From d17b33131c14864bd1eae275f49a3f148e21cf29 Mon Sep 17 00:00:00 2001
From: Leo Chan <leochanj@live.unc.edu>
Date: Thu, 22 Oct 2020 01:53:21 -0400
Subject: Squashed commit of the sb-vbs branch.

Includes the SD-VBS benchmarks modified to:
- Use libextra to loop as realtime jobs
- Preallocate memory before starting their main computation
- Accept input via stdin instead of via argc

Does not include the SD-VBS matlab code.

Fixes libextra execution in LITMUS^RT.
---
 SD-VBS/benchmarks/tracking/src/c/calcGoodFeature.c | 78 ++++++++++++++++++++++
 1 file changed, 78 insertions(+)
 create mode 100644 SD-VBS/benchmarks/tracking/src/c/calcGoodFeature.c

(limited to 'SD-VBS/benchmarks/tracking/src/c/calcGoodFeature.c')

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;
+}
-- 
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