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);
+ }
}
}
}
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.
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
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:
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;
}
projects / platform / upstream / opencv.git / commitdiff