mirror of https://github.com/opencv/opencv.git
Vadim Pisarevsky
11 years ago
parent
fdf1996e2e
commit
302a5adcc2
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// License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2010-2012, Institute Of Software Chinese Academy Of Science, all rights reserved. |
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// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// @Authors |
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// Niko Li, newlife20080214@gmail.com |
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// Wang Weiyan, wangweiyanster@gmail.com |
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// Jia Haipeng, jiahaipeng95@gmail.com |
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// Nathan, liujun@multicorewareinc.com |
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// Peng Xiao, pengxiao@outlook.com |
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// Erping Pang, erping@multicorewareinc.com |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials provided with the distribution. |
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// |
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// * The name of the copyright holders may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors as is and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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// |
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|
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#define CV_HAAR_FEATURE_MAX 3 |
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|
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#define calc_sum(rect,offset) (sum[(rect).p0+offset] - sum[(rect).p1+offset] - sum[(rect).p2+offset] + sum[(rect).p3+offset]) |
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#define calc_sum1(rect,offset,i) (sum[(rect).p0[i]+offset] - sum[(rect).p1[i]+offset] - sum[(rect).p2[i]+offset] + sum[(rect).p3[i]+offset]) |
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|
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typedef int sumtype; |
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typedef float sqsumtype; |
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|
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#ifndef STUMP_BASED |
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#define STUMP_BASED 1 |
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#endif |
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|
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typedef struct __attribute__((aligned (128) )) GpuHidHaarTreeNode |
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{ |
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int p[CV_HAAR_FEATURE_MAX][4] __attribute__((aligned (64))); |
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float weight[CV_HAAR_FEATURE_MAX]; |
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float threshold; |
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float alpha[3] __attribute__((aligned (16))); |
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int left __attribute__((aligned (4))); |
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int right __attribute__((aligned (4))); |
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} |
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GpuHidHaarTreeNode; |
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|
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|
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//typedef struct __attribute__((aligned (32))) GpuHidHaarClassifier |
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//{ |
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// int count __attribute__((aligned (4))); |
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// GpuHidHaarTreeNode* node __attribute__((aligned (8))); |
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// float* alpha __attribute__((aligned (8))); |
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//} |
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//GpuHidHaarClassifier; |
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|
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typedef struct __attribute__((aligned (64))) GpuHidHaarStageClassifier |
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{ |
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int count __attribute__((aligned (4))); |
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float threshold __attribute__((aligned (4))); |
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int two_rects __attribute__((aligned (4))); |
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int reserved0 __attribute__((aligned (8))); |
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int reserved1 __attribute__((aligned (8))); |
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int reserved2 __attribute__((aligned (8))); |
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int reserved3 __attribute__((aligned (8))); |
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} |
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GpuHidHaarStageClassifier; |
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|
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|
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//typedef struct __attribute__((aligned (64))) GpuHidHaarClassifierCascade |
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//{ |
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// int count __attribute__((aligned (4))); |
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// int is_stump_based __attribute__((aligned (4))); |
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// int has_tilted_features __attribute__((aligned (4))); |
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// int is_tree __attribute__((aligned (4))); |
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// int pq0 __attribute__((aligned (4))); |
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// int pq1 __attribute__((aligned (4))); |
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// int pq2 __attribute__((aligned (4))); |
