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提交 0ddd16cf 编写于 作者: V Vladislav Vinogradov
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calcHist & equalizeHist

上级 7e57648e
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Showing with 91 addition and 193 deletion
modules/gpu/include/opencv2/gpu/gpu.hpp
浏览文件 @ 0ddd16cf
......@@ -1028,11 +1028,9 @@ CV_EXPORTS void histRange(const GpuMat& src, GpuMat hist[4], const GpuMat levels
//! Calculates histogram for 8u one channel image
//! Output hist will have one row, 256 cols and CV32SC1 type.
CV_EXPORTS void calcHist(const GpuMat& src, GpuMat& hist, Stream& stream = Stream::Null());
CV_EXPORTS void calcHist(const GpuMat& src, GpuMat& hist, GpuMat& buf, Stream& stream = Stream::Null());
//! normalizes the grayscale image brightness and contrast by normalizing its histogram
CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, Stream& stream = Stream::Null());
CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, GpuMat& buf, Stream& stream = Stream::Null());
//////////////////////////////// StereoBM_GPU ////////////////////////////////
......
modules/gpu/perf/perf_imgproc.cpp
浏览文件 @ 0ddd16cf
......@@ -581,13 +581,12 @@ PERF_TEST_P(Sz, ImgProc_CalcHist, GPU_TYPICAL_MAT_SIZES)
{
cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat d_hist;
cv::gpu::GpuMat d_buf;
cv::gpu::calcHist(d_src, d_hist, d_buf);
cv::gpu::calcHist(d_src, d_hist);
TEST_CYCLE()
{
cv::gpu::calcHist(d_src, d_hist, d_buf);
cv::gpu::calcHist(d_src, d_hist);
}
GPU_SANITY_CHECK(d_hist);
......
modules/gpu/src/cuda/hist.cu
浏览文件 @ 0ddd16cf
......@@ -43,182 +43,115 @@
#if !defined CUDA_DISABLER
#include "internal_shared.hpp"
#include "opencv2/gpu/device/utility.hpp"
#include "opencv2/gpu/device/saturate_cast.hpp"
#include "opencv2/gpu/device/common.hpp"
#include "opencv2/gpu/device/functional.hpp"
#include "opencv2/gpu/device/emulation.hpp"
#include "opencv2/gpu/device/transform.hpp"
namespace cv { namespace gpu { namespace device
{
#define UINT_BITS 32U
//Warps == subhistograms per threadblock
#define WARP_COUNT 6
//Threadblock size
#define HISTOGRAM256_THREADBLOCK_SIZE (WARP_COUNT * OPENCV_GPU_WARP_SIZE)
#define HISTOGRAM256_BIN_COUNT 256
//Shared memory per threadblock
#define HISTOGRAM256_THREADBLOCK_MEMORY (WARP_COUNT * HISTOGRAM256_BIN_COUNT)
#define PARTIAL_HISTOGRAM256_COUNT 240
#define MERGE_THREADBLOCK_SIZE 256
using namespace cv::gpu;
using namespace cv::gpu::device;
#define USE_SMEM_ATOMICS (defined (__CUDA_ARCH__) && (__CUDA_ARCH__ >= 120))
namespace hist
namespace
{
__global__ void histogram256(const uchar* src, int cols, int rows, size_t step, int* hist)
{
#if (!USE_SMEM_ATOMICS)
#define TAG_MASK ( (1U << (UINT_BITS - OPENCV_GPU_LOG_WARP_SIZE)) - 1U )
__forceinline__ __device__ void addByte(volatile uint* s_WarpHist, uint data, uint threadTag)
{
uint count;
do
{
count = s_WarpHist[data] & TAG_MASK;
count = threadTag | (count + 1);
s_WarpHist[data] = count;
} while (s_WarpHist[data] != count);
}
#else
__shared__ int shist[256];
#define TAG_MASK 0xFFFFFFFFU
const int y = blockIdx.x * blockDim.y + threadIdx.y;
const int tid = threadIdx.y * blockDim.x + threadIdx.x;
__forceinline__ __device__ void addByte(uint* s_WarpHist, uint data, uint threadTag)
{
atomicAdd(s_WarpHist + data, 1);
}
shist[tid] = 0;
__syncthreads();
#endif
