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1) NPP_staging as sources. Binaries removed.

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2) NVidia tests for GPU
3) FD sample that uses NVidia's interface.
This commit is contained in:
Anatoly Baksheev
2011-02-04 15:15:25 +00:00
parent 811f6fbe92
commit 0747f2d863
52 changed files with 6042 additions and 1240 deletions
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3rdparty/NPP_staging/NPP_staging_static_Windows_32_v1.lib
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/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NOTICE TO USER:
*
* This source code is subject to NVIDIA ownership rights under U.S. and
* international Copyright laws.
*
* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH
* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
* OR PERFORMANCE OF THIS SOURCE CODE.
*
* U.S. Government End Users. This source code is a "commercial item" as
* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of
* "commercial computer software" and "commercial computer software
* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)
* and is provided to the U.S. Government only as a commercial end item.
* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
* source code with only those rights set forth herein.
*/
#ifndef _npp_staging_h_
#define _npp_staging_h_
/**
* \file npp_staging.h
* NPP Staging Library (will become part of NPP next release)
*/
#ifdef __cplusplus
/** \defgroup ctassert Compile-time assert functionality
* @{
*/
/**
* Compile-time assert namespace
*/
namespace NppStCTprep
{
template <bool x>
struct CT_ASSERT_FAILURE;
template <>
struct CT_ASSERT_FAILURE<true> {};
template <int x>
struct assertTest{};
}
#define NPPST_CT_PREP_PASTE_AUX(a,b) a##b ///< Concatenation indirection macro
#define NPPST_CT_PREP_PASTE(a,b) NPPST_CT_PREP_PASTE_AUX(a, b) ///< Concatenation macro
/**
* Performs compile-time assertion of a condition on the file scope
*/
#define NPPST_CT_ASSERT(X) \
typedef NppStCTprep::assertTest<sizeof(NppStCTprep::CT_ASSERT_FAILURE< (bool)(X) >)> \
NPPST_CT_PREP_PASTE(__ct_assert_typedef_, __LINE__)
/*@}*/
#endif
/** \defgroup typedefs NPP Integral and compound types of guaranteed size
* @{
*/
typedef bool NppStBool; ///< Bool of size less than integer
typedef long long NppSt64s; ///< 64-bit signed integer
typedef unsigned long long NppSt64u; ///< 64-bit unsigned integer
typedef int NppSt32s; ///< 32-bit signed integer
typedef unsigned int NppSt32u; ///< 32-bit unsigned integer
typedef short NppSt16s; ///< 16-bit signed short
typedef unsigned short NppSt16u; ///< 16-bit unsigned short
typedef char NppSt8s; ///< 8-bit signed char
typedef unsigned char NppSt8u; ///< 8-bit unsigned char
typedef float NppSt32f; ///< 32-bit IEEE-754 (single precision) float
typedef double NppSt64f; ///< 64-bit IEEE-754 (double precision) float
/**
* 2D Rectangle, 8-bit unsigned fields
* This struct contains position and size information of a rectangle in two space
*/
struct NppStRect8u
{
NppSt8u x; ///< x-coordinate of upper left corner
NppSt8u y; ///< y-coordinate of upper left corner
NppSt8u width; ///< Rectangle width
NppSt8u height; ///< Rectangle height
#ifdef __cplusplus
NppStRect8u() : x(0), y(0), width(0), height(0) {};
NppStRect8u(NppSt8u x, NppSt8u y, NppSt8u width, NppSt8u height) : x(x), y(y), width(width), height(height) {}
#endif
};
/**
* 2D Rectangle, 32-bit signed fields
* This struct contains position and size information of a rectangle in two space
*/
struct NppStRect32s
{
NppSt32s x; ///< x-coordinate of upper left corner
NppSt32s y; ///< y-coordinate of upper left corner
NppSt32s width; ///< Rectangle width
NppSt32s height; ///< Rectangle height
#ifdef __cplusplus
NppStRect32s() : x(0), y(0), width(0), height(0) {};
NppStRect32s(NppSt32s x, NppSt32s y, NppSt32s width, NppSt32s height) : x(x), y(y), width(width), height(height) {}
#endif
};
/**
* 2D Rectangle, 32-bit unsigned fields
* This struct contains position and size information of a rectangle in two space
*/
struct NppStRect32u
{
NppSt32u x; ///< x-coordinate of upper left corner
NppSt32u y; ///< y-coordinate of upper left corner
NppSt32u width; ///< Rectangle width
NppSt32u height; ///< Rectangle height
#ifdef __cplusplus
NppStRect32u() : x(0), y(0), width(0), height(0) {};
NppStRect32u(NppSt32u x, NppSt32u y, NppSt32u width, NppSt32u height) : x(x), y(y), width(width), height(height) {}
#endif
};
/**
* 2D Size, 32-bit signed fields
* This struct typically represents the size of a a rectangular region in two space
*/
struct NppStSize32s
{
NppSt32s width; ///< Rectangle width
NppSt32s height; ///< Rectangle height
#ifdef __cplusplus
NppStSize32s() : width(0), height(0) {};
NppStSize32s(NppSt32s width, NppSt32s height) : width(width), height(height) {}
#endif
};
/**
* 2D Size, 32-bit unsigned fields
* This struct typically represents the size of a a rectangular region in two space
*/
struct NppStSize32u
{
NppSt32u width; ///< Rectangle width
NppSt32u height; ///< Rectangle height
#ifdef __cplusplus
NppStSize32u() : width(0), height(0) {};
NppStSize32u(NppSt32u width, NppSt32u height) : width(width), height(height) {}
#endif
};
/**
* Error Status Codes
*
* Almost all NPP function return error-status information using
* these return codes.
* Negative return codes indicate errors, positive return codes indicate
* warnings, a return code of 0 indicates success.
*/
enum NppStStatus
{
//already present in NPP
__NPP_SUCCESS = 0, ///< Successful operation (same as NPP_NO_ERROR)
__NPP_ERROR = -1, ///< Unknown error
__NPP_CUDA_KERNEL_EXECUTION_ERROR = -3, ///< CUDA kernel execution error
__NPP_NULL_POINTER_ERROR = -4, ///< NULL pointer argument error
__NPP_TEXTURE_BIND_ERROR = -24, ///< CUDA texture binding error or non-zero offset returned
__NPP_MEMCPY_ERROR = -13, ///< CUDA memory copy error
__NPP_MEM_ALLOC_ERR = -12, ///< CUDA memory allocation error
__NPP_MEMFREE_ERR = -15, ///< CUDA memory deallocation error
//to be added
NPP_INVALID_ROI, ///< Invalid region of interest argument
NPP_INVALID_STEP, ///< Invalid image lines step argument (check sign, alignment, relation to image width)
NPP_INVALID_SCALE, ///< Invalid scale parameter passed
NPP_MEM_INSUFFICIENT_BUFFER, ///< Insufficient user-allocated buffer
NPP_MEM_RESIDENCE_ERROR, ///< Memory residence error detected (check if pointers should be device or pinned)
NPP_MEM_INTERNAL_ERROR, ///< Internal memory management error
};
/*@}*/
#ifdef __cplusplus
/** \defgroup ct_typesize_checks Client-side sizeof types compile-time check
* @{
*/
NPPST_CT_ASSERT(sizeof(NppStBool) <= 4);
NPPST_CT_ASSERT(sizeof(NppSt64s) == 8);
NPPST_CT_ASSERT(sizeof(NppSt64u) == 8);
NPPST_CT_ASSERT(sizeof(NppSt32s) == 4);
NPPST_CT_ASSERT(sizeof(NppSt32u) == 4);
NPPST_CT_ASSERT(sizeof(NppSt16s) == 2);
NPPST_CT_ASSERT(sizeof(NppSt16u) == 2);
NPPST_CT_ASSERT(sizeof(NppSt8s) == 1);
NPPST_CT_ASSERT(sizeof(NppSt8u) == 1);
NPPST_CT_ASSERT(sizeof(NppSt32f) == 4);
NPPST_CT_ASSERT(sizeof(NppSt64f) == 8);
NPPST_CT_ASSERT(sizeof(NppStRect8u) == sizeof(NppSt32u));
NPPST_CT_ASSERT(sizeof(NppStRect32s) == 4 * sizeof(NppSt32s));
NPPST_CT_ASSERT(sizeof(NppStRect32u) == 4 * sizeof(NppSt32u));
NPPST_CT_ASSERT(sizeof(NppStSize32u) == 2 * sizeof(NppSt32u));
/*@}*/
#endif
#ifdef __cplusplus
extern "C" {
#endif
/** \defgroup core_npp NPP Core
* Basic functions for CUDA streams management.
* WARNING: These functions couldn't be exported into DLL, so they can be used only with static version of NPP_staging
* @{
*/
/**
* Gets an active CUDA stream used by NPP (Not an API yet!)
* \return Current CUDA stream
*/
cudaStream_t nppStGetActiveCUDAstream();
/**
* Sets an active CUDA stream used by NPP (Not an API yet!)
* \param cudaStream [IN] cudaStream CUDA stream to become current
* \return CUDA stream used before
*/
cudaStream_t nppStSetActiveCUDAstream(cudaStream_t cudaStream);
/*@}*/
/** \defgroup nppi NPP Image Processing
* @{
*/
/**
* Downsamples (decimates) an image using the nearest neighbor algorithm. 32-bit unsigned pixels, single channel.
*
* \param d_src [IN] Source image pointer (CUDA device memory)
* \param srcStep [IN] Source image line step
* \param d_dst [OUT] Destination image pointer (CUDA device memory)
* \param dstStep [IN] Destination image line step
* \param srcRoi [IN] Region of interest in the source image
* \param scale [IN] Downsampling scale factor (positive integer)
* \param readThruTexture [IN] Performance hint to cache source in texture (true) or read directly (false)
*
* \return NPP status code
*/
NppStStatus nppiStDownsampleNearest_32u_C1R(NppSt32u *d_src, NppSt32u srcStep,
NppSt32u *d_dst, NppSt32u dstStep,
NppStSize32u srcRoi, NppSt32u scale,
NppStBool readThruTexture);
/**
* Downsamples (decimates) an image using the nearest neighbor algorithm. 32-bit signed pixels, single channel.
* \see nppiStDownsampleNearest_32u_C1R
*/
NppStStatus nppiStDownsampleNearest_32s_C1R(NppSt32s *d_src, NppSt32u srcStep,
NppSt32s *d_dst, NppSt32u dstStep,
NppStSize32u srcRoi, NppSt32u scale,
NppStBool readThruTexture);
/**
* Downsamples (decimates) an image using the nearest neighbor algorithm. 32-bit float pixels, single channel.
* \see nppiStDownsampleNearest_32u_C1R
*/
NppStStatus nppiStDownsampleNearest_32f_C1R(NppSt32f *d_src, NppSt32u srcStep,
NppSt32f *d_dst, NppSt32u dstStep,
NppStSize32u srcRoi, NppSt32u scale,
NppStBool readThruTexture);
/**
* Downsamples (decimates) an image using the nearest neighbor algorithm. 64-bit unsigned pixels, single channel.
* \see nppiStDownsampleNearest_32u_C1R
*/
NppStStatus nppiStDownsampleNearest_64u_C1R(NppSt64u *d_src, NppSt32u srcStep,
NppSt64u *d_dst, NppSt32u dstStep,
NppStSize32u srcRoi, NppSt32u scale,
NppStBool readThruTexture);
/**
* Downsamples (decimates) an image using the nearest neighbor algorithm. 64-bit signed pixels, single channel.
* \see nppiStDownsampleNearest_32u_C1R
*/
NppStStatus nppiStDownsampleNearest_64s_C1R(NppSt64s *d_src, NppSt32u srcStep,
NppSt64s *d_dst, NppSt32u dstStep,
NppStSize32u srcRoi, NppSt32u scale,
NppStBool readThruTexture);
/**
* Downsamples (decimates) an image using the nearest neighbor algorithm. 64-bit float pixels, single channel.
* \see nppiStDownsampleNearest_32u_C1R
*/
NppStStatus nppiStDownsampleNearest_64f_C1R(NppSt64f *d_src, NppSt32u srcStep,
NppSt64f *d_dst, NppSt32u dstStep,
NppStSize32u srcRoi, NppSt32u scale,
NppStBool readThruTexture);
/**
* Downsamples (decimates) an image using the nearest neighbor algorithm. 32-bit unsigned pixels, single channel. Host implementation.
*
* \param h_src [IN] Source image pointer (Host or pinned memory)
* \param srcStep [IN] Source image line step
* \param h_dst [OUT] Destination image pointer (Host or pinned memory)
* \param dstStep [IN] Destination image line step
* \param srcRoi [IN] Region of interest in the source image
* \param scale [IN] Downsampling scale factor (positive integer)
*
* \return NPP status code
*/
NppStStatus nppiStDownsampleNearest_32u_C1R_host(NppSt32u *h_src, NppSt32u srcStep,
NppSt32u *h_dst, NppSt32u dstStep,
NppStSize32u srcRoi, NppSt32u scale);
/**
* Downsamples (decimates) an image using the nearest neighbor algorithm. 32-bit signed pixels, single channel. Host implementation.
* \see nppiStDownsampleNearest_32u_C1R_host
*/
NppStStatus nppiStDownsampleNearest_32s_C1R_host(NppSt32s *h_src, NppSt32u srcStep,
NppSt32s *h_dst, NppSt32u dstStep,
NppStSize32u srcRoi, NppSt32u scale);
/**
* Downsamples (decimates) an image using the nearest neighbor algorithm. 32-bit float pixels, single channel. Host implementation.
* \see nppiStDownsampleNearest_32u_C1R_host
*/
NppStStatus nppiStDownsampleNearest_32f_C1R_host(NppSt32f *h_src, NppSt32u srcStep,
NppSt32f *h_dst, NppSt32u dstStep,
NppStSize32u srcRoi, NppSt32u scale);
/**
* Downsamples (decimates) an image using the nearest neighbor algorithm. 64-bit unsigned pixels, single channel. Host implementation.
* \see nppiStDownsampleNearest_32u_C1R_host
*/
NppStStatus nppiStDownsampleNearest_64u_C1R_host(NppSt64u *h_src, NppSt32u srcStep,
NppSt64u *h_dst, NppSt32u dstStep,
NppStSize32u srcRoi, NppSt32u scale);
/**
* Downsamples (decimates) an image using the nearest neighbor algorithm. 64-bit signed pixels, single channel. Host implementation.
* \see nppiStDownsampleNearest_32u_C1R_host
*/
NppStStatus nppiStDownsampleNearest_64s_C1R_host(NppSt64s *h_src, NppSt32u srcStep,
NppSt64s *h_dst, NppSt32u dstStep,
NppStSize32u srcRoi, NppSt32u scale);
/**
* Downsamples (decimates) an image using the nearest neighbor algorithm. 64-bit float pixels, single channel. Host implementation.
* \see nppiStDownsampleNearest_32u_C1R_host
*/
NppStStatus nppiStDownsampleNearest_64f_C1R_host(NppSt64f *h_src, NppSt32u srcStep,
NppSt64f *h_dst, NppSt32u dstStep,
NppStSize32u srcRoi, NppSt32u scale);
/**
* Computes standard deviation for each rectangular region of the input image using integral images.
*
* \param d_sum [IN] Integral image pointer (CUDA device memory)
* \param sumStep [IN] Integral image line step
* \param d_sqsum [IN] Squared integral image pointer (CUDA device memory)
* \param sqsumStep [IN] Squared integral image line step
* \param d_norm [OUT] Stddev image pointer (CUDA device memory). Each pixel contains stddev of a rect with top-left corner at the original location in the image
* \param normStep [IN] Stddev image line step
* \param roi [IN] Region of interest in the source image
* \param rect [IN] Rectangular region to calculate stddev over
* \param scaleArea [IN] Multiplication factor to account decimated scale
* \param readThruTexture [IN] Performance hint to cache source in texture (true) or read directly (false)
*
* \return NPP status code
*/
NppStStatus nppiStRectStdDev_32f_C1R(NppSt32u *d_sum, NppSt32u sumStep,
NppSt64u *d_sqsum, NppSt32u sqsumStep,
NppSt32f *d_norm, NppSt32u normStep,
NppStSize32u roi, NppStRect32u rect,
NppSt32f scaleArea, NppStBool readThruTexture);
/**
* Computes standard deviation for each rectangular region of the input image using integral images. Host implementation
*
* \param h_sum [IN] Integral image pointer (Host or pinned memory)
* \param sumStep [IN] Integral image line step
* \param h_sqsum [IN] Squared integral image pointer (Host or pinned memory)
* \param sqsumStep [IN] Squared integral image line step
* \param h_norm [OUT] Stddev image pointer (Host or pinned memory). Each pixel contains stddev of a rect with top-left corner at the original location in the image
* \param normStep [IN] Stddev image line step
* \param roi [IN] Region of interest in the source image
* \param rect [IN] Rectangular region to calculate stddev over
* \param scaleArea [IN] Multiplication factor to account decimated scale
*
* \return NPP status code
*/
NppStStatus nppiStRectStdDev_32f_C1R_host(NppSt32u *h_sum, NppSt32u sumStep,
NppSt64u *h_sqsum, NppSt32u sqsumStep,
NppSt32f *h_norm, NppSt32u normStep,
NppStSize32u roi, NppStRect32u rect,
NppSt32f scaleArea);
/**
* Transposes an image. 32-bit unsigned pixels, single channel
*
* \param d_src [IN] Source image pointer (CUDA device memory)
* \param srcStride [IN] Source image line step
* \param d_dst [OUT] Destination image pointer (CUDA device memory)
* \param dstStride [IN] Destination image line step
* \param srcRoi [IN] Region of interest of the source image
*
* \return NPP status code
*/
NppStStatus nppiStTranspose_32u_C1R(NppSt32u *d_src, NppSt32u srcStride,
NppSt32u *d_dst, NppSt32u dstStride, NppStSize32u srcRoi);
/**
* Transposes an image. 32-bit signed pixels, single channel
* \see nppiStTranspose_32u_C1R
*/
NppStStatus nppiStTranspose_32s_C1R(NppSt32s *d_src, NppSt32u srcStride,
NppSt32s *d_dst, NppSt32u dstStride, NppStSize32u srcRoi);
/**
* Transposes an image. 32-bit float pixels, single channel
* \see nppiStTranspose_32u_C1R
*/
NppStStatus nppiStTranspose_32f_C1R(NppSt32f *d_src, NppSt32u srcStride,
NppSt32f *d_dst, NppSt32u dstStride, NppStSize32u srcRoi);
/**
* Transposes an image. 64-bit unsigned pixels, single channel
* \see nppiStTranspose_32u_C1R
*/
NppStStatus nppiStTranspose_64u_C1R(NppSt64u *d_src, NppSt32u srcStride,
NppSt64u *d_dst, NppSt32u dstStride, NppStSize32u srcRoi);
/**
* Transposes an image. 64-bit signed pixels, single channel
* \see nppiStTranspose_32u_C1R
*/
NppStStatus nppiStTranspose_64s_C1R(NppSt64s *d_src, NppSt32u srcStride,
NppSt64s *d_dst, NppSt32u dstStride, NppStSize32u srcRoi);
/**
* Transposes an image. 64-bit float pixels, single channel
* \see nppiStTranspose_32u_C1R
*/
NppStStatus nppiStTranspose_64f_C1R(NppSt64f *d_src, NppSt32u srcStride,
NppSt64f *d_dst, NppSt32u dstStride, NppStSize32u srcRoi);
/**
* Transposes an image. 32-bit unsigned pixels, single channel. Host implementation
*
* \param h_src [IN] Source image pointer (Host or pinned memory)
* \param srcStride [IN] Source image line step
* \param h_dst [OUT] Destination image pointer (Host or pinned memory)
* \param dstStride [IN] Destination image line step
* \param srcRoi [IN] Region of interest of the source image
*
* \return NPP status code
*/
NppStStatus nppiStTranspose_32u_C1R_host(NppSt32u *h_src, NppSt32u srcStride,
NppSt32u *h_dst, NppSt32u dstStride, NppStSize32u srcRoi);
/**
* Transposes an image. 32-bit signed pixels, single channel. Host implementation
* \see nppiStTranspose_32u_C1R_host
*/
NppStStatus nppiStTranspose_32s_C1R_host(NppSt32s *h_src, NppSt32u srcStride,
NppSt32s *h_dst, NppSt32u dstStride, NppStSize32u srcRoi);
/**
* Transposes an image. 32-bit float pixels, single channel. Host implementation
* \see nppiStTranspose_32u_C1R_host
*/
NppStStatus nppiStTranspose_32f_C1R_host(NppSt32f *h_src, NppSt32u srcStride,
NppSt32f *h_dst, NppSt32u dstStride, NppStSize32u srcRoi);
/**
* Transposes an image. 64-bit unsigned pixels, single channel. Host implementation
* \see nppiStTranspose_32u_C1R_host
*/
NppStStatus nppiStTranspose_64u_C1R_host(NppSt64u *h_src, NppSt32u srcStride,
NppSt64u *h_dst, NppSt32u dstStride, NppStSize32u srcRoi);
/**
* Transposes an image. 64-bit signed pixels, single channel. Host implementation
* \see nppiStTranspose_32u_C1R_host
*/
NppStStatus nppiStTranspose_64s_C1R_host(NppSt64s *h_src, NppSt32u srcStride,
NppSt64s *h_dst, NppSt32u dstStride, NppStSize32u srcRoi);
/**
* Transposes an image. 64-bit float pixels, single channel. Host implementation
* \see nppiStTranspose_32u_C1R_host
*/
NppStStatus nppiStTranspose_64f_C1R_host(NppSt64f *h_src, NppSt32u srcStride,
NppSt64f *h_dst, NppSt32u dstStride, NppStSize32u srcRoi);
/**
* Calculates the size of the temporary buffer for integral image creation
*
* \param roiSize [IN] Size of the input image
* \param pBufsize [OUT] Pointer to host variable that returns the size of the temporary buffer (in bytes)
*
* \return NPP status code
*/
NppStStatus nppiStIntegralGetSize_8u32u(NppStSize32u roiSize, NppSt32u *pBufsize);
/**
* Calculates the size of the temporary buffer for integral image creation
* \see nppiStIntegralGetSize_8u32u
*/
NppStStatus nppiStIntegralGetSize_32f32f(NppStSize32u roiSize, NppSt32u *pBufsize);
/**
* Creates an integral image representation for the input image
*
* \param d_src [IN] Source image pointer (CUDA device memory)
* \param srcStep [IN] Source image line step
* \param d_dst [OUT] Destination integral image pointer (CUDA device memory)
* \param dstStep [IN] Destination image line step
* \param roiSize [IN] Region of interest of the source image
* \param pBuffer [IN] Pointer to the pre-allocated temporary buffer (CUDA device memory)
* \param bufSize [IN] Size of the pBuffer in bytes
*
* \return NPP status code
*/
NppStStatus nppiStIntegral_8u32u_C1R(NppSt8u *d_src, NppSt32u srcStep,
NppSt32u *d_dst, NppSt32u dstStep, NppStSize32u roiSize,
NppSt8u *pBuffer, NppSt32u bufSize);
/**
* Creates an integral image representation for the input image
* \see nppiStIntegral_8u32u_C1R
*/
NppStStatus nppiStIntegral_32f32f_C1R(NppSt32f *d_src, NppSt32u srcStep,
NppSt32f *d_dst, NppSt32u dstStep, NppStSize32u roiSize,
NppSt8u *pBuffer, NppSt32u bufSize);
/**
* Creates an integral image representation for the input image. Host implementation
*
* \param h_src [IN] Source image pointer (Host or pinned memory)
* \param srcStep [IN] Source image line step
* \param h_dst [OUT] Destination integral image pointer (Host or pinned memory)
* \param dstStep [IN] Destination image line step
* \param roiSize [IN] Region of interest of the source image
*
* \return NPP status code
*/
NppStStatus nppiStIntegral_8u32u_C1R_host(NppSt8u *h_src, NppSt32u srcStep,
NppSt32u *h_dst, NppSt32u dstStep, NppStSize32u roiSize);
/**
* Creates an integral image representation for the input image. Host implementation
* \see nppiStIntegral_8u32u_C1R_host
*/
NppStStatus nppiStIntegral_32f32f_C1R_host(NppSt32f *h_src, NppSt32u srcStep,
NppSt32f *h_dst, NppSt32u dstStep, NppStSize32u roiSize);
/**
* Calculates the size of the temporary buffer for squared integral image creation
*
* \param roiSize [IN] Size of the input image
* \param pBufsize [OUT] Pointer to host variable that returns the size of the temporary buffer (in bytes)
*
* \return NPP status code
*/
NppStStatus nppiStSqrIntegralGetSize_8u64u(NppStSize32u roiSize, NppSt32u *pBufsize);
/**
* Creates a squared integral image representation for the input image
*
* \param d_src [IN] Source image pointer (CUDA device memory)
* \param srcStep [IN] Source image line step
* \param d_dst [OUT] Destination squared integral image pointer (CUDA device memory)
* \param dstStep [IN] Destination image line step
* \param roiSize [IN] Region of interest of the source image
* \param pBuffer [IN] Pointer to the pre-allocated temporary buffer (CUDA device memory)
* \param bufSize [IN] Size of the pBuffer in bytes
*
* \return NPP status code
*/
NppStStatus nppiStSqrIntegral_8u64u_C1R(NppSt8u *d_src, NppSt32u srcStep,
NppSt64u *d_dst, NppSt32u dstStep, NppStSize32u roiSize,
NppSt8u *pBuffer, NppSt32u bufSize);
/**
* Creates a squared integral image representation for the input image. Host implementation
*
* \param h_src [IN] Source image pointer (Host or pinned memory)
* \param srcStep [IN] Source image line step
* \param h_dst [OUT] Destination squared integral image pointer (Host or pinned memory)
* \param dstStep [IN] Destination image line step
* \param roiSize [IN] Region of interest of the source image
*
* \return NPP status code
*/
NppStStatus nppiStSqrIntegral_8u64u_C1R_host(NppSt8u *h_src, NppSt32u srcStep,
NppSt64u *h_dst, NppSt32u dstStep, NppStSize32u roiSize);
/*@}*/
/** \defgroup npps NPP Signal Processing
* @{
*/
/**
* Calculates the size of the temporary buffer for vector compaction. 32-bit unsigned values
*
* \param srcLen [IN] Length of the input vector in elements
* \param pBufsize [OUT] Pointer to host variable that returns the size of the temporary buffer (in bytes)
*
* \return NPP status code
*/
NppStStatus nppsStCompactGetSize_32u(NppSt32u srcLen, NppSt32u *pBufsize);
/**
* Calculates the size of the temporary buffer for vector compaction. 32-bit signed values
* \see nppsStCompactGetSize_32u
*/
NppStStatus nppsStCompactGetSize_32s(NppSt32u srcLen, NppSt32u *pBufsize);
/**
* Calculates the size of the temporary buffer for vector compaction. 32-bit float values
* \see nppsStCompactGetSize_32u
*/
NppStStatus nppsStCompactGetSize_32f(NppSt32u srcLen, NppSt32u *pBufsize);
/**
* Compacts the input vector by removing elements of specified value. 32-bit unsigned values
*
* \param d_src [IN] Source vector pointer (CUDA device memory)
* \param srcLen [IN] Source vector length
* \param d_dst [OUT] Destination vector pointer (CUDA device memory)