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// int pq3 __attribute__((aligned (4))); |
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// int p0 __attribute__((aligned (4))); |
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// int p1 __attribute__((aligned (4))); |
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// int p2 __attribute__((aligned (4))); |
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// int p3 __attribute__((aligned (4))); |
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// float inv_window_area __attribute__((aligned (4))); |
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//} GpuHidHaarClassifierCascade; |
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|
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|
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#ifdef PACKED_CLASSIFIER |
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// this code is scalar, one pixel -> one workitem |
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__kernel void gpuRunHaarClassifierCascadePacked( |
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global const GpuHidHaarStageClassifier * stagecascadeptr, |
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global const int4 * info, |
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global const GpuHidHaarTreeNode * nodeptr, |
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global const int * restrict sum, |
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global const float * restrict sqsum, |
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volatile global int4 * candidate, |
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const int pixelstep, |
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const int loopcount, |
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const int start_stage, |
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const int split_stage, |
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const int end_stage, |
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const int startnode, |
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const int splitnode, |
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const int4 p, |
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const int4 pq, |
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const float correction, |
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global const int* pNodesPK, |
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global const int4* pWGInfo |
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) |
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|
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{ |
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// this version used information provided for each workgroup |
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// no empty WG |
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int gid = (int)get_group_id(0); |
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int lid_x = (int)get_local_id(0); |
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int lid_y = (int)get_local_id(1); |
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int lid = lid_y*LSx+lid_x; |
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int4 WGInfo = pWGInfo[gid]; |
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int GroupX = (WGInfo.y >> 16)&0xFFFF; |
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int GroupY = (WGInfo.y >> 0 )& 0xFFFF; |
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int Width = (WGInfo.x >> 16)&0xFFFF; |
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int Height = (WGInfo.x >> 0 )& 0xFFFF; |
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int ImgOffset = WGInfo.z; |
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float ScaleFactor = as_float(WGInfo.w); |
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#define DATA_SIZE_X (LSx+WND_SIZE_X) |
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#define DATA_SIZE_Y (LSy+WND_SIZE_Y) |
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#define DATA_SIZE (DATA_SIZE_X*DATA_SIZE_Y) |
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local int SumL[DATA_SIZE]; |
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// read input data window into local mem |
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for(int i = 0; i<DATA_SIZE; i+=(LSx*LSy)) |
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{ |
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int index = i+lid; // index in shared local memory |
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if(index<DATA_SIZE) |
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{// calc global x,y coordinat and read data from there |
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int x = min(GroupX + (index % (DATA_SIZE_X)),Width-1); |
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int y = min(GroupY + (index / (DATA_SIZE_X)),Height-1); |
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SumL[index] = sum[ImgOffset+y*pixelstep+x]; |
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} |
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} |
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barrier(CLK_LOCAL_MEM_FENCE); |
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// calc variance_norm_factor for all stages |
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float variance_norm_factor; |
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int nodecounter= startnode; |
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int4 info1 = p; |
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int4 info2 = pq; |
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{ |
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int xl = lid_x; |
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int yl = lid_y; |
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int OffsetLocal = yl * DATA_SIZE_X + xl; |
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int OffsetGlobal = (GroupY+yl)* pixelstep + (GroupX+xl); |
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// add shift to get position on scaled image |