__forceinline__ __device__ void addWord(uint* s_WarpHist, uint data, uint tag, uint pos_x, uint cols)
if (y < rows)
{
uint x = pos_x << 2;
if (x + 0 < cols) addByte(s_WarpHist, (data >> 0) & 0xFFU, tag);
if (x + 1 < cols) addByte(s_WarpHist, (data >> 8) & 0xFFU, tag);
if (x + 2 < cols) addByte(s_WarpHist, (data >> 16) & 0xFFU, tag);
if (x + 3 < cols) addByte(s_WarpHist, (data >> 24) & 0xFFU, tag);
}
const unsigned int* rowPtr = (const unsigned int*) (src + y * step);
__global__ void histogram256(const PtrStep<uint> d_Data, uint* d_PartialHistograms, uint dataCount, uint cols)
{
//Per-warp subhistogram storage
__shared__ uint s_Hist[HISTOGRAM256_THREADBLOCK_MEMORY];
uint* s_WarpHist= s_Hist + (threadIdx.x >> OPENCV_GPU_LOG_WARP_SIZE) * HISTOGRAM256_BIN_COUNT;
//Clear shared memory storage for current threadblock before processing
#pragma unroll
for (uint i = 0; i < (HISTOGRAM256_THREADBLOCK_MEMORY / HISTOGRAM256_THREADBLOCK_SIZE); i++)
s_Hist[threadIdx.x + i * HISTOGRAM256_THREADBLOCK_SIZE] = 0;
//Cycle through the entire data set, update subhistograms for each warp
const uint tag = threadIdx.x << (UINT_BITS - OPENCV_GPU_LOG_WARP_SIZE);
__syncthreads();
const uint colsui = d_Data.step / sizeof(uint);
for(uint pos = blockIdx.x * blockDim.x + threadIdx.x; pos < dataCount; pos += blockDim.x * gridDim.x)
const int cols_4 = cols / 4;
for (int x = threadIdx.x; x < cols_4; x += blockDim.x)
{
uint pos_y = pos / colsui;
uint pos_x = pos % colsui;
uint data = d_Data.ptr(pos_y)[pos_x];
addWord(s_WarpHist, data, tag, pos_x, cols);
unsigned int data = rowPtr[x];
Emulation::smem::atomicAdd(&shist[(data >> 0) & 0xFFU], 1);
Emulation::smem::atomicAdd(&shist[(data >> 8) & 0xFFU], 1);
Emulation::smem::atomicAdd(&shist[(data >> 16) & 0xFFU], 1);
Emulation::smem::atomicAdd(&shist[(data >> 24) & 0xFFU], 1);
}
//Merge per-warp histograms into per-block and write to global memory
__syncthreads();
for(uint bin = threadIdx.x; bin < HISTOGRAM256_BIN_COUNT; bin += HISTOGRAM256_THREADBLOCK_SIZE)
if (cols % 4 != 0 && threadIdx.x == 0)
{
uint sum = 0;
for (uint i = 0; i < WARP_COUNT; i++)
sum += s_Hist[bin + i * HISTOGRAM256_BIN_COUNT] & TAG_MASK;
d_PartialHistograms[blockIdx.x * HISTOGRAM256_BIN_COUNT + bin] = sum;
for (int x = cols_4 * 4; x < cols; ++x)
{
unsigned int data = ((const uchar*)rowPtr)[x];
Emulation::smem::atomicAdd(&shist[data], 1);
}
}
}
////////////////////////////////////////////////////////////////////////////////
// Merge histogram256() output
// Run one threadblock per bin; each threadblock adds up the same bin counter
// from every partial histogram. Reads are uncoalesced, but mergeHistogram256
// takes only a fraction of total processing time
////////////////////////////////////////////////////////////////////////////////
__global__ void mergeHistogram256(const uint* d_PartialHistograms, int* d_Histogram)
{
uint sum = 0;
__syncthreads();
#pragma unroll
for (uint i = threadIdx.x; i < PARTIAL_HISTOGRAM256_COUNT; i += MERGE_THREADBLOCK_SIZE)
sum += d_PartialHistograms[blockIdx.x + i * HISTOGRAM256_BIN_COUNT];
const int histVal = shist[tid];
if (histVal > 0)
::atomicAdd(hist + tid, histVal);
}
}
__shared__ uint data[MERGE_THREADBLOCK_SIZE];
data[threadIdx.x] = sum;
for (uint stride = MERGE_THREADBLOCK_SIZE / 2; stride > 0; stride >>= 1)
{
__syncthreads();
if(threadIdx.x < stride)
data[threadIdx.x] += data[threadIdx.x + stride];
