added gpu belief propagation stereo matching

modules/gpu/include/opencv2/gpu/gpu.hpp

@@ -43,6 +43,7 @@
 #ifndef __OPENCV_GPU_HPP__
 #define __OPENCV_GPU_HPP__
 
+#include <vector>
 #include "opencv2/core/core.hpp"
 #include "opencv2/gpu/devmem2d.hpp"
 
@@ -368,6 +369,42 @@ namespace cv
         private:
             GpuMat minSSD, leBuf, riBuf;
         };
+        
+        //////////////////////// StereoBeliefPropagation_GPU /////////////////////////
+        
+        class CV_EXPORTS StereoBeliefPropagation_GPU
+        {
+        public:
+            enum { DEFAULT_NDISP  = 64 };
+            enum { DEFAULT_ITERS  = 5  };
+            enum { DEFAULT_LEVELS = 5  };
+
+            static const float DEFAULT_DISC_COST;
+            static const float DEFAULT_DATA_COST;
+            static const float DEFAULT_LAMBDA_COST;
+
+            explicit StereoBeliefPropagation_GPU(int   ndisp     = DEFAULT_NDISP, 
+                                                 int   iters     = DEFAULT_ITERS, 
+                                                 int   levels    = DEFAULT_LEVELS, 
+                                                 float disc_cost = DEFAULT_DISC_COST, 
+                                                 float data_cost = DEFAULT_DATA_COST, 
+                                                 float lambda    = DEFAULT_LAMBDA_COST);
+
+            void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity);
+            
+            int ndisp;
+
+            int iters;
+            int levels;
+            
+            float disc_cost;
+            float data_cost;
+            float lambda;
+        private:
+            GpuMat u, d, l, r, u2, d2, l2, r2;
+
+            std::vector<GpuMat> datas;
+        };        
     }
 }
 #include "opencv2/gpu/matrix_operations.hpp"