* \param p_dstLen [OUT] Pointer to the destination vector length (Pinned memory or NULL)
* \param elemRemove [IN] The value to be removed
* \param pBuffer [IN] Pointer to the pre-allocated temporary buffer (CUDA device memory)
* \param bufSize [IN] Size of the pBuffer in bytes
*
* \return NPP status code
*/
NppStStatus nppsStCompact_32u(NppSt32u *d_src, NppSt32u srcLen,
NppSt32u *d_dst, NppSt32u *p_dstLen,
NppSt32u elemRemove,
NppSt8u *pBuffer, NppSt32u bufSize);
/**
* Compacts the input vector by removing elements of specified value. 32-bit signed values
* \see nppsStCompact_32u
*/
NppStStatus nppsStCompact_32s(NppSt32s *d_src, NppSt32u srcLen,
NppSt32s *d_dst, NppSt32u *p_dstLen,
NppSt32s elemRemove,
NppSt8u *pBuffer, NppSt32u bufSize);
/**
* Compacts the input vector by removing elements of specified value. 32-bit float values
* \see nppsStCompact_32u
*/
NppStStatus nppsStCompact_32f(NppSt32f *d_src, NppSt32u srcLen,
NppSt32f *d_dst, NppSt32u *p_dstLen,
NppSt32f elemRemove,
NppSt8u *pBuffer, NppSt32u bufSize);
/**
* Compacts the input vector by removing elements of specified value. 32-bit unsigned values. Host implementation
*
* \param h_src [IN] Source vector pointer (CUDA device memory)
* \param srcLen [IN] Source vector length
* \param h_dst [OUT] Destination vector pointer (CUDA device memory)
* \param dstLen [OUT] Pointer to the destination vector length (can be NULL)
* \param elemRemove [IN] The value to be removed
*
* \return NPP status code
*/
NppStStatus nppsStCompact_32u_host(NppSt32u *h_src, NppSt32u srcLen,
NppSt32u *h_dst, NppSt32u *dstLen, NppSt32u elemRemove);
/**
* Compacts the input vector by removing elements of specified value. 32-bit signed values. Host implementation
* \see nppsStCompact_32u_host
*/
NppStStatus nppsStCompact_32s_host(NppSt32s *h_src, NppSt32u srcLen,
NppSt32s *h_dst, NppSt32u *dstLen, NppSt32s elemRemove);
/**
* Compacts the input vector by removing elements of specified value. 32-bit float values. Host implementation
* \see nppsStCompact_32u_host
*/
NppStStatus nppsStCompact_32f_host(NppSt32f *h_src, NppSt32u srcLen,
NppSt32f *h_dst, NppSt32u *dstLen, NppSt32f elemRemove);
/*@}*/
#ifdef __cplusplus
}
#endif
#endif // _npp_staging_h_
modules/gpu/CMakeLists.txt
+9 -12
View File
@@ -36,10 +36,12 @@ file(GLOB lib_device_hdrs "src/opencv2/gpu/device/*.h*")
source_group("Device" FILES ${lib_device_hdrs})
if (HAVE_CUDA AND MSVC)
file(GLOB ncv_srcs "src/nvidia/*.cpp")
file(GLOB ncv_hdrs "src/nvidia/*.h*")
file(GLOB ncv_cuda "src/nvidia/*.cu")
source_group("Src\\NVidia" FILES ${ncv_srcs} ${ncv_hdrs} ${ncv_cuda})
file(GLOB_RECURSE ncv_srcs "src/nvidia/*.cpp")
file(GLOB_RECURSE ncv_cuda "src/nvidia/*.cu")
file(GLOB_RECURSE ncv_hdr1 "src/nvidia/*.hpp")
file(GLOB_RECURSE ncv_hdr2 "src/nvidia/*.h")
source_group("Src\\NVidia" FILES ${ncv_srcs} ${ncv_hdr1} ${ncv_hdr2} ${ncv_cuda})
include_directories("src/nvidia/core" "src/nvidia/NPP_staging")
endif()
if (HAVE_CUDA)
@@ -74,17 +76,13 @@ if (HAVE_CUDA)
string(REPLACE "/EHsc-" "/EHs" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
string(REPLACE "/EHsc-" "/EHs" CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE}")
string(REPLACE "/EHsc-" "/EHs" CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG}")
endif()
include(FindNPP_staging.cmake)
include_directories(${NPPST_INC})
endif()
CUDA_COMPILE(cuda_objs ${lib_cuda} ${ncv_cuda})
#CUDA_BUILD_CLEAN_TARGET()
endif()
add_library(${the_target} ${lib_srcs} ${lib_hdrs} ${lib_int_hdrs} ${lib_cuda} ${lib_cuda_hdrs} ${lib_device_hdrs} ${ncv_srcs} ${ncv_hdrs} ${ncv_cuda} ${cuda_objs})
add_library(${the_target} ${lib_srcs} ${lib_hdrs} ${lib_int_hdrs} ${lib_cuda} ${lib_cuda_hdrs} ${lib_device_hdrs} ${ncv_srcs} ${ncv_hdr1} ${ncv_hdr2} ${ncv_cuda} ${cuda_objs})
if(PCHSupport_FOUND)
set(pch_header ${CMAKE_CURRENT_SOURCE_DIR}/src/precomp.hpp)
@@ -117,8 +115,7 @@ set_target_properties(${the_target} PROPERTIES
target_link_libraries(${the_target} ${OPENCV_LINKER_LIBS} ${IPP_LIBS} ${DEPS} )
if (HAVE_CUDA)
target_link_libraries(${the_target} ${CUDA_LIBRARIES} ${CUDA_NPP_LIBRARIES})
target_link_libraries(${the_target} ${NPPST_LIB})
target_link_libraries(${the_target} ${CUDA_LIBRARIES} ${CUDA_NPP_LIBRARIES})
CUDA_ADD_CUFFT_TO_TARGET(${the_target})
endif()
modules/gpu/FindNPP_staging.cmake
-24
View File
@@ -1,24 +0,0 @@
if(CMAKE_SIZEOF_VOID_P EQUAL 4)
set(BIT_SUFF 32)
else()
set(BIT_SUFF 64)
endif()
if (APPLE)
set(PLATFORM_SUFF Darwin)
elseif (UNIX)
set(PLATFORM_SUFF Linux)
else()
set(PLATFORM_SUFF Windows)
endif()
set(LIB_FILE NPP_staging_static_${PLATFORM_SUFF}_${BIT_SUFF}_v1)
find_library(NPPST_LIB
NAMES "${LIB_FILE}" "lib${LIB_FILE}"
PATHS "${CMAKE_SOURCE_DIR}/3rdparty/NPP_staging"
DOC "NPP staging library"
)
SET(NPPST_INC "${CMAKE_SOURCE_DIR}//3rdparty/NPP_staging")
modules/gpu/src/arithm.cpp
+7 -7
View File
@@ -83,25 +83,25 @@ void cv::gpu::transpose(const GpuMat& src, GpuMat& dst)
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( nppiTranspose_8u_C1R(src.ptr<Npp8u>(), src.step, dst.ptr<Npp8u>(), dst.step, sz) );
nppSafeCall( nppiTranspose_8u_C1R(src.ptr<Npp8u>(), src.step, dst.ptr<Npp8u>(), dst.step, sz) );
}
else if (src.elemSize() == 4)
{
NppStSize32u sz;
NcvSize32u sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( nppiStTranspose_32u_C1R(const_cast<NppSt32u*>(src.ptr<NppSt32u>()), src.step,
dst.ptr<NppSt32u>(), dst.step, sz) );
nppSafeCall( nppiStTranspose_32u_C1R(const_cast<Ncv32u*>(src.ptr<Ncv32u>()), src.step,
dst.ptr<Ncv32u>(), dst.step, sz) );
}
else // if (src.elemSize() == 8)
{
NppStSize32u sz;
NcvSize32u sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( nppiStTranspose_64u_C1R(const_cast<NppSt64u*>(src.ptr<NppSt64u>()), src.step,
dst.ptr<NppSt64u>(), dst.step, sz) );
nppSafeCall( nppiStTranspose_64u_C1R(const_cast<Ncv64u*>(src.ptr<Ncv64u>()), src.step,
dst.ptr<Ncv64u>(), dst.step, sz) );
}
cudaSafeCall( cudaThreadSynchronize() );
modules/gpu/src/cascadeclassifier.cpp
+4 -4
View File
@@ -126,7 +126,7 @@ struct cv::gpu::CascadeClassifier_GPU::CascadeClassifierImpl
minNeighbors,
scaleStep, 1,
flags,
*gpuAllocator, *cpuAllocator, devProp.major, devProp.minor, 0);
*gpuAllocator, *cpuAllocator, devProp, 0);
ncvAssertReturnNcvStat(ncvStat);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);
@@ -146,8 +146,8 @@ private:
ncvAssertCUDAReturn(cudaGetDeviceProperties(&devProp, devId), NCV_CUDA_ERROR);
// Load the classifier from file (assuming its size is about 1 mb) using a simple allocator
gpuCascadeAllocator = new NCVMemNativeAllocator(NCVMemoryTypeDevice);
cpuCascadeAllocator = new NCVMemNativeAllocator(NCVMemoryTypeHostPinned);
gpuCascadeAllocator = new NCVMemNativeAllocator(NCVMemoryTypeDevice, devProp.textureAlignment);
cpuCascadeAllocator = new NCVMemNativeAllocator(NCVMemoryTypeHostPinned, devProp.textureAlignment);
ncvAssertPrintReturn(gpuCascadeAllocator->isInitialized(), "Error creating cascade GPU allocator", NCV_CUDA_ERROR);
ncvAssertPrintReturn(cpuCascadeAllocator->isInitialized(), "Error creating cascade CPU allocator", NCV_CUDA_ERROR);
@@ -212,7 +212,7 @@ private:
roi.height = d_src.height();
Ncv32u numDetections;
ncvStat = ncvDetectObjectsMultiScale_device(d_src, roi, d_rects, numDetections, haar, *h_haarStages,
*d_haarStages, *d_haarNodes, *d_haarFeatures, haar.ClassifierSize, 4, 1.2f, 1, 0, gpuCounter, cpuCounter, devProp.major, devProp.minor, 0);
*d_haarStages, *d_haarNodes, *d_haarFeatures, haar.ClassifierSize, 4, 1.2f, 1, 0, gpuCounter, cpuCounter, devProp, 0);
ncvAssertReturnNcvStat(ncvStat);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);
modules/gpu/src/imgproc_gpu.cpp
+16 -12
View File
@@ -560,16 +560,19 @@ void cv::gpu::integralBuffered(const GpuMat& src, GpuMat& sum, GpuMat& buffer)
sum.create(src.rows + 1, src.cols + 1, CV_32S);
NppStSize32u roiSize;
NcvSize32u roiSize;
roiSize.width = src.cols;
roiSize.height = src.rows;
NppSt32u bufSize;
nppSafeCall( nppiStIntegralGetSize_8u32u(roiSize, &bufSize) );
cudaDeviceProp prop;
cudaSafeCall( cudaGetDeviceProperties(&prop, cv::gpu::getDevice()) );
Ncv32u bufSize;
nppSafeCall( nppiStIntegralGetSize_8u32u(roiSize, &bufSize, prop) );
ensureSizeIsEnough(1, bufSize, CV_8UC1, buffer);
nppSafeCall( nppiStIntegral_8u32u_C1R(const_cast<NppSt8u*>(src.ptr<NppSt8u>()), src.step,
sum.ptr<NppSt32u>(), sum.step, roiSize, buffer.ptr<NppSt8u>(), bufSize) );
nppSafeCall( nppiStIntegral_8u32u_C1R(const_cast<Ncv8u*>(src.ptr<Ncv8u>()), src.step,
sum.ptr<Ncv32u>(), sum.step, roiSize, buffer.ptr<Ncv8u>(), bufSize, prop) );
cudaSafeCall( cudaThreadSynchronize() );
}
@@ -600,19 +603,20 @@ void cv::gpu::sqrIntegral(const GpuMat& src, GpuMat& sqsum)
{
CV_Assert(src.type() == CV_8U);
NppStSize32u roiSize;
NcvSize32u roiSize;
roiSize.width = src.cols;
roiSize.height = src.rows;
NppSt32u bufSize;
nppSafeCall(nppiStSqrIntegralGetSize_8u64u(roiSize, &bufSize));
cudaDeviceProp prop;
cudaSafeCall( cudaGetDeviceProperties(&prop, cv::gpu::getDevice()) );
Ncv32u bufSize;
nppSafeCall(nppiStSqrIntegralGetSize_8u64u(roiSize, &bufSize, prop));
GpuMat buf(1, bufSize, CV_8U);
sqsum.create(src.rows + 1, src.cols + 1, CV_64F);
nppSafeCall(nppiStSqrIntegral_8u64u_C1R(
const_cast<NppSt8u*>(src.ptr<NppSt8u>(0)), src.step,
sqsum.ptr<NppSt64u>(0), sqsum.step, roiSize,
buf.ptr<NppSt8u>(0), bufSize));
nppSafeCall(nppiStSqrIntegral_8u64u_C1R(const_cast<Ncv8u*>(src.ptr<Ncv8u>(0)), src.step,
sqsum.ptr<Ncv64u>(0), sqsum.step, roiSize, buf.ptr<Ncv8u>(0), bufSize, prop));
cudaSafeCall( cudaThreadSynchronize() );
}
modules/gpu/src/nvidia/NCVHaarObjectDetection.cu
+42 -40
View File
@@ -57,8 +57,8 @@
#include <algorithm>
#include "npp.h"
#include "NCV.hpp"
#include "NPP_staging/NPP_staging.hpp"
#include "NCVRuntimeTemplates.hpp"
#include "NCVHaarObjectDetection.hpp"
@@ -970,8 +970,7 @@ NCVStatus ncvApplyHaarClassifierCascade_device(NCVMatrix<Ncv32u> &d_integralImag
Ncv32f scaleArea,
INCVMemAllocator &gpuAllocator,
INCVMemAllocator &cpuAllocator,
Ncv32u devPropMajor,
Ncv32u devPropMinor,
cudaDeviceProp &devProp,
cudaStream_t cuStream)
{
ncvAssertReturn(d_integralImage.memType() == d_weights.memType() &&
@@ -1077,15 +1076,15 @@ NCVStatus ncvApplyHaarClassifierCascade_device(NCVMatrix<Ncv32u> &d_integralImag
Ncv32f scaleAreaPixels = scaleArea * ((haar.ClassifierSize.width - 2*HAAR_STDDEV_BORDER) *
(haar.ClassifierSize.height - 2*HAAR_STDDEV_BORDER));
NcvBool bTexCacheCascade = devPropMajor < 2;
NcvBool bTexCacheCascade = devProp.major < 2;
NcvBool bTexCacheIImg = true; //this works better even on Fermi so far
NcvBool bDoAtomicCompaction = devPropMajor >= 2 || (devPropMajor == 1 && devPropMinor >= 3);
NcvBool bDoAtomicCompaction = devProp.major >= 2 || (devProp.major == 1 && devProp.minor >= 3);
NCVVector<Ncv32u> *d_ptrNowData = &d_vecPixelMask;
NCVVector<Ncv32u> *d_ptrNowTmp = &d_vecPixelMaskTmp;
Ncv32u szNppCompactTmpBuf;
nppsStCompactGetSize_32u(d_vecPixelMask.length(), &szNppCompactTmpBuf);
nppsStCompactGetSize_32u(d_vecPixelMask.length(), &szNppCompactTmpBuf, devProp);
if (bDoAtomicCompaction)
{
szNppCompactTmpBuf = 0;
@@ -1185,11 +1184,11 @@ NCVStatus ncvApplyHaarClassifierCascade_device(NCVMatrix<Ncv32u> &d_integralImag
}
else
{
NppStStatus nppSt;
NCVStatus nppSt;
nppSt = nppsStCompact_32u(d_ptrNowTmp->ptr(), d_vecPixelMask.length(),
d_ptrNowData->ptr(), hp_numDet, OBJDET_MASK_ELEMENT_INVALID_32U,
d_tmpBufCompact.ptr(), szNppCompactTmpBuf);
ncvAssertReturn(nppSt == NPP_SUCCESS, NCV_NPP_ERROR);
d_tmpBufCompact.ptr(), szNppCompactTmpBuf, devProp);
ncvAssertReturn(nppSt == NPPST_SUCCESS, NCV_NPP_ERROR);
}
numDetections = *hp_numDet;
}
@@ -1240,11 +1239,11 @@ NCVStatus ncvApplyHaarClassifierCascade_device(NCVMatrix<Ncv32u> &d_integralImag
}
else
{
NppStStatus nppSt;
NCVStatus nppSt;
nppSt = nppsStCompact_32u(d_ptrNowData->ptr(), d_vecPixelMask.length(),
d_ptrNowTmp->ptr(), hp_numDet, OBJDET_MASK_ELEMENT_INVALID_32U,
d_tmpBufCompact.ptr(), szNppCompactTmpBuf);
ncvAssertReturn(nppSt == NPP_SUCCESS, NCV_NPP_ERROR);
d_tmpBufCompact.ptr(), szNppCompactTmpBuf, devProp);
ncvAssertReturnNcvStat(nppSt);
}
swap(d_ptrNowData, d_ptrNowTmp);
@@ -1310,11 +1309,11 @@ NCVStatus ncvApplyHaarClassifierCascade_device(NCVMatrix<Ncv32u> &d_integralImag
}
else
{
NppStStatus nppSt;
NCVStatus nppSt;
nppSt = nppsStCompact_32u(d_ptrNowData->ptr(), numDetections,
d_ptrNowTmp->ptr(), hp_numDet, OBJDET_MASK_ELEMENT_INVALID_32U,
d_tmpBufCompact.ptr(), szNppCompactTmpBuf);
ncvAssertReturn(nppSt == NPP_SUCCESS, NCV_NPP_ERROR);
d_tmpBufCompact.ptr(), szNppCompactTmpBuf, devProp);
ncvAssertReturnNcvStat(nppSt);
}
swap(d_ptrNowData, d_ptrNowTmp);
@@ -1371,11 +1370,11 @@ NCVStatus ncvApplyHaarClassifierCascade_device(NCVMatrix<Ncv32u> &d_integralImag
}
else
{
NppStStatus nppSt;
NCVStatus nppSt;
nppSt = nppsStCompact_32u(d_ptrNowData->ptr(), numDetections,
d_ptrNowTmp->ptr(), hp_numDet, OBJDET_MASK_ELEMENT_INVALID_32U,
d_tmpBufCompact.ptr(), szNppCompactTmpBuf);
ncvAssertReturn(nppSt == NPP_SUCCESS, NCV_NPP_ERROR);
d_tmpBufCompact.ptr(), szNppCompactTmpBuf, devProp);
ncvAssertReturnNcvStat(nppSt);
}
swap(d_ptrNowData, d_ptrNowTmp);
@@ -1715,8 +1714,7 @@ NCVStatus ncvDetectObjectsMultiScale_device(NCVMatrix<Ncv8u> &d_srcImg,
INCVMemAllocator &gpuAllocator,
INCVMemAllocator &cpuAllocator,
Ncv32u devPropMajor,
Ncv32u devPropMinor,
cudaDeviceProp &devProp,
cudaStream_t cuStream)
{
ncvAssertReturn(d_srcImg.memType() == d_dstRects.memType() &&
@@ -1773,12 +1771,12 @@ NCVStatus ncvDetectObjectsMultiScale_device(NCVMatrix<Ncv8u> &d_srcImg,
NCVVectorAlloc<NcvRect32u> h_hypothesesIntermediate(cpuAllocator, d_srcImg.width() * d_srcImg.height());
ncvAssertReturn(h_hypothesesIntermediate.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
NppStStatus nppStat;
NCVStatus nppStat;
Ncv32u szTmpBufIntegral, szTmpBufSqIntegral;
nppStat = nppiStIntegralGetSize_8u32u(NppStSize32u(d_srcImg.width(), d_srcImg.height()), &szTmpBufIntegral);
ncvAssertReturn(nppStat == NPP_SUCCESS, NCV_NPP_ERROR);
nppStat = nppiStSqrIntegralGetSize_8u64u(NppStSize32u(d_srcImg.width(), d_srcImg.height()), &szTmpBufSqIntegral);
ncvAssertReturn(nppStat == NPP_SUCCESS, NCV_NPP_ERROR);
nppStat = nppiStIntegralGetSize_8u32u(NcvSize32u(d_srcImg.width(), d_srcImg.height()), &szTmpBufIntegral, devProp);
ncvAssertReturnNcvStat(nppStat);
nppStat = nppiStSqrIntegralGetSize_8u64u(NcvSize32u(d_srcImg.width(), d_srcImg.height()), &szTmpBufSqIntegral, devProp);
ncvAssertReturnNcvStat(nppStat);
NCVVectorAlloc<Ncv8u> d_tmpIIbuf(gpuAllocator, std::max(szTmpBufIntegral, szTmpBufSqIntegral));
ncvAssertReturn(d_tmpIIbuf.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
@@ -1786,15 +1784,15 @@ NCVStatus ncvDetectObjectsMultiScale_device(NCVMatrix<Ncv8u> &d_srcImg,
nppStat = nppiStIntegral_8u32u_C1R(d_srcImg.ptr(), d_srcImg.pitch(),
d_integralImage.ptr(), d_integralImage.pitch(),
NppStSize32u(d_srcImg.width(), d_srcImg.height()),
d_tmpIIbuf.ptr(), szTmpBufIntegral);
ncvAssertReturn(nppStat == NPP_SUCCESS, NCV_NPP_ERROR);
NcvSize32u(d_srcImg.width(), d_srcImg.height()),
d_tmpIIbuf.ptr(), szTmpBufIntegral, devProp);
ncvAssertReturnNcvStat(nppStat);
nppStat = nppiStSqrIntegral_8u64u_C1R(d_srcImg.ptr(), d_srcImg.pitch(),
d_sqIntegralImage.ptr(), d_sqIntegralImage.pitch(),
NppStSize32u(d_srcImg.width(), d_srcImg.height()),
d_tmpIIbuf.ptr(), szTmpBufSqIntegral);
ncvAssertReturn(nppStat == NPP_SUCCESS, NCV_NPP_ERROR);
NcvSize32u(d_srcImg.width(), d_srcImg.height()),
d_tmpIIbuf.ptr(), szTmpBufSqIntegral, devProp);
ncvAssertReturnNcvStat(nppStat);
NCV_SKIP_COND_END
@@ -1859,7 +1857,7 @@ NCVStatus ncvDetectObjectsMultiScale_device(NCVMatrix<Ncv8u> &d_srcImg,
Ncv32u scale = scalesVector[i];
NcvSize32u srcRoi, scaledIIRoi, searchRoi;
NppStSize32u srcIIRoi;
NcvSize32u srcIIRoi;
srcRoi.width = d_srcImg.width();
srcRoi.height = d_srcImg.height();
srcIIRoi.width = srcRoi.width + 1;
@@ -1875,15 +1873,15 @@ NCVStatus ncvDetectObjectsMultiScale_device(NCVMatrix<Ncv8u> &d_srcImg,
d_integralImage.ptr(), d_integralImage.pitch(),
d_scaledIntegralImage.ptr(), d_scaledIntegralImage.pitch(),
srcIIRoi, scale, true);
ncvAssertReturn(nppStat == NPP_SUCCESS, NCV_NPP_ERROR);
ncvAssertReturnNcvStat(nppStat);
nppStat = nppiStDownsampleNearest_64u_C1R(
d_sqIntegralImage.ptr(), d_sqIntegralImage.pitch(),
d_scaledSqIntegralImage.ptr(), d_scaledSqIntegralImage.pitch(),
srcIIRoi, scale, true);
ncvAssertReturn(nppStat == NPP_SUCCESS, NCV_NPP_ERROR);
ncvAssertReturnNcvStat(nppStat);
const NppStRect32u rect(
const NcvRect32u rect(
HAAR_STDDEV_BORDER,
HAAR_STDDEV_BORDER,
haar.ClassifierSize.width - 2*HAAR_STDDEV_BORDER,
@@ -1892,9 +1890,9 @@ NCVStatus ncvDetectObjectsMultiScale_device(NCVMatrix<Ncv8u> &d_srcImg,
d_scaledIntegralImage.ptr(), d_scaledIntegralImage.pitch(),
d_scaledSqIntegralImage.ptr(), d_scaledSqIntegralImage.pitch(),
d_rectStdDev.ptr(), d_rectStdDev.pitch(),
NppStSize32u(searchRoi.width, searchRoi.height), rect,
NcvSize32u(searchRoi.width, searchRoi.height), rect,
(Ncv32f)scale*scale, true);
ncvAssertReturn(nppStat == NPP_SUCCESS, NCV_NPP_ERROR);
ncvAssertReturnNcvStat(nppStat);
NCV_SKIP_COND_END
@@ -1904,8 +1902,8 @@ NCVStatus ncvDetectObjectsMultiScale_device(NCVMatrix<Ncv8u> &d_srcImg,
detectionsOnThisScale,
haar, h_HaarStages, d_HaarStages, d_HaarNodes, d_HaarFeatures, false,
searchRoi, pixelStep, (Ncv32f)scale*scale,
gpuAllocator, cpuAllocator, devPropMajor, devPropMinor, cuStream);
ncvAssertReturn(ncvStat == NCV_SUCCESS, ncvStat);
gpuAllocator, cpuAllocator, devProp, cuStream);
ncvAssertReturnNcvStat(nppStat);
NCV_SKIP_COND_BEGIN
@@ -2250,6 +2248,10 @@ NCVStatus ncvGrowDetectionsVector_host(NCVVector<Ncv32u> &pixelMask,
return ncvStat;
}
NCVStatus ncvFilterHypotheses_host(NCVVector<NcvRect32u> &hypotheses,
Ncv32u &numHypotheses,
Ncv32u minNeighbors,
@@ -2539,7 +2541,7 @@ NCVStatus ncvHaarLoadFromFile_host(const std::string &filename,
}
NCVStatus ncvHaarStoreNVBIN_host(std::string &filename,
NCVStatus ncvHaarStoreNVBIN_host(const std::string &filename,
HaarClassifierCascadeDescriptor haar,
NCVVector<HaarStage64> &h_HaarStages,
NCVVector<HaarClassifierNode128> &h_HaarNodes,
modules/gpu/src/nvidia/NCVHaarObjectDetection.hpp
+25 -26
View File
@@ -75,13 +75,13 @@ struct HaarFeature64
#define HaarFeature64_CreateCheck_MaxRectField 0xFF
__host__ NCVStatus setRect(Ncv32u rectX, Ncv32u rectY, Ncv32u rectWidth, Ncv32u rectHeight, Ncv32u clsWidth, Ncv32u clsHeight)
__host__ NCVStatus setRect(Ncv32u rectX, Ncv32u rectY, Ncv32u rectWidth, Ncv32u rectHeight, Ncv32u /*clsWidth*/, Ncv32u /*clsHeight*/)
{
ncvAssertReturn(rectWidth <= HaarFeature64_CreateCheck_MaxRectField && rectHeight <= HaarFeature64_CreateCheck_MaxRectField, NCV_HAAR_TOO_LARGE_FEATURES);
((NcvRect8u*)&(this->_ui2.x))->x = rectX;
((NcvRect8u*)&(this->_ui2.x))->y = rectY;
((NcvRect8u*)&(this->_ui2.x))->width = rectWidth;
((NcvRect8u*)&(this->_ui2.x))->height = rectHeight;
((NcvRect8u*)&(this->_ui2.x))->x = (Ncv8u)rectX;
((NcvRect8u*)&(this->_ui2.x))->y = (Ncv8u)rectY;
((NcvRect8u*)&(this->_ui2.x))->width = (Ncv8u)rectWidth;
((NcvRect8u*)&(this->_ui2.x))->height = (Ncv8u)rectHeight;
return NCV_SUCCESS;
}
@@ -306,11 +306,11 @@ struct HaarStage64
};
NPPST_CT_ASSERT(sizeof(HaarFeature64) == 8);
NPPST_CT_ASSERT(sizeof(HaarFeatureDescriptor32) == 4);
NPPST_CT_ASSERT(sizeof(HaarClassifierNodeDescriptor32) == 4);
NPPST_CT_ASSERT(sizeof(HaarClassifierNode128) == 16);
NPPST_CT_ASSERT(sizeof(HaarStage64) == 8);
NCV_CT_ASSERT(sizeof(HaarFeature64) == 8);
NCV_CT_ASSERT(sizeof(HaarFeatureDescriptor32) == 4);
NCV_CT_ASSERT(sizeof(HaarClassifierNodeDescriptor32) == 4);
NCV_CT_ASSERT(sizeof(HaarClassifierNode128) == 16);
NCV_CT_ASSERT(sizeof(HaarStage64) == 8);
//==============================================================================
@@ -347,7 +347,7 @@ enum
NCVPipeObjDet_VisualizeInPlace = 0x004,
};
NCV_EXPORTS
NCVStatus ncvDetectObjectsMultiScale_device(NCVMatrix<Ncv8u> &d_srcImg,
NcvSize32u srcRoi,
NCVVector<NcvRect32u> &d_dstRects,
@@ -367,15 +367,14 @@ NCVStatus ncvDetectObjectsMultiScale_device(NCVMatrix<Ncv8u> &d_srcImg,
INCVMemAllocator &gpuAllocator,
INCVMemAllocator &cpuAllocator,
Ncv32u devPropMajor,
Ncv32u devPropMinor,
cudaDeviceProp &devProp,
cudaStream_t cuStream);
#define OBJDET_MASK_ELEMENT_INVALID_32U 0xFFFFFFFF
#define HAAR_STDDEV_BORDER 1
NCV_EXPORTS
NCVStatus ncvApplyHaarClassifierCascade_device(NCVMatrix<Ncv32u> &d_integralImage,
NCVMatrix<Ncv32f> &d_weights,
NCVMatrixAlloc<Ncv32u> &d_pixelMask,
@@ -391,11 +390,10 @@ NCVStatus ncvApplyHaarClassifierCascade_device(NCVMatrix<Ncv32u> &d_integralImag
Ncv32f scaleArea,
INCVMemAllocator &gpuAllocator,
INCVMemAllocator &cpuAllocator,
Ncv32u devPropMajor,
Ncv32u devPropMinor,
cudaDeviceProp &devProp,
cudaStream_t cuStream);
NCV_EXPORTS
NCVStatus ncvApplyHaarClassifierCascade_host(NCVMatrix<Ncv32u> &h_integralImage,
NCVMatrix<Ncv32f> &h_weights,
NCVMatrixAlloc<Ncv32u> &h_pixelMask,
@@ -409,7 +407,7 @@ NCVStatus ncvApplyHaarClassifierCascade_host(NCVMatrix<Ncv32u> &h_integralImage,
Ncv32u pixelStep,
Ncv32f scaleArea);
NCV_EXPORTS
NCVStatus ncvDrawRects_8u_device(Ncv8u *d_dst,
Ncv32u dstStride,
Ncv32u dstWidth,
@@ -419,7 +417,7 @@ NCVStatus ncvDrawRects_8u_device(Ncv8u *d_dst,
Ncv8u color,
cudaStream_t cuStream);
NCV_EXPORTS
NCVStatus ncvDrawRects_32u_device(Ncv32u *d_dst,
Ncv32u dstStride,
Ncv32u dstWidth,
@@ -429,7 +427,7 @@ NCVStatus ncvDrawRects_32u_device(Ncv32u *d_dst,
Ncv32u color,
cudaStream_t cuStream);
NCV_EXPORTS
NCVStatus ncvDrawRects_8u_host(Ncv8u *h_dst,
Ncv32u dstStride,
Ncv32u dstWidth,
@@ -438,7 +436,7 @@ NCVStatus ncvDrawRects_8u_host(Ncv8u *h_dst,
Ncv32u numRects,
Ncv8u color);
NCV_EXPORTS
NCVStatus ncvDrawRects_32u_host(Ncv32u *h_dst,
Ncv32u dstStride,
Ncv32u dstWidth,
@@ -450,7 +448,7 @@ NCVStatus ncvDrawRects_32u_host(Ncv32u *h_dst,
#define RECT_SIMILARITY_PROPORTION 0.2f
NCV_EXPORTS
NCVStatus ncvGrowDetectionsVector_device(NCVVector<Ncv32u> &pixelMask,
Ncv32u numPixelMaskDetections,
NCVVector<NcvRect32u> &hypotheses,
@@ -461,7 +459,7 @@ NCVStatus ncvGrowDetectionsVector_device(NCVVector<Ncv32u> &pixelMask,
Ncv32f curScale,
cudaStream_t cuStream);
NCV_EXPORTS
NCVStatus ncvGrowDetectionsVector_host(NCVVector<Ncv32u> &pixelMask,
Ncv32u numPixelMaskDetections,
NCVVector<NcvRect32u> &hypotheses,
@@ -471,18 +469,18 @@ NCVStatus ncvGrowDetectionsVector_host(NCVVector<Ncv32u> &pixelMask,
Ncv32u rectHeight,
Ncv32f curScale);
NCV_EXPORTS
NCVStatus ncvFilterHypotheses_host(NCVVector<NcvRect32u> &hypotheses,
Ncv32u &numHypotheses,
Ncv32u minNeighbors,
Ncv32f intersectEps,
NCVVector<Ncv32u> *hypothesesWeights);
NCV_EXPORTS
NCVStatus ncvHaarGetClassifierSize(const std::string &filename, Ncv32u &numStages,
Ncv32u &numNodes, Ncv32u &numFeatures);
NCV_EXPORTS
NCVStatus ncvHaarLoadFromFile_host(const std::string &filename,
HaarClassifierCascadeDescriptor &haar,
NCVVector<HaarStage64> &h_HaarStages,
@@ -490,6 +488,7 @@ NCVStatus ncvHaarLoadFromFile_host(const std::string &filename,
NCVVector<HaarFeature64> &h_HaarFeatures);
NCV_EXPORTS
NCVStatus ncvHaarStoreNVBIN_host(const std::string &filename,
HaarClassifierCascadeDescriptor haar,
NCVVector<HaarStage64> &h_HaarStages,
modules/gpu/src/nvidia/NPP_staging/NPP_staging.cu
+1704
View File
File diff suppressed because it is too large Load Diff
modules/gpu/src/nvidia/NPP_staging/NPP_staging.hpp
+637
View File
@@ -0,0 +1,637 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2009-2010, NVIDIA Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef _npp_staging_hpp_
#define _npp_staging_hpp_
#include "NCV.hpp"
/**
* \file NPP_staging.hpp
* NPP Staging Library
*/
/** \defgroup core_npp NPPST Core
* Basic functions for CUDA streams management.
