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this is code for finding and tracking feature points

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<h2> Chapter 2: Selecting and Tracking Features </h2>

Below is a sample program that selects the 100 best features in
an image and tracks them in the next image.  Instead of
requiring images to be in a particular format, KLT needs only a
pointer to the raw image data (in row-major format), along with
the dimensions of the image.  (The data is assumed to be unsigned
char's, but this can be <a href="noUchar.html"> changed </a> by 
modifying the code and recompiling.)  We will now describe 
the code, line-by-line.<p>

The header file <code>"pnmio.h"</code> is necessary only for the helper 
function pgmReadFile, while <code>"klt.h"</code> must always be included
in programs using KLT.<p>
 
<b>KLTCreateTrackingContext()</b> creates a tracking context, which
is a structure containing various parameters (such as window size,
sigma for Gaussian smoothing, minimum distance between features, etc.)
that govern the computation.  The nature of the structure is not 
important for now.<p>

<b>KLTCreateFeatureList()</b> creates a feature list, which is
a structure containing all the features in a given image.  
The parameter of the function is the number of features desired. <p>

The helper function <b>pgmReadFile()</b> has been provided 
for reading PGM images, but it can be removed if the raw image
data is obtained in another manner.  Passing <code>NULL</code> as the second
parameter causes pgmReadFile() to allocate memory for the image.<p>

<b>KLTSelectGoodFeatures()</b> finds the <i>N</i> best features
in an image and stores them in a feature list in descending
order of goodness.  <i>N</i> is the size of the feature list (in this
case 100).  (Note:  It is also possible to 
select all the features above a certain level of goodness, rather than 
just the <i>N</i> best features, by
simply changing the <code>min_eigenvalue</code> field of the tracking context, 
as described in Chapter 7.)<p>

The <b>for loop</b> demonstrates how to access data in the
feature list.
A feature list is simply an array of <code>fl->nFeatures</code> 
features, where each feature has
three members: <i>x</i> location, <i>y</i> location, and value.  
The first two are
<code>floats</code>, and the second is an <code>int</code>.  
The location is given in pixels
with the origin in the upper-left corner.  The value is a constant
multiplied by the minimum eigenvalue of the window surrounding the
feature -- the larger the value, the better the feature.  (NOTE:
Because <code>fl->nFeatures</code> indicates the amount of memory allocated,
it must not be changed by hand.)<p>

<b>KLTWriteFeatureListToPPM()</b> writes the grey-level image to
a PPM file and overlays the features in red.  This image can then
be viewed with a tool like <code>xv</code>.<p>

<b>KLTWriteFeatureList()</b> writes the features to a file (If 
the second parameter is NULL, then the features are written to
<code>stderr</code> instead).  You will probably want to keep the
third parameter NULL, which causes the file to be binary.  Binary 
files are better because they are smaller and cause no truncation error.
However, if a text file is desired, then the third parameter must be a
string beginning with '%' and ending with either 'd' or 'f', as in
"%3d" or "%5.1f", which specifies the format of the feature locations
to be either decimal or floating point.  
Once the feature list is written, the file can be viewed off-line (if it
is text) or read back into
a feature list using the command KLTReadFeatureList().<p>

<b>KLTTrackFeatures()</b> tracks the features from the first image
to the second image and overwrites the feature list.  If a feature
is successfully tracked, then its value becomes zero; otherwise, it becomes
negative.  Note that this function does not resort the features,
but rather keeps them in place so that correspondences between the
images are maintained.  Chapter 5 describes the KLT_FeatureTable structure, 
which is useful for storing the feature list before it is overwritten.<p>

And that's all there is to selecting and tracking features.  The remaining
chapters describe various extensions that may be helpful.<p>

<hr>

<h3> Example 1</h3>

<pre width=80>
/**********************************************************************
Finds the 100 best features in an image, and tracks these
features to the next image.  Saves the feature
locations (before and after tracking) to text files and to PPM files, 
and prints the features to the screen.
**********************************************************************/

#include "pnmio.h"
#include "klt.h"

void main()
{
     unsigned char *img1, *img2;
     KLT_TrackingContext tc;
     KLT_FeatureList fl;
     int nFeatures = 100;
     int ncols, nrows;
     int i;

     tc = KLTCreateTrackingContext();
	KLTPrintTrackingContext(tc);
     fl = KLTCreateFeatureList(nFeatures);

     img1 = pgmReadFile("img0.pgm", NULL, &ncols, &nrows);
     img2 = pgmReadFile("img1.pgm", NULL, &ncols, &nrows);

     KLTSelectGoodFeatures(tc, img1, ncols, nrows, fl);

     printf("\nIn first image:\n");
     for (i = 0 ; i < fl->nFeatures ; i++)  {
          printf("Feature #%d:  (%f,%f) with value of %d\n",
               i, fl->feature[i]->x, fl->feature[i]->y, fl->feature[i]->val);
     }

     KLTWriteFeatureListToPPM(fl, img1, ncols, nrows, "feat1.ppm");
     KLTWriteFeatureList(fl, "feat1.txt", "%3d");

     KLTTrackFeatures(tc, img1, img2, ncols, nrows, fl);

     printf("\nIn second image:\n");
     for (i = 0 ; i < fl->nFeatures ; i++)  {
          printf("Feature #%d:  (%f,%f) with value of %d\n",
               i, fl->feature[i]->x, fl->feature[i]->y, fl->feature[i]->val);
     }

     KLTWriteFeatureListToPPM(fl, img2, ncols, nrows, "feat2.ppm");
     KLTWriteFeatureList(fl, "feat2.fl", NULL);  /* binary file */
     KLTWriteFeatureList(fl, "feat2.txt", "%5.1f");  /* text file */
}
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