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Merge pull request #9042 from terfendail:haar_avx
AVX optimized implementation of haar migrated to separate file
This commit is contained in:
Alexander Alekhin
GitHub
commit
431e2e6d68
369
modules/objdetect/src/haar.avx.cpp
Normal file
369
modules/objdetect/src/haar.avx.cpp
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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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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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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 Intel Corporation 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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/* Haar features calculation */
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#include "precomp.hpp"
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#include "haar.hpp"
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namespace cv_haar_avx
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{
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// AVX version icvEvalHidHaarClassifier. Process 8 CvHidHaarClassifiers per call. Check AVX support before invocation!!
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#if CV_HAAR_USE_AVX
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double icvEvalHidHaarClassifierAVX(CvHidHaarClassifier* classifier,
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double variance_norm_factor, size_t p_offset)
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{
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int CV_DECL_ALIGNED(32) idxV[8] = { 0,0,0,0,0,0,0,0 };
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uchar flags[8] = { 0,0,0,0,0,0,0,0 };
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CvHidHaarTreeNode* nodes[8];
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double res = 0;
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uchar exitConditionFlag = 0;
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for (;;)
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{
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float CV_DECL_ALIGNED(32) tmp[8] = { 0,0,0,0,0,0,0,0 };
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nodes[0] = (classifier + 0)->node + idxV[0];
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nodes[1] = (classifier + 1)->node + idxV[1];
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nodes[2] = (classifier + 2)->node + idxV[2];
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nodes[3] = (classifier + 3)->node + idxV[3];
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nodes[4] = (classifier + 4)->node + idxV[4];
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nodes[5] = (classifier + 5)->node + idxV[5];
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nodes[6] = (classifier + 6)->node + idxV[6];
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nodes[7] = (classifier + 7)->node + idxV[7];
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__m256 t = _mm256_set1_ps(static_cast<float>(variance_norm_factor));
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t = _mm256_mul_ps(t, _mm256_set_ps(nodes[7]->threshold,
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nodes[6]->threshold,
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nodes[5]->threshold,
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nodes[4]->threshold,
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nodes[3]->threshold,
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nodes[2]->threshold,
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nodes[1]->threshold,
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nodes[0]->threshold));
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__m256 offset = _mm256_set_ps(calc_sumf(nodes[7]->feature.rect[0], p_offset),
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calc_sumf(nodes[6]->feature.rect[0], p_offset),
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calc_sumf(nodes[5]->feature.rect[0], p_offset),
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calc_sumf(nodes[4]->feature.rect[0], p_offset),
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calc_sumf(nodes[3]->feature.rect[0], p_offset),
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calc_sumf(nodes[2]->feature.rect[0], p_offset),
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calc_sumf(nodes[1]->feature.rect[0], p_offset),
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calc_sumf(nodes[0]->feature.rect[0], p_offset));
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__m256 weight = _mm256_set_ps(nodes[7]->feature.rect[0].weight,
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nodes[6]->feature.rect[0].weight,
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nodes[5]->feature.rect[0].weight,
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nodes[4]->feature.rect[0].weight,
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nodes[3]->feature.rect[0].weight,
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nodes[2]->feature.rect[0].weight,
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nodes[1]->feature.rect[0].weight,
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nodes[0]->feature.rect[0].weight);
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__m256 sum = _mm256_mul_ps(offset, weight);
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offset = _mm256_set_ps(calc_sumf(nodes[7]->feature.rect[1], p_offset),
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calc_sumf(nodes[6]->feature.rect[1], p_offset),
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calc_sumf(nodes[5]->feature.rect[1], p_offset),
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calc_sumf(nodes[4]->feature.rect[1], p_offset),
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calc_sumf(nodes[3]->feature.rect[1], p_offset),
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calc_sumf(nodes[2]->feature.rect[1], p_offset),
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calc_sumf(nodes[1]->feature.rect[1], p_offset),
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calc_sumf(nodes[0]->feature.rect[1], p_offset));
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weight = _mm256_set_ps(nodes[7]->feature.rect[1].weight,
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nodes[6]->feature.rect[1].weight,
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nodes[5]->feature.rect[1].weight,
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nodes[4]->feature.rect[1].weight,
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nodes[3]->feature.rect[1].weight,
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nodes[2]->feature.rect[1].weight,
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nodes[1]->feature.rect[1].weight,
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nodes[0]->feature.rect[1].weight);
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sum = _mm256_add_ps(sum, _mm256_mul_ps(offset, weight));
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if (nodes[0]->feature.rect[2].p0)
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tmp[0] = calc_sumf(nodes[0]->feature.rect[2], p_offset) * nodes[0]->feature.rect[2].weight;
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if (nodes[1]->feature.rect[2].p0)
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tmp[1] = calc_sumf(nodes[1]->feature.rect[2], p_offset) * nodes[1]->feature.rect[2].weight;
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if (nodes[2]->feature.rect[2].p0)
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tmp[2] = calc_sumf(nodes[2]->feature.rect[2], p_offset) * nodes[2]->feature.rect[2].weight;
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if (nodes[3]->feature.rect[2].p0)
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tmp[3] = calc_sumf(nodes[3]->feature.rect[2], p_offset) * nodes[3]->feature.rect[2].weight;
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if (nodes[4]->feature.rect[2].p0)
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tmp[4] = calc_sumf(nodes[4]->feature.rect[2], p_offset) * nodes[4]->feature.rect[2].weight;
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if (nodes[5]->feature.rect[2].p0)
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tmp[5] = calc_sumf(nodes[5]->feature.rect[2], p_offset) * nodes[5]->feature.rect[2].weight;
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if (nodes[6]->feature.rect[2].p0)
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tmp[6] = calc_sumf(nodes[6]->feature.rect[2], p_offset) * nodes[6]->feature.rect[2].weight;
