fixed warnings in OpenCL kernels

modules/ocl/src/opencl/bgfg_mog.cl

@@ -48,22 +48,22 @@
 #define T_MEAN_VAR float
 #define CONVERT_TYPE convert_uchar_sat
 #define F_ZERO (0.0f)
-float cvt(uchar val)
+inline float cvt(uchar val)
 {
     return val;
 }
 
-float sqr(float val)
+inline float sqr(float val)
 {
     return val * val;
 }
 
-float sum(float val)
+inline float sum(float val)
 {
     return val;
 }
 
-float clamp1(float var, float learningRate, float diff, float minVar)
+static float clamp1(float var, float learningRate, float diff, float minVar)
 {
     return fmax(var + learningRate * (diff * diff - var), minVar);
 }
@@ -72,7 +72,7 @@ float clamp1(float var, float learningRate, float diff, float minVar)
 #define T_MEAN_VAR float4
 #define CONVERT_TYPE convert_uchar4_sat
 #define F_ZERO (0.0f, 0.0f, 0.0f, 0.0f)
-float4 cvt(const uchar4 val)
+inline float4 cvt(const uchar4 val)
 {
     float4 result;
     result.x = val.x;
@@ -83,17 +83,17 @@ float4 cvt(const uchar4 val)
     return result;
 }
 
-float sqr(const float4 val)
+inline float sqr(const float4 val)
 {
     return val.x * val.x + val.y * val.y + val.z * val.z;
 }
 
-float sum(const float4 val)
+inline float sum(const float4 val)
 {
     return (val.x + val.y + val.z);
 }
 
-float4 clamp1(const float4 var, float learningRate, const float4 diff, float minVar)
+static float4 clamp1(const float4 var, float learningRate, const float4 diff, float minVar)
 {
     float4 result;
     result.x = fmax(var.x + learningRate * (diff.x * diff.x - var.x), minVar);
@@ -116,14 +116,14 @@ typedef struct
     uchar c_shadowVal;
 }con_srtuct_t;
 
-void swap(__global float* ptr, int x, int y, int k, int rows, int ptr_step)
+static void swap(__global float* ptr, int x, int y, int k, int rows, int ptr_step)
 {
     float val = ptr[(k * rows + y) * ptr_step + x];
     ptr[(k * rows + y) * ptr_step + x] = ptr[((k + 1) * rows + y) * ptr_step + x];
     ptr[((k + 1) * rows + y) * ptr_step + x] = val;
 }
 
-void swap4(__global float4* ptr, int x, int y, int k, int rows, int ptr_step)
+static void swap4(__global float4* ptr, int x, int y, int k, int rows, int ptr_step)
 {
     float4 val = ptr[(k * rows + y) * ptr_step + x];
     ptr[(k * rows + y) * ptr_step + x] = ptr[((k + 1) * rows + y) * ptr_step + x];
@@ -412,7 +412,7 @@ __kernel void mog2_kernel(__global T_FRAME * frame, __global int* fgmask, __glob
 
             if (_weight < -prune)
             {
-                _weight = 0.0;
+                _weight = 0.0f;
                 nmodes--;
             }
 

modules/ocl/src/opencl/haarobjectdetect.cl

@@ -292,7 +292,6 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
     for(int scalei = 0; scalei <loopcount; scalei++)
     {
         int4 scaleinfo1= info[scalei];
-        int width = (scaleinfo1.x & 0xffff0000) >> 16;
         int height = scaleinfo1.x & 0xffff;
         int grpnumperline =(scaleinfo1.y & 0xffff0000) >> 16;
         int totalgrp = scaleinfo1.y & 0xffff;

modules/ocl/src/opencl/haarobjectdetect_scaled2.cl

@@ -136,8 +136,6 @@ __kernel void gpuRunHaarClassifierCascade_scaled2(
     {
         int4 scaleinfo1;
         scaleinfo1 = info[scalei];
-        int width = (scaleinfo1.x & 0xffff0000) >> 16;
-        int height = scaleinfo1.x & 0xffff;
         int grpnumperline = (scaleinfo1.y & 0xffff0000) >> 16;
         int totalgrp = scaleinfo1.y & 0xffff;
         float factor = as_float(scaleinfo1.w);

modules/ocl/src/opencl/tvl1flow.cl

@@ -69,23 +69,16 @@ __global float* dx, __global float* dy, int dx_step)
 
 }
 
-float bicubicCoeff(float x_)
+static float bicubicCoeff(float x_)
 {
 
     float x = fabs(x_);
     if (x <= 1.0f)
-    {
         return x * x * (1.5f * x - 2.5f) + 1.0f;
-    }
     else if (x < 2.0f)
-    {
         return x * (x * (-0.5f * x + 2.5f) - 4.0f) + 2.0f;
-    }
     else
-    {
         return 0.0f;
-    }
-
 }
 
 __kernel void warpBackwardKernel(__global const float* I0, int I0_step, int I0_col, int I0_row,
@@ -170,12 +163,10 @@ __kernel void warpBackwardKernel(__global const float* I0, int I0_step, int I0_c
 
 }
 
-float readImage(__global const float *image,  const int x,  const int y,  const int rows,  const int cols, const int elemCntPerRow)
+static float readImage(__global const float *image,  const int x,  const int y,  const int rows,  const int cols, const int elemCntPerRow)
 {
     int i0 = clamp(x, 0, cols - 1);
     int j0 = clamp(y, 0, rows - 1);
-    int i1 = clamp(x + 1, 0, cols - 1);
-    int j1 = clamp(y + 1, 0, rows - 1);
 
     return image[j0 * elemCntPerRow + i0];
 }
@@ -303,7 +294,7 @@ __kernel void estimateDualVariablesKernel(__global const float* u1, int u1_col,
 
 }
 
-float divergence(__global const float* v1, __global const float* v2, int y, int x, int v1_step, int v2_step)
+static float divergence(__global const float* v1, __global const float* v2, int y, int x, int v1_step, int v2_step)
 {
 
     if (x > 0 && y > 0)
@@ -407,5 +398,4 @@ __kernel void estimateUKernel(__global const float* I1wx, int I1wx_col, int I1wx
             error[y * I1wx_step + x] = n1 + n2;
         }
     }
-
 }