8 files changed, 887 insertions, 0 deletions

diff --git a/SD-VBS/benchmarks/tracking/src/c/calcAreaSum.c b/SD-VBS/benchmarks/tracking/src/c/calcAreaSum.c
new file mode 100644
index 0000000..40f03dc
--- /dev/null
+++ b/SD-VBS/benchmarks/tracking/src/c/calcAreaSum.c
@@ -0,0 +1,81 @@
+/********************************
+Author: Sravanthi Kota Venkata
+********************************/
+
+#include "tracking.h"
+
+/** Compute the sum of pixels over pixel neighborhood.
+    Neighborhood = winSize*winSize 
+    This will be useful when we compute the displacement
+    of the neighborhood across frames instead of tracking each pixel.
+
+    Input:  src Image
+            # rows, cols, size of window
+    Output: windowed-sum image, ret.
+
+    Example:
+    
+    winSize = 4, cols = 8, rows = 16
+    nave_half = 2, nave = 4
+    Say, the first row of the image is:
+                3 8 6 2 4 8 9 5
+    a1 =    0 0 3 8 6 2 4 8 9 5 0 0 (append nave_half zeros to left and right border)
+    asum =  (sum the first nave # pixels - 0 0 3 8 ) = 11
+    ret(0,0) =  11
+    For ret(0,1), we need to move the window to the right by one pixel and subtract
+    from a1sum the leftmost pixel. So, we add value 6 and subtract value at a1(0,0), = 0 here.
+    ret(0,1) = 17 = a1sum
+    For ret(0,2), a1sum - a1(0,1) + a1(2+nave) = 17 - 0 + 2 = 19 = a1sum
+    For ret(0,3), a1sum - a1(0,2) + a1(3+nave) = 19 - 3 + 4 = 20 = a1sum
+
+    We proceed this way for all the rows and then perform summantion across all cols.
+**/
+F2D* calcAreaSum(F2D* src, int cols, int rows, int winSize)
+{
+    int nave, nave_half, i, j, k;
+    F2D *ret, *a1;
+    float a1sum;
+    
+    nave = winSize;
+    nave_half = floor((nave+1)/2);
+
+    ret = fMallocHandle(rows, cols);
+    a1 = fSetArray(1, cols+nave,0);
+
+    for(i=0; i<rows; i++)
+    {
+        for(j=0; j<cols; j++) {
+            asubsref(a1,j+nave_half) = subsref(src,i,j);
+                }
+        a1sum = 0;
+        for(k=0; k<nave; k++) {
+            a1sum += asubsref(a1,k);
+                }
+        for(j=0; j<cols; j++)
+        {
+            subsref(ret,i,j) = a1sum;
+            a1sum += asubsref(a1,j+nave) - asubsref(a1,j);
+        }
+    }
+    fFreeHandle(a1);
+
+    a1 = fSetArray(1, rows+nave,0);
+    for(i=0; i<cols; i++)
+    {
+        for(j=0; j<rows; j++) {
+            asubsref(a1,j+nave_half) = subsref(ret,j,i);
+                }
+        a1sum = 0;
+        for(k=0; k<nave; k++) {
+            a1sum += asubsref(a1,k);
+                }
+        for(j=0; j<rows; j++)
+        {
+            subsref(ret,j,i) = a1sum;
+            a1sum += asubsref(a1,j+nave) - asubsref(a1,j);
+        }
+    }
+    fFreeHandle(a1);
+
+    return ret; 
+}
diff --git a/SD-VBS/benchmarks/tracking/src/c/calcGoodFeature.c b/SD-VBS/benchmarks/tracking/src/c/calcGoodFeature.c
new file mode 100644
index 0000000..0ef1862
--- /dev/null
+++ b/SD-VBS/benchmarks/tracking/src/c/calcGoodFeature.c
@@ -0,0 +1,78 @@
+/********************************
+Author: Sravanthi Kota Venkata
+********************************/
+
+#include "tracking.h"
+
+/** Computes lambda matrix, strength at each pixel
+    
+    det = determinant( [ IverticalEdgeSq IhorzVertEdge; IhorzVertEdge IhorizontalEdgeSq] ) ;
+    tr = IverticalEdgeSq + IhorizontalEdgeSq;
+    lamdba = det/tr;
+
+    Lambda is the measure of the strength of pixel
+    neighborhood. By strength we mean the amount of 
+    edge information it has, which translates to
+    sharp features in the image.