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if (nodes[7]->feature.rect[2].p0)
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tmp[7] = calc_sumf(nodes[7]->feature.rect[2], p_offset) * nodes[7]->feature.rect[2].weight;
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sum = _mm256_add_ps(sum, _mm256_load_ps(tmp));
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__m256 left = _mm256_set_ps(static_cast<float>(nodes[7]->left), static_cast<float>(nodes[6]->left),
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static_cast<float>(nodes[5]->left), static_cast<float>(nodes[4]->left),
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static_cast<float>(nodes[3]->left), static_cast<float>(nodes[2]->left),
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static_cast<float>(nodes[1]->left), static_cast<float>(nodes[0]->left));
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__m256 right = _mm256_set_ps(static_cast<float>(nodes[7]->right), static_cast<float>(nodes[6]->right),
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static_cast<float>(nodes[5]->right), static_cast<float>(nodes[4]->right),
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static_cast<float>(nodes[3]->right), static_cast<float>(nodes[2]->right),
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static_cast<float>(nodes[1]->right), static_cast<float>(nodes[0]->right));
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_mm256_store_si256((__m256i*)idxV, _mm256_cvttps_epi32(_mm256_blendv_ps(right, left, _mm256_cmp_ps(sum, t, _CMP_LT_OQ))));
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for (int i = 0; i < 8; i++)
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{
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if (idxV[i] <= 0)
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{
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if (!flags[i])
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{
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exitConditionFlag++;
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flags[i] = 1;
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res += (classifier + i)->alpha[-idxV[i]];
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}
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idxV[i] = 0;
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}
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}
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if (exitConditionFlag == 8)
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return res;
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}
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}
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double icvEvalHidHaarStumpClassifierAVX(CvHidHaarClassifier* classifier,
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double variance_norm_factor, size_t p_offset)
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{
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float CV_DECL_ALIGNED(32) tmp[8] = { 0,0,0,0,0,0,0,0 };
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CvHidHaarTreeNode* nodes[8];
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nodes[0] = classifier[0].node;
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nodes[1] = classifier[1].node;
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nodes[2] = classifier[2].node;
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nodes[3] = classifier[3].node;
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nodes[4] = classifier[4].node;
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nodes[5] = classifier[5].node;
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nodes[6] = classifier[6].node;
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nodes[7] = classifier[7].node;
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__m256 t = _mm256_set1_ps(static_cast<float>(variance_norm_factor));
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t = _mm256_mul_ps(t, _mm256_set_ps(nodes[7]->threshold,
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nodes[6]->threshold,
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nodes[5]->threshold,
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nodes[4]->threshold,
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nodes[3]->threshold,
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nodes[2]->threshold,
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nodes[1]->threshold,
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nodes[0]->threshold));
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__m256 offset = _mm256_set_ps(calc_sumf(nodes[7]->feature.rect[0], p_offset),
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calc_sumf(nodes[6]->feature.rect[0], p_offset),
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calc_sumf(nodes[5]->feature.rect[0], p_offset),
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calc_sumf(nodes[4]->feature.rect[0], p_offset),
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calc_sumf(nodes[3]->feature.rect[0], p_offset),
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calc_sumf(nodes[2]->feature.rect[0], p_offset),
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calc_sumf(nodes[1]->feature.rect[0], p_offset),
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calc_sumf(nodes[0]->feature.rect[0], p_offset));
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__m256 weight = _mm256_set_ps(nodes[7]->feature.rect[0].weight,
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nodes[6]->feature.rect[0].weight,
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nodes[5]->feature.rect[0].weight,
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nodes[4]->feature.rect[0].weight,
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nodes[3]->feature.rect[0].weight,
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nodes[2]->feature.rect[0].weight,
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nodes[1]->feature.rect[0].weight,
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nodes[0]->feature.rect[0].weight);
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__m256 sum = _mm256_mul_ps(offset, weight);
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offset = _mm256_set_ps(calc_sumf(nodes[7]->feature.rect[1], p_offset),
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calc_sumf(nodes[6]->feature.rect[1], p_offset),
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calc_sumf(nodes[5]->feature.rect[1], p_offset),
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calc_sumf(nodes[4]->feature.rect[1], p_offset),
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calc_sumf(nodes[3]->feature.rect[1], p_offset),
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calc_sumf(nodes[2]->feature.rect[1], p_offset),
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calc_sumf(nodes[1]->feature.rect[1], p_offset),
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calc_sumf(nodes[0]->feature.rect[1], p_offset));
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weight = _mm256_set_ps(nodes[7]->feature.rect[1].weight,
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nodes[6]->feature.rect[1].weight,
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nodes[5]->feature.rect[1].weight,
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nodes[4]->feature.rect[1].weight,
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nodes[3]->feature.rect[1].weight,
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nodes[2]->feature.rect[1].weight,
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nodes[1]->feature.rect[1].weight,
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nodes[0]->feature.rect[1].weight);
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sum = _mm256_add_ps(sum, _mm256_mul_ps(offset, weight));
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if (nodes[0]->feature.rect[2].p0)
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tmp[0] = calc_sumf(nodes[0]->feature.rect[2], p_offset) * nodes[0]->feature.rect[2].weight;
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if (nodes[1]->feature.rect[2].p0)
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tmp[1] = calc_sumf(nodes[1]->feature.rect[2], p_offset) * nodes[1]->feature.rect[2].weight;
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if (nodes[2]->feature.rect[2].p0)
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tmp[2] = calc_sumf(nodes[2]->feature.rect[2], p_offset) * nodes[2]->feature.rect[2].weight;
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if (nodes[3]->feature.rect[2].p0)
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tmp[3] = calc_sumf(nodes[3]->feature.rect[2], p_offset) * nodes[3]->feature.rect[2].weight;
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if (nodes[4]->feature.rect[2].p0)
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tmp[4] = calc_sumf(nodes[4]->feature.rect[2], p_offset) * nodes[4]->feature.rect[2].weight;
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if (nodes[5]->feature.rect[2].p0)
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tmp[5] = calc_sumf(nodes[5]->feature.rect[2], p_offset) * nodes[5]->feature.rect[2].weight;
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if (nodes[6]->feature.rect[2].p0)
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tmp[6] = calc_sumf(nodes[6]->feature.rect[2], p_offset) * nodes[6]->feature.rect[2].weight;
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if (nodes[7]->feature.rect[2].p0)
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tmp[7] = calc_sumf(nodes[7]->feature.rect[2], p_offset) * nodes[7]->feature.rect[2].weight;
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sum = _mm256_add_ps(sum, _mm256_load_ps(tmp));
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__m256 alpha0 = _mm256_set_ps(classifier[7].alpha[0],
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classifier[6].alpha[0],
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classifier[5].alpha[0],
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classifier[4].alpha[0],
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classifier[3].alpha[0],
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classifier[2].alpha[0],
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classifier[1].alpha[0],
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classifier[0].alpha[0]);
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__m256 alpha1 = _mm256_set_ps(classifier[7].alpha[1],
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classifier[6].alpha[1],
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classifier[5].alpha[1],
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classifier[4].alpha[1],
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classifier[3].alpha[1],
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classifier[2].alpha[1],
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classifier[1].alpha[1],
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classifier[0].alpha[1]);
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__m256 outBuf = _mm256_blendv_ps(alpha0, alpha1, _mm256_cmp_ps(t, sum, _CMP_LE_OQ));
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outBuf = _mm256_hadd_ps(outBuf, outBuf);
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outBuf = _mm256_hadd_ps(outBuf, outBuf);
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_mm256_store_ps(tmp, outBuf);
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return (tmp[0] + tmp[4]);
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}
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double icvEvalHidHaarStumpClassifierTwoRectAVX(CvHidHaarClassifier* classifier,
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double variance_norm_factor, size_t p_offset)
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{
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float CV_DECL_ALIGNED(32) buf[8];
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CvHidHaarTreeNode* nodes[8];
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nodes[0] = classifier[0].node;
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nodes[1] = classifier[1].node;
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nodes[2] = classifier[2].node;
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nodes[3] = classifier[3].node;
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nodes[4] = classifier[4].node;
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nodes[5] = classifier[5].node;
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nodes[6] = classifier[6].node;
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nodes[7] = classifier[7].node;
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__m256 t = _mm256_set1_ps(static_cast<float>(variance_norm_factor));
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t = _mm256_mul_ps(t, _mm256_set_ps(nodes[7]->threshold,
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nodes[6]->threshold,
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nodes[5]->threshold,
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nodes[4]->threshold,
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nodes[3]->threshold,
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nodes[2]->threshold,
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nodes[1]->threshold,
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nodes[0]->threshold));
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__m256 offset = _mm256_set_ps(calc_sumf(nodes[7]->feature.rect[0], p_offset),
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calc_sumf(nodes[6]->feature.rect[0], p_offset),
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calc_sumf(nodes[5]->feature.rect[0], p_offset),
|
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calc_sumf(nodes[4]->feature.rect[0], p_offset),
|
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calc_sumf(nodes[3]->feature.rect[0], p_offset),
|
||||
calc_sumf(nodes[2]->feature.rect[0], p_offset),
|
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calc_sumf(nodes[1]->feature.rect[0], p_offset),
|
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calc_sumf(nodes[0]->feature.rect[0], p_offset));
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__m256 weight = _mm256_set_ps(nodes[7]->feature.rect[0].weight,
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nodes[6]->feature.rect[0].weight,
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nodes[5]->feature.rect[0].weight,
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nodes[4]->feature.rect[0].weight,
|
||||
nodes[3]->feature.rect[0].weight,
|
||||
nodes[2]->feature.rect[0].weight,
|
||||
nodes[1]->feature.rect[0].weight,
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||||
nodes[0]->feature.rect[0].weight);
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__m256 sum = _mm256_mul_ps(offset, weight);
|
||||
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offset = _mm256_set_ps(calc_sumf(nodes[7]->feature.rect[1], p_offset),
|
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calc_sumf(nodes[6]->feature.rect[1], p_offset),
|
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calc_sumf(nodes[5]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[4]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[3]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[2]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[1]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[0]->feature.rect[1], p_offset));
|
||||
|
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weight = _mm256_set_ps(nodes[7]->feature.rect[1].weight,
|
||||
nodes[6]->feature.rect[1].weight,
|
||||
nodes[5]->feature.rect[1].weight,
|
||||
nodes[4]->feature.rect[1].weight,
|
||||
nodes[3]->feature.rect[1].weight,
|
||||
nodes[2]->feature.rect[1].weight,
|
||||
nodes[1]->feature.rect[1].weight,
|
||||
nodes[0]->feature.rect[1].weight);
|
||||
|
||||
sum = _mm256_add_ps(sum, _mm256_mul_ps(offset, weight));
|
||||
|
||||
__m256 alpha0 = _mm256_set_ps(classifier[7].alpha[0],
|
||||
classifier[6].alpha[0],
|
||||
classifier[5].alpha[0],
|
||||
classifier[4].alpha[0],
|
||||
classifier[3].alpha[0],
|
||||
classifier[2].alpha[0],
|
||||
classifier[1].alpha[0],
|
||||
classifier[0].alpha[0]);
|
||||
__m256 alpha1 = _mm256_set_ps(classifier[7].alpha[1],
|
||||
classifier[6].alpha[1],
|
||||
classifier[5].alpha[1],
|
||||
classifier[4].alpha[1],
|
||||
classifier[3].alpha[1],
|
||||
classifier[2].alpha[1],
|
||||
classifier[1].alpha[1],
|
||||
classifier[0].alpha[1]);
|
||||
|
||||
_mm256_store_ps(buf, _mm256_blendv_ps(alpha0, alpha1, _mm256_cmp_ps(t, sum, _CMP_LE_OQ)));
|
||||
return (buf[0] + buf[1] + buf[2] + buf[3] + buf[4] + buf[5] + buf[6] + buf[7]);
|
||||
}
|
||||
|
||||
#endif //CV_HAAR_USE_AVX
|
||||
|
||||
}
|
||||
|
||||
/* End of file. */
|
||||
@ -45,6 +45,10 @@
|
||||
#include "opencv2/imgproc/imgproc_c.h"
|
||||
#include "opencv2/objdetect/objdetect_c.h"
|
||||
#include <stdio.h>
|
||||
#include "haar.hpp"
|
||||
#if CV_HAAR_FEATURE_MAX_LOCAL != CV_HAAR_FEATURE_MAX
|
||||
#error CV_HAAR_FEATURE_MAX definition changed. Adjust CV_HAAR_FEATURE_MAX_LOCAL value please.
|
||||
#endif
|
||||
|
||||
#if CV_SSE2
|
||||
# if 1 /*!CV_SSE4_1 && !CV_SSE4_2*/
|
||||
@ -53,8 +57,7 @@
|
||||
# endif
|
||||
#endif
|
||||
|
||||
#if 0 /*CV_AVX*/
|
||||
# define CV_HAAR_USE_AVX 1
|
||||
#if CV_HAAR_USE_AVX
|
||||
# if defined _MSC_VER
|
||||
# pragma warning( disable : 4752 )
|
||||
# endif
|
||||
@ -68,38 +71,6 @@
|
||||
#define CV_ADJUST_FEATURES 1
|
||||
#define CV_ADJUST_WEIGHTS 0
|
||||
|
||||
typedef int sumtype;
|
||||
typedef double sqsumtype;
|
||||
|
||||
typedef struct CvHidHaarFeature
|
||||
{
|
||||
struct
|
||||
{
|
||||
sumtype *p0, *p1, *p2, *p3;
|
||||
float weight;
|
||||
}
|
||||
rect[CV_HAAR_FEATURE_MAX];
|
||||
} CvHidHaarFeature;
|
||||
|
||||
|
||||
typedef struct CvHidHaarTreeNode
|
||||
{
|
||||
CvHidHaarFeature feature;
|
||||
float threshold;
|
||||
int left;
|
||||
int right;
|
||||
} CvHidHaarTreeNode;
|
||||
|
||||
|
||||
typedef struct CvHidHaarClassifier
|
||||
{
|
||||
int count;
|
||||
//CvHaarFeature* orig_feature;
|
||||
CvHidHaarTreeNode* node;
|
||||
float* alpha;
|
||||
} CvHidHaarClassifier;
|
||||
|
||||
|
||||
typedef struct CvHidHaarStageClassifier
|
||||
{
|
||||
int count;
|
||||
@ -420,10 +391,6 @@ icvCreateHidHaarClassifierCascade( CvHaarClassifierCascade* cascade )
|
||||
#define calc_sum(rect,offset) \
|
||||
((rect).p0[offset] - (rect).p1[offset] - (rect).p2[offset] + (rect).p3[offset])
|
||||
|
||||
#define calc_sumf(rect,offset) \
|
||||
static_cast<float>((rect).p0[offset] - (rect).p1[offset] - (rect).p2[offset] + (rect).p3[offset])
|
||||
|
||||
|
||||
CV_IMPL void
|
||||
cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* _cascade,
|
||||
const CvArr* _sum,
|
||||
@ -640,129 +607,6 @@ cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* _cascade,
|
||||
}
|
||||
|
||||
|
||||
// AVX version icvEvalHidHaarClassifier. Process 8 CvHidHaarClassifiers per call. Check AVX support before invocation!!
|
||||
#ifdef CV_HAAR_USE_AVX
|
||||
CV_INLINE
|
||||
double icvEvalHidHaarClassifierAVX( CvHidHaarClassifier* classifier,
|
||||
double variance_norm_factor, size_t p_offset )
|
||||
{
|
||||
int CV_DECL_ALIGNED(32) idxV[8] = {0,0,0,0,0,0,0,0};
|
||||
uchar flags[8] = {0,0,0,0,0,0,0,0};
|
||||
CvHidHaarTreeNode* nodes[8];
|
||||
double res = 0;
|
||||
uchar exitConditionFlag = 0;
|
||||
for(;;)
|
||||
{
|
||||
float CV_DECL_ALIGNED(32) tmp[8] = {0,0,0,0,0,0,0,0};
|
||||
nodes[0] = (classifier+0)->node + idxV[0];
|
||||
nodes[1] = (classifier+1)->node + idxV[1];
|
||||
nodes[2] = (classifier+2)->node + idxV[2];
|
||||
nodes[3] = (classifier+3)->node + idxV[3];
|
||||
nodes[4] = (classifier+4)->node + idxV[4];
|
||||
nodes[5] = (classifier+5)->node + idxV[5];
|
||||
nodes[6] = (classifier+6)->node + idxV[6];
|
||||
nodes[7] = (classifier+7)->node + idxV[7];
|
||||
|
||||
__m256 t = _mm256_set1_ps(static_cast<float>(variance_norm_factor));
|
||||
|
||||
t = _mm256_mul_ps(t, _mm256_set_ps(nodes[7]->threshold,
|
||||
nodes[6]->threshold,
|
||||
nodes[5]->threshold,
|
||||
nodes[4]->threshold,
|
||||
nodes[3]->threshold,
|
||||
nodes[2]->threshold,
|
||||
nodes[1]->threshold,
|
||||
nodes[0]->threshold));
|
||||
|
||||
__m256 offset = _mm256_set_ps(calc_sumf(nodes[7]->feature.rect[0], p_offset),
|
||||
calc_sumf(nodes[6]->feature.rect[0], p_offset),
|
||||
calc_sumf(nodes[5]->feature.rect[0], p_offset),
|
||||
calc_sumf(nodes[4]->feature.rect[0], p_offset),
|
||||
calc_sumf(nodes[3]->feature.rect[0], p_offset),
|
||||
calc_sumf(nodes[2]->feature.rect[0], p_offset),
|
||||
calc_sumf(nodes[1]->feature.rect[0], p_offset),
|
||||
calc_sumf(nodes[0]->feature.rect[0], p_offset));
|
||||
|
||||
__m256 weight = _mm256_set_ps(nodes[7]->feature.rect[0].weight,
|
||||
nodes[6]->feature.rect[0].weight,
|
||||
nodes[5]->feature.rect[0].weight,
|
||||
nodes[4]->feature.rect[0].weight,
|
||||
nodes[3]->feature.rect[0].weight,
|
||||
nodes[2]->feature.rect[0].weight,
|
||||
nodes[1]->feature.rect[0].weight,
|
||||
nodes[0]->feature.rect[0].weight);
|
||||
|
||||
__m256 sum = _mm256_mul_ps(offset, weight);
|
||||
|
||||
offset = _mm256_set_ps(calc_sumf(nodes[7]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[6]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[5]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[4]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[3]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[2]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[1]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[0]->feature.rect[1], p_offset));
