mirror of
https://github.com/opencv/opencv.git
synced 2024-11-29 05:29:54 +08:00
Merge pull request #91 from taka-no-me/warnings/windows
This commit is contained in:
Andrey Kamaev
commit
b88323afc3
2
3rdparty/libtiff/CMakeLists.txt
vendored
2
3rdparty/libtiff/CMakeLists.txt
vendored
@ -95,6 +95,8 @@ ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4244) # vs2008
|
||||
ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4267 /wd4305 /wd4306) # vs2008 Win64
|
||||
ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4703) # vs2012
|
||||
|
||||
ocv_warnings_disable(CMAKE_C_FLAGS /wd4267 /wd4244 /wd4018)
|
||||
|
||||
if(UNIX AND (CMAKE_COMPILER_IS_GNUCXX OR CV_ICC))
|
||||
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fPIC")
|
||||
endif()
|
||||
|
||||
@ -806,6 +806,7 @@ struct Mutex::Impl
|
||||
int refcount;
|
||||
};
|
||||
|
||||
#ifndef __GNUC__
|
||||
int _interlockedExchangeAdd(int* addr, int delta)
|
||||
{
|
||||
#if defined _MSC_VER && _MSC_VER >= 1500
|
||||
@ -814,6 +815,7 @@ int _interlockedExchangeAdd(int* addr, int delta)
|
||||
return (int)InterlockedExchangeAdd((long volatile*)addr, delta);
|
||||
#endif
|
||||
}
|
||||
#endif // __GNUC__
|
||||
|
||||
#elif defined __APPLE__
|
||||
|
||||
|
||||
@ -44,6 +44,10 @@ The references are:
|
||||
#include "precomp.hpp"
|
||||
#include "fast_score.hpp"
|
||||
|
||||
#if defined _MSC_VER
|
||||
# pragma warning( disable : 4127)
|
||||
#endif
|
||||
|
||||
namespace cv
|
||||
{
|
||||
|
||||
|
||||
@ -120,10 +120,14 @@ PERF_TEST_P( TestWarpPerspectiveNear_t, WarpPerspectiveNear,
|
||||
resize(src, src, size);
|
||||
|
||||
int shift = src.cols*0.04;
|
||||
Mat srcVertices = (Mat_<Vec2f>(1, 4) << Vec2f(0, 0), Vec2f(size.width-1, 0),
|
||||
Vec2f(size.width-1, size.height-1), Vec2f(0, size.height-1));
|
||||
Mat dstVertices = (Mat_<Vec2f>(1, 4) << Vec2f(0, shift), Vec2f(size.width-shift/2, 0),
|
||||
Vec2f(size.width-shift, size.height-shift), Vec2f(shift/2, size.height-1));
|
||||
Mat srcVertices = (Mat_<Vec2f>(1, 4) << Vec2f(0, 0),
|
||||
Vec2f(static_cast<float>(size.width-1), 0),
|
||||
Vec2f(static_cast<float>(size.width-1), static_cast<float>(size.height-1)),
|
||||
Vec2f(0, static_cast<float>(size.height-1)));
|
||||
Mat dstVertices = (Mat_<Vec2f>(1, 4) << Vec2f(0, static_cast<float>(shift)),
|
||||
Vec2f(static_cast<float>(size.width-shift/2), 0),
|
||||
Vec2f(static_cast<float>(size.width-shift), static_cast<float>(size.height-shift)),
|
||||
Vec2f(static_cast<float>(shift/2), static_cast<float>(size.height-1)));
|
||||
Mat warpMat = getPerspectiveTransform(srcVertices, dstVertices);
|
||||
|
||||
Mat dst(size, type);
|
||||
|
||||
@ -1,176 +0,0 @@
|
||||
/*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
|
||||
//
|
||||