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OffsetGlobal += ImgOffset; |
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float mean = |
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SumL[info1.y*DATA_SIZE_X+info1.x+OffsetLocal] - |
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SumL[info1.y*DATA_SIZE_X+info1.z+OffsetLocal] - |
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SumL[info1.w*DATA_SIZE_X+info1.x+OffsetLocal] + |
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SumL[info1.w*DATA_SIZE_X+info1.z+OffsetLocal]; |
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float sq = |
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sqsum[info2.y*pixelstep+info2.x+OffsetGlobal] - |
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sqsum[info2.y*pixelstep+info2.z+OffsetGlobal] - |
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sqsum[info2.w*pixelstep+info2.x+OffsetGlobal] + |
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sqsum[info2.w*pixelstep+info2.z+OffsetGlobal]; |
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mean *= correction; |
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sq *= correction; |
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variance_norm_factor = sq - mean * mean; |
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variance_norm_factor = (variance_norm_factor >=0.f) ? sqrt(variance_norm_factor) : 1.f; |
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}// end calc variance_norm_factor for all stages |
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int result = (1.0f>0.0f); |
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for(int stageloop = start_stage; (stageloop < end_stage) && result; stageloop++ ) |
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{// iterate until candidate is exist |
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float stage_sum = 0.0f; |
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__global GpuHidHaarStageClassifier* stageinfo = (__global GpuHidHaarStageClassifier*) |
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((__global uchar*)stagecascadeptr+stageloop*sizeof(GpuHidHaarStageClassifier)); |
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int stagecount = stageinfo->count; |
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float stagethreshold = stageinfo->threshold; |
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int lcl_off = (lid_y*DATA_SIZE_X)+(lid_x); |
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for(int nodeloop = 0; nodeloop < stagecount; nodecounter++,nodeloop++ ) |
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{ |
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// simple macro to extract shorts from int |
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#define M0(_t) ((_t)&0xFFFF) |
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#define M1(_t) (((_t)>>16)&0xFFFF) |
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// load packed node data from global memory (L3) into registers |
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global const int4* pN = (__global int4*)(pNodesPK+nodecounter*NODE_SIZE); |
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int4 n0 = pN[0]; |
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int4 n1 = pN[1]; |
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int4 n2 = pN[2]; |
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float nodethreshold = as_float(n2.y) * variance_norm_factor; |
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// calc sum of intensity pixels according to node information |
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float classsum = |
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(SumL[M0(n0.x)+lcl_off] - SumL[M1(n0.x)+lcl_off] - SumL[M0(n0.y)+lcl_off] + SumL[M1(n0.y)+lcl_off]) * as_float(n1.z) + |
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(SumL[M0(n0.z)+lcl_off] - SumL[M1(n0.z)+lcl_off] - SumL[M0(n0.w)+lcl_off] + SumL[M1(n0.w)+lcl_off]) * as_float(n1.w) + |
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(SumL[M0(n1.x)+lcl_off] - SumL[M1(n1.x)+lcl_off] - SumL[M0(n1.y)+lcl_off] + SumL[M1(n1.y)+lcl_off]) * as_float(n2.x); |
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//accumulate stage responce |
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stage_sum += (classsum >= nodethreshold) ? as_float(n2.w) : as_float(n2.z); |
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} |
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result = (stage_sum >= stagethreshold); |
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}// next stage if needed |
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if(result) |
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{// all stages will be passed and there is a detected face on the tested position |
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int index = 1+atomic_inc((volatile global int*)candidate); //get index to write global data with face info |
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if(index<OUTPUTSZ) |
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{ |
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int x = GroupX+lid_x; |
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int y = GroupY+lid_y; |
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int4 candidate_result; |
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candidate_result.x = convert_int_rtn(x*ScaleFactor); |
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candidate_result.y = convert_int_rtn(y*ScaleFactor); |
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candidate_result.z = convert_int_rtn(ScaleFactor*WND_SIZE_X); |
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candidate_result.w = convert_int_rtn(ScaleFactor*WND_SIZE_Y); |
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candidate[index] = candidate_result; |
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} |
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} |
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}//end gpuRunHaarClassifierCascade |
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#else |
