renamed OpenCL kernel filename; made some final changes
This commit is contained in:
Vadim Pisarevsky
parent
b7553d4e2e
commit
ec3f22cee2
@ -1133,7 +1133,7 @@ bool CascadeClassifierImpl::detectSingleScale( InputArray _image, Size processin
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bool CascadeClassifierImpl::ocl_detectSingleScale( InputArray _image, Size processingRectSize,
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int yStep, double factor, Size sumSize0 )
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{
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const int VECTOR_SIZE = 4;
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const int VECTOR_SIZE = 1;
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Ptr<HaarEvaluator> haar = featureEvaluator.dynamicCast<HaarEvaluator>();
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if( haar.empty() )
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return false;
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@ -1142,7 +1142,7 @@ bool CascadeClassifierImpl::ocl_detectSingleScale( InputArray _image, Size proce
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if( cascadeKernel.empty() )
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{
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cascadeKernel.create("runHaarClassifierStump", ocl::objdetect::haarobjectdetect_oclsrc,
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cascadeKernel.create("runHaarClassifierStump", ocl::objdetect::cascadedetect_oclsrc,
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format("-D VECTOR_SIZE=%d", VECTOR_SIZE));
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if( cascadeKernel.empty() )
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return false;
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@ -40,7 +40,7 @@ __kernel void runHaarClassifierStump(
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int2 imgsize, int xyscale, float factor,
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int4 normrect, int2 windowsize, int maxFaces)
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{
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int ix = get_global_id(0)*xyscale*VECTOR_SIZE;
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int ix = get_global_id(0)*xyscale;
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int iy = get_global_id(1)*xyscale;
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sumstep /= sizeof(int);
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sqsumstep /= sizeof(int);
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@ -94,7 +94,6 @@ __kernel void runHaarClassifierStump(
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if( stageIdx == nstages )
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{
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int nfaces = atomic_inc(facepos);
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//printf("detected face #d!!!!\n", nfaces);
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if( nfaces < maxFaces )
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{
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volatile __global int* face = facepos + 1 + nfaces*4;
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@ -1,323 +0,0 @@
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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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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// Wu Xinglong, wxl370@126.com
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// Sen Liu, swjtuls1987@126.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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//M*/
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#define CV_HAAR_FEATURE_MAX 3
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typedef int sumtype;
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typedef float sqsumtype;
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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] /*__attribute__((aligned (16)))*/;
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float threshold /*__attribute__((aligned (4)))*/;
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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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//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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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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//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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__kernel void gpuRunHaarClassifierCascade_scaled2(
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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 sum,
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global const float *restrict sqsum,
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global int4 *candidate,
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const int rows,
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const int cols,
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const int step,
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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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global int4 *p,
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global float *correction,
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const int nodecount)
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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_id = mad24(lclidy, grpszx, lclidx);
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__local int glboutindex[1];
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__local int lclcount[1];
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__local int lcloutindex[64];
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glboutindex[0] = 0;
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int outputoff = mul24(grpidx, 256);
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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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int max_idx = rows * cols - 1;
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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 grpnumperline = (scaleinfo1.y & 0xffff0000) >> 16;
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int totalgrp = scaleinfo1.y & 0xffff;
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float factor = as_float(scaleinfo1.w);
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float correction_t = correction[scalei];
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float ystep = max(2.0f, factor);
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for (int grploop = get_group_id(0); grploop < totalgrp; grploop += grpnumx)
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{
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int4 cascadeinfo = p[scalei];
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int grpidy = grploop / grpnumperline;
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int grpidx = grploop - mul24(grpidy, grpnumperline);
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int ix = mad24(grpidx, grpszx, lclidx);
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int iy = mad24(grpidy, grpszy, lclidy);
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int x = round(ix * ystep);
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int y = round(iy * ystep);
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lcloutindex[lcl_id] = 0;
