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minor warning fix

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pull/17/merge
marina.kolpakova 13 years ago
parent d395bf1522
commit c8a54f67d4
10 changed files with 24 additions and 24 deletions
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  1. 4
      modules/gpu/src/cuda/bf_radius_match.cu
  2. 4
      modules/gpu/src/cuda/canny.cu
  3. 2
      modules/gpu/src/cuda/column_filter.cu
  4. 4
      modules/gpu/src/cuda/fast.cu
  5. 2
      modules/gpu/src/cuda/hist.cu
  6. 4
      modules/gpu/src/cuda/integral_image.cu
  7. 16
      modules/gpu/src/cuda/matrix_reductions.cu
  8. 8
      modules/gpu/src/cuda/pyrlk.cu
  9. 2
      modules/gpu/src/cuda/row_filter.cu
  10. 2
      modules/gpu/src/nvidia/NPP_staging/NPP_staging.cu

4
modules/gpu/src/cuda/bf_radius_match.cu
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@ -56,7 +56,7 @@ namespace cv { namespace gpu { namespace device
__global__ void matchUnrolled(const DevMem2D_<T> query, int imgIdx, const DevMem2D_<T> train, float maxDistance, const Mask mask,
PtrStepi bestTrainIdx, PtrStepi bestImgIdx, PtrStepf bestDistance, unsigned int* nMatches, int maxCount)
{
#if __CUDA_ARCH__ >= 110
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 110)
extern __shared__ int smem[];
@ -168,7 +168,7 @@ namespace cv { namespace gpu { namespace device
__global__ void match(const DevMem2D_<T> query, int imgIdx, const DevMem2D_<T> train, float maxDistance, const Mask mask,
PtrStepi bestTrainIdx, PtrStepi bestImgIdx, PtrStepf bestDistance, unsigned int* nMatches, int maxCount)
{
#if __CUDA_ARCH__ >= 110
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 110)
extern __shared__ int smem[];

4
modules/gpu/src/cuda/canny.cu
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@ -261,7 +261,7 @@ namespace cv { namespace gpu { namespace device
__global__ void edgesHysteresisLocal(PtrStepi map, ushort2* st, int rows, int cols)
{
#if __CUDA_ARCH__ >= 120
#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ >= 120)
__shared__ int smem[18][18];
@ -358,7 +358,7 @@ namespace cv { namespace gpu { namespace device
__global__ void edgesHysteresisGlobal(PtrStepi map, ushort2* st1, ushort2* st2, int rows, int cols, int count)
{
#if __CUDA_ARCH__ >= 120
#if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 120
const int stack_size = 512;

2
modules/gpu/src/cuda/column_filter.cu
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@ -64,7 +64,7 @@ namespace cv { namespace gpu { namespace device
template <int KSIZE, typename T, typename D, typename B>
__global__ void linearColumnFilter(const DevMem2D_<T> src, PtrStep<D> dst, const int anchor, const B brd)
{
#if __CUDA_ARCH__ >= 200
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 200)
const int BLOCK_DIM_X = 16;
const int BLOCK_DIM_Y = 16;
const int PATCH_PER_BLOCK = 4;

4
modules/gpu/src/cuda/fast.cu
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@ -223,7 +223,7 @@ namespace cv { namespace gpu { namespace device
template <bool calcScore, class Mask>
__global__ void calcKeypoints(const DevMem2Db img, const Mask mask, short2* kpLoc, const unsigned int maxKeypoints, PtrStepi score, const int threshold)
{
#if __CUDA_ARCH__ >= 110
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 110)
const int j = threadIdx.x + blockIdx.x * blockDim.x + 3;
const int i = threadIdx.y + blockIdx.y * blockDim.y + 3;
@ -325,7 +325,7 @@ namespace cv { namespace gpu { namespace device
__global__ void nonmaxSupression(const short2* kpLoc, int count, const DevMem2Di scoreMat, short2* locFinal, float* responseFinal)
{
#if __CUDA_ARCH__ >= 110
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 110)
const int kpIdx = threadIdx.x + blockIdx.x * blockDim.x;

2
modules/gpu/src/cuda/hist.cu
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@ -63,7 +63,7 @@ namespace cv { namespace gpu { namespace device
#define MERGE_THREADBLOCK_SIZE 256
#define USE_SMEM_ATOMICS (__CUDA_ARCH__ >= 120)
#define USE_SMEM_ATOMICS (defined (__CUDA_ARCH__) && (__CUDA_ARCH__ >= 120))
namespace hist
{

4
modules/gpu/src/cuda/integral_image.cu
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@ -59,7 +59,7 @@ namespace cv { namespace gpu { namespace device
__global__ void shfl_integral_horizontal(const PtrStep_<uint4> img, PtrStep_<uint4> integral)
{
#if __CUDA_ARCH__ >= 300
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 300)
__shared__ int sums[128];
const int id = threadIdx.x;
@ -299,7 +299,7 @@ namespace cv { namespace gpu { namespace device
// block sums.
__global__ void shfl_integral_vertical(DevMem2D_<unsigned int> integral)
{
#if __CUDA_ARCH__ >= 300
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 300)
__shared__ unsigned int sums[32][9];
const int tidx = blockIdx.x * blockDim.x + threadIdx.x;