|
||||
|
||||
weight = _mm256_set_ps(nodes[7]->feature.rect[1].weight,
|
||||
nodes[6]->feature.rect[1].weight,
|
||||
nodes[5]->feature.rect[1].weight,
|
||||
nodes[4]->feature.rect[1].weight,
|
||||
nodes[3]->feature.rect[1].weight,
|
||||
nodes[2]->feature.rect[1].weight,
|
||||
nodes[1]->feature.rect[1].weight,
|
||||
nodes[0]->feature.rect[1].weight);
|
||||
|
||||
sum = _mm256_add_ps(sum, _mm256_mul_ps(offset, weight));
|
||||
|
||||
if( nodes[0]->feature.rect[2].p0 )
|
||||
tmp[0] = calc_sumf(nodes[0]->feature.rect[2], p_offset) * nodes[0]->feature.rect[2].weight;
|
||||
if( nodes[1]->feature.rect[2].p0 )
|
||||
tmp[1] = calc_sumf(nodes[1]->feature.rect[2], p_offset) * nodes[1]->feature.rect[2].weight;
|
||||
if( nodes[2]->feature.rect[2].p0 )
|
||||
tmp[2] = calc_sumf(nodes[2]->feature.rect[2], p_offset) * nodes[2]->feature.rect[2].weight;
|
||||
if( nodes[3]->feature.rect[2].p0 )
|
||||
tmp[3] = calc_sumf(nodes[3]->feature.rect[2], p_offset) * nodes[3]->feature.rect[2].weight;
|
||||
if( nodes[4]->feature.rect[2].p0 )
|
||||
tmp[4] = calc_sumf(nodes[4]->feature.rect[2], p_offset) * nodes[4]->feature.rect[2].weight;
|
||||
if( nodes[5]->feature.rect[2].p0 )
|
||||
tmp[5] = calc_sumf(nodes[5]->feature.rect[2], p_offset) * nodes[5]->feature.rect[2].weight;
|
||||
if( nodes[6]->feature.rect[2].p0 )
|
||||
tmp[6] = calc_sumf(nodes[6]->feature.rect[2], p_offset) * nodes[6]->feature.rect[2].weight;
|
||||
if( nodes[7]->feature.rect[2].p0 )
|
||||
tmp[7] = calc_sumf(nodes[7]->feature.rect[2], p_offset) * nodes[7]->feature.rect[2].weight;
|
||||
|
||||
sum = _mm256_add_ps(sum,_mm256_load_ps(tmp));
|
||||
|
||||
__m256 left = _mm256_set_ps(static_cast<float>(nodes[7]->left), static_cast<float>(nodes[6]->left),
|
||||
static_cast<float>(nodes[5]->left), static_cast<float>(nodes[4]->left),
|
||||
static_cast<float>(nodes[3]->left), static_cast<float>(nodes[2]->left),
|
||||
static_cast<float>(nodes[1]->left), static_cast<float>(nodes[0]->left));
|
||||
__m256 right = _mm256_set_ps(static_cast<float>(nodes[7]->right),static_cast<float>(nodes[6]->right),
|
||||
static_cast<float>(nodes[5]->right),static_cast<float>(nodes[4]->right),
|
||||
static_cast<float>(nodes[3]->right),static_cast<float>(nodes[2]->right),
|
||||
static_cast<float>(nodes[1]->right),static_cast<float>(nodes[0]->right));
|
||||
|
||||
_mm256_store_si256((__m256i*)idxV, _mm256_cvttps_epi32(_mm256_blendv_ps(right, left, _mm256_cmp_ps(sum, t, _CMP_LT_OQ))));
|
||||
|
||||
for(int i = 0; i < 8; i++)
|
||||
{
|
||||
if(idxV[i]<=0)
|
||||
{
|
||||
if(!flags[i])
|
||||
{
|
||||
exitConditionFlag++;
|
||||
flags[i] = 1;
|
||||
res += (classifier+i)->alpha[-idxV[i]];
|
||||
}
|
||||
idxV[i]=0;
|
||||
}
|
||||
}
|
||||
if(exitConditionFlag == 8)
|
||||
return res;
|
||||
}
|
||||
}
|
||||
#endif //CV_HAAR_USE_AVX
|
||||
|
||||
CV_INLINE
|
||||
double icvEvalHidHaarClassifier( CvHidHaarClassifier* classifier,
|
||||
double variance_norm_factor,
|
||||
@ -823,8 +667,8 @@ static int
|
||||
cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
|
||||
CvPoint pt, double& stage_sum, int start_stage )
|
||||
{
|
||||
#ifdef CV_HAAR_USE_AVX
|
||||
bool haveAVX = cv::checkHardwareSupport(CV_CPU_AVX);
|
||||
#if CV_HAAR_USE_AVX
|
||||
bool haveAVX = CV_CPU_HAS_SUPPORT_AVX;
|
||||
#else
|
||||
# ifdef CV_HAAR_USE_SSE
|
||||
bool haveSSE2 = cv::checkHardwareSupport(CV_CPU_SSE2);
|
||||
@ -870,14 +714,14 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
|
||||
stage_sum = 0.0;
|
||||
j = 0;
|
||||
|
||||
#ifdef CV_HAAR_USE_AVX
|
||||
#if CV_HAAR_USE_AVX
|
||||
if(haveAVX)
|
||||
{
|
||||
for( ; j <= ptr->count - 8; j += 8 )
|
||||
{
|
||||
stage_sum += icvEvalHidHaarClassifierAVX(
|
||||
ptr->classifier + j,
|
||||
variance_norm_factor, p_offset );
|
||||
stage_sum += cv_haar_avx::icvEvalHidHaarClassifierAVX(
|
||||
ptr->classifier + j,
|
||||
variance_norm_factor, p_offset );
|
||||
}
|
||||
}
|
||||
#endif
|
||||
@ -901,106 +745,20 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
|
||||
}
|
||||
else if( cascade->isStumpBased )
|
||||
{
|
||||
#ifdef CV_HAAR_USE_AVX
|
||||
#if CV_HAAR_USE_AVX
|
||||