* @{
*/
/**
* Gets an active CUDA stream used by NPPST
* NOT THREAD SAFE
* \return Current CUDA stream
*/
cudaStream_t nppStGetActiveCUDAstream();
/**
* Sets an active CUDA stream used by NPPST
* NOT THREAD SAFE
* \param cudaStream [IN] cudaStream CUDA stream to become current
* \return CUDA stream used before
*/
cudaStream_t nppStSetActiveCUDAstream(cudaStream_t cudaStream);
/*@}*/
/** \defgroup nppi NPPST Image Processing
* @{
*/
/**
* Downsamples (decimates) an image using the nearest neighbor algorithm. 32-bit unsigned pixels, single channel.
*
* \param d_src [IN] Source image pointer (CUDA device memory)
* \param srcStep [IN] Source image line step
* \param d_dst [OUT] Destination image pointer (CUDA device memory)
* \param dstStep [IN] Destination image line step
* \param srcRoi [IN] Region of interest in the source image
* \param scale [IN] Downsampling scale factor (positive integer)
* \param readThruTexture [IN] Performance hint to cache source in texture (true) or read directly (false)
*
* \return NCV status code
*/
NCV_EXPORTS
NCVStatus nppiStDownsampleNearest_32u_C1R(Ncv32u *d_src, Ncv32u srcStep,
Ncv32u *d_dst, Ncv32u dstStep,
NcvSize32u srcRoi, Ncv32u scale,
NcvBool readThruTexture);
/**
* Downsamples (decimates) an image using the nearest neighbor algorithm. 32-bit signed pixels, single channel.
* \see nppiStDownsampleNearest_32u_C1R
*/
NCV_EXPORTS
NCVStatus nppiStDownsampleNearest_32s_C1R(Ncv32s *d_src, Ncv32u srcStep,
Ncv32s *d_dst, Ncv32u dstStep,
NcvSize32u srcRoi, Ncv32u scale,
NcvBool readThruTexture);
/**
* Downsamples (decimates) an image using the nearest neighbor algorithm. 32-bit float pixels, single channel.
* \see nppiStDownsampleNearest_32u_C1R
*/
NCV_EXPORTS
NCVStatus nppiStDownsampleNearest_32f_C1R(Ncv32f *d_src, Ncv32u srcStep,
Ncv32f *d_dst, Ncv32u dstStep,
NcvSize32u srcRoi, Ncv32u scale,
NcvBool readThruTexture);
/**
* Downsamples (decimates) an image using the nearest neighbor algorithm. 64-bit unsigned pixels, single channel.
* \see nppiStDownsampleNearest_32u_C1R
*/
NCV_EXPORTS
NCVStatus nppiStDownsampleNearest_64u_C1R(Ncv64u *d_src, Ncv32u srcStep,
Ncv64u *d_dst, Ncv32u dstStep,
NcvSize32u srcRoi, Ncv32u scale,
NcvBool readThruTexture);
/**
* Downsamples (decimates) an image using the nearest neighbor algorithm. 64-bit signed pixels, single channel.
* \see nppiStDownsampleNearest_32u_C1R
*/
NCV_EXPORTS
NCVStatus nppiStDownsampleNearest_64s_C1R(Ncv64s *d_src, Ncv32u srcStep,
Ncv64s *d_dst, Ncv32u dstStep,
NcvSize32u srcRoi, Ncv32u scale,
NcvBool readThruTexture);
/**
* Downsamples (decimates) an image using the nearest neighbor algorithm. 64-bit float pixels, single channel.
* \see nppiStDownsampleNearest_32u_C1R
*/
NCV_EXPORTS
NCVStatus nppiStDownsampleNearest_64f_C1R(Ncv64f *d_src, Ncv32u srcStep,
Ncv64f *d_dst, Ncv32u dstStep,
NcvSize32u srcRoi, Ncv32u scale,
NcvBool readThruTexture);
/**
* Downsamples (decimates) an image using the nearest neighbor algorithm. 32-bit unsigned pixels, single channel. Host implementation.
*
* \param h_src [IN] Source image pointer (Host or pinned memory)
* \param srcStep [IN] Source image line step
* \param h_dst [OUT] Destination image pointer (Host or pinned memory)
* \param dstStep [IN] Destination image line step
* \param srcRoi [IN] Region of interest in the source image
* \param scale [IN] Downsampling scale factor (positive integer)
*
* \return NCV status code
*/
NCV_EXPORTS
NCVStatus nppiStDownsampleNearest_32u_C1R_host(Ncv32u *h_src, Ncv32u srcStep,
Ncv32u *h_dst, Ncv32u dstStep,
NcvSize32u srcRoi, Ncv32u scale);
/**
* Downsamples (decimates) an image using the nearest neighbor algorithm. 32-bit signed pixels, single channel. Host implementation.
* \see nppiStDownsampleNearest_32u_C1R_host
*/
NCV_EXPORTS
NCVStatus nppiStDownsampleNearest_32s_C1R_host(Ncv32s *h_src, Ncv32u srcStep,
Ncv32s *h_dst, Ncv32u dstStep,
NcvSize32u srcRoi, Ncv32u scale);
/**
* Downsamples (decimates) an image using the nearest neighbor algorithm. 32-bit float pixels, single channel. Host implementation.
* \see nppiStDownsampleNearest_32u_C1R_host
*/
NCV_EXPORTS
NCVStatus nppiStDownsampleNearest_32f_C1R_host(Ncv32f *h_src, Ncv32u srcStep,
Ncv32f *h_dst, Ncv32u dstStep,
NcvSize32u srcRoi, Ncv32u scale);
/**
* Downsamples (decimates) an image using the nearest neighbor algorithm. 64-bit unsigned pixels, single channel. Host implementation.
* \see nppiStDownsampleNearest_32u_C1R_host
*/
NCV_EXPORTS
NCVStatus nppiStDownsampleNearest_64u_C1R_host(Ncv64u *h_src, Ncv32u srcStep,
Ncv64u *h_dst, Ncv32u dstStep,
NcvSize32u srcRoi, Ncv32u scale);
/**
* Downsamples (decimates) an image using the nearest neighbor algorithm. 64-bit signed pixels, single channel. Host implementation.
* \see nppiStDownsampleNearest_32u_C1R_host
*/
NCV_EXPORTS
NCVStatus nppiStDownsampleNearest_64s_C1R_host(Ncv64s *h_src, Ncv32u srcStep,
Ncv64s *h_dst, Ncv32u dstStep,
NcvSize32u srcRoi, Ncv32u scale);
/**
* Downsamples (decimates) an image using the nearest neighbor algorithm. 64-bit float pixels, single channel. Host implementation.
* \see nppiStDownsampleNearest_32u_C1R_host
*/
NCV_EXPORTS
NCVStatus nppiStDownsampleNearest_64f_C1R_host(Ncv64f *h_src, Ncv32u srcStep,
Ncv64f *h_dst, Ncv32u dstStep,
NcvSize32u srcRoi, Ncv32u scale);
/**
* Computes standard deviation for each rectangular region of the input image using integral images.
*
* \param d_sum [IN] Integral image pointer (CUDA device memory)
* \param sumStep [IN] Integral image line step
* \param d_sqsum [IN] Squared integral image pointer (CUDA device memory)
* \param sqsumStep [IN] Squared integral image line step
* \param d_norm [OUT] Stddev image pointer (CUDA device memory). Each pixel contains stddev of a rect with top-left corner at the original location in the image
* \param normStep [IN] Stddev image line step
* \param roi [IN] Region of interest in the source image
* \param rect [IN] Rectangular region to calculate stddev over
* \param scaleArea [IN] Multiplication factor to account decimated scale
* \param readThruTexture [IN] Performance hint to cache source in texture (true) or read directly (false)
*
* \return NCV status code
*/
NCV_EXPORTS
NCVStatus nppiStRectStdDev_32f_C1R(Ncv32u *d_sum, Ncv32u sumStep,
Ncv64u *d_sqsum, Ncv32u sqsumStep,
Ncv32f *d_norm, Ncv32u normStep,
NcvSize32u roi, NcvRect32u rect,
Ncv32f scaleArea, NcvBool readThruTexture);
/**
* Computes standard deviation for each rectangular region of the input image using integral images. Host implementation
*
* \param h_sum [IN] Integral image pointer (Host or pinned memory)
* \param sumStep [IN] Integral image line step
* \param h_sqsum [IN] Squared integral image pointer (Host or pinned memory)
* \param sqsumStep [IN] Squared integral image line step
* \param h_norm [OUT] Stddev image pointer (Host or pinned memory). Each pixel contains stddev of a rect with top-left corner at the original location in the image
* \param normStep [IN] Stddev image line step
* \param roi [IN] Region of interest in the source image
* \param rect [IN] Rectangular region to calculate stddev over
* \param scaleArea [IN] Multiplication factor to account decimated scale
*
* \return NCV status code
*/
NCV_EXPORTS
NCVStatus nppiStRectStdDev_32f_C1R_host(Ncv32u *h_sum, Ncv32u sumStep,
Ncv64u *h_sqsum, Ncv32u sqsumStep,
Ncv32f *h_norm, Ncv32u normStep,
NcvSize32u roi, NcvRect32u rect,
Ncv32f scaleArea);
/**
* Transposes an image. 32-bit unsigned pixels, single channel
*
* \param d_src [IN] Source image pointer (CUDA device memory)
* \param srcStride [IN] Source image line step
* \param d_dst [OUT] Destination image pointer (CUDA device memory)
* \param dstStride [IN] Destination image line step
* \param srcRoi [IN] Region of interest of the source image
*
* \return NCV status code
*/
NCV_EXPORTS
NCVStatus nppiStTranspose_32u_C1R(Ncv32u *d_src, Ncv32u srcStride,
Ncv32u *d_dst, Ncv32u dstStride, NcvSize32u srcRoi);
/**
* Transposes an image. 32-bit signed pixels, single channel
* \see nppiStTranspose_32u_C1R
*/
NCV_EXPORTS
NCVStatus nppiStTranspose_32s_C1R(Ncv32s *d_src, Ncv32u srcStride,
Ncv32s *d_dst, Ncv32u dstStride, NcvSize32u srcRoi);
/**
* Transposes an image. 32-bit float pixels, single channel
* \see nppiStTranspose_32u_C1R
*/
NCV_EXPORTS
NCVStatus nppiStTranspose_32f_C1R(Ncv32f *d_src, Ncv32u srcStride,
Ncv32f *d_dst, Ncv32u dstStride, NcvSize32u srcRoi);
/**
* Transposes an image. 64-bit unsigned pixels, single channel
* \see nppiStTranspose_32u_C1R
*/
NCV_EXPORTS
NCVStatus nppiStTranspose_64u_C1R(Ncv64u *d_src, Ncv32u srcStride,
Ncv64u *d_dst, Ncv32u dstStride, NcvSize32u srcRoi);
/**
* Transposes an image. 64-bit signed pixels, single channel
* \see nppiStTranspose_32u_C1R
*/
NCV_EXPORTS
NCVStatus nppiStTranspose_64s_C1R(Ncv64s *d_src, Ncv32u srcStride,
Ncv64s *d_dst, Ncv32u dstStride, NcvSize32u srcRoi);
/**
* Transposes an image. 64-bit float pixels, single channel
* \see nppiStTranspose_32u_C1R
*/
NCV_EXPORTS
NCVStatus nppiStTranspose_64f_C1R(Ncv64f *d_src, Ncv32u srcStride,
Ncv64f *d_dst, Ncv32u dstStride, NcvSize32u srcRoi);
/**
* Transposes an image. 32-bit unsigned pixels, single channel. Host implementation
*
* \param h_src [IN] Source image pointer (Host or pinned memory)
* \param srcStride [IN] Source image line step
* \param h_dst [OUT] Destination image pointer (Host or pinned memory)
* \param dstStride [IN] Destination image line step
* \param srcRoi [IN] Region of interest of the source image
*
* \return NCV status code
*/
NCV_EXPORTS
NCVStatus nppiStTranspose_32u_C1R_host(Ncv32u *h_src, Ncv32u srcStride,
Ncv32u *h_dst, Ncv32u dstStride, NcvSize32u srcRoi);
/**
* Transposes an image. 32-bit signed pixels, single channel. Host implementation
* \see nppiStTranspose_32u_C1R_host
*/
NCV_EXPORTS
NCVStatus nppiStTranspose_32s_C1R_host(Ncv32s *h_src, Ncv32u srcStride,
Ncv32s *h_dst, Ncv32u dstStride, NcvSize32u srcRoi);
/**
* Transposes an image. 32-bit float pixels, single channel. Host implementation
* \see nppiStTranspose_32u_C1R_host
*/
NCV_EXPORTS
NCVStatus nppiStTranspose_32f_C1R_host(Ncv32f *h_src, Ncv32u srcStride,
Ncv32f *h_dst, Ncv32u dstStride, NcvSize32u srcRoi);
/**
* Transposes an image. 64-bit unsigned pixels, single channel. Host implementation
* \see nppiStTranspose_32u_C1R_host
*/
NCV_EXPORTS
NCVStatus nppiStTranspose_64u_C1R_host(Ncv64u *h_src, Ncv32u srcStride,
Ncv64u *h_dst, Ncv32u dstStride, NcvSize32u srcRoi);
/**
* Transposes an image. 64-bit signed pixels, single channel. Host implementation
* \see nppiStTranspose_32u_C1R_host
*/
NCV_EXPORTS
NCVStatus nppiStTranspose_64s_C1R_host(Ncv64s *h_src, Ncv32u srcStride,
Ncv64s *h_dst, Ncv32u dstStride, NcvSize32u srcRoi);
/**
* Transposes an image. 64-bit float pixels, single channel. Host implementation
* \see nppiStTranspose_32u_C1R_host
*/
NCV_EXPORTS
NCVStatus nppiStTranspose_64f_C1R_host(Ncv64f *h_src, Ncv32u srcStride,
Ncv64f *h_dst, Ncv32u dstStride, NcvSize32u srcRoi);
/**
* Calculates the size of the temporary buffer for integral image creation
*
* \param roiSize [IN] Size of the input image
* \param pBufsize [OUT] Pointer to host variable that returns the size of the temporary buffer (in bytes)
* \param devProp [IN] CUDA device properties structure, containing texture alignment information
*
* \return NCV status code
*/
NCV_EXPORTS
NCVStatus nppiStIntegralGetSize_8u32u(NcvSize32u roiSize, Ncv32u *pBufsize, cudaDeviceProp &devProp);
/**
* Calculates the size of the temporary buffer for integral image creation
* \see nppiStIntegralGetSize_8u32u
*/
NCV_EXPORTS
NCVStatus nppiStIntegralGetSize_32f32f(NcvSize32u roiSize, Ncv32u *pBufsize, cudaDeviceProp &devProp);
/**
* Creates an integral image representation for the input image
*
* \param d_src [IN] Source image pointer (CUDA device memory)
* \param srcStep [IN] Source image line step
* \param d_dst [OUT] Destination integral image pointer (CUDA device memory)
* \param dstStep [IN] Destination image line step
* \param roiSize [IN] Region of interest of the source image
* \param pBuffer [IN] Pointer to the pre-allocated temporary buffer (CUDA device memory)
* \param bufSize [IN] Size of the pBuffer in bytes
* \param devProp [IN] CUDA device properties structure, containing texture alignment information
*
* \return NCV status code
*/
NCV_EXPORTS
NCVStatus nppiStIntegral_8u32u_C1R(Ncv8u *d_src, Ncv32u srcStep,
Ncv32u *d_dst, Ncv32u dstStep, NcvSize32u roiSize,
Ncv8u *pBuffer, Ncv32u bufSize, cudaDeviceProp &devProp);
/**
* Creates an integral image representation for the input image
* \see nppiStIntegral_8u32u_C1R
*/
NCV_EXPORTS
NCVStatus nppiStIntegral_32f32f_C1R(Ncv32f *d_src, Ncv32u srcStep,
Ncv32f *d_dst, Ncv32u dstStep, NcvSize32u roiSize,
Ncv8u *pBuffer, Ncv32u bufSize, cudaDeviceProp &devProp);
/**
* Creates an integral image representation for the input image. Host implementation
*
* \param h_src [IN] Source image pointer (Host or pinned memory)
* \param srcStep [IN] Source image line step
* \param h_dst [OUT] Destination integral image pointer (Host or pinned memory)
* \param dstStep [IN] Destination image line step
* \param roiSize [IN] Region of interest of the source image
*
* \return NCV status code
*/
NCV_EXPORTS
NCVStatus nppiStIntegral_8u32u_C1R_host(Ncv8u *h_src, Ncv32u srcStep,
Ncv32u *h_dst, Ncv32u dstStep, NcvSize32u roiSize);
/**
* Creates an integral image representation for the input image. Host implementation
* \see nppiStIntegral_8u32u_C1R_host
*/
NCV_EXPORTS
NCVStatus nppiStIntegral_32f32f_C1R_host(Ncv32f *h_src, Ncv32u srcStep,
Ncv32f *h_dst, Ncv32u dstStep, NcvSize32u roiSize);
/**
* Calculates the size of the temporary buffer for squared integral image creation
*
* \param roiSize [IN] Size of the input image
* \param pBufsize [OUT] Pointer to host variable that returns the size of the temporary buffer (in bytes)
* \param devProp [IN] CUDA device properties structure, containing texture alignment information
*
* \return NCV status code
*/
NCV_EXPORTS
NCVStatus nppiStSqrIntegralGetSize_8u64u(NcvSize32u roiSize, Ncv32u *pBufsize, cudaDeviceProp &devProp);
/**
* Creates a squared integral image representation for the input image
*
* \param d_src [IN] Source image pointer (CUDA device memory)
* \param srcStep [IN] Source image line step
* \param d_dst [OUT] Destination squared integral image pointer (CUDA device memory)
* \param dstStep [IN] Destination image line step
* \param roiSize [IN] Region of interest of the source image
* \param pBuffer [IN] Pointer to the pre-allocated temporary buffer (CUDA device memory)
* \param bufSize [IN] Size of the pBuffer in bytes
* \param devProp [IN] CUDA device properties structure, containing texture alignment information
*
* \return NCV status code
*/
NCV_EXPORTS
NCVStatus nppiStSqrIntegral_8u64u_C1R(Ncv8u *d_src, Ncv32u srcStep,
Ncv64u *d_dst, Ncv32u dstStep, NcvSize32u roiSize,
Ncv8u *pBuffer, Ncv32u bufSize, cudaDeviceProp &devProp);
/**
* Creates a squared integral image representation for the input image. Host implementation
*
* \param h_src [IN] Source image pointer (Host or pinned memory)
* \param srcStep [IN] Source image line step
* \param h_dst [OUT] Destination squared integral image pointer (Host or pinned memory)
* \param dstStep [IN] Destination image line step
* \param roiSize [IN] Region of interest of the source image
*
* \return NCV status code
*/
NCV_EXPORTS
NCVStatus nppiStSqrIntegral_8u64u_C1R_host(Ncv8u *h_src, Ncv32u srcStep,
Ncv64u *h_dst, Ncv32u dstStep, NcvSize32u roiSize);
/*@}*/
/** \defgroup npps NPPST Signal Processing
* @{
*/
/**
* Calculates the size of the temporary buffer for vector compaction. 32-bit unsigned values
*
* \param srcLen [IN] Length of the input vector in elements
* \param pBufsize [OUT] Pointer to host variable that returns the size of the temporary buffer (in bytes)
* \param devProp [IN] CUDA device properties structure, containing texture alignment information
*
* \return NCV status code
*/
NCV_EXPORTS
NCVStatus nppsStCompactGetSize_32u(Ncv32u srcLen, Ncv32u *pBufsize, cudaDeviceProp &devProp);
/**
* Calculates the size of the temporary buffer for vector compaction. 32-bit signed values
* \see nppsStCompactGetSize_32u
*/
NCVStatus nppsStCompactGetSize_32s(Ncv32u srcLen, Ncv32u *pBufsize, cudaDeviceProp &devProp);
/**
* Calculates the size of the temporary buffer for vector compaction. 32-bit float values
* \see nppsStCompactGetSize_32u
*/
NCVStatus nppsStCompactGetSize_32f(Ncv32u srcLen, Ncv32u *pBufsize, cudaDeviceProp &devProp);
/**
* Compacts the input vector by removing elements of specified value. 32-bit unsigned values
*
* \param d_src [IN] Source vector pointer (CUDA device memory)
* \param srcLen [IN] Source vector length
* \param d_dst [OUT] Destination vector pointer (CUDA device memory)
* \param p_dstLen [OUT] Pointer to the destination vector length (Pinned memory or NULL)
* \param elemRemove [IN] The value to be removed
* \param pBuffer [IN] Pointer to the pre-allocated temporary buffer (CUDA device memory)
* \param bufSize [IN] Size of the pBuffer in bytes
* \param devProp [IN] CUDA device properties structure, containing texture alignment information
*
* \return NCV status code
*/
NCV_EXPORTS
NCVStatus nppsStCompact_32u(Ncv32u *d_src, Ncv32u srcLen,
Ncv32u *d_dst, Ncv32u *p_dstLen,
Ncv32u elemRemove, Ncv8u *pBuffer,
Ncv32u bufSize, cudaDeviceProp &devProp);
/**
* Compacts the input vector by removing elements of specified value. 32-bit signed values
* \see nppsStCompact_32u
*/
NCV_EXPORTS
NCVStatus nppsStCompact_32s(Ncv32s *d_src, Ncv32u srcLen,
Ncv32s *d_dst, Ncv32u *p_dstLen,
Ncv32s elemRemove, Ncv8u *pBuffer,
Ncv32u bufSize, cudaDeviceProp &devProp);
/**
* Compacts the input vector by removing elements of specified value. 32-bit float values
* \see nppsStCompact_32u
*/
NCV_EXPORTS
NCVStatus nppsStCompact_32f(Ncv32f *d_src, Ncv32u srcLen,
Ncv32f *d_dst, Ncv32u *p_dstLen,
Ncv32f elemRemove, Ncv8u *pBuffer,
Ncv32u bufSize, cudaDeviceProp &devProp);
/**
* Compacts the input vector by removing elements of specified value. 32-bit unsigned values. Host implementation
*
* \param h_src [IN] Source vector pointer (CUDA device memory)
* \param srcLen [IN] Source vector length
* \param h_dst [OUT] Destination vector pointer (CUDA device memory)
* \param dstLen [OUT] Pointer to the destination vector length (can be NULL)
* \param elemRemove [IN] The value to be removed
*
* \return NCV status code
*/
NCV_EXPORTS
NCVStatus nppsStCompact_32u_host(Ncv32u *h_src, Ncv32u srcLen,
Ncv32u *h_dst, Ncv32u *dstLen, Ncv32u elemRemove);
/**
* Compacts the input vector by removing elements of specified value. 32-bit signed values. Host implementation
* \see nppsStCompact_32u_host
*/
NCV_EXPORTS
NCVStatus nppsStCompact_32s_host(Ncv32s *h_src, Ncv32u srcLen,
Ncv32s *h_dst, Ncv32u *dstLen, Ncv32s elemRemove);
/**
* Compacts the input vector by removing elements of specified value. 32-bit float values. Host implementation
* \see nppsStCompact_32u_host
*/
NCV_EXPORTS
NCVStatus nppsStCompact_32f_host(Ncv32f *h_src, Ncv32u srcLen,
Ncv32f *h_dst, Ncv32u *dstLen, Ncv32f elemRemove);
/*@}*/
#endif // _npp_staging_hpp_
modules/gpu/src/nvidia/NCV.cpp → modules/gpu/src/nvidia/core/NCV.cpp
+39 -38
View File
@@ -40,15 +40,13 @@
//M*/
#include <precomp.hpp>
#if !defined (HAVE_CUDA)
#else /* !defined (HAVE_CUDA) */
#include <ios>
#include <stdarg.h>
#include "NCV.hpp"
@@ -94,17 +92,6 @@ void ncvSetDebugOutputHandler(NCVDebugOutputHandler *func)
//==============================================================================
NCVStatus GPUAlignmentValue(Ncv32u &alignment)
{
int curDev;
cudaDeviceProp curProp;
ncvAssertCUDAReturn(cudaGetDevice(&curDev), NCV_CUDA_ERROR);
ncvAssertCUDAReturn(cudaGetDeviceProperties(&curProp, curDev), NCV_CUDA_ERROR);
alignment = curProp.textureAlignment; //GPUAlignmentValue(curProp.major);
return NCV_SUCCESS;
}
Ncv32u alignUp(Ncv32u what, Ncv32u alignment)
{
Ncv32u alignMask = alignment-1;
@@ -216,7 +203,7 @@ NCVMemStackAllocator::NCVMemStackAllocator(Ncv32u alignment)
}
NCVMemStackAllocator::NCVMemStackAllocator(NCVMemoryType memT, size_t capacity, Ncv32u alignment)
NCVMemStackAllocator::NCVMemStackAllocator(NCVMemoryType memT, size_t capacity, Ncv32u alignment, void *reusePtr)
:
currentSize(0),
_maxSize(0),
@@ -229,17 +216,26 @@ NCVMemStackAllocator::NCVMemStackAllocator(NCVMemoryType memT, size_t capacity,
allocBegin = NULL;
switch (memT)
if (reusePtr == NULL)
{
case NCVMemoryTypeDevice:
ncvAssertCUDAReturn(cudaMalloc(&allocBegin, capacity), );
break;
case NCVMemoryTypeHostPinned:
ncvAssertCUDAReturn(cudaMallocHost(&allocBegin, capacity), );
break;
case NCVMemoryTypeHostPageable:
allocBegin = (Ncv8u *)malloc(capacity);
break;
bReusesMemory = false;
switch (memT)
{
case NCVMemoryTypeDevice:
ncvAssertCUDAReturn(cudaMalloc(&allocBegin, capacity), );
break;
case NCVMemoryTypeHostPinned:
ncvAssertCUDAReturn(cudaMallocHost(&allocBegin, capacity), );
break;
case NCVMemoryTypeHostPageable:
allocBegin = (Ncv8u *)malloc(capacity);
break;
}
}
else
{
bReusesMemory = true;
allocBegin = (Ncv8u *)reusePtr;
}
if (capacity == 0)
@@ -260,18 +256,23 @@ NCVMemStackAllocator::~NCVMemStackAllocator()
if (allocBegin != NULL)
{
ncvAssertPrintCheck(currentSize == 0, "NCVMemStackAllocator dtor:: not all objects were deallocated properly, forcing destruction");
switch (_memType)
if (!bReusesMemory)
{
case NCVMemoryTypeDevice:
ncvAssertCUDAReturn(cudaFree(allocBegin), );
break;
case NCVMemoryTypeHostPinned:
ncvAssertCUDAReturn(cudaFreeHost(allocBegin), );
break;
case NCVMemoryTypeHostPageable:
free(allocBegin);
break;
switch (_memType)
{
case NCVMemoryTypeDevice:
ncvAssertCUDAReturn(cudaFree(allocBegin), );
break;
case NCVMemoryTypeHostPinned:
ncvAssertCUDAReturn(cudaFreeHost(allocBegin), );
break;
case NCVMemoryTypeHostPageable:
free(allocBegin);
break;
}
}
allocBegin = NULL;
}
}
@@ -356,14 +357,14 @@ size_t NCVMemStackAllocator::maxSize(void) const
//===================================================================
NCVMemNativeAllocator::NCVMemNativeAllocator(NCVMemoryType memT)
NCVMemNativeAllocator::NCVMemNativeAllocator(NCVMemoryType memT, Ncv32u alignment)
:
currentSize(0),
_maxSize(0),
_memType(memT)
_memType(memT),
_alignment(alignment)
{
ncvAssertPrintReturn(memT != NCVMemoryTypeNone, "NCVMemNativeAllocator ctor:: counting not permitted for this allocator type", );
ncvAssertPrintReturn(NCV_SUCCESS == GPUAlignmentValue(this->_alignment), "NCVMemNativeAllocator ctor:: couldn't get device _alignment", );
}
modules/gpu/src/nvidia/NCV.hpp → modules/gpu/src/nvidia/core/NCV.hpp
+116 -51
View File
@@ -42,8 +42,49 @@
#ifndef _ncv_hpp_
#define _ncv_hpp_
#if (defined WIN32 || defined _WIN32 || defined WINCE) && defined CVAPI_EXPORTS //&& !defined(__CUDACC__)
#define NCV_EXPORTS __declspec(dllexport)
#else
#define NCV_EXPORTS
#endif
#include <cuda_runtime.h>
#include "npp_staging.h"
//==============================================================================
//
// Compile-time assert functionality
//
//==============================================================================
/**
* Compile-time assert namespace
*/
namespace NcvCTprep
{
template <bool x>
struct CT_ASSERT_FAILURE;
template <>
struct CT_ASSERT_FAILURE<true> {};
template <int x>
struct assertTest{};
}
#define NCV_CT_PREP_PASTE_AUX(a,b) a##b ///< Concatenation indirection macro
#define NCV_CT_PREP_PASTE(a,b) NCV_CT_PREP_PASTE_AUX(a, b) ///< Concatenation macro
/**
* Performs compile-time assertion of a condition on the file scope
*/
#define NCV_CT_ASSERT(X) \
typedef NcvCTprep::assertTest<sizeof(NcvCTprep::CT_ASSERT_FAILURE< (bool)(X) >)> \
NCV_CT_PREP_PASTE(__ct_assert_typedef_, __LINE__)
//==============================================================================
@@ -82,62 +123,72 @@ typedef float Ncv32f;
typedef double Ncv64f;
typedef struct
struct NcvRect8u
{
Ncv8u x;
Ncv8u y;
Ncv8u width;
Ncv8u height;
} NcvRect8u;
NcvRect8u() : x(0), y(0), width(0), height(0) {};
NcvRect8u(Ncv8u x, Ncv8u y, Ncv8u width, Ncv8u height) : x(x), y(y), width(width), height(height) {}
};
typedef struct
struct NcvRect32s
{
Ncv32s x; ///< x-coordinate of upper left corner.
