#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);
}
#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;
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);
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];
if (_weight < -prune)
{
- _weight = 0.0;
+ _weight = 0.0f;
nmodes--;
}
}
-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,
}
-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];
}
}
-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)
error[y * I1wx_step + x] = n1 + n2;
}
}
-
}