if(haveAVX)
|
||||
{
|
||||
CvHidHaarClassifier* classifiers[8];
|
||||
CvHidHaarTreeNode* nodes[8];
|
||||
for( i = start_stage; i < cascade->count; i++ )
|
||||
{
|
||||
stage_sum = 0.0;
|
||||
j = 0;
|
||||
float CV_DECL_ALIGNED(32) buf[8];
|
||||
if( cascade->stage_classifier[i].two_rects )
|
||||
{
|
||||
for( ; j <= cascade->stage_classifier[i].count - 8; j += 8 )
|
||||
{
|
||||
classifiers[0] = cascade->stage_classifier[i].classifier + j;
|
||||
nodes[0] = classifiers[0]->node;
|
||||
classifiers[1] = cascade->stage_classifier[i].classifier + j + 1;
|
||||
nodes[1] = classifiers[1]->node;
|
||||
classifiers[2] = cascade->stage_classifier[i].classifier + j + 2;
|
||||
nodes[2] = classifiers[2]->node;
|
||||
classifiers[3] = cascade->stage_classifier[i].classifier + j + 3;
|
||||
nodes[3] = classifiers[3]->node;
|
||||
classifiers[4] = cascade->stage_classifier[i].classifier + j + 4;
|
||||
nodes[4] = classifiers[4]->node;
|
||||
classifiers[5] = cascade->stage_classifier[i].classifier + j + 5;
|
||||
nodes[5] = classifiers[5]->node;
|
||||
classifiers[6] = cascade->stage_classifier[i].classifier + j + 6;
|
||||
nodes[6] = classifiers[6]->node;
|
||||
classifiers[7] = cascade->stage_classifier[i].classifier + j + 7;
|
||||
nodes[7] = classifiers[7]->node;
|
||||
|
||||
__m256 t = _mm256_set1_ps(static_cast<float>(variance_norm_factor));
|
||||
t = _mm256_mul_ps(t, _mm256_set_ps(nodes[7]->threshold,
|
||||
nodes[6]->threshold,
|
||||
nodes[5]->threshold,
|
||||
nodes[4]->threshold,
|
||||
nodes[3]->threshold,
|
||||
nodes[2]->threshold,
|
||||
nodes[1]->threshold,
|
||||
nodes[0]->threshold));
|
||||
|
||||
__m256 offset = _mm256_set_ps(calc_sumf(nodes[7]->feature.rect[0], p_offset),
|
||||
calc_sumf(nodes[6]->feature.rect[0], p_offset),
|
||||
calc_sumf(nodes[5]->feature.rect[0], p_offset),
|
||||
calc_sumf(nodes[4]->feature.rect[0], p_offset),
|
||||
calc_sumf(nodes[3]->feature.rect[0], p_offset),
|
||||
calc_sumf(nodes[2]->feature.rect[0], p_offset),
|
||||
calc_sumf(nodes[1]->feature.rect[0], p_offset),
|
||||
calc_sumf(nodes[0]->feature.rect[0], p_offset));
|
||||
|
||||
__m256 weight = _mm256_set_ps(nodes[7]->feature.rect[0].weight,
|
||||
nodes[6]->feature.rect[0].weight,
|
||||
nodes[5]->feature.rect[0].weight,
|
||||
nodes[4]->feature.rect[0].weight,
|
||||
nodes[3]->feature.rect[0].weight,
|
||||
nodes[2]->feature.rect[0].weight,
|
||||
nodes[1]->feature.rect[0].weight,
|
||||
nodes[0]->feature.rect[0].weight);
|
||||
|
||||
__m256 sum = _mm256_mul_ps(offset, weight);
|
||||
|
||||
offset = _mm256_set_ps(calc_sumf(nodes[7]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[6]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[5]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[4]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[3]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[2]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[1]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[0]->feature.rect[1], p_offset));
|
||||
|
||||
weight = _mm256_set_ps(nodes[7]->feature.rect[1].weight,
|
||||
nodes[6]->feature.rect[1].weight,
|
||||
nodes[5]->feature.rect[1].weight,
|
||||
nodes[4]->feature.rect[1].weight,
|
||||
nodes[3]->feature.rect[1].weight,
|
||||
nodes[2]->feature.rect[1].weight,
|
||||
nodes[1]->feature.rect[1].weight,
|
||||
nodes[0]->feature.rect[1].weight);
|
||||
|
||||
sum = _mm256_add_ps(sum, _mm256_mul_ps(offset,weight));
|
||||
|
||||
__m256 alpha0 = _mm256_set_ps(classifiers[7]->alpha[0],
|
||||
classifiers[6]->alpha[0],
|
||||
classifiers[5]->alpha[0],
|
||||
classifiers[4]->alpha[0],
|
||||
classifiers[3]->alpha[0],
|
||||
classifiers[2]->alpha[0],
|
||||
classifiers[1]->alpha[0],
|
||||
classifiers[0]->alpha[0]);
|
||||
__m256 alpha1 = _mm256_set_ps(classifiers[7]->alpha[1],
|
||||
classifiers[6]->alpha[1],
|
||||
classifiers[5]->alpha[1],
|
||||
classifiers[4]->alpha[1],
|
||||
classifiers[3]->alpha[1],
|
||||
classifiers[2]->alpha[1],
|
||||
classifiers[1]->alpha[1],
|
||||
classifiers[0]->alpha[1]);
|
||||
|
||||
_mm256_store_ps(buf, _mm256_blendv_ps(alpha0, alpha1, _mm256_cmp_ps(t, sum, _CMP_LE_OQ)));
|
||||
stage_sum += (buf[0]+buf[1]+buf[2]+buf[3]+buf[4]+buf[5]+buf[6]+buf[7]);
|
||||
stage_sum += cv_haar_avx::icvEvalHidHaarStumpClassifierTwoRectAVX(
|
||||
cascade->stage_classifier[i].classifier + j,