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lclcount[0] = 0;
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int nodecounter;
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float mean, variance_norm_factor;
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//if((ix < width) && (iy < height))
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{
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const int p_offset = mad24(y, step, x);
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cascadeinfo.x += p_offset;
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cascadeinfo.z += p_offset;
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mean = (sum[clamp(mad24(cascadeinfo.y, step, cascadeinfo.x), 0, max_idx)]
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- sum[clamp(mad24(cascadeinfo.y, step, cascadeinfo.z), 0, max_idx)] -
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sum[clamp(mad24(cascadeinfo.w, step, cascadeinfo.x), 0, max_idx)]
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+ sum[clamp(mad24(cascadeinfo.w, step, cascadeinfo.z), 0, max_idx)])
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* correction_t;
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variance_norm_factor = sqsum[clamp(mad24(cascadeinfo.y, step, cascadeinfo.x), 0, max_idx)]
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- sqsum[clamp(mad24(cascadeinfo.y, step, cascadeinfo.z), 0, max_idx)] -
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sqsum[clamp(mad24(cascadeinfo.w, step, cascadeinfo.x), 0, max_idx)]
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+ sqsum[clamp(mad24(cascadeinfo.w, step, cascadeinfo.z), 0, max_idx)];
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variance_norm_factor = variance_norm_factor * correction_t - mean * mean;
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variance_norm_factor = variance_norm_factor >= 0.f ? sqrt(variance_norm_factor) : 1.f;
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bool result = true;
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nodecounter = startnode + nodecount * scalei;
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for (int stageloop = start_stage; (stageloop < end_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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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 += p_offset;
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info1.z += p_offset;
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info2.x += p_offset;
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info2.z += p_offset;
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info3.x += p_offset;
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info3.z += p_offset;
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float classsum = (sum[clamp(mad24(info1.y, step, info1.x), 0, max_idx)]
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- sum[clamp(mad24(info1.y, step, info1.z), 0, max_idx)] -
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sum[clamp(mad24(info1.w, step, info1.x), 0, max_idx)]
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+ sum[clamp(mad24(info1.w, step, info1.z), 0, max_idx)]) * w.x;
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classsum += (sum[clamp(mad24(info2.y, step, info2.x), 0, max_idx)]
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- sum[clamp(mad24(info2.y, step, info2.z), 0, max_idx)] -
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sum[clamp(mad24(info2.w, step, info2.x), 0, max_idx)]
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+ sum[clamp(mad24(info2.w, step, info2.z), 0, max_idx)]) * w.y;
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classsum += (sum[clamp(mad24(info3.y, step, info3.x), 0, max_idx)]
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- sum[clamp(mad24(info3.y, step, info3.z), 0, max_idx)] -
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sum[clamp(mad24(info3.w, step, info3.x), 0, max_idx)]
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+ sum[clamp(mad24(info3.w, step, info3.z), 0, max_idx)]) * w.z;
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bool passThres = (classsum >= nodethreshold) ? 1 : 0;
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#if STUMP_BASED
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stage_sum += passThres ? alpha3.y : alpha3.x;
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nodecounter++;
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nodeloop++;
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#else
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bool isRootNode = (nodecounter & 1) == 0;
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if(isRootNode)
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{
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if( (passThres && currentnodeptr->right) ||
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(!passThres && currentnodeptr->left))
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{
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nodecounter ++;
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}
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else
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{
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stage_sum += alpha3.x;
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nodecounter += 2;
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nodeloop ++;
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}
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}
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else
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{
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stage_sum += (passThres ? alpha3.z : alpha3.y);
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nodecounter ++;
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nodeloop ++;
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}
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#endif
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}
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result = (stage_sum >= stageinfo->threshold) ? 1 : 0;
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}
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barrier(CLK_LOCAL_MEM_FENCE);
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if (result)
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{
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int queueindex = atomic_inc(lclcount);
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lcloutindex[queueindex] = (y << 16) | x;
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}
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barrier(CLK_LOCAL_MEM_FENCE);
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int queuecount = lclcount[0];
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if (lcl_id < queuecount)
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{
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int temp = lcloutindex[lcl_id];
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int x = temp & 0xffff;
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int y = (temp & (int)0xffff0000) >> 16;
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temp = atomic_inc(glboutindex);
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int4 candidate_result;
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candidate_result.zw = (int2)convert_int_rte(factor * 20.f);
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candidate_result.x = x;
