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I have following picture and try to find the largest rectangle with OpenCV with these lines

std::vector< std::vector<cv::Point> > contours;
cv::findContours(result,contours,CV_RETR_LIST,CV_CHAIN_APPROX_SIMPLE);

But the statements above causes memory heap error. Can anyone give me a clue why this is happening? I have been stretching my hairs for last couple of hours.

I think it's something to do with cv::Point allocator since call stack indicates it.

Update: I just ran the program with CvFindContours instead without any problem. So it must be OpenCV 2.3.1.

Update2: Thanks to @karlphillip answer, I revisited my project and it was my Visual Studio project setting. I was linking MFC as static library because of annoying memork leak message. That was the cause of the problem. When I use MFC as shared DLL, the problem went away.

enter image description here

karlphillip
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Tae-Sung Shin
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  • What OS is this? Linux? Windows? Can you provide a minimal application to reproduce the problem? These 2 lines doesn't seem to be the cause of the error. – karlphillip Apr 18 '12 at 12:59
  • @karlphillip It's Windows 7 64bit. you are right. Sorry for my laziness. I will try to come up with a way to make a small test app. – Tae-Sung Shin Apr 18 '12 at 14:07

1 Answers1

2

I've just tested the following application with OpenCV 2.3.1 on both Linux and Windows XP (32bits) and I had no problems.

Unless you can write a minimal application to reproduce the problem you are observing, this is as far as I go.

This is the input image, and the code is right below:

#include <cv.h>
#include <highgui.h>

using namespace cv;

double angle( cv::Point pt1, cv::Point pt2, cv::Point pt0 ) 
{
    double dx1 = pt1.x - pt0.x;
    double dy1 = pt1.y - pt0.y;
    double dx2 = pt2.x - pt0.x;
    double dy2 = pt2.y - pt0.y;
    return (dx1*dx2 + dy1*dy2)/sqrt((dx1*dx1 + dy1*dy1)*(dx2*dx2 + dy2*dy2) + 1e-10);
}


void find_squares(Mat& image, vector<vector<Point> >& squares)
{
    // blur will enhance edge detection
    Mat blurred(image);
    medianBlur(image, blurred, 9);

    Mat gray0(blurred.size(), CV_8U), gray;
    vector<vector<Point> > contours;

    // find squares in every color plane of the image
    for (int c = 0; c < 3; c++)
    {
        int ch[] = {c, 0};
        mixChannels(&blurred, 1, &gray0, 1, ch, 1);

        // try several threshold levels
        const int threshold_level = 2;
        for (int l = 0; l < threshold_level; l++)
        {
            // Use Canny instead of zero threshold level!
            // Canny helps to catch squares with gradient shading
            if (l == 0)
            {
                Canny(gray0, gray, 10, 20, 3); // 

                // Dilate helps to remove potential holes between edge segments
                dilate(gray, gray, Mat(), Point(-1,-1));
            }
            else
            {
                    gray = gray0 >= (l+1) * 255 / threshold_level;
            }

            // Find contours and store them in a list
            findContours(gray, contours, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE);

            // Test contours
            vector<Point> approx;
            for (size_t i = 0; i < contours.size(); i++)
            {
                    // approximate contour with accuracy proportional
                    // to the contour perimeter
                    approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);

                    // Note: absolute value of an area is used because
                    // area may be positive or negative - in accordance with the
                    // contour orientation
                    if (approx.size() == 4 &&
                            fabs(contourArea(Mat(approx))) > 1000 &&
                            isContourConvex(Mat(approx)))
                    {
                            double maxCosine = 0;

                            for (int j = 2; j < 5; j++)
                            {
                                    double cosine = fabs(angle(approx[j%4], approx[j-2], approx[j-1]));
                                    maxCosine = MAX(maxCosine, cosine);
                            }

                            if (maxCosine < 0.3)
                                    squares.push_back(approx);
                    }
            }
        }
    }
}

int main()
{    
    Mat img = imread("paper.jpg");

    vector<vector<Point> > squares;
    find_squares(img, squares);

    std::cout << "squares size: " << squares.size() << std::endl;
    getchar();

    return 0;
}
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karlphillip
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