私はopencvの顔認識コードをEclipseのubuntuで実行すると、セグメンテーションフォールトのコアがダンプされます

Question

Cのビデオから顔認識のコードが得られました。+ +コードはWindows上でうまく動作しますが、私はubuntuを実行し、私が持っている完全なコードはここにある可能性があります：int im_width = images[0].cols;私は持っています：

#include "opencv2/core.hpp" 
#include "opencv2/face.hpp" 
#include "opencv2/highgui.hpp" 
#include "opencv2/imgproc.hpp" 
#include "opencv2/objdetect.hpp" 

#include <iostream> 
#include <fstream> 
#include <sstream> 
#include <ctime> 

using namespace cv; 
using namespace std; 
using namespace cv::face; 



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 = string(argv[1]); 
    string fn_csv = string(argv[2]); 
    int deviceId = atoi(argv[3]); 
    // 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(unsigned 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; 
} 

Answer 1

vector<Mat> images;が空であると思われますので、images[0]にアクセスするとクラッシュしています。

imagesが、デバッガを使用して、またはその状態を印刷することによって空でないことを確認します。次のようになります。

int im_width = 0; 
int im_height = 0; 

if(images.size()) 
{ 
    im_width = images[0].cols; 
    im_height = images[0].rows; 
} 
else 
{ 
    std::cout << "Images is Empty!" << std::endl; 
} 

if((im_width > 0) && (im_height > 0)) 
{ 
    // Continue on with valid dimensions 
}