使用OpenCV进行视频人脸识别时出现未处理的异常

我试图在OpenCV提供的视频样本中使用人脸识别。我所做的唯一修改是：我没有使用命令行参数来提供CSV和级联分类器路径，而是直接在代码中提供它们。代码如下：

#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;
}

但是，当我尝试运行示例时（它构建得很好），我得到了这些错误，它要求我中断：

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.

错误发生在我初始化Fisher识别器的位置：

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

Ptr model=createFisherFaceRecognizer（）；
模型->列车（图像、标签）；

我在Visual Studio 2012和OpenCV 2.4.2（构建配置：x64版本）中使用64位Windows 7

我已经尝试过人脸检测和人脸提取，它们在我的计算机上工作得很好（），因此我的Visual studio项目设置（链接器或C/C++）显然没有问题

但它仍然解决不了任何问题

---

我现在做的有什么问题吗？

我们已经通过邮件得出结论。这是因为CSV文件中只提供了一个标签：

C:\Training\extract0.jpg;0 C:\Training\extract1.jpg;0 C:\Training\extract2.jpg;0 C:\Training\extract0.jpg；0 C:\Training\extract1.jpg；0 C:\Training\extract2.jpg；0 Fisherfaces方法至少需要两个类来学习模型。本应捕获此案例，OpenCV 2.4.2在我的系统上引发以下异常：

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 OpenCV错误：LDA文件/home/philipp/github/libfacerec/src/subspace.cpp第150行中的参数错误（至少需要两个类来执行LDA。原因：只给出了一个类！） 在引发“cv:：Exception”的实例后调用terminate what（）：/home/philipp/github/libfacerec/src/subspace.cpp:150:error:（-5）执行LDA至少需要两个类。原因：只上了一节课！函数内lda

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这使得训练数据中的错误非常明显。我不知道为什么Windows 7安装中不会引发此异常，但我将设置一个测试系统，以便尽快复制并进行相应的修复。

异常通常会说明出现了什么问题。您应该处理它并查看它的消息。@AndreyKamaev我已经试过调试它，问题似乎出在代码

图像中的这一行上。推回（imread（path，0））read_csv（）

function中的code>。它不会在

图像

数组中添加图像数据，这就是Fisher训练和预测函数给出异常的原因。

imread（）

实现或CSV文件中的路径格式是否有问题？当我只有一个标签时，是否有其他选择？ 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