}
if(threadIdx.x == 0)
d_Histogram[blockIdx.x] = saturate_cast<int>(data[0]);
}
namespace hist
{
void histogram256(PtrStepSzb src, int* hist, cudaStream_t stream)
{
const dim3 block(32, 8);
const dim3 grid(divUp(src.rows, block.y));
void histogram256_gpu(PtrStepSzb src, int* hist, uint* buf, cudaStream_t stream)
{
histogram256<<<PARTIAL_HISTOGRAM256_COUNT, HISTOGRAM256_THREADBLOCK_SIZE, 0, stream>>>(
PtrStepSz<uint>(src),
buf,
static_cast<uint>(src.rows * src.step / sizeof(uint)),
src.cols);
::histogram256<<<grid, block, 0, stream>>>(src.data, src.cols, src.rows, src.step, hist);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
}
mergeHistogram256<<<HISTOGRAM256_BIN_COUNT, MERGE_THREADBLOCK_SIZE, 0, stream>>>(buf, hist);
/////////////////////////////////////////////////////////////////////////
cudaSafeCall( cudaGetLastError() );
namespace
{
__constant__ int c_lut[256];
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
struct EqualizeHist : unary_function<uchar, uchar>
{
float scale;
__constant__ int c_lut[256];
__host__ EqualizeHist(float _scale) : scale(_scale) {}
__global__ void equalizeHist(const PtrStepSzb src, PtrStepb dst)
__device__ __forceinline__ uchar operator ()(uchar val) const
{
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x < src.cols && y < src.rows)
{
const uchar val = src.ptr(y)[x];
const int lut = c_lut[val];
dst.ptr(y)[x] = __float2int_rn(255.0f / (src.cols * src.rows) * lut);
}
const int lut = c_lut[val];
return __float2int_rn(scale * lut);
}
};
}
void equalizeHist_gpu(PtrStepSzb src, PtrStepSzb dst, const int* lut, cudaStream_t stream)
{
dim3 block(16, 16);
dim3 grid(divUp(src.cols, block.x), divUp(src.rows, block.y));
namespace cv { namespace gpu { namespace device
{
template <> struct TransformFunctorTraits<EqualizeHist> : DefaultTransformFunctorTraits<EqualizeHist>
{
enum { smart_shift = 4 };
};
}}}
namespace hist
{
void equalizeHist(PtrStepSzb src, PtrStepSzb dst, const int* lut, cudaStream_t stream)
{
if (stream == 0)
cudaSafeCall( cudaMemcpyToSymbol(c_lut, lut, 256 * sizeof(int), 0, cudaMemcpyDeviceToDevice) );
else
cudaSafeCall( cudaMemcpyToSymbolAsync(c_lut, lut, 256 * sizeof(int), 0, cudaMemcpyDeviceToDevice, stream) );
equalizeHist<<<grid, block, 0, stream>>>(src, dst);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
} // namespace hist
}}} // namespace cv { namespace gpu { namespace device
const float scale = 255.0f / (src.cols * src.rows);
transform(src, dst, EqualizeHist(scale), WithOutMask(), stream);
}
}
#endif /* CUDA_DISABLER */
\ No newline at end of file
#endif /* CUDA_DISABLER */
modules/gpu/src/imgproc.cpp
浏览文件 @ 0ddd16cf
......@@ -71,9 +71,7 @@ void cv::gpu::histRange(const GpuMat&, GpuMat&, const GpuMat&, GpuMat&, Stream&)
void cv::gpu::histRange(const GpuMat&, GpuMat*, const GpuMat*, Stream&) { throw_nogpu(); }
void cv::gpu::histRange(const GpuMat&, GpuMat*, const GpuMat*, GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::calcHist(const GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::calcHist(const GpuMat&, GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::equalizeHist(const GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::equalizeHist(const GpuMat&, GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::equalizeHist(const GpuMat&, GpuMat&, GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::cornerHarris(const GpuMat&, GpuMat&, int, int, double, int) { throw_nogpu(); }