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__kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCascade( |
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global GpuHidHaarStageClassifier * stagecascadeptr, |
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global int4 * info, |
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global GpuHidHaarTreeNode * nodeptr, |
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global const int * restrict sum1, |
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global const float * restrict sqsum1, |
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global int4 * candidate, |
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const int pixelstep, |
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const int loopcount, |
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const int start_stage, |
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const int split_stage, |
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const int end_stage, |
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const int startnode, |
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const int splitnode, |
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const int4 p, |
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const int4 pq, |
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const float correction) |
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{ |
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int grpszx = get_local_size(0); |
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int grpszy = get_local_size(1); |
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int grpnumx = get_num_groups(0); |
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int grpidx = get_group_id(0); |
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int lclidx = get_local_id(0); |
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int lclidy = get_local_id(1); |
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int lcl_sz = mul24(grpszx,grpszy); |
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int lcl_id = mad24(lclidy,grpszx,lclidx); |
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__local int lclshare[1024]; |
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__local int* lcldata = lclshare;//for save win data |
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__local int* glboutindex = lcldata + 28*28;//for save global out index |
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__local int* lclcount = glboutindex + 1;//for save the numuber of temp pass pixel |
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__local int* lcloutindex = lclcount + 1;//for save info of temp pass pixel |
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__local float* partialsum = (__local float*)(lcloutindex + (lcl_sz<<1)); |
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glboutindex[0]=0; |
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int outputoff = mul24(grpidx,256); |
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|
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//assume window size is 20X20 |
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#define WINDOWSIZE 20+1 |
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//make sure readwidth is the multiple of 4 |
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//ystep =1, from host code |
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int readwidth = ((grpszx-1 + WINDOWSIZE+3)>>2)<<2; |
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int readheight = grpszy-1+WINDOWSIZE; |
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int read_horiz_cnt = readwidth >> 2;//each read int4 |
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int total_read = mul24(read_horiz_cnt,readheight); |
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int read_loop = (total_read + lcl_sz - 1) >> 6; |
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candidate[outputoff+(lcl_id<<2)] = (int4)0; |
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candidate[outputoff+(lcl_id<<2)+1] = (int4)0; |
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candidate[outputoff+(lcl_id<<2)+2] = (int4)0; |
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candidate[outputoff+(lcl_id<<2)+3] = (int4)0; |
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for(int scalei = 0; scalei <loopcount; scalei++) |
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{ |
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int4 scaleinfo1= info[scalei]; |
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int height = scaleinfo1.x & 0xffff; |
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int grpnumperline =(scaleinfo1.y & 0xffff0000) >> 16; |
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int totalgrp = scaleinfo1.y & 0xffff; |
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int imgoff = scaleinfo1.z; |
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float factor = as_float(scaleinfo1.w); |
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__global const int * sum = sum1 + imgoff; |
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__global const float * sqsum = sqsum1 + imgoff; |
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for(int grploop=grpidx; grploop<totalgrp; grploop+=grpnumx) |
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{ |
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int grpidy = grploop / grpnumperline; |
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int grpidx = grploop - mul24(grpidy, grpnumperline); |
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int x = mad24(grpidx,grpszx,lclidx); |
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int y = mad24(grpidy,grpszy,lclidy); |
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int grpoffx = x-lclidx; |
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int grpoffy = y-lclidy; |
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for(int i=0; i<read_loop; i++) |
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{ |
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int pos_id = mad24(i,lcl_sz,lcl_id); |
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pos_id = pos_id < total_read ? pos_id : 0; |
