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| int main(int argc, const char *argv[]) {
// Check for valid command line arguments, print usage
// if no arguments were given.
/* if (argc != 4) {
cout << "usage: " << argv[0] << " </path/to/haar_cascade> </path/to/csv.ext> </path/to/device id>" << endl;
cout << "\t </path/to/haar_cascade> -- Path to the Haar Cascade for face detection." << endl;
cout << "\t </path/to/csv.ext> -- Path to the CSV file with the face database." << endl;
cout << "\t <device id> -- The webcam device id to grab frames from." << endl;
exit(1);
}
// Get the path to your CSV:
string fn_haar = string(argv[1]);
string fn_csv = string(argv[2]);
int deviceId = atoi(argv[3]); */
string fn_haar="D:\\opencv\\sources\\data\\haarcascades\\haarcascade_frontalface_alt.xml";
string fn_csv = "D:\\opencv\\csv.ext";
int deviceId = 0;
// These vectors hold the images and corresponding labels:
vector<Mat> images;
vector<int> labels;
// Read in the data (fails if no valid input filename is given, but you'll get an error message):
try {
read_csv(fn_csv, images, labels);
} catch (cv::Exception& e) {
cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
// nothing more we can do
exit(1);
}
// Get the height from the first image. We'll need this
// later in code to reshape the images to their original
// size AND we need to reshape incoming faces to this size:
int im_width = images[0].cols;
int im_height = images[0].rows;
// Create a FaceRecognizer and train it on the given images:
Ptr<FaceRecognizer> model = createLBPHFaceRecognizer();
model->train(images, labels);
// That's it for learning the Face Recognition model. You now
// need to create the classifier for the task of Face Detection.
// We are going to use the haar cascade you have specified in the
// command line arguments:
//
CascadeClassifier haar_cascade;
haar_cascade.load(fn_haar);
Mat original;
original= imread("D:\\hugh.jpg", CV_LOAD_IMAGE_COLOR);
// Convert the current frame to grayscale:
Mat gray;
cvtColor(original, gray, CV_BGR2GRAY);
// Find the faces in the frame:
int prediction;
vector< Rect_<int> > faces;
vector<pair<vector<int>,vector< Rect_<int> >>> label;
haar_cascade.detectMultiScale(gray, faces);
vector<int> pred (faces.size());
// At this point you have the position of the faces in
// faces. Now we'll get the faces, make a prediction and
// annotate it in the video. Cool or what?
for(unsigned int i = 0; i < faces.size(); i++) {
nbFaces=faces.size();
// Process face by face:
Rect face_i = faces[i];
//Tableau[i]=faces[i];
// Crop the face from the image. So simple with OpenCV C++:
Mat face = gray(face_i);
// Resizing the face is necessary for Eigenfaces and Fisherfaces. You can easily
// verify this, by reading through the face recognition tutorial coming with OpenCV.
// Resizing IS NOT NEEDED for Local Binary Patterns Histograms, so preparing the
// input data really depends on the algorithm used.
//
// I strongly encourage you to play around with the algorithms. See which work best
// in your scenario, LBPH should always be a contender for robust face recognition.
//
// Since I am showing the Fisherfaces algorithm here, I also show how to resize the
// face you have just found:
Mat face_resized;
cv::resize(face, face_resized, Size(im_width, im_height), 1.0, 1.0, INTER_CUBIC);
// Now perform the prediction, see how easy that is:
prediction = model->predict(face_resized);
pred[i]=prediction;
// And finally write all we've found out to the original image!
// First of all draw a green rectangle around the detected face:
rectangle(original, face_i, CV_RGB(0, 255,0), 1);
// Create the text we will annotate the box with:
string box_text = format("Prediction = %d", prediction);
// Calculate the position for annotated text (make sure we don't
// put illegal values in there):
int pos_x = max(face_i.tl().x - 10, 0);
int pos_y = max(face_i.tl().y - 10, 0);
// And now put it into the image:
putText(original, box_text, Point(pos_x, pos_y), FONT_HERSHEY_PLAIN, 1, CV_RGB(0,255,0), 2);
}
label.push_back(make_pair(pred,faces));
imwrite("D:\\ImageScanée.jpg", original);
namedWindow( "face_recognizer", 0 );
//Detect click on window and call mouseEvent
cvSetMouseCallback("face_recognizer", mouseEvent, (void*)&label);
// Show the result:
imshow("face_recognizer", original);
// And display it:
waitKey(0);
return 0;
} |
