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Commit c0b3424a authored by Vladislav Vinogradov's avatar Vladislav Vinogradov
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gpuimgproc module fixes

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modules/gpuimgproc/CMakeLists.txt
View file @ c0b3424a
...@@ -4,6 +4,6 @@ endif() ...@@ -4,6 +4,6 @@ endif()
set(the_description "GPU-accelerated Image Processing") set(the_description "GPU-accelerated Image Processing")
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wundef -Wmissing-declarations -Wshadow -Wunused-parameter) ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4127 /wd4100 /wd4324 /wd4512 -Wundef -Wmissing-declarations -Wshadow -Wunused-parameter)
ocv_define_module(gpuimgproc opencv_imgproc opencv_gpuarithm opencv_gpufilters) ocv_define_module(gpuimgproc opencv_imgproc opencv_gpufilters OPTIONAL opencv_gpuarithm)
modules/gpuimgproc/doc/color.rst 0 → 100644
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Color space processing
======================
.. highlight:: cpp
gpu::cvtColor
-----------------
Converts an image from one color space to another.
.. ocv:function:: void gpu::cvtColor(const GpuMat& src, GpuMat& dst, int code, int dcn = 0, Stream& stream = Stream::Null())
:param src: Source image with ``CV_8U`` , ``CV_16U`` , or ``CV_32F`` depth and 1, 3, or 4 channels.
:param dst: Destination image with the same size and depth as ``src`` .
:param code: Color space conversion code. For details, see :ocv:func:`cvtColor` . Conversion to/from Luv and Bayer color spaces is not supported.
:param dcn: Number of channels in the destination image. If the parameter is 0, the number of the channels is derived automatically from ``src`` and the ``code`` .
:param stream: Stream for the asynchronous version.
3-channel color spaces (like ``HSV``, ``XYZ``, and so on) can be stored in a 4-channel image for better performance.
.. seealso:: :ocv:func:`cvtColor`
gpu::swapChannels
-----------------
Exchanges the color channels of an image in-place.
.. ocv:function:: void gpu::swapChannels(GpuMat& image, const int dstOrder[4], Stream& stream = Stream::Null())
:param image: Source image. Supports only ``CV_8UC4`` type.
:param dstOrder: Integer array describing how channel values are permutated. The n-th entry of the array contains the number of the channel that is stored in the n-th channel of the output image. E.g. Given an RGBA image, aDstOrder = [3,2,1,0] converts this to ABGR channel order.
:param stream: Stream for the asynchronous version.
The methods support arbitrary permutations of the original channels, including replication.
gpu::alphaComp
-------------------
Composites two images using alpha opacity values contained in each image.
.. ocv:function:: void gpu::alphaComp(const GpuMat& img1, const GpuMat& img2, GpuMat& dst, int alpha_op, Stream& stream = Stream::Null())
:param img1: First image. Supports ``CV_8UC4`` , ``CV_16UC4`` , ``CV_32SC4`` and ``CV_32FC4`` types.
:param img2: Second image. Must have the same size and the same type as ``img1`` .
:param dst: Destination image.
:param alpha_op: Flag specifying the alpha-blending operation:
* **ALPHA_OVER**
* **ALPHA_IN**
* **ALPHA_OUT**
* **ALPHA_ATOP**
* **ALPHA_XOR**
* **ALPHA_PLUS**
* **ALPHA_OVER_PREMUL**
* **ALPHA_IN_PREMUL**
* **ALPHA_OUT_PREMUL**
* **ALPHA_ATOP_PREMUL**
* **ALPHA_XOR_PREMUL**
* **ALPHA_PLUS_PREMUL**
* **ALPHA_PREMUL**
:param stream: Stream for the asynchronous version.
modules/gpuimgproc/doc/feature_detection.rst 0 → 100644
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Feature Detection
=================
.. highlight:: cpp
gpu::cornerHarris
---------------------
Computes the Harris cornerness criteria at each image pixel.
.. ocv:function:: void gpu::cornerHarris(const GpuMat& src, GpuMat& dst, int blockSize, int ksize, double k, int borderType=BORDER_REFLECT101)
:param src: Source image. Only ``CV_8UC1`` and ``CV_32FC1`` images are supported for now.
:param dst: Destination image containing cornerness values. It has the same size as ``src`` and ``CV_32FC1`` type.
:param blockSize: Neighborhood size.
:param ksize: Aperture parameter for the Sobel operator.
:param k: Harris detector free parameter.
:param borderType: Pixel extrapolation method. Only ``BORDER_REFLECT101`` and ``BORDER_REPLICATE`` are supported for now.
.. seealso:: :ocv:func:`cornerHarris`
gpu::cornerMinEigenVal
--------------------------
Computes the minimum eigen value of a 2x2 derivative covariation matrix at each pixel (the cornerness criteria).
.. ocv:function:: void gpu::cornerMinEigenVal(const GpuMat& src, GpuMat& dst, int blockSize, int ksize, int borderType=BORDER_REFLECT101)
.. ocv:function:: void gpu::cornerMinEigenVal(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, int blockSize, int ksize, int borderType=BORDER_REFLECT101)
.. ocv:function:: void gpu::cornerMinEigenVal(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, GpuMat& buf, int blockSize, int ksize, int borderType=BORDER_REFLECT101, Stream& stream = Stream::Null())
:param src: Source image. Only ``CV_8UC1`` and ``CV_32FC1`` images are supported for now.
:param dst: Destination image containing cornerness values. The size is the same. The type is ``CV_32FC1`` .
:param blockSize: Neighborhood size.
:param ksize: Aperture parameter for the Sobel operator.
:param borderType: Pixel extrapolation method. Only ``BORDER_REFLECT101`` and ``BORDER_REPLICATE`` are supported for now.
.. seealso:: :ocv:func:`cornerMinEigenVal`
gpu::GoodFeaturesToTrackDetector_GPU
------------------------------------
.. ocv:class:: gpu::GoodFeaturesToTrackDetector_GPU
Class used for strong corners detection on an image. ::
class GoodFeaturesToTrackDetector_GPU
{
public:
explicit GoodFeaturesToTrackDetector_GPU(int maxCorners_ = 1000, double qualityLevel_ = 0.01, double minDistance_ = 0.0,
int blockSize_ = 3, bool useHarrisDetector_ = false, double harrisK_ = 0.04);
void operator ()(const GpuMat& image, GpuMat& corners, const GpuMat& mask = GpuMat());
int maxCorners;
double qualityLevel;
double minDistance;
int blockSize;
bool useHarrisDetector;
double harrisK;
void releaseMemory();
};
The class finds the most prominent corners in the image.
.. seealso:: :ocv:func:`goodFeaturesToTrack`
modules/gpuimgproc/doc/gpuimgproc.rst
View file @ c0b3424a
************************************* ********************************************
gpu. GPU-accelerated Image Processing gpuimgproc. GPU-accelerated Image Processing
************************************* ********************************************
.. toctree:: .. toctree::
:maxdepth: 1 :maxdepth: 1
image_processing color
histogram
hough
feature_detection
imgproc
modules/gpuimgproc/doc/histogram.rst 0 → 100644
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Histogram Calculation
=====================
.. highlight:: cpp
gpu::evenLevels
-------------------
Computes levels with even distribution.
.. ocv:function:: void gpu::evenLevels(GpuMat& levels, int nLevels, int lowerLevel, int upperLevel)
:param levels: Destination array. ``levels`` has 1 row, ``nLevels`` columns, and the ``CV_32SC1`` type.
:param nLevels: Number of computed levels. ``nLevels`` must be at least 2.
:param lowerLevel: Lower boundary value of the lowest level.
:param upperLevel: Upper boundary value of the greatest level.
gpu::histEven
-----------------
Calculates a histogram with evenly distributed bins.
.. ocv:function:: void gpu::histEven(const GpuMat& src, GpuMat& hist, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::histEven(const GpuMat& src, GpuMat& hist, GpuMat& buf, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::histEven( const GpuMat& src, GpuMat hist[4], int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream=Stream::Null() )
.. ocv:function:: void gpu::histEven( const GpuMat& src, GpuMat hist[4], GpuMat& buf, int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream=Stream::Null() )
:param src: Source image. ``CV_8U``, ``CV_16U``, or ``CV_16S`` depth and 1 or 4 channels are supported. For a four-channel image, all channels are processed separately.
:param hist: Destination histogram with one row, ``histSize`` columns, and the ``CV_32S`` type.
:param histSize: Size of the histogram.
:param lowerLevel: Lower boundary of lowest-level bin.
:param upperLevel: Upper boundary of highest-level bin.
:param buf: Optional buffer to avoid extra memory allocations (for many calls with the same sizes).
:param stream: Stream for the asynchronous version.
gpu::histRange
------------------
Calculates a histogram with bins determined by the ``levels`` array.
.. ocv:function:: void gpu::histRange(const GpuMat& src, GpuMat& hist, const GpuMat& levels, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::histRange(const GpuMat& src, GpuMat& hist, const GpuMat& levels, GpuMat& buf, Stream& stream = Stream::Null())
:param src: Source image. ``CV_8U`` , ``CV_16U`` , or ``CV_16S`` depth and 1 or 4 channels are supported. For a four-channel image, all channels are processed separately.
:param hist: Destination histogram with one row, ``(levels.cols-1)`` columns, and the ``CV_32SC1`` type.
:param levels: Number of levels in the histogram.
:param buf: Optional buffer to avoid extra memory allocations (for many calls with the same sizes).
:param stream: Stream for the asynchronous version.
gpu::calcHist
------------------
Calculates histogram for one channel 8-bit image.
.. ocv:function:: void gpu::calcHist(const GpuMat& src, GpuMat& hist, Stream& stream = Stream::Null())
:param src: Source image.
:param hist: Destination histogram with one row, 256 columns, and the ``CV_32SC1`` type.
:param stream: Stream for the asynchronous version.
gpu::equalizeHist
------------------
Equalizes the histogram of a grayscale image.
.. ocv:function:: void gpu::equalizeHist(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, GpuMat& buf, Stream& stream = Stream::Null())
:param src: Source image.
:param dst: Destination image.
:param hist: Destination histogram with one row, 256 columns, and the ``CV_32SC1`` type.
:param buf: Optional buffer to avoid extra memory allocations (for many calls with the same sizes).
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`equalizeHist`
modules/gpuimgproc/doc/hough.rst 0 → 100644
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Hough Transform
===============
.. highlight:: cpp
gpu::HoughLines
---------------
Finds lines in a binary image using the classical Hough transform.
.. ocv:function:: void gpu::HoughLines(const GpuMat& src, GpuMat& lines, float rho, float theta, int threshold, bool doSort = false, int maxLines = 4096)
.. ocv:function:: void gpu::HoughLines(const GpuMat& src, GpuMat& lines, HoughLinesBuf& buf, float rho, float theta, int threshold, bool doSort = false, int maxLines = 4096)
:param src: 8-bit, single-channel binary source image.
:param lines: Output vector of lines. Each line is represented by a two-element vector :math:`(\rho, \theta)` . :math:`\rho` is the distance from the coordinate origin :math:`(0,0)` (top-left corner of the image). :math:`\theta` is the line rotation angle in radians ( :math:`0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}` ).
:param rho: Distance resolution of the accumulator in pixels.
:param theta: Angle resolution of the accumulator in radians.
:param threshold: Accumulator threshold parameter. Only those lines are returned that get enough votes ( :math:`>\texttt{threshold}` ).
:param doSort: Performs lines sort by votes.