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int lcl_y = pos_id / read_horiz_cnt; |
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int lcl_x = pos_id - mul24(lcl_y, read_horiz_cnt); |
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int glb_x = grpoffx + (lcl_x<<2); |
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int glb_y = grpoffy + lcl_y; |
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int glb_off = mad24(min(glb_y, height + WINDOWSIZE - 1),pixelstep,glb_x); |
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int4 data = *(__global int4*)&sum[glb_off]; |
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int lcl_off = mad24(lcl_y, readwidth, lcl_x<<2); |
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vstore4(data, 0, &lcldata[lcl_off]); |
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} |
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lcloutindex[lcl_id] = 0; |
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lclcount[0] = 0; |
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int result = 1; |
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int nodecounter= startnode; |
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float mean, variance_norm_factor; |
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barrier(CLK_LOCAL_MEM_FENCE); |
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int lcl_off = mad24(lclidy,readwidth,lclidx); |
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int4 cascadeinfo1, cascadeinfo2; |
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cascadeinfo1 = p; |
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cascadeinfo2 = pq; |
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cascadeinfo1.x +=lcl_off; |
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cascadeinfo1.z +=lcl_off; |
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mean = (lcldata[mad24(cascadeinfo1.y,readwidth,cascadeinfo1.x)] - lcldata[mad24(cascadeinfo1.y,readwidth,cascadeinfo1.z)] - |
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lcldata[mad24(cascadeinfo1.w,readwidth,cascadeinfo1.x)] + lcldata[mad24(cascadeinfo1.w,readwidth,cascadeinfo1.z)]) |
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*correction; |
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int p_offset = mad24(y, pixelstep, x); |
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cascadeinfo2.x +=p_offset; |
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cascadeinfo2.z +=p_offset; |
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variance_norm_factor =sqsum[mad24(cascadeinfo2.y, pixelstep, cascadeinfo2.x)] - sqsum[mad24(cascadeinfo2.y, pixelstep, cascadeinfo2.z)] - |
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sqsum[mad24(cascadeinfo2.w, pixelstep, cascadeinfo2.x)] + sqsum[mad24(cascadeinfo2.w, pixelstep, cascadeinfo2.z)]; |
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variance_norm_factor = variance_norm_factor * correction - mean * mean; |
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variance_norm_factor = variance_norm_factor >=0.f ? sqrt(variance_norm_factor) : 1.f; |
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for(int stageloop = start_stage; (stageloop < split_stage) && result; stageloop++ ) |
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{ |
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float stage_sum = 0.f; |
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__global GpuHidHaarStageClassifier* stageinfo = (__global GpuHidHaarStageClassifier*) |
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((__global uchar*)stagecascadeptr+stageloop*sizeof(GpuHidHaarStageClassifier)); |
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int stagecount = stageinfo->count; |
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float stagethreshold = stageinfo->threshold; |
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for(int nodeloop = 0; nodeloop < stagecount; ) |
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{ |
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__global GpuHidHaarTreeNode* currentnodeptr = (__global GpuHidHaarTreeNode*) |
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(((__global uchar*)nodeptr) + nodecounter * sizeof(GpuHidHaarTreeNode)); |
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int4 info1 = *(__global int4*)(&(currentnodeptr->p[0][0])); |
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int4 info2 = *(__global int4*)(&(currentnodeptr->p[1][0])); |
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int4 info3 = *(__global int4*)(&(currentnodeptr->p[2][0])); |
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float4 w = *(__global float4*)(&(currentnodeptr->weight[0])); |
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float3 alpha3 = *(__global float3*)(&(currentnodeptr->alpha[0])); |
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float nodethreshold = w.w * variance_norm_factor; |
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info1.x +=lcl_off; |
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info1.z +=lcl_off; |
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info2.x +=lcl_off; |
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info2.z +=lcl_off; |
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float classsum = (lcldata[mad24(info1.y,readwidth,info1.x)] - lcldata[mad24(info1.y,readwidth,info1.z)] - |
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lcldata[mad24(info1.w,readwidth,info1.x)] + lcldata[mad24(info1.w,readwidth,info1.z)]) * w.x; |
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|
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classsum += (lcldata[mad24(info2.y,readwidth,info2.x)] - lcldata[mad24(info2.y,readwidth,info2.z)] - |
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lcldata[mad24(info2.w,readwidth,info2.x)] + lcldata[mad24(info2.w,readwidth,info2.z)]) * w.y; |
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|
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info3.x +=lcl_off; |
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info3.z +=lcl_off; |
