changed StereoBeliefPropagation_GPU output disparity default type to CV_32S

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

@@ -387,8 +387,8 @@ namespace cv
             //! number of levels, truncation of discontinuity cost, truncation of data cost and weighting of data cost.
             StereoBeliefPropagation_GPU(int ndisp, int iters, int levels, float disc_cost, float data_cost, float lambda);
 
-            //! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair
-            //! Output disparity has CV_8U type.
+            //! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair,
+            //! if disparity is empty output type will be CV_32S else output type will be disparity.type().
             void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity);
             
             //! Acync version
@@ -409,8 +409,8 @@ namespace cv
             float lambda;
         private:
             GpuMat u, d, l, r, u2, d2, l2, r2;
-
-            std::vector<GpuMat> datas;
+            std::vector<GpuMat> datas;            
+            GpuMat out;
         };        
     }
 }

modules/gpu/src/beliefpropagation_gpu.cpp

@@ -63,6 +63,7 @@ static const float DEFAULT_DATA_COST   = 10.0f;
 static const float DEFAULT_LAMBDA_COST = 0.07f;
 
 typedef DevMem2D_<float> DevMem2Df;
+typedef DevMem2D_<int> DevMem2Di;
 
 namespace cv { namespace gpu { namespace impl {
     extern "C" void load_constants(int ndisp, float disc_cost, float data_cost, float lambda);
@@ -70,31 +71,27 @@ namespace cv { namespace gpu { namespace impl {
     extern "C" void data_down_kernel_caller(int dst_cols, int dst_rows, int src_rows, const DevMem2Df& src, DevMem2Df dst, const cudaStream_t& stream);
     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, const cudaStream_t& stream);
     extern "C" void call_all_iterations(int cols, int rows, int iters, DevMem2Df& u, DevMem2Df& d, DevMem2Df& l, DevMem2Df& r, const DevMem2Df& data, const cudaStream_t& stream);
-    extern "C" void output_caller(const DevMem2Df& u, const DevMem2Df& d, const DevMem2Df& l, const DevMem2Df& r, const DevMem2Df& data, DevMem2D disp, const cudaStream_t& stream);
+    extern "C" void output_caller(const DevMem2Df& u, const DevMem2Df& d, const DevMem2Df& l, const DevMem2Df& r, const DevMem2Df& data, DevMem2Di disp, const cudaStream_t& stream);
 }}}
 
 cv::gpu::StereoBeliefPropagation_GPU::StereoBeliefPropagation_GPU(int ndisp_, int iters_, int levels_)
  : ndisp(ndisp_), iters(iters_), levels(levels_), disc_cost(DEFAULT_DISC_COST), data_cost(DEFAULT_DATA_COST), lambda(DEFAULT_LAMBDA_COST), datas(levels_) 
 {
-    const int max_supported_ndisp = 1 << (sizeof(unsigned char) * 8);
-
-    CV_Assert(0 < ndisp && ndisp <= max_supported_ndisp);
+    CV_Assert(0 < ndisp);
     CV_Assert(ndisp % 8 == 0);
 }
 
 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(0 < ndisp);
     CV_Assert(ndisp % 8 == 0);
 }
 
 static void stereo_bp_gpu_operator(int ndisp, int iters, int levels, float disc_cost, float data_cost, float lambda, 
                                    GpuMat& u, GpuMat& d, GpuMat& l, GpuMat& r, 
-                                   GpuMat& u2, GpuMat& d2, GpuMat& l2, GpuMat& r2, 
-                                   vector<GpuMat>& datas, 
+                                   GpuMat& u2, GpuMat& d2, GpuMat& l2, GpuMat& r2,
+                                   vector<GpuMat>& datas, GpuMat& out,
                                    const GpuMat& left, const GpuMat& right, GpuMat& disp, 
                                    const cudaStream_t& stream)
 {
@@ -111,8 +108,6 @@ static void stereo_bp_gpu_operator(int ndisp, int iters, int levels, float disc_
     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);  
@@ -146,10 +141,16 @@ static void stereo_bp_gpu_operator(int ndisp, int iters, int levels, float disc_
     }       
 
     impl::load_constants(ndisp, disc_cost, data_cost, lambda);
-     
-    vector<int> cols_all(levels);
-    vector<int> rows_all(levels);
-    vector<int> iters_all(levels);
+
+    datas.resize(levels);
+    
+    AutoBuffer<int> cols_all_buf(levels);
+    AutoBuffer<int> rows_all_buf(levels);
+    AutoBuffer<int> iters_all_buf(levels);
+
+    int *cols_all = cols_all_buf;
+    int *rows_all = rows_all_buf;
+    int *iters_all = iters_all_buf;
 
     cols_all[0] = cols;
     rows_all[0] = rows;
@@ -190,18 +191,34 @@ static void stereo_bp_gpu_operator(int ndisp, int iters, int levels, float disc_
 
         mem_idx = (mem_idx + 1) & 1;
     }
+    
+    if (disp.empty())
+        disp.create(rows, cols, CV_32S);
 
-    impl::output_caller(u, d, l, r, datas.front(), disp, stream);
+    if (disp.type() == CV_32S)
+    {
+        disp = zero;
+        impl::output_caller(u, d, l, r, datas.front(), disp, stream);
+    }
+    else
+    {    
+        out.create(rows, cols, CV_32S);
+        out = zero;
+
+        impl::output_caller(u, d, l, r, datas.front(), out, stream);
+        
+        out.convertTo(disp, disp.type());
+    }
 }
 
 void cv::gpu::StereoBeliefPropagation_GPU::operator()(const GpuMat& left, const GpuMat& right, GpuMat& disp)
 {    
-    ::stereo_bp_gpu_operator(ndisp, iters, levels, disc_cost, data_cost, lambda, u, d, l, r, u2, d2, l2, r2, datas, left, right, disp, 0);
+    ::stereo_bp_gpu_operator(ndisp, iters, levels, disc_cost, data_cost, lambda, u, d, l, r, u2, d2, l2, r2, datas, out, left, right, disp, 0);
 }
 
 void cv::gpu::StereoBeliefPropagation_GPU::operator()(const GpuMat& left, const GpuMat& right, GpuMat& disp, const CudaStream& stream)
 {
-    ::stereo_bp_gpu_operator(ndisp, iters, levels, disc_cost, data_cost, lambda, u, d, l, r, u2, d2, l2, r2, datas, left, right, disp, StreamAccessor::getStream(stream));
+    ::stereo_bp_gpu_operator(ndisp, iters, levels, disc_cost, data_cost, lambda, u, d, l, r, u2, d2, l2, r2, datas, out, left, right, disp, StreamAccessor::getStream(stream));
 }
 
 bool cv::gpu::StereoBeliefPropagation_GPU::checkIfGpuCallReasonable()

modules/gpu/src/cuda/beliefpropagation.cu

@@ -353,42 +353,41 @@ namespace cv { namespace gpu { namespace impl {
 
 namespace beliefpropagation_gpu
 {  
-    __global__ void output(int cols, int rows, float *u, float *d, float *l, float *r, float* data, size_t step, unsigned char *disp, size_t res_step) 
+    __global__ void output(int cols, int rows, float *u, float *d, float *l, float *r, float* data, size_t step, int *disp, size_t res_step) 
     {   
         int x = blockIdx.x * blockDim.x + threadIdx.x;
         int y = blockIdx.y * blockDim.y + threadIdx.y;
 
-        if (y > 0 && y < rows - 1)
-            if (x > 0 && x < cols - 1)
-            {
-                float *us = u + (y + 1) * step + x;
-                float *ds = d + (y - 1) * step + x;
-                float *ls = l + y * step + (x + 1);
-                float *rs = r + y * step + (x - 1);
-                float *dt = data + y * step + x;
+        if (y > 0 && y < rows - 1 && x > 0 && x < cols - 1)
+        {
+            float *us = u + (y + 1) * step + x;
+            float *ds = d + (y - 1) * step + x;
+            float *ls = l + y * step + (x + 1);
+            float *rs = r + y * step + (x - 1);
+            float *dt = data + y * step + x;
 
-                size_t disp_step = rows * step;
+            size_t disp_step = rows * step;
 
-                int best = 0;
-                float best_val = FLT_MAX;
-                for (int d = 0; d < cndisp; ++d) 
-                {
-                    float val = us[d * disp_step] + ds[d * disp_step] + ls[d * disp_step] + rs[d * disp_step] + dt[d * disp_step];
+            int best = 0;
+            float best_val = FLT_MAX;
+            for (int d = 0; d < cndisp; ++d) 
+            {
+                float val = us[d * disp_step] + ds[d * disp_step] + ls[d * disp_step] + rs[d * disp_step] + dt[d * disp_step];
 
-                    if (val < best_val) 
-                    {
-                        best_val = val;
-                        best = d;
-                    }
+                if (val < best_val) 
+                {
+                    best_val = val;
+                    best = d;
                 }
-
-                disp[res_step * y + x] = best & 0xFF;                           
             }
+
+            disp[res_step * y + x] = best;                           
+        }
     }
 }
 
 namespace cv { namespace gpu { namespace impl {
-    extern "C" void output_caller(const DevMem2D_<float>& u, const DevMem2D_<float>& d, const DevMem2D_<float>& l, const DevMem2D_<float>& r, const DevMem2D_<float>& data, DevMem2D disp, const cudaStream_t& stream)
+    extern "C" void output_caller(const DevMem2D_<float>& u, const DevMem2D_<float>& d, const DevMem2D_<float>& l, const DevMem2D_<float>& r, const DevMem2D_<float>& data, DevMem2D_<int> disp, const cudaStream_t& stream)
     {    
         dim3 threads(32, 8, 1);
         dim3 grid(1, 1, 1);
@@ -398,12 +397,12 @@ namespace cv { namespace gpu { namespace impl {
 
         if (stream == 0)
         {
-            beliefpropagation_gpu::output<<<grid, threads>>>(disp.cols, disp.rows, u.ptr, d.ptr, l.ptr, r.ptr, data.ptr, u.step/sizeof(float), disp.ptr, disp.step);
+            beliefpropagation_gpu::output<<<grid, threads>>>(disp.cols, disp.rows, u.ptr, d.ptr, l.ptr, r.ptr, data.ptr, u.step/sizeof(float), disp.ptr, disp.step/sizeof(int));
             cudaSafeCall( cudaThreadSynchronize() );
         }
         else
         {            
-            beliefpropagation_gpu::output<<<grid, threads, 0, stream>>>(disp.cols, disp.rows, u.ptr, d.ptr, l.ptr, r.ptr, data.ptr, u.step/sizeof(float), disp.ptr, disp.step);
+            beliefpropagation_gpu::output<<<grid, threads, 0, stream>>>(disp.cols, disp.rows, u.ptr, d.ptr, l.ptr, r.ptr, data.ptr, u.step/sizeof(float), disp.ptr, disp.step/sizeof(int));
         }
     }
 }}}
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