void cv::gpu::cornerHarris(const GpuMat&, GpuMat&, GpuMat&, GpuMat&, int, int, double, int) { throw_nogpu(); }
......@@ -989,36 +987,20 @@ void cv::gpu::histRange(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4
hist_callers[src.depth()](src, hist, levels, buf, StreamAccessor::getStream(stream));
}
namespace cv { namespace gpu { namespace device
{
namespace hist
{
void histogram256_gpu(PtrStepSzb src, int* hist, unsigned int* buf, cudaStream_t stream);
const int PARTIAL_HISTOGRAM256_COUNT = 240;
const int HISTOGRAM256_BIN_COUNT = 256;
void equalizeHist_gpu(PtrStepSzb src, PtrStepSzb dst, const int* lut, cudaStream_t stream);
}
}}}
void cv::gpu::calcHist(const GpuMat& src, GpuMat& hist, Stream& stream)
namespace hist
{
GpuMat buf;
calcHist(src, hist, buf, stream);
void histogram256(PtrStepSzb src, int* hist, cudaStream_t stream);
void equalizeHist(PtrStepSzb src, PtrStepSzb dst, const int* lut, cudaStream_t stream);
}
void cv::gpu::calcHist(const GpuMat& src, GpuMat& hist, GpuMat& buf, Stream& stream)
void cv::gpu::calcHist(const GpuMat& src, GpuMat& hist, Stream& stream)
{
using namespace ::cv::gpu::device::hist;
CV_Assert(src.type() == CV_8UC1);
hist.create(1, 256, CV_32SC1);
hist.setTo(Scalar::all(0));
ensureSizeIsEnough(1, PARTIAL_HISTOGRAM256_COUNT * HISTOGRAM256_BIN_COUNT, CV_32SC1, buf);
histogram256_gpu(src, hist.ptr<int>(), buf.ptr<unsigned int>(), StreamAccessor::getStream(stream));
hist::histogram256(src, hist.ptr<int>(), StreamAccessor::getStream(stream));
}
void cv::gpu::equalizeHist(const GpuMat& src, GpuMat& dst, Stream& stream)
......@@ -1028,16 +1010,8 @@ void cv::gpu::equalizeHist(const GpuMat& src, GpuMat& dst, Stream& stream)
equalizeHist(src, dst, hist, buf, stream);
}
void cv::gpu::equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, Stream& stream)
{
GpuMat buf;
equalizeHist(src, dst, hist, buf, stream);
}
void cv::gpu::equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, GpuMat& buf, Stream& s)
{
using namespace ::cv::gpu::device::hist;
CV_Assert(src.type() == CV_8UC1);
dst.create(src.size(), src.type());
......@@ -1045,15 +1019,12 @@ void cv::gpu::equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, GpuMat&
int intBufSize;
nppSafeCall( nppsIntegralGetBufferSize_32s(256, &intBufSize) );
int bufSize = static_cast<int>(std::max(256 * 240 * sizeof(int), intBufSize + 256 * sizeof(int)));
ensureSizeIsEnough(1, bufSize, CV_8UC1, buf);
ensureSizeIsEnough(1, intBufSize + 256 * sizeof(int), CV_8UC1, buf);
GpuMat histBuf(1, 256 * 240, CV_32SC1, buf.ptr());
GpuMat intBuf(1, intBufSize, CV_8UC1, buf.ptr());
GpuMat lut(1, 256, CV_32S, buf.ptr() + intBufSize);
calcHist(src, hist, histBuf, s);
calcHist(src, hist, s);
cudaStream_t stream = StreamAccessor::getStream(s);
......@@ -1061,10 +1032,7 @@ void cv::gpu::equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, GpuMat&
nppSafeCall( nppsIntegral_32s(hist.ptr<Npp32s>(), lut.ptr<Npp32s>(), 256, intBuf.ptr<Npp8u>()) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
equalizeHist_gpu(src, dst, lut.ptr<int>(), stream);
hist::equalizeHist(src, dst, lut.ptr<int>(), stream);
}
////////////////////////////////////////////////////////////////////////
......
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