Ncv32s y; ///< y-coordinate of upper left corner.
Ncv32s width; ///< Rectangle width.
Ncv32s height; ///< Rectangle height.
} NcvRect32s;
NcvRect32s() : x(0), y(0), width(0), height(0) {};
NcvRect32s(Ncv32s x, Ncv32s y, Ncv32s width, Ncv32s height) : x(x), y(y), width(width), height(height) {}
};
typedef struct
struct NcvRect32u
{
Ncv32u x; ///< x-coordinate of upper left corner.
Ncv32u y; ///< y-coordinate of upper left corner.
Ncv32u width; ///< Rectangle width.
Ncv32u height; ///< Rectangle height.
} NcvRect32u;
NcvRect32u() : x(0), y(0), width(0), height(0) {};
NcvRect32u(Ncv32u x, Ncv32u y, Ncv32u width, Ncv32u height) : x(x), y(y), width(width), height(height) {}
};
typedef struct
struct NcvSize32s
{
Ncv32s width; ///< Rectangle width.
Ncv32s height; ///< Rectangle height.
} NcvSize32s;
NcvSize32s() : width(0), height(0) {};
NcvSize32s(Ncv32s width, Ncv32s height) : width(width), height(height) {}
};
typedef struct
struct NcvSize32u
{
Ncv32u width; ///< Rectangle width.
Ncv32u height; ///< Rectangle height.
} NcvSize32u;
NcvSize32u() : width(0), height(0) {};
NcvSize32u(Ncv32u width, Ncv32u height) : width(width), height(height) {}
};
NPPST_CT_ASSERT(sizeof(NcvBool) <= 4);
NPPST_CT_ASSERT(sizeof(Ncv64s) == 8);
NPPST_CT_ASSERT(sizeof(Ncv64u) == 8);
NPPST_CT_ASSERT(sizeof(Ncv32s) == 4);
NPPST_CT_ASSERT(sizeof(Ncv32u) == 4);
NPPST_CT_ASSERT(sizeof(Ncv16s) == 2);
NPPST_CT_ASSERT(sizeof(Ncv16u) == 2);
NPPST_CT_ASSERT(sizeof(Ncv8s) == 1);
NPPST_CT_ASSERT(sizeof(Ncv8u) == 1);
NPPST_CT_ASSERT(sizeof(Ncv32f) == 4);
NPPST_CT_ASSERT(sizeof(Ncv64f) == 8);
NPPST_CT_ASSERT(sizeof(NcvRect8u) == sizeof(Ncv32u));
NPPST_CT_ASSERT(sizeof(NcvRect32s) == 4 * sizeof(Ncv32s));
NPPST_CT_ASSERT(sizeof(NcvRect32u) == 4 * sizeof(Ncv32u));
NPPST_CT_ASSERT(sizeof(NcvSize32u) == 2 * sizeof(Ncv32u));
NCV_CT_ASSERT(sizeof(NcvBool) <= 4);
NCV_CT_ASSERT(sizeof(Ncv64s) == 8);
NCV_CT_ASSERT(sizeof(Ncv64u) == 8);
NCV_CT_ASSERT(sizeof(Ncv32s) == 4);
NCV_CT_ASSERT(sizeof(Ncv32u) == 4);
NCV_CT_ASSERT(sizeof(Ncv16s) == 2);
NCV_CT_ASSERT(sizeof(Ncv16u) == 2);
NCV_CT_ASSERT(sizeof(Ncv8s) == 1);
NCV_CT_ASSERT(sizeof(Ncv8u) == 1);
NCV_CT_ASSERT(sizeof(Ncv32f) == 4);
NCV_CT_ASSERT(sizeof(Ncv64f) == 8);
NCV_CT_ASSERT(sizeof(NcvRect8u) == sizeof(Ncv32u));
NCV_CT_ASSERT(sizeof(NcvRect32s) == 4 * sizeof(Ncv32s));
NCV_CT_ASSERT(sizeof(NcvRect32u) == 4 * sizeof(Ncv32u));
NCV_CT_ASSERT(sizeof(NcvSize32u) == 2 * sizeof(Ncv32u));
//==============================================================================
@@ -162,13 +213,13 @@ const Ncv32u K_LOG2_WARP_SIZE = 5;
#define NCV_CT_PREP_STRINGIZE(x) NCV_CT_PREP_STRINGIZE_AUX(x)
void ncvDebugOutput(const char *msg, ...);
NCV_EXPORTS void ncvDebugOutput(const char *msg, ...);
typedef void NCVDebugOutputHandler(const char* msg);
void ncvSetDebugOutputHandler(NCVDebugOutputHandler* func);
NCV_EXPORTS void ncvSetDebugOutputHandler(NCVDebugOutputHandler* func);
#define ncvAssertPrintCheck(pred, msg) \
@@ -222,6 +273,7 @@ void ncvSetDebugOutputHandler(NCVDebugOutputHandler* func);
*/
enum NCVStatus
{
//NCV statuses
NCV_SUCCESS,
NCV_CUDA_ERROR,
@@ -257,6 +309,24 @@ enum NCVStatus
NCV_NOIMPL_HAAR_TILTED_FEATURES,
NCV_WARNING_HAAR_DETECTIONS_VECTOR_OVERFLOW,
//NPP statuses
NPPST_SUCCESS = NCV_SUCCESS, ///< Successful operation (same as NPP_NO_ERROR)
NPPST_ERROR, ///< Unknown error
NPPST_CUDA_KERNEL_EXECUTION_ERROR, ///< CUDA kernel execution error
NPPST_NULL_POINTER_ERROR, ///< NULL pointer argument error
NPPST_TEXTURE_BIND_ERROR, ///< CUDA texture binding error or non-zero offset returned
NPPST_MEMCPY_ERROR, ///< CUDA memory copy error
NPPST_MEM_ALLOC_ERR, ///< CUDA memory allocation error
NPPST_MEMFREE_ERR, ///< CUDA memory deallocation error
//NPPST statuses
NPPST_INVALID_ROI, ///< Invalid region of interest argument
NPPST_INVALID_STEP, ///< Invalid image lines step argument (check sign, alignment, relation to image width)
NPPST_INVALID_SCALE, ///< Invalid scale parameter passed
NPPST_MEM_INSUFFICIENT_BUFFER, ///< Insufficient user-allocated buffer
NPPST_MEM_RESIDENCE_ERROR, ///< Memory residence error detected (check if pointers should be device or pinned)
NPPST_MEM_INTERNAL_ERROR, ///< Internal memory management error
};
@@ -285,11 +355,11 @@ enum NCVStatus
typedef struct _NcvTimer *NcvTimer;
NcvTimer ncvStartTimer(void);
NCV_EXPORTS NcvTimer ncvStartTimer(void);
double ncvEndQueryTimerUs(NcvTimer t);
NCV_EXPORTS double ncvEndQueryTimerUs(NcvTimer t);
double ncvEndQueryTimerMs(NcvTimer t);
NCV_EXPORTS double ncvEndQueryTimerMs(NcvTimer t);
//==============================================================================
@@ -299,16 +369,10 @@ double ncvEndQueryTimerMs(NcvTimer t);
//==============================================================================
/**
* Alignment of GPU memory chunks in bytes
*/
NCVStatus GPUAlignmentValue(Ncv32u &alignment);
/**
* Calculates the aligned top bound value
*/
Ncv32u alignUp(Ncv32u what, Ncv32u alignment);
NCV_EXPORTS Ncv32u alignUp(Ncv32u what, Ncv32u alignment);
/**
@@ -326,7 +390,7 @@ enum NCVMemoryType
/**
* NCVMemPtr
*/
struct NCVMemPtr
struct NCV_EXPORTS NCVMemPtr
{
void *ptr;
NCVMemoryType memtype;
@@ -337,7 +401,7 @@ struct NCVMemPtr
/**
* NCVMemSegment
*/
struct NCVMemSegment
struct NCV_EXPORTS NCVMemSegment
{
NCVMemPtr begin;
size_t size;
@@ -348,7 +412,7 @@ struct NCVMemSegment
/**
* INCVMemAllocator (Interface)
*/
class INCVMemAllocator
class NCV_EXPORTS INCVMemAllocator
{
public:
virtual ~INCVMemAllocator() = 0;
@@ -370,7 +434,7 @@ inline INCVMemAllocator::~INCVMemAllocator() {}
/**
* NCVMemStackAllocator
*/
class NCVMemStackAllocator : public INCVMemAllocator
class NCV_EXPORTS NCVMemStackAllocator : public INCVMemAllocator
{
NCVMemStackAllocator();
NCVMemStackAllocator(const NCVMemStackAllocator &);
@@ -378,7 +442,7 @@ class NCVMemStackAllocator : public INCVMemAllocator
public:
explicit NCVMemStackAllocator(Ncv32u alignment);
NCVMemStackAllocator(NCVMemoryType memT, size_t capacity, Ncv32u alignment);
NCVMemStackAllocator(NCVMemoryType memT, size_t capacity, Ncv32u alignment, void *reusePtr=NULL);
virtual ~NCVMemStackAllocator();
virtual NCVStatus alloc(NCVMemSegment &seg, size_t size);
@@ -400,17 +464,18 @@ private:
Ncv8u *end;
size_t currentSize;
size_t _maxSize;
NcvBool bReusesMemory;
};
/**
* NCVMemNativeAllocator
*/
class NCVMemNativeAllocator : public INCVMemAllocator
class NCV_EXPORTS NCVMemNativeAllocator : public INCVMemAllocator
{
public:
NCVMemNativeAllocator(NCVMemoryType memT);
NCVMemNativeAllocator(NCVMemoryType memT, Ncv32u alignment);
virtual ~NCVMemNativeAllocator();
virtual NCVStatus alloc(NCVMemSegment &seg, size_t size);
@@ -438,9 +503,9 @@ private:
/**
* Copy dispatcher
*/
NCVStatus memSegCopyHelper(void *dst, NCVMemoryType dstType,
const void *src, NCVMemoryType srcType,
size_t sz, cudaStream_t cuStream);
NCV_EXPORTS NCVStatus memSegCopyHelper(void *dst, NCVMemoryType dstType,
const void *src, NCVMemoryType srcType,
size_t sz, cudaStream_t cuStream);
/**
@@ -514,6 +579,7 @@ class NCVVectorAlloc : public NCVVector<T>
{
NCVVectorAlloc();
NCVVectorAlloc(const NCVVectorAlloc &);
NCVVectorAlloc& operator=(const NCVVectorAlloc<T>&);
public:
@@ -563,8 +629,7 @@ public:
return allocatedMem;
}
private:
private:
INCVMemAllocator &allocator;
NCVMemSegment allocatedMem;
};
@@ -707,7 +772,7 @@ class NCVMatrixAlloc : public NCVMatrix<T>
{
NCVMatrixAlloc();
NCVMatrixAlloc(const NCVMatrixAlloc &);
NCVMatrixAlloc& operator=(const NCVMatrixAlloc &);
public:
NCVMatrixAlloc(INCVMemAllocator &allocator, Ncv32u width, Ncv32u height, Ncv32u pitch=0)
modules/gpu/src/nvidia/NCVRuntimeTemplates.hpp → modules/gpu/src/nvidia/core/NCVRuntimeTemplates.hpp
+48 -7
View File
@@ -1,3 +1,51 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2009-2010, NVIDIA Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef _ncvruntimetemplates_hpp_
#define _ncvruntimetemplates_hpp_
#include <stdarg.h>
#include <vector>
////////////////////////////////////////////////////////////////////////////////
// The Loki Library
// Copyright (c) 2001 by Andrei Alexandrescu
@@ -14,13 +62,6 @@
// http://loki-lib.sourceforge.net/index.php?n=Main.License
////////////////////////////////////////////////////////////////////////////////
#ifndef _ncvruntimetemplates_hpp_
#define _ncvruntimetemplates_hpp_
#include <stdarg.h>
#include <vector>
namespace Loki
{
//==============================================================================
modules/gpu/src/opencv2/gpu/device/transform.hpp
+135 -135
View File
@@ -68,51 +68,51 @@ namespace cv { namespace gpu { namespace device
//! Read Write Traits
template <size_t src_elem_size, size_t dst_elem_size>
struct UnReadWriteTraits_
{
enum {shift=1};
};
template <size_t src_elem_size>
struct UnReadWriteTraits_<src_elem_size, 1>
{
enum {shift=4};
};
template <size_t src_elem_size>
struct UnReadWriteTraits_<src_elem_size, 2>
{
enum {shift=2};
template <size_t src_elem_size, size_t dst_elem_size>
struct UnReadWriteTraits_
{
enum {shift=1};
};
template <typename T, typename D> struct UnReadWriteTraits
{
enum {shift=UnReadWriteTraits_<sizeof(T), sizeof(D)>::shift};
typedef typename TypeVec<T, shift>::vec_t read_type;
typedef typename TypeVec<D, shift>::vec_t write_type;
template <size_t src_elem_size>
struct UnReadWriteTraits_<src_elem_size, 1>
{
enum {shift=4};
};
template <size_t src_elem_size>
struct UnReadWriteTraits_<src_elem_size, 2>
{
enum {shift=2};
};
template <typename T, typename D> struct UnReadWriteTraits
{
enum {shift=UnReadWriteTraits_<sizeof(T), sizeof(D)>::shift};
typedef typename TypeVec<T, shift>::vec_t read_type;
typedef typename TypeVec<D, shift>::vec_t write_type;
};
template <size_t src_elem_size1, size_t src_elem_size2, size_t dst_elem_size>
struct BinReadWriteTraits_
{
enum {shift=1};
template <size_t src_elem_size1, size_t src_elem_size2, size_t dst_elem_size>
struct BinReadWriteTraits_
{
enum {shift=1};
};
template <size_t src_elem_size1, size_t src_elem_size2>
struct BinReadWriteTraits_<src_elem_size1, src_elem_size2, 1>
{
enum {shift=4};
template <size_t src_elem_size1, size_t src_elem_size2>
struct BinReadWriteTraits_<src_elem_size1, src_elem_size2, 1>
{
enum {shift=4};
};
template <size_t src_elem_size1, size_t src_elem_size2>
struct BinReadWriteTraits_<src_elem_size1, src_elem_size2, 2>
{
enum {shift=2};
template <size_t src_elem_size1, size_t src_elem_size2>
struct BinReadWriteTraits_<src_elem_size1, src_elem_size2, 2>
{
enum {shift=2};
};
template <typename T1, typename T2, typename D> struct BinReadWriteTraits
{
enum {shift=BinReadWriteTraits_<sizeof(T1), sizeof(T2), sizeof(D)>::shift};
typedef typename TypeVec<T1, shift>::vec_t read_type1;
typedef typename TypeVec<T2, shift>::vec_t read_type2;
typedef typename TypeVec<D , shift>::vec_t write_type;
template <typename T1, typename T2, typename D> struct BinReadWriteTraits
{
enum {shift=BinReadWriteTraits_<sizeof(T1), sizeof(T2), sizeof(D)>::shift};
typedef typename TypeVec<T1, shift>::vec_t read_type1;
typedef typename TypeVec<T2, shift>::vec_t read_type2;
typedef typename TypeVec<D , shift>::vec_t write_type;
};
//! Transform kernels
@@ -122,14 +122,14 @@ namespace cv { namespace gpu { namespace device
{
template <typename T, typename D, typename UnOp, typename Mask>
static __device__ void unroll(const T& src, D& dst, const Mask& mask, UnOp& op, int x_shifted, int y)
{
{
if (mask(y, x_shifted))
dst.x = op(src.x);
}
template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
static __device__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, BinOp& op, int x_shifted, int y)
{
{
if (mask(y, x_shifted))
dst.x = op(src1.x, src2.x);
}
@@ -138,18 +138,18 @@ namespace cv { namespace gpu { namespace device
{
template <typename T, typename D, typename UnOp, typename Mask>
static __device__ void unroll(const T& src, D& dst, const Mask& mask, UnOp& op, int x_shifted, int y)
{
{
if (mask(y, x_shifted))
dst.x = op(src.x);
dst.x = op(src.x);
if (mask(y, x_shifted + 1))
dst.y = op(src.y);
}
template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
static __device__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, BinOp& op, int x_shifted, int y)
{
{
if (mask(y, x_shifted))
dst.x = op(src1.x, src2.x);
dst.x = op(src1.x, src2.x);
if (mask(y, x_shifted + 1))
dst.y = op(src1.y, src2.y);
}
@@ -158,22 +158,22 @@ namespace cv { namespace gpu { namespace device
{
template <typename T, typename D, typename UnOp, typename Mask>
static __device__ void unroll(const T& src, D& dst, const Mask& mask, UnOp& op, int x_shifted, int y)
{
{
if (mask(y, x_shifted))
dst.x = op(src.x);
dst.x = op(src.x);
if (mask(y, x_shifted + 1))
dst.y = op(src.y);
dst.y = op(src.y);
if (mask(y, x_shifted + 2))
dst.z = op(src.z);
}
template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
static __device__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, BinOp& op, int x_shifted, int y)
{
{
if (mask(y, x_shifted))
dst.x = op(src1.x, src2.x);
dst.x = op(src1.x, src2.x);
if (mask(y, x_shifted + 1))
dst.y = op(src1.y, src2.y);
dst.y = op(src1.y, src2.y);
if (mask(y, x_shifted + 2))
dst.z = op(src1.z, src2.z);
}
@@ -182,65 +182,65 @@ namespace cv { namespace gpu { namespace device
{
template <typename T, typename D, typename UnOp, typename Mask>
static __device__ void unroll(const T& src, D& dst, const Mask& mask, UnOp& op, int x_shifted, int y)
{
{
if (mask(y, x_shifted))
dst.x = op(src.x);
dst.x = op(src.x);
if (mask(y, x_shifted + 1))
dst.y = op(src.y);
dst.y = op(src.y);
if (mask(y, x_shifted + 2))
dst.z = op(src.z);
dst.z = op(src.z);
if (mask(y, x_shifted + 3))
dst.w = op(src.w);
}
template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
static __device__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, BinOp& op, int x_shifted, int y)
{
{
if (mask(y, x_shifted))
dst.x = op(src1.x, src2.x);
dst.x = op(src1.x, src2.x);
if (mask(y, x_shifted + 1))
dst.y = op(src1.y, src2.y);
dst.y = op(src1.y, src2.y);
if (mask(y, x_shifted + 2))
dst.z = op(src1.z, src2.z);
dst.z = op(src1.z, src2.z);
if (mask(y, x_shifted + 3))
dst.w = op(src1.w, src2.w);
}
};
template <typename T, typename D, typename UnOp, typename Mask>
__global__ static void transformSmart(const DevMem2D_<T> src_, PtrStep_<D> dst_, const Mask mask, UnOp op)
{
typedef typename UnReadWriteTraits<T, D>::read_type read_type;
typedef typename UnReadWriteTraits<T, D>::write_type write_type;
const int shift = UnReadWriteTraits<T, D>::shift;
const int x = threadIdx.x + blockIdx.x * blockDim.x;
const int y = threadIdx.y + blockIdx.y * blockDim.y;
const int x_shifted = x * shift;
if (y < src_.rows)
{
const T* src = src_.ptr(y);
D* dst = dst_.ptr(y);
if (x_shifted + shift - 1 < src_.cols)
{
read_type src_n_el = ((const read_type*)src)[x];
write_type dst_n_el;
OpUnroller<shift>::unroll(src_n_el, dst_n_el, mask, op, x_shifted, y);
((write_type*)dst)[x] = dst_n_el;
}
else
{
for (int real_x = x_shifted; real_x < src_.cols; ++real_x)
{
if (mask(y, real_x))
dst[real_x] = op(src[real_x]);
}
}
}
template <typename T, typename D, typename UnOp, typename Mask>
__global__ static void transformSmart(const DevMem2D_<T> src_, PtrStep_<D> dst_, const Mask mask, UnOp op)
{
typedef typename UnReadWriteTraits<T, D>::read_type read_type;
typedef typename UnReadWriteTraits<T, D>::write_type write_type;
const int shift = UnReadWriteTraits<T, D>::shift;
const int x = threadIdx.x + blockIdx.x * blockDim.x;
const int y = threadIdx.y + blockIdx.y * blockDim.y;
const int x_shifted = x * shift;
if (y < src_.rows)
{
const T* src = src_.ptr(y);
D* dst = dst_.ptr(y);
if (x_shifted + shift - 1 < src_.cols)
{
read_type src_n_el = ((const read_type*)src)[x];
write_type dst_n_el;
OpUnroller<shift>::unroll(src_n_el, dst_n_el, mask, op, x_shifted, y);
((write_type*)dst)[x] = dst_n_el;
}
else
{
for (int real_x = x_shifted; real_x < src_.cols; ++real_x)
{
if (mask(y, real_x))
dst[real_x] = op(src[real_x]);
}
}
}
}
template <typename T, typename D, typename UnOp, typename Mask>
@@ -255,44 +255,44 @@ namespace cv { namespace gpu { namespace device
}
}
template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
__global__ static void transformSmart(const DevMem2D_<T1> src1_, const PtrStep_<T2> src2_, PtrStep_<D> dst_,
const Mask mask, BinOp op)
{
typedef typename BinReadWriteTraits<T1, T2, D>::read_type1 read_type1;
typedef typename BinReadWriteTraits<T1, T2, D>::read_type2 read_type2;
typedef typename BinReadWriteTraits<T1, T2, D>::write_type write_type;
const int shift = BinReadWriteTraits<T1, T2, D>::shift;
const int x = threadIdx.x + blockIdx.x * blockDim.x;
const int y = threadIdx.y + blockIdx.y * blockDim.y;
const int x_shifted = x * shift;
if (y < src1_.rows)
{
const T1* src1 = src1_.ptr(y);
const T2* src2 = src2_.ptr(y);
D* dst = dst_.ptr(y);
if (x_shifted + shift - 1 < src1_.cols)
{
read_type1 src1_n_el = ((const read_type1*)src1)[x];
read_type2 src2_n_el = ((const read_type2*)src2)[x];
write_type dst_n_el;
OpUnroller<shift>::unroll(src1_n_el, src2_n_el, dst_n_el, mask, op, x_shifted, y);
((write_type*)dst)[x] = dst_n_el;
}
else
{
for (int real_x = x_shifted; real_x < src1_.cols; ++real_x)
{
if (mask(y, real_x))
dst[real_x] = op(src1[real_x], src2[real_x]);
}
}
}
template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
__global__ static void transformSmart(const DevMem2D_<T1> src1_, const PtrStep_<T2> src2_, PtrStep_<D> dst_,
const Mask mask, BinOp op)
{
typedef typename BinReadWriteTraits<T1, T2, D>::read_type1 read_type1;
typedef typename BinReadWriteTraits<T1, T2, D>::read_type2 read_type2;
typedef typename BinReadWriteTraits<T1, T2, D>::write_type write_type;
const int shift = BinReadWriteTraits<T1, T2, D>::shift;
const int x = threadIdx.x + blockIdx.x * blockDim.x;
const int y = threadIdx.y + blockIdx.y * blockDim.y;
const int x_shifted = x * shift;
if (y < src1_.rows)
{
const T1* src1 = src1_.ptr(y);