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classsum += (lcldata[mad24(info3.y,readwidth,info3.x)] - lcldata[mad24(info3.y,readwidth,info3.z)] - |
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lcldata[mad24(info3.w,readwidth,info3.x)] + lcldata[mad24(info3.w,readwidth,info3.z)]) * w.z; |
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|
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bool passThres = classsum >= nodethreshold; |
||||
#if STUMP_BASED |
||||
stage_sum += passThres ? alpha3.y : alpha3.x; |
||||
nodecounter++; |
||||
nodeloop++; |
||||
#else |
||||
bool isRootNode = (nodecounter & 1) == 0; |
||||
if(isRootNode) |
||||
{ |
||||
if( (passThres && currentnodeptr->right) || |
||||
(!passThres && currentnodeptr->left)) |
||||
{ |
||||
nodecounter ++; |
||||
} |
||||
else |
||||
{ |
||||
stage_sum += alpha3.x; |
||||
nodecounter += 2; |
||||
nodeloop ++; |
||||
} |
||||
} |
||||
else |
||||
{ |
||||
stage_sum += passThres ? alpha3.z : alpha3.y; |
||||
nodecounter ++; |
||||
nodeloop ++; |
||||
} |
||||
#endif |
||||
} |
||||
|
||||
result = (stage_sum >= stagethreshold) ? 1 : 0; |
||||
} |
||||
if(factor < 2) |
||||
{ |
||||
if(result && lclidx %2 ==0 && lclidy %2 ==0 ) |
||||
{ |
||||
int queueindex = atomic_inc(lclcount); |
||||
lcloutindex[queueindex<<1] = (lclidy << 16) | lclidx; |
||||
lcloutindex[(queueindex<<1)+1] = as_int((float)variance_norm_factor); |
||||
} |
||||
} |
||||
else |
||||
{ |
||||
if(result) |
||||
{ |
||||
int queueindex = atomic_inc(lclcount); |
||||
lcloutindex[queueindex<<1] = (lclidy << 16) | lclidx; |
||||
lcloutindex[(queueindex<<1)+1] = as_int((float)variance_norm_factor); |
||||
} |
||||
} |
||||
barrier(CLK_LOCAL_MEM_FENCE); |
||||
int queuecount = lclcount[0]; |
||||
barrier(CLK_LOCAL_MEM_FENCE); |
||||
nodecounter = splitnode; |
||||
for(int stageloop = split_stage; stageloop< end_stage && queuecount>0; stageloop++) |
||||
{ |
||||
lclcount[0]=0; |
||||
barrier(CLK_LOCAL_MEM_FENCE); |
||||
|
||||
//int2 stageinfo = *(global int2*)(stagecascadeptr+stageloop); |
||||
__global GpuHidHaarStageClassifier* stageinfo = (__global GpuHidHaarStageClassifier*) |
||||
((__global uchar*)stagecascadeptr+stageloop*sizeof(GpuHidHaarStageClassifier)); |
||||
int stagecount = stageinfo->count; |
||||
float stagethreshold = stageinfo->threshold; |
||||
|
||||
int perfscale = queuecount > 4 ? 3 : 2; |
||||
int queuecount_loop = (queuecount + (1<<perfscale)-1) >> perfscale; |
||||
int lcl_compute_win = lcl_sz >> perfscale; |
||||
int lcl_compute_win_id = (lcl_id >>(6-perfscale)); |
||||
int lcl_loops = (stagecount + lcl_compute_win -1) >> (6-perfscale); |
||||
int lcl_compute_id = lcl_id - (lcl_compute_win_id << (6-perfscale)); |
||||
for(int queueloop=0; queueloop<queuecount_loop; queueloop++) |
||||
{ |
||||
float stage_sum = 0.f; |
||||
int temp_coord = lcloutindex[lcl_compute_win_id<<1]; |
||||
float variance_norm_factor = as_float(lcloutindex[(lcl_compute_win_id<<1)+1]); |
||||
int queue_pixel = mad24(((temp_coord & (int)0xffff0000)>>16),readwidth,temp_coord & 0xffff); |
||||
|
||||
if(lcl_compute_win_id < queuecount) |
||||
{ |
||||
int tempnodecounter = lcl_compute_id; |
||||
float part_sum = 0.f; |
||||
const int stump_factor = STUMP_BASED ? 1 : 2; |
||||
int root_offset = 0; |
||||
for(int lcl_loop=0; lcl_loop<lcl_loops && tempnodecounter<stagecount;) |
||||
{ |
||||
__global GpuHidHaarTreeNode* currentnodeptr = (__global GpuHidHaarTreeNode*) |
||||
(((__global uchar*)nodeptr) + sizeof(GpuHidHaarTreeNode) * ((nodecounter + tempnodecounter) * stump_factor + root_offset)); |
||||
|
||||
int4 info1 = *(__global int4*)(&(currentnodeptr->p[0][0])); |
||||
int4 info2 = *(__global int4*)(&(currentnodeptr->p[1][0])); |
||||
int4 info3 = *(__global int4*)(&(currentnodeptr->p[2][0])); |
||||
float4 w = *(__global float4*)(&(currentnodeptr->weight[0])); |
||||
float3 alpha3 = *(__global float3*)(&(currentnodeptr->alpha[0])); |
||||
float nodethreshold = w.w * variance_norm_factor; |
||||
|
||||
info1.x +=queue_pixel; |
||||
info1.z +=queue_pixel; |
||||
info2.x +=queue_pixel; |
||||
info2.z +=queue_pixel; |
||||
|
||||
float classsum = (lcldata[mad24(info1.y,readwidth,info1.x)] - lcldata[mad24(info1.y,readwidth,info1.z)] - |
||||
lcldata[mad24(info1.w,readwidth,info1.x)] + lcldata[mad24(info1.w,readwidth,info1.z)]) * w.x; |
||||
|
||||
|
||||
classsum += (lcldata[mad24(info2.y,readwidth,info2.x)] - lcldata[mad24(info2.y,readwidth,info2.z)] - |
||||
lcldata[mad24(info2.w,readwidth,info2.x)] + lcldata[mad24(info2.w,readwidth,info2.z)]) * w.y; |
||||
|
||||
info3.x +=queue_pixel; |
||||
info3.z +=queue_pixel; |
||||
classsum += (lcldata[mad24(info3.y,readwidth,info3.x)] - lcldata[mad24(info3.y,readwidth,info3.z)] - |
||||
lcldata[mad24(info3.w,readwidth,info3.x)] + lcldata[mad24(info3.w,readwidth,info3.z)]) * w.z; |
||||
|
||||
bool passThres = classsum >= nodethreshold; |
||||
#if STUMP_BASED |
||||
part_sum += passThres ? alpha3.y : alpha3.x; |
||||
tempnodecounter += lcl_compute_win; |
||||
lcl_loop++; |
||||
#else |
||||
if(root_offset == 0) |
||||
{ |
||||
if( (passThres && currentnodeptr->right) || |
||||
(!passThres && currentnodeptr->left)) |
||||
{ |
||||
root_offset = 1; |
||||
} |
||||
else |
||||
{ |
||||
part_sum += alpha3.x; |
||||
tempnodecounter += lcl_compute_win; |
||||
lcl_loop++; |
||||
} |
||||
} |
||||
else |
||||
{ |
||||
part_sum += passThres ? alpha3.z : alpha3.y; |
||||
tempnodecounter += lcl_compute_win; |
||||
lcl_loop++; |
||||
root_offset = 0; |
||||
} |
||||
#endif |
||||
}//end for(int lcl_loop=0;lcl_loop<lcl_loops;lcl_loop++) |
||||
partialsum[lcl_id]=part_sum; |
||||
} |
||||
barrier(CLK_LOCAL_MEM_FENCE); |
||||
if(lcl_compute_win_id < queuecount) |
||||
{ |
||||
for(int i=0; i<lcl_compute_win && (lcl_compute_id==0); i++) |
||||
{ |
||||
stage_sum += partialsum[lcl_id+i]; |
||||
} |
||||
if(stage_sum >= stagethreshold && (lcl_compute_id==0)) |
||||
{ |
||||
int queueindex = atomic_inc(lclcount); |
||||
lcloutindex[queueindex<<1] = temp_coord; |
||||
lcloutindex[(queueindex<<1)+1] = as_int(variance_norm_factor); |
||||
} |
||||
lcl_compute_win_id +=(1<<perfscale); |
||||
} |
||||
barrier(CLK_LOCAL_MEM_FENCE); |
||||
}//end for(int queueloop=0;queueloop<queuecount_loop;queueloop++) |