// 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*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
|
||||
/*======================= KALMAN FILTER AS TRACKER =========================*/
|
||||
/* State vector is (x,y,w,h,dx,dy,dw,dh). */
|
||||
/* Measurement is (x,y,w,h) */
|
||||
|
||||
/* Dynamic matrix A: */
|
||||
const float A8[] = { 1, 0, 0, 0, 1, 0, 0, 0,
|
||||
0, 1, 0, 0, 0, 1, 0, 0,
|
||||
0, 0, 1, 0, 0, 0, 1, 0,
|
||||
0, 0, 0, 1, 0, 0, 0, 1,
|
||||
0, 0, 0, 0, 1, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 1, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 1, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 1};
|
||||
|
||||
/* Measurement matrix H: */
|
||||
const float H8[] = { 1, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 1, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 1, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 1, 0, 0, 0, 0};
|
||||
|
||||
/* Matices for zero size velocity: */
|
||||
/* Dynamic matrix A: */
|
||||
const float A6[] = { 1, 0, 0, 0, 1, 0,
|
||||
0, 1, 0, 0, 0, 1,
|
||||
0, 0, 1, 0, 0, 0,
|
||||
0, 0, 0, 1, 0, 0,
|
||||
0, 0, 0, 0, 1, 0,
|
||||
0, 0, 0, 0, 0, 1};
|
||||
|
||||
/* Measurement matrix H: */
|
||||
const float H6[] = { 1, 0, 0, 0, 0, 0,
|
||||
0, 1, 0, 0, 0, 0,
|
||||
0, 0, 1, 0, 0, 0,
|
||||
0, 0, 0, 1, 0, 0};
|
||||
|
||||
#define STATE_NUM 6
|
||||
#define A A6
|
||||
#define H H6
|
||||
class CvBlobTrackerOneKalman:public CvBlobTrackerOne
|
||||
{
|
||||
private:
|
||||
CvBlob m_Blob;
|
||||
CvKalman* m_pKalman;
|
||||
int m_Frame;
|
||||
|
||||
public:
|
||||
CvBlobTrackerOneKalman()
|
||||
{
|
||||
m_Frame = 0;
|
||||
m_pKalman = cvCreateKalman(STATE_NUM,4);
|
||||
memcpy( m_pKalman->transition_matrix->data.fl, A, sizeof(A));
|
||||
memcpy( m_pKalman->measurement_matrix->data.fl, H, sizeof(H));
|
||||
cvSetIdentity( m_pKalman->process_noise_cov, cvRealScalar(1e-5) );
|
||||
cvSetIdentity( m_pKalman->measurement_noise_cov, cvRealScalar(1e-1) );
|
||||
// CV_MAT_ELEM(*m_pKalman->measurement_noise_cov, float, 2,2) *= (float)pow(20,2);
|
||||
// CV_MAT_ELEM(*m_pKalman->measurement_noise_cov, float, 3,3) *= (float)pow(20,2);
|
||||
cvSetIdentity( m_pKalman->error_cov_post, cvRealScalar(1));
|
||||
cvZero(m_pKalman->state_post);
|
||||
cvZero(m_pKalman->state_pre);
|
||||
|
||||
SetModuleName("Kalman");
|
||||
}
|
||||
|
||||
~CvBlobTrackerOneKalman()
|
||||
{
|
||||
cvReleaseKalman(&m_pKalman);
|
||||
}
|
||||
|
||||
virtual void Init(CvBlob* pBlob, IplImage* /*pImg*/, IplImage* /*pImgFG*/ = NULL)
|
||||
{
|
||||
m_Blob = pBlob[0];
|
||||
m_pKalman->state_post->data.fl[0] = CV_BLOB_X(pBlob);
|
||||
m_pKalman->state_post->data.fl[1] = CV_BLOB_Y(pBlob);
|
||||
m_pKalman->state_post->data.fl[2] = CV_BLOB_WX(pBlob);
|
||||
m_pKalman->state_post->data.fl[3] = CV_BLOB_WY(pBlob);
|
||||
}
|