:param maxLines: Maximum number of output lines.
:param buf: Optional buffer to avoid extra memory allocations (for many calls with the same sizes).
.. seealso:: :ocv:func:`HoughLines`
gpu::HoughLinesDownload
-----------------------
Downloads results from :ocv:func:`gpu::HoughLines` to host memory.
.. ocv:function:: void gpu::HoughLinesDownload(const GpuMat& d_lines, OutputArray h_lines, OutputArray h_votes = noArray())
:param d_lines: Result of :ocv:func:`gpu::HoughLines` .
:param h_lines: Output host array.
:param h_votes: Optional output array for line's votes.
.. seealso:: :ocv:func:`gpu::HoughLines`
gpu::HoughCircles
-----------------
Finds circles in a grayscale image using the Hough transform.
.. ocv:function:: void gpu::HoughCircles(const GpuMat& src, GpuMat& circles, int method, float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles = 4096)
.. ocv:function:: void gpu::HoughCircles(const GpuMat& src, GpuMat& circles, HoughCirclesBuf& buf, int method, float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles = 4096)
:param src: 8-bit, single-channel grayscale input image.
:param circles: Output vector of found circles. Each vector is encoded as a 3-element floating-point vector :math:`(x, y, radius)` .
:param method: Detection method to use. Currently, the only implemented method is ``CV_HOUGH_GRADIENT`` , which is basically *21HT* , described in [Yuen90]_.
:param dp: Inverse ratio of the accumulator resolution to the image resolution. For example, if ``dp=1`` , the accumulator has the same resolution as the input image. If ``dp=2`` , the accumulator has half as big width and height.
:param minDist: Minimum distance between the centers of the detected circles. If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed.
:param cannyThreshold: The higher threshold of the two passed to the :ocv:func:`gpu::Canny` edge detector (the lower one is twice smaller).
:param votesThreshold: The accumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected.
:param minRadius: Minimum circle radius.
:param maxRadius: Maximum circle radius.
:param maxCircles: Maximum number of output circles.
:param buf: Optional buffer to avoid extra memory allocations (for many calls with the same sizes).
.. seealso:: :ocv:func:`HoughCircles`
gpu::HoughCirclesDownload
-------------------------
Downloads results from :ocv:func:`gpu::HoughCircles` to host memory.
.. ocv:function:: void gpu::HoughCirclesDownload(const GpuMat& d_circles, OutputArray h_circles)
:param d_circles: Result of :ocv:func:`gpu::HoughCircles` .
:param h_circles: Output host array.
.. seealso:: :ocv:func:`gpu::HoughCircles`
modules/gpuimgproc/perf/perf_bilateral_filter.cpp 0 → 100644
View file @ c0b3424a
/*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) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., 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*/
#include "perf_precomp.hpp"
using namespace std;
using namespace testing;
using namespace perf;
//////////////////////////////////////////////////////////////////////
// BilateralFilter
DEF_PARAM_TEST(Sz_Depth_Cn_KernelSz, cv::Size, MatDepth, MatCn, int);
PERF_TEST_P(Sz_Depth_Cn_KernelSz, BilateralFilter,
Combine(GPU_TYPICAL_MAT_SIZES,
Values(CV_8U, CV_32F),
GPU_CHANNELS_1_3,
Values(3, 5, 9)))
{
declare.time(60.0);
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int channels = GET_PARAM(2);
const int kernel_size = GET_PARAM(3);
const float sigma_color = 7;
const float sigma_spatial = 5;
const int borderMode = cv::BORDER_REFLECT101;
const int type = CV_MAKE_TYPE(depth, channels);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE() cv::gpu::bilateralFilter(d_src, dst, kernel_size, sigma_color, sigma_spatial, borderMode);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::bilateralFilter(src, dst, kernel_size, sigma_color, sigma_spatial, borderMode);
CPU_SANITY_CHECK(dst);
}
}
modules/gpuimgproc/perf/perf_blend.cpp 0 → 100644
View file @ c0b3424a
/*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) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., 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*/
#include "perf_precomp.hpp"
using namespace std;
using namespace testing;
using namespace perf;
//////////////////////////////////////////////////////////////////////
// BlendLinear
PERF_TEST_P(Sz_Depth_Cn, BlendLinear,
Combine(GPU_TYPICAL_MAT_SIZES,
Values(CV_8U, CV_32F),
GPU_CHANNELS_1_3_4))
{
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int channels = GET_PARAM(2);
const int type = CV_MAKE_TYPE(depth, channels);
cv::Mat img1(size, type);
cv::Mat img2(size, type);
declare.in(img1, img2, WARMUP_RNG);
const cv::Mat weights1(size, CV_32FC1, cv::Scalar::all(0.5));
const cv::Mat weights2(size, CV_32FC1, cv::Scalar::all(0.5));
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_img1(img1);
const cv::gpu::GpuMat d_img2(img2);
const cv::gpu::GpuMat d_weights1(weights1);
const cv::gpu::GpuMat d_weights2(weights2);
cv::gpu::GpuMat dst;
TEST_CYCLE() cv::gpu::blendLinear(d_img1, d_img2, d_weights1, d_weights2, dst);
GPU_SANITY_CHECK(dst);
}
else
{
FAIL_NO_CPU();
}
}
modules/gpuimgproc/perf/perf_canny.cpp 0 → 100644
View file @ c0b3424a
/*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) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., 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*/
#include "perf_precomp.hpp"
using namespace std;
using namespace testing;
using namespace perf;
//////////////////////////////////////////////////////////////////////
// Canny
DEF_PARAM_TEST(Image_AppertureSz_L2gradient, string, int, bool);
PERF_TEST_P(Image_AppertureSz_L2gradient, Canny,
Combine(Values("perf/800x600.png", "perf/1280x1024.png", "perf/1680x1050.png"),
Values(3, 5),
Bool()))
{
const string fileName = GET_PARAM(0);
const int apperture_size = GET_PARAM(1);
const bool useL2gradient = GET_PARAM(2);
const cv::Mat image = readImage(fileName, cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(image.empty());
const double low_thresh = 50.0;
const double high_thresh = 100.0;
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_image(image);
cv::gpu::GpuMat dst;
cv::gpu::CannyBuf d_buf;
TEST_CYCLE() cv::gpu::Canny(d_image, d_buf, dst, low_thresh, high_thresh, apperture_size, useL2gradient);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::Canny(image, dst, low_thresh, high_thresh, apperture_size, useL2gradient);
CPU_SANITY_CHECK(dst);
}
}
modules/gpuimgproc/perf/perf_color.cpp 0 → 100644
View file @ c0b3424a
/*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) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., 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*/
#include "perf_precomp.hpp"
using namespace std;
using namespace testing;
using namespace perf;
//////////////////////////////////////////////////////////////////////
// CvtColor
DEF_PARAM_TEST(Sz_Depth_Code, cv::Size, MatDepth, CvtColorInfo);
PERF_TEST_P(Sz_Depth_Code, CvtColor,
Combine(GPU_TYPICAL_MAT_SIZES,
Values(CV_8U, CV_32F),
Values(CvtColorInfo(4, 4, cv::COLOR_RGBA2BGRA),
CvtColorInfo(4, 1, cv::COLOR_BGRA2GRAY),
CvtColorInfo(1, 4, cv::COLOR_GRAY2BGRA),
CvtColorInfo(3, 3, cv::COLOR_BGR2XYZ),
CvtColorInfo(3, 3, cv::COLOR_XYZ2BGR),
CvtColorInfo(3, 3, cv::COLOR_BGR2YCrCb),
CvtColorInfo(3, 3, cv::COLOR_YCrCb2BGR),
CvtColorInfo(3, 3, cv::COLOR_BGR2YUV),
CvtColorInfo(3, 3, cv::COLOR_YUV2BGR),
CvtColorInfo(3, 3, cv::COLOR_BGR2HSV),
CvtColorInfo(3, 3, cv::COLOR_HSV2BGR),
CvtColorInfo(3, 3, cv::COLOR_BGR2HLS),
CvtColorInfo(3, 3, cv::COLOR_HLS2BGR),
CvtColorInfo(3, 3, cv::COLOR_BGR2Lab),
CvtColorInfo(3, 3, cv::COLOR_LBGR2Lab),
CvtColorInfo(3, 3, cv::COLOR_BGR2Luv),
CvtColorInfo(3, 3, cv::COLOR_LBGR2Luv),
CvtColorInfo(3, 3, cv::COLOR_Lab2BGR),
CvtColorInfo(3, 3, cv::COLOR_Lab2LBGR),
CvtColorInfo(3, 3, cv::COLOR_Luv2RGB),
CvtColorInfo(3, 3, cv::COLOR_Luv2LRGB))))
{
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const CvtColorInfo info = GET_PARAM(2);
cv::Mat src(size, CV_MAKETYPE(depth, info.scn));
cv::randu(src, 0, depth == CV_8U ? 255.0 : 1.0);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE() cv::gpu::cvtColor(d_src, dst, info.code, info.dcn);
GPU_SANITY_CHECK(dst, 1e-4);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::cvtColor(src, dst, info.code, info.dcn);
CPU_SANITY_CHECK(dst);
}
}
PERF_TEST_P(Sz_Depth_Code, CvtColorBayer,
Combine(GPU_TYPICAL_MAT_SIZES,
Values(CV_8U, CV_16U),
Values(CvtColorInfo(1, 3, cv::COLOR_BayerBG2BGR),
CvtColorInfo(1, 3, cv::COLOR_BayerGB2BGR),
CvtColorInfo(1, 3, cv::COLOR_BayerRG2BGR),
CvtColorInfo(1, 3, cv::COLOR_BayerGR2BGR),
CvtColorInfo(1, 1, cv::COLOR_BayerBG2GRAY),
CvtColorInfo(1, 1, cv::COLOR_BayerGB2GRAY),
CvtColorInfo(1, 1, cv::COLOR_BayerRG2GRAY),
CvtColorInfo(1, 1, cv::COLOR_BayerGR2GRAY))))
{
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const CvtColorInfo info = GET_PARAM(2);
cv::Mat src(size, CV_MAKETYPE(depth, info.scn));
declare.in(src, WARMUP_RNG);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE() cv::gpu::cvtColor(d_src, dst, info.code, info.dcn);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::cvtColor(src, dst, info.code, info.dcn);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// Demosaicing
CV_ENUM(DemosaicingCode,
cv::COLOR_BayerBG2BGR, cv::COLOR_BayerGB2BGR, cv::COLOR_BayerRG2BGR, cv::COLOR_BayerGR2BGR,
cv::COLOR_BayerBG2GRAY, cv::COLOR_BayerGB2GRAY, cv::COLOR_BayerRG2GRAY, cv::COLOR_BayerGR2GRAY,
cv::gpu::COLOR_BayerBG2BGR_MHT, cv::gpu::COLOR_BayerGB2BGR_MHT, cv::gpu::COLOR_BayerRG2BGR_MHT, cv::gpu::COLOR_BayerGR2BGR_MHT,
