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authorsiddhu89902017-04-19 11:56:09 +0530
committersiddhu89902017-04-19 11:56:09 +0530
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treea2eac389b3e3e67c9182e8369b50eb78b5e698cb /2.3-1/thirdparty/raspberrypi/includes/opencv2/gpu/device/block.hpp
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Copyright message updated in added files and libraries separated in 'thirdparty' folder
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+/*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*/
+
+#ifndef __OPENCV_GPU_DEVICE_BLOCK_HPP__
+#define __OPENCV_GPU_DEVICE_BLOCK_HPP__
+
+namespace cv { namespace gpu { namespace device
+{
+ struct Block
+ {
+ static __device__ __forceinline__ unsigned int id()
+ {
+ return blockIdx.x;
+ }
+
+ static __device__ __forceinline__ unsigned int stride()
+ {
+ return blockDim.x * blockDim.y * blockDim.z;
+ }
+
+ static __device__ __forceinline__ void sync()
+ {
+ __syncthreads();
+ }
+
+ static __device__ __forceinline__ int flattenedThreadId()
+ {
+ return threadIdx.z * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x;
+ }
+
+ template<typename It, typename T>
+ static __device__ __forceinline__ void fill(It beg, It end, const T& value)
+ {
+ int STRIDE = stride();
+ It t = beg + flattenedThreadId();
+
+ for(; t < end; t += STRIDE)
+ *t = value;
+ }
+
+ template<typename OutIt, typename T>
+ static __device__ __forceinline__ void yota(OutIt beg, OutIt end, T value)
+ {
+ int STRIDE = stride();
+ int tid = flattenedThreadId();
+ value += tid;
+
+ for(OutIt t = beg + tid; t < end; t += STRIDE, value += STRIDE)
+ *t = value;
+ }
+
+ template<typename InIt, typename OutIt>
+ static __device__ __forceinline__ void copy(InIt beg, InIt end, OutIt out)
+ {
+ int STRIDE = stride();
+ InIt t = beg + flattenedThreadId();
+ OutIt o = out + (t - beg);
+
+ for(; t < end; t += STRIDE, o += STRIDE)
+ *o = *t;
+ }
+
+ template<typename InIt, typename OutIt, class UnOp>
+ static __device__ __forceinline__ void transfrom(InIt beg, InIt end, OutIt out, UnOp op)
+ {
+ int STRIDE = stride();
+ InIt t = beg + flattenedThreadId();
+ OutIt o = out + (t - beg);
+
+ for(; t < end; t += STRIDE, o += STRIDE)
+ *o = op(*t);
+ }
+
+ template<typename InIt1, typename InIt2, typename OutIt, class BinOp>
+ static __device__ __forceinline__ void transfrom(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op)
+ {
+ int STRIDE = stride();
+ InIt1 t1 = beg1 + flattenedThreadId();
+ InIt2 t2 = beg2 + flattenedThreadId();
+ OutIt o = out + (t1 - beg1);
+
+ for(; t1 < end1; t1 += STRIDE, t2 += STRIDE, o += STRIDE)
+ *o = op(*t1, *t2);
+ }
+
+ template<int CTA_SIZE, typename T, class BinOp>
+ static __device__ __forceinline__ void reduce(volatile T* buffer, BinOp op)
+ {
+ int tid = flattenedThreadId();
+ T val = buffer[tid];
+
+ if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); }
+ if (CTA_SIZE >= 512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); }
+ if (CTA_SIZE >= 256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); }
+ if (CTA_SIZE >= 128) { if (tid < 64) buffer[tid] = val = op(val, buffer[tid + 64]); __syncthreads(); }
+
+ if (tid < 32)
+ {
+ if (CTA_SIZE >= 64) { buffer[tid] = val = op(val, buffer[tid + 32]); }
+ if (CTA_SIZE >= 32) { buffer[tid] = val = op(val, buffer[tid + 16]); }
+ if (CTA_SIZE >= 16) { buffer[tid] = val = op(val, buffer[tid + 8]); }
+ if (CTA_SIZE >= 8) { buffer[tid] = val = op(val, buffer[tid + 4]); }
+ if (CTA_SIZE >= 4) { buffer[tid] = val = op(val, buffer[tid + 2]); }
+ if (CTA_SIZE >= 2) { buffer[tid] = val = op(val, buffer[tid + 1]); }
+ }
+ }
+
+ template<int CTA_SIZE, typename T, class BinOp>
+ static __device__ __forceinline__ T reduce(volatile T* buffer, T init, BinOp op)
+ {
+ int tid = flattenedThreadId();
+ T val = buffer[tid] = init;
+ __syncthreads();
+
+ if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); }
+ if (CTA_SIZE >= 512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); }
+ if (CTA_SIZE >= 256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); }
+ if (CTA_SIZE >= 128) { if (tid < 64) buffer[tid] = val = op(val, buffer[tid + 64]); __syncthreads(); }
+
+ if (tid < 32)
+ {
+ if (CTA_SIZE >= 64) { buffer[tid] = val = op(val, buffer[tid + 32]); }
+ if (CTA_SIZE >= 32) { buffer[tid] = val = op(val, buffer[tid + 16]); }
+ if (CTA_SIZE >= 16) { buffer[tid] = val = op(val, buffer[tid + 8]); }
+ if (CTA_SIZE >= 8) { buffer[tid] = val = op(val, buffer[tid + 4]); }
+ if (CTA_SIZE >= 4) { buffer[tid] = val = op(val, buffer[tid + 2]); }
+ if (CTA_SIZE >= 2) { buffer[tid] = val = op(val, buffer[tid + 1]); }
+ }
+ __syncthreads();
+ return buffer[0];
+ }
+
+ template <typename T, class BinOp>
+ static __device__ __forceinline__ void reduce_n(T* data, unsigned int n, BinOp op)
+ {
+ int ftid = flattenedThreadId();
+ int sft = stride();
+
+ if (sft < n)
+ {
+ for (unsigned int i = sft + ftid; i < n; i += sft)
+ data[ftid] = op(data[ftid], data[i]);
+
+ __syncthreads();
+
+ n = sft;
+ }
+
+ while (n > 1)
+ {
+ unsigned int half = n/2;
+
+ if (ftid < half)
+ data[ftid] = op(data[ftid], data[n - ftid - 1]);
+
+ __syncthreads();
+
+ n = n - half;
+ }
+ }
+ };
+}}}
+
+#endif /* __OPENCV_GPU_DEVICE_BLOCK_HPP__ */