Rozpoznawanie twarzy w wideo przy użyciu OpenCV daje nieobsługiwany wyjątek

Question

Próbuję użyć rozpoznawania twarzy w przykładzie wideo dostarczonym z OpenCV. Jedyna modyfikacja jaką zrobiłem to: Zamiast używać argumentów wiersza poleceń do dostarczania ścieżek klasyfikatora CSV i Cascade, podałem je bezpośrednio w kodzie. Jest to kod:

#include "stdafx.h" 
#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<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."; 
     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(atoi(classlabel.c_str())); 
     } 
    } 
} 

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 = "C:\\OpenCV-2.4.2\\opencv\\data\\haarcascades\\haarcascade_frontalface_default.xml"; 
string fn_csv = "C:\\Users\\gaspl\\Desktop\\train.txt"; 
int deviceId = 1; 
// 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 = 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: 
     int prediction = model->predict(face_resized); 
     // 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 = std::max(face_i.tl().x - 10, 0); 
     int pos_y = std::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, 1.0, CV_RGB(0,255,0), 2.0); 
    } 
    // Show the result: 
    imshow("face_recognizer", original); 
    // And display it: 
    char key = (char) waitKey(20); 
    // Exit this loop on escape: 
    if(key == 27) 
     break; 
    } 
    return 0; 
} 

To właśnie moja train.txt plik CSV wygląda następująco:

C:\\Training\\extract0.jpg;0 
C:\\Training\\extract1.jpg;0 
C:\\Training\\extract2.jpg;0 

Jednak kiedy próbuję uruchomić próbki (buduje w porządku), otrzymuję te błędy i prosi abym Break:

First-chance exception at 0x000007FEFE04CAED in facrec.exe: Microsoft C++ exception: cv::Exception at memory location 0x000000000025C530. 
Unhandled exception at at 0x000007FEFE04CAED in facrec.exe: Microsoft C++ exception: cv::Exception at memory location 0x000000000025C530. 

błąd występuje w punkcie, gdzie mogę zainicjować Fisher Recognizer:

Ptr<FaceRecognizer> model = createFisherFaceRecognizer(); 
model->train(images, labels); 

używam Windows 7 64-bit z Visual Studio 2012 i OpenCV 2.4.2 (Build Configuration: 64-Release)

Próbowałem już wykrywania twarzy i wydobycie twarzy i działają dobrze na moim komputerze (If anyone wants to check out the code), więc najwyraźniej nie ma problemu z moimi ustawieniami projektu Visual Studio (Linker lub C/C++).

There is a similar question here ale nadal nie rozwiązuje niczego.

Coś nie tak z tym, co robię?

Answer 1

Jak już zawarliśmy pocztą. Dzieje się tak, ponieważ tylko jedna etykieta jest podany w pliku CSV:

metoda

 
C:\Training\extract0.jpg;0 
C:\Training\extract1.jpg;0 
C:\Training\extract2.jpg;0 

W Fisherfaces potrzebuje co najmniej dwie klasy, aby dowiedzieć się model. Sprawa ta powinna zostać złapany i OpenCV 2.4.2 rzuca następujący wyjątek w moim systemie:

 
OpenCV Error: Bad argument (At least two classes are needed to perform a LDA. Reason: Only one class was given!) in lda, file /home/philipp/github/libfacerec/src/subspace.cpp, line 150 
terminate called after throwing an instance of 'cv::Exception' 
    what(): /home/philipp/github/libfacerec/src/subspace.cpp:150: error: (-5) At least two classes are needed to perform a LDA. Reason: Only one class was given! in function lda 

co sprawia, że ​​błąd w prezentowanych danych treningowych dość jasne. Nie wiem, dlaczego ten wyjątek nie został zgłoszony w instalacji systemu Windows 7, ale skonfiguruję system testowy do odtworzenia tak szybko, jak to możliwe i odpowiednio go poprawię.