const T2* src2 = src2_.ptr(y);
D* dst = dst_.ptr(y);
if (x_shifted + shift - 1 < src1_.cols)
{
read_type1 src1_n_el = ((const read_type1*)src1)[x];
read_type2 src2_n_el = ((const read_type2*)src2)[x];
write_type dst_n_el;
OpUnroller<shift>::unroll(src1_n_el, src2_n_el, dst_n_el, mask, op, x_shifted, y);
((write_type*)dst)[x] = dst_n_el;
}
else
{
for (int real_x = x_shifted; real_x < src1_.cols; ++real_x)
{
if (mask(y, real_x))
dst[real_x] = op(src1[real_x], src2[real_x]);
}
}
}
}
template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
@@ -355,11 +355,11 @@ namespace cv
template <typename T, typename D, typename UnOp, typename Mask>
static void call(const DevMem2D_<T>& src, const DevMem2D_<D>& dst, UnOp op, const Mask& mask,
cudaStream_t stream = 0)
{
{
const int shift = device::UnReadWriteTraits<T, D>::shift;
dim3 threads(16, 16, 1);
dim3 grid(1, 1, 1);
dim3 grid(1, 1, 1);
grid.x = divUp(src.cols, threads.x * shift);
grid.y = divUp(src.rows, threads.y);
@@ -373,7 +373,7 @@ namespace cv
template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
static void call(const DevMem2D_<T1>& src1, const DevMem2D_<T2>& src2, const DevMem2D_<D>& dst,
BinOp op, const Mask& mask, cudaStream_t stream = 0)
{
{
const int shift = device::BinReadWriteTraits<T1, T2, D>::shift;
dim3 threads(16, 16, 1);
@@ -392,7 +392,7 @@ namespace cv
template <typename T, typename D, typename UnOp, typename Mask>
static void transform_caller(const DevMem2D_<T>& src, const DevMem2D_<D>& dst, UnOp op, const Mask& mask,
cudaStream_t stream = 0)
{
{
TransformChooser<device::VecTraits<T>::cn == 1 && device::VecTraits<D>::cn == 1 && device::UnReadWriteTraits<T, D>::shift != 1>::call(src, dst, op, mask, stream);
}
modules/gpu/src/precomp.hpp
+3 -3
View File
@@ -69,9 +69,9 @@
#include "cufft.h"
#include "opencv2/gpu/stream_accessor.hpp"
#include "npp.h"
#include "npp_staging.h"
#include "nvidia/NCV.hpp"
#include "nvidia/core/NCV.hpp"
#include "nvidia/NPP_staging/npp_staging.hpp"
#include "nvidia/NCVHaarObjectDetection.hpp"
#define CUDART_MINIMUM_REQUIRED_VERSION 3020
modules/gtest/src/gtestcv.cpp
+1 -1
View File
@@ -1378,7 +1378,7 @@ cmpEpsFlt_(const _Tp* src1, const _Tp* src2, size_t total, int imaxdiff, size_t
{
_Tp a = src1[i], b = src2[i];
if( a < 0 ) a ^= C; if( b < 0 ) b ^= C;
_Tp d = std::abs(a - b);
_Tp d = std::abs(double(a - b));
if( d > imaxdiff )
{
idx = i + startidx;
samples/CMakeLists.txt
+2 -2
View File
@@ -3,8 +3,8 @@
#
# ----------------------------------------------------------------------------
add_subdirectory(c)
add_subdirectory(cpp)
#add_subdirectory(c)
#add_subdirectory(cpp)
add_subdirectory(gpu)
if(0)
samples/gpu/CMakeLists.txt
+3 -1
View File
@@ -14,10 +14,12 @@ if (BUILD_EXAMPLES)
"${CMAKE_SOURCE_DIR}/modules/legacy/include"
"${CMAKE_SOURCE_DIR}/modules/contrib/include"
"${CMAKE_SOURCE_DIR}/modules/gpu/include"
"${CMAKE_SOURCE_DIR}/modules/gpu/src/nvidia"
"${CMAKE_SOURCE_DIR}/modules/gpu/src/nvidia/core"
)
if(HAVE_CUDA)
include_directories(${CUDA_INCLUDE_DIRS})
include_directories(${CUDA_INCLUDE_DIRS} ${CMAKE_SOURCE_DIR}/modules/gpu/src/nvidia ${CMAKE_SOURCE_DIR}/modules/gpu/src/nvidia/core)
endif()
if(CMAKE_COMPILER_IS_GNUCXX)
modules/gpu/src/nvidia/FaceDetectionFeed.cpp_NvidiaAPI_sample → samples/gpu/cascadeclassifier_nvidia_api.cpp
+88 -90
View File
@@ -1,60 +1,25 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2009-2010, NVIDIA Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#pragma warning( disable : 4201 4408 4127 4100)
#include <cstdio>
#include "cvconfig.h"
#if !defined(HAVE_CUDA)
int main( int argc, const char** argv ) { return printf("Please compile the librarary with CUDA support."), -1; }
#else
#include <cuda_runtime.h>
#define CV_NO_BACKWARD_COMPATIBILITY
#include "opencv2/opencv.hpp"
#include "NCVHaarObjectDetection.hpp"
using namespace cv;
using namespace std;
const Size preferredVideoFrameSize(640, 480);
string preferredClassifier = "haarcascade_frontalface_alt.xml";
string wndTitle = "NVIDIA Computer Vision SDK :: Face Detection in Video Feed";
const Size2i preferredVideoFrameSize(640, 480);
std::string preferredClassifier = "haarcascade_frontalface_alt.xml";
std::string wndTitle = "NVIDIA Computer Vision SDK :: Face Detection in Video Feed";
void printSyntax(void)
@@ -62,7 +27,6 @@ void printSyntax(void)
printf("Syntax: FaceDetectionFeed.exe [-c cameranum | -v filename] classifier.xml\n");
}
void imagePrintf(Mat& img, int lineOffsY, Scalar color, const char *format, ...)
{
int fontFace = CV_FONT_HERSHEY_PLAIN;
@@ -83,7 +47,6 @@ void imagePrintf(Mat& img, int lineOffsY, Scalar color, const char *format, ...)
va_end(arg_ptr);
}
NCVStatus process(Mat *srcdst,
Ncv32u width, Ncv32u height,
NcvBool bShowAllHypotheses, NcvBool bLargestFace,
@@ -104,15 +67,16 @@ NCVStatus process(Mat *srcdst,
ncvAssertReturn(d_src.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
NCVMatrixAlloc<Ncv8u> h_src(cpuAllocator, width, height);
ncvAssertReturn(h_src.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
NCVVectorAlloc<NcvRect32u> d_rects(gpuAllocator, 100);
NCVVectorAlloc<NcvRect32u> d_rects(gpuAllocator, 100);
ncvAssertReturn(d_rects.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
Mat h_src_hdr(Size(width, height), CV_8U, h_src.ptr(), h_src.stride());
NCV_SKIP_COND_BEGIN
for (Ncv32u i=0; i<(Ncv32u)srcdst->rows; i++)
{
memcpy(h_src.ptr() + i * h_src.stride(), srcdst->ptr(i), srcdst->cols);
}
NCV_SKIP_COND_BEGIN
(*srcdst).copyTo(h_src_hdr);
ncvStat = h_src.copySolid(d_src, 0);
ncvAssertReturnNcvStat(ncvStat);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);
@@ -130,8 +94,9 @@ NCVStatus process(Mat *srcdst,
haar.ClassifierSize,
bShowAllHypotheses ? 0 : 4,
1.2f, 1,
(bLargestFace ? NCVPipeObjDet_FindLargestObject : 0) | NCVPipeObjDet_VisualizeInPlace,
gpuAllocator, cpuAllocator, devProp.major, devProp.minor, 0);
(bLargestFace ? NCVPipeObjDet_FindLargestObject : 0)
| NCVPipeObjDet_VisualizeInPlace,
gpuAllocator, cpuAllocator, devProp, 0);
ncvAssertReturnNcvStat(ncvStat);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);
@@ -141,14 +106,16 @@ NCVStatus process(Mat *srcdst,
ncvAssertReturnNcvStat(ncvStat);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);
h_src_hdr.copyTo(*srcdst);
for (Ncv32u i=0; i<(Ncv32u)srcdst->rows; i++)
{
memcpy(srcdst->ptr(i), h_src.ptr() + i * h_src.stride(), srcdst->cols);
}
NCV_SKIP_COND_END
return NCV_SUCCESS;
}
int main( int argc, const char** argv )
{
NCVStatus ncvStat;
@@ -160,14 +127,19 @@ int main( int argc, const char** argv )
printf(" Space - Switch between NCV and OpenCV\n");
printf(" L - Switch between FullSearch and LargestFace modes\n");
printf(" U - Toggle unfiltered hypotheses visualization in FullSearch\n");
if (argc != 4 && argc != 1)
return printSyntax(), -1;
VideoCapture capture;
Size frameSize;
bool bQuit = false;
if (argc == 1 || strcmp(argv[1], "-c") == 0)
Size2i frameSize;
if (argc != 4 && argc != 1)
{
printSyntax();
return -1;
}
if (argc == 1 || strcmp(argv[1], "-c") == 0)
{
// Camera input is specified
int camIdx = (argc == 3) ? atoi(argv[2]) : 0;
@@ -192,14 +164,26 @@ int main( int argc, const char** argv )
return printSyntax(), -1;
NcvBool bUseOpenCV = true;
NcvBool bLargestFace = true;
NcvBool bShowAllHypotheses = false;
NcvBool bLargestFace = false; //LargestFace=true is used usually during training
NcvBool bShowAllHypotheses = false;
string classifierFile = (argc == 1) ? preferredClassifier : argv[3];
CascadeClassifier classifierOpenCV;
std::string classifierFile;
if (argc == 1)
{
classifierFile = preferredClassifier;
}
else
{
classifierFile.assign(argv[3]);
}
if (!classifierOpenCV.load(classifierFile))
return printf("Error (in OpenCV) opening classifier\n"), printSyntax(), -1;
{
printf("Error (in OpenCV) opening classifier\n");
printSyntax();
return -1;
}
int devId;
ncvAssertCUDAReturn(cudaGetDevice(&devId), -1);
@@ -214,9 +198,9 @@ int main( int argc, const char** argv )
//
//==============================================================================
NCVMemNativeAllocator gpuCascadeAllocator(NCVMemoryTypeDevice);
NCVMemNativeAllocator gpuCascadeAllocator(NCVMemoryTypeDevice, devProp.textureAlignment);
ncvAssertPrintReturn(gpuCascadeAllocator.isInitialized(), "Error creating cascade GPU allocator", -1);
NCVMemNativeAllocator cpuCascadeAllocator(NCVMemoryTypeHostPinned);
NCVMemNativeAllocator cpuCascadeAllocator(NCVMemoryTypeHostPinned, devProp.textureAlignment);
ncvAssertPrintReturn(cpuCascadeAllocator.isInitialized(), "Error creating cascade CPU allocator", -1);
Ncv32u haarNumStages, haarNumNodes, haarNumFeatures;
@@ -278,32 +262,36 @@ int main( int argc, const char** argv )
// Main processing loop
//
//==============================================================================
namedWindow(wndTitle, 1);
namedWindow(wndTitle, 1);
Mat frame, gray, frameDisp;
for(;;)
do
{
// For camera and video file, capture the next image
// For camera and video file, capture the next image
capture >> frame;
if (frame.empty())
break;
Mat gray;
cvtColor(frame, gray, CV_BGR2GRAY);
//
// process
//
NcvSize32u minSize = haar.ClassifierSize;
if (bLargestFace)
{
Ncv32u ratioX = preferredVideoFrameSize.width / minSize.width;
Ncv32u ratioY = preferredVideoFrameSize.height / minSize.height;
Ncv32u ratioSmallest = std::min(ratioX, ratioY);
ratioSmallest = (Ncv32u)std::max(ratioSmallest / 2.5f, 1.f);
ratioSmallest = std::max((Ncv32u)(ratioSmallest / 2.5f), (Ncv32u)1);
minSize.width *= ratioSmallest;
minSize.height *= ratioSmallest;
}
Ncv32f avgTime;
NcvTimer timer = ncvStartTimer();
if (!bUseOpenCV)
@@ -324,15 +312,16 @@ int main( int argc, const char** argv )
rectsOpenCV,
1.2f,
bShowAllHypotheses && !bLargestFace ? 0 : 4,
(bLargestFace ? CV_HAAR_FIND_BIGGEST_OBJECT : 0) | CV_HAAR_SCALE_IMAGE,
(bLargestFace ? CV_HAAR_FIND_BIGGEST_OBJECT : 0)
| CV_HAAR_SCALE_IMAGE,
Size(minSize.width, minSize.height));
for (size_t rt = 0; rt < rectsOpenCV.size(); ++rt)
rectangle(gray, rectsOpenCV[rt], Scalar(255));
}
Ncv32f avgTime = (Ncv32f)ncvEndQueryTimerMs(timer);
avgTime = (Ncv32f)ncvEndQueryTimerMs(timer);
cvtColor(gray, frameDisp, CV_GRAY2BGR);
imagePrintf(frameDisp, 0, CV_RGB(255, 0,0), "Space - Switch NCV%s / OpenCV%s", bUseOpenCV?"":" (ON)", bUseOpenCV?" (ON)":"");
@@ -347,16 +336,25 @@ int main( int argc, const char** argv )
case ' ':
bUseOpenCV = !bUseOpenCV;
break;
case 'L':case 'l':
case 'L':
case 'l':
bLargestFace = !bLargestFace;
break;
case 'U':case 'u':
case 'U':
case 'u':
bShowAllHypotheses = !bShowAllHypotheses;
break;
case 27:
return 0;
bQuit = true;
break;
}
}
} while (!bQuit);
cvDestroyWindow(wndTitle.c_str());
return 0;
}
#endif
tests/gpu/CMakeLists.txt
+16 -3
View File
@@ -3,12 +3,15 @@
# ----------------------------------------------------------------------------
project(opencv_test_gpu)
set(the_target "opencv_test_gpu")
file(GLOB test_srcs "src/*.cpp")
source_group("Src" FILES ${test_srcs})
file(GLOB test_hdrs "src/*.h*")
source_group("Src" FILES ${test_srcs})
source_group("Include" FILES ${test_hdrs})
set(the_target "opencv_test_gpu")
include_directories (
"${CMAKE_SOURCE_DIR}/include/opencv"
@@ -26,11 +29,21 @@ include_directories (
"${CMAKE_SOURCE_DIR}/modules/ml/include"
"${CMAKE_CURRENT_SOURCE_DIR}/src"
"${CMAKE_CURRENT_BINARY_DIR}"
"${CMAKE_SOURCE_DIR}/modules/gpu/src/nvidia"
"${CMAKE_SOURCE_DIR}/modules/gpu/src/nvidia/core"
)
include_directories(../cxts)
add_executable(${the_target} ${test_srcs} ${test_hdrs})
if(HAVE_CUDA)
include_directories(${CUDA_INCLUDE_DIRS} ${CMAKE_SOURCE_DIR}/modules/gpu/src/nvidia ${CMAKE_SOURCE_DIR}/modules/gpu/src/nvidia/core ${CMAKE_SOURCE_DIR}/modules/gpu/src/nvidia/NPP_staging)
file(GLOB nvidia "src/nvidia/*.*")
SET(ncv_cpp ../../modules/gpu/src/nvidia/core/NCV.cpp)
source_group("Src\\nvidia" FILES ${nvidia})
endif()
add_executable(${the_target} ${test_srcs} ${test_hdrs} ${nvidia} ${ncv_cpp})
# Additional target properties
set_target_properties(${the_target} PROPERTIES
tests/gpu/src/nvidia/NCVAutoTestLister.hpp
+127
View File
@@ -0,0 +1,127 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _ncvautotestlister_hpp_
#define _ncvautotestlister_hpp_
#include <vector>
#include "NCVTest.hpp"
class NCVAutoTestLister
{
public:
NCVAutoTestLister(std::string testSuiteName, NcvBool bStopOnFirstFail=false, NcvBool bCompactOutput=true)
:
testSuiteName(testSuiteName),
bStopOnFirstFail(bStopOnFirstFail),
bCompactOutput(bCompactOutput)
{
}
void add(INCVTest *test)
{
this->tests.push_back(test);
}
void invoke()
{
Ncv32u nPassed = 0;
Ncv32u nFailed = 0;
Ncv32u nFailedMem = 0;
if (bCompactOutput)
{
printf("Test suite '%s' with %d tests\n",
testSuiteName.c_str(),
(int)(this->tests.size()));
}
for (Ncv32u i=0; i<this->tests.size(); i++)
{
INCVTest &curTest = *tests[i];
NCVTestReport curReport;
bool res = curTest.executeTest(curReport);
if (!bCompactOutput)
{
printf("Test %3i %16s; Consumed mem GPU = %8d, CPU = %8d; %s\n",
i,
curTest.getName().c_str(),
curReport.statsNums["MemGPU"],
curReport.statsNums["MemCPU"],
curReport.statsText["rcode"].c_str());
}
if (res)
{
nPassed++;
if (bCompactOutput)
{
printf(".");
}
}
else
{
if (!curReport.statsText["rcode"].compare("FAILED"))
{
nFailed++;
if (bCompactOutput)
{
printf("x");
}
if (bStopOnFirstFail)
{
break;
}
}
else
{
nFailedMem++;
if (bCompactOutput)
{
printf("m");
}
}
}
fflush(stdout);
}
if (bCompactOutput)
{
printf("\n");
}
printf("Test suite '%s' complete: %d total, %d passed, %d memory errors, %d failed\n\n",
testSuiteName.c_str(),
(int)(this->tests.size()),
nPassed,
nFailedMem,
nFailed);
}
~NCVAutoTestLister()
{
for (Ncv32u i=0; i<this->tests.size(); i++)
{
delete tests[i];
}
}
private:
NcvBool bStopOnFirstFail;
NcvBool bCompactOutput;
std::string testSuiteName;
std::vector<INCVTest *> tests;
};
#endif // _ncvautotestlister_hpp_
tests/gpu/src/nvidia/NCVTest.hpp
+211
View File
@@ -0,0 +1,211 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _ncvtest_hpp_
#define _ncvtest_hpp_
#pragma warning( disable : 4201 4408 4127 4100)
#include <string>
#include <vector>
#include <map>
#include <memory>
#include <algorithm>
#include <fstream>
#include <cuda_runtime.h>
#include "NPP_staging.hpp"
struct NCVTestReport
{
std::map<std::string, Ncv32u> statsNums;
std::map<std::string, std::string> statsText;
};
class INCVTest
{
public:
virtual bool executeTest(NCVTestReport &report) = 0;
virtual std::string getName() const = 0;
};
class NCVTestProvider : public INCVTest
{
public:
NCVTestProvider(std::string testName)
:
testName(testName)
{
int devId;
ncvAssertPrintReturn(cudaSuccess == cudaGetDevice(&devId), "Error returned from cudaGetDevice", );
ncvAssertPrintReturn(cudaSuccess == cudaGetDeviceProperties(&this->devProp, devId), "Error returned from cudaGetDeviceProperties", );
}
virtual bool init() = 0;
virtual bool process() = 0;
virtual bool deinit() = 0;
virtual bool toString(std::ofstream &strOut) = 0;
virtual std::string getName() const
{
return this->testName;
}
virtual ~NCVTestProvider()
{
deinitMemory();
}
virtual bool executeTest(NCVTestReport &report)
{
bool res;
report.statsText["rcode"] = "FAILED";
res = initMemory(report);
if (!res)
{
dumpToFile(report);
deinitMemory();
return false;
}
res = init();
if (!res)
{
dumpToFile(report);
deinit();
deinitMemory();
return false;
}
res = process();
if (!res)
{
dumpToFile(report);
deinit();
deinitMemory();
return false;
}
res = deinit();
if (!res)
{
dumpToFile(report);
deinitMemory();
return false;
}
deinitMemory();
report.statsText["rcode"] = "Passed";
return true;
}
protected:
cudaDeviceProp devProp;
std::auto_ptr<INCVMemAllocator> allocatorGPU;
std::auto_ptr<INCVMemAllocator> allocatorCPU;
private:
std::string testName;
bool initMemory(NCVTestReport &report)
{
this->allocatorGPU.reset(new NCVMemStackAllocator(devProp.textureAlignment));
this->allocatorCPU.reset(new NCVMemStackAllocator(devProp.textureAlignment));
if (!this->allocatorGPU.get()->isInitialized() ||
!this->allocatorCPU.get()->isInitialized())
{
report.statsText["rcode"] = "Memory FAILED";
return false;
}
if (!this->process())
{
report.statsText["rcode"] = "Memory FAILED";
return false;
}
Ncv32u maxGPUsize = (Ncv32u)this->allocatorGPU.get()->maxSize();
Ncv32u maxCPUsize = (Ncv32u)this->allocatorCPU.get()->maxSize();
report.statsNums["MemGPU"] = maxGPUsize;
report.statsNums["MemCPU"] = maxCPUsize;
this->allocatorGPU.reset(new NCVMemStackAllocator(NCVMemoryTypeDevice, maxGPUsize, devProp.textureAlignment));
this->allocatorCPU.reset(new NCVMemStackAllocator(NCVMemoryTypeHostPinned, maxCPUsize, devProp.textureAlignment));
if (!this->allocatorGPU.get()->isInitialized() ||
!this->allocatorCPU.get()->isInitialized())
{
report.statsText["rcode"] = "Memory FAILED";
return false;
}
return true;
}
void deinitMemory()
{
this->allocatorGPU.reset();
this->allocatorCPU.reset();
}
void dumpToFile(NCVTestReport &report)
{
bool bReasonMem = (0 == report.statsText["rcode"].compare("Memory FAILED"));
std::string fname = "TestDump_";
fname += (bReasonMem ? "m_" : "") + this->testName + ".log";
std::ofstream stream(fname.c_str(), std::ios::trunc | std::ios::out);
if (!stream.is_open()) return;
stream << "NCV Test Failure Log: " << this->testName << std::endl;
stream << "====================================================" << std::endl << std::endl;
stream << "Test initialization report: " << std::endl;
for (std::map<std::string,std::string>::iterator it=report.statsText.begin();
it != report.statsText.end(); it++)
{
stream << it->first << "=" << it->second << std::endl;
}
for (std::map<std::string,Ncv32u>::iterator it=report.statsNums.begin();
it != report.statsNums.end(); it++)
{
stream << it->first << "=" << it->second << std::endl;
}
stream << std::endl;
stream << "Test initialization parameters: " << std::endl;
bool bSerializeRes = false;
try
{
bSerializeRes = this->toString(stream);
}
catch (...)
