Hello eveyone,
I would like to use receive string instead of int from prediction function.
my .csv file looks like that:
path/to/username1/username1-01.png;username1
path/to/username1/username1-02.png;username1
path/to/username2/username2-01.png;username2
path/to/username2/username2-02.png;username2
and so on
And I would like to get recognized label, for example username2 as variable, so I could write it to a text file. There is my code below, and I cannot use string format at the very bottom (string prediction = model->predict(face_resized);)
Can anyone help me with that?
> #include "opencv2/core/core.hpp"
> #include "opencv2/contrib/contrib.hpp"
> #include "opencv2/highgui/highgui.hpp"
> #include "opencv2/imgproc/imgproc.hpp"
> #include "opencv2/objdetect/objdetect.hpp"
>
> #include <iostream>
> #include <fstream>
> #include <sstream>
>
> using namespace cv; using namespace
> std;
>
> static void read_csv(const string&
> filename, vector<Mat>& images,
> vector<string>& 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.";
> CV_Error(CV_StsBadArg, error_message);
> }
> string line, path, classlabel;
> while (getline(file, line)) {
> stringstream liness(line);
> getline(liness, path, separator);
> getline(liness, classlabel);
> if(!path.empty() && !classlabel.empty()) {
> images.push_back(imread(path, 0));
> labels.push_back((classlabel.c_str()));
> }
> } }
>
> int main(int argc, const char *argv[])
> { remove( "path_to_user.txt" );
> //change PATH!!!
> 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]);
> string deviceId = string(argv[3]);
> // These vectors hold the images and corresponding labels:
> vector<Mat> images;
> vector<string> 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 = createFisherFaceRecognizer();
> 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);
> // Get a handle to the Video device:
> VideoCapture cap(deviceId);
> // Check if we can use this device at all:
> if(!cap.isOpened()) {
> cerr << "Capture Device ID " << deviceId << "cannot be opened." <<
> endl;
> return -1;
> }
> // Holds the current frame from the Video device:
> Mat frame;
> for(;;) {
> cap >> frame;
> // Clone the current frame:
> Mat original = frame.clone();
> // Convert the current frame to grayscale:
> Mat gray;
> cvtColor(original, gray, CV_BGR2GRAY);
> // Find the faces in the frame:
> vector< Rect_<int> > faces;
> haar_cascade.detectMultiScale(gray,
> faces);
> // 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(int i = 0; i < faces.size(); i++) {
> // Process face by face:
> Rect face_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:
> string prediction = model->predict(face_resized);
> }
> }
> return 0; }
Cheers!