Riconoscimento viso in video utilizzando OpenCV fornisce un'eccezione non gestita

Question

Sto tentando di utilizzare il riconoscimento visi nel campione di video fornito con OpenCV. L'unica modifica che ho fatto è: invece di usare gli argomenti della riga di comando per fornire i percorsi dei classificatori CSV e Cascade, li ho dati direttamente nel codice. Questo è il codice:

#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; 
} 

Questo è ciò che il mio file CSV train.txt assomiglia:

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

Tuttavia quando provo a eseguire l'esempio (si costruisce bene), ottengo questi errori e chiede mi 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. 

l'errore si verifica nel punto in cui ho inizializzare la Fisher Recognizer:

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

Sto usando Windows 7 64-bit con Visual Studio 2012 e OpenCV 2.4.2 (Build Configurazione: 64-Release)

ho già provato rilevamento dei volti e l'estrazione faccia e funzionano bene sul mio computer (If anyone wants to check out the code), quindi non c'è apparentemente alcun problema con le mie impostazioni di progetto di Visual Studio (Linker o C/C++).

There is a similar question here ma ancora non risolve nulla.

Qualcosa di sbagliato in quello che sto facendo?

Answer 1

Come abbiamo già concluso per posta. Questo accade perché una sola etichetta viene dato nel file CSV:

metodo

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

I Fisherfaces ha bisogno di almeno due classi di imparare un modello. Questo caso avrebbe dovuto essere catturato e OpenCV 2.4.2 getta la seguente eccezione sul mio sistema:

 
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 

Il che rende l'errore nei dati di addestramento abbastanza chiaro. Non lo so, perché questa eccezione non viene lanciata nell'installazione di Windows 7, ma installerò un sistema di test per riprodurlo il prima possibile e correggerlo di conseguenza.