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Exceptions now go into CV_Error. Added thresholding to the FaceRecognizer and updated the demo accordingly.

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This commit is contained in:
Philipp Wagner
2012-06-10 22:23:18 +00:00
parent ee1b671279
commit cd7d93f362
4 changed files with 155 additions and 88 deletions
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61
samples/cpp/facerec_demo.cpp
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@@ -41,7 +41,7 @@ static Mat toGrayscale(InputArray _src) {
static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, 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.";
string error_message = "No valid input file was given, please check the given filename.";
CV_Error(CV_StsBadArg, error_message);
}
string line, path, classlabel;
@@ -58,7 +58,7 @@ 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 no arguments were given.
if (argc != 2) {
cout << "usage: " << argv[0] << " <csv.ext>" << endl;
exit(1);
@@ -79,8 +79,8 @@ int main(int argc, const char *argv[]) {
}
// Quit if there are not enough images for this demo.
if(images.size() <= 1) {
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);
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
@@ -102,30 +102,51 @@ int main(int argc, const char *argv[]) {
// 10 principal components (read Eigenfaces), then call
// the factory method like this:
//
// cv::createEigenFaceRecognizer(10);
// cv::createEigenFaceRecognizer(10);
//
// If you want to create a FaceRecognizer with a
// confidennce threshold, call it with:
//
// cv::createEigenFaceRecognizer(10, 123.0);
//
Ptr<FaceRecognizer> model = createEigenFaceRecognizer();
model->train(images, labels);
// The following line predicts the label of a given
// test image. In this example no thresholding is
// done.
int predicted = model->predict(testSample);
// Show the prediction and actual class of the given
// sample:
string result_message = format("Predicted class=%d / Actual class=%d.", predicted, testLabel);
// test image:
int predictedLabel = model->predict(testSample);
//
// To get the confidence of a prediction call it with:
//
// model with:
// int predictedLabel = -1;
// double confidence = 0.0;
// model->predict(testSample, predictedLabel, confidence);
//
string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);
cout << result_message << endl;
// Sometimes you'll need to get some 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.
//
// Here is how to get the eigenvalues of this Eigenfaces model:
// 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
// without retraining the model:
//
model->set("threshold", 0.0);
// Now the threshold is of this model is 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;
// Now 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 ("Eigenfaces"):
// 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;
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.