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candidate_result.y = y;
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int i = outputoff+temp+lcl_id;
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if(candidate[i].z == 0)
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{
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candidate[i] = candidate_result;
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}
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else
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{
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for(i=i+1;;i++)
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{
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if(candidate[i].z == 0)
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{
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candidate[i] = candidate_result;
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break;
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}
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}
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}
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}
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barrier(CLK_LOCAL_MEM_FENCE);
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}
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}
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}
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}
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__kernel void gpuscaleclassifier(global GpuHidHaarTreeNode *orinode, global GpuHidHaarTreeNode *newnode, float scale, float weight_scale, const int nodenum)
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{
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const int counter = get_global_id(0);
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int tr_x[3], tr_y[3], tr_h[3], tr_w[3], i = 0;
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GpuHidHaarTreeNode t1 = *(__global GpuHidHaarTreeNode*)
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(((__global uchar*)orinode) + counter * sizeof(GpuHidHaarTreeNode));
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__global GpuHidHaarTreeNode* pNew = (__global GpuHidHaarTreeNode*)
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(((__global uchar*)newnode) + (counter + nodenum) * sizeof(GpuHidHaarTreeNode));
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#pragma unroll
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for (i = 0; i < 3; i++)
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{
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tr_x[i] = (int)(t1.p[i][0] * scale + 0.5f);
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tr_y[i] = (int)(t1.p[i][1] * scale + 0.5f);
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tr_w[i] = (int)(t1.p[i][2] * scale + 0.5f);
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tr_h[i] = (int)(t1.p[i][3] * scale + 0.5f);
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}
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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]);
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#pragma unroll
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for (i = 0; i < 3; i++)
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{
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pNew->p[i][0] = tr_x[i];
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pNew->p[i][1] = tr_y[i];
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pNew->p[i][2] = tr_x[i] + tr_w[i];
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pNew->p[i][3] = tr_y[i] + tr_h[i];
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pNew->weight[i] = t1.weight[i] * weight_scale;
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}
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pNew->left = t1.left;
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pNew->right = t1.right;
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pNew->threshold = t1.threshold;
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pNew->alpha[0] = t1.alpha[0];
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pNew->alpha[1] = t1.alpha[1];
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pNew->alpha[2] = t1.alpha[2];
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}
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@ -201,13 +201,12 @@ void detectAndDraw( UMat& img, Mat& canvas, CascadeClassifier& cascade,
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t = (double)getTickCount();
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cvtColor( img, gray, COLOR_BGR2GRAY );
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resize( gray, smallImg, Size(), scale0, scale0, INTER_LINEAR );
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cvtColor(smallImg, canvas, COLOR_GRAY2BGR);
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equalizeHist( smallImg, smallImg );
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resize( img, smallImg, Size(), scale0, scale0, INTER_LINEAR );
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cvtColor( smallImg, gray, COLOR_BGR2GRAY );
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equalizeHist( gray, gray );
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cascade.detectMultiScale( smallImg, faces,
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1.1, 2, 0
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cascade.detectMultiScale( gray, faces,
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1.1, 3, 0
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//|CASCADE_FIND_BIGGEST_OBJECT
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//|CASCADE_DO_ROUGH_SEARCH
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|CASCADE_SCALE_IMAGE
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@ -215,8 +214,8 @@ void detectAndDraw( UMat& img, Mat& canvas, CascadeClassifier& cascade,
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Size(30, 30) );
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if( tryflip )
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{
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flip(smallImg, smallImg, 1);
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cascade.detectMultiScale( smallImg, faces2,
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flip(gray, gray, 1);
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cascade.detectMultiScale( gray, faces2,
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1.1, 2, 0
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//|CASCADE_FIND_BIGGEST_OBJECT
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//|CASCADE_DO_ROUGH_SEARCH
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@ -229,7 +228,7 @@ void detectAndDraw( UMat& img, Mat& canvas, CascadeClassifier& cascade,
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}
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}
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t = (double)getTickCount() - t;
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cvtColor(smallImg, canvas, COLOR_GRAY2BGR);
|
||||
smallImg.copyTo(canvas);
|
||||
|
||||
double fps = getTickFrequency()/t;
|
||||
|
||||
@ -257,7 +256,7 @@ void detectAndDraw( UMat& img, Mat& canvas, CascadeClassifier& cascade,
|
||||
color, 3, 8, 0);
|
||||
if( nestedCascade.empty() )
|
||||
continue;
|
||||
UMat smallImgROI = smallImg(*r);
|
||||
UMat smallImgROI = gray(*r);
|
||||
nestedCascade.detectMultiScale( smallImgROI, nestedObjects,
|
||||
1.1, 2, 0
|
||||
//|CASCADE_FIND_BIGGEST_OBJECT
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user