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I need to make a program that can detect different objects from a quadcopter, only problem is I'm getting tons of little random shapes appearing. I borrowed the code from here: http://opencv-srf.blogspot.com/2011/09/object-detection-tracking-using-contours.html and tried it it on some images taken from the quad copter. Here is the result of the program: https://i.stack.imgur.com/YfFKV.jpg. It should've only seen the silverish box near the top, but there's all the little shapes it sees. I know logically what to do; just not draw lines around the shape if its area is below a certain pixel amount... but I have no clue how to do it. I altered the coded slightly and included it below. How would I go about doing this?

Code:

#include "opencv2/core/core.hpp"
#include "opencv2/flann/miniflann.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/photo/photo.hpp"
#include "opencv2/video/video.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/ml/ml.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/contrib/contrib.hpp"
#include "opencv2/core/core_c.h"
#include "opencv2/highgui/highgui_c.h"
#include "opencv2/imgproc/imgproc_c.h"

using namespace cv;
using namespace std;

int main()
{

IplImage* img = cvLoadImage("C:/Users/wyndr_000/Documents/Visual Studio 2013/Projects/OpenCV2410Test2/OpenCV2410Test2/Testpic2.png");

//show the original image
cvNamedWindow("Raw");
cvShowImage("Raw", img);

//converting the original image into grayscale
IplImage* imgGrayScale = cvCreateImage(cvGetSize(img), 8, 1);
cvCvtColor(img, imgGrayScale, CV_BGR2GRAY);

//thresholding the grayscale image to get better results
cvThreshold(imgGrayScale, imgGrayScale, 128, 255, CV_THRESH_BINARY);

CvSeq* contours;  //hold the pointer to a contour in the memory block
CvSeq* result;   //hold sequence of points of a contour
CvMemStorage *storage = cvCreateMemStorage(0); //storage area for all contours

//finding all contours in the image
cvFindContours(imgGrayScale, storage, &contours, sizeof(CvContour), CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE, cvPoint(0, 0));

//iterating through each contour
while (contours)
{
    //obtain a sequence of points of contour, pointed by the variable 'contour'
    result = cvApproxPoly(contours, sizeof(CvContour), storage, CV_POLY_APPROX_DP, cvContourPerimeter(contours)*0.02, 0);


        //if there are 3  vertices  in the contour(It should be a triangle)
        if (result->total == 3)
        {
            //iterating through each point
            CvPoint *pt[3];
            for (int i = 0; i < 3; i++){
                pt[i] = (CvPoint*)cvGetSeqElem(result, i);
            }

            //drawing lines around the triangle
            cvLine(img, *pt[0], *pt[1], cvScalar(255, 0, 0), 4);
            cvLine(img, *pt[1], *pt[2], cvScalar(255, 0, 0), 4);
            cvLine(img, *pt[2], *pt[0], cvScalar(255, 0, 0), 4);

        }

        //if there are 4 vertices in the contour(It should be a quadrilateral)
        else if (result->total == 4)
        {
            //iterating through each point
            CvPoint *pt[4];
            for (int i = 0; i < 4; i++){
                pt[i] = (CvPoint*)cvGetSeqElem(result, i);
            }

            //drawing lines around the quadrilateral
            cvLine(img, *pt[0], *pt[1], cvScalar(0, 255, 0), 4);
            cvLine(img, *pt[1], *pt[2], cvScalar(0, 255, 0), 4);
            cvLine(img, *pt[2], *pt[3], cvScalar(0, 255, 0), 4);
            cvLine(img, *pt[3], *pt[0], cvScalar(0, 255, 0), 4);
        }

        //if there are 7  vertices  in the contour(It should be a heptagon)
        else if (result->total == 7)
        {
            //iterating through each point
            CvPoint *pt[7];
            for (int i = 0; i < 7; i++){
                pt[i] = (CvPoint*)cvGetSeqElem(result, i);
            }

            //drawing lines around the heptagon
            cvLine(img, *pt[0], *pt[1], cvScalar(0, 0, 255), 4);
            cvLine(img, *pt[1], *pt[2], cvScalar(0, 0, 255), 4);
            cvLine(img, *pt[2], *pt[3], cvScalar(0, 0, 255), 4);
            cvLine(img, *pt[3], *pt[4], cvScalar(0, 0, 255), 4);
            cvLine(img, *pt[4], *pt[5], cvScalar(0, 0, 255), 4);
            cvLine(img, *pt[5], *pt[6], cvScalar(0, 0, 255), 4);
            cvLine(img, *pt[6], *pt[0], cvScalar(0, 0, 255), 4);
        }

    //obtain the next contour
    contours = contours->h_next;
}


//show the image in which identified shapes are marked   
cvNamedWindow("Tracked");
cvShowImage("Tracked", img);

cvWaitKey(0); //wait for a key press

//cleaning up
cvDestroyAllWindows();
cvReleaseMemStorage(&storage);
cvReleaseImage(&img);
cvReleaseImage(&imgGrayScale);

return 0;
} 
desertnaut
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Wyndrix
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  • Have you considered using [`contourArea`](http://docs.opencv.org/modules/imgproc/doc/structural_analysis_and_shape_descriptors.html#contourarea) to find all of the areas of each contour, and for any contour area that's below a certain amount, use [`drawContours`](http://docs.opencv.org/modules/imgproc/doc/structural_analysis_and_shape_descriptors.html#drawcontours) in a black colour to zero them out? – rayryeng Apr 08 '15 at 07:54
  • I know there's a function contourArea, but I don't understand how to use it. I looked it up but I don't understand how to use it here. – Wyndrix Apr 08 '15 at 15:35
  • `contourArea` determines the total area bounded by a closed contour. It's basically summing all of the white pixels that a contour encloses. All you have to do is find all of the areas bounded by each contour, and filter out those that are less than a prescribed minimum. – rayryeng Apr 08 '15 at 16:04

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