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Merge pull request #2972 from apavlenko:24_face_rec_sample

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pull/2901/merge
Vadim Pisarevsky 11 years ago
parent d31a0eab9e 64d8cf1e2e
commit 246a793d82
1 changed files with 75 additions and 60 deletions
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  1. 135
      samples/cpp/facerec_demo.cpp

135
samples/cpp/facerec_demo.cpp
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@ -27,35 +27,38 @@
using namespace cv;
using namespace std;
static Mat toGrayscale(InputArray _src) {
Mat src = _src.getMat();
// only allow one channel
if(src.channels() != 1) {
CV_Error(CV_StsBadArg, "Only Matrices with one channel are supported");
}
// create and return normalized image
Mat dst;
cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
return dst;
}
static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, std::map<int, string>& labelsInfo, char separator = ';') {
std::ifstream file(filename.c_str(), ifstream::in);
if (!file) {
string error_message = "No valid input file was given, please check the given filename.";
CV_Error(CV_StsBadArg, error_message);
}
ifstream csv(filename.c_str());
if (!csv) CV_Error(CV_StsBadArg, "No valid input file was given, please check the given filename.");
string line, path, classlabel, info;
while (getline(file, line)) {
while (getline(csv, line)) {
stringstream liness(line);
path.clear(); classlabel.clear(); info.clear();
getline(liness, path, separator);
getline(liness, classlabel, separator);
getline(liness, info, separator);
if(!path.empty() && !classlabel.empty()) {
images.push_back(imread(path, 0));
labels.push_back(atoi(classlabel.c_str()));
cout << "Processing " << path << endl;
int label = atoi(classlabel.c_str());
if(!info.empty())
labelsInfo.insert(std::make_pair(labels.back(), info));
labelsInfo.insert(std::make_pair(label, info));
// 'path' can be file, dir or wildcard path
String root(path.c_str());
vector<String> files;
glob(root, files, true);
for(vector<String>::const_iterator f = files.begin(); f != files.end(); ++f) {
cout << "\t" << *f << endl;
Mat img = imread(*f, CV_LOAD_IMAGE_GRAYSCALE);
static int w=-1, h=-1;
static bool showSmallSizeWarning = true;
if(w>0 && h>0 && (w!=img.cols || h!=img.rows)) cout << "\t* Warning: images should be of the same size!" << endl;
if(showSmallSizeWarning && (img.cols<50 || img.rows<50)) {
cout << "* Warning: for better results images should be not smaller than 50x50!" << endl;
showSmallSizeWarning = false;
}
images.push_back(img);
labels.push_back(label);
}
}
}
}
@ -63,8 +66,17 @@ static void read_csv(const string& filename, vector<Mat>& images, vector<int>& l
int main(int argc, const char *argv[]) {
// Check for valid command line arguments, print usage
// if no arguments were given.
if (argc != 2) {
cout << "usage: " << argv[0] << " <csv.ext>" << endl;
if (argc != 2 && argc != 3) {
cout << "Usage: " << argv[0] << " <csv> [arg2]\n"
<< "\t<csv> - path to config file in CSV format\n"
<< "\targ2 - if the 2nd argument is provided (with any value) "
<< "the advanced stuff is run and shown to console.\n"
<< "The CSV config file consists of the following lines:\n"
<< "<path>;<label>[;<comment>]\n"
<< "\t<path> - file, dir or wildcard path\n"
<< "\t<label> - non-negative integer person label\n"
<< "\t<comment> - optional comment string (e.g. person name)"
<< endl;
exit(1);
}
// Get the path to your CSV.
@ -88,10 +100,6 @@ int main(int argc, const char *argv[]) {
string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
CV_Error(CV_StsError, error_message);
}
// Get the height from the first image. We'll need this
// later in code to reshape the images to their original
// size:
int height = images[0].rows;