cv::gpu::COLOR_BayerBG2GRAY_MHT, cv::gpu::COLOR_BayerGB2GRAY_MHT, cv::gpu::COLOR_BayerRG2GRAY_MHT, cv::gpu::COLOR_BayerGR2GRAY_MHT)
DEF_PARAM_TEST(Sz_Code, cv::Size, DemosaicingCode);
PERF_TEST_P(Sz_Code, Demosaicing,
Combine(GPU_TYPICAL_MAT_SIZES,
DemosaicingCode::all()))
{
const cv::Size size = GET_PARAM(0);
const int code = GET_PARAM(1);
cv::Mat src(size, CV_8UC1);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE() cv::gpu::demosaicing(d_src, dst, code);
GPU_SANITY_CHECK(dst);
}
else
{
if (code >= cv::COLOR_COLORCVT_MAX)
{
FAIL_NO_CPU();
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::cvtColor(src, dst, code);
CPU_SANITY_CHECK(dst);
}
}
}
//////////////////////////////////////////////////////////////////////
// SwapChannels
PERF_TEST_P(Sz, SwapChannels,
GPU_TYPICAL_MAT_SIZES)
{
const cv::Size size = GetParam();
cv::Mat src(size, CV_8UC4);
declare.in(src, WARMUP_RNG);
const int dstOrder[] = {2, 1, 0, 3};
if (PERF_RUN_GPU())
{
cv::gpu::GpuMat dst(src);
TEST_CYCLE() cv::gpu::swapChannels(dst, dstOrder);
GPU_SANITY_CHECK(dst);
}
else
{
FAIL_NO_CPU();
}
}
//////////////////////////////////////////////////////////////////////
// AlphaComp
CV_ENUM(AlphaOp, cv::gpu::ALPHA_OVER, cv::gpu::ALPHA_IN, cv::gpu::ALPHA_OUT, cv::gpu::ALPHA_ATOP, cv::gpu::ALPHA_XOR, cv::gpu::ALPHA_PLUS, cv::gpu::ALPHA_OVER_PREMUL, cv::gpu::ALPHA_IN_PREMUL, cv::gpu::ALPHA_OUT_PREMUL, cv::gpu::ALPHA_ATOP_PREMUL, cv::gpu::ALPHA_XOR_PREMUL, cv::gpu::ALPHA_PLUS_PREMUL, cv::gpu::ALPHA_PREMUL)
DEF_PARAM_TEST(Sz_Type_Op, cv::Size, MatType, AlphaOp);
PERF_TEST_P(Sz_Type_Op, AlphaComp,
Combine(GPU_TYPICAL_MAT_SIZES,
Values(CV_8UC4, CV_16UC4, CV_32SC4, CV_32FC4),
AlphaOp::all()))
{
const cv::Size size = GET_PARAM(0);
const int type = GET_PARAM(1);
const int alpha_op = GET_PARAM(2);
cv::Mat img1(size, type);
cv::Mat img2(size, type);
declare.in(img1, img2, WARMUP_RNG);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_img1(img1);
const cv::gpu::GpuMat d_img2(img2);
cv::gpu::GpuMat dst;
TEST_CYCLE() cv::gpu::alphaComp(d_img1, d_img2, dst, alpha_op);
GPU_SANITY_CHECK(dst, 1e-3, ERROR_RELATIVE);
}
else
{
FAIL_NO_CPU();
}
}
modules/gpuimgproc/perf/perf_corners.cpp 0 → 100644
View file @ c0b3424a
/*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) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., 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*/
#include "perf_precomp.hpp"
using namespace std;
using namespace testing;
using namespace perf;
//////////////////////////////////////////////////////////////////////
// CornerHarris
DEF_PARAM_TEST(Image_Type_Border_BlockSz_ApertureSz, string, MatType, BorderMode, int, int);
PERF_TEST_P(Image_Type_Border_BlockSz_ApertureSz, CornerHarris,
Combine(Values<string>("gpu/stereobm/aloe-L.png"),
Values(CV_8UC1, CV_32FC1),
Values(BorderMode(cv::BORDER_REFLECT101), BorderMode(cv::BORDER_REPLICATE), BorderMode(cv::BORDER_REFLECT)),
Values(3, 5, 7),
Values(0, 3, 5, 7)))
{
const string fileName = GET_PARAM(0);
const int type = GET_PARAM(1);
const int borderMode = GET_PARAM(2);
const int blockSize = GET_PARAM(3);
const int apertureSize = GET_PARAM(4);
cv::Mat img = readImage(fileName, cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img.empty());
img.convertTo(img, type, type == CV_32F ? 1.0 / 255.0 : 1.0);
const double k = 0.5;
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_img(img);
cv::gpu::GpuMat dst;
cv::gpu::GpuMat d_Dx;
cv::gpu::GpuMat d_Dy;
cv::gpu::GpuMat d_buf;
TEST_CYCLE() cv::gpu::cornerHarris(d_img, dst, d_Dx, d_Dy, d_buf, blockSize, apertureSize, k, borderMode);
GPU_SANITY_CHECK(dst, 1e-4);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::cornerHarris(img, dst, blockSize, apertureSize, k, borderMode);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// CornerMinEigenVal
PERF_TEST_P(Image_Type_Border_BlockSz_ApertureSz, CornerMinEigenVal,
Combine(Values<string>("gpu/stereobm/aloe-L.png"),
Values(CV_8UC1, CV_32FC1),
Values(BorderMode(cv::BORDER_REFLECT101), BorderMode(cv::BORDER_REPLICATE), BorderMode(cv::BORDER_REFLECT)),
Values(3, 5, 7),
Values(0, 3, 5, 7)))
{
const string fileName = GET_PARAM(0);
const int type = GET_PARAM(1);
const int borderMode = GET_PARAM(2);
const int blockSize = GET_PARAM(3);
const int apertureSize = GET_PARAM(4);
cv::Mat img = readImage(fileName, cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img.empty());
img.convertTo(img, type, type == CV_32F ? 1.0 / 255.0 : 1.0);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_img(img);
cv::gpu::GpuMat dst;
cv::gpu::GpuMat d_Dx;
cv::gpu::GpuMat d_Dy;
cv::gpu::GpuMat d_buf;
TEST_CYCLE() cv::gpu::cornerMinEigenVal(d_img, dst, d_Dx, d_Dy, d_buf, blockSize, apertureSize, borderMode);
GPU_SANITY_CHECK(dst, 1e-4);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::cornerMinEigenVal(img, dst, blockSize, apertureSize, borderMode);
CPU_SANITY_CHECK(dst);
}
}
modules/gpuimgproc/perf/perf_gftt.cpp 0 → 100644
View file @ c0b3424a
/*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) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., 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*/
#include "perf_precomp.hpp"
using namespace std;
using namespace testing;
using namespace perf;
//////////////////////////////////////////////////////
// GoodFeaturesToTrack
DEF_PARAM_TEST(Image_MinDistance, string, double);
PERF_TEST_P(Image_MinDistance, GoodFeaturesToTrack,
Combine(Values<string>("gpu/perf/aloe.png"),
Values(0.0, 3.0)))
{
const string fileName = GET_PARAM(0);
const double minDistance = GET_PARAM(1);
const cv::Mat image = readImage(fileName, cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(image.empty());
const int maxCorners = 8000;
const double qualityLevel = 0.01;
if (PERF_RUN_GPU())
{
cv::gpu::GoodFeaturesToTrackDetector_GPU d_detector(maxCorners, qualityLevel, minDistance);
const cv::gpu::GpuMat d_image(image);
cv::gpu::GpuMat pts;
TEST_CYCLE() d_detector(d_image, pts);
GPU_SANITY_CHECK(pts);
}
else
{
cv::Mat pts;
TEST_CYCLE() cv::goodFeaturesToTrack(image, pts, maxCorners, qualityLevel, minDistance);
CPU_SANITY_CHECK(pts);
}
}
modules/gpuimgproc/perf/perf_histogram.cpp 0 → 100644
View file @ c0b3424a
/*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) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., 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*/
#include "perf_precomp.hpp"
using namespace std;
using namespace testing;
using namespace perf;
//////////////////////////////////////////////////////////////////////
// HistEvenC1
PERF_TEST_P(Sz_Depth, HistEvenC1,
Combine(GPU_TYPICAL_MAT_SIZES,
Values(CV_8U, CV_16U, CV_16S)))
{
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
cv::Mat src(size, depth);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
cv::gpu::GpuMat d_buf;
TEST_CYCLE() cv::gpu::histEven(d_src, dst, d_buf, 30, 0, 180);
GPU_SANITY_CHECK(dst);
}
else
{
const int hbins = 30;
const float hranges[] = {0.0f, 180.0f};
const int histSize[] = {hbins};
const float* ranges[] = {hranges};
const int channels[] = {0};
cv::Mat dst;
TEST_CYCLE() cv::calcHist(&src, 1, channels, cv::Mat(), dst, 1, histSize, ranges);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// HistEvenC4
PERF_TEST_P(Sz_Depth, HistEvenC4,
Combine(GPU_TYPICAL_MAT_SIZES,
Values(CV_8U, CV_16U, CV_16S)))
{
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
cv::Mat src(size, CV_MAKE_TYPE(depth, 4));
declare.in(src, WARMUP_RNG);
int histSize[] = {30, 30, 30, 30};
int lowerLevel[] = {0, 0, 0, 0};
int upperLevel[] = {180, 180, 180, 180};
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat d_hist[4];
cv::gpu::GpuMat d_buf;
TEST_CYCLE() cv::gpu::histEven(d_src, d_hist, d_buf, histSize, lowerLevel, upperLevel);
cv::Mat cpu_hist0, cpu_hist1, cpu_hist2, cpu_hist3;
d_hist[0].download(cpu_hist0);
d_hist[1].download(cpu_hist1);
d_hist[2].download(cpu_hist2);
d_hist[3].download(cpu_hist3);
SANITY_CHECK(cpu_hist0);
SANITY_CHECK(cpu_hist1);
SANITY_CHECK(cpu_hist2);
SANITY_CHECK(cpu_hist3);
}
else
{
FAIL_NO_CPU();
}
}
//////////////////////////////////////////////////////////////////////
// CalcHist
PERF_TEST_P(Sz, CalcHist,
GPU_TYPICAL_MAT_SIZES)
{
const cv::Size size = GetParam();
cv::Mat src(size, CV_8UC1);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE() cv::gpu::calcHist(d_src, dst);
GPU_SANITY_CHECK(dst);
}
else
{
FAIL_NO_CPU();
}
}
//////////////////////////////////////////////////////////////////////
// EqualizeHist
PERF_TEST_P(Sz, EqualizeHist,
GPU_TYPICAL_MAT_SIZES)
{
const cv::Size size = GetParam();
cv::Mat src(size, CV_8UC1);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
cv::gpu::GpuMat d_hist;
cv::gpu::GpuMat d_buf;
TEST_CYCLE() cv::gpu::equalizeHist(d_src, dst, d_hist, d_buf);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::equalizeHist(src, dst);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// CLAHE
DEF_PARAM_TEST(Sz_ClipLimit, cv::Size, double);
PERF_TEST_P(Sz_ClipLimit, CLAHE,
Combine(GPU_TYPICAL_MAT_SIZES,
Values(0.0, 40.0)))
{
const cv::Size size = GET_PARAM(0);
const double clipLimit = GET_PARAM(1);
cv::Mat src(size, CV_8UC1);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_GPU())
{
cv::Ptr<cv::gpu::CLAHE> clahe = cv::gpu::createCLAHE(clipLimit);
cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE() clahe->apply(d_src, dst);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Ptr<cv::CLAHE> clahe = cv::createCLAHE(clipLimit);
cv::Mat dst;
TEST_CYCLE() clahe->apply(src, dst);
CPU_SANITY_CHECK(dst);
}
}
modules/gpuimgproc/perf/perf_match_template.cpp 0 → 100644