{
}
if (!bSerializeRes)
{
stream << "Couldn't retrieve object dump" << std::endl;
}
stream.flush();
}
};
#endif // _ncvtest_hpp_
tests/gpu/src/nvidia/NCVTestSourceProvider.hpp
+161
View File
@@ -0,0 +1,161 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _ncvtestsourceprovider_hpp_
#define _ncvtestsourceprovider_hpp_
#include <memory>
#include "NCV.hpp"
#include <opencv2/highgui/highgui.hpp>
template <class T>
class NCVTestSourceProvider
{
public:
NCVTestSourceProvider(Ncv32u seed, T rangeLow, T rangeHigh, Ncv32u maxWidth, Ncv32u maxHeight)
:
bInit(false)
{
ncvAssertPrintReturn(rangeLow < rangeHigh, "NCVTestSourceProvider ctor:: Invalid range", );
int devId;
cudaDeviceProp devProp;
ncvAssertPrintReturn(cudaSuccess == cudaGetDevice(&devId), "Error returned from cudaGetDevice", );
ncvAssertPrintReturn(cudaSuccess == cudaGetDeviceProperties(&devProp, devId), "Error returned from cudaGetDeviceProperties", );
//Ncv32u maxWpitch = alignUp(maxWidth * sizeof(T), devProp.textureAlignment);
allocatorCPU.reset(new NCVMemNativeAllocator(NCVMemoryTypeHostPinned, devProp.textureAlignment));
data.reset(new NCVMatrixAlloc<T>(*this->allocatorCPU.get(), maxWidth, maxHeight));
ncvAssertPrintReturn(data.get()->isMemAllocated(), "NCVTestSourceProvider ctor:: Matrix not allocated", );
this->dataWidth = maxWidth;
this->dataHeight = maxHeight;
srand(seed);
for (Ncv32u i=0; i<maxHeight; i++)
{
for (Ncv32u j=0; j<data.get()->stride(); j++)
{
data.get()->ptr()[i * data.get()->stride() + j] =
(T)(((1.0 * rand()) / RAND_MAX) * (rangeHigh - rangeLow) + rangeLow);
}
}
this->bInit = true;
}
NCVTestSourceProvider(std::string pgmFilename)
:
bInit(false)
{
ncvAssertPrintReturn(sizeof(T) == 1, "NCVTestSourceProvider ctor:: PGM constructor complies only with 8bit types", );
cv::Mat image = cv::imread(pgmFilename);
ncvAssertPrintReturn(!image.empty(), "NCVTestSourceProvider ctor:: PGM file error", );
int devId;
cudaDeviceProp devProp;
ncvAssertPrintReturn(cudaSuccess == cudaGetDevice(&devId), "Error returned from cudaGetDevice", );
ncvAssertPrintReturn(cudaSuccess == cudaGetDeviceProperties(&devProp, devId), "Error returned from cudaGetDeviceProperties", );
allocatorCPU.reset(new NCVMemNativeAllocator(NCVMemoryTypeHostPinned, devProp.textureAlignment));
data.reset(new NCVMatrixAlloc<T>(*this->allocatorCPU.get(), image.cols, image.rows));
ncvAssertPrintReturn(data.get()->isMemAllocated(), "NCVTestSourceProvider ctor:: Matrix not allocated", );
this->dataWidth = image.cols;
this->dataHeight = image.rows;
cv::Mat hdr(image.size(), CV_8UC1, data.get()->ptr(), data.get()->pitch());
image.copyTo(hdr);
this->bInit = true;
}
NcvBool fill(NCVMatrix<T> &dst)
{
ncvAssertReturn(this->isInit() &&
dst.memType() == allocatorCPU.get()->memType(), false);
if (dst.width() == 0 || dst.height() == 0)
{
return true;
}
for (Ncv32u i=0; i<dst.height(); i++)
{
Ncv32u srcLine = i % this->dataHeight;
Ncv32u srcFullChunks = dst.width() / this->dataWidth;
for (Ncv32u j=0; j<srcFullChunks; j++)
{
memcpy(dst.ptr() + i * dst.stride() + j * this->dataWidth,
this->data.get()->ptr() + this->data.get()->stride() * srcLine,
this->dataWidth * sizeof(T));
}
Ncv32u srcLastChunk = dst.width() % this->dataWidth;
memcpy(dst.ptr() + i * dst.stride() + srcFullChunks * this->dataWidth,
this->data.get()->ptr() + this->data.get()->stride() * srcLine,
srcLastChunk * sizeof(T));
}
return true;
}
NcvBool fill(NCVVector<T> &dst)
{
ncvAssertReturn(this->isInit() &&
dst.memType() == allocatorCPU.get()->memType(), false);
if (dst.length() == 0)
{
return true;
}
Ncv32u srcLen = this->dataWidth * this->dataHeight;
Ncv32u srcFullChunks = (Ncv32u)dst.length() / srcLen;
for (Ncv32u j=0; j<srcFullChunks; j++)
{
memcpy(dst.ptr() + j * srcLen, this->data.get()->ptr(), srcLen * sizeof(T));
}
Ncv32u srcLastChunk = dst.length() % srcLen;
memcpy(dst.ptr() + srcFullChunks * srcLen, this->data.get()->ptr(), srcLastChunk * sizeof(T));
return true;
}
~NCVTestSourceProvider()
{
data.reset();
allocatorCPU.reset();
}
private:
NcvBool isInit(void)
{
return this->bInit;
}
NcvBool bInit;
std::auto_ptr< INCVMemAllocator > allocatorCPU;
std::auto_ptr< NCVMatrixAlloc<T> > data;
Ncv32u dataWidth;
Ncv32u dataHeight;
};
#endif // _ncvtestsourceprovider_hpp_
tests/gpu/src/nvidia/TestCompact.cpp
+129
View File
@@ -0,0 +1,129 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#include "TestCompact.h"
TestCompact::TestCompact(std::string testName, NCVTestSourceProvider<Ncv32u> &src,
Ncv32u length, Ncv32u badElem, Ncv32u badElemPercentage)
:
NCVTestProvider(testName),
src(src),
length(length),
badElem(badElem),
badElemPercentage(badElemPercentage > 100 ? 100 : badElemPercentage)
{
}
bool TestCompact::toString(std::ofstream &strOut)
{
strOut << "length=" << length << std::endl;
strOut << "badElem=" << badElem << std::endl;
strOut << "badElemPercentage=" << badElemPercentage << std::endl;
return true;
}
bool TestCompact::init()
{
return true;
}
bool TestCompact::process()
{
NCVStatus ncvStat;
bool rcode = false;
NCVVectorAlloc<Ncv32u> h_vecSrc(*this->allocatorCPU.get(), this->length);
ncvAssertReturn(h_vecSrc.isMemAllocated(), false);
NCVVectorAlloc<Ncv32u> d_vecSrc(*this->allocatorGPU.get(), this->length);
ncvAssertReturn(d_vecSrc.isMemAllocated(), false);
NCVVectorAlloc<Ncv32u> h_vecDst(*this->allocatorCPU.get(), this->length);
ncvAssertReturn(h_vecDst.isMemAllocated(), false);
NCVVectorAlloc<Ncv32u> d_vecDst(*this->allocatorGPU.get(), this->length);
ncvAssertReturn(d_vecDst.isMemAllocated(), false);
NCVVectorAlloc<Ncv32u> h_vecDst_d(*this->allocatorCPU.get(), this->length);
ncvAssertReturn(h_vecDst_d.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_vecSrc), false);
for (Ncv32u i=0; i<this->length; i++)
{
Ncv32u tmp = (h_vecSrc.ptr()[i]) & 0xFF;
tmp = tmp * 99 / 255;
if (tmp < this->badElemPercentage)
{
h_vecSrc.ptr()[i] = this->badElem;
}
}
NCV_SKIP_COND_END
NCVVectorAlloc<Ncv32u> h_dstLen(*this->allocatorCPU.get(), 1);
ncvAssertReturn(h_dstLen.isMemAllocated(), false);
Ncv32u bufSize;
ncvStat = nppsStCompactGetSize_32u(this->length, &bufSize, this->devProp);
ncvAssertReturn(NPPST_SUCCESS == ncvStat, false);
NCVVectorAlloc<Ncv8u> d_tmpBuf(*this->allocatorGPU.get(), bufSize);
ncvAssertReturn(d_tmpBuf.isMemAllocated(), false);
Ncv32u h_outElemNum_h = 0;
NCV_SKIP_COND_BEGIN
ncvStat = h_vecSrc.copySolid(d_vecSrc, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppsStCompact_32u(d_vecSrc.ptr(), this->length,
d_vecDst.ptr(), h_dstLen.ptr(), this->badElem,
d_tmpBuf.ptr(), bufSize, this->devProp);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = d_vecDst.copySolid(h_vecDst_d, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppsStCompact_32u_host(h_vecSrc.ptr(), this->length, h_vecDst.ptr(), &h_outElemNum_h, this->badElem);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
if (h_dstLen.ptr()[0] != h_outElemNum_h)
{
bLoopVirgin = false;
}
else
{
for (Ncv32u i=0; bLoopVirgin && i < h_outElemNum_h; i++)
{
if (h_vecDst.ptr()[i] != h_vecDst_d.ptr()[i])
{
bLoopVirgin = false;
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
bool TestCompact::deinit()
{
return true;
}
tests/gpu/src/nvidia/TestCompact.h
+41
View File
@@ -0,0 +1,41 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testhypothesescompact_h_
#define _testhypothesescompact_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
class TestCompact : public NCVTestProvider
{
public:
TestCompact(std::string testName, NCVTestSourceProvider<Ncv32u> &src,
Ncv32u length, Ncv32u badElem, Ncv32u badElemPercentage);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestCompact(const TestCompact&);
TestCompact& operator=(const TestCompact&);
NCVTestSourceProvider<Ncv32u> &src;
Ncv32u length;
Ncv32u badElem;
Ncv32u badElemPercentage;
};
#endif // _testhypothesescompact_h_
tests/gpu/src/nvidia/TestDrawRects.cpp
+163
View File
@@ -0,0 +1,163 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#include "TestDrawRects.h"
#include "NCVHaarObjectDetection.hpp"
template <class T>
TestDrawRects<T>::TestDrawRects(std::string testName, NCVTestSourceProvider<T> &src, NCVTestSourceProvider<Ncv32u> &src32u,
Ncv32u width, Ncv32u height, Ncv32u numRects, T color)
:
NCVTestProvider(testName),
src(src),
src32u(src32u),
width(width),
height(height),
numRects(numRects),
color(color)
{
}
template <class T>
bool TestDrawRects<T>::toString(std::ofstream &strOut)
{
strOut << "sizeof(T)=" << sizeof(T) << std::endl;
strOut << "width=" << width << std::endl;
strOut << "height=" << height << std::endl;
strOut << "numRects=" << numRects << std::endl;
strOut << "color=" << color << std::endl;
return true;
}
template <class T>
bool TestDrawRects<T>::init()
{
return true;
}
template <class T>
bool TestDrawRects<T>::process()
{
NCVStatus ncvStat;
bool rcode = false;
NCVMatrixAlloc<T> d_img(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_img.isMemAllocated(), false);
NCVMatrixAlloc<T> h_img(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_img.isMemAllocated(), false);
NCVMatrixAlloc<T> h_img_d(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_img_d.isMemAllocated(), false);
NCVVectorAlloc<NcvRect32u> d_rects(*this->allocatorGPU.get(), this->numRects);
ncvAssertReturn(d_rects.isMemAllocated(), false);
NCVVectorAlloc<NcvRect32u> h_rects(*this->allocatorCPU.get(), this->numRects);
ncvAssertReturn(h_rects.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_img), false);
ncvStat = h_img.copySolid(d_img, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), false);
//fill vector of rectangles with random rects covering the input
NCVVectorReuse<Ncv32u> h_rects_as32u(h_rects.getSegment());
ncvAssertReturn(h_rects_as32u.isMemReused(), false);
ncvAssertReturn(this->src32u.fill(h_rects_as32u), false);
for (Ncv32u i=0; i<this->numRects; i++)
{
h_rects.ptr()[i].x = (Ncv32u)(((1.0 * h_rects.ptr()[i].x) / RAND_MAX) * (this->width-2));
h_rects.ptr()[i].y = (Ncv32u)(((1.0 * h_rects.ptr()[i].y) / RAND_MAX) * (this->height-2));
h_rects.ptr()[i].width = (Ncv32u)(((1.0 * h_rects.ptr()[i].width) / RAND_MAX) * (this->width+10 - h_rects.ptr()[i].x));
h_rects.ptr()[i].height = (Ncv32u)(((1.0 * h_rects.ptr()[i].height) / RAND_MAX) * (this->height+10 - h_rects.ptr()[i].y));
}
ncvStat = h_rects.copySolid(d_rects, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), false);
if (sizeof(T) == sizeof(Ncv32u))
{
ncvStat = ncvDrawRects_32u_device((Ncv32u *)d_img.ptr(), d_img.stride(), this->width, this->height,
(NcvRect32u *)d_rects.ptr(), this->numRects, this->color, 0);
}
else if (sizeof(T) == sizeof(Ncv8u))
{
ncvStat = ncvDrawRects_8u_device((Ncv8u *)d_img.ptr(), d_img.stride(), this->width, this->height,
(NcvRect32u *)d_rects.ptr(), this->numRects, (Ncv8u)this->color, 0);
}
else
{
ncvAssertPrintReturn(false, "Incorrect drawrects test instance", false);
}
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
NCV_SKIP_COND_END
ncvStat = d_img.copySolid(h_img_d, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), false);
NCV_SKIP_COND_BEGIN
if (sizeof(T) == sizeof(Ncv32u))
{
ncvStat = ncvDrawRects_32u_host((Ncv32u *)h_img.ptr(), h_img.stride(), this->width, this->height,
(NcvRect32u *)h_rects.ptr(), this->numRects, this->color);
}
else if (sizeof(T) == sizeof(Ncv8u))
{
ncvStat = ncvDrawRects_8u_host((Ncv8u *)h_img.ptr(), h_img.stride(), this->width, this->height,
(NcvRect32u *)h_rects.ptr(), this->numRects, (Ncv8u)this->color);
}
else
{
ncvAssertPrintReturn(false, "Incorrect drawrects test instance", false);
}
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
//const Ncv64f relEPS = 0.005;
for (Ncv32u i=0; bLoopVirgin && i < h_img.height(); i++)
{
for (Ncv32u j=0; bLoopVirgin && j < h_img.width(); j++)
{
if (h_img.ptr()[h_img.stride()*i+j] != h_img_d.ptr()[h_img_d.stride()*i+j])
{
bLoopVirgin = false;
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
template <class T>
bool TestDrawRects<T>::deinit()
{
return true;
}
template class TestDrawRects<Ncv8u>;
template class TestDrawRects<Ncv32u>;
tests/gpu/src/nvidia/TestDrawRects.h
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/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testdrawrects_h_
#define _testdrawrects_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
template <class T>
class TestDrawRects : public NCVTestProvider
{
public:
TestDrawRects(std::string testName, NCVTestSourceProvider<T> &src, NCVTestSourceProvider<Ncv32u> &src32u,
Ncv32u width, Ncv32u height, Ncv32u numRects, T color);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestDrawRects(const TestDrawRects&);
TestDrawRects& operator=(const TestDrawRects&);
NCVTestSourceProvider<T> &src;
NCVTestSourceProvider<Ncv32u> &src32u;
Ncv32u width;
Ncv32u height;
Ncv32u numRects;
T color;
};
#endif // _testdrawrects_h_
tests/gpu/src/nvidia/TestHaarCascadeApplication.cpp
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/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#include <float.h>
#include "TestHaarCascadeApplication.h"
#include "NCVHaarObjectDetection.hpp"
TestHaarCascadeApplication::TestHaarCascadeApplication(std::string testName, NCVTestSourceProvider<Ncv8u> &src,
std::string cascadeName, Ncv32u width, Ncv32u height)
:
NCVTestProvider(testName),
src(src),
cascadeName(cascadeName),
width(width),
height(height)
{
}
bool TestHaarCascadeApplication::toString(std::ofstream &strOut)
{
strOut << "cascadeName=" << cascadeName << std::endl;
strOut << "width=" << width << std::endl;
strOut << "height=" << height << std::endl;
return true;
}
bool TestHaarCascadeApplication::init()
{
return true;
}
bool TestHaarCascadeApplication::process()
{
NCVStatus ncvStat;
bool rcode = false;
Ncv32u numStages, numNodes, numFeatures;
ncvStat = ncvHaarGetClassifierSize(this->cascadeName, numStages, numNodes, numFeatures);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
NCVVectorAlloc<HaarStage64> h_HaarStages(*this->allocatorCPU.get(), numStages);
ncvAssertReturn(h_HaarStages.isMemAllocated(), false);
NCVVectorAlloc<HaarClassifierNode128> h_HaarNodes(*this->allocatorCPU.get(), numNodes);
ncvAssertReturn(h_HaarNodes.isMemAllocated(), false);
NCVVectorAlloc<HaarFeature64> h_HaarFeatures(*this->allocatorCPU.get(), numFeatures);
ncvAssertReturn(h_HaarFeatures.isMemAllocated(), false);
NCVVectorAlloc<HaarStage64> d_HaarStages(*this->allocatorGPU.get(), numStages);
ncvAssertReturn(d_HaarStages.isMemAllocated(), false);
NCVVectorAlloc<HaarClassifierNode128> d_HaarNodes(*this->allocatorGPU.get(), numNodes);
ncvAssertReturn(d_HaarNodes.isMemAllocated(), false);
NCVVectorAlloc<HaarFeature64> d_HaarFeatures(*this->allocatorGPU.get(), numFeatures);
ncvAssertReturn(d_HaarFeatures.isMemAllocated(), false);
HaarClassifierCascadeDescriptor haar;
haar.ClassifierSize.width = haar.ClassifierSize.height = 1;
haar.bNeedsTiltedII = false;
haar.NumClassifierRootNodes = numNodes;
haar.NumClassifierTotalNodes = numNodes;
haar.NumFeatures = numFeatures;
haar.NumStages = numStages;
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvStat = ncvHaarLoadFromFile_host(this->cascadeName, haar, h_HaarStages, h_HaarNodes, h_HaarFeatures);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvAssertReturn(NCV_SUCCESS == h_HaarStages.copySolid(d_HaarStages, 0), false);
ncvAssertReturn(NCV_SUCCESS == h_HaarNodes.copySolid(d_HaarNodes, 0), false);
ncvAssertReturn(NCV_SUCCESS == h_HaarFeatures.copySolid(d_HaarFeatures, 0), false);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), false);
NCV_SKIP_COND_END
NcvSize32s srcRoi, srcIIRoi, searchRoi;
srcRoi.width = this->width;
srcRoi.height = this->height;
srcIIRoi.width = srcRoi.width + 1;
srcIIRoi.height = srcRoi.height + 1;
searchRoi.width = srcIIRoi.width - haar.ClassifierSize.width;
searchRoi.height = srcIIRoi.height - haar.ClassifierSize.height;
if (searchRoi.width <= 0 || searchRoi.height <= 0)
{
return false;
}
NcvSize32u searchRoiU(searchRoi.width, searchRoi.height);
NCVMatrixAlloc<Ncv8u> d_img(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_img.isMemAllocated(), false);
NCVMatrixAlloc<Ncv8u> h_img(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_img.isMemAllocated(), false);
Ncv32u integralWidth = this->width + 1;
Ncv32u integralHeight = this->height + 1;
NCVMatrixAlloc<Ncv32u> d_integralImage(*this->allocatorGPU.get(), integralWidth, integralHeight);
ncvAssertReturn(d_integralImage.isMemAllocated(), false);
NCVMatrixAlloc<Ncv64u> d_sqIntegralImage(*this->allocatorGPU.get(), integralWidth, integralHeight);
ncvAssertReturn(d_sqIntegralImage.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32u> h_integralImage(*this->allocatorCPU.get(), integralWidth, integralHeight);
ncvAssertReturn(h_integralImage.isMemAllocated(), false);
NCVMatrixAlloc<Ncv64u> h_sqIntegralImage(*this->allocatorCPU.get(), integralWidth, integralHeight);
ncvAssertReturn(h_sqIntegralImage.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32f> d_rectStdDev(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_rectStdDev.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32u> d_pixelMask(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_pixelMask.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32f> h_rectStdDev(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_rectStdDev.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32u> h_pixelMask(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_pixelMask.isMemAllocated(), false);
NCVVectorAlloc<NcvRect32u> d_hypotheses(*this->allocatorGPU.get(), this->width * this->height);
ncvAssertReturn(d_hypotheses.isMemAllocated(), false);
NCVVectorAlloc<NcvRect32u> h_hypotheses(*this->allocatorCPU.get(), this->width * this->height);
ncvAssertReturn(h_hypotheses.isMemAllocated(), false);
NCVStatus nppStat;
Ncv32u szTmpBufIntegral, szTmpBufSqIntegral;
nppStat = nppiStIntegralGetSize_8u32u(NcvSize32u(this->width, this->height), &szTmpBufIntegral, this->devProp);
ncvAssertReturn(nppStat == NPPST_SUCCESS, false);
nppStat = nppiStSqrIntegralGetSize_8u64u(NcvSize32u(this->width, this->height), &szTmpBufSqIntegral, this->devProp);
ncvAssertReturn(nppStat == NPPST_SUCCESS, false);
NCVVectorAlloc<Ncv8u> d_tmpIIbuf(*this->allocatorGPU.get(), std::max(szTmpBufIntegral, szTmpBufSqIntegral));
ncvAssertReturn(d_tmpIIbuf.isMemAllocated(), false);
Ncv32u detectionsOnThisScale_d = 0;
Ncv32u detectionsOnThisScale_h = 0;
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_img), false);
ncvStat = h_img.copySolid(d_img, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), false);
nppStat = nppiStIntegral_8u32u_C1R(d_img.ptr(), d_img.pitch(),
d_integralImage.ptr(), d_integralImage.pitch(),
NcvSize32u(d_img.width(), d_img.height()),
d_tmpIIbuf.ptr(), szTmpBufIntegral, this->devProp);
ncvAssertReturn(nppStat == NPPST_SUCCESS, false);
nppStat = nppiStSqrIntegral_8u64u_C1R(d_img.ptr(), d_img.pitch(),
d_sqIntegralImage.ptr(), d_sqIntegralImage.pitch(),
NcvSize32u(d_img.width(), d_img.height()),
d_tmpIIbuf.ptr(), szTmpBufSqIntegral, this->devProp);
ncvAssertReturn(nppStat == NPPST_SUCCESS, false);
const NcvRect32u rect(
HAAR_STDDEV_BORDER,
HAAR_STDDEV_BORDER,
haar.ClassifierSize.width - 2*HAAR_STDDEV_BORDER,
haar.ClassifierSize.height - 2*HAAR_STDDEV_BORDER);
nppStat = nppiStRectStdDev_32f_C1R(
d_integralImage.ptr(), d_integralImage.pitch(),
d_sqIntegralImage.ptr(), d_sqIntegralImage.pitch(),
d_rectStdDev.ptr(), d_rectStdDev.pitch(),
NcvSize32u(searchRoi.width, searchRoi.height), rect,
1.0f, true);
ncvAssertReturn(nppStat == NPPST_SUCCESS, false);
ncvStat = d_integralImage.copySolid(h_integralImage, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvStat = d_rectStdDev.copySolid(h_rectStdDev, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
for (Ncv32u i=0; i<searchRoiU.height; i++)
{
for (Ncv32u j=0; j<h_pixelMask.stride(); j++)
{
if (j<searchRoiU.width)
{
h_pixelMask.ptr()[i*h_pixelMask.stride()+j] = (i << 16) | j;
}
else
{
h_pixelMask.ptr()[i*h_pixelMask.stride()+j] = OBJDET_MASK_ELEMENT_INVALID_32U;
}
}
}
ncvAssertReturn(cudaSuccess == cudaStreamSynchronize(0), false);
Ncv32u fpu_oldcw, fpu_cw;
_controlfp_s(&fpu_cw, 0, 0);
fpu_oldcw = fpu_cw;
_controlfp_s(&fpu_cw, _PC_24, _MCW_PC);
ncvStat = ncvApplyHaarClassifierCascade_host(
h_integralImage, h_rectStdDev, h_pixelMask,
detectionsOnThisScale_h,
haar, h_HaarStages, h_HaarNodes, h_HaarFeatures, false,
searchRoiU, 1, 1.0f);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
_controlfp_s(&fpu_cw, fpu_oldcw, _MCW_PC);
NCV_SKIP_COND_END
int devId;
ncvAssertCUDAReturn(cudaGetDevice(&devId), false);
cudaDeviceProp devProp;
ncvAssertCUDAReturn(cudaGetDeviceProperties(&devProp, devId), false);
ncvStat = ncvApplyHaarClassifierCascade_device(
d_integralImage, d_rectStdDev, d_pixelMask,
detectionsOnThisScale_d,
haar, h_HaarStages, d_HaarStages, d_HaarNodes, d_HaarFeatures, false,
searchRoiU, 1, 1.0f,
*this->allocatorGPU.get(), *this->allocatorCPU.get(),
devProp, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
NCVMatrixAlloc<Ncv32u> h_pixelMask_d(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_pixelMask_d.isMemAllocated(), false);
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
ncvStat = d_pixelMask.copySolid(h_pixelMask_d, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
if (detectionsOnThisScale_d != detectionsOnThisScale_h)
{
bLoopVirgin = false;
}
else
{
std::sort(h_pixelMask_d.ptr(), h_pixelMask_d.ptr() + detectionsOnThisScale_d);
for (Ncv32u i=0; i<detectionsOnThisScale_d && bLoopVirgin; i++)
{
if (h_pixelMask.ptr()[i] != h_pixelMask_d.ptr()[i])
{
bLoopVirgin = false;
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
bool TestHaarCascadeApplication::deinit()
{
return true;
}
tests/gpu/src/nvidia/TestHaarCascadeApplication.h
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/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testhaarcascadeapplication_h_
#define _testhaarcascadeapplication_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
class TestHaarCascadeApplication : public NCVTestProvider
{
public:
TestHaarCascadeApplication(std::string testName, NCVTestSourceProvider<Ncv8u> &src,
std::string cascadeName, Ncv32u width, Ncv32u height);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestHaarCascadeApplication(const TestHaarCascadeApplication&);
TestHaarCascadeApplication& operator=(const TestHaarCascadeApplication&);
NCVTestSourceProvider<Ncv8u> &src;
std::string cascadeName;
Ncv32u width;
Ncv32u height;
};
#endif // _testhaarcascadeapplication_h_
tests/gpu/src/nvidia/TestHaarCascadeLoader.cpp
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/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#include "TestHaarCascadeLoader.h"
#include "NCVHaarObjectDetection.hpp"
TestHaarCascadeLoader::TestHaarCascadeLoader(std::string testName, std::string cascadeName)
:
NCVTestProvider(testName),
cascadeName(cascadeName)
{
}
bool TestHaarCascadeLoader::toString(std::ofstream &strOut)
{
strOut << "cascadeName=" << cascadeName << std::endl;
return true;
}
bool TestHaarCascadeLoader::init()
{
return true;
}
bool TestHaarCascadeLoader::process()
{
NCVStatus ncvStat;
bool rcode = false;
Ncv32u numStages, numNodes, numFeatures;
Ncv32u numStages_2 = 0, numNodes_2 = 0, numFeatures_2 = 0;
ncvStat = ncvHaarGetClassifierSize(this->cascadeName, numStages, numNodes, numFeatures);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
NCVVectorAlloc<HaarStage64> h_HaarStages(*this->allocatorCPU.get(), numStages);
ncvAssertReturn(h_HaarStages.isMemAllocated(), false);
NCVVectorAlloc<HaarClassifierNode128> h_HaarNodes(*this->allocatorCPU.get(), numNodes);
ncvAssertReturn(h_HaarNodes.isMemAllocated(), false);
NCVVectorAlloc<HaarFeature64> h_HaarFeatures(*this->allocatorCPU.get(), numFeatures);
ncvAssertReturn(h_HaarFeatures.isMemAllocated(), false);
NCVVectorAlloc<HaarStage64> h_HaarStages_2(*this->allocatorCPU.get(), numStages);
ncvAssertReturn(h_HaarStages_2.isMemAllocated(), false);
NCVVectorAlloc<HaarClassifierNode128> h_HaarNodes_2(*this->allocatorCPU.get(), numNodes);
ncvAssertReturn(h_HaarNodes_2.isMemAllocated(), false);
NCVVectorAlloc<HaarFeature64> h_HaarFeatures_2(*this->allocatorCPU.get(), numFeatures);
ncvAssertReturn(h_HaarFeatures_2.isMemAllocated(), false);
HaarClassifierCascadeDescriptor haar;