||||
|
||||
queuecount = lclcount[0]; |
||||
barrier(CLK_LOCAL_MEM_FENCE); |
||||
nodecounter += stagecount; |
||||
}//end for(int stageloop = splitstage; stageloop< endstage && queuecount>0;stageloop++) |
||||
|
||||
if(lcl_id<queuecount) |
||||
{ |
||||
int temp = lcloutindex[lcl_id<<1]; |
||||
int x = mad24(grpidx,grpszx,temp & 0xffff); |
||||
int y = mad24(grpidy,grpszy,((temp & (int)0xffff0000) >> 16)); |
||||
temp = glboutindex[0]; |
||||
int4 candidate_result; |
||||
candidate_result.zw = (int2)convert_int_rte(factor*20.f); |
||||
candidate_result.x = convert_int_rte(x*factor); |
||||
candidate_result.y = convert_int_rte(y*factor); |
||||
atomic_inc(glboutindex); |
||||
|
||||
int i = outputoff+temp+lcl_id; |
||||
if(candidate[i].z == 0) |
||||
{ |
||||
candidate[i] = candidate_result; |
||||
} |
||||
else |
||||
{ |
||||
for(i=i+1;;i++) |
||||
{ |
||||
if(candidate[i].z == 0) |
||||
{ |
||||
candidate[i] = candidate_result; |
||||
break; |
||||
} |
||||
} |
||||
} |
||||
} |
||||
barrier(CLK_LOCAL_MEM_FENCE); |
||||
}//end for(int grploop=grpidx;grploop<totalgrp;grploop+=grpnumx) |
||||
}//end for(int scalei = 0; scalei <loopcount; scalei++) |
||||
} |
||||
#endif |
||||
@ -0,0 +1,323 @@ |
||||
/*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) 2010-2012, Institute Of Software Chinese Academy Of Science, all rights reserved. |
||||
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved. |
||||
// Third party copyrights are property of their respective owners. |
||||
// |
||||
// @Authors |
||||
// Wu Xinglong, wxl370@126.com |
||||
// Sen Liu, swjtuls1987@126.com |
||||
// Peng Xiao, pengxiao@outlook.com |
||||
// Erping Pang, erping@multicorewareinc.com |
||||
// 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*/ |
||||
|
||||
#define CV_HAAR_FEATURE_MAX 3 |
||||
typedef int sumtype; |
||||
typedef float sqsumtype; |
||||
|
||||
typedef struct __attribute__((aligned(128))) GpuHidHaarTreeNode |
||||
{ |
||||
int p[CV_HAAR_FEATURE_MAX][4] __attribute__((aligned(64))); |
||||
float weight[CV_HAAR_FEATURE_MAX] /*__attribute__((aligned (16)))*/; |
||||
float threshold /*__attribute__((aligned (4)))*/; |
||||
float alpha[3] __attribute__((aligned(16))); |
||||
int left __attribute__((aligned(4))); |
||||
int right __attribute__((aligned(4))); |
||||
} |
||||
GpuHidHaarTreeNode; |
||||
//typedef struct __attribute__((aligned(32))) GpuHidHaarClassifier |
||||
//{ |
||||
// int count __attribute__((aligned(4))); |
||||
// GpuHidHaarTreeNode *node __attribute__((aligned(8))); |
||||
// float *alpha __attribute__((aligned(8))); |
||||
//} |
||||
//GpuHidHaarClassifier; |
||||
typedef struct __attribute__((aligned(64))) GpuHidHaarStageClassifier |
||||
{ |
||||
int count __attribute__((aligned(4))); |
||||
float threshold __attribute__((aligned(4))); |
||||
int two_rects __attribute__((aligned(4))); |
||||
int reserved0 __attribute__((aligned(8))); |
||||
int reserved1 __attribute__((aligned(8))); |
||||
int reserved2 __attribute__((aligned(8))); |
||||
int reserved3 __attribute__((aligned(8))); |
||||
} |
||||
GpuHidHaarStageClassifier; |
||||
//typedef struct __attribute__((aligned(64))) GpuHidHaarClassifierCascade |
||||
//{ |
||||
// int count __attribute__((aligned(4))); |
||||
// int is_stump_based __attribute__((aligned(4))); |
||||
// int has_tilted_features __attribute__((aligned(4))); |
||||
// int is_tree __attribute__((aligned(4))); |
||||
// int pq0 __attribute__((aligned(4))); |
||||
// int pq1 __attribute__((aligned(4))); |
||||
// int pq2 __attribute__((aligned(4))); |
||||
// int pq3 __attribute__((aligned(4))); |
||||
// int p0 __attribute__((aligned(4))); |
||||
// int p1 __attribute__((aligned(4))); |
||||
// int p2 __attribute__((aligned(4))); |
||||
// int p3 __attribute__((aligned(4))); |
||||
// float inv_window_area __attribute__((aligned(4))); |
||||
//} GpuHidHaarClassifierCascade; |
||||
|
||||
__kernel void gpuRunHaarClassifierCascade_scaled2( |
||||
global GpuHidHaarStageClassifier *stagecascadeptr_, |
||||
global int4 *info, |
||||
global GpuHidHaarTreeNode *nodeptr_, |
||||
global const int *restrict sum, |
||||
global const float *restrict sqsum, |
||||
global int4 *candidate, |
||||
const int rows, |
||||
const int cols, |
||||
const int step, |
||||
const int loopcount, |
||||
const int start_stage, |
||||
const int split_stage, |
||||
const int end_stage, |
||||
const int startnode, |
||||
global int4 *p, |
||||
global float *correction, |
||||
const int nodecount) |
||||
{ |
||||
int grpszx = get_local_size(0); |
||||
int grpszy = get_local_size(1); |
||||
int grpnumx = get_num_groups(0); |
||||
int grpidx = get_group_id(0); |
||||
int lclidx = get_local_id(0); |
||||
int lclidy = get_local_id(1); |
||||
int lcl_id = mad24(lclidy, grpszx, lclidx); |
||||
__local int glboutindex[1]; |
||||
__local int lclcount[1]; |
||||
__local int lcloutindex[64]; |
||||
glboutindex[0] = 0; |
||||
int outputoff = mul24(grpidx, 256); |
||||
candidate[outputoff + (lcl_id << 2)] = (int4)0; |
||||
candidate[outputoff + (lcl_id << 2) + 1] = (int4)0; |
||||
candidate[outputoff + (lcl_id << 2) + 2] = (int4)0; |
||||
candidate[outputoff + (lcl_id << 2) + 3] = (int4)0; |
||||
int max_idx = rows * cols - 1; |
||||
for (int scalei = 0; scalei < loopcount; scalei++) |
||||
{ |
||||
int4 scaleinfo1 = info[scalei]; |
||||
int grpnumperline = (scaleinfo1.y & 0xffff0000) >> 16; |
||||
int totalgrp = scaleinfo1.y & 0xffff; |
||||
float factor = as_float(scaleinfo1.w); |
||||
float correction_t = correction[scalei]; |
||||
float ystep = max(2.0f, factor); |
||||
|
||||
for (int grploop = get_group_id(0); grploop < totalgrp; grploop += grpnumx) |
||||
{ |
||||
int4 cascadeinfo = p[scalei]; |
||||
int grpidy = grploop / grpnumperline; |
||||
int grpidx = grploop - mul24(grpidy, grpnumperline); |
||||
int ix = mad24(grpidx, grpszx, lclidx); |
||||
int iy = mad24(grpidy, grpszy, lclidy); |
||||
int x = round(ix * ystep); |
||||
int y = round(iy * ystep); |
||||
lcloutindex[lcl_id] = 0; |
||||
lclcount[0] = 0; |
||||
int nodecounter; |
||||
float mean, variance_norm_factor; |
||||
//if((ix < width) && (iy < height)) |
||||
{ |
||||
const int p_offset = mad24(y, step, x); |
||||
cascadeinfo.x += p_offset; |