16
modules/gpu/src/cuda/matrix_reductions.cu
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@ -215,7 +215,7 @@ namespace cv { namespace gpu { namespace device
maxval[blockIdx.y * gridDim.x + blockIdx.x] = (T)smaxval[0];
}
#if __CUDA_ARCH__ >= 110
#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ >= 110)
__shared__ bool is_last;
if (tid == 0)
@ -535,7 +535,7 @@ namespace cv { namespace gpu { namespace device
findMinMaxLocInSmem<nthreads, best_type>(sminval, smaxval, sminloc, smaxloc, tid);
#if __CUDA_ARCH__ >= 110
#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ >= 110)
__shared__ bool is_last;
if (tid == 0)
@ -841,7 +841,7 @@ namespace cv { namespace gpu { namespace device
sumInSmem<nthreads, uint>(scount, tid);
#if __CUDA_ARCH__ >= 110
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 110)
__shared__ bool is_last;
if (tid == 0)
@ -1034,7 +1034,7 @@ namespace cv { namespace gpu { namespace device
sumInSmem<nthreads, R>(smem, tid);
#if __CUDA_ARCH__ >= 110
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 110)
__shared__ bool is_last;
if (tid == 0)
@ -1115,7 +1115,7 @@ namespace cv { namespace gpu { namespace device
sumInSmem<nthreads, R>(smem, tid);
sumInSmem<nthreads, R>(smem + nthreads, tid);
#if __CUDA_ARCH__ >= 110
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 110)
__shared__ bool is_last;
if (tid == 0)
@ -1222,7 +1222,7 @@ namespace cv { namespace gpu { namespace device
sumInSmem<nthreads, R>(smem + nthreads, tid);
sumInSmem<nthreads, R>(smem + 2 * nthreads, tid);
#if __CUDA_ARCH__ >= 110
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 110
__shared__ bool is_last;
if (tid == 0)
@ -1339,7 +1339,7 @@ namespace cv { namespace gpu { namespace device
sumInSmem<nthreads, R>(smem + 2 * nthreads, tid);
sumInSmem<nthreads, R>(smem + 3 * nthreads, tid);
#if __CUDA_ARCH__ >= 110
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 110)
__shared__ bool is_last;
if (tid == 0)
@ -1975,7 +1975,7 @@ namespace cv { namespace gpu { namespace device
for (int c = 0; c < cn; ++c)
myVal[c] = op.startValue();
#if __CUDA_ARCH__ >= 200
#if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 200
// For cc >= 2.0 prefer L1 cache
for (int x = threadIdx.x; x < src.cols; x += 256)

8
modules/gpu/src/cuda/pyrlk.cu
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@ -82,7 +82,7 @@ namespace cv { namespace gpu { namespace device
smem3[tid] = val3;
__syncthreads();
#if __CUDA_ARCH__ > 110
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ > 110)
if (tid < 128)
{
smem1[tid] = val1 += smem1[tid + 128];
@ -138,7 +138,7 @@ namespace cv { namespace gpu { namespace device
smem2[tid] = val2;
__syncthreads();
#if __CUDA_ARCH__ > 110
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ > 110)
if (tid < 128)
{
smem1[tid] = val1 += smem1[tid + 128];
@ -184,7 +184,7 @@ namespace cv { namespace gpu { namespace device
smem1[tid] = val1;
__syncthreads();
#if __CUDA_ARCH__ > 110
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ > 110)
if (tid < 128)
{
smem1[tid] = val1 += smem1[tid + 128];
@ -271,7 +271,7 @@ namespace cv { namespace gpu { namespace device
template <int cn, int PATCH_X, int PATCH_Y, bool calcErr>
__global__ void lkSparse(const float2* prevPts, float2* nextPts, uchar* status, float* err, const int level, const int rows, const int cols)
{
#if __CUDA_ARCH__ <= 110
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ <= 110)
__shared__ float smem1[128];
__shared__ float smem2[128];
__shared__ float smem3[128];

2
modules/gpu/src/cuda/row_filter.cu
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@ -64,7 +64,7 @@ namespace cv { namespace gpu { namespace device
template <int KSIZE, typename T, typename D, typename B>
__global__ void linearRowFilter(const DevMem2D_<T> src, PtrStep<D> dst, const int anchor, const B brd)
{
#if __CUDA_ARCH__ >= 200
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 200)
const int BLOCK_DIM_X = 32;
const int BLOCK_DIM_Y = 8;
const int PATCH_PER_BLOCK = 4;

2
modules/gpu/src/nvidia/NPP_staging/NPP_staging.cu
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@ -2070,7 +2070,7 @@ NCVStatus nppiStInterpolateFrames(const NppStInterpolationState *pState)
//==============================================================================
#if __CUDA_ARCH__ < 200
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ < 200)
// FP32 atomic add
static __forceinline__ __device__ float _atomicAdd(float *addr, float val)

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