// The following lines simply get the last images from
// your dataset and remove it from the vector. This is
// done, so that the training data (which we learn the
@ -118,6 +126,9 @@ int main(int argc, const char *argv[]) {
Ptr<FaceRecognizer> model = createEigenFaceRecognizer();
model->setLabelsInfo(labelsInfo);
model->train(images, labels);
string saveModelPath = "face-rec-model.txt";
cout << "Saving the trained model to " << saveModelPath << endl;
model->save(saveModelPath);
// The following line predicts the label of a given
// test image:
@ -133,39 +144,43 @@ int main(int argc, const char *argv[]) {
cout << result_message << endl;
if( (predictedLabel == testLabel) && !model->getLabelInfo(predictedLabel).empty() )
cout << format("%d-th label's info: %s", predictedLabel, model->getLabelInfo(predictedLabel).c_str()) << endl;
// Sometimes you'll need to get/set internal model data,
// which isn't exposed by the public cv::FaceRecognizer.
// Since each cv::FaceRecognizer is derived from a
// cv::Algorithm, you can query the data.
//
// First we'll use it to set the threshold of the FaceRecognizer
// to 0.0 without retraining the model. This can be useful if
// you are evaluating the model:
//
model->set("threshold", 0.0);
// Now the threshold of this model is set to 0.0. A prediction
// now returns -1, as it's impossible to have a distance below
// it
predictedLabel = model->predict(testSample);
cout << "Predicted class = " << predictedLabel << endl;
// Here is how to get the eigenvalues of this Eigenfaces model:
Mat eigenvalues = model->getMat("eigenvalues");
// And we can do the same to display the Eigenvectors (read Eigenfaces):
Mat W = model->getMat("eigenvectors");
// From this we will display the (at most) first 10 Eigenfaces:
for (int i = 0; i < min(10, W.cols); i++) {
string msg = format("Eigenvalue #%d = %.5f", i, eigenvalues.at<double>(i));
cout << msg << endl;
// get eigenvector #i
Mat ev = W.col(i).clone();
// Reshape to original size & normalize to [0...255] for imshow.
Mat grayscale = toGrayscale(ev.reshape(1, height));
// Show the image & apply a Jet colormap for better sensing.
Mat cgrayscale;
applyColorMap(grayscale, cgrayscale, COLORMAP_JET);
imshow(format("%d", i), cgrayscale);
}
waitKey(0);
// advanced stuff
if(argc>2) {
// Sometimes you'll need to get/set internal model data,
// which isn't exposed by the public cv::FaceRecognizer.
// Since each cv::FaceRecognizer is derived from a
// cv::Algorithm, you can query the data.
//
// First we'll use it to set the threshold of the FaceRecognizer
// to 0.0 without retraining the model. This can be useful if
// you are evaluating the model:
//
model->set("threshold", 0.0);
// Now the threshold of this model is set to 0.0. A prediction
// now returns -1, as it's impossible to have a distance below
// it
predictedLabel = model->predict(testSample);
cout << "Predicted class = " << predictedLabel << endl;
// Here is how to get the eigenvalues of this Eigenfaces model:
Mat eigenvalues = model->getMat("eigenvalues");
// And we can do the same to display the Eigenvectors (read Eigenfaces):
Mat W = model->getMat("eigenvectors");
// From this we will display the (at most) first 10 Eigenfaces:
for (int i = 0; i < min(10, W.cols); i++) {
string msg = format("Eigenvalue #%d = %.5f", i, eigenvalues.at<double>(i));
cout << msg << endl;
// get eigenvector #i
Mat ev = W.col(i).clone();
// Reshape to original size & normalize to [0...255] for imshow.
Mat grayscale;
normalize(ev.reshape(1), grayscale, 0, 255, NORM_MINMAX, CV_8UC1);
// Show the image & apply a Jet colormap for better sensing.
Mat cgrayscale;
applyColorMap(grayscale, cgrayscale, COLORMAP_JET);
imshow(format("%d", i), cgrayscale);
}
waitKey(0);
}
return 0;
}

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