Saturday, July 26, 2014

Message board

Update (02 April, 2012) :
If anyone wants to publish a paper in biometrics, which you feel is revolutionary, try the BTAS conference. It was an international conference on biometrics and has very high value. The deadline is April, 15th

Update (29 April, 2012):
And I wrote a post about - how to become a hacker
My projects are at https://github.com/dileep98490

Update (28 June, 2012):
There is a conference "What's in a Face" whose main aim is to improve the research on Human Face. The deadline is July 3rd. It's an international conference, and even if you think you could not attend (due to monetary problems), try sending the paper. If you get acceptance, then it is something that you can write proudly in your resume.

Best of luck :)

Update (05 July, 2012):
Have released the SURF source code. Enjoy :)

Update (24 September, 2012):
Have joined a telecommunication company. Learning a lot in the telecom domain :)
Anyway, my first paper
 http://www.springerlink.com/content/r744311v038r1488/ 

Update (29 Nov, 2014)
Started a new blog, for more tech news in less words. The below is the link
http://intertechie.blogspot.in/ 

Ceemple - C++ based technical computing

Today, let me give you a glimpse of a wonderful tool called Ceemple, which I believe is still in beta stage, but can be downloaded from www.ceemple.com.



It is one package, fit all tool for C++; where on installing Ceemple, you get all the computational and graphical libraries, which you can use hassle free, while concentrating only on development.

Many of you told me about the problems being faced, while installing openCV in windows and how you failed to integrate it to into your existing Code Blocks or Dev C++ software. Now it's all done for you by the Ceemple team. You just need to download, install and zup... ready to go. It is too easy. Also, you have a hell lot of other libraries including Matlab, Python etc., which you can use for building real things - super fast. If you are a less software guy and more of a researcher, this tool is the boon for you, and it's open source nature makes it tasty enough for gulping directly. For a detailed comparative analysis and the usefulness of the tool, you can visit this link

Thursday, July 5, 2012

SURF Source Code (Part-2)

Many of you might have already figured out, how to strip the code. The below is the stripped version. Here, we can just supply the gallery image and probe image, the same way as the previous post as arg1 and arg2. But here, no GUI utilities are being used. Just the output needed is printed to standard output. This drastically reduces the time for the program execution. Also, I have removed the flann method of matching, since any one (findpairs and flannfindpairs - functions) can be used and both the methods given have the same recognition performance.

/*
 * A Demo to OpenCV Implementation of SURF
 * Further Information Refer to "SURF: Speed-Up Robust Feature"
 * Author: Liu Liu
 * liuliu.1987+opencv@gmail.com
 * Modified by : http://www.opencvuser.blogspot.com
 * Modifying Author : Dileep Kumar Kotha
 */

#include <cv.h>
#include <highgui.h>
#include <ctype.h>
#include <stdio.h>
#include <stdlib.h>

#include <iostream>
#include <vector>

using namespace std;

static double dis=0;//Dileep:For calculating the distance




IplImage *image = 0;

double
compareSURFDescriptors( const float* d1, const float* d2, double best, int length )
{
    double total_cost = 0;
    assert( length % 4 == 0 );
    for( int i = 0; i < length; i += 4 )
    {
        double t0 = d1[i] - d2[i];
        double t1 = d1[i+1] - d2[i+1];
        double t2 = d1[i+2] - d2[i+2];
        double t3 = d1[i+3] - d2[i+3];
        total_cost += t0*t0 + t1*t1 + t2*t2 + t3*t3;
/*
We are sending a total cost, that's slightly greater or smaller than the best 