||||
|
||||
virtual CvBlob* Process(CvBlob* pBlob, IplImage* /*pImg*/, IplImage* /*pImgFG*/ = NULL)
|
||||
{
|
||||
CvBlob* pBlobRes = &m_Blob;
|
||||
float Z[4];
|
||||
CvMat Zmat = cvMat(4,1,CV_32F,Z);
|
||||
m_Blob = pBlob[0];
|
||||
|
||||
if(m_Frame < 2)
|
||||
{ /* First call: */
|
||||
m_pKalman->state_post->data.fl[0+4] = CV_BLOB_X(pBlob)-m_pKalman->state_post->data.fl[0];
|
||||
m_pKalman->state_post->data.fl[1+4] = CV_BLOB_Y(pBlob)-m_pKalman->state_post->data.fl[1];
|
||||
if(m_pKalman->DP>6)
|
||||
{
|
||||
m_pKalman->state_post->data.fl[2+4] = CV_BLOB_WX(pBlob)-m_pKalman->state_post->data.fl[2];
|
||||
m_pKalman->state_post->data.fl[3+4] = CV_BLOB_WY(pBlob)-m_pKalman->state_post->data.fl[3];
|
||||
}
|
||||
m_pKalman->state_post->data.fl[0] = CV_BLOB_X(pBlob);
|
||||
m_pKalman->state_post->data.fl[1] = CV_BLOB_Y(pBlob);
|
||||
m_pKalman->state_post->data.fl[2] = CV_BLOB_WX(pBlob);
|
||||
m_pKalman->state_post->data.fl[3] = CV_BLOB_WY(pBlob);
|
||||
memcpy(m_pKalman->state_pre->data.fl,m_pKalman->state_post->data.fl,sizeof(float)*STATE_NUM);
|
||||
}
|
||||
else
|
||||
{ /* Another call: */
|
||||
Z[0] = CV_BLOB_X(pBlob);
|
||||
Z[1] = CV_BLOB_Y(pBlob);
|
||||
Z[2] = CV_BLOB_WX(pBlob);
|
||||
Z[3] = CV_BLOB_WY(pBlob);
|
||||
cvKalmanCorrect(m_pKalman,&Zmat);
|
||||
cvKalmanPredict(m_pKalman,0);
|
||||
cvMatMulAdd(m_pKalman->measurement_matrix, m_pKalman->state_pre, NULL, &Zmat);
|
||||
CV_BLOB_X(pBlobRes) = Z[0];
|
||||
CV_BLOB_Y(pBlobRes) = Z[1];
|
||||
CV_BLOB_WX(pBlobRes) = Z[2];
|
||||
CV_BLOB_WY(pBlobRes) = Z[3];
|
||||
}
|
||||
m_Frame++;
|
||||
return pBlobRes;
|
||||
}
|
||||
virtual void Release()
|
||||
{
|
||||
delete this;
|
||||
}
|
||||
}; /* class CvBlobTrackerOneKalman */
|
||||
|
||||
#if 0
|
||||
static CvBlobTrackerOne* cvCreateModuleBlobTrackerOneKalman()
|
||||
{
|
||||
return (CvBlobTrackerOne*) new CvBlobTrackerOneKalman;
|
||||
}
|
||||
|
||||
|
||||
CvBlobTracker* cvCreateBlobTrackerKalman()
|
||||
{
|
||||
return cvCreateBlobTrackerList(cvCreateModuleBlobTrackerOneKalman);
|
||||
}
|
||||
#endif
|
||||
@ -54,6 +54,9 @@
|
||||
|
||||
#if CV_AVX
|
||||
# define CV_HAAR_USE_AVX 1
|
||||
# if defined _MSC_VER
|
||||
# pragma warning( disable : 4752 )
|
||||
# endif
|
||||
#else
|
||||
# if CV_SSE2 || CV_SSE3
|
||||
# define CV_HAAR_USE_SSE 1
|
||||
@ -412,6 +415,9 @@ 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,
|
||||
@ -652,7 +658,7 @@ double icvEvalHidHaarClassifierAVX( CvHidHaarClassifier* classifier,
|
||||
nodes[6] = (classifier+6)->node + idxV[6];
|
||||
nodes[7] = (classifier+7)->node + idxV[7];
|
||||
|
||||
__m256 t = _mm256_set1_ps(variance_norm_factor);
|
||||
__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,
|
||||
@ -663,14 +669,14 @@ double icvEvalHidHaarClassifierAVX( CvHidHaarClassifