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this is code for finding and tracking feature points

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<h2> Chapter 5: Storing All the Features in a Single Structure/File </h2>

Up to now, all the features have been contained in a feature list, 
which is overwritten when the features are tracked.  Sometimes, however,
it is desirable to keep the features from the last frame, or from the
last few frames, or from all the frames.  In these cases, it would be 
tedious to have to create a new feature list for each image.  Instead, 
we create what is called a feature table.
While a feature list is a one-dimensional array of features from a
single image, a feature table is a two-dimensional array of features
from many images.  Each column of the table is a feature list.<p>

The sample code below shows the use of the sequential mode from the
last chapter, as well as the use of a feature table.  
If the constant <code>REPLACE</code> is defined, then lost features
are replaced; otherwise they are not.  In the former case, new
features can be identified in the table because they have positive
(rather than zero) values. Now let us examine the unfamiliar lines of
code. <p>

<b>KLTCreateFeatureTable()</b> creates a feature table, given the 
number of frames and the number
of features to store.  Although in this example the number of frames is
the same as the total number of actual images, this does not have to be
the case if all the features do not need to be stored.<p>

<b>KLTStoreFeatureList()</b> stores a feature list as the <i>i</i>th
column of a feature table, where <i>i</i> is given by the third parameter
(<i>i</i> must be between <code>0</code> and 
<code>ft->nFrames-1</code>, inclusive).
The dimensions of the feature list and feature table must be compatible,
meaning that they must both contain the same number of features.
It is perfectly legal to overwrite a column that has already been used,
although this is not done in the current example.<p>

<b>KLTWriteFeatureTable()</b> writes a feature table to a file, in
a manner similar to that of KLTWriteFeatureList(), which was
described in Chapter 2.<p>

<hr>

<h3> Example 3</h3>

<pre width=80>
/**********************************************************************
Finds the 150 best features in an image and tracks them through the 
next two images.  The sequential mode is set in order to speed
processing.  The features are stored in a feature table, which is then
saved to a text file; each feature list is also written to a PPM file.
**********************************************************************/

#include &lt;stdlib.h&gt;
#include &lt;stdio.h&gt;
#include "pnmio.h"
#include "klt.h"

#define REPLACE

void main()
{
     unsigned char *img1, *img2;
     char fnamein[100], fnameout[100];
     KLT_TrackingContext tc;
     KLT_FeatureList fl;
     KLT_FeatureTable ft;
     int nFeatures = 150, nFrames = 10;
     int ncols, nrows;
     int i;

     tc = KLTCreateTrackingContext();
     fl = KLTCreateFeatureList(nFeatures);
     ft = KLTCreateFeatureTable(nFrames, nFeatures);
     tc->sequentialMode = TRUE;

     img1 = pgmReadFile("img0.pgm", NULL, &ncols, &nrows);
     img2 = (unsigned char *) malloc(ncols*nrows*sizeof(unsigned char));

     KLTSelectGoodFeatures(tc, img1, ncols, nrows, fl);
     KLTStoreFeatureList(fl, ft, 0);
     KLTWriteFeatureListToPPM(fl, img1, ncols, nrows, "feat0.ppm");

     for (i = 1 ; i < nFrames ; i++)  {
          sprintf(fnamein, "img%d.pgm", i);
          pgmReadFile(fnamein, img2, &ncols, &nrows);
          KLTTrackFeatures(tc, img1, img2, ncols, nrows, fl);
#ifdef REPLACE
          KLTReplaceLostFeatures(tc, img2, ncols, nrows, fl);
#endif
          KLTStoreFeatureList(fl, ft, i);
          sprintf(fnameout, "feat%d.ppm", i);
          KLTWriteFeatureListToPPM(fl, img2, ncols, nrows, fnameout);
     }
     KLTWriteFeatureTable(ft, "features.txt", "%5.1f");
}
</pre>

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