cout << "Saving the trained model to " << saveModelPath << endl;
//model->save(saveModelPath);
if (trainMethod == "dev") // train on personsDevTrain.txt
{
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);
}
model->train(images, labels);
//string saveModelPath = "face-rec-model.txt";
//cout << "Saving the trained model to " << saveModelPath << endl;
//model->save(saveModelPath);
}
vector<double> p;
for (unsigned int j=0; j<numSplits; ++j)
{
if (trainMethod == "split") // train on the remaining 9 splits from pairs.txt
{
images.clear();
labels.clear();
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());
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);
}
}
model->train(images, labels);
}
unsigned int incorrect = 0, correct = 0;
vector < Ptr<Object> > &curr = dataset->getTest(j);
for (unsigned int i=0; i<curr.size(); ++i)
@@ -168,7 +203,7 @@ int main(int argc, const char *argv[])
sigma += (*it - mu)*(*it - mu);
}
sigma = sqrt(sigma/p.size());
double se = sigma/sqrt(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;