HaarClassifierCascadeDescriptor haar_2;
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
const std::string testNvbinName = "test.nvbin";
ncvStat = ncvHaarLoadFromFile_host(this->cascadeName, haar, h_HaarStages, h_HaarNodes, h_HaarFeatures);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvStat = ncvHaarStoreNVBIN_host(testNvbinName, haar, h_HaarStages, h_HaarNodes, h_HaarFeatures);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvStat = ncvHaarGetClassifierSize(testNvbinName, numStages_2, numNodes_2, numFeatures_2);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvStat = ncvHaarLoadFromFile_host(testNvbinName, haar_2, h_HaarStages_2, h_HaarNodes_2, h_HaarFeatures_2);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
if (
numStages_2 != numStages ||
numNodes_2 != numNodes ||
numFeatures_2 != numFeatures ||
haar.NumStages != haar_2.NumStages ||
haar.NumClassifierRootNodes != haar_2.NumClassifierRootNodes ||
haar.NumClassifierTotalNodes != haar_2.NumClassifierTotalNodes ||
haar.NumFeatures != haar_2.NumFeatures ||
haar.ClassifierSize.width != haar_2.ClassifierSize.width ||
haar.ClassifierSize.height != haar_2.ClassifierSize.height ||
haar.bNeedsTiltedII != haar_2.bNeedsTiltedII ||
haar.bHasStumpsOnly != haar_2.bHasStumpsOnly )
{
bLoopVirgin = false;
}
if (memcmp(h_HaarStages.ptr(), h_HaarStages_2.ptr(), haar.NumStages * sizeof(HaarStage64)) ||
memcmp(h_HaarNodes.ptr(), h_HaarNodes_2.ptr(), haar.NumClassifierTotalNodes * sizeof(HaarClassifierNode128)) ||
memcmp(h_HaarFeatures.ptr(), h_HaarFeatures_2.ptr(), haar.NumFeatures * sizeof(HaarFeature64)) )
{
bLoopVirgin = false;
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
bool TestHaarCascadeLoader::deinit()
{
return true;
}
tests/gpu/src/nvidia/TestHaarCascadeLoader.h
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/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testhaarcascadeloader_h_
#define _testhaarcascadeloader_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
class TestHaarCascadeLoader : public NCVTestProvider
{
public:
TestHaarCascadeLoader(std::string testName, std::string cascadeName);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
std::string cascadeName;
};
#endif // _testhaarcascadeloader_h_
tests/gpu/src/nvidia/TestHypothesesFilter.cpp
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/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#include "TestHypothesesFilter.h"
#include "NCVHaarObjectDetection.hpp"
TestHypothesesFilter::TestHypothesesFilter(std::string testName, NCVTestSourceProvider<Ncv32u> &src,
Ncv32u numDstRects, Ncv32u minNeighbors, Ncv32f eps)
:
NCVTestProvider(testName),
src(src),
numDstRects(numDstRects),
minNeighbors(minNeighbors),
eps(eps)
{
}
bool TestHypothesesFilter::toString(std::ofstream &strOut)
{
strOut << "numDstRects=" << numDstRects << std::endl;
strOut << "minNeighbors=" << minNeighbors << std::endl;
strOut << "eps=" << eps << std::endl;
return true;
}
bool TestHypothesesFilter::init()
{
this->canvasWidth = 4096;
this->canvasHeight = 4096;
return true;
}
bool compareRects(const NcvRect32u &r1, const NcvRect32u &r2, Ncv32f eps)
{
double delta = eps*(std::min(r1.width, r2.width) + std::min(r1.height, r2.height))*0.5;
return std::abs((Ncv32s)r1.x - (Ncv32s)r2.x) <= delta &&
std::abs((Ncv32s)r1.y - (Ncv32s)r2.y) <= delta &&
std::abs((Ncv32s)r1.x + (Ncv32s)r1.width - (Ncv32s)r2.x - (Ncv32s)r2.width) <= delta &&
std::abs((Ncv32s)r1.y + (Ncv32s)r1.height - (Ncv32s)r2.y - (Ncv32s)r2.height) <= delta;
}
inline bool operator < (const NcvRect32u &a, const NcvRect32u &b)
{
return a.x < b.x;
}
bool TestHypothesesFilter::process()
{
NCVStatus ncvStat;
bool rcode = false;
NCVVectorAlloc<Ncv32u> h_random32u(*this->allocatorCPU.get(), this->numDstRects * sizeof(NcvRect32u) / sizeof(Ncv32u));
ncvAssertReturn(h_random32u.isMemAllocated(), false);
Ncv32u srcSlotSize = 2 * this->minNeighbors + 1;
NCVVectorAlloc<NcvRect32u> h_vecSrc(*this->allocatorCPU.get(), this->numDstRects*srcSlotSize);
ncvAssertReturn(h_vecSrc.isMemAllocated(), false);
NCVVectorAlloc<NcvRect32u> h_vecDst_groundTruth(*this->allocatorCPU.get(), this->numDstRects);
ncvAssertReturn(h_vecDst_groundTruth.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorCPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_random32u), false);
Ncv32u randCnt = 0;
Ncv64f randVal;
for (Ncv32u i=0; i<this->numDstRects; i++)
{
h_vecDst_groundTruth.ptr()[i].x = i * this->canvasWidth / this->numDstRects + this->canvasWidth / (this->numDstRects * 4);
h_vecDst_groundTruth.ptr()[i].y = i * this->canvasHeight / this->numDstRects + this->canvasHeight / (this->numDstRects * 4);
h_vecDst_groundTruth.ptr()[i].width = this->canvasWidth / (this->numDstRects * 2);
h_vecDst_groundTruth.ptr()[i].height = this->canvasHeight / (this->numDstRects * 2);
Ncv32u numNeighbors = this->minNeighbors + 1 + (Ncv32u)(((1.0 * h_random32u.ptr()[i]) * (this->minNeighbors + 1)) / 0xFFFFFFFF);
numNeighbors = (numNeighbors > srcSlotSize) ? srcSlotSize : numNeighbors;
//fill in strong hypotheses (2 * ((1.0 * randVal) / 0xFFFFFFFF) - 1)
for (Ncv32u j=0; j<numNeighbors; j++)
{
randVal = (1.0 * h_random32u.ptr()[randCnt++]) / 0xFFFFFFFF; randCnt = randCnt % h_random32u.length();
h_vecSrc.ptr()[srcSlotSize * i + j].x =
h_vecDst_groundTruth.ptr()[i].x +
(Ncv32s)(h_vecDst_groundTruth.ptr()[i].width * this->eps * (randVal - 0.5));
randVal = (1.0 * h_random32u.ptr()[randCnt++]) / 0xFFFFFFFF; randCnt = randCnt % h_random32u.length();
h_vecSrc.ptr()[srcSlotSize * i + j].y =
h_vecDst_groundTruth.ptr()[i].y +
(Ncv32s)(h_vecDst_groundTruth.ptr()[i].height * this->eps * (randVal - 0.5));
h_vecSrc.ptr()[srcSlotSize * i + j].width = h_vecDst_groundTruth.ptr()[i].width;
h_vecSrc.ptr()[srcSlotSize * i + j].height = h_vecDst_groundTruth.ptr()[i].height;
}
//generate weak hypotheses (to be removed in processing)
for (Ncv32u j=numNeighbors; j<srcSlotSize; j++)
{
randVal = (1.0 * h_random32u.ptr()[randCnt++]) / 0xFFFFFFFF; randCnt = randCnt % h_random32u.length();
h_vecSrc.ptr()[srcSlotSize * i + j].x =
this->canvasWidth + h_vecDst_groundTruth.ptr()[i].x +
(Ncv32s)(h_vecDst_groundTruth.ptr()[i].width * this->eps * (randVal - 0.5));
randVal = (1.0 * h_random32u.ptr()[randCnt++]) / 0xFFFFFFFF; randCnt = randCnt % h_random32u.length();
h_vecSrc.ptr()[srcSlotSize * i + j].y =
this->canvasHeight + h_vecDst_groundTruth.ptr()[i].y +
(Ncv32s)(h_vecDst_groundTruth.ptr()[i].height * this->eps * (randVal - 0.5));
h_vecSrc.ptr()[srcSlotSize * i + j].width = h_vecDst_groundTruth.ptr()[i].width;
h_vecSrc.ptr()[srcSlotSize * i + j].height = h_vecDst_groundTruth.ptr()[i].height;
}
}
//shuffle
for (Ncv32u i=0; i<this->numDstRects*srcSlotSize-1; i++)
{
Ncv32u randVal = h_random32u.ptr()[randCnt++]; randCnt = randCnt % h_random32u.length();
Ncv32u secondSwap = randVal % (this->numDstRects*srcSlotSize-1 - i);
NcvRect32u tmp = h_vecSrc.ptr()[i + secondSwap];
h_vecSrc.ptr()[i + secondSwap] = h_vecSrc.ptr()[i];
h_vecSrc.ptr()[i] = tmp;
}
NCV_SKIP_COND_END
Ncv32u numHypothesesSrc = h_vecSrc.length();
NCV_SKIP_COND_BEGIN
ncvStat = ncvFilterHypotheses_host(h_vecSrc, numHypothesesSrc, this->minNeighbors, this->eps, NULL);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
NCV_SKIP_COND_END
//verification
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
if (numHypothesesSrc != this->numDstRects)
{
bLoopVirgin = false;
}
else
{
std::vector<NcvRect32u> tmpRects(numHypothesesSrc);
memcpy(&tmpRects[0], h_vecSrc.ptr(), numHypothesesSrc * sizeof(NcvRect32u));
std::sort(tmpRects.begin(), tmpRects.end());
for (Ncv32u i=0; i<numHypothesesSrc && bLoopVirgin; i++)
{
if (!compareRects(tmpRects[i], h_vecDst_groundTruth.ptr()[i], this->eps))
{
bLoopVirgin = false;
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
bool TestHypothesesFilter::deinit()
{
return true;
}
tests/gpu/src/nvidia/TestHypothesesFilter.h
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/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testhypothesesfilter_h_
#define _testhypothesesfilter_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
class TestHypothesesFilter : public NCVTestProvider
{
public:
TestHypothesesFilter(std::string testName, NCVTestSourceProvider<Ncv32u> &src,
Ncv32u numDstRects, Ncv32u minNeighbors, Ncv32f eps);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestHypothesesFilter(const TestHypothesesFilter&);
TestHypothesesFilter& operator=(const TestHypothesesFilter&);
NCVTestSourceProvider<Ncv32u> &src;
Ncv32u numDstRects;
Ncv32u minNeighbors;
Ncv32f eps;
Ncv32u canvasWidth;
Ncv32u canvasHeight;
};
#endif // _testhypothesesfilter_h_
tests/gpu/src/nvidia/TestHypothesesGrow.cpp
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/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#include "TestHypothesesGrow.h"
#include "NCVHaarObjectDetection.hpp"
TestHypothesesGrow::TestHypothesesGrow(std::string testName, NCVTestSourceProvider<Ncv32u> &src,
Ncv32u rectWidth, Ncv32u rectHeight, Ncv32f rectScale,
Ncv32u maxLenSrc, Ncv32u lenSrc, Ncv32u maxLenDst, Ncv32u lenDst)
:
NCVTestProvider(testName),
src(src),
rectWidth(rectWidth),
rectHeight(rectHeight),
rectScale(rectScale),
maxLenSrc(maxLenSrc),
lenSrc(lenSrc),
maxLenDst(maxLenDst),
lenDst(lenDst)
{
}
bool TestHypothesesGrow::toString(std::ofstream &strOut)
{
strOut << "rectWidth=" << rectWidth << std::endl;
strOut << "rectHeight=" << rectHeight << std::endl;
strOut << "rectScale=" << rectScale << std::endl;
strOut << "maxLenSrc=" << maxLenSrc << std::endl;
strOut << "lenSrc=" << lenSrc << std::endl;
strOut << "maxLenDst=" << maxLenDst << std::endl;
strOut << "lenDst=" << lenDst << std::endl;
return true;
}
bool TestHypothesesGrow::init()
{
return true;
}
bool TestHypothesesGrow::process()
{
NCVStatus ncvStat;
bool rcode = false;
NCVVectorAlloc<Ncv32u> h_vecSrc(*this->allocatorCPU.get(), this->maxLenSrc);
ncvAssertReturn(h_vecSrc.isMemAllocated(), false);
NCVVectorAlloc<Ncv32u> d_vecSrc(*this->allocatorGPU.get(), this->maxLenSrc);
ncvAssertReturn(d_vecSrc.isMemAllocated(), false);
NCVVectorAlloc<NcvRect32u> h_vecDst(*this->allocatorCPU.get(), this->maxLenDst);
ncvAssertReturn(h_vecDst.isMemAllocated(), false);
NCVVectorAlloc<NcvRect32u> d_vecDst(*this->allocatorGPU.get(), this->maxLenDst);
ncvAssertReturn(d_vecDst.isMemAllocated(), false);
NCVVectorAlloc<NcvRect32u> h_vecDst_d(*this->allocatorCPU.get(), this->maxLenDst);
ncvAssertReturn(h_vecDst_d.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_vecSrc), false);
memset(h_vecDst.ptr(), 0, h_vecDst.length() * sizeof(NcvRect32u));
NCVVectorReuse<Ncv32u> h_vecDst_as32u(h_vecDst.getSegment(), lenDst * sizeof(NcvRect32u) / sizeof(Ncv32u));
ncvAssertReturn(h_vecDst_as32u.isMemReused(), false);
ncvAssertReturn(this->src.fill(h_vecDst_as32u), false);
memcpy(h_vecDst_d.ptr(), h_vecDst.ptr(), h_vecDst.length() * sizeof(NcvRect32u));
NCV_SKIP_COND_END
ncvStat = h_vecSrc.copySolid(d_vecSrc, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvStat = h_vecDst.copySolid(d_vecDst, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), false);
Ncv32u h_outElemNum_d = 0;
Ncv32u h_outElemNum_h = 0;
NCV_SKIP_COND_BEGIN
h_outElemNum_d = this->lenDst;
ncvStat = ncvGrowDetectionsVector_device(d_vecSrc, this->lenSrc,
d_vecDst, h_outElemNum_d, this->maxLenDst,
this->rectWidth, this->rectHeight, this->rectScale, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvStat = d_vecDst.copySolid(h_vecDst_d, 0);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), false);
h_outElemNum_h = this->lenDst;
ncvStat = ncvGrowDetectionsVector_host(h_vecSrc, this->lenSrc,
h_vecDst, h_outElemNum_h, this->maxLenDst,
this->rectWidth, this->rectHeight, this->rectScale);
ncvAssertReturn(ncvStat == NCV_SUCCESS, false);
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
if (h_outElemNum_d != h_outElemNum_h)
{
bLoopVirgin = false;
}
else
{
if (memcmp(h_vecDst.ptr(), h_vecDst_d.ptr(), this->maxLenDst * sizeof(NcvRect32u)))
{
bLoopVirgin = false;
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
bool TestHypothesesGrow::deinit()
{
return true;
}
tests/gpu/src/nvidia/TestHypothesesGrow.h
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/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testhypothesesgrow_h_
#define _testhypothesesgrow_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
class TestHypothesesGrow : public NCVTestProvider
{
public:
TestHypothesesGrow(std::string testName, NCVTestSourceProvider<Ncv32u> &src,
Ncv32u rectWidth, Ncv32u rectHeight, Ncv32f rectScale,
Ncv32u maxLenSrc, Ncv32u lenSrc, Ncv32u maxLenDst, Ncv32u lenDst);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestHypothesesGrow(const TestHypothesesGrow&);
TestHypothesesGrow& operator=(const TestHypothesesGrow&);
NCVTestSourceProvider<Ncv32u> &src;
Ncv32u rectWidth;
Ncv32u rectHeight;
Ncv32f rectScale;
Ncv32u maxLenSrc;
Ncv32u lenSrc;
Ncv32u maxLenDst;
Ncv32u lenDst;
};
#endif // _testhypothesesgrow_h_
tests/gpu/src/nvidia/TestIntegralImage.cpp
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/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#include <math.h>
#include "TestIntegralImage.h"
template <class T_in, class T_out>
TestIntegralImage<T_in, T_out>::TestIntegralImage(std::string testName, NCVTestSourceProvider<T_in> &src,
Ncv32u width, Ncv32u height)
:
NCVTestProvider(testName),
src(src),
width(width),
height(height)
{
}
template <class T_in, class T_out>
bool TestIntegralImage<T_in, T_out>::toString(std::ofstream &strOut)
{
strOut << "sizeof(T_in)=" << sizeof(T_in) << std::endl;
strOut << "sizeof(T_out)=" << sizeof(T_out) << std::endl;
strOut << "width=" << width << std::endl;
strOut << "height=" << height << std::endl;
return true;
}
template <class T_in, class T_out>
bool TestIntegralImage<T_in, T_out>::init()
{
return true;
}
template <class T_in, class T_out>
bool TestIntegralImage<T_in, T_out>::process()
{
NCVStatus ncvStat;
bool rcode = false;
Ncv32u widthII = this->width + 1;
Ncv32u heightII = this->height + 1;
NCVMatrixAlloc<T_in> d_img(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_img.isMemAllocated(), false);
NCVMatrixAlloc<T_in> h_img(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_img.isMemAllocated(), false);
NCVMatrixAlloc<T_out> d_imgII(*this->allocatorGPU.get(), widthII, heightII);
ncvAssertReturn(d_imgII.isMemAllocated(), false);
NCVMatrixAlloc<T_out> h_imgII(*this->allocatorCPU.get(), widthII, heightII);
ncvAssertReturn(h_imgII.isMemAllocated(), false);
NCVMatrixAlloc<T_out> h_imgII_d(*this->allocatorCPU.get(), widthII, heightII);
ncvAssertReturn(h_imgII_d.isMemAllocated(), false);
Ncv32u bufSize;
if (sizeof(T_in) == sizeof(Ncv8u))
{
ncvStat = nppiStIntegralGetSize_8u32u(NcvSize32u(this->width, this->height), &bufSize, this->devProp);
ncvAssertReturn(NPPST_SUCCESS == ncvStat, false);
}
else if (sizeof(T_in) == sizeof(Ncv32f))
{
ncvStat = nppiStIntegralGetSize_32f32f(NcvSize32u(this->width, this->height), &bufSize, this->devProp);
ncvAssertReturn(NPPST_SUCCESS == ncvStat, false);
}
else
{
ncvAssertPrintReturn(false, "Incorrect integral image test instance", false);
}
NCVVectorAlloc<Ncv8u> d_tmpBuf(*this->allocatorGPU.get(), bufSize);
ncvAssertReturn(d_tmpBuf.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_img), false);
ncvStat = h_img.copySolid(d_img, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
if (sizeof(T_in) == sizeof(Ncv8u))
{
ncvStat = nppiStIntegral_8u32u_C1R((Ncv8u *)d_img.ptr(), d_img.pitch(),
(Ncv32u *)d_imgII.ptr(), d_imgII.pitch(),
NcvSize32u(this->width, this->height),
d_tmpBuf.ptr(), bufSize, this->devProp);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
}
else if (sizeof(T_in) == sizeof(Ncv32f))
{
ncvStat = nppiStIntegral_32f32f_C1R((Ncv32f *)d_img.ptr(), d_img.pitch(),
(Ncv32f *)d_imgII.ptr(), d_imgII.pitch(),
NcvSize32u(this->width, this->height),
d_tmpBuf.ptr(), bufSize, this->devProp);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
}
else
{
ncvAssertPrintReturn(false, "Incorrect integral image test instance", false);
}
ncvStat = d_imgII.copySolid(h_imgII_d, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
if (sizeof(T_in) == sizeof(Ncv8u))
{
ncvStat = nppiStIntegral_8u32u_C1R_host((Ncv8u *)h_img.ptr(), h_img.pitch(),
(Ncv32u *)h_imgII.ptr(), h_imgII.pitch(),
NcvSize32u(this->width, this->height));
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
}
else if (sizeof(T_in) == sizeof(Ncv32f))
{
ncvStat = nppiStIntegral_32f32f_C1R_host((Ncv32f *)h_img.ptr(), h_img.pitch(),
(Ncv32f *)h_imgII.ptr(), h_imgII.pitch(),
NcvSize32u(this->width, this->height));
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
}
else
{
ncvAssertPrintReturn(false, "Incorrect integral image test instance", false);
}
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
for (Ncv32u i=0; bLoopVirgin && i < h_img.height() + 1; i++)
{
for (Ncv32u j=0; bLoopVirgin && j < h_img.width() + 1; j++)
{
if (sizeof(T_in) == sizeof(Ncv8u))
{
if (h_imgII.ptr()[h_imgII.stride()*i+j] != h_imgII_d.ptr()[h_imgII_d.stride()*i+j])
{
bLoopVirgin = false;
}
}
else if (sizeof(T_in) == sizeof(Ncv32f))
{
if (fabsf((float)h_imgII.ptr()[h_imgII.stride()*i+j] - (float)h_imgII_d.ptr()[h_imgII_d.stride()*i+j]) > 0.01f)
{
bLoopVirgin = false;
}
}
else
{
ncvAssertPrintReturn(false, "Incorrect integral image test instance", false);
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
template <class T_in, class T_out>
bool TestIntegralImage<T_in, T_out>::deinit()
{
return true;
}
template class TestIntegralImage<Ncv8u, Ncv32u>;
template class TestIntegralImage<Ncv32f, Ncv32f>;
tests/gpu/src/nvidia/TestIntegralImage.h
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/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testintegralimage_h_
#define _testintegralimage_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
template <class T_in, class T_out>
class TestIntegralImage : public NCVTestProvider
{
public:
TestIntegralImage(std::string testName, NCVTestSourceProvider<T_in> &src,
Ncv32u width, Ncv32u height);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestIntegralImage(const TestIntegralImage&);
TestIntegralImage& operator=(const TestIntegralImage&);
NCVTestSourceProvider<T_in> &src;
Ncv32u width;
Ncv32u height;
};
#endif // _testintegralimage_h_
tests/gpu/src/nvidia/TestIntegralImageSquared.cpp
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/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#include "TestIntegralImageSquared.h"
TestIntegralImageSquared::TestIntegralImageSquared(std::string testName, NCVTestSourceProvider<Ncv8u> &src,
Ncv32u width, Ncv32u height)
:
NCVTestProvider(testName),
src(src),
width(width),
height(height)
{
}
bool TestIntegralImageSquared::toString(std::ofstream &strOut)
{
strOut << "width=" << width << std::endl;
strOut << "height=" << height << std::endl;
return true;
}
bool TestIntegralImageSquared::init()
{
return true;
}
bool TestIntegralImageSquared::process()
{
NCVStatus ncvStat;
bool rcode = false;
Ncv32u widthSII = this->width + 1;
Ncv32u heightSII = this->height + 1;
NCVMatrixAlloc<Ncv8u> d_img(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_img.isMemAllocated(), false);
NCVMatrixAlloc<Ncv8u> h_img(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_img.isMemAllocated(), false);
NCVMatrixAlloc<Ncv64u> d_imgSII(*this->allocatorGPU.get(), widthSII, heightSII);
ncvAssertReturn(d_imgSII.isMemAllocated(), false);
NCVMatrixAlloc<Ncv64u> h_imgSII(*this->allocatorCPU.get(), widthSII, heightSII);
ncvAssertReturn(h_imgSII.isMemAllocated(), false);
NCVMatrixAlloc<Ncv64u> h_imgSII_d(*this->allocatorCPU.get(), widthSII, heightSII);
ncvAssertReturn(h_imgSII_d.isMemAllocated(), false);
Ncv32u bufSize;
ncvStat = nppiStSqrIntegralGetSize_8u64u(NcvSize32u(this->width, this->height), &bufSize, this->devProp);
ncvAssertReturn(NPPST_SUCCESS == ncvStat, false);
NCVVectorAlloc<Ncv8u> d_tmpBuf(*this->allocatorGPU.get(), bufSize);
ncvAssertReturn(d_tmpBuf.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_img), false);
ncvStat = h_img.copySolid(d_img, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppiStSqrIntegral_8u64u_C1R(d_img.ptr(), d_img.pitch(),
d_imgSII.ptr(), d_imgSII.pitch(),
NcvSize32u(this->width, this->height),
d_tmpBuf.ptr(), bufSize, this->devProp);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = d_imgSII.copySolid(h_imgSII_d, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppiStSqrIntegral_8u64u_C1R_host(h_img.ptr(), h_img.pitch(),
h_imgSII.ptr(), h_imgSII.pitch(),
NcvSize32u(this->width, this->height));
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
for (Ncv32u i=0; bLoopVirgin && i < h_img.height() + 1; i++)
{
for (Ncv32u j=0; bLoopVirgin && j < h_img.width() + 1; j++)
{
if (h_imgSII.ptr()[h_imgSII.stride()*i+j] != h_imgSII_d.ptr()[h_imgSII_d.stride()*i+j])
{
bLoopVirgin = false;
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
bool TestIntegralImageSquared::deinit()
{
return true;
}
tests/gpu/src/nvidia/TestIntegralImageSquared.h
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/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testintegralimagesquared_h_
#define _testintegralimagesquared_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
class TestIntegralImageSquared : public NCVTestProvider
{
public:
TestIntegralImageSquared(std::string testName, NCVTestSourceProvider<Ncv8u> &src,
Ncv32u width, Ncv32u height);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestIntegralImageSquared(const TestIntegralImageSquared&);
TestIntegralImageSquared& operator=(const TestIntegralImageSquared&);
NCVTestSourceProvider<Ncv8u> &src;
Ncv32u width;
Ncv32u height;
};
#endif // _testintegralimagesquared_h_
tests/gpu/src/nvidia/TestRectStdDev.cpp
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/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#include <math.h>
#include "TestRectStdDev.h"
TestRectStdDev::TestRectStdDev(std::string testName, NCVTestSourceProvider<Ncv8u> &src,
Ncv32u width, Ncv32u height, NcvRect32u rect, Ncv32f scaleFactor,
NcvBool bTextureCache)
:
NCVTestProvider(testName),
src(src),
width(width),
height(height),
rect(rect),
scaleFactor(scaleFactor),
bTextureCache(bTextureCache)
{
}
bool TestRectStdDev::toString(std::ofstream &strOut)
{
strOut << "width=" << width << std::endl;
strOut << "height=" << height << std::endl;
strOut << "rect=[" << rect.x << ", " << rect.y << ", " << rect.width << ", " << rect.height << "]\n";
strOut << "scaleFactor=" << scaleFactor << std::endl;
strOut << "bTextureCache=" << bTextureCache << std::endl;
return true;
}
bool TestRectStdDev::init()
{
return true;
}
bool TestRectStdDev::process()
{
NCVStatus ncvStat;
bool rcode = false;
Ncv32s _normWidth = (Ncv32s)this->width - this->rect.x - this->rect.width + 1;
Ncv32s _normHeight = (Ncv32s)this->height - this->rect.y - this->rect.height + 1;
if (_normWidth <= 0 || _normHeight <= 0)
{
return true;
}
Ncv32u normWidth = (Ncv32u)_normWidth;
Ncv32u normHeight = (Ncv32u)_normHeight;
NcvSize32u szNormRoi(normWidth, normHeight);
Ncv32u widthII = this->width + 1;
Ncv32u heightII = this->height + 1;
Ncv32u widthSII = this->width + 1;
Ncv32u heightSII = this->height + 1;
NCVMatrixAlloc<Ncv8u> d_img(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_img.isMemAllocated(), false);
NCVMatrixAlloc<Ncv8u> h_img(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_img.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32u> d_imgII(*this->allocatorGPU.get(), widthII, heightII);
ncvAssertReturn(d_imgII.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32u> h_imgII(*this->allocatorCPU.get(), widthII, heightII);
ncvAssertReturn(h_imgII.isMemAllocated(), false);
NCVMatrixAlloc<Ncv64u> d_imgSII(*this->allocatorGPU.get(), widthSII, heightSII);
ncvAssertReturn(d_imgSII.isMemAllocated(), false);
NCVMatrixAlloc<Ncv64u> h_imgSII(*this->allocatorCPU.get(), widthSII, heightSII);
ncvAssertReturn(h_imgSII.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32f> d_norm(*this->allocatorGPU.get(), normWidth, normHeight);
ncvAssertReturn(d_norm.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32f> h_norm(*this->allocatorCPU.get(), normWidth, normHeight);
ncvAssertReturn(h_norm.isMemAllocated(), false);