  */
      if( total_cost > best )
            break;
    }
    return total_cost;
}


int
naiveNearestNeighbor( const float* vec, int laplacian,
                      const CvSeq* model_keypoints,
                      const CvSeq* model_descriptors )
{
    int length = (int)(model_descriptors->elem_size/sizeof(float));
    int i, neighbor = -1;
    double d, dist1 = 1e6, dist2 = 1e6;
    CvSeqReader reader, kreader;
    cvStartReadSeq( model_keypoints, &kreader, 0 );
    cvStartReadSeq( model_descriptors, &reader, 0 );

    for( i = 0; i < model_descriptors->total; i++ )
    {
        const CvSURFPoint* kp = (const CvSURFPoint*)kreader.ptr;
        const float* mvec = (const float*)reader.ptr;
     CV_NEXT_SEQ_ELEM( kreader.seq->elem_size, kreader );
        CV_NEXT_SEQ_ELEM( reader.seq->elem_size, reader );
        if( laplacian != kp->laplacian )
            continue;
        d = compareSURFDescriptors( vec, mvec, dist2, length );

        if( d < dist1 )
        {
            dist2 = dist1;
            dist1 = d;
            neighbor = i;
        }
        else if ( d < dist2 )
            dist2 = d;
    }
dis=dis+dist1;

/*Dileep:We are finding the distance from every descriptor of probe image to every descriptor of the galley image. Finally in the findpairs function, we divide this distance with the total no. of descriptors to get the average of all the distances
*/
    if ( dist1 < 0.6*dist2 )
        return neighbor;
    return -1;
}

void
findPairs( const CvSeq* objectKeypoints, const CvSeq* objectDescriptors,
           const CvSeq* imageKeypoints, const CvSeq* imageDescriptors, vector<int>& ptpairs )
{ 
    int i;
    CvSeqReader reader, kreader;
    cvStartReadSeq( objectKeypoints, &kreader );
    cvStartReadSeq( objectDescriptors, &reader );
    ptpairs.clear();

    for( i = 0; i < objectDescriptors->total; i++ )
    {
        const CvSURFPoint* kp = (const CvSURFPoint*)kreader.ptr;
        const float* descriptor = (const float*)reader.ptr;
        CV_NEXT_SEQ_ELEM( kreader.seq->elem_size, kreader );
        CV_NEXT_SEQ_ELEM( reader.seq->elem_size, reader );
        int nearest_neighbor = naiveNearestNeighbor( descriptor, kp->laplacian, imageKeypoints, imageDescriptors);
//Dileep:For every descriptor, we are trying to find it's nearest neighbour in the probe image
        if( nearest_neighbor >= 0 )
        {
            ptpairs.push_back(i);
            ptpairs.push_back(nearest_neighbor);
        }
    }

printf("\n%lf\n",(dis/objectDescriptors->total));////Dileep:Here's where I am outputting the distance between the images
/*Dileep: If you are using this for recognition, write this distance to a file along with the name of the image you are matching against. After doing this for several images, you can then sort them in ascending order to find the best possible match - the one with the smallest distance. Here, I am outputting the distance to stdout
*/
}

int main(int argc, char** argv)
{
    const char* object_filename = argc == 3 ? argv[1] : "box.png";
    const char* scene_filename = argc == 3 ? argv[2] : "box_in_scene.png";
//Dileep:When you are excuting the object file, please write Command:./objectfile probe_image Gallery_image
/*Dileep:
Probe_image - This is the image for which you need to find the match
Gallery_image - This is one of the set of images, you use for matching

You keep the same probe image same, repeatedly changing the gallery image and outputting the distance in the format
<Gallery_name distance> into a file
Finally you can sort the distances in ascending order. And the one with the shortest distance - You can output it's name as the best possible match

It may become tedious to continually write the same command multiple times, changing the gallery file name. Try to use shell script with a for loop
*/
    CvMemStorage* storage = cvCreateMemStorage(0);

    

   

    IplImage* object = cvLoadImage( object_filename, CV_LOAD_IMAGE_GRAYSCALE );
    IplImage* image = cvLoadImage( scene_filename, CV_LOAD_IMAGE_GRAYSCALE );
    if( !object || !image )
    {
        fprintf( stderr, "Can not load %s and/or %s\n"
            "Usage: find_obj [<object_filename> <scene_filename>]\n",
            object_filename, scene_filename );
        exit(-1);
    }

