OpenCV Max locations

Question

I am working on an OpenCV project and am using cvMatchTemplate to locate part of an image I am then using cvMinMaxLoc to find the maximum area, therefore best match, my prob

Answer 1

Try cvThreshold(src, dst, threshold, CV_THRESH_BINARY)

This would return an image in dst with all pixels above threshold as white and all others as black. You would then iterate through all the pixels and check if it is greater than 0 then that is a location you want. Something like this

   char* data = dst->imageData;
   int size = (dst->height) * (dst->width)

   for (int i=0; i<size; i++)
   {
       if(data[i] > 0)
          //copy i into your array
   }

Answer 2

I modified the matchTemplate tutorial to get you started. It basically uses a queue to track the top X match points, and later plots all of them. Hope that is helpful!

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <iostream>
#include <vector>
#include <limits>
#include <queue>

using namespace cv;
using namespace std;

void maxLocs(const Mat& src, queue<Point>& dst, size_t size)
{
    float maxValue = -1.0f * numeric_limits<float>::max();
    float* srcData = reinterpret_cast<float*>(src.data);

    for(int i = 0; i < src.rows; i++)
    {
        for(int j = 0; j < src.cols; j++)
        {
            if(srcData[i*src.cols + j] > maxValue)
            {
                maxValue = srcData[i*src.cols + j];

                dst.push(Point(j, i));

                // pop the smaller one off the end if we reach the size threshold.
                if(dst.size() > size)
                {
                    dst.pop();
                }
            }
        }
    }
}

/// Global Variables
Mat img; Mat templ; Mat result;
string image_window = "Source Image";
string result_window = "Result window";

int match_method;
int max_Trackbar = 5;

/// Function Headers
void MatchingMethod( int, void* );

int main(int argc, char* argv[])
{
    /// Load image and template
    img = imread( "dogs.jpg", 1 );
    templ = imread( "dog_templ.jpg", 1 );

    /// Create windows
    namedWindow( image_window, CV_WINDOW_AUTOSIZE );
    namedWindow( result_window, CV_WINDOW_AUTOSIZE );

    /// Create Trackbar
    string trackbar_label = "Method: \n 0: SQDIFF \n 1: SQDIFF NORMED \n 2: TM CCORR \n 3: TM CCORR NORMED \n 4: TM COEFF \n 5: TM COEFF NORMED";
    createTrackbar( trackbar_label, image_window, &match_method, max_Trackbar, MatchingMethod );

    MatchingMethod( 0, 0 );

    waitKey(0);

    return 0;
}

/**
 * @function MatchingMethod
 * @brief Trackbar callback
 */
void MatchingMethod( int, void* )
{
    /// Source image to display
    Mat img_display;
    img.copyTo( img_display );

    /// Create the result matrix
    int result_cols =  img.cols - templ.cols + 1;
    int result_rows = img.rows - templ.rows + 1;

    result.create( result_cols, result_rows, CV_32FC1 );

    /// Do the Matching and Normalize
    matchTemplate( img, templ, result, match_method );
    normalize( result, result, 0, 1, NORM_MINMAX, -1, Mat() );

    /// For SQDIFF and SQDIFF_NORMED, the best matches are lower values. For all the other methods, the higher the better
    if( match_method  == CV_TM_SQDIFF || match_method == CV_TM_SQDIFF_NORMED )
    {
        result = 1.0 - result;
    }

    // get the top 100 maximums...
    queue<Point> locations;
    maxLocs(result, locations, 100);

    /// Show me what you got
    while(!locations.empty())
    {
        Point matchLoc = locations.front();
        rectangle( img_display, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
        rectangle( result, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
        locations.pop();
    }

    imshow( image_window, img_display );
    imshow( result_window, result );

    return;
}