modules/gpu/src/beliefpropagation_gpu.cpp

+/*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 GpuMaterials 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;
+using namespace std;
+
+const float cv::gpu::StereoBeliefPropagation_GPU::DEFAULT_DISC_COST   = 1.7f;
+const float cv::gpu::StereoBeliefPropagation_GPU::DEFAULT_DATA_COST   = 10.0f;
+const float cv::gpu::StereoBeliefPropagation_GPU::DEFAULT_LAMBDA_COST = 0.07f;
+
+#if !defined (HAVE_CUDA)
+
+cv::gpu::StereoBeliefPropagation_GPU::StereoBeliefPropagation_GPU(int, int, int, float, float, float) { throw_nogpu(); }
+
+void cv::gpu::StereoBeliefPropagation_GPU::operator() (const GpuMat&, const GpuMat&, GpuMat&) { throw_nogpu(); }
+
+#else /* !defined (HAVE_CUDA) */
+
+typedef DevMem2D_<float> DevMem2Df;
+
+namespace cv { namespace gpu { namespace impl {
+    extern "C" void load_constants(int ndisp, float disc_cost, float data_cost, float lambda);
+    extern "C" void comp_data_caller(const DevMem2D& l, const DevMem2D& r, DevMem2Df mdata);
+    extern "C" void data_down_kernel_caller(int dst_cols, int dst_rows, int src_rows, const DevMem2Df& src, DevMem2Df dst);
+    extern "C" void level_up(int dst_idx, int dst_cols, int dst_rows, int src_rows, DevMem2Df* mu, DevMem2Df* md, DevMem2Df* ml, DevMem2Df* mr);
+    extern "C" void call_all_iterations(int cols, int rows, int iters, DevMem2Df& u, DevMem2Df& d, DevMem2Df& l, DevMem2Df& r, const DevMem2Df& data);
+    extern "C" void output_caller(const DevMem2Df& u, const DevMem2Df& d, const DevMem2Df& l, const DevMem2Df& r, const DevMem2Df& data, DevMem2D disp);
+}}}
+
+cv::gpu::StereoBeliefPropagation_GPU::StereoBeliefPropagation_GPU(int ndisp_, int iters_, int levels_, float disc_cost_, float data_cost_, float lambda_)
+    : ndisp(ndisp_), iters(iters_), levels(levels_), disc_cost(disc_cost_), data_cost(data_cost_), lambda(lambda_), datas(levels_) 
+{
+    const int max_supported_ndisp = 1 << (sizeof(unsigned char) * 8);
+
+    CV_Assert(0 < ndisp && ndisp <= max_supported_ndisp);
+    CV_Assert(ndisp % 8 == 0);
+}
+
+void cv::gpu::StereoBeliefPropagation_GPU::operator()(const GpuMat& left, const GpuMat& right, GpuMat& disp)
+{    
+    CV_DbgAssert(left.cols == right.cols && left.rows == right.rows && left.type() == right.type() && left.type() == CV_8U);
+
+    const Scalar zero = Scalar::all(0);
+
+    int rows = left.rows;
+    int cols = left.cols;
+
+    int divisor = (int)pow(2.f, levels - 1.0f);
+    int lowest_cols = cols / divisor;
+    int lowest_rows = rows / divisor;
+    const int min_image_dim_size = 20;
+    CV_Assert(min(lowest_cols, lowest_rows) > min_image_dim_size);    
+
+    disp.create(rows, cols, CV_8U);
+
+    u.create(rows * ndisp, cols, CV_32F);  
+    d.create(rows * ndisp, cols, CV_32F);  
+    l.create(rows * ndisp, cols, CV_32F);  
+    r.create(rows * ndisp, cols, CV_32F);  
+
+    if (levels & 1)
+    {
+        u = zero; //can clear less area
+        d = zero;
+        l = zero;
+        r = zero;
+    }
+
+    if (levels > 1)
+    {
+        int less_rows = (rows + 1) / 2;
+        int less_cols = (cols + 1) / 2;
+
+        u2.create(less_rows * ndisp, less_cols, CV_32F);
+        d2.create(less_rows * ndisp, less_cols, CV_32F);
+        l2.create(less_rows * ndisp, less_cols, CV_32F);
+        r2.create(less_rows * ndisp, less_cols, CV_32F);
+
+        if ((levels & 1) == 0)
+        {
+            u2 = zero;
+            d2 = zero;
+            l2 = zero;
+            r2 = zero;    
+        }
+    }       
+
+    impl::load_constants(ndisp, disc_cost, data_cost, lambda);
+     
+    vector<int> cols_all(levels);
+    vector<int> rows_all(levels);
+    vector<int> iters_all(levels);
+
+    cols_all[0] = cols;
+    rows_all[0] = rows;
+    iters_all[0] = iters;
+
+    datas[0].create(rows * ndisp, cols, CV_32F);
+    //datas[0] = Scalar(data_cost); //DOTO did in kernel, but not sure if correct
+
+    impl::comp_data_caller(left, right, datas.front());
+
+    for (int i = 1; i < levels; i++) 
+    {
+        cols_all[i] = (cols_all[i-1] + 1)/2;
+        rows_all[i] = (rows_all[i-1] + 1)/2;
+
+        // this is difference from Felzenszwalb algorithm
+        // we reduce iters num for each next level
+        iters_all[i] = max(2 * iters_all[i-1] / 3, 1);
+
+        datas[i].create(rows_all[i] * ndisp, cols_all[i], CV_32F);               
+
+        impl::data_down_kernel_caller(cols_all[i], rows_all[i], rows_all[i-1], datas[i-1], datas[i]);
+    }
+    
+    DevMem2D_<float> mus[] = {u, u2}; 
+    DevMem2D_<float> mds[] = {d, d2};
+    DevMem2D_<float> mrs[] = {r, r2}; 
+    DevMem2D_<float> mls[] = {l, l2};
+
+    int mem_idx = (levels & 1) ? 0 : 1;
+
+    for (int i = levels - 1; i >= 0; i--) // for lower level we have already computed messages by setting to zero
+    {                        
+        if (i != levels - 1) 
+            impl::level_up(mem_idx, cols_all[i], rows_all[i], rows_all[i+1], mus, mds, mls, mrs);
+
+        impl::call_all_iterations(cols_all[i], rows_all[i], iters_all[i], mus[mem_idx], mds[mem_idx], mls[mem_idx], mrs[mem_idx], datas[i]);
+
+        mem_idx = (mem_idx + 1) & 1;
+    }
+
+    impl::output_caller(u, d, l, r, datas.front(), disp);
+}
+
+#endif /* !defined (HAVE_CUDA) */