    CvSeq *objectKeypoints = 0, *objectDescriptors = 0;
    CvSeq *imageKeypoints = 0, *imageDescriptors = 0;
    int i;
    CvSURFParams params = cvSURFParams(500, 1);

    double tt = (double)cvGetTickCount();
    cvExtractSURF( object, 0, &objectKeypoints, &objectDescriptors, storage, params );
    printf("Object Descriptors: %d\n", objectDescriptors->total);
    cvExtractSURF( image, 0, &imageKeypoints, &imageDescriptors, storage, params );
    printf("Image Descriptors: %d\n", imageDescriptors->total);
    tt = (double)cvGetTickCount() - tt;
    printf( "Extraction time = %gms\n", tt/(cvGetTickFrequency()*1000.));




    vector<int> ptpairs;

    findPairs( objectKeypoints, objectDescriptors, imageKeypoints, imageDescriptors, ptpairs );

  
    return 0;
}


SURF Souce Code (Part-1)

When you install openCV, openCV samples folder also gets installed. The samples will be present in your InstallationDirectory/samples/C. For me it's present in OpenCV-2.1.0/samples/c.This folder contains the sample codes of many good openCV programmes that can be used for a wide variety of purposes. One more thing is that, it also contains the compiled object files along with the source code for each programme. The programme , we will be looking is find_obj.cpp and the compiled code will be with the name find_obj. This programme uses SURF to do an object detection. The original code, written by Liu, is modified by me to give the below code. Note that the comments made by me, start with Dileep:
/*
 * A Demo to OpenCV Implementation of SURF
 * Further Information Refer to "SURF: Speed-Up Robust Feature"
 * Author: Liu Liu
 * liuliu.1987+opencv@gmail.com
 * Modified by : http://www.opencvuser.blogspot.com
 * Modifying Author : Dileep Kumar Kotha
 */

#include <cv.h>
#include <highgui.h>
#include <ctype.h>
#include <stdio.h>
#include <stdlib.h>

#include <iostream>
#include <vector>

using namespace std;

static double dis=0;//For calculating the average descriptor distance
// define whether to use approximate nearest-neighbor search
//#define USE_FLANN


IplImage *image = 0;

double
compareSURFDescriptors( const float* d1, const float* d2, double best, int length )
{
    double total_cost = 0;
    assert( length % 4 == 0 );
    for( int i = 0; i < length; i += 4 )
    {
        double t0 = d1[i] - d2[i];
        double t1 = d1[i+1] - d2[i+1];
        double t2 = d1[i+2] - d2[i+2];
        double t3 = d1[i+3] - d2[i+3];
        total_cost += t0*t0 + t1*t1 + t2*t2 + t3*t3;
/*
We are sending a total cost, that's slightly greater or smaller than the best 

  */
      if( total_cost > best )
            break;
    }
    return total_cost;
}


int
naiveNearestNeighbor( const float* vec, int laplacian,
                      const CvSeq* model_keypoints,
                      const CvSeq* model_descriptors )
{
    int length = (int)(model_descriptors->elem_size/sizeof(float));
    int i, neighbor = -1;
    double d, dist1 = 1e6, dist2 = 1e6;
    CvSeqReader reader, kreader;
    cvStartReadSeq( model_keypoints, &kreader, 0 );
    cvStartReadSeq( model_descriptors, &reader, 0 );

    for( i = 0; i < model_descriptors->total; i++ )
    {
        const CvSURFPoint* kp = (const CvSURFPoint*)kreader.ptr;
        const float* mvec = (const float*)reader.ptr;
     CV_NEXT_SEQ_ELEM( kreader.seq->elem_size, kreader );
        CV_NEXT_SEQ_ELEM( reader.seq->elem_size, reader );
        if( laplacian != kp->laplacian )
            continue;
        d = compareSURFDescriptors( vec, mvec, dist2, length );

        if( d < dist1 )
        {
            dist2 = dist1;
            dist1 = d;
            neighbor = i;
        }
        else if ( d < dist2 )
            dist2 = d;
    }
dis=dis+dist1;

    if ( dist1 < 0.6*dist2 )
        return neighbor;
    return -1;
}

void
findPairs( const CvSeq* objectKeypoints, const CvSeq* objectDescriptors,
           const CvSeq* imageKeypoints, const CvSeq* imageDescriptors, vector<int>& ptpairs )
{ 
    int i;
    CvSeqReader reader, kreader;
    cvStartReadSeq( objectKeypoints, &kreader );
    cvStartReadSeq( objectDescriptors, &reader );
    ptpairs.clear();