+
+    Input:  Edge images - vertical and horizontal
+            Window size (neighborhood size)
+    Output: Lambda, strength of pixel neighborhood
+
+    Given the edge images, we compute strength based
+    on how strong the edges are within each neighborhood.
+
+**/
+
+F2D* calcGoodFeature(F2D* verticalEdgeImage, F2D* horizontalEdgeImage, int cols, int rows, int winSize)
+{
+    int i, j, k, ind;
+    F2D *verticalEdgeSq, *horizontalEdgeSq, *horzVertEdge;
+    F2D *tr, *det, *lambda;
+    F2D *cummulative_verticalEdgeSq, *cummulative_horzVertEdge, *cummulative_horizontalEdgeSq;
+
+    verticalEdgeSq = fMallocHandle(rows, cols);
+    horzVertEdge = fMallocHandle(rows, cols);
+    horizontalEdgeSq = fMallocHandle(rows, cols);
+    
+    for( i=0; i<rows; i++)
+    {
+        for( j=0; j<cols; j++)
+        {
+            subsref(verticalEdgeSq,i,j) = subsref(verticalEdgeImage,i,j) * subsref(verticalEdgeImage,i,j);
+            subsref(horzVertEdge,i,j) = subsref(verticalEdgeImage,i,j) * subsref(horizontalEdgeImage,i,j);
+            subsref(horizontalEdgeSq,i,j) = subsref(horizontalEdgeImage,i,j) * subsref(horizontalEdgeImage,i,j);
+        }
+    }
+    
+    cummulative_verticalEdgeSq = calcAreaSum(verticalEdgeSq, cols, rows, winSize); 
+    cummulative_horzVertEdge = calcAreaSum(horzVertEdge, cols, rows, winSize);
+    cummulative_horizontalEdgeSq = calcAreaSum(horizontalEdgeSq, cols, rows, winSize);    
+     
+    tr = fMallocHandle(rows, cols);
+    det = fMallocHandle(rows, cols);
+    lambda = fMallocHandle(rows, cols);
+    
+    for( i=0; i<rows; i++)
+    {
+        for( j=0; j<cols; j++)
+        {
+            subsref(tr,i,j) = subsref(cummulative_verticalEdgeSq,i,j) + subsref(cummulative_horizontalEdgeSq,i,j);
+            subsref(det,i,j) = subsref(cummulative_verticalEdgeSq,i,j) * subsref(cummulative_horizontalEdgeSq,i,j) - subsref(cummulative_horzVertEdge,i,j) * subsref(cummulative_horzVertEdge,i,j);
+            subsref(lambda,i,j) = ( subsref(det,i,j) / (subsref(tr,i,j)+0.00001) ) ;
+        }
+    }
+   
+    fFreeHandle(verticalEdgeSq);
+    fFreeHandle(horzVertEdge);
+    fFreeHandle(horizontalEdgeSq);
+
+    fFreeHandle(cummulative_verticalEdgeSq);
+    fFreeHandle(cummulative_horzVertEdge);
+    fFreeHandle(cummulative_horizontalEdgeSq);
+    
+    fFreeHandle(tr);
+    fFreeHandle(det);
+     
+    return lambda;
+}
diff --git a/SD-VBS/benchmarks/tracking/src/c/calcPyrLKTrack.c b/SD-VBS/benchmarks/tracking/src/c/calcPyrLKTrack.c
new file mode 100644
index 0000000..3718673
--- /dev/null
+++ b/SD-VBS/benchmarks/tracking/src/c/calcPyrLKTrack.c
@@ -0,0 +1,179 @@
+/********************************
+Author: Sravanthi Kota Venkata
+********************************/
+
+#include "tracking.h"
+
+/** calcPyrLKTrack tries to find the displacement (translation in x and y) of features computed in the previous frame.
+    To compute the displacement, we interpolate the existing feature position around the pixel neighborhood.
+    For better results, we perform interpolatations across all pyramid levels.
+
+    Input:  Previous frame blurred images, vertical and horizontal
+            Current frame edge images, vertical and horizontal
+            Current frame blurred images, for level 0 and 1
+            Number of features from previous frame