執行環境：VS2012+OpenCV2.4.8。

存在問題：
在使用OpenCV的人臉檢測時，用到了detectMultiScale函式，使用方法為：face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, cv::Size(30, 30) );
其中faces為vector，在faces這個vector釋放時，會出現_CrtIsValidHeapPointer的bug。這應該是由於detectMultiScale函式封裝在OpenCV的dll中，在檢測到人臉後會對faces這個vector進行操作，而在該變數使用結束釋放時，再次對其進行操作時，就會報錯。

程式碼如下：

#include "stdafx.h"
#include <opencv2/opencv.hpp>
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"

#include <iostream>
#include <stdio.h>

using namespace std;
using namespace cv;

// Global variables
// Copy this file from opencv/data/haarscascades to target folder
 

string face_cascade_name = "F:\\opencv\\opencv\\sources\\data\\haarcascades\\haarcascade_frontalface_alt.xml";
CascadeClassifier *face_cascade;
string window_name = "Capture - Face detection";
int filenumber; // Number of file to be saved
string filename;

// Function Headers
class faceDetection
{
private
 
:
    std::vector<Rect> faces;
public:
    faceDetection();
    ~faceDetection();
    void detectAndDisplay(Mat frame);
};
faceDetection::faceDetection()
{
}
faceDetection::~faceDetection()
{
}

// Function detectAndDisplay
void faceDetection::detectAndDisplay(Mat frame)
{
    // 報錯問題所在，。
    // std::vector<Rect> faces;

    Mat frame_gray;
    Mat crop;
    Mat res;
    Mat gray;
    string text;
    stringstream sstm;

    cvtColor(frame, frame_gray, COLOR_BGR2GRAY);
    equalizeHist(frame_gray, frame_gray);

    // Detect faces
    face_cascade->detectMultiScale(frame_gray, faces, 1.1, 2, 0 | CASCADE_SCALE_IMAGE, Size(30, 30));

    // Set Region of Interest
    cv::Rect roi_b;
    cv::Rect roi_c;

    size_t ic = 0; // ic is index of current element
    int ac = 0; // ac is area of current element

    size_t ib = 0; // ib is index of biggest element
    int ab = 0; // ab is area of biggest element

    for (ic = 0; ic < faces.size(); ic++) // Iterate through all current elements (detected faces)

    {
        roi_c.x = faces[ic].x;
        roi_c.y = faces[ic].y;
        roi_c.width = (faces[ic].width);
        roi_c.height = (faces[ic].height);

        ac = roi_c.width * roi_c.height; // Get the area of current element (detected face)

        roi_b.x = faces[ib].x;
        roi_b.y = faces[ib].y;
        roi_b.width = (faces[ib].width);
        roi_b.height = (faces[ib].height);

        ab = roi_b.width * roi_b.height; // Get the area of biggest element, at beginning it is same as "current" element

        if (ac > ab)
        {
            ib = ic;
            roi_b.x = faces[ib].x;
            roi_b.y = faces[ib].y;
            roi_b.width = (faces[ib].width);
            roi_b.height = (faces[ib].height);
        }

        crop = frame(roi_b);
        resize(crop, res, Size(128, 128), 0, 0, INTER_LINEAR); // This will be needed later while saving images
        cvtColor(crop, gray, CV_BGR2GRAY); // Convert cropped image to Grayscale

        // Form a filename
        filename = "";
        stringstream ssfn;
        ssfn << filenumber << ".png";
        filename = ssfn.str();
        filenumber++;

        imwrite(filename, gray);

        Point pt1(faces[ic].x, faces[ic].y); // Display detected faces on main window - live stream from camera
        Point pt2((faces[ic].x + faces[ic].height), (faces[ic].y + faces[ic].width));
        rectangle(frame, pt1, pt2, Scalar(0, 255, 0), 2, 8, 0);
    }

    // Show image
    sstm << "Crop area size: " << roi_b.width << "x" << roi_b.height << " Filename: " << filename;
    text = sstm.str();

    putText(frame, text, cvPoint(30, 30), FONT_HERSHEY_COMPLEX_SMALL, 0.8, cvScalar(0, 0, 255), 1, CV_AA);
    imshow("original", frame);
    // waitKey();

    if (!crop.empty())
    {
        imshow("detected", crop);
        // waitKey();
    }
    else
        destroyWindow("detected");

}


// Function main
int main()
{
    faceDetection *face;
    face = new faceDetection;
    // Load the cascade
    face_cascade = new CascadeClassifier;
    if (!face_cascade->load(face_cascade_name)){
        printf("--(!)Error loading\n");
        return (-1);
    }

    // Read the image file
    Mat frame = imread("lp.jpg");

    // Apply the classifier to the frame
    if (!frame.empty()){
        face->detectAndDisplay(frame);
    }
    else{
        printf(" --(!) No captured frame -- Break!");
        //break;
    }

    int c = waitKey(1000);

    if (27 == char(c)){
       // break;
    }

    return 0;
}