View file @ c0b3424a
/*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) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., 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*/
#include "perf_precomp.hpp"
using namespace std;
using namespace testing;
using namespace perf;
////////////////////////////////////////////////////////////////////////////////
// MatchTemplate8U
CV_ENUM(TemplateMethod, TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED)
DEF_PARAM_TEST(Sz_TemplateSz_Cn_Method, cv::Size, cv::Size, MatCn, TemplateMethod);
PERF_TEST_P(Sz_TemplateSz_Cn_Method, MatchTemplate8U,
Combine(GPU_TYPICAL_MAT_SIZES,
Values(cv::Size(5, 5), cv::Size(16, 16), cv::Size(30, 30)),
GPU_CHANNELS_1_3_4,
TemplateMethod::all()))
{
declare.time(300.0);
const cv::Size size = GET_PARAM(0);
const cv::Size templ_size = GET_PARAM(1);
const int cn = GET_PARAM(2);
const int method = GET_PARAM(3);
cv::Mat image(size, CV_MAKE_TYPE(CV_8U, cn));
cv::Mat templ(templ_size, CV_MAKE_TYPE(CV_8U, cn));
declare.in(image, templ, WARMUP_RNG);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_image(image);
const cv::gpu::GpuMat d_templ(templ);
cv::gpu::GpuMat dst;
TEST_CYCLE() cv::gpu::matchTemplate(d_image, d_templ, dst, method);
GPU_SANITY_CHECK(dst, 1e-5, ERROR_RELATIVE);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::matchTemplate(image, templ, dst, method);
CPU_SANITY_CHECK(dst);
}
};
////////////////////////////////////////////////////////////////////////////////
// MatchTemplate32F
PERF_TEST_P(Sz_TemplateSz_Cn_Method, MatchTemplate32F,
Combine(GPU_TYPICAL_MAT_SIZES,
Values(cv::Size(5, 5), cv::Size(16, 16), cv::Size(30, 30)),
GPU_CHANNELS_1_3_4,
Values(TemplateMethod(cv::TM_SQDIFF), TemplateMethod(cv::TM_CCORR))))
{
declare.time(300.0);
const cv::Size size = GET_PARAM(0);
const cv::Size templ_size = GET_PARAM(1);
const int cn = GET_PARAM(2);
int method = GET_PARAM(3);
cv::Mat image(size, CV_MAKE_TYPE(CV_32F, cn));
cv::Mat templ(templ_size, CV_MAKE_TYPE(CV_32F, cn));
declare.in(image, templ, WARMUP_RNG);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_image(image);
const cv::gpu::GpuMat d_templ(templ);
cv::gpu::GpuMat dst;
TEST_CYCLE() cv::gpu::matchTemplate(d_image, d_templ, dst, method);
GPU_SANITY_CHECK(dst, 1e-6, ERROR_RELATIVE);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::matchTemplate(image, templ, dst, method);
CPU_SANITY_CHECK(dst);
}
}
modules/gpuimgproc/perf/perf_mean_shift.cpp 0 → 100644
View file @ c0b3424a
/*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) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., 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*/
#include "perf_precomp.hpp"
using namespace std;
using namespace testing;
using namespace perf;
//////////////////////////////////////////////////////////////////////
// MeanShiftFiltering
DEF_PARAM_TEST_1(Image, string);
PERF_TEST_P(Image, MeanShiftFiltering,
Values<string>("gpu/meanshift/cones.png"))
{
declare.time(300.0);
const cv::Mat img = readImage(GetParam());
ASSERT_FALSE(img.empty());
cv::Mat rgba;
cv::cvtColor(img, rgba, cv::COLOR_BGR2BGRA);
const int sp = 50;
const int sr = 50;
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(rgba);
cv::gpu::GpuMat dst;
TEST_CYCLE() cv::gpu::meanShiftFiltering(d_src, dst, sp, sr);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::pyrMeanShiftFiltering(img, dst, sp, sr);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// MeanShiftProc
PERF_TEST_P(Image, MeanShiftProc,
Values<string>("gpu/meanshift/cones.png"))
{
declare.time(300.0);
const cv::Mat img = readImage(GetParam());
ASSERT_FALSE(img.empty());
cv::Mat rgba;
cv::cvtColor(img, rgba, cv::COLOR_BGR2BGRA);
const int sp = 50;
const int sr = 50;
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(rgba);
cv::gpu::GpuMat dstr;
cv::gpu::GpuMat dstsp;
TEST_CYCLE() cv::gpu::meanShiftProc(d_src, dstr, dstsp, sp, sr);
GPU_SANITY_CHECK(dstr);
GPU_SANITY_CHECK(dstsp);
}
else
{
FAIL_NO_CPU();
}
}
//////////////////////////////////////////////////////////////////////
// MeanShiftSegmentation
PERF_TEST_P(Image, MeanShiftSegmentation,
Values<string>("gpu/meanshift/cones.png"))
{
declare.time(300.0);
const cv::Mat img = readImage(GetParam());
ASSERT_FALSE(img.empty());
cv::Mat rgba;
cv::cvtColor(img, rgba, cv::COLOR_BGR2BGRA);
const int sp = 10;
const int sr = 10;
const int minsize = 20;
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(rgba);
cv::Mat dst;
TEST_CYCLE() cv::gpu::meanShiftSegmentation(d_src, dst, sp, sr, minsize);
GPU_SANITY_CHECK(dst);
}
else
{
FAIL_NO_CPU();
}
}
modules/gpuimgproc/src/blend.cpp
View file @ c0b3424a
...@@ -51,6 +51,9 @@ void cv::gpu::blendLinear(const GpuMat&, const GpuMat&, const GpuMat&, const Gpu ...@@ -51,6 +51,9 @@ void cv::gpu::blendLinear(const GpuMat&, const GpuMat&, const GpuMat&, const Gpu
#else #else
////////////////////////////////////////////////////////////////////////
// blendLinear
namespace cv { namespace gpu { namespace cudev namespace cv { namespace gpu { namespace cudev
{ {
namespace blend namespace blend
......
modules/gpuimgproc/src/canny.cpp 0 → 100644
View file @ c0b3424a
/*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) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., 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*/
#include "precomp.hpp"
using namespace cv;
using namespace cv::gpu;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
void cv::gpu::Canny(const GpuMat&, GpuMat&, double, double, int, bool) { throw_no_cuda(); }
void cv::gpu::Canny(const GpuMat&, CannyBuf&, GpuMat&, double, double, int, bool) { throw_no_cuda(); }
void cv::gpu::Canny(const GpuMat&, const GpuMat&, GpuMat&, double, double, bool) { throw_no_cuda(); }
void cv::gpu::Canny(const GpuMat&, const GpuMat&, CannyBuf&, GpuMat&, double, double, bool) { throw_no_cuda(); }
void cv::gpu::CannyBuf::create(const Size&, int) { throw_no_cuda(); }
void cv::gpu::CannyBuf::release() { throw_no_cuda(); }
#else /* !defined (HAVE_CUDA) */
void cv::gpu::CannyBuf::create(const Size& image_size, int apperture_size)
{
if (apperture_size > 0)
{
ensureSizeIsEnough(image_size, CV_32SC1, dx);
ensureSizeIsEnough(image_size, CV_32SC1, dy);
if (apperture_size != 3)
{
filterDX = createDerivFilter_GPU(CV_8UC1, CV_32S, 1, 0, apperture_size, BORDER_REPLICATE);
filterDY = createDerivFilter_GPU(CV_8UC1, CV_32S, 0, 1, apperture_size, BORDER_REPLICATE);
}
}
ensureSizeIsEnough(image_size, CV_32FC1, mag);
ensureSizeIsEnough(image_size, CV_32SC1, map);
ensureSizeIsEnough(1, image_size.area(), CV_16UC2, st1);
ensureSizeIsEnough(1, image_size.area(), CV_16UC2, st2);
}
void cv::gpu::CannyBuf::release()
{
dx.release();
dy.release();
mag.release();
map.release();
st1.release();
st2.release();
}
namespace canny
{
void calcMagnitude(PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzi dx, PtrStepSzi dy, PtrStepSzf mag, bool L2Grad);
void calcMagnitude(PtrStepSzi dx, PtrStepSzi dy, PtrStepSzf mag, bool L2Grad);
void calcMap(PtrStepSzi dx, PtrStepSzi dy, PtrStepSzf mag, PtrStepSzi map, float low_thresh, float high_thresh);
void edgesHysteresisLocal(PtrStepSzi map, ushort2* st1);
void edgesHysteresisGlobal(PtrStepSzi map, ushort2* st1, ushort2* st2);
void getEdges(PtrStepSzi map, PtrStepSzb dst);
}
namespace
{
void CannyCaller(const GpuMat& dx, const GpuMat& dy, CannyBuf& buf, GpuMat& dst, float low_thresh, float high_thresh)
{
using namespace canny;
buf.map.setTo(Scalar::all(0));
calcMap(dx, dy, buf.mag, buf.map, low_thresh, high_thresh);
edgesHysteresisLocal(buf.map, buf.st1.ptr<ushort2>());
edgesHysteresisGlobal(buf.map, buf.st1.ptr<ushort2>(), buf.st2.ptr<ushort2>());
getEdges(buf.map, dst);
}
}
void cv::gpu::Canny(const GpuMat& src, GpuMat& dst, double low_thresh, double high_thresh, int apperture_size, bool L2gradient)
{
CannyBuf buf;
Canny(src, buf, dst, low_thresh, high_thresh, apperture_size, L2gradient);
}
void cv::gpu::Canny(const GpuMat& src, CannyBuf& buf, GpuMat& dst, double low_thresh, double high_thresh, int apperture_size, bool L2gradient)
{
using namespace canny;
CV_Assert(src.type() == CV_8UC1);
if (!deviceSupports(SHARED_ATOMICS))
CV_Error(cv::Error::StsNotImplemented, "The device doesn't support shared atomics");
if( low_thresh > high_thresh )
std::swap( low_thresh, high_thresh);
dst.create(src.size(), CV_8U);
buf.create(src.size(), apperture_size);
if (apperture_size == 3)
{
Size wholeSize;
Point ofs;
src.locateROI(wholeSize, ofs);
GpuMat srcWhole(wholeSize, src.type(), src.datastart, src.step);
calcMagnitude(srcWhole, ofs.x, ofs.y, buf.dx, buf.dy, buf.mag, L2gradient);
}
else
{
buf.filterDX->apply(src, buf.dx, Rect(0, 0, src.cols, src.rows));
buf.filterDY->apply(src, buf.dy, Rect(0, 0, src.cols, src.rows));
calcMagnitude(buf.dx, buf.dy, buf.mag, L2gradient);
}
CannyCaller(buf.dx, buf.dy, buf, dst, static_cast<float>(low_thresh), static_cast<float>(high_thresh));
}
void cv::gpu::Canny(const GpuMat& dx, const GpuMat& dy, GpuMat& dst, double low_thresh, double high_thresh, bool L2gradient)
{
CannyBuf buf;
Canny(dx, dy, buf, dst, low_thresh, high_thresh, L2gradient);
}
void cv::gpu::Canny(const GpuMat& dx, const GpuMat& dy, CannyBuf& buf, GpuMat& dst, double low_thresh, double high_thresh, bool L2gradient)
{
using namespace canny;
CV_Assert(TargetArchs::builtWith(SHARED_ATOMICS) && DeviceInfo().supports(SHARED_ATOMICS));
CV_Assert(dx.type() == CV_32SC1 && dy.type() == CV_32SC1 && dx.size() == dy.size());
if( low_thresh > high_thresh )
std::swap( low_thresh, high_thresh);
dst.create(dx.size(), CV_8U);