|
||||
variance_norm_factor, p_offset);
|
||||
}
|
||||
|
||||
for( ; j < cascade->stage_classifier[i].count; j++ )
|
||||
@ -1018,117 +776,9 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
|
||||
{
|
||||
for( ; j <= (cascade->stage_classifier[i].count)-8; j+=8 )
|
||||
{
|
||||
float CV_DECL_ALIGNED(32) tmp[8] = {0,0,0,0,0,0,0,0};
|
||||
|
||||
classifiers[0] = cascade->stage_classifier[i].classifier + j;
|
||||
nodes[0] = classifiers[0]->node;
|
||||
classifiers[1] = cascade->stage_classifier[i].classifier + j + 1;
|
||||
nodes[1] = classifiers[1]->node;
|
||||
classifiers[2] = cascade->stage_classifier[i].classifier + j + 2;
|
||||
nodes[2] = classifiers[2]->node;
|
||||
classifiers[3] = cascade->stage_classifier[i].classifier + j + 3;
|
||||
nodes[3] = classifiers[3]->node;
|
||||
classifiers[4] = cascade->stage_classifier[i].classifier + j + 4;
|
||||
nodes[4] = classifiers[4]->node;
|
||||
classifiers[5] = cascade->stage_classifier[i].classifier + j + 5;
|
||||
nodes[5] = classifiers[5]->node;
|
||||
classifiers[6] = cascade->stage_classifier[i].classifier + j + 6;
|
||||
nodes[6] = classifiers[6]->node;
|
||||
classifiers[7] = cascade->stage_classifier[i].classifier + j + 7;
|
||||
nodes[7] = classifiers[7]->node;
|
||||
|
||||
__m256 t = _mm256_set1_ps(static_cast<float>(variance_norm_factor));
|
||||
|
||||
t = _mm256_mul_ps(t, _mm256_set_ps(nodes[7]->threshold,
|
||||
nodes[6]->threshold,
|
||||
nodes[5]->threshold,
|
||||
nodes[4]->threshold,
|
||||
nodes[3]->threshold,
|
||||
nodes[2]->threshold,
|
||||
nodes[1]->threshold,
|
||||
nodes[0]->threshold));
|
||||
|
||||
__m256 offset = _mm256_set_ps(calc_sumf(nodes[7]->feature.rect[0], p_offset),
|
||||
calc_sumf(nodes[6]->feature.rect[0], p_offset),
|
||||
calc_sumf(nodes[5]->feature.rect[0], p_offset),
|
||||
calc_sumf(nodes[4]->feature.rect[0], p_offset),
|
||||
calc_sumf(nodes[3]->feature.rect[0], p_offset),
|
||||
calc_sumf(nodes[2]->feature.rect[0], p_offset),
|
||||
calc_sumf(nodes[1]->feature.rect[0], p_offset),
|
||||
calc_sumf(nodes[0]->feature.rect[0], p_offset));
|
||||
|
||||
__m256 weight = _mm256_set_ps(nodes[7]->feature.rect[0].weight,
|
||||
nodes[6]->feature.rect[0].weight,
|
||||
nodes[5]->feature.rect[0].weight,
|
||||
nodes[4]->feature.rect[0].weight,
|
||||
nodes[3]->feature.rect[0].weight,
|
||||
nodes[2]->feature.rect[0].weight,
|
||||
nodes[1]->feature.rect[0].weight,
|
||||
nodes[0]->feature.rect[0].weight);
|
||||
|
||||
__m256 sum = _mm256_mul_ps(offset, weight);
|
||||
|
||||
offset = _mm256_set_ps(calc_sumf(nodes[7]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[6]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[5]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[4]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[3]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[2]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[1]->feature.rect[1], p_offset),
|
||||
calc_sumf(nodes[0]->feature.rect[1], p_offset));
|
||||
|
||||
weight = _mm256_set_ps(nodes[7]->feature.rect[1].weight,
|
||||
nodes[6]->feature.rect[1].weight,
|
||||
nodes[5]->feature.rect[1].weight,
|
||||
nodes[4]->feature.rect[1].weight,
|
||||
nodes[3]->feature.rect[1].weight,
|
||||
nodes[2]->feature.rect[1].weight,
|
||||
nodes[1]->feature.rect[1].weight,
|
||||
nodes[0]->feature.rect[1].weight);
|
||||
|
||||
sum = _mm256_add_ps(sum, _mm256_mul_ps(offset, weight));
|
||||
|
||||
if( nodes[0]->feature.rect[2].p0 )
|
||||
tmp[0] = calc_sumf(nodes[0]->feature.rect[2],p_offset) * nodes[0]->feature.rect[2].weight;
|
||||
if( nodes[1]->feature.rect[2].p0 )
|
||||
tmp[1] = calc_sumf(nodes[1]->feature.rect[2],p_offset) * nodes[1]->feature.rect[2].weight;
|
||||
if( nodes[2]->feature.rect[2].p0 )
|
||||
tmp[2] = calc_sumf(nodes[2]->feature.rect[2],p_offset) * nodes[2]->feature.rect[2].weight;
|
||||
if( nodes[3]->feature.rect[2].p0 )
|
||||
tmp[3] = calc_sumf(nodes[3]->feature.rect[2],p_offset) * nodes[3]->feature.rect[2].weight;
|
||||
if( nodes[4]->feature.rect[2].p0 )
|
||||
tmp[4] = calc_sumf(nodes[4]->feature.rect[2],p_offset) * nodes[4]->feature.rect[2].weight;
|
||||
if( nodes[5]->feature.rect[2].p0 )
|
||||
tmp[5] = calc_sumf(nodes[5]->feature.rect[2],p_offset) * nodes[5]->feature.rect[2].weight;
|
||||
if( nodes[6]->feature.rect[2].p0 )
|