    for( i = 0; i < objectDescriptors->total; i++ )
    {
        const CvSURFPoint* kp = (const CvSURFPoint*)kreader.ptr;
        const float* descriptor = (const float*)reader.ptr;
        CV_NEXT_SEQ_ELEM( kreader.seq->elem_size, kreader );
        CV_NEXT_SEQ_ELEM( reader.seq->elem_size, reader );
        int nearest_neighbor = naiveNearestNeighbor( descriptor, kp->laplacian, imageKeypoints, imageDescriptors);
        if( nearest_neighbor >= 0 )
        {
            ptpairs.push_back(i);
            ptpairs.push_back(nearest_neighbor);
        }
    }

printf("\n%lf\n",(dis/objectDescriptors->total));////Dileep:Here's where I am outputting the distance between the images
/*Dileep: If you are using this for recognition, write this distance to a file along with the name of the image you are matching against. After doing this for several images, you can then sort them in ascending order to find the best possible match - the one with the smallest distance. Here, I am outputting the distance to stdout
*/
}
////Dileep:I am commenting the below one since it's of flann method and what ever the method we choose to find distance, the result is the same
/*
void
flannFindPairs( const CvSeq*, const CvSeq* objectDescriptors,
           const CvSeq*, const CvSeq* imageDescriptors, vector<int>& ptpairs )
{
 int length = (int)(objectDescriptors->elem_size/sizeof(float));

    cv::Mat m_object(objectDescriptors->total, length, CV_32F);
 cv::Mat m_image(imageDescriptors->total, length, CV_32F);


 // copy descriptors
    CvSeqReader obj_reader;
 float* obj_ptr = m_object.ptr<float>(0);
    cvStartReadSeq( objectDescriptors, &obj_reader );
    for(int i = 0; i < objectDescriptors->total; i++ )
    {
        const float* descriptor = (const float*)obj_reader.ptr;
        CV_NEXT_SEQ_ELEM( obj_reader.seq->elem_size, obj_reader );
        memcpy(obj_ptr, descriptor, length*sizeof(float));
        obj_ptr += length;
    }
    CvSeqReader img_reader;
 float* img_ptr = m_image.ptr<float>(0);
    cvStartReadSeq( imageDescriptors, &img_reader );
    for(int i = 0; i < imageDescriptors->total; i++ )
    {
        const float* descriptor = (const float*)img_reader.ptr;
        CV_NEXT_SEQ_ELEM( img_reader.seq->elem_size, img_reader );
        memcpy(img_ptr, descriptor, length*sizeof(float));
        img_ptr += length;
    }

    // find nearest neighbors using FLANN
    cv::Mat m_indices(objectDescriptors->total, 2, CV_32S);
    cv::Mat m_dists(objectDescriptors->total, 2, CV_32F);
    cv::flann::Index flann_index(m_image, cv::flann::KDTreeIndexParams(4));  // using 4 randomized kdtrees
    flann_index.knnSearch(m_object, m_indices, m_dists, 2, cv::flann::SearchParams(64) ); // maximum number of leafs checked

    int* indices_ptr = m_indices.ptr<int>(0);
    float* dists_ptr = m_dists.ptr<float>(0);
    for (int i=0;i<m_indices.rows;++i) {
     if (dists_ptr[2*i]<0.6*dists_ptr[2*i+1]) {
      ptpairs.push_back(i);
      ptpairs.push_back(indices_ptr[2*i]);
     }
    }
}


// a rough implementation for object location 
int
locatePlanarObject( const CvSeq* objectKeypoints, const CvSeq* objectDescriptors,
                    const CvSeq* imageKeypoints, const CvSeq* imageDescriptors,
                    const CvPoint src_corners[4], CvPoint dst_corners[4] )
{
    double h[9];
    CvMat _h = cvMat(3, 3, CV_64F, h);
    vector<int> ptpairs;
    vector<CvPoint2D32f> pt1, pt2;
    CvMat _pt1, _pt2;
    int i, n;