buf.create(dx.size(), -1);
calcMagnitude(dx, dy, buf.mag, L2gradient);
CannyCaller(dx, dy, buf, dst, static_cast<float>(low_thresh), static_cast<float>(high_thresh));
}
#endif /* !defined (HAVE_CUDA) */
modules/gpuimgproc/src/color.cpp
View file @ c0b3424a
...@@ -48,10 +48,16 @@ using namespace cv::gpu; ...@@ -48,10 +48,16 @@ using namespace cv::gpu;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER) #if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
void cv::gpu::cvtColor(const GpuMat&, GpuMat&, int, int, Stream&) { throw_no_cuda(); } void cv::gpu::cvtColor(const GpuMat&, GpuMat&, int, int, Stream&) { throw_no_cuda(); }
void cv::gpu::demosaicing(const GpuMat&, GpuMat&, int, int, Stream&) { throw_no_cuda(); } void cv::gpu::demosaicing(const GpuMat&, GpuMat&, int, int, Stream&) { throw_no_cuda(); }
void cv::gpu::swapChannels(GpuMat&, const int[], Stream&) { throw_no_cuda(); } void cv::gpu::swapChannels(GpuMat&, const int[], Stream&) { throw_no_cuda(); }
void cv::gpu::gammaCorrection(const GpuMat&, GpuMat&, bool, Stream&) { throw_no_cuda(); } void cv::gpu::gammaCorrection(const GpuMat&, GpuMat&, bool, Stream&) { throw_no_cuda(); }
void cv::gpu::alphaComp(const GpuMat&, const GpuMat&, GpuMat&, int, Stream&) { throw_no_cuda(); }
#else /* !defined (HAVE_CUDA) */ #else /* !defined (HAVE_CUDA) */
#include "cvt_color_internal.h" #include "cvt_color_internal.h"
...@@ -1581,7 +1587,7 @@ namespace ...@@ -1581,7 +1587,7 @@ namespace
(void)src; (void)src;
(void)dst; (void)dst;
(void)st; (void)st;
CV_Error( CV_StsBadFlag, "Unknown/unsupported color conversion code" ); CV_Error( cv::Error::StsBadFlag, "Unknown/unsupported color conversion code" );
#else #else
CV_Assert(src.type() == CV_8UC4 || src.type() == CV_16UC4); CV_Assert(src.type() == CV_8UC4 || src.type() == CV_16UC4);
...@@ -1676,6 +1682,9 @@ namespace ...@@ -1676,6 +1682,9 @@ namespace
} }
} }
////////////////////////////////////////////////////////////////////////
// cvtColor
void cv::gpu::cvtColor(const GpuMat& src, GpuMat& dst, int code, int dcn, Stream& stream) void cv::gpu::cvtColor(const GpuMat& src, GpuMat& dst, int code, int dcn, Stream& stream)
{ {
typedef void (*func_t)(const GpuMat& src, GpuMat& dst, int dcn, Stream& stream); typedef void (*func_t)(const GpuMat& src, GpuMat& dst, int dcn, Stream& stream);
...@@ -1859,6 +1868,9 @@ void cv::gpu::cvtColor(const GpuMat& src, GpuMat& dst, int code, int dcn, Stream ...@@ -1859,6 +1868,9 @@ void cv::gpu::cvtColor(const GpuMat& src, GpuMat& dst, int code, int dcn, Stream
func(src, dst, dcn, stream); func(src, dst, dcn, stream);
} }
////////////////////////////////////////////////////////////////////////
// demosaicing
void cv::gpu::demosaicing(const GpuMat& src, GpuMat& dst, int code, int dcn, Stream& stream) void cv::gpu::demosaicing(const GpuMat& src, GpuMat& dst, int code, int dcn, Stream& stream)
{ {
const int depth = src.depth(); const int depth = src.depth();
...@@ -1927,6 +1939,9 @@ void cv::gpu::demosaicing(const GpuMat& src, GpuMat& dst, int code, int dcn, Str ...@@ -1927,6 +1939,9 @@ void cv::gpu::demosaicing(const GpuMat& src, GpuMat& dst, int code, int dcn, Str
} }
} }
////////////////////////////////////////////////////////////////////////
// swapChannels
void cv::gpu::swapChannels(GpuMat& image, const int dstOrder[4], Stream& s) void cv::gpu::swapChannels(GpuMat& image, const int dstOrder[4], Stream& s)
{ {
CV_Assert(image.type() == CV_8UC4); CV_Assert(image.type() == CV_8UC4);
...@@ -1945,6 +1960,9 @@ void cv::gpu::swapChannels(GpuMat& image, const int dstOrder[4], Stream& s) ...@@ -1945,6 +1960,9 @@ void cv::gpu::swapChannels(GpuMat& image, const int dstOrder[4], Stream& s)
cudaSafeCall( cudaDeviceSynchronize() ); cudaSafeCall( cudaDeviceSynchronize() );
} }
////////////////////////////////////////////////////////////////////////
// gammaCorrection
void cv::gpu::gammaCorrection(const GpuMat& src, GpuMat& dst, bool forward, Stream& stream) void cv::gpu::gammaCorrection(const GpuMat& src, GpuMat& dst, bool forward, Stream& stream)
{ {
#if (CUDA_VERSION < 5000) #if (CUDA_VERSION < 5000)
...@@ -1986,4 +2004,77 @@ void cv::gpu::gammaCorrection(const GpuMat& src, GpuMat& dst, bool forward, Stre ...@@ -1986,4 +2004,77 @@ void cv::gpu::gammaCorrection(const GpuMat& src, GpuMat& dst, bool forward, Stre
#endif #endif
} }
////////////////////////////////////////////////////////////////////////
// alphaComp
namespace
{
template <int DEPTH> struct NppAlphaCompFunc
{
typedef typename NPPTypeTraits<DEPTH>::npp_type npp_t;
typedef NppStatus (*func_t)(const npp_t* pSrc1, int nSrc1Step, const npp_t* pSrc2, int nSrc2Step, npp_t* pDst, int nDstStep, NppiSize oSizeROI, NppiAlphaOp eAlphaOp);
};
template <int DEPTH, typename NppAlphaCompFunc<DEPTH>::func_t func> struct NppAlphaComp
{
typedef typename NPPTypeTraits<DEPTH>::npp_type npp_t;
static void call(const GpuMat& img1, const GpuMat& img2, GpuMat& dst, NppiAlphaOp eAlphaOp, cudaStream_t stream)
{
NppStreamHandler h(stream);
NppiSize oSizeROI;
oSizeROI.width = img1.cols;
oSizeROI.height = img2.rows;
nppSafeCall( func(img1.ptr<npp_t>(), static_cast<int>(img1.step), img2.ptr<npp_t>(), static_cast<int>(img2.step),
dst.ptr<npp_t>(), static_cast<int>(dst.step), oSizeROI, eAlphaOp) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
};
}
void cv::gpu::alphaComp(const GpuMat& img1, const GpuMat& img2, GpuMat& dst, int alpha_op, Stream& stream)
{
static const NppiAlphaOp npp_alpha_ops[] = {
NPPI_OP_ALPHA_OVER,
NPPI_OP_ALPHA_IN,
NPPI_OP_ALPHA_OUT,
NPPI_OP_ALPHA_ATOP,
NPPI_OP_ALPHA_XOR,
NPPI_OP_ALPHA_PLUS,
NPPI_OP_ALPHA_OVER_PREMUL,
NPPI_OP_ALPHA_IN_PREMUL,
NPPI_OP_ALPHA_OUT_PREMUL,
NPPI_OP_ALPHA_ATOP_PREMUL,
NPPI_OP_ALPHA_XOR_PREMUL,
NPPI_OP_ALPHA_PLUS_PREMUL,
NPPI_OP_ALPHA_PREMUL
};
typedef void (*func_t)(const GpuMat& img1, const GpuMat& img2, GpuMat& dst, NppiAlphaOp eAlphaOp, cudaStream_t stream);
static const func_t funcs[] =
{
NppAlphaComp<CV_8U, nppiAlphaComp_8u_AC4R>::call,
0,
NppAlphaComp<CV_16U, nppiAlphaComp_16u_AC4R>::call,
0,
NppAlphaComp<CV_32S, nppiAlphaComp_32s_AC4R>::call,
NppAlphaComp<CV_32F, nppiAlphaComp_32f_AC4R>::call
};
CV_Assert( img1.type() == CV_8UC4 || img1.type() == CV_16UC4 || img1.type() == CV_32SC4 || img1.type() == CV_32FC4 );
CV_Assert( img1.size() == img2.size() && img1.type() == img2.type() );
dst.create(img1.size(), img1.type());
const func_t func = funcs[img1.depth()];
func(img1, img2, dst, npp_alpha_ops[alpha_op], StreamAccessor::getStream(stream));
}
#endif /* !defined (HAVE_CUDA) */ #endif /* !defined (HAVE_CUDA) */
modules/gpuimgproc/src/corners.cpp 0 → 100644
View file @ c0b3424a
/*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) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., 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*/
#include "precomp.hpp"
using namespace cv;
using namespace cv::gpu;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
void cv::gpu::cornerHarris(const GpuMat&, GpuMat&, int, int, double, int) { throw_no_cuda(); }
void cv::gpu::cornerHarris(const GpuMat&, GpuMat&, GpuMat&, GpuMat&, int, int, double, int) { throw_no_cuda(); }
void cv::gpu::cornerHarris(const GpuMat&, GpuMat&, GpuMat&, GpuMat&, GpuMat&, int, int, double, int, Stream&) { throw_no_cuda(); }
void cv::gpu::cornerMinEigenVal(const GpuMat&, GpuMat&, int, int, int) { throw_no_cuda(); }
void cv::gpu::cornerMinEigenVal(const GpuMat&, GpuMat&, GpuMat&, GpuMat&, int, int, int) { throw_no_cuda(); }
void cv::gpu::cornerMinEigenVal(const GpuMat&, GpuMat&, GpuMat&, GpuMat&, GpuMat&, int, int, int, Stream&) { throw_no_cuda(); }
#else /* !defined (HAVE_CUDA) */
namespace cv { namespace gpu { namespace cudev
{
namespace imgproc
{
void cornerHarris_gpu(int block_size, float k, PtrStepSzf Dx, PtrStepSzf Dy, PtrStepSzf dst, int border_type, cudaStream_t stream);
void cornerMinEigenVal_gpu(int block_size, PtrStepSzf Dx, PtrStepSzf Dy, PtrStepSzf dst, int border_type, cudaStream_t stream);
}
}}}
namespace
{
void extractCovData(const GpuMat& src, GpuMat& Dx, GpuMat& Dy, GpuMat& buf, int blockSize, int ksize, int borderType, Stream& stream)
{
double scale = static_cast<double>(1 << ((ksize > 0 ? ksize : 3) - 1)) * blockSize;
if (ksize < 0)
scale *= 2.;
if (src.depth() == CV_8U)
scale *= 255.;
scale = 1./scale;
Dx.create(src.size(), CV_32F);
Dy.create(src.size(), CV_32F);
if (ksize > 0)
{
Sobel(src, Dx, CV_32F, 1, 0, buf, ksize, scale, borderType, -1, stream);
Sobel(src, Dy, CV_32F, 0, 1, buf, ksize, scale, borderType, -1, stream);
}
else
{
Scharr(src, Dx, CV_32F, 1, 0, buf, scale, borderType, -1, stream);
Scharr(src, Dy, CV_32F, 0, 1, buf, scale, borderType, -1, stream);
}
}
}
void cv::gpu::cornerHarris(const GpuMat& src, GpuMat& dst, int blockSize, int ksize, double k, int borderType)
{
GpuMat Dx, Dy;
cornerHarris(src, dst, Dx, Dy, blockSize, ksize, k, borderType);
}
void cv::gpu::cornerHarris(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, int blockSize, int ksize, double k, int borderType)
{
GpuMat buf;
cornerHarris(src, dst, Dx, Dy, buf, blockSize, ksize, k, borderType);
}
void cv::gpu::cornerHarris(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, GpuMat& buf, int blockSize, int ksize, double k, int borderType, Stream& stream)
{
using namespace cv::gpu::cudev::imgproc;
CV_Assert(borderType == cv::BORDER_REFLECT101 || borderType == cv::BORDER_REPLICATE || borderType == cv::BORDER_REFLECT);
extractCovData(src, Dx, Dy, buf, blockSize, ksize, borderType, stream);
dst.create(src.size(), CV_32F);