modules/gpu/src/cudastream.cpp

@@ -52,18 +52,18 @@ void cv::gpu::CudaStream::create() { throw_nogpu(); }
 void cv::gpu::CudaStream::release() { throw_nogpu(); }
 cv::gpu::CudaStream::CudaStream() : impl(0) { throw_nogpu(); }
 cv::gpu::CudaStream::~CudaStream() { throw_nogpu(); }
-cv::gpu::CudaStream::CudaStream(const CudaStream& stream) { throw_nogpu(); }
-CudaStream& cv::gpu::CudaStream::operator=(const CudaStream& stream) { throw_nogpu(); return *this; }
+cv::gpu::CudaStream::CudaStream(const CudaStream& /*stream*/) { throw_nogpu(); }
+CudaStream& cv::gpu::CudaStream::operator=(const CudaStream& /*stream*/) { throw_nogpu(); return *this; }
 bool cv::gpu::CudaStream::queryIfComplete() { throw_nogpu(); return true; }
 void cv::gpu::CudaStream::waitForCompletion() { throw_nogpu(); }
-void cv::gpu::CudaStream::enqueueDownload(const GpuMat& src, Mat& dst) { throw_nogpu(); }
-void cv::gpu::CudaStream::enqueueDownload(const GpuMat& src, MatPL& dst) { throw_nogpu(); }
-void cv::gpu::CudaStream::enqueueUpload(const MatPL& src, GpuMat& dst) { throw_nogpu(); }
-void cv::gpu::CudaStream::enqueueUpload(const Mat& src, GpuMat& dst) { throw_nogpu(); }
-void cv::gpu::CudaStream::enqueueCopy(const GpuMat& src, GpuMat& dst) { throw_nogpu(); }
-void cv::gpu::CudaStream::enqueueMemSet(const GpuMat& src, Scalar val) { throw_nogpu(); }
-void cv::gpu::CudaStream::enqueueMemSet(const GpuMat& src, Scalar val, const GpuMat& mask) { throw_nogpu(); }
-void cv::gpu::CudaStream::enqueueConvert(const GpuMat& src, GpuMat& dst, int type, double a, double b) { throw_nogpu(); }
+void cv::gpu::CudaStream::enqueueDownload(const GpuMat& /*src*/, Mat& /*dst*/) { throw_nogpu(); }
+void cv::gpu::CudaStream::enqueueDownload(const GpuMat& /*src*/, MatPL& /*dst*/) { throw_nogpu(); }
+void cv::gpu::CudaStream::enqueueUpload(const MatPL& /*src*/, GpuMat& /*dst*/) { throw_nogpu(); }
+void cv::gpu::CudaStream::enqueueUpload(const Mat& /*src*/, GpuMat& /*dst*/) { throw_nogpu(); }
+void cv::gpu::CudaStream::enqueueCopy(const GpuMat& /*src*/, GpuMat& /*dst*/) { throw_nogpu(); }
+void cv::gpu::CudaStream::enqueueMemSet(const GpuMat& /*src*/, Scalar /*val*/) { throw_nogpu(); }
+void cv::gpu::CudaStream::enqueueMemSet(const GpuMat& /*src*/, Scalar /*val*/, const GpuMat& /*mask*/) { throw_nogpu(); }
+void cv::gpu::CudaStream::enqueueConvert(const GpuMat& /*src*/, GpuMat& /*dst*/, int /*type*/, double /*a*/, double /*b*/) { throw_nogpu(); }
 
 #else /* !defined (HAVE_CUDA) */
 

modules/gpu/src/precomp.hpp

@@ -54,13 +54,12 @@
 #include <limits>
 
 #include "opencv2/gpu/gpu.hpp"
-#include "opencv2/gpu/stream_accessor.hpp"
-
 
 #if defined(HAVE_CUDA)
 
     #include "cuda_shared.hpp"
     #include "cuda_runtime_api.h"
+    #include "opencv2/gpu/stream_accessor.hpp"
 
 #else /* defined(HAVE_CUDA) */
 

modules/gpu/src/stereobm_gpu.cpp

@@ -98,7 +98,7 @@ bool cv::gpu::StereoBM_GPU::checkIfGpuCallReasonable()
     return false;
 }
 
-void stereo_gpu_operator ( GpuMat& minSSD,  GpuMat& leBuf, GpuMat&  riBuf,  int preset, int ndisp, int winSize, float avergeTexThreshold, const GpuMat& left, const GpuMat& right, GpuMat& disparity, const cudaStream_t & stream)
+static void stereo_bm_gpu_operator ( GpuMat& minSSD,  GpuMat& leBuf, GpuMat&  riBuf,  int preset, int ndisp, int winSize, float avergeTexThreshold, const GpuMat& left, const GpuMat& right, GpuMat& disparity, const cudaStream_t & stream)
 {
     CV_DbgAssert(left.rows == right.rows && left.cols == right.cols);
     CV_DbgAssert(left.type() == CV_8UC1);
@@ -131,12 +131,12 @@ void stereo_gpu_operator ( GpuMat& minSSD,  GpuMat& leBuf, GpuMat&  riBuf,  int
 
 void cv::gpu::StereoBM_GPU::operator() ( const GpuMat& left, const GpuMat& right, GpuMat& disparity)
 {
-    ::stereo_gpu_operator(minSSD, leBuf, riBuf, preset, ndisp, winSize, avergeTexThreshold, left, right, disparity, 0);
+    ::stereo_bm_gpu_operator(minSSD, leBuf, riBuf, preset, ndisp, winSize, avergeTexThreshold, left, right, disparity, 0);
 }
 
 void cv::gpu::StereoBM_GPU::operator() ( const GpuMat& left, const GpuMat& right, GpuMat& disparity, const CudaStream& stream)
 {
-    ::stereo_gpu_operator(minSSD, leBuf, riBuf, preset, ndisp, winSize, avergeTexThreshold, left, right, disparity, StreamAccessor::getStream(stream));
+    ::stereo_bm_gpu_operator(minSSD, leBuf, riBuf, preset, ndisp, winSize, avergeTexThreshold, left, right, disparity, StreamAccessor::getStream(stream));
 }
 
 #endif /* !defined (HAVE_CUDA) */