Extracting complete image frames from video file using VLC

If you wish to extract the entire image frames from a video and if you have vlc player installed in your PC, the following command can be useful.

C:\VideoLAN\VLC>vlc "path\to\video\file.avi"
--video-filter=scene --vout=dummy --start-time=1
--stop-time=5 --scene-ratio=1 --scene-prefix=img-
--scene-path=path\to\destination\ vlc://quit

Since vlc supports most of the popular video formats you can simply change the video file extension and use it directly.

opencv#8: Find the vertical projection of an image

Often, it will be necessary to find the sum of pixels in each column of an image. This is called the vertical projection. An application may in optical character recognition (OCR), where we can use the vertical projection to separate connected characters. The troughs in vertical projection mostly correspond to the junction of two characters.

This piece of code find the sum of column elements of a binary/gray image and stores it in a vector. It also finds the maximum value of vertical projection.

IplImage* img;
// find the vertical projection
std::vector v_vl_proj; // holds the column sum values    
double max_vl_proj = 0; // holds the maximum value    
for( int i=0;i<img->width;++i )
{
    CvMat col;
    CvScalar col_sum;
    cvGetCol( img, &col, i ); // get individual columns    
    col_sum = cvSum( &col ); // find the sum of ith column
    v_vl_proj.push_back( col_sum.val[0] ); // push back to vector
    if( col_sum.val[0]>max_vl_proj )    max_vl_proj = col_sum.val[0];    
}

The same logic can be used to find the row sum of the image.

Matlab: Window based Otsu thresholding

This function divides the gray level image into a predefined set of subimages (This can be controlled). Calculates the otsu threshold on each subimage and threshold it accordingly. The function returns a BW image that is thresholded according to window based otsu thresholding.

function IMBw = LocalOtsuThresh( IMGray, NW )

[M N] = size( IMGray );
IMBw = zeros(M, N);

if nargin < 2
    NW = 4;
end

for m = 1:NW
    for n = 1:NW
        IMTmp = uint8(IMGray( 1+(m-1)*floor(M/NW):m*floor(M/NW),...   
            1+(n-1)*floor(N/NW):n*floor(N/NW) ));
        IMTmpBw = im2bw( IMTmp, graythresh( IMTmp ) );
        
        IMBw( 1+(m-1)*floor(M/NW):m*floor(M/NW),...
            1+(n-1)*floor(N/NW):n*floor(N/NW) ) = IMTmpBw;
    end
end

NW defines the number of subimages. Default is 4. So in default case the gray level image will be divided into 16 subimages.

OpenCV#7: Simple motion detection

This is a simple motion detection algorithm using frame difference method. This example assumes that the video frames are stored in your system in a particular directory.

#include <iostream>
#include <sstream>

#include "cv.h"
#include "highgui.h"

using namespace std;

int main()
{

    IplImage* frame_prev = cvLoadImage( "dir_name/frame0.jpg", CV_LOAD_IMAGE_GRAYSCALE );
    int M = frame_prev->width;
    int N = frame_prev->height;
    IplImage* frame_curr;
    IplImage* frame_diff = cvCreateImage( cvGetSize(frame_prev), 8, 1 );

    for( int i=1; i<=500; ++i )
    {
        ostringstream oss;
        oss << "dir_name/frame";
        oss << i;
        oss << ".jpg";

        // motion detection - simple frame difference algorithm
        frame_curr = cvLoadImage( oss.str().c_str(), CV_LOAD_IMAGE_GRAYSCALE );
        cvAbsDiff( frame_curr, frame_prev, frame_diff );
        cvThreshold( frame_diff, frame_diff, 30, 255, CV_THRESH_BINARY );
        cvCopy( frame_curr, frame_prev );

        cvNamedWindow( "Motion Detection", CV_WINDOW_AUTOSIZE );
        cvShowImage( "Motion Detection", frame_diff );
        if( (cvWaitKey(25) & 255) == 27 ) break;    // wait for some time or 'ESC' key press    
    }

    cvReleaseImage( &frame_prev );
    cvDestroyWindow( "Motion Detection" );

    return 0;
}

OpenCV#6: Find the cross correlation of two opencv images

This function calculates the cross correlation between two images of the type IplImage*. Returns the resulting value.

double cross_correlation( IplImage* img1, IplImage* img2 )
{
double corr;

int M = img1->width;
int N = img1->height;

BwImage img_1( img1 ); // using opencv wrapper for accessing image pixels
BwImage img_2( img2 );

CvScalar img1_avg = cvAvg( img1, NULL );
CvScalar img2_avg = cvAvg( img2, NULL );

double sum_img1_img2 = 0;
double sum_img1_2 = 0;
double sum_img2_2 = 0;

for( int m=0; m<M; ++m )
{
   for( int n=0; n<N; ++n )
   {
       sum_img1_img2   = sum_img1_img2 + (img_1[m][n]-img1_avg.val[0])*(img_2[m][n]-img2_avg.val[0]);  
       sum_img1_2      = sum_img1_2 + (img_1[m][n]-img1_avg.val[0])*(img_1[m][n]-img1_avg.val[0]);
       sum_img2_2      = sum_img2_2 + (img_2[m][n]-img2_avg.val[0])*(img_2[m][n]-img2_avg.val[0]);
   }
}

corr = sum_img1_img2/sqrt(sum_img1_2*sum_img2_2);

return corr;
}