||||
tmp[6] = calc_sumf(nodes[6]->feature.rect[2],p_offset) * nodes[6]->feature.rect[2].weight;
|
||||
if( nodes[7]->feature.rect[2].p0 )
|
||||
tmp[7] = calc_sumf(nodes[7]->feature.rect[2],p_offset) * nodes[7]->feature.rect[2].weight;
|
||||
|
||||
sum = _mm256_add_ps(sum, _mm256_load_ps(tmp));
|
||||
|
||||
__m256 alpha0 = _mm256_set_ps(classifiers[7]->alpha[0],
|
||||
classifiers[6]->alpha[0],
|
||||
classifiers[5]->alpha[0],
|
||||
classifiers[4]->alpha[0],
|
||||
classifiers[3]->alpha[0],
|
||||
classifiers[2]->alpha[0],
|
||||
classifiers[1]->alpha[0],
|
||||
classifiers[0]->alpha[0]);
|
||||
__m256 alpha1 = _mm256_set_ps(classifiers[7]->alpha[1],
|
||||
classifiers[6]->alpha[1],
|
||||
classifiers[5]->alpha[1],
|
||||
classifiers[4]->alpha[1],
|
||||
classifiers[3]->alpha[1],
|
||||
classifiers[2]->alpha[1],
|
||||
classifiers[1]->alpha[1],
|
||||
classifiers[0]->alpha[1]);
|
||||
|
||||
__m256 outBuf = _mm256_blendv_ps(alpha0, alpha1, _mm256_cmp_ps(t, sum, _CMP_LE_OQ ));
|
||||
outBuf = _mm256_hadd_ps(outBuf, outBuf);
|
||||
outBuf = _mm256_hadd_ps(outBuf, outBuf);
|
||||
_mm256_store_ps(buf, outBuf);
|
||||
stage_sum += (buf[0] + buf[4]);
|
||||
stage_sum += cv_haar_avx::icvEvalHidHaarStumpClassifierAVX(
|
||||
cascade->stage_classifier[i].classifier + j,
|
||||
variance_norm_factor, p_offset);
|
||||
}
|
||||
|
||||
for( ; j < cascade->stage_classifier[i].count; j++ )
|
||||
@ -1241,14 +891,14 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
|
||||
stage_sum = 0.0;
|
||||
int k = 0;
|
||||
|
||||
#ifdef CV_HAAR_USE_AVX
|
||||
#if CV_HAAR_USE_AVX
|
||||
if(haveAVX)
|
||||
{
|
||||
for( ; k < cascade->stage_classifier[i].count - 8; k += 8 )
|
||||
{
|
||||
stage_sum += icvEvalHidHaarClassifierAVX(
|
||||
cascade->stage_classifier[i].classifier + k,
|
||||
variance_norm_factor, p_offset );
|
||||
stage_sum += cv_haar_avx::icvEvalHidHaarClassifierAVX(
|
||||
cascade->stage_classifier[i].classifier + k,
|
||||
variance_norm_factor, p_offset );
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
||||
101
modules/objdetect/src/haar.hpp
Normal file
101
modules/objdetect/src/haar.hpp
Normal file
@ -0,0 +1,101 @@
|
||||
/*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.
|
||||
//
|
||||
//
|
||||
// Intel License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000, Intel Corporation, 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 Intel Corporation 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*/
|
||||
|
||||
/* Haar features calculation */
|
||||
|
||||
#ifndef OPENCV_OBJDETECT_HAAR_HPP
|
||||
#define OPENCV_OBJDETECT_HAAR_HPP
|
||||
|
||||
#define CV_HAAR_FEATURE_MAX_LOCAL 3
|
||||
|
||||
typedef int sumtype;
|
||||
typedef double sqsumtype;
|
||||
|
||||
typedef struct CvHidHaarFeature
|
||||
{
|
||||
struct
|
||||
{
|
||||
sumtype *p0, *p1, *p2, *p3;
|
||||
float weight;
|
||||
}
|
||||
rect[CV_HAAR_FEATURE_MAX_LOCAL];
|
||||
} CvHidHaarFeature;
|
||||
|
||||
|
||||
typedef struct CvHidHaarTreeNode
|
||||
{
|
||||
CvHidHaarFeature feature;
|
||||
float threshold;
|
||||
int left;
|
||||
int right;
|
||||
} CvHidHaarTreeNode;
|
||||
|
||||
|
||||
typedef struct CvHidHaarClassifier
|
||||
{
|
||||
int count;
|
||||
//CvHaarFeature* orig_feature;
|
||||
CvHidHaarTreeNode* node;
|
||||
float* alpha;
|
||||
} CvHidHaarClassifier;
|
||||
|
||||
#define calc_sumf(rect,offset) \
|
||||
static_cast<float>((rect).p0[offset] - (rect).p1[offset] - (rect).p2[offset] + (rect).p3[offset])
|
||||
|
||||
namespace cv_haar_avx
|
||||
{
|
||||
#if 0 /*CV_TRY_AVX*/
|
||||
#define CV_HAAR_USE_AVX 1
|
||||
#else
|
||||
#define CV_HAAR_USE_AVX 0
|
||||
#endif
|
||||
|
||||
#if CV_HAAR_USE_AVX
|
||||
// AVX version icvEvalHidHaarClassifier. Process 8 CvHidHaarClassifiers per call. Check AVX support before invocation!!
|
||||
double icvEvalHidHaarClassifierAVX(CvHidHaarClassifier* classifier, double variance_norm_factor, size_t p_offset);
|
||||
double icvEvalHidHaarStumpClassifierAVX(CvHidHaarClassifier* classifier, double variance_norm_factor, size_t p_offset);
|
||||
double icvEvalHidHaarStumpClassifierTwoRectAVX(CvHidHaarClassifier* classifier, double variance_norm_factor, size_t p_offset);
|
||||
#endif
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
/* End of file. */
|
||||
Loading…
Reference in New Issue
Block a user