#ifdef USE_FLANN
    flannFindPairs( objectKeypoints, objectDescriptors, imageKeypoints, imageDescriptors, ptpairs );
#else
    findPairs( objectKeypoints, objectDescriptors, imageKeypoints, imageDescriptors, ptpairs );
#endif

    n = ptpairs.size()/2;
    if( n < 4 )
        return 0;

    pt1.resize(n);
    pt2.resize(n);
    for( i = 0; i < n; i++ )
    {
        pt1[i] = ((CvSURFPoint*)cvGetSeqElem(objectKeypoints,ptpairs[i*2]))->pt;
        pt2[i] = ((CvSURFPoint*)cvGetSeqElem(imageKeypoints,ptpairs[i*2+1]))->pt;
    }

    _pt1 = cvMat(1, n, CV_32FC2, &pt1[0] );
    _pt2 = cvMat(1, n, CV_32FC2, &pt2[0] );
    if( !cvFindHomography( &_pt1, &_pt2, &_h, CV_RANSAC, 5 ))
        return 0;

    for( i = 0; i < 4; i++ )
    {
        double x = src_corners[i].x, y = src_corners[i].y;
        double Z = 1./(h[6]*x + h[7]*y + h[8]);
        double X = (h[0]*x + h[1]*y + h[2])*Z;
        double Y = (h[3]*x + h[4]*y + h[5])*Z;
        dst_corners[i] = cvPoint(cvRound(X), cvRound(Y));
    }

    return 1;
}
*/

int main(int argc, char** argv)
{
    const char* object_filename = argc == 3 ? argv[1] : "box.png";
    const char* scene_filename = argc == 3 ? argv[2] : "box_in_scene.png";

    CvMemStorage* storage = cvCreateMemStorage(0);

    cvNamedWindow("Object", 1);
    cvNamedWindow("Object Correspond", 1);

    static CvScalar colors[] = 
    {
        {{0,0,255}},
        {{0,128,255}},
        {{0,255,255}},
        {{0,255,0}},
        {{255,128,0}},
        {{255,255,0}},
        {{255,0,0}},
        {{255,0,255}},
        {{255,255,255}}
    };

    IplImage* object = cvLoadImage( object_filename, CV_LOAD_IMAGE_GRAYSCALE );
    IplImage* image = cvLoadImage( scene_filename, CV_LOAD_IMAGE_GRAYSCALE );
    if( !object || !image )
    {
        fprintf( stderr, "Can not load %s and/or %s\n"
            "Usage: find_obj [<object_filename> <scene_filename>]\n",
            object_filename, scene_filename );
        exit(-1);
    }
    IplImage* object_color = cvCreateImage(cvGetSize(object), 8, 3);
    cvCvtColor( object, object_color, CV_GRAY2BGR );
    
    CvSeq *objectKeypoints = 0, *objectDescriptors = 0;
    CvSeq *imageKeypoints = 0, *imageDescriptors = 0;
    int i;
    CvSURFParams params = cvSURFParams(500, 1);

    double tt = (double)cvGetTickCount();
    cvExtractSURF( object, 0, &objectKeypoints, &objectDescriptors, storage, params );
    printf("Object Descriptors: %d\n", objectDescriptors->total);
    cvExtractSURF( image, 0, &imageKeypoints, &imageDescriptors, storage, params );
    printf("Image Descriptors: %d\n", imageDescriptors->total);
    tt = (double)cvGetTickCount() - tt;
    printf( "Extraction time = %gms\n", tt/(cvGetTickFrequency()*1000.));
    CvPoint src_corners[4] = {{0,0}, {object->width,0}, {object->width, object->height}, {0, object->height}};
    CvPoint dst_corners[4];
    IplImage* correspond = cvCreateImage( cvSize(image->width, object->height+image->height), 8, 1 );
    cvSetImageROI( correspond, cvRect( 0, 0, object->width, object->height ) );
    cvCopy( object, correspond );
    cvSetImageROI( correspond, cvRect( 0, object->height, correspond->width, correspond->height ) );
    cvCopy( image, correspond );
    cvResetImageROI( correspond );

#ifdef USE_FLANN
    printf("Using approximate nearest neighbor search\n");
#endif
/*
    if( locatePlanarObject( objectKeypoints, objectDescriptors, imageKeypoints,
        imageDescriptors, src_corners, dst_corners ))
    {
        for( i = 0; i < 4; i++ )
        {
            CvPoint r1 = dst_corners[i%4];
            CvPoint r2 = dst_corners[(i+1)%4];
            cvLine( correspond, cvPoint(r1.x, r1.y+object->height ),
                cvPoint(r2.x, r2.y+object->height ), colors[8] );
        }
    }
*/


    vector<int> ptpairs;
#ifdef USE_FLANN
    flannFindPairs( objectKeypoints, objectDescriptors, imageKeypoints, imageDescriptors, ptpairs );
#else
    findPairs( objectKeypoints, objectDescriptors, imageKeypoints, imageDescriptors, ptpairs );
#endif

////Dileep:It is used to draw the the lines and rectangles in the windows that display images. If you want just the distance, you can comment the whole 
////below code
    for( i = 0; i < (int)ptpairs.size(); i += 2 )
    {
        CvSURFPoint* r1 = (CvSURFPoint*)cvGetSeqElem( objectKeypoints, ptpairs[i] );
        CvSURFPoint* r2 = (CvSURFPoint*)cvGetSeqElem( imageKeypoints, ptpairs[i+1] );
        cvLine( correspond, cvPointFrom32f(r1->pt),
            cvPoint(cvRound(r2->pt.x), cvRound(r2->pt.y+object->height)), colors[8] );
    }

    cvShowImage( "Object Correspond", correspond );
    for( i = 0; i < objectKeypoints->total; i++ )
    {
        CvSURFPoint* r = (CvSURFPoint*)cvGetSeqElem( objectKeypoints, i );
        CvPoint center;
        int radius;
        center.x = cvRound(r->pt.x);
        center.y = cvRound(r->pt.y);
        radius = cvRound(r->size*1.2/9.*2);
        cvCircle( object_color, center, radius, colors[0], 1, 8, 0 );
    }
    cvShowImage( "Object", object_color );

    cvWaitKey(0);
//cvSaveImage("Object_1.png",correspond);
//cvSaveImage("Object_2.png",object_color);

    cvDestroyWindow("Object");
    cvDestroyWindow("Object SURF");
    cvDestroyWindow("Object Correspond");

    return 0;
}

There are enough comments in it, explaining what each part does. compile it. If you compile it to the object file find_obj, the command below works.

./find_obj

Otherwise, replace it with a.out. Also, copy the two images in the samples folder named box.png and box_in_scene.png into the folder where you run the above command.

Now for ./find_obj Since we are not supplying arguements, it will take the default box.png and box_in_scene.png and tries to find the first object inside the second object. The below figure appears.

SURF example


What's actually happening here is object detection. But I included a "dis" variable and also printing it to standard ouput, so that you will also know the average distance of all the descriptors. If you want, you can keep two photographs of the same person with different facial expressions in the same folder and supply them as arg1 and arg2 (in which case you will get distance as zero, ofcourse :P)

./findobj arg1 arg2 

If we keep on changing the second arguement(the image we are supplying as arg2), we can actually use it for recognition. Then the one with the least distance is the best possible match for the first argument (the image we are supplying as arg1). In the next post, I will strip all the unnecessary components of this code and make this usable for recognition. You can try to do it yourself.

Saturday, March 3, 2012

SURF Source code

When you install openCV, openCV samples folder also gets installed. The samples will be present in your InstallationDirectory/samples/C. For me it's present in OpenCV-2.1.0/samples/c.This folder contains the sample codes of many good openCV programmes that can be used for a wide variety of purposes. One more thing is that, it also contains the compiled object files along with the source code for each programme. The programme , we will be looking is find_obj.cpp and the compiled code will be with the name find_obj. This programme uses SURF to do an object detection.

I have modified the code to make it work for recognition. I did my summer project at IIT Kharagpur, and there we performed the experiments on face recognition with SURF. For the first time in face recognition history, we worked with color FERET database. We wrote a paper, describing our approach and writing the results of our work. The Research paper was sent to the proceedings of an international conference. Till that gets published, I cannot share my work. But it's so simple and intuitive, when you look at the code I mentioned. Best of luck with your work.

Update (29/06/12) :
I will release the modified source code on or before 8th July