cornerHarris_gpu(blockSize, static_cast<float>(k), Dx, Dy, dst, borderType, StreamAccessor::getStream(stream));
}
void cv::gpu::cornerMinEigenVal(const GpuMat& src, GpuMat& dst, int blockSize, int ksize, int borderType)
{
GpuMat Dx, Dy;
cornerMinEigenVal(src, dst, Dx, Dy, blockSize, ksize, borderType);
}
void cv::gpu::cornerMinEigenVal(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, int blockSize, int ksize, int borderType)
{
GpuMat buf;
cornerMinEigenVal(src, dst, Dx, Dy, buf, blockSize, ksize, borderType);
}
void cv::gpu::cornerMinEigenVal(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, GpuMat& buf, int blockSize, int ksize, int borderType, Stream& stream)
{
using namespace ::cv::gpu::cudev::imgproc;
CV_Assert(borderType == cv::BORDER_REFLECT101 || borderType == cv::BORDER_REPLICATE || borderType == cv::BORDER_REFLECT);
extractCovData(src, Dx, Dy, buf, blockSize, ksize, borderType, stream);
dst.create(src.size(), CV_32F);
cornerMinEigenVal_gpu(blockSize, Dx, Dy, dst, borderType, StreamAccessor::getStream(stream));
}
#endif /* !defined (HAVE_CUDA) */
modules/gpuimgproc/src/cuda/imgproc.cu → modules/gpuimgproc/src/cuda/corners.cu
View file @ c0b3424a
...@@ -52,137 +52,6 @@ namespace cv { namespace gpu { namespace cudev ...@@ -52,137 +52,6 @@ namespace cv { namespace gpu { namespace cudev
{ {
namespace imgproc namespace imgproc
{ {
/////////////////////////////////// MeanShiftfiltering ///////////////////////////////////////////////
texture<uchar4, 2> tex_meanshift;
__device__ short2 do_mean_shift(int x0, int y0, unsigned char* out,
size_t out_step, int cols, int rows,
int sp, int sr, int maxIter, float eps)
{
int isr2 = sr*sr;
uchar4 c = tex2D(tex_meanshift, x0, y0 );
// iterate meanshift procedure
for( int iter = 0; iter < maxIter; iter++ )
{
int count = 0;
int s0 = 0, s1 = 0, s2 = 0, sx = 0, sy = 0;
float icount;
//mean shift: process pixels in window (p-sigmaSp)x(p+sigmaSp)
int minx = x0-sp;
int miny = y0-sp;
int maxx = x0+sp;
int maxy = y0+sp;
for( int y = miny; y <= maxy; y++)
{
int rowCount = 0;
for( int x = minx; x <= maxx; x++ )
{
uchar4 t = tex2D( tex_meanshift, x, y );
int norm2 = (t.x - c.x) * (t.x - c.x) + (t.y - c.y) * (t.y - c.y) + (t.z - c.z) * (t.z - c.z);
if( norm2 <= isr2 )
{
s0 += t.x; s1 += t.y; s2 += t.z;
sx += x; rowCount++;
}
}
count += rowCount;
sy += y*rowCount;
}
if( count == 0 )
break;
icount = 1.f/count;
int x1 = __float2int_rz(sx*icount);
int y1 = __float2int_rz(sy*icount);
s0 = __float2int_rz(s0*icount);
s1 = __float2int_rz(s1*icount);
s2 = __float2int_rz(s2*icount);
int norm2 = (s0 - c.x) * (s0 - c.x) + (s1 - c.y) * (s1 - c.y) + (s2 - c.z) * (s2 - c.z);
bool stopFlag = (x0 == x1 && y0 == y1) || (::abs(x1-x0) + ::abs(y1-y0) + norm2 <= eps);
x0 = x1; y0 = y1;
c.x = s0; c.y = s1; c.z = s2;
if( stopFlag )
break;
}
int base = (blockIdx.y * blockDim.y + threadIdx.y) * out_step + (blockIdx.x * blockDim.x + threadIdx.x) * 4 * sizeof(uchar);
*(uchar4*)(out + base) = c;
return make_short2((short)x0, (short)y0);
}
__global__ void meanshift_kernel(unsigned char* out, size_t out_step, int cols, int rows, int sp, int sr, int maxIter, float eps )
{
int x0 = blockIdx.x * blockDim.x + threadIdx.x;
int y0 = blockIdx.y * blockDim.y + threadIdx.y;
if( x0 < cols && y0 < rows )
do_mean_shift(x0, y0, out, out_step, cols, rows, sp, sr, maxIter, eps);
}
__global__ void meanshiftproc_kernel(unsigned char* outr, size_t outrstep,
unsigned char* outsp, size_t outspstep,
int cols, int rows,
int sp, int sr, int maxIter, float eps)
{
int x0 = blockIdx.x * blockDim.x + threadIdx.x;
int y0 = blockIdx.y * blockDim.y + threadIdx.y;
if( x0 < cols && y0 < rows )
{
int basesp = (blockIdx.y * blockDim.y + threadIdx.y) * outspstep + (blockIdx.x * blockDim.x + threadIdx.x) * 2 * sizeof(short);
*(short2*)(outsp + basesp) = do_mean_shift(x0, y0, outr, outrstep, cols, rows, sp, sr, maxIter, eps);
}
}
void meanShiftFiltering_gpu(const PtrStepSzb& src, PtrStepSzb dst, int sp, int sr, int maxIter, float eps, cudaStream_t stream)
{
dim3 grid(1, 1, 1);
dim3 threads(32, 8, 1);
grid.x = divUp(src.cols, threads.x);
grid.y = divUp(src.rows, threads.y);
cudaChannelFormatDesc desc = cudaCreateChannelDesc<uchar4>();
cudaSafeCall( cudaBindTexture2D( 0, tex_meanshift, src.data, desc, src.cols, src.rows, src.step ) );
meanshift_kernel<<< grid, threads, 0, stream >>>( dst.data, dst.step, dst.cols, dst.rows, sp, sr, maxIter, eps );
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
//cudaSafeCall( cudaUnbindTexture( tex_meanshift ) );
}
void meanShiftProc_gpu(const PtrStepSzb& src, PtrStepSzb dstr, PtrStepSzb dstsp, int sp, int sr, int maxIter, float eps, cudaStream_t stream)
{
dim3 grid(1, 1, 1);
dim3 threads(32, 8, 1);
grid.x = divUp(src.cols, threads.x);
grid.y = divUp(src.rows, threads.y);
cudaChannelFormatDesc desc = cudaCreateChannelDesc<uchar4>();
cudaSafeCall( cudaBindTexture2D( 0, tex_meanshift, src.data, desc, src.cols, src.rows, src.step ) );
meanshiftproc_kernel<<< grid, threads, 0, stream >>>( dstr.data, dstr.step, dstsp.data, dstsp.step, dstr.cols, dstr.rows, sp, sr, maxIter, eps );
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
//cudaSafeCall( cudaUnbindTexture( tex_meanshift ) );
}
/////////////////////////////////////////// Corner Harris ///////////////////////////////////////////////// /////////////////////////////////////////// Corner Harris /////////////////////////////////////////////////
texture<float, cudaTextureType2D, cudaReadModeElementType> harrisDxTex(0, cudaFilterModePoint, cudaAddressModeClamp); texture<float, cudaTextureType2D, cudaReadModeElementType> harrisDxTex(0, cudaFilterModePoint, cudaAddressModeClamp);
...@@ -399,8 +268,7 @@ namespace cv { namespace gpu { namespace cudev ...@@ -399,8 +268,7 @@ namespace cv { namespace gpu { namespace cudev
if (stream == 0) if (stream == 0)
cudaSafeCall(cudaDeviceSynchronize()); cudaSafeCall(cudaDeviceSynchronize());
} }
} // namespace imgproc }
}}} // namespace cv { namespace gpu { namespace cudev { }}}
#endif /* CUDA_DISABLER */ #endif
modules/gpuimgproc/src/cuda/mean_shift.cu 0 → 100644
View file @ c0b3424a
/*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) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., 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*/
#if !defined CUDA_DISABLER
#include "opencv2/core/cuda/common.hpp"
#include "opencv2/core/cuda/vec_traits.hpp"
#include "opencv2/core/cuda/vec_math.hpp"
#include "opencv2/core/cuda/saturate_cast.hpp"
#include "opencv2/core/cuda/border_interpolate.hpp"
namespace cv { namespace gpu { namespace cudev
{
namespace imgproc
{
texture<uchar4, 2> tex_meanshift;
__device__ short2 do_mean_shift(int x0, int y0, unsigned char* out,
size_t out_step, int cols, int rows,
int sp, int sr, int maxIter, float eps)
{
int isr2 = sr*sr;
uchar4 c = tex2D(tex_meanshift, x0, y0 );
// iterate meanshift procedure
for( int iter = 0; iter < maxIter; iter++ )
{
int count = 0;
int s0 = 0, s1 = 0, s2 = 0, sx = 0, sy = 0;
float icount;
//mean shift: process pixels in window (p-sigmaSp)x(p+sigmaSp)
int minx = x0-sp;
int miny = y0-sp;
int maxx = x0+sp;
int maxy = y0+sp;
for( int y = miny; y <= maxy; y++)
{
int rowCount = 0;
for( int x = minx; x <= maxx; x++ )
{
uchar4 t = tex2D( tex_meanshift, x, y );
int norm2 = (t.x - c.x) * (t.x - c.x) + (t.y - c.y) * (t.y - c.y) + (t.z - c.z) * (t.z - c.z);
if( norm2 <= isr2 )
{
s0 += t.x; s1 += t.y; s2 += t.z;
sx += x; rowCount++;
}
}
count += rowCount;
sy += y*rowCount;
}
if( count == 0 )
break;
icount = 1.f/count;
int x1 = __float2int_rz(sx*icount);
int y1 = __float2int_rz(sy*icount);
s0 = __float2int_rz(s0*icount);
s1 = __float2int_rz(s1*icount);
s2 = __float2int_rz(s2*icount);
int norm2 = (s0 - c.x) * (s0 - c.x) + (s1 - c.y) * (s1 - c.y) + (s2 - c.z) * (s2 - c.z);
bool stopFlag = (x0 == x1 && y0 == y1) || (::abs(x1-x0) + ::abs(y1-y0) + norm2 <= eps);
x0 = x1; y0 = y1;
c.x = s0; c.y = s1; c.z = s2;
if( stopFlag )
break;
}
int base = (blockIdx.y * blockDim.y + threadIdx.y) * out_step + (blockIdx.x * blockDim.x + threadIdx.x) * 4 * sizeof(uchar);
*(uchar4*)(out + base) = c;
return make_short2((short)x0, (short)y0);
}
__global__ void meanshift_kernel(unsigned char* out, size_t out_step, int cols, int rows, int sp, int sr, int maxIter, float eps )
{
int x0 = blockIdx.x * blockDim.x + threadIdx.x;
int y0 = blockIdx.y * blockDim.y + threadIdx.y;
if( x0 < cols && y0 < rows )
do_mean_shift(x0, y0, out, out_step, cols, rows, sp, sr, maxIter, eps);
}
void meanShiftFiltering_gpu(const PtrStepSzb& src, PtrStepSzb dst, int sp, int sr, int maxIter, float eps, cudaStream_t stream)
{
dim3 grid(1, 1, 1);
dim3 threads(32, 8, 1);
grid.x = divUp(src.cols, threads.x);
grid.y = divUp(src.rows, threads.y);
cudaChannelFormatDesc desc = cudaCreateChannelDesc<uchar4>();
cudaSafeCall( cudaBindTexture2D( 0, tex_meanshift, src.data, desc, src.cols, src.rows, src.step ) );
meanshift_kernel<<< grid, threads, 0, stream >>>( dst.data, dst.step, dst.cols, dst.rows, sp, sr, maxIter, eps );
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
__global__ void meanshiftproc_kernel(unsigned char* outr, size_t outrstep,
unsigned char* outsp, size_t outspstep,
int cols, int rows,
int sp, int sr, int maxIter, float eps)
{
int x0 = blockIdx.x * blockDim.x + threadIdx.x;
int y0 = blockIdx.y * blockDim.y + threadIdx.y;
if( x0 < cols && y0 < rows )
{
int basesp = (blockIdx.y * blockDim.y + threadIdx.y) * outspstep + (blockIdx.x * blockDim.x + threadIdx.x) * 2 * sizeof(short);
*(short2*)(outsp + basesp) = do_mean_shift(x0, y0, outr, outrstep, cols, rows, sp, sr, maxIter, eps);