||||
cascadeinfo.z += p_offset; |
||||
mean = (sum[clamp(mad24(cascadeinfo.y, step, cascadeinfo.x), 0, max_idx)] |
||||
- sum[clamp(mad24(cascadeinfo.y, step, cascadeinfo.z), 0, max_idx)] - |
||||
sum[clamp(mad24(cascadeinfo.w, step, cascadeinfo.x), 0, max_idx)] |
||||
+ sum[clamp(mad24(cascadeinfo.w, step, cascadeinfo.z), 0, max_idx)]) |
||||
* correction_t; |
||||
variance_norm_factor = sqsum[clamp(mad24(cascadeinfo.y, step, cascadeinfo.x), 0, max_idx)] |
||||
- sqsum[clamp(mad24(cascadeinfo.y, step, cascadeinfo.z), 0, max_idx)] - |
||||
sqsum[clamp(mad24(cascadeinfo.w, step, cascadeinfo.x), 0, max_idx)] |
||||
+ sqsum[clamp(mad24(cascadeinfo.w, step, cascadeinfo.z), 0, max_idx)]; |
||||
variance_norm_factor = variance_norm_factor * correction_t - mean * mean; |
||||
variance_norm_factor = variance_norm_factor >= 0.f ? sqrt(variance_norm_factor) : 1.f; |
||||
bool result = true; |
||||
nodecounter = startnode + nodecount * scalei; |
||||
for (int stageloop = start_stage; (stageloop < end_stage) && result; stageloop++) |
||||
{ |
||||
float stage_sum = 0.f; |
||||
__global GpuHidHaarStageClassifier* stageinfo = (__global GpuHidHaarStageClassifier*) |
||||
(((__global uchar*)stagecascadeptr_)+stageloop*sizeof(GpuHidHaarStageClassifier)); |
||||
int stagecount = stageinfo->count; |
||||
for (int nodeloop = 0; nodeloop < stagecount;) |
||||
{ |
||||
__global GpuHidHaarTreeNode* currentnodeptr = (__global GpuHidHaarTreeNode*) |
||||
(((__global uchar*)nodeptr_) + nodecounter * sizeof(GpuHidHaarTreeNode)); |
||||
int4 info1 = *(__global int4 *)(&(currentnodeptr->p[0][0])); |
||||
int4 info2 = *(__global int4 *)(&(currentnodeptr->p[1][0])); |
||||
int4 info3 = *(__global int4 *)(&(currentnodeptr->p[2][0])); |
||||
float4 w = *(__global float4 *)(&(currentnodeptr->weight[0])); |
||||
float3 alpha3 = *(__global float3*)(&(currentnodeptr->alpha[0])); |
||||
float nodethreshold = w.w * variance_norm_factor; |
||||
|
||||
info1.x += p_offset; |
||||
info1.z += p_offset; |
||||
info2.x += p_offset; |
||||
info2.z += p_offset; |
||||
info3.x += p_offset; |
||||
info3.z += p_offset; |
||||
float classsum = (sum[clamp(mad24(info1.y, step, info1.x), 0, max_idx)] |
||||
- sum[clamp(mad24(info1.y, step, info1.z), 0, max_idx)] - |
||||
sum[clamp(mad24(info1.w, step, info1.x), 0, max_idx)] |
||||
+ sum[clamp(mad24(info1.w, step, info1.z), 0, max_idx)]) * w.x; |
||||
classsum += (sum[clamp(mad24(info2.y, step, info2.x), 0, max_idx)] |
||||
- sum[clamp(mad24(info2.y, step, info2.z), 0, max_idx)] - |
||||
sum[clamp(mad24(info2.w, step, info2.x), 0, max_idx)] |
||||
+ sum[clamp(mad24(info2.w, step, info2.z), 0, max_idx)]) * w.y; |
||||
classsum += (sum[clamp(mad24(info3.y, step, info3.x), 0, max_idx)] |
||||
- sum[clamp(mad24(info3.y, step, info3.z), 0, max_idx)] - |
||||
sum[clamp(mad24(info3.w, step, info3.x), 0, max_idx)] |
||||
+ sum[clamp(mad24(info3.w, step, info3.z), 0, max_idx)]) * w.z; |
||||
|
||||
bool passThres = (classsum >= nodethreshold) ? 1 : 0; |
||||
|
||||
#if STUMP_BASED |
||||
stage_sum += passThres ? alpha3.y : alpha3.x; |
||||
nodecounter++; |
||||
nodeloop++; |
||||
#else |
||||
bool isRootNode = (nodecounter & 1) == 0; |
||||
if(isRootNode) |
||||
{ |
||||
if( (passThres && currentnodeptr->right) || |
||||
(!passThres && currentnodeptr->left)) |
||||
{ |
||||
nodecounter ++; |
||||
} |
||||
else |
||||
{ |
||||
stage_sum += alpha3.x; |
||||
nodecounter += 2; |
||||
nodeloop ++; |
||||
} |
||||
} |
||||
else |
||||
{ |
||||
stage_sum += (passThres ? alpha3.z : alpha3.y); |
||||
nodecounter ++; |
||||
nodeloop ++; |
||||
} |
||||
#endif |
||||
} |
||||
|
||||
result = (stage_sum >= stageinfo->threshold) ? 1 : 0; |
||||
} |
||||
|
||||
barrier(CLK_LOCAL_MEM_FENCE); |
||||
|
||||
if (result) |
||||
{ |
||||
int queueindex = atomic_inc(lclcount); |
||||
lcloutindex[queueindex] = (y << 16) | x; |
||||
} |
||||
barrier(CLK_LOCAL_MEM_FENCE); |
||||
int queuecount = lclcount[0]; |
||||
|
||||
if (lcl_id < queuecount) |
||||
{ |
||||
int temp = lcloutindex[lcl_id]; |
||||
int x = temp & 0xffff; |
||||
int y = (temp & (int)0xffff0000) >> 16; |
||||
temp = atomic_inc(glboutindex); |
||||
int4 candidate_result; |
||||
candidate_result.zw = (int2)convert_int_rte(factor * 20.f); |
||||
candidate_result.x = x; |
||||
candidate_result.y = y; |
||||
|
||||
int i = outputoff+temp+lcl_id; |
||||
if(candidate[i].z == 0) |
||||
{ |
||||
candidate[i] = candidate_result; |
||||
} |
||||
else |
||||
{ |
||||
for(i=i+1;;i++) |
||||
{ |
||||
if(candidate[i].z == 0) |
||||
{ |
||||
candidate[i] = candidate_result; |
||||
break; |
||||
} |
||||
} |
||||
} |
||||
} |
||||
|
||||
barrier(CLK_LOCAL_MEM_FENCE); |
||||
} |
||||
} |
||||
} |
||||
} |
||||
__kernel void gpuscaleclassifier(global GpuHidHaarTreeNode *orinode, global GpuHidHaarTreeNode *newnode, float scale, float weight_scale, const int nodenum) |
||||
{ |
||||
const int counter = get_global_id(0); |
||||
int tr_x[3], tr_y[3], tr_h[3], tr_w[3], i = 0; |
||||
GpuHidHaarTreeNode t1 = *(__global GpuHidHaarTreeNode*) |
||||
(((__global uchar*)orinode) + counter * sizeof(GpuHidHaarTreeNode)); |
||||
__global GpuHidHaarTreeNode* pNew = (__global GpuHidHaarTreeNode*) |
||||
(((__global uchar*)newnode) + (counter + nodenum) * sizeof(GpuHidHaarTreeNode)); |
||||
|
||||
#pragma unroll |
||||
for (i = 0; i < 3; i++) |
||||
{ |
||||
tr_x[i] = (int)(t1.p[i][0] * scale + 0.5f); |
||||
tr_y[i] = (int)(t1.p[i][1] * scale + 0.5f); |
||||
tr_w[i] = (int)(t1.p[i][2] * scale + 0.5f); |
||||
tr_h[i] = (int)(t1.p[i][3] * scale + 0.5f); |
||||
} |
||||
|
||||
t1.weight[0] = -(t1.weight[1] * tr_h[1] * tr_w[1] + t1.weight[2] * tr_h[2] * tr_w[2]) / (tr_h[0] * tr_w[0]); |
||||
|
||||
#pragma unroll |
||||
for (i = 0; i < 3; i++) |
||||
{ |
||||
pNew->p[i][0] = tr_x[i]; |
||||
pNew->p[i][1] = tr_y[i]; |
||||
pNew->p[i][2] = tr_x[i] + tr_w[i]; |
||||
pNew->p[i][3] = tr_y[i] + tr_h[i]; |
||||
pNew->weight[i] = t1.weight[i] * weight_scale; |
||||
} |
||||
|
||||
pNew->left = t1.left; |
||||
pNew->right = t1.right; |
||||
pNew->threshold = t1.threshold; |
||||
pNew->alpha[0] = t1.alpha[0]; |
||||
pNew->alpha[1] = t1.alpha[1]; |
||||
pNew->alpha[2] = t1.alpha[2]; |
||||
} |
||||
Loading…
Reference in new issue