1 /*M/////////////////////////////////////////////////////////////////////////////////////// 2 // 3 // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. 4 // 5 // By downloading, copying, installing or using the software you agree to this license. 6 // If you do not agree to this license, do not download, install, 7 // copy or use the software. 8 // 9 // 10 // License Agreement 11 // For Open Source Computer Vision Library 12 // 13 // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. 14 // Copyright (C) 2009, Willow Garage Inc., all rights reserved. 15 // Third party copyrights are property of their respective owners. 16 // 17 // Redistribution and use in source and binary forms, with or without modification, 18 // are permitted provided that the following conditions are met: 19 // 20 // * Redistribution's of source code must retain the above copyright notice, 21 // this list of conditions and the following disclaimer. 22 // 23 // * Redistribution's in binary form must reproduce the above copyright notice, 24 // this list of conditions and the following disclaimer in the documentation 25 // and/or other materials provided with the distribution. 26 // 27 // * The name of the copyright holders may not be used to endorse or promote products 28 // derived from this software without specific prior written permission. 29 // 30 // This software is provided by the copyright holders and contributors "as is" and 31 // any express or implied warranties, including, but not limited to, the implied 32 // warranties of merchantability and fitness for a particular purpose are disclaimed. 33 // In no event shall the Intel Corporation or contributors be liable for any direct, 34 // indirect, incidental, special, exemplary, or consequential damages 35 // (including, but not limited to, procurement of substitute goods or services; 36 // loss of use, data, or profits; or business interruption) however caused 37 // and on any theory of liability, whether in contract, strict liability, 38 // or tort (including negligence or otherwise) arising in any way out of 39 // the use of this software, even if advised of the possibility of such damage. 40 // 41 //M*/ 42 43 #if !defined CUDA_DISABLER 44 45 #include "opencv2/core/cuda/common.hpp" 46 #include "opencv2/core/cuda/saturate_cast.hpp" 47 #include "opencv2/core/cuda/limits.hpp" 48 #include "opencv2/core/cuda/reduce.hpp" 49 #include "opencv2/core/cuda/functional.hpp" 50 51 #include "cuda/stereocsbp.hpp" 52 53 namespace cv { namespace cuda { namespace device 54 { 55 namespace stereocsbp 56 { 57 /////////////////////////////////////////////////////////////// 58 /////////////////////// init data cost //////////////////////// 59 /////////////////////////////////////////////////////////////// 60 61 template <int channels> static float __device__ pixeldiff(const uchar* left, const uchar* right, float max_data_term); 62 template<> __device__ __forceinline__ float pixeldiff<1>(const uchar* left, const uchar* right, float max_data_term) 63 { 64 return fminf( ::abs((int)*left - *right), max_data_term); 65 } 66 template<> __device__ __forceinline__ float pixeldiff<3>(const uchar* left, const uchar* right, float max_data_term) 67 { 68 float tb = 0.114f * ::abs((int)left[0] - right[0]); 69 float tg = 0.587f * ::abs((int)left[1] - right[1]); 70 float tr = 0.299f * ::abs((int)left[2] - right[2]); 71 72 return fminf(tr + tg + tb, max_data_term); 73 } 74 template<> __device__ __forceinline__ float pixeldiff<4>(const uchar* left, const uchar* right, float max_data_term) 75 { 76 uchar4 l = *((const uchar4*)left); 77 uchar4 r = *((const uchar4*)right); 78 79 float tb = 0.114f * ::abs((int)l.x - r.x); 80 float tg = 0.587f * ::abs((int)l.y - r.y); 81 float tr = 0.299f * ::abs((int)l.z - r.z); 82 83 return fminf(tr + tg + tb, max_data_term); 84 } 85 86 template <typename T> 87 __global__ void get_first_k_initial_global(uchar *ctemp, T* data_cost_selected_, T *selected_disp_pyr, int h, int w, int nr_plane, int ndisp, 88 size_t msg_step, size_t disp_step) 89 { 90 int x = blockIdx.x * blockDim.x + threadIdx.x; 91 int y = blockIdx.y * blockDim.y + threadIdx.y; 92 93 if (y < h && x < w) 94 { 95 T* selected_disparity = selected_disp_pyr + y * msg_step + x; 96 T* data_cost_selected = data_cost_selected_ + y * msg_step + x; 97 T* data_cost = (T*)ctemp + y * msg_step + x; 98 99 for(int i = 0; i < nr_plane; i++) 100 { 101 T minimum = device::numeric_limits<T>::max(); 102 int id = 0; 103 for(int d = 0; d < ndisp; d++) 104 { 105 T cur = data_cost[d * disp_step]; 106 if(cur < minimum) 107 { 108 minimum = cur; 109 id = d; 110 } 111 } 112 113 data_cost_selected[i * disp_step] = minimum; 114 selected_disparity[i * disp_step] = id; 115 data_cost [id * disp_step] = numeric_limits<T>::max(); 116 } 117 } 118 } 119 120 121 template <typename T> 122 __global__ void get_first_k_initial_local(uchar *ctemp, T* data_cost_selected_, T* selected_disp_pyr, int h, int w, int nr_plane, int ndisp, 123 size_t msg_step, size_t disp_step) 124 { 125 int x = blockIdx.x * blockDim.x + threadIdx.x; 126 int y = blockIdx.y * blockDim.y + threadIdx.y; 127 128 if (y < h && x < w) 129 { 130 T* selected_disparity = selected_disp_pyr + y * msg_step + x; 131 T* data_cost_selected = data_cost_selected_ + y * msg_step + x; 132 T* data_cost = (T*)ctemp + y * msg_step + x; 133 134 int nr_local_minimum = 0; 135 136 T prev = data_cost[0 * disp_step]; 137 T cur = data_cost[1 * disp_step]; 138 T next = data_cost[2 * disp_step]; 139 140 for (int d = 1; d < ndisp - 1 && nr_local_minimum < nr_plane; d++) 141 { 142 if (cur < prev && cur < next) 143 { 144 data_cost_selected[nr_local_minimum * disp_step] = cur; 145 selected_disparity[nr_local_minimum * disp_step] = d; 146 147 data_cost[d * disp_step] = numeric_limits<T>::max(); 148 149 nr_local_minimum++; 150 } 151 prev = cur; 152 cur = next; 153 next = data_cost[(d + 1) * disp_step]; 154 } 155 156 for (int i = nr_local_minimum; i < nr_plane; i++) 157 { 158 T minimum = numeric_limits<T>::max(); 159 int id = 0; 160 161 for (int d = 0; d < ndisp; d++) 162 { 163 cur = data_cost[d * disp_step]; 164 if (cur < minimum) 165 { 166 minimum = cur; 167 id = d; 168 } 169 } 170 data_cost_selected[i * disp_step] = minimum; 171 selected_disparity[i * disp_step] = id; 172 173 data_cost[id * disp_step] = numeric_limits<T>::max(); 174 } 175 } 176 } 177 178 template <typename T, int channels> 179 __global__ void init_data_cost(const uchar *cleft, const uchar *cright, uchar *ctemp, size_t cimg_step, 180 int h, int w, int level, int ndisp, float data_weight, float max_data_term, 181 int min_disp, size_t msg_step, size_t disp_step) 182 { 183 int x = blockIdx.x * blockDim.x + threadIdx.x; 184 int y = blockIdx.y * blockDim.y + threadIdx.y; 185 186 if (y < h && x < w) 187 { 188 int y0 = y << level; 189 int yt = (y + 1) << level; 190 191 int x0 = x << level; 192 int xt = (x + 1) << level; 193 194 T* data_cost = (T*)ctemp + y * msg_step + x; 195 196 for(int d = 0; d < ndisp; ++d) 197 { 198 float val = 0.0f; 199 for(int yi = y0; yi < yt; yi++) 200 { 201 for(int xi = x0; xi < xt; xi++) 202 { 203 int xr = xi - d; 204 if(d < min_disp || xr < 0) 205 val += data_weight * max_data_term; 206 else 207 { 208 const uchar* lle = cleft + yi * cimg_step + xi * channels; 209 const uchar* lri = cright + yi * cimg_step + xr * channels; 210 211 val += data_weight * pixeldiff<channels>(lle, lri, max_data_term); 212 } 213 } 214 } 215 data_cost[disp_step * d] = saturate_cast<T>(val); 216 } 217 } 218 } 219 220 template <typename T, int winsz, int channels> 221 __global__ void init_data_cost_reduce(const uchar *cleft, const uchar *cright, uchar *ctemp, size_t cimg_step, 222 int level, int rows, int cols, int h, int ndisp, float data_weight, float max_data_term, 223 int min_disp, size_t msg_step, size_t disp_step) 224 { 225 int x_out = blockIdx.x; 226 int y_out = blockIdx.y % h; 227 int d = (blockIdx.y / h) * blockDim.z + threadIdx.z; 228 229 int tid = threadIdx.x; 230 231 if (d < ndisp) 232 { 233 int x0 = x_out << level; 234 int y0 = y_out << level; 235 236 int len = ::min(y0 + winsz, rows) - y0; 237 238 float val = 0.0f; 239 if (x0 + tid < cols) 240 { 241 if (x0 + tid - d < 0 || d < min_disp) 242 val = data_weight * max_data_term * len; 243 else 244 { 245 const uchar* lle = cleft + y0 * cimg_step + channels * (x0 + tid ); 246 const uchar* lri = cright + y0 * cimg_step + channels * (x0 + tid - d); 247 248 for(int y = 0; y < len; ++y) 249 { 250 val += data_weight * pixeldiff<channels>(lle, lri, max_data_term); 