}
}
void meanShiftProc_gpu(const PtrStepSzb& src, PtrStepSzb dstr, PtrStepSzb dstsp, int sp, int sr, int maxIter, float eps, cudaStream_t stream)
{
dim3 grid(1, 1, 1);
dim3 threads(32, 8, 1);
grid.x = divUp(src.cols, threads.x);
grid.y = divUp(src.rows, threads.y);
cudaChannelFormatDesc desc = cudaCreateChannelDesc<uchar4>();
cudaSafeCall( cudaBindTexture2D( 0, tex_meanshift, src.data, desc, src.cols, src.rows, src.step ) );
meanshiftproc_kernel<<< grid, threads, 0, stream >>>( dstr.data, dstr.step, dstsp.data, dstsp.step, dstr.cols, dstr.rows, sp, sr, maxIter, eps );
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
}
}}}
#endif
modules/gpuimgproc/src/gftt.cpp
View file @ c0b3424a
...@@ -62,6 +62,12 @@ namespace cv { namespace gpu { namespace cudev ...@@ -62,6 +62,12 @@ namespace cv { namespace gpu { namespace cudev
void cv::gpu::GoodFeaturesToTrackDetector_GPU::operator ()(const GpuMat& image, GpuMat& corners, const GpuMat& mask) void cv::gpu::GoodFeaturesToTrackDetector_GPU::operator ()(const GpuMat& image, GpuMat& corners, const GpuMat& mask)
{ {
#ifndef HAVE_OPENCV_GPUARITHM
(void) image;
(void) corners;
(void) mask;
throw_no_cuda();
#else
using namespace cv::gpu::cudev::gfft; using namespace cv::gpu::cudev::gfft;
CV_Assert(qualityLevel > 0 && minDistance >= 0 && maxCorners >= 0); CV_Assert(qualityLevel > 0 && minDistance >= 0 && maxCorners >= 0);
...@@ -75,7 +81,7 @@ void cv::gpu::GoodFeaturesToTrackDetector_GPU::operator ()(const GpuMat& image, ...@@ -75,7 +81,7 @@ void cv::gpu::GoodFeaturesToTrackDetector_GPU::operator ()(const GpuMat& image,
cornerMinEigenVal(image, eig_, Dx_, Dy_, buf_, blockSize, 3); cornerMinEigenVal(image, eig_, Dx_, Dy_, buf_, blockSize, 3);
double maxVal = 0; double maxVal = 0;
minMax(eig_, 0, &maxVal, GpuMat(), minMaxbuf_); gpu::minMax(eig_, 0, &maxVal, GpuMat(), minMaxbuf_);
ensureSizeIsEnough(1, std::max(1000, static_cast<int>(image.size().area() * 0.05)), CV_32FC2, tmpCorners_); ensureSizeIsEnough(1, std::max(1000, static_cast<int>(image.size().area() * 0.05)), CV_32FC2, tmpCorners_);
...@@ -164,6 +170,7 @@ void cv::gpu::GoodFeaturesToTrackDetector_GPU::operator ()(const GpuMat& image, ...@@ -164,6 +170,7 @@ void cv::gpu::GoodFeaturesToTrackDetector_GPU::operator ()(const GpuMat& image,
corners.upload(Mat(1, static_cast<int>(tmp2.size()), CV_32FC2, &tmp2[0])); corners.upload(Mat(1, static_cast<int>(tmp2.size()), CV_32FC2, &tmp2[0]));
} }
#endif
} }
#endif /* !defined (HAVE_CUDA) */ #endif /* !defined (HAVE_CUDA) */
modules/gpuimgproc/src/match_template.cpp
View file @ c0b3424a
...@@ -45,7 +45,7 @@ ...@@ -45,7 +45,7 @@
using namespace cv; using namespace cv;
using namespace cv::gpu; using namespace cv::gpu;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER) #if !defined (HAVE_CUDA) || !defined (HAVE_OPENCV_GPUARITHM) || defined (CUDA_DISABLER)
void cv::gpu::matchTemplate(const GpuMat&, const GpuMat&, GpuMat&, int, Stream&) { throw_no_cuda(); } void cv::gpu::matchTemplate(const GpuMat&, const GpuMat&, GpuMat&, int, Stream&) { throw_no_cuda(); }
...@@ -172,15 +172,15 @@ namespace ...@@ -172,15 +172,15 @@ namespace
return; return;
} }
ConvolveBuf convolve_buf; gpu::ConvolveBuf convolve_buf;
convolve_buf.user_block_size = buf.user_block_size; convolve_buf.user_block_size = buf.user_block_size;
if (image.channels() == 1) if (image.channels() == 1)
convolve(image.reshape(1), templ.reshape(1), result, true, convolve_buf, stream); gpu::convolve(image.reshape(1), templ.reshape(1), result, true, convolve_buf, stream);
else else
{ {
GpuMat result_; GpuMat result_;
convolve(image.reshape(1), templ.reshape(1), result_, true, convolve_buf, stream); gpu::convolve(image.reshape(1), templ.reshape(1), result_, true, convolve_buf, stream);
extractFirstChannel_32F(result_, result, image.channels(), StreamAccessor::getStream(stream)); extractFirstChannel_32F(result_, result, image.channels(), StreamAccessor::getStream(stream));
} }
} }
...@@ -216,9 +216,9 @@ namespace ...@@ -216,9 +216,9 @@ namespace
matchTemplate_CCORR_8U(image, templ, result, buf, stream); matchTemplate_CCORR_8U(image, templ, result, buf, stream);
buf.image_sqsums.resize(1); buf.image_sqsums.resize(1);
sqrIntegral(image.reshape(1), buf.image_sqsums[0], stream); gpu::sqrIntegral(image.reshape(1), buf.image_sqsums[0], stream);
unsigned long long templ_sqsum = (unsigned long long)sqrSum(templ.reshape(1))[0]; unsigned long long templ_sqsum = (unsigned long long)gpu::sqrSum(templ.reshape(1))[0];
normalize_8U(templ.cols, templ.rows, buf.image_sqsums[0], templ_sqsum, result, image.channels(), StreamAccessor::getStream(stream)); normalize_8U(templ.cols, templ.rows, buf.image_sqsums[0], templ_sqsum, result, image.channels(), StreamAccessor::getStream(stream));
} }
...@@ -243,9 +243,9 @@ namespace ...@@ -243,9 +243,9 @@ namespace
} }
buf.image_sqsums.resize(1); buf.image_sqsums.resize(1);
sqrIntegral(image.reshape(1), buf.image_sqsums[0], stream); gpu::sqrIntegral(image.reshape(1), buf.image_sqsums[0], stream);
unsigned long long templ_sqsum = (unsigned long long)sqrSum(templ.reshape(1))[0]; unsigned long long templ_sqsum = (unsigned long long)gpu::sqrSum(templ.reshape(1))[0];
matchTemplate_CCORR_8U(image, templ, result, buf, stream); matchTemplate_CCORR_8U(image, templ, result, buf, stream);
matchTemplatePrepared_SQDIFF_8U(templ.cols, templ.rows, buf.image_sqsums[0], templ_sqsum, result, image.channels(), StreamAccessor::getStream(stream)); matchTemplatePrepared_SQDIFF_8U(templ.cols, templ.rows, buf.image_sqsums[0], templ_sqsum, result, image.channels(), StreamAccessor::getStream(stream));
...@@ -256,9 +256,9 @@ namespace ...@@ -256,9 +256,9 @@ namespace
const GpuMat& image, const GpuMat& templ, GpuMat& result, MatchTemplateBuf &buf, Stream& stream) const GpuMat& image, const GpuMat& templ, GpuMat& result, MatchTemplateBuf &buf, Stream& stream)
{ {
buf.image_sqsums.resize(1); buf.image_sqsums.resize(1);
sqrIntegral(image.reshape(1), buf.image_sqsums[0], stream); gpu::sqrIntegral(image.reshape(1), buf.image_sqsums[0], stream);
unsigned long long templ_sqsum = (unsigned long long)sqrSum(templ.reshape(1))[0]; unsigned long long templ_sqsum = (unsigned long long)gpu::sqrSum(templ.reshape(1))[0];
matchTemplate_CCORR_8U(image, templ, result, buf, stream); matchTemplate_CCORR_8U(image, templ, result, buf, stream);
matchTemplatePrepared_SQDIFF_NORMED_8U(templ.cols, templ.rows, buf.image_sqsums[0], templ_sqsum, result, image.channels(), StreamAccessor::getStream(stream)); matchTemplatePrepared_SQDIFF_NORMED_8U(templ.cols, templ.rows, buf.image_sqsums[0], templ_sqsum, result, image.channels(), StreamAccessor::getStream(stream));
...@@ -273,19 +273,19 @@ namespace ...@@ -273,19 +273,19 @@ namespace
if (image.channels() == 1) if (image.channels() == 1)
{ {
buf.image_sums.resize(1); buf.image_sums.resize(1);
integral(image, buf.image_sums[0], stream); gpu::integral(image, buf.image_sums[0], stream);
unsigned int templ_sum = (unsigned int)sum(templ)[0]; unsigned int templ_sum = (unsigned int)sum(templ)[0];
matchTemplatePrepared_CCOFF_8U(templ.cols, templ.rows, buf.image_sums[0], templ_sum, result, StreamAccessor::getStream(stream)); matchTemplatePrepared_CCOFF_8U(templ.cols, templ.rows, buf.image_sums[0], templ_sum, result, StreamAccessor::getStream(stream));
} }
else else
{ {
split(image, buf.images); gpu::split(image, buf.images);
buf.image_sums.resize(buf.images.size()); buf.image_sums.resize(buf.images.size());
for (int i = 0; i < image.channels(); ++i) for (int i = 0; i < image.channels(); ++i)
integral(buf.images[i], buf.image_sums[i], stream); gpu::integral(buf.images[i], buf.image_sums[i], stream);
Scalar templ_sum = sum(templ); Scalar templ_sum = gpu::sum(templ);
switch (image.channels()) switch (image.channels())
{ {
...@@ -333,12 +333,12 @@ namespace ...@@ -333,12 +333,12 @@ namespace
if (image.channels() == 1) if (image.channels() == 1)
{ {
buf.image_sums.resize(1); buf.image_sums.resize(1);
integral(image, buf.image_sums[0], stream); gpu::integral(image, buf.image_sums[0], stream);
buf.image_sqsums.resize(1); buf.image_sqsums.resize(1);
sqrIntegral(image, buf.image_sqsums[0], stream); gpu::sqrIntegral(image, buf.image_sqsums[0], stream);
unsigned int templ_sum = (unsigned int)sum(templ)[0]; unsigned int templ_sum = (unsigned int)gpu::sum(templ)[0];
unsigned long long templ_sqsum = (unsigned long long)sqrSum(templ)[0]; unsigned long long templ_sqsum = (unsigned long long)gpu::sqrSum(templ)[0];
matchTemplatePrepared_CCOFF_NORMED_8U( matchTemplatePrepared_CCOFF_NORMED_8U(
templ.cols, templ.rows, buf.image_sums[0], buf.image_sqsums[0], templ.cols, templ.rows, buf.image_sums[0], buf.image_sqsums[0],
...@@ -346,17 +346,17 @@ namespace ...@@ -346,17 +346,17 @@ namespace
} }
else else
{ {
split(image, buf.images); gpu::split(image, buf.images);
buf.image_sums.resize(buf.images.size()); buf.image_sums.resize(buf.images.size());
buf.image_sqsums.resize(buf.images.size()); buf.image_sqsums.resize(buf.images.size());
for (int i = 0; i < image.channels(); ++i) for (int i = 0; i < image.channels(); ++i)
{ {
integral(buf.images[i], buf.image_sums[i], stream); gpu::integral(buf.images[i], buf.image_sums[i], stream);
sqrIntegral(buf.images[i], buf.image_sqsums[i], stream); gpu::sqrIntegral(buf.images[i], buf.image_sqsums[i], stream);
} }
Scalar templ_sum = sum(templ); Scalar templ_sum = gpu::sum(templ);
Scalar templ_sqsum = sqrSum(templ); Scalar templ_sqsum = gpu::sqrSum(templ);
switch (image.channels()) switch (image.channels())
{ {
......