NCVMatrixAlloc<Ncv32f> h_norm_d(*this->allocatorCPU.get(), normWidth, normHeight);
ncvAssertReturn(h_norm_d.isMemAllocated(), false);
Ncv32u bufSizeII, bufSizeSII;
ncvStat = nppiStIntegralGetSize_8u32u(NcvSize32u(this->width, this->height), &bufSizeII, this->devProp);
ncvAssertReturn(NPPST_SUCCESS == ncvStat, false);
ncvStat = nppiStSqrIntegralGetSize_8u64u(NcvSize32u(this->width, this->height), &bufSizeSII, this->devProp);
ncvAssertReturn(NPPST_SUCCESS == ncvStat, false);
Ncv32u bufSize = bufSizeII > bufSizeSII ? bufSizeII : bufSizeSII;
NCVVectorAlloc<Ncv8u> d_tmpBuf(*this->allocatorGPU.get(), bufSize);
ncvAssertReturn(d_tmpBuf.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_img), false);
ncvStat = h_img.copySolid(d_img, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppiStIntegral_8u32u_C1R(d_img.ptr(), d_img.pitch(),
d_imgII.ptr(), d_imgII.pitch(),
NcvSize32u(this->width, this->height),
d_tmpBuf.ptr(), bufSize, this->devProp);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppiStSqrIntegral_8u64u_C1R(d_img.ptr(), d_img.pitch(),
d_imgSII.ptr(), d_imgSII.pitch(),
NcvSize32u(this->width, this->height),
d_tmpBuf.ptr(), bufSize, this->devProp);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppiStRectStdDev_32f_C1R(d_imgII.ptr(), d_imgII.pitch(),
d_imgSII.ptr(), d_imgSII.pitch(),
d_norm.ptr(), d_norm.pitch(),
szNormRoi, this->rect,
this->scaleFactor,
this->bTextureCache);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = d_norm.copySolid(h_norm_d, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppiStIntegral_8u32u_C1R_host(h_img.ptr(), h_img.pitch(),
h_imgII.ptr(), h_imgII.pitch(),
NcvSize32u(this->width, this->height));
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppiStSqrIntegral_8u64u_C1R_host(h_img.ptr(), h_img.pitch(),
h_imgSII.ptr(), h_imgSII.pitch(),
NcvSize32u(this->width, this->height));
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
ncvStat = nppiStRectStdDev_32f_C1R_host(h_imgII.ptr(), h_imgII.pitch(),
h_imgSII.ptr(), h_imgSII.pitch(),
h_norm.ptr(), h_norm.pitch(),
szNormRoi, this->rect,
this->scaleFactor);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
const Ncv64f relEPS = 0.005;
for (Ncv32u i=0; bLoopVirgin && i < h_norm.height(); i++)
{
for (Ncv32u j=0; bLoopVirgin && j < h_norm.width(); j++)
{
Ncv64f absErr = fabs(h_norm.ptr()[h_norm.stride()*i+j] - h_norm_d.ptr()[h_norm_d.stride()*i+j]);
Ncv64f relErr = absErr / h_norm.ptr()[h_norm.stride()*i+j];
if (relErr > relEPS)
{
bLoopVirgin = false;
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
bool TestRectStdDev::deinit()
{
return true;
}
tests/gpu/src/nvidia/TestRectStdDev.h
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/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testrectstddev_h_
#define _testrectstddev_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
class TestRectStdDev : public NCVTestProvider
{
public:
TestRectStdDev(std::string testName, NCVTestSourceProvider<Ncv8u> &src,
Ncv32u width, Ncv32u height, NcvRect32u rect, Ncv32f scaleFactor,
NcvBool bTextureCache);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestRectStdDev(const TestRectStdDev&);
TestRectStdDev& operator=(const TestRectStdDev&);
NCVTestSourceProvider<Ncv8u> &src;
NcvRect32u rect;
Ncv32u width;
Ncv32u height;
Ncv32f scaleFactor;
NcvBool bTextureCache;
};
#endif // _testrectstddev_h_
tests/gpu/src/nvidia/TestResize.cpp
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/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#include <math.h>
#include "TestResize.h"
template <class T>
TestResize<T>::TestResize(std::string testName, NCVTestSourceProvider<T> &src,
Ncv32u width, Ncv32u height, Ncv32u scaleFactor, NcvBool bTextureCache)
:
NCVTestProvider(testName),
src(src),
width(width),
height(height),
scaleFactor(scaleFactor),
bTextureCache(bTextureCache)
{
}
template <class T>
bool TestResize<T>::toString(std::ofstream &strOut)
{
strOut << "sizeof(T)=" << sizeof(T) << std::endl;
strOut << "width=" << width << std::endl;
strOut << "scaleFactor=" << scaleFactor << std::endl;
strOut << "bTextureCache=" << bTextureCache << std::endl;
return true;
}
template <class T>
bool TestResize<T>::init()
{
return true;
}
template <class T>
bool TestResize<T>::process()
{
NCVStatus ncvStat;
bool rcode = false;
Ncv32s smallWidth = this->width / this->scaleFactor;
Ncv32s smallHeight = this->height / this->scaleFactor;
if (smallWidth == 0 || smallHeight == 0)
{
return true;
}
NcvSize32u srcSize(this->width, this->height);
NCVMatrixAlloc<T> d_img(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_img.isMemAllocated(), false);
NCVMatrixAlloc<T> h_img(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_img.isMemAllocated(), false);
NCVMatrixAlloc<T> d_small(*this->allocatorGPU.get(), smallWidth, smallHeight);
ncvAssertReturn(d_small.isMemAllocated(), false);
NCVMatrixAlloc<T> h_small(*this->allocatorCPU.get(), smallWidth, smallHeight);
ncvAssertReturn(h_small.isMemAllocated(), false);
NCVMatrixAlloc<T> h_small_d(*this->allocatorCPU.get(), smallWidth, smallHeight);
ncvAssertReturn(h_small_d.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_img), false);
NCV_SKIP_COND_END
ncvStat = h_img.copySolid(d_img, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_BEGIN
if (sizeof(T) == sizeof(Ncv32u))
{
ncvStat = nppiStDownsampleNearest_32u_C1R((Ncv32u *)d_img.ptr(), d_img.pitch(),
(Ncv32u *)d_small.ptr(), d_small.pitch(),
srcSize, this->scaleFactor,
this->bTextureCache);
}
else if (sizeof(T) == sizeof(Ncv64u))
{
ncvStat = nppiStDownsampleNearest_64u_C1R((Ncv64u *)d_img.ptr(), d_img.pitch(),
(Ncv64u *)d_small.ptr(), d_small.pitch(),
srcSize, this->scaleFactor,
this->bTextureCache);
}
else
{
ncvAssertPrintReturn(false, "Incorrect downsample test instance", false);
}
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_END
ncvStat = d_small.copySolid(h_small_d, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_BEGIN
if (sizeof(T) == sizeof(Ncv32u))
{
ncvStat = nppiStDownsampleNearest_32u_C1R_host((Ncv32u *)h_img.ptr(), h_img.pitch(),
(Ncv32u *)h_small.ptr(), h_small.pitch(),
srcSize, this->scaleFactor);
}
else if (sizeof(T) == sizeof(Ncv64u))
{
ncvStat = nppiStDownsampleNearest_64u_C1R_host((Ncv64u *)h_img.ptr(), h_img.pitch(),
(Ncv64u *)h_small.ptr(), h_small.pitch(),
srcSize, this->scaleFactor);
}
else
{
ncvAssertPrintReturn(false, "Incorrect downsample test instance", false);
}
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
//const Ncv64f relEPS = 0.005;
for (Ncv32u i=0; bLoopVirgin && i < h_small.height(); i++)
{
for (Ncv32u j=0; bLoopVirgin && j < h_small.width(); j++)
{
if (h_small.ptr()[h_small.stride()*i+j] != h_small_d.ptr()[h_small_d.stride()*i+j])
{
bLoopVirgin = false;
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
template <class T>
bool TestResize<T>::deinit()
{
return true;
}
template class TestResize<Ncv32u>;
template class TestResize<Ncv64u>;
tests/gpu/src/nvidia/TestResize.h
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/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testresize_h_
#define _testresize_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
template <class T>
class TestResize : public NCVTestProvider
{
public:
TestResize(std::string testName, NCVTestSourceProvider<T> &src,
Ncv32u width, Ncv32u height, Ncv32u scaleFactor, NcvBool bTextureCache);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestResize(const TestResize&);
TestResize& operator=(const TestResize&);
NCVTestSourceProvider<T> &src;
Ncv32u width;
Ncv32u height;
Ncv32u scaleFactor;
NcvBool bTextureCache;
};
#endif // _testresize_h_
tests/gpu/src/nvidia/TestTranspose.cpp
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/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#include <math.h>
#include "TestTranspose.h"
template <class T>
TestTranspose<T>::TestTranspose(std::string testName, NCVTestSourceProvider<T> &src,
Ncv32u width, Ncv32u height)
:
NCVTestProvider(testName),
src(src),
width(width),
height(height)
{
}
template <class T>
bool TestTranspose<T>::toString(std::ofstream &strOut)
{
strOut << "sizeof(T)=" << sizeof(T) << std::endl;
strOut << "width=" << width << std::endl;
return true;
}
template <class T>
bool TestTranspose<T>::init()
{
return true;
}
template <class T>
bool TestTranspose<T>::process()
{
NCVStatus ncvStat;
bool rcode = false;
NcvSize32u srcSize(this->width, this->height);
NCVMatrixAlloc<T> d_img(*this->allocatorGPU.get(), this->width, this->height);
ncvAssertReturn(d_img.isMemAllocated(), false);
NCVMatrixAlloc<T> h_img(*this->allocatorCPU.get(), this->width, this->height);
ncvAssertReturn(h_img.isMemAllocated(), false);
NCVMatrixAlloc<T> d_dst(*this->allocatorGPU.get(), this->height, this->width);
ncvAssertReturn(d_dst.isMemAllocated(), false);
NCVMatrixAlloc<T> h_dst(*this->allocatorCPU.get(), this->height, this->width);
ncvAssertReturn(h_dst.isMemAllocated(), false);
NCVMatrixAlloc<T> h_dst_d(*this->allocatorCPU.get(), this->height, this->width);
ncvAssertReturn(h_dst_d.isMemAllocated(), false);
NCV_SET_SKIP_COND(this->allocatorGPU.get()->isCounting());
NCV_SKIP_COND_BEGIN
ncvAssertReturn(this->src.fill(h_img), false);
NCV_SKIP_COND_END
ncvStat = h_img.copySolid(d_img, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_BEGIN
if (sizeof(T) == sizeof(Ncv32u))
{
ncvStat = nppiStTranspose_32u_C1R((Ncv32u *)d_img.ptr(), d_img.pitch(),
(Ncv32u *)d_dst.ptr(), d_dst.pitch(),
NcvSize32u(this->width, this->height));
}
else if (sizeof(T) == sizeof(Ncv64u))
{
ncvStat = nppiStTranspose_64u_C1R((Ncv64u *)d_img.ptr(), d_img.pitch(),
(Ncv64u *)d_dst.ptr(), d_dst.pitch(),
NcvSize32u(this->width, this->height));
}
else
{
ncvAssertPrintReturn(false, "Incorrect transpose test instance", false);
}
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_END
ncvStat = d_dst.copySolid(h_dst_d, 0);
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_BEGIN
if (sizeof(T) == sizeof(Ncv32u))
{
ncvStat = nppiStTranspose_32u_C1R_host((Ncv32u *)h_img.ptr(), h_img.pitch(),
(Ncv32u *)h_dst.ptr(), h_dst.pitch(),
NcvSize32u(this->width, this->height));
}
else if (sizeof(T) == sizeof(Ncv64u))
{
ncvStat = nppiStTranspose_64u_C1R_host((Ncv64u *)h_img.ptr(), h_img.pitch(),
(Ncv64u *)h_dst.ptr(), h_dst.pitch(),
NcvSize32u(this->width, this->height));
}
else
{
ncvAssertPrintReturn(false, "Incorrect downsample test instance", false);
}
ncvAssertReturn(ncvStat == NPPST_SUCCESS, false);
NCV_SKIP_COND_END
//bit-to-bit check
bool bLoopVirgin = true;
NCV_SKIP_COND_BEGIN
//const Ncv64f relEPS = 0.005;
for (Ncv32u i=0; bLoopVirgin && i < this->width; i++)
{
for (Ncv32u j=0; bLoopVirgin && j < this->height; j++)
{
if (h_dst.ptr()[h_dst.stride()*i+j] != h_dst_d.ptr()[h_dst_d.stride()*i+j])
{
bLoopVirgin = false;
}
}
}
NCV_SKIP_COND_END
if (bLoopVirgin)
{
rcode = true;
}
return rcode;
}
template <class T>
bool TestTranspose<T>::deinit()
{
return true;
}
template class TestTranspose<Ncv32u>;
template class TestTranspose<Ncv64u>;
tests/gpu/src/nvidia/TestTranspose.h
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/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
#ifndef _testtranspose_h_
#define _testtranspose_h_
#include "NCVTest.hpp"
#include "NCVTestSourceProvider.hpp"
template <class T>
class TestTranspose : public NCVTestProvider
{
public:
TestTranspose(std::string testName, NCVTestSourceProvider<T> &src,
Ncv32u width, Ncv32u height);
virtual bool init();
virtual bool process();
virtual bool deinit();
virtual bool toString(std::ofstream &strOut);
private:
TestTranspose(const TestTranspose&);
TestTranspose& operator=(const TestTranspose&);
NCVTestSourceProvider<T> &src;
Ncv32u width;
Ncv32u height;
};
#endif // _testtranspose_h_
tests/gpu/src/nvidia/main_nvidia.cpp
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#pragma warning (disable : 4408 4201 4100)
#include <cstdio>
#include "NCV.hpp"
#include "NCVHaarObjectDetection.hpp"
#include "TestIntegralImage.h"
#include "TestIntegralImageSquared.h"
#include "TestRectStdDev.h"
#include "TestResize.h"
#include "TestCompact.h"
#include "TestTranspose.h"
#include "TestDrawRects.h"
#include "TestHypothesesGrow.h"
#include "TestHypothesesFilter.h"
#include "TestHaarCascadeLoader.h"
#include "TestHaarCascadeApplication.h"
#include "NCVAutoTestLister.hpp"
#include "NCVTestSourceProvider.hpp"
template <class T_in, class T_out>
void generateIntegralTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<T_in> &src,
Ncv32u maxWidth, Ncv32u maxHeight)
{
for (Ncv32f _i=1.0; _i<maxWidth; _i*=1.2f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf_s(testName, sizeof(testName), "LinIntImgW%dH%d", i, 2);
testLister.add(new TestIntegralImage<T_in, T_out>(testName, src, i, 2));
}
for (Ncv32f _i=1.0; _i<maxHeight; _i*=1.2f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf_s(testName, sizeof(testName), "LinIntImgW%dH%d", 2, i);
testLister.add(new TestIntegralImage<T_in, T_out>(testName, src, 2, i));
}
//test VGA
testLister.add(new TestIntegralImage<T_in, T_out>("LinIntImg_VGA", src, 640, 480));
//TODO: add tests of various resolutions up to 4096x4096
}
void generateSquaredIntegralTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<Ncv8u> &src,
Ncv32u maxWidth, Ncv32u maxHeight)
{
for (Ncv32f _i=1.0; _i<maxWidth; _i*=1.2f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf_s(testName, sizeof(testName), "SqIntImgW%dH%d", i, 32);
testLister.add(new TestIntegralImageSquared(testName, src, i, 32));
}
for (Ncv32f _i=1.0; _i<maxHeight; _i*=1.2f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf_s(testName, sizeof(testName), "SqIntImgW%dH%d", 32, i);
testLister.add(new TestIntegralImageSquared(testName, src, 32, i));
}
//test VGA
testLister.add(new TestIntegralImageSquared("SqLinIntImg_VGA", src, 640, 480));
//TODO: add tests of various resolutions up to 4096x4096
}
void generateRectStdDevTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<Ncv8u> &src,
Ncv32u maxWidth, Ncv32u maxHeight)
{
NcvRect32u rect(1,1,18,18);
for (Ncv32f _i=32; _i<maxHeight/2 && _i < maxWidth/2; _i*=1.2f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf_s(testName, sizeof(testName), "RectStdDevW%dH%d", i*2, i);
testLister.add(new TestRectStdDev(testName, src, i*2, i, rect, 1, true));
testLister.add(new TestRectStdDev(testName, src, i*2, i, rect, 1.5, false));
testLister.add(new TestRectStdDev(testName, src, i-1, i*2-1, rect, 1, false));
testLister.add(new TestRectStdDev(testName, src, i-1, i*2-1, rect, 2.5, true));
}
//test VGA
testLister.add(new TestRectStdDev("RectStdDev_VGA", src, 640, 480, rect, 1, true));
//TODO: add tests of various resolutions up to 4096x4096
}
template <class T>
void generateResizeTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<T> &src)
{
//test VGA
for (Ncv32u i=1; i<480; i+=3)
{
char testName[80];
sprintf_s(testName, sizeof(testName), "TestResize_VGA_s%d", i);
testLister.add(new TestResize<T>(testName, src, 640, 480, i, true));
testLister.add(new TestResize<T>(testName, src, 640, 480, i, false));
}
//test HD
for (Ncv32u i=1; i<1080; i+=5)
{
char testName[80];
sprintf_s(testName, sizeof(testName), "TestResize_1080_s%d", i);
testLister.add(new TestResize<T>(testName, src, 1920, 1080, i, true));
testLister.add(new TestResize<T>(testName, src, 1920, 1080, i, false));
}
//TODO: add tests of various resolutions up to 4096x4096
}
void generateNPPSTVectorTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<Ncv32u> &src, Ncv32u maxLength)
{
//compaction
for (Ncv32f _i=256.0; _i<maxLength; _i*=1.1f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf_s(testName, sizeof(testName), "Compaction%d", i);
testLister.add(new TestCompact(testName, src, i, 0xFFFFFFFF, 30));
}
for (Ncv32u i=1; i<260; i++)
{
char testName[80];
sprintf_s(testName, sizeof(testName), "Compaction%d", i);
testLister.add(new TestCompact(testName, src, i, 0xC001C0DE, 70));
testLister.add(new TestCompact(testName, src, i, 0xC001C0DE, 0));
testLister.add(new TestCompact(testName, src, i, 0xC001C0DE, 100));
}
for (Ncv32u i=256*256-256; i<256*256+257; i++)
{
char testName[80];
sprintf_s(testName, sizeof(testName), "Compaction%d", i);
testLister.add(new TestCompact(testName, src, i, 0xFFFFFFFF, 40));
}
for (Ncv32u i=256*256*256-10; i<256*256*256+10; i++)
{
char testName[80];
sprintf_s(testName, sizeof(testName), "Compaction%d", i);
testLister.add(new TestCompact(testName, src, i, 0x00000000, 2));
}
}
template <class T>
void generateTransposeTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<T> &src)
{
for (int i=2; i<64; i+=4)
{
for (int j=2; j<64; j+=4)
{
char testName[80];
sprintf_s(testName, sizeof(testName), "TestTranspose_%dx%d", i, j);
testLister.add(new TestTranspose<T>(testName, src, i, j));
}
}
for (int i=1; i<128; i+=1)
{
for (int j=1; j<2; j+=1)
{
char testName[80];
sprintf_s(testName, sizeof(testName), "TestTranspose_%dx%d", i, j);
testLister.add(new TestTranspose<T>(testName, src, i, j));
}
}
testLister.add(new TestTranspose<T>("TestTranspose_VGA", src, 640, 480));
testLister.add(new TestTranspose<T>("TestTranspose_HD1080", src, 1920, 1080));
}
template <class T>
void generateDrawRectsTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<T> &src, NCVTestSourceProvider<Ncv32u> &src32u,
Ncv32u maxWidth, Ncv32u maxHeight)
{
for (Ncv32f _i=16.0; _i<maxWidth; _i*=1.1f)
{
Ncv32u i = (Ncv32u)_i;
Ncv32u j = maxHeight * i / maxWidth;
if (!j) continue;
char testName[80];
sprintf_s(testName, sizeof(testName), "DrawRectsW%dH%d", i, j);
if (sizeof(T) == sizeof(Ncv32u))
{
testLister.add(new TestDrawRects<T>(testName, src, src32u, i, j, i*j/1000+1, (T)0xFFFFFFFF));
}
else if (sizeof(T) == sizeof(Ncv8u))
{
testLister.add(new TestDrawRects<T>(testName, src, src32u, i, j, i*j/1000+1, (T)0xFF));
}
else
{
ncvAssertPrintCheck(false, "Attempted to instantiate non-existing DrawRects test suite");
}
}
//test VGA
testLister.add(new TestDrawRects<T>("DrawRects_VGA", src, src32u, 640, 480, 640*480/1000, (T)0xFF));
//TODO: add tests of various resolutions up to 4096x4096
}
void generateVectorTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<Ncv32u> &src, Ncv32u maxLength)
{
//growth
for (Ncv32f _i=10.0; _i<maxLength; _i*=1.1f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf_s(testName, sizeof(testName), "VectorGrow%d", i);
testLister.add(new TestHypothesesGrow(testName, src, 20, 20, 2.2f, i, i/2, i, i/4));
testLister.add(new TestHypothesesGrow(testName, src, 10, 42, 1.2f, i, i, i, 0));
}
testLister.add(new TestHypothesesGrow("VectorGrow01b", src, 10, 42, 1.2f, 10, 0, 10, 1));
testLister.add(new TestHypothesesGrow("VectorGrow11b", src, 10, 42, 1.2f, 10, 1, 10, 1));
testLister.add(new TestHypothesesGrow("VectorGrow10b", src, 10, 42, 1.2f, 10, 1, 10, 0));
testLister.add(new TestHypothesesGrow("VectorGrow00b", src, 10, 42, 1.2f, 10, 0, 10, 0));
}
void generateHypothesesFiltrationTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<Ncv32u> &src, Ncv32u maxLength)
{
for (Ncv32f _i=1.0; _i<maxLength; _i*=1.1f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf_s(testName, sizeof(testName), "HypFilter%d", i);
testLister.add(new TestHypothesesFilter(testName, src, i, 3, 0.2f));
testLister.add(new TestHypothesesFilter(testName, src, i, 0, 0.2f));
testLister.add(new TestHypothesesFilter(testName, src, i, 1, 0.1f));
}
}
void generateHaarLoaderTests(NCVAutoTestLister &testLister)
{
testLister.add(new TestHaarCascadeLoader("haarcascade_eye.xml", "haarcascade_eye.xml"));
testLister.add(new TestHaarCascadeLoader("haarcascade_frontalface_alt.xml", "haarcascade_frontalface_alt.xml"));
testLister.add(new TestHaarCascadeLoader("haarcascade_frontalface_alt2.xml", "haarcascade_frontalface_alt2.xml"));
testLister.add(new TestHaarCascadeLoader("haarcascade_frontalface_alt_tree.xml", "haarcascade_frontalface_alt_tree.xml"));
testLister.add(new TestHaarCascadeLoader("haarcascade_eye_tree_eyeglasses.xml", "haarcascade_eye_tree_eyeglasses.xml"));
}
void generateHaarApplicationTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<Ncv8u> &src,
Ncv32u maxWidth, Ncv32u maxHeight)
{
for (Ncv32u i=20; i<512; i+=11)
{
for (Ncv32u j=20; j<128; j+=5)
{
char testName[80];
sprintf_s(testName, sizeof(testName), "HaarAppl%d_%d", i, j);
testLister.add(new TestHaarCascadeApplication(testName, src, "haarcascade_frontalface_alt.xml", j, i));
}
}
for (Ncv32f _i=20.0; _i<maxWidth; _i*=1.1f)
{
Ncv32u i = (Ncv32u)_i;
char testName[80];
sprintf_s(testName, sizeof(testName), "HaarAppl%d", i);
testLister.add(new TestHaarCascadeApplication(testName, src, "haarcascade_frontalface_alt.xml", i, i));
}
}
static void devNullOutput(const char *msg)
{
}
int main_nvidia()
{
printf("Testing NVIDIA Computer Vision SDK\n");
printf("==================================\n");
ncvSetDebugOutputHandler(devNullOutput);
NCVAutoTestLister testListerII("NPPST Integral Image" );//,,true, false);
NCVAutoTestLister testListerSII("NPPST Squared Integral Image" );//,,true, false);
NCVAutoTestLister testListerRStdDev("NPPST RectStdDev" );//,,true, false);
NCVAutoTestLister testListerResize("NPPST Resize" );//,,true, false);
NCVAutoTestLister testListerNPPSTVectorOperations("NPPST Vector Operations" );//,,true, false);
NCVAutoTestLister testListerTranspose("NPPST Transpose" );//,,true, false);
NCVAutoTestLister testListerVectorOperations("Vector Operations" );//,,true, false);
NCVAutoTestLister testListerHaarLoader("Haar Cascade Loader" );//,,true, false);
NCVAutoTestLister testListerHaarAppl("Haar Cascade Application" );//,,true, false);
NCVAutoTestLister testListerHypFiltration("Hypotheses Filtration" );//,,true, false);
NCVAutoTestLister testListerVisualize("Visualization" );//,,true, false);
printf("Initializing data source providers\n");
NCVTestSourceProvider<Ncv32u> testSrcRandom_32u(2010, 0, 0xFFFFFFFF, 4096, 4096);
NCVTestSourceProvider<Ncv8u> testSrcRandom_8u(2010, 0, 255, 4096, 4096);
NCVTestSourceProvider<Ncv64u> testSrcRandom_64u(2010, 0, 0xFFFFFFFFFFFFFFFF, 4096, 4096);
NCVTestSourceProvider<Ncv8u> testSrcFacesVGA_8u("../../data/group_1_640x480_VGA.pgm");
NCVTestSourceProvider<Ncv32f> testSrcRandom_32f(2010, -1.0f, 1.0f, 4096, 4096);
printf("Generating NPPST test suites\n");
generateIntegralTests<Ncv8u, Ncv32u>(testListerII, testSrcRandom_8u, 4096, 4096);
generateIntegralTests<Ncv32f, Ncv32f>(testListerII, testSrcRandom_32f, 4096, 4096);
generateSquaredIntegralTests(testListerSII, testSrcRandom_8u, 4096, 4096);
generateRectStdDevTests(testListerRStdDev, testSrcRandom_8u, 4096, 4096);
generateResizeTests(testListerResize, testSrcRandom_32u);
generateResizeTests(testListerResize, testSrcRandom_64u);
generateNPPSTVectorTests(testListerNPPSTVectorOperations, testSrcRandom_32u, 4096*4096);
generateTransposeTests(testListerTranspose, testSrcRandom_32u);
generateTransposeTests(testListerTranspose, testSrcRandom_64u);
printf("Generating NCV test suites\n");
generateDrawRectsTests(testListerVisualize, testSrcRandom_8u, testSrcRandom_32u, 4096, 4096);
generateDrawRectsTests(testListerVisualize, testSrcRandom_32u, testSrcRandom_32u, 4096, 4096);
generateVectorTests(testListerVectorOperations, testSrcRandom_32u, 4096*4096);
generateHypothesesFiltrationTests(testListerHypFiltration, testSrcRandom_32u, 1024);
generateHaarLoaderTests(testListerHaarLoader);
generateHaarApplicationTests(testListerHaarAppl, testSrcFacesVGA_8u, 1280, 720);
testListerII.invoke();
testListerSII.invoke();
testListerRStdDev.invoke();
testListerResize.invoke();
testListerNPPSTVectorOperations.invoke();
testListerTranspose.invoke();
testListerVisualize.invoke();
testListerVectorOperations.invoke();
testListerHypFiltration.invoke();
testListerHaarLoader.invoke();
testListerHaarAppl.invoke();
return 0;
}
tests/gpu/src/nvidia_tests.cpp
+63
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@@ -0,0 +1,63 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "gputest.hpp"
#include "cvconfig.h"
class CV_NVidiaTestsCaller : public CvTest
{
public:
CV_NVidiaTestsCaller() : CvTest("GPU-NVidia", "NVidia") {}
virtual ~CV_NVidiaTestsCaller() {}
protected:
void run( int )
{
#if defined(HAVE_CUDA)
int main_nvidia();
main_nvidia();
ts->set_failed_test_info(CvTS::OK);
#else
ts->set_failed_test_info(CvTS::SKIPPED);
#endif
}
} CV_NVidiaTestsCaller_test;