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/********************************
Author: Sravanthi Kota Venkata
********************************/
#include "tracking.h"
#include <malloc.h>
#include "extra.h"
#define TRACKING_MEM 1<<29
int main(int argc, char* argv[])
{
SET_UP
mallopt(M_TOP_PAD, TRACKING_MEM);
mallopt(M_MMAP_MAX, 0);
int i, j, k, N_FEA, WINSZ, LK_ITER, rows, cols;
int endR, endC;
F2D *blurredImage, *previousFrameBlurred_level1, *previousFrameBlurred_level2, *blurred_level1, *blurred_level2;
F2D *verticalEdgeImage, *horizontalEdgeImage, *verticalEdge_level1, *verticalEdge_level2, *horizontalEdge_level1, *horizontalEdge_level2, *interestPnt;
F2D *lambda, *lambdaTemp, *features;
I2D *Ic, *status;
float SUPPRESION_RADIUS;
F2D *newpoints;
int numFind, m, n;
F2D *np_temp;
char im1[100];
int counter=2;
float accuracy = 0.03;
int count;
N_FEA = 1600;
WINSZ = 4;
SUPPRESION_RADIUS = 10.0;
LK_ITER = 20;
#ifdef test
WINSZ = 2;
N_FEA = 100;
LK_ITER = 2;
counter = 2;
accuracy = 0.1;
#endif
#ifdef sim_fast
WINSZ = 2;
N_FEA = 100;
LK_ITER = 2;
counter = 4;
#endif
#ifdef sim
WINSZ = 2;
N_FEA = 200;
LK_ITER = 2;
counter = 4;
#endif
#ifdef sqcif
WINSZ = 8;
N_FEA = 500;
LK_ITER = 15;
counter = 2;
#endif
#ifdef qcif
WINSZ = 12;
N_FEA = 400;
LK_ITER = 15;
counter = 4;
#endif
#ifdef cif
WINSZ = 20;
N_FEA = 500;
LK_ITER = 20;
counter = 4;
#endif
#ifdef vga
WINSZ = 32;
N_FEA = 400;
LK_ITER = 20;
counter = 4;
#endif
#ifdef wuxga
WINSZ = 64;
N_FEA = 500;
LK_ITER = 20;
counter = 4;
#endif
#ifdef fullhd
WINSZ = 48;
N_FEA = 500;
LK_ITER = 20;
counter = 4;
#endif
I2D* images[counter];
/** Read input image **/
for(count=1; count<=counter; count++)
{
/** Read image **/
printf("Input image %d: ", count);
scanf("%s", im1);
images[count - 1] = readImage(im1);
if(count == 1) Ic = readImage(im1);
}
rows = Ic->height;
cols = Ic->width;
printf("start\n");
for_each_job{
/** IMAGE PRE-PROCESSING **/
/** Blur the image to remove noise - weighted avergae filter **/
blurredImage = imageBlur(Ic);
/** Scale down the image to build Image Pyramid. We find features across all scales of the image **/
blurred_level1 = blurredImage; /** Scale 0 **/
blurred_level2 = imageResize(blurredImage); /** Scale 1 **/
/** Edge Images - From pre-processed images, build gradient images, both horizontal and vertical **/
verticalEdgeImage = calcSobel_dX(blurredImage);
horizontalEdgeImage = calcSobel_dY(blurredImage);
/** Edge images are used for feature detection. So, using the verticalEdgeImage and horizontalEdgeImage images, we compute feature strength
across all pixels. Lambda matrix is the feature strength matrix returned by calcGoodFeature **/
lambda = calcGoodFeature(verticalEdgeImage, horizontalEdgeImage, verticalEdgeImage->width, verticalEdgeImage->height, WINSZ);
endR = lambda->height;
endC = lambda->width;
lambdaTemp = fReshape(lambda, endR*endC, 1);
/** We sort the lambda matrix based on the strengths **/
/** Fill features matrix with top N_FEA features **/
fFreeHandle(lambdaTemp);
lambdaTemp = fillFeatures(lambda, N_FEA, WINSZ);
features = fTranspose(lambdaTemp);
/** Suppress features that have approximately similar strength and belong to close neighborhood **/