modules/gpuimgproc/src/mean_shift.cpp 0 → 100644
View file @ c0b3424a
/*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) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., 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*/
#include "precomp.hpp"
using namespace cv;
using namespace cv::gpu;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
void cv::gpu::meanShiftFiltering(const GpuMat&, GpuMat&, int, int, TermCriteria, Stream&) { throw_no_cuda(); }
void cv::gpu::meanShiftProc(const GpuMat&, GpuMat&, GpuMat&, int, int, TermCriteria, Stream&) { throw_no_cuda(); }
#else /* !defined (HAVE_CUDA) */
////////////////////////////////////////////////////////////////////////
// meanShiftFiltering_GPU
namespace cv { namespace gpu { namespace cudev
{
namespace imgproc
{
void meanShiftFiltering_gpu(const PtrStepSzb& src, PtrStepSzb dst, int sp, int sr, int maxIter, float eps, cudaStream_t stream);
}
}}}
void cv::gpu::meanShiftFiltering(const GpuMat& src, GpuMat& dst, int sp, int sr, TermCriteria criteria, Stream& stream)
{
using namespace ::cv::gpu::cudev::imgproc;
if( src.empty() )
CV_Error( cv::Error::StsBadArg, "The input image is empty" );
if( src.depth() != CV_8U || src.channels() != 4 )
CV_Error( cv::Error::StsUnsupportedFormat, "Only 8-bit, 4-channel images are supported" );
dst.create( src.size(), CV_8UC4 );
if( !(criteria.type & TermCriteria::MAX_ITER) )
criteria.maxCount = 5;
int maxIter = std::min(std::max(criteria.maxCount, 1), 100);
float eps;
if( !(criteria.type & TermCriteria::EPS) )
eps = 1.f;
eps = (float)std::max(criteria.epsilon, 0.0);
meanShiftFiltering_gpu(src, dst, sp, sr, maxIter, eps, StreamAccessor::getStream(stream));
}
////////////////////////////////////////////////////////////////////////
// meanShiftProc_GPU
namespace cv { namespace gpu { namespace cudev
{
namespace imgproc
{
void meanShiftProc_gpu(const PtrStepSzb& src, PtrStepSzb dstr, PtrStepSzb dstsp, int sp, int sr, int maxIter, float eps, cudaStream_t stream);
}
}}}
void cv::gpu::meanShiftProc(const GpuMat& src, GpuMat& dstr, GpuMat& dstsp, int sp, int sr, TermCriteria criteria, Stream& stream)
{
using namespace ::cv::gpu::cudev::imgproc;
if( src.empty() )
CV_Error( cv::Error::StsBadArg, "The input image is empty" );
if( src.depth() != CV_8U || src.channels() != 4 )
CV_Error( cv::Error::StsUnsupportedFormat, "Only 8-bit, 4-channel images are supported" );
dstr.create( src.size(), CV_8UC4 );
dstsp.create( src.size(), CV_16SC2 );
if( !(criteria.type & TermCriteria::MAX_ITER) )
criteria.maxCount = 5;
int maxIter = std::min(std::max(criteria.maxCount, 1), 100);
float eps;
if( !(criteria.type & TermCriteria::EPS) )
eps = 1.f;
eps = (float)std::max(criteria.epsilon, 0.0);
meanShiftProc_gpu(src, dstr, dstsp, sp, sr, maxIter, eps, StreamAccessor::getStream(stream));
}
#endif /* !defined (HAVE_CUDA) */
modules/gpuimgproc/src/precomp.hpp
View file @ c0b3424a
...@@ -45,9 +45,14 @@ ...@@ -45,9 +45,14 @@
#include "opencv2/gpuimgproc.hpp" #include "opencv2/gpuimgproc.hpp"
#include "opencv2/gpufilters.hpp" #include "opencv2/gpufilters.hpp"
#include "opencv2/gpuarithm.hpp"
#include "opencv2/core/private.hpp" #include "opencv2/core/private.hpp"
#include "opencv2/core/gpu_private.hpp" #include "opencv2/core/gpu_private.hpp"
#include "opencv2/opencv_modules.hpp"
#ifdef HAVE_OPENCV_GPUARITHM
# include "opencv2/gpuarithm.hpp"
#endif
#endif /* __OPENCV_PRECOMP_H__ */ #endif /* __OPENCV_PRECOMP_H__ */
modules/gpuimgproc/test/test_bilateral_filter.cpp 0 → 100644
View file @ c0b3424a
/*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) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., 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*/
#include "test_precomp.hpp"
#ifdef HAVE_CUDA
using namespace cvtest;
////////////////////////////////////////////////////////
// BilateralFilter
PARAM_TEST_CASE(BilateralFilter, cv::gpu::DeviceInfo, cv::Size, MatType)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int type;
int kernel_size;
float sigma_color;
float sigma_spatial;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
kernel_size = 5;
sigma_color = 10.f;
sigma_spatial = 3.5f;
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_TEST_P(BilateralFilter, Accuracy)
{
cv::Mat src = randomMat(size, type);
src.convertTo(src, type);
cv::gpu::GpuMat dst;
cv::gpu::bilateralFilter(loadMat(src), dst, kernel_size, sigma_color, sigma_spatial);
cv::Mat dst_gold;
cv::bilateralFilter(src, dst_gold, kernel_size, sigma_color, sigma_spatial);
EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-3 : 1.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, BilateralFilter, testing::Combine(
ALL_DEVICES,
testing::Values(cv::Size(128, 128), cv::Size(113, 113), cv::Size(639, 481)),
testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_32FC1), MatType(CV_32FC3))
));
#endif // HAVE_CUDA
modules/gpuimgproc/test/test_blend.cpp 0 → 100644
View file @ c0b3424a
/*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) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., 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*/
#include "test_precomp.hpp"
#ifdef HAVE_CUDA
using namespace cvtest;
////////////////////////////////////////////////////////////////////////////
// Blend
namespace
{
template <typename T>
void blendLinearGold(const cv::Mat& img1, const cv::Mat& img2, const cv::Mat& weights1, const cv::Mat& weights2, cv::Mat& result_gold)
{
result_gold.create(img1.size(), img1.type());
int cn = img1.channels();
for (int y = 0; y < img1.rows; ++y)
{
const float* weights1_row = weights1.ptr<float>(y);
const float* weights2_row = weights2.ptr<float>(y);
const T* img1_row = img1.ptr<T>(y);
const T* img2_row = img2.ptr<T>(y);
T* result_gold_row = result_gold.ptr<T>(y);
for (int x = 0; x < img1.cols * cn; ++x)
{
float w1 = weights1_row[x / cn];
float w2 = weights2_row[x / cn];
result_gold_row[x] = static_cast<T>((img1_row[x] * w1 + img2_row[x] * w2) / (w1 + w2 + 1e-5f));
}
}
}
}
PARAM_TEST_CASE(Blend, cv::gpu::DeviceInfo, cv::Size, MatType, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int type;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
useRoi = GET_PARAM(3);
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_TEST_P(Blend, Accuracy)
{
int depth = CV_MAT_DEPTH(type);
cv::Mat img1 = randomMat(size, type, 0.0, depth == CV_8U ? 255.0 : 1.0);
cv::Mat img2 = randomMat(size, type, 0.0, depth == CV_8U ? 255.0 : 1.0);
cv::Mat weights1 = randomMat(size, CV_32F, 0, 1);
cv::Mat weights2 = randomMat(size, CV_32F, 0, 1);
cv::gpu::GpuMat result;
cv::gpu::blendLinear(loadMat(img1, useRoi), loadMat(img2, useRoi), loadMat(weights1, useRoi), loadMat(weights2, useRoi), result);
cv::Mat result_gold;
if (depth == CV_8U)
blendLinearGold<uchar>(img1, img2, weights1, weights2, result_gold);
else
blendLinearGold<float>(img1, img2, weights1, weights2, result_gold);
EXPECT_MAT_NEAR(result_gold, result, CV_MAT_DEPTH(type) == CV_8U ? 1.0 : 1e-5);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Blend, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)),
WHOLE_SUBMAT));
#endif // HAVE_CUDA
modules/gpuimgproc/test/test_canny.cpp 0 → 100644
View file @ c0b3424a
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modules/gpuimgproc/test/test_corners.cpp 0 → 100644
View file @ c0b3424a
This diff is collapsed.
modules/gpuimgproc/test/test_gftt.cpp 0 → 100644
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modules/gpuimgproc/test/test_histogram.cpp 0 → 100644
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modules/gpuimgproc/test/test_mean_shift.cpp 0 → 100644
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