Detect different shaped contours?

edit

EDIT 2 : you can change width&height ratio if(dist0 > dist1 *4) and angle if( fabs(angle) > 35 & fabs(angle) < 150 )

input & output Image:

#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"

using namespace cv;
using namespace std;

//! Compute the distance between two points
/*! Compute the Euclidean distance between two points
*
* @Param a Point a
* @Param b Point b
*/
static double distanceBtwPoints(const cv::Point2f &a, const cv::Point2f &b)
{
    double xDiff = a.x - b.x;
    double yDiff = a.y - b.y;

    return std::sqrt((xDiff * xDiff) + (yDiff * yDiff));
}

int main( int argc, char** argv )
{
    Mat src,gray;
    src = imread(argv[1]);
    if(src.empty())
        return -1;

    cvtColor( src, gray, COLOR_BGR2GRAY );
    gray = gray < 200;

    vector<vector<Point> > contours;

    findContours(gray.clone(), contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);

    RotatedRect _minAreaRect;

    for (size_t i = 0; i < contours.size(); ++i)
    {
        _minAreaRect = minAreaRect( Mat(contours[i]) );
        Point2f pts[4];
        _minAreaRect.points(pts);

        double dist0 = distanceBtwPoints(pts[0], pts[1]);
        double dist1 = distanceBtwPoints(pts[1], pts[2]);

        double angle = 0;
        if(dist0 > dist1 *4)
            angle =atan2(pts[0].y - pts[1].y,pts[0].x - pts[1].x) * 180.0 / CV_PI;
        if(dist1 > dist0 *4)
            angle =atan2(pts[1].y - pts[2].y,pts[1].x - pts[2].x) * 180.0 / CV_PI;

        if( fabs(angle) > 35 & fabs(angle) < 150 )
            for( int j = 0; j < 4; j++ )
                line(src, pts[j], pts[(j+1)%4], Scalar(0, 0, 255), 1, LINE_AA);
    }
    imshow("result", src);
    waitKey(0);
    return 0;
}

EDIT 1: you can improve the code below by changing

 if( _minAreaRect.angle < -30 & (dist0 > dist1 *4 | dist1 > dist0 *4) )

here you can change width&height ratio and angle of RotatedRect ( need your care )

input & output Image:

#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"

using namespace cv;
using namespace std;

//! Compute the distance between two points
/*! Compute the Euclidean distance between two points
*
* @Param a Point a
* @Param b Point b
*/
static double distanceBtwPoints(const cv::Point2f &a, const cv::Point2f &b)
{
    double xDiff = a.x - b.x;
    double yDiff = a.y - b.y;

    return std::sqrt((xDiff * xDiff) + (yDiff * yDiff));
}

int main( int argc, char** argv )
{
    Mat src,gray;
    src = imread(argv[1]);
    if(src.empty())
        return -1;

    cvtColor( src, gray, COLOR_BGR2GRAY );
    gray = gray < 200;

    vector<vector<Point> > contours;

    findContours(gray.clone(), contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);

    RotatedRect _minAreaRect;

    for (size_t i = 0; i < contours.size(); ++i)
    {
        _minAreaRect = minAreaRect( Mat(contours[i]) );
        Point2f pts[4];
        _minAreaRect.points(pts);

        double dist0 = distanceBtwPoints(pts[0], pts[1]);
        double dist1 = distanceBtwPoints(pts[1], pts[2]);

        if( _minAreaRect.angle < -30 & (dist0 > dist1 *4 | dist1 > dist0 *4) )
            for( int j = 0; j < 4; j++ )
                line(src, pts[j], pts[(j+1)%4], Scalar(0, 0, 255), 1, LINE_AA);
    }
    imshow("result", src);
    waitKey(0);
    return 0;
}

you can filter contours by using height and width of bounding rectangels .

like if ( minRect.height > minRect.width*4 ) as shown with the code below:

#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"

using namespace cv;
using namespace std;

int main( int argc, char** argv )
{
    Mat src,gray;
    src = imread(argv ...