interestPnt = getANMS(features, SUPPRESION_RADIUS);
/** Refill interestPnt in features matrix **/
fFreeHandle(features);
features = fSetArray(2, interestPnt->height, 0);
for(i=0; i<2; i++) {
for(j=0; j<interestPnt->height; j++) {
subsref(features,i,j) = subsref(interestPnt,j,i);
}
}
fFreeHandle(verticalEdgeImage);
fFreeHandle(horizontalEdgeImage);
fFreeHandle(interestPnt);
fFreeHandle(lambda);
fFreeHandle(lambdaTemp);
/** Until now, we processed base frame. The following for loop processes other frames **/
for(count=1; count<=counter; count++)
{
/** Read image **/
//sprintf(im1, "%s/%d.bmp", argv[1], count);
//Ic = readImage(im1);
I2D* Icc = images[count-1];
rows = Icc->height;
cols = Icc->width;
/** Blur image to remove noise **/
blurredImage = imageBlur(Icc);
previousFrameBlurred_level1 = fDeepCopy(blurred_level1);
previousFrameBlurred_level2 = fDeepCopy(blurred_level2);
fFreeHandle(blurred_level1);
fFreeHandle(blurred_level2);
/** Image pyramid **/
blurred_level1 = blurredImage;
blurred_level2 = imageResize(blurredImage);
/** Gradient image computation, for all scales **/
verticalEdge_level1 = calcSobel_dX(blurred_level1);
horizontalEdge_level1 = calcSobel_dY(blurred_level1);
verticalEdge_level2 = calcSobel_dX(blurred_level2);
horizontalEdge_level2 = calcSobel_dY(blurred_level2);
newpoints = fSetArray(2, features->width, 0);
/** Based on features computed in the previous frame, find correspondence in the current frame. "status" returns the index of corresponding features **/
status = calcPyrLKTrack(previousFrameBlurred_level1, previousFrameBlurred_level2, verticalEdge_level1, verticalEdge_level2, horizontalEdge_level1, horizontalEdge_level2, blurred_level1, blurred_level2, features, features->width, WINSZ, accuracy, LK_ITER, newpoints);
fFreeHandle(verticalEdge_level1);
fFreeHandle(verticalEdge_level2);
fFreeHandle(horizontalEdge_level1);
fFreeHandle(horizontalEdge_level2);
fFreeHandle(previousFrameBlurred_level1);
fFreeHandle(previousFrameBlurred_level2);
//printf("height = %d, width = %d, numFind = %d\n", newpoints->height, newpoints->width);
/** Populate newpoints with features that had correspondence with previous frame features **/
np_temp = fDeepCopy(newpoints);
if(status->width > 0 )
{
k = 0;
numFind=0;
for(i=0; i<status->width; i++)
{
if( asubsref(status,i) == 1)
numFind++;
}
fFreeHandle(newpoints);
newpoints = fSetArray(2, numFind, 0);
for(i=0; i<status->width; i++)
{
if( asubsref(status,i) == 1)
{
subsref(newpoints,0,k) = subsref(np_temp,0,i);
subsref(newpoints,1,k++) = subsref(np_temp,1,i);
}
}
}
iFreeHandle(status);
fFreeHandle(np_temp);
fFreeHandle(features);
/** Populate newpoints into features **/
features = fDeepCopy(newpoints);
fFreeHandle(newpoints);
}
}
printf("end..\n");
#ifdef CHECK
/* Self checking */
{
int ret=0;
float tol = 2.0;
#ifdef GENERATE_OUTPUT
fWriteMatrix(features, argv[1]);
#endif
ret = fSelfCheck(features, "expected_C.txt", tol);
if (ret == -1)
printf("Error in Tracking Map\n");
}
#endif
fFreeHandle(blurred_level1);
fFreeHandle(blurred_level2);
fFreeHandle(features);
for(count=1; count<=counter; count++)
{
free(images[count - 1] );
}
iFreeHandle(Ic);
WRITE_TO_FILE
return 0;
}
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