251 252 lle += cimg_step; 253 lri += cimg_step; 254 } 255 } 256 } 257 258 extern __shared__ float smem[]; 259 260 reduce<winsz>(smem + winsz * threadIdx.z, val, tid, plus<float>()); 261 262 T* data_cost = (T*)ctemp + y_out * msg_step + x_out; 263 264 if (tid == 0) 265 data_cost[disp_step * d] = saturate_cast<T>(val); 266 } 267 } 268 269 270 template <typename T> 271 void init_data_cost_caller_(const uchar *cleft, const uchar *cright, uchar *ctemp, size_t cimg_step, int /*rows*/, int /*cols*/, int h, int w, int level, int ndisp, int channels, float data_weight, float max_data_term, int min_disp, size_t msg_step, size_t disp_step, cudaStream_t stream) 272 { 273 dim3 threads(32, 8, 1); 274 dim3 grid(1, 1, 1); 275 276 grid.x = divUp(w, threads.x); 277 grid.y = divUp(h, threads.y); 278 279 switch (channels) 280 { 281 case 1: init_data_cost<T, 1><<<grid, threads, 0, stream>>>(cleft, cright, ctemp, cimg_step, h, w, level, ndisp, data_weight, max_data_term, min_disp, msg_step, disp_step); break; 282 case 3: init_data_cost<T, 3><<<grid, threads, 0, stream>>>(cleft, cright, ctemp, cimg_step, h, w, level, ndisp, data_weight, max_data_term, min_disp, msg_step, disp_step); break; 283 case 4: init_data_cost<T, 4><<<grid, threads, 0, stream>>>(cleft, cright, ctemp, cimg_step, h, w, level, ndisp, data_weight, max_data_term, min_disp, msg_step, disp_step); break; 284 default: CV_Error(cv::Error::BadNumChannels, "Unsupported channels count"); 285 } 286 } 287 288 template <typename T, int winsz> 289 void init_data_cost_reduce_caller_(const uchar *cleft, const uchar *cright, uchar *ctemp, size_t cimg_step, int rows, int cols, int h, int w, int level, int ndisp, int channels, float data_weight, float max_data_term, int min_disp, size_t msg_step, size_t disp_step, cudaStream_t stream) 290 { 291 const int threadsNum = 256; 292 const size_t smem_size = threadsNum * sizeof(float); 293 294 dim3 threads(winsz, 1, threadsNum / winsz); 295 dim3 grid(w, h, 1); 296 grid.y *= divUp(ndisp, threads.z); 297 298 switch (channels) 299 { 300 case 1: init_data_cost_reduce<T, winsz, 1><<<grid, threads, smem_size, stream>>>(cleft, cright, ctemp, cimg_step, level, rows, cols, h, ndisp, data_weight, max_data_term, min_disp, msg_step, disp_step); break; 301 case 3: init_data_cost_reduce<T, winsz, 3><<<grid, threads, smem_size, stream>>>(cleft, cright, ctemp, cimg_step, level, rows, cols, h, ndisp, data_weight, max_data_term, min_disp, msg_step, disp_step); break; 302 case 4: init_data_cost_reduce<T, winsz, 4><<<grid, threads, smem_size, stream>>>(cleft, cright, ctemp, cimg_step, level, rows, cols, h, ndisp, data_weight, max_data_term, min_disp, msg_step, disp_step); break; 303 default: CV_Error(cv::Error::BadNumChannels, "Unsupported channels count"); 304 } 305 } 306 307 template<class T> 308 void init_data_cost(const uchar *cleft, const uchar *cright, uchar *ctemp, size_t cimg_step, int rows, int cols, T* disp_selected_pyr, T* data_cost_selected, size_t msg_step, 309 int h, int w, int level, int nr_plane, int ndisp, int channels, float data_weight, float max_data_term, int min_disp, bool use_local_init_data_cost, cudaStream_t stream) 310 { 311 312 typedef void (*InitDataCostCaller)(const uchar *cleft, const uchar *cright, uchar *ctemp, size_t cimg_step, int cols, int rows, int w, int h, int level, int ndisp, int channels, float data_weight, float max_data_term, int min_disp, size_t msg_step, size_t disp_step, cudaStream_t stream); 313 314 static const InitDataCostCaller init_data_cost_callers[] = 315 { 316 init_data_cost_caller_<T>, init_data_cost_caller_<T>, init_data_cost_reduce_caller_<T, 4>, 317 init_data_cost_reduce_caller_<T, 8>, init_data_cost_reduce_caller_<T, 16>, init_data_cost_reduce_caller_<T, 32>, 318 init_data_cost_reduce_caller_<T, 64>, init_data_cost_reduce_caller_<T, 128>, init_data_cost_reduce_caller_<T, 256> 319 }; 320 321 size_t disp_step = msg_step * h; 322 323 init_data_cost_callers[level](cleft, cright, ctemp, cimg_step, rows, cols, h, w, level, ndisp, channels, data_weight, max_data_term, min_disp, msg_step, disp_step, stream); 324 cudaSafeCall( cudaGetLastError() ); 325 326 if (stream == 0) 327 cudaSafeCall( cudaDeviceSynchronize() ); 328 329 dim3 threads(32, 8, 1); 330 dim3 grid(1, 1, 1); 331 332 grid.x = divUp(w, threads.x); 333 grid.y = divUp(h, threads.y); 334 335 if (use_local_init_data_cost == true) 336 get_first_k_initial_local<<<grid, threads, 0, stream>>> (ctemp, data_cost_selected, disp_selected_pyr, h, w, nr_plane, ndisp, msg_step, disp_step); 337 else 338 get_first_k_initial_global<<<grid, threads, 0, stream>>>(ctemp, data_cost_selected, disp_selected_pyr, h, w, nr_plane, ndisp, msg_step, disp_step); 339 340 cudaSafeCall( cudaGetLastError() ); 341 342 if (stream == 0) 343 cudaSafeCall( cudaDeviceSynchronize() ); 344 } 345 346 template void init_data_cost<short>(const uchar *cleft, const uchar *cright, uchar *ctemp, size_t cimg_step, int rows, int cols, short* disp_selected_pyr, short* data_cost_selected, size_t msg_step, 347 int h, int w, int level, int nr_plane, int ndisp, int channels, float data_weight, float max_data_term, int min_disp, bool use_local_init_data_cost, cudaStream_t stream); 348 349 template void init_data_cost<float>(const uchar *cleft, const uchar *cright, uchar *ctemp, size_t cimg_step, int rows, int cols, float* disp_selected_pyr, float* data_cost_selected, size_t msg_step, 350 int h, int w, int level, int nr_plane, int ndisp, int channels, float data_weight, float max_data_term, int min_disp, bool use_local_init_data_cost, cudaStream_t stream); 351 352 /////////////////////////////////////////////////////////////// 353 ////////////////////// compute data cost ////////////////////// 354 /////////////////////////////////////////////////////////////// 355 356 template <typename T, int channels> 357 __global__ void compute_data_cost(const uchar *cleft, const uchar *cright, size_t cimg_step, const T* selected_disp_pyr, T* data_cost_, int h, int w, int level, int nr_plane, float data_weight, float max_data_term, int min_disp, size_t msg_step, size_t disp_step1, size_t disp_step2) 358 { 359 int x = blockIdx.x * blockDim.x + threadIdx.x; 360 int y = blockIdx.y * blockDim.y + threadIdx.y; 361 362 if (y < h && x < w) 363 { 364 int y0 = y << level; 365 int yt = (y + 1) << level; 366 367 int x0 = x << level; 368 int xt = (x + 1) << level; 369 370 const T* selected_disparity = selected_disp_pyr + y/2 * msg_step + x/2; 371 T* data_cost = data_cost_ + y * msg_step + x; 372 373 for(int d = 0; d < nr_plane; d++) 374 { 375 float val = 0.0f; 376 for(int yi = y0; yi < yt; yi++) 377 { 378 for(int xi = x0; xi < xt; xi++) 379 { 380 int sel_disp = selected_disparity[d * disp_step2]; 381 int xr = xi - sel_disp; 382 383 if (xr < 0 || sel_disp < min_disp) 384 val += data_weight * max_data_term; 385 else 386 { 387 const uchar* left_x = cleft + yi * cimg_step + xi * channels; 388 const uchar* right_x = cright + yi * cimg_step + xr * channels; 389 390 val += data_weight * pixeldiff<channels>(left_x, right_x, max_data_term); 391 } 392 } 393 } 394 data_cost[disp_step1 * d] = saturate_cast<T>(val); 395 } 396 } 397 } 398 399 template <typename T, int winsz, int channels> 400 __global__ void compute_data_cost_reduce(const uchar *cleft, const uchar *cright, size_t cimg_step, const T* selected_disp_pyr, T* data_cost_, int level, int rows, int cols, int h, int nr_plane, float data_weight, float max_data_term, int min_disp, size_t msg_step, size_t disp_step1, size_t disp_step2) 401 { 402 int x_out = blockIdx.x; 403 int y_out = blockIdx.y % h; 404 int d = (blockIdx.y / h) * blockDim.z + threadIdx.z; 405 406 int tid = threadIdx.x; 407 408 const T* selected_disparity = selected_disp_pyr + y_out/2 * msg_step + x_out/2; 409 T* data_cost = data_cost_ + y_out * msg_step + x_out; 410 411 if (d < nr_plane) 412 { 413 int sel_disp = selected_disparity[d * disp_step2]; 414 415 int x0 = x_out << level; 416 int y0 = y_out << level; 417 418 int len = ::min(y0 + winsz, rows) - y0; 419 420 float val = 0.0f; 421 if (x0 + tid < cols) 422 { 423 if (x0 + tid - sel_disp < 0 || sel_disp < min_disp) 424 val = data_weight * max_data_term * len; 425 else 426 { 427 const uchar* lle = cleft + y0 * cimg_step + channels * (x0 + tid ); 428 const uchar* lri = cright + y0 * cimg_step + channels * (x0 + tid - sel_disp); 429 430 for(int y = 0; y < len; ++y) 431 { 432 val += data_weight * pixeldiff<channels>(lle, lri, max_data_term); 433 434 lle += cimg_step; 435 lri += cimg_step; 436 } 437 } 438 } 439 440 extern __shared__ float smem[]; 441 442 reduce<winsz>(smem + winsz * threadIdx.z, val, tid, plus<float>()); 443 444 if (tid == 0) 445 data_cost[disp_step1 * d] = saturate_cast<T>(val); 446 } 447 } 448 449 template <typename T> 450 void compute_data_cost_caller_(const uchar *cleft, const uchar *cright, size_t cimg_step, const T* disp_selected_pyr, T* data_cost, int /*rows*/, int /*cols*/, 451 int h, int w, int level, int nr_plane, int channels, float data_weight, float max_data_term, int min_disp, size_t msg_step, size_t disp_step1, size_t disp_step2, cudaStream_t stream) 452 { 453 dim3 threads(32, 8, 1); 454 dim3 grid(1, 1, 1); 455 456 grid.x = divUp(w, threads.x); 457 grid.y = divUp(h, threads.y); 458 459 switch(channels) 460 { 461 case 1: compute_data_cost<T, 1><<<grid, threads, 0, stream>>>(cleft, cright, cimg_step, disp_selected_pyr, data_cost, h, w, level, nr_plane, data_weight, max_data_term, min_disp, msg_step, disp_step1, disp_step2); break; 462 case 3: compute_data_cost<T, 3><<<grid, threads, 0, stream>>>(cleft, cright, cimg_step, disp_selected_pyr, data_cost, h, w, level, nr_plane, data_weight, max_data_term, min_disp, msg_step, disp_step1, disp_step2); break; 463 case 4: compute_data_cost<T, 4><<<grid, threads, 0, stream>>>(cleft, cright, cimg_step, disp_selected_pyr, data_cost, h, w, level, nr_plane, data_weight, max_data_term, min_disp, msg_step, disp_step1, disp_step2); break; 464 default: CV_Error(cv::Error::BadNumChannels, "Unsupported channels count"); 465 } 466 } 467 468 template <typename T, int winsz> 469 void compute_data_cost_reduce_caller_(const uchar *cleft, const uchar *cright, size_t cimg_step, const T* disp_selected_pyr, T* data_cost, int rows, int cols, 470 int h, int w, int level, int nr_plane, int channels, float data_weight, float max_data_term, int min_disp, size_t msg_step, size_t disp_step1, size_t disp_step2, cudaStream_t stream) 471 { 472 const int threadsNum = 256; 473 const size_t smem_size = threadsNum * sizeof(float); 474 475 dim3 threads(winsz, 1, threadsNum / winsz); 476 dim3 grid(w, h, 1); 477 grid.y *= divUp(nr_plane, threads.z); 478 479 switch (channels) 480 { 481 case 1: compute_data_cost_reduce<T, winsz, 1><<<grid, threads, smem_size, stream>>>(cleft, cright, cimg_step, disp_selected_pyr, data_cost, level, rows, cols, h, nr_plane, data_weight, max_data_term, min_disp, msg_step, disp_step1, disp_step2); break; 482 case 3: compute_data_cost_reduce<T, winsz, 3><<<grid, threads, smem_size, stream>>>(cleft, cright, cimg_step, disp_selected_pyr, data_cost, level, rows, cols, h, nr_plane, data_weight, max_data_term, min_disp, msg_step, disp_step1, disp_step2); break; 483 case 4: compute_data_cost_reduce<T, winsz, 4><<<grid, threads, smem_size, stream>>>(cleft, cright, cimg_step, disp_selected_pyr, data_cost, level, rows, cols, h, nr_plane, data_weight, max_data_term, min_disp, msg_step, disp_step1, disp_step2); break; 484 default: CV_Error(cv::Error::BadNumChannels, "Unsupported channels count"); 485 } 486 } 487 488 template<class T> 489 void compute_data_cost(const uchar *cleft, const uchar *cright, size_t cimg_step, const T* disp_selected_pyr, T* data_cost, size_t msg_step, 490 int rows, int cols, int h, int w, int h2, int level, int nr_plane, int channels, float data_weight, float max_data_term, 491 int min_disp, cudaStream_t stream) 492 { 493 typedef void (*ComputeDataCostCaller)(const uchar *cleft, const uchar *cright, size_t cimg_step, const T* disp_selected_pyr, T* data_cost, int rows, int cols, 494 int h, int w, int level, int nr_plane, int channels, float data_weight, float max_data_term, int min_disp, size_t msg_step, size_t disp_step1, size_t disp_step2, cudaStream_t stream); 495 496 static const ComputeDataCostCaller callers[] = 497 { 498 compute_data_cost_caller_<T>, compute_data_cost_caller_<T>, compute_data_cost_reduce_caller_<T, 4>, 499 compute_data_cost_reduce_caller_<T, 8>, compute_data_cost_reduce_caller_<T, 16>, compute_data_cost_reduce_caller_<T, 32>, 500 compute_data_cost_reduce_caller_<T, 64>, compute_data_cost_reduce_caller_<T, 128>, compute_data_cost_reduce_caller_<T, 256> 501 }; 502 503 size_t disp_step1 = msg_step * h; 504 size_t disp_step2 = msg_step * h2; 505 506 callers[level](cleft, cright, cimg_step, disp_selected_pyr, data_cost, rows, cols, h, w, level, nr_plane, channels, data_weight, max_data_term, min_disp, msg_step, disp_step1, disp_step2, stream); 507 cudaSafeCall( cudaGetLastError() ); 508 509 if (stream == 0) 510 cudaSafeCall( cudaDeviceSynchronize() ); 511 } 512 513 template void compute_data_cost(const uchar *cleft, const uchar *cright, size_t cimg_step, const short* disp_selected_pyr, short* data_cost, size_t msg_step, 514 int rows, int cols, int h, int w, int h2, int level, int nr_plane, int channels, float data_weight, float max_data_term, int min_disp, cudaStream_t stream); 515 516 template void compute_data_cost(const uchar *cleft, const uchar *cright, size_t cimg_step, const float* disp_selected_pyr, float* data_cost, size_t msg_step, 517 int rows, int cols, int h, int w, int h2, int level, int nr_plane, int channels, float data_weight, float max_data_term, int min_disp, cudaStream_t stream); 518 519 520 /////////////////////////////////////////////////////////////// 521 //////////////////////// init message ///////////////////////// 522 /////////////////////////////////////////////////////////////// 523 524 525 template <typename T> 526 __device__ void get_first_k_element_increase(T* u_new, T* d_new, T* l_new, T* r_new, 527 const T* u_cur, const T* d_cur, const T* l_cur, const T* r_cur, 528 T* data_cost_selected, T* disparity_selected_new, T* data_cost_new, 529 const T* data_cost_cur, const T* disparity_selected_cur, 530 int nr_plane, int nr_plane2, size_t disp_step1, size_t disp_step2) 531 { 532 for(int i = 0; i < nr_plane; i++) 533 { 534 T minimum = numeric_limits<T>::max(); 535 int id = 0; 536 for(int j = 0; j < nr_plane2; j++) 537 { 538 T cur = data_cost_new[j * disp_step1]; 539 if(cur < minimum) 540 { 541 minimum = cur; 542 id = j; 543 } 544 } 545 546 data_cost_selected[i * disp_step1] = data_cost_cur[id * disp_step1]; 547 disparity_selected_new[i * disp_step1] = disparity_selected_cur[id * disp_step2]; 548 549 u_new[i * disp_step1] = u_cur[id * disp_step2]; 550 d_new[i * disp_step1] = d_cur[id * disp_step2]; 551 l_new[i * disp_step1] = l_cur[id * disp_step2]; 552 r_new[i * disp_step1] = r_cur[id * disp_step2]; 553 554 data_cost_new[id * disp_step1] = numeric_limits<T>::max(); 555 } 556 } 557 558 template <typename T> 559 __global__ void init_message(uchar *ctemp, T* u_new_, T* d_new_, T* l_new_, T* r_new_, 560 const T* u_cur_, const T* d_cur_, const T* l_cur_, const T* r_cur_, 561 T* selected_disp_pyr_new, const T* selected_disp_pyr_cur, 562 T* data_cost_selected_, const T* data_cost_, 563 int h, int w, int nr_plane, int h2, int w2, int nr_plane2, 564 size_t msg_step, size_t disp_step1, size_t disp_step2) 565 { 566 int x = blockIdx.x * blockDim.x + threadIdx.x; 567 int y = blockIdx.y * blockDim.y + threadIdx.y; 568 569 if (y < h && x < w) 570 { 571 const T* u_cur = u_cur_ + ::min(h2-1, y/2 + 1) * msg_step + x/2; 572 const T* d_cur = d_cur_ + ::max(0, y/2 - 1) * msg_step + x/2; 573 const T* l_cur = l_cur_ + (y/2) * msg_step + ::min(w2-1, x/2 + 1); 574 const T* r_cur = r_cur_ + (y/2) * msg_step + ::max(0, x/2 - 1); 575 576 T* data_cost_new = (T*)ctemp + y * msg_step + x; 577 578 const T* disparity_selected_cur = selected_disp_pyr_cur + y/2 * msg_step + x/2; 579 const T* data_cost = data_cost_ + y * msg_step + x; 580 581 for(int d = 0; d < nr_plane2; d++) 582 { 583 int idx2 = d * disp_step2; 584 585 T val = data_cost[d * disp_step1] + u_cur[idx2] + d_cur[idx2] + l_cur[idx2] + r_cur[idx2]; 586 data_cost_new[d * disp_step1] = val; 587 } 588 589 T* data_cost_selected = data_cost_selected_ + y * msg_step + x; 590 T* disparity_selected_new = selected_disp_pyr_new + y * msg_step + x; 591 592 T* u_new = u_new_ + y * msg_step + x; 593 T* d_new = d_new_ + y * msg_step + x; 594 T* l_new = l_new_ + y * msg_step + x; 595 T* r_new = r_new_ + y * msg_step + x; 596 597 u_cur = u_cur_ + y/2 * msg_step + x/2; 598 d_cur = d_cur_ + y/2 * msg_step + x/2; 599 l_cur = l_cur_ + y/2 * msg_step + x/2; 600 r_cur = r_cur_ + y/2 * msg_step + x/2; 601 602 get_first_k_element_increase(u_new, d_new, l_new, r_new, u_cur, d_cur, l_cur, r_cur, 603 data_cost_selected, disparity_selected_new, data_cost_new, 604 data_cost, disparity_selected_cur, nr_plane, nr_plane2, 605 disp_step1, disp_step2); 606 } 607 } 608 609 610 template<class T> 611 void init_message(uchar *ctemp, T* u_new, T* d_new, T* l_new, T* r_new, 612 const T* u_cur, const T* d_cur, const T* l_cur, const T* r_cur, 613 T* selected_disp_pyr_new, const T* selected_disp_pyr_cur, 614 T* data_cost_selected, const T* data_cost, size_t msg_step, 615 int h, int w, int nr_plane, int h2, int w2, int nr_plane2, cudaStream_t stream) 616 { 617 618 size_t disp_step1 = msg_step * h; 619 size_t disp_step2 = msg_step * h2; 620 621 dim3 threads(32, 8, 1); 622 dim3 grid(1, 1, 1); 623 624 grid.x = divUp(w, threads.x); 625 grid.y = divUp(h, threads.y); 626 627 init_message<<<grid, threads, 0, stream>>>(ctemp, u_new, d_new, l_new, r_new, 628 u_cur, d_cur, l_cur, r_cur, 629 selected_disp_pyr_new, selected_disp_pyr_cur, 630 data_cost_selected, data_cost, 631 h, w, nr_plane, h2, w2, nr_plane2, 632 msg_step, disp_step1, disp_step2); 633 cudaSafeCall( cudaGetLastError() ); 634 635 if (stream == 0) 636 cudaSafeCall( cudaDeviceSynchronize() ); 637 } 638 639 640 template void init_message(uchar *ctemp, short* u_new, short* d_new, short* l_new, short* r_new, 641 const short* u_cur, const short* d_cur, const short* l_cur, const short* r_cur, 642 short* selected_disp_pyr_new, const short* selected_disp_pyr_cur, 643 short* data_cost_selected, const short* data_cost, size_t msg_step, 644 int h, int w, int nr_plane, int h2, int w2, int nr_plane2, cudaStream_t stream); 645 646 template void init_message(uchar *ctemp, float* u_new, float* d_new, float* l_new, float* r_new, 647 const float* u_cur, const float* d_cur, const float* l_cur, const float* r_cur, 648 float* selected_disp_pyr_new, const float* selected_disp_pyr_cur, 649 float* data_cost_selected, const float* data_cost, size_t msg_step, 650 int h, int w, int nr_plane, int h2, int w2, int nr_plane2, cudaStream_t stream); 651 652 /////////////////////////////////////////////////////////////// 653 //////////////////// calc all iterations ///////////////////// 654 /////////////////////////////////////////////////////////////// 655 656 template <typename T> 657 __device__ void message_per_pixel(const T* data, T* msg_dst, const T* msg1, const T* msg2, const T* msg3, 658 const T* dst_disp, const T* src_disp, int nr_plane, int max_disc_term, float disc_single_jump, volatile T* temp, 659 size_t disp_step) 660 { 661 T minimum = numeric_limits<T>::max(); 662 663 for(int d = 0; d < nr_plane; d++) 664 { 665 int idx = d * disp_step; 666 T val = data[idx] + msg1[idx] + msg2[idx] + msg3[idx]; 667 668 if(val < minimum) 669 minimum = val; 670 671 msg_dst[idx] = val; 672 } 673 674 float sum = 0; 675 for(int d = 0; d < nr_plane; d++) 676 { 677 float cost_min = minimum + max_disc_term; 678 T src_disp_reg = src_disp[d * disp_step]; 679 680 for(int d2 = 0; d2 < nr_plane; d2++) 681 cost_min = fmin(cost_min, msg_dst[d2 * disp_step] + disc_single_jump * ::abs(dst_disp[d2 * disp_step] - src_disp_reg)); 682 683 temp[d * disp_step] = saturate_cast<T>(cost_min); 684 sum += cost_min; 685 } 686 sum /= nr_plane; 687 688 for(int d = 0; d < nr_plane; d++) 689 msg_dst[d * disp_step] = saturate_cast<T>(temp[d * disp_step] - sum); 690 } 691 692 template <typename T> 693 __global__ void compute_message(uchar *ctemp, T* u_, T* d_, T* l_, T* r_, const T* data_cost_selected, const T* selected_disp_pyr_cur, int h, int w, int nr_plane, int i, int max_disc_term, float disc_single_jump, size_t msg_step, size_t disp_step) 694 { 695 int y = blockIdx.y * blockDim.y + threadIdx.y; 696 int x = ((blockIdx.x * blockDim.x + threadIdx.x) << 1) + ((y + i) & 1); 697 698 if (y > 0 && y < h - 1 && x > 0 && x < w - 1) 699 { 700 const T* data = data_cost_selected + y * msg_step + x; 701 702 T* u = u_ + y * msg_step + x; 703 T* d = d_ + y * msg_step + x; 704 T* l = l_ + y * msg_step + x; 705 T* r = r_ + y * msg_step + x; 706 707 const T* disp = selected_disp_pyr_cur + y * msg_step + x; 708 709 T* temp = (T*)ctemp + y * msg_step + x; 710 711 message_per_pixel(data, u, r - 1, u + msg_step, l + 1, disp, disp - msg_step, nr_plane, max_disc_term, disc_single_jump, temp, disp_step); 712 message_per_pixel(data, d, d - msg_step, r - 1, l + 1, disp, disp + msg_step, nr_plane, max_disc_term, disc_single_jump, temp, disp_step); 713 message_per_pixel(data, l, u + msg_step, d - msg_step, l + 1, disp, disp - 1, nr_plane, max_disc_term, disc_single_jump, temp, disp_step); 714 message_per_pixel(data, r, u + msg_step, d - msg_step, r - 1, disp, disp + 1, nr_plane, max_disc_term, disc_single_jump, temp, disp_step); 715 } 716 } 717 718 719 template<class T> 720 void calc_all_iterations(uchar *ctemp, T* u, T* d, T* l, T* r, const T* data_cost_selected, 721 const T* selected_disp_pyr_cur, size_t msg_step, int h, int w, int nr_plane, int iters, int max_disc_term, float disc_single_jump, cudaStream_t stream) 722 { 723 size_t disp_step = msg_step * h; 724 725 dim3 threads(32, 8, 1); 726 dim3 grid(1, 1, 1); 727 728 grid.x = divUp(w, threads.x << 1); 729 grid.y = divUp(h, threads.y); 730 731 for(int t = 0; t < iters; ++t) 732 { 733 compute_message<<<grid, threads, 0, stream>>>(ctemp, u, d, l, r, data_cost_selected, selected_disp_pyr_cur, h, w, nr_plane, t & 1, max_disc_term, disc_single_jump, msg_step, disp_step); 734 cudaSafeCall( cudaGetLastError() ); 735 } 736 if (stream == 0) 737 cudaSafeCall( cudaDeviceSynchronize() ); 738 }; 739 740 template void calc_all_iterations(uchar *ctemp, short* u, short* d, short* l, short* r, const short* data_cost_selected, const short* selected_disp_pyr_cur, size_t msg_step, 741 int h, int w, int nr_plane, int iters, int max_disc_term, float disc_single_jump, cudaStream_t stream); 742 743 template void calc_all_iterations(uchar *ctemp, float* u, float* d, float* l, float* r, const float* data_cost_selected, const float* selected_disp_pyr_cur, size_t msg_step, 744 int h, int w, int nr_plane, int iters, int max_disc_term, float disc_single_jump, cudaStream_t stream); 745 746 747 /////////////////////////////////////////////////////////////// 748 /////////////////////////// output //////////////////////////// 749 /////////////////////////////////////////////////////////////// 750 751 752 template <typename T> 753 __global__ void compute_disp(const T* u_, const T* d_, const T* l_, const T* r_, 754 const T* data_cost_selected, const T* disp_selected_pyr, 755 PtrStepSz<short> disp, int nr_plane, size_t msg_step, size_t disp_step) 756 { 757 int x = blockIdx.x * blockDim.x + threadIdx.x; 758 int y = blockIdx.y * blockDim.y + threadIdx.y; 759 760 if (y > 0 && y < disp.rows - 1 && x > 0 && x < disp.cols - 1) 761 { 762 const T* data = data_cost_selected + y * msg_step + x; 763 const T* disp_selected = disp_selected_pyr + y * msg_step + x; 764 765 const T* u = u_ + (y+1) * msg_step + (x+0); 766 const T* d = d_ + (y-1) * msg_step + (x+0); 767 const T* l = l_ + (y+0) * msg_step + (x+1); 768 const T* r = r_ + (y+0) * msg_step + (x-1); 769 770 int best = 0; 771 T best_val = numeric_limits<T>::max(); 772 for (int i = 0; i < nr_plane; ++i) 773 { 774 int idx = i * disp_step; 775 T val = data[idx]+ u[idx] + d[idx] + l[idx] + r[idx]; 776 777 if (val < best_val) 778 { 779 best_val = val; 780 best = saturate_cast<short>(disp_selected[idx]); 781 } 782 } 783 disp(y, x) = best; 784 } 785 } 786 787 template<class T> 788 void compute_disp(const T* u, const T* d, const T* l, const T* r, const T* data_cost_selected, const T* disp_selected, size_t msg_step, 789 const PtrStepSz<short>& disp, int nr_plane, cudaStream_t stream) 790 { 791 size_t disp_step = disp.rows * msg_step; 792 793 dim3 threads(32, 8, 1); 794 dim3 grid(1, 1, 1); 795 796 grid.x = divUp(disp.cols, threads.x); 797 grid.y = divUp(disp.rows, threads.y); 798 799 compute_disp<<<grid, threads, 0, stream>>>(u, d, l, r, data_cost_selected, disp_selected, disp, nr_plane, msg_step, disp_step); 800 cudaSafeCall( cudaGetLastError() ); 801 802 if (stream == 0) 803 cudaSafeCall( cudaDeviceSynchronize() ); 804 } 805 806 template void compute_disp(const short* u, const short* d, const short* l, const short* r, const short* data_cost_selected, const short* disp_selected, size_t msg_step, 807 const PtrStepSz<short>& disp, int nr_plane, cudaStream_t stream); 808 809 template void compute_disp(const float* u, const float* d, const float* l, const float* r, const float* data_cost_selected, const float* disp_selected, size_t msg_step, 810 const PtrStepSz<short>& disp, int nr_plane, cudaStream_t stream); 811 } // namespace stereocsbp 812 }}} // namespace cv { namespace cuda { namespace cudev { 813 814 #endif /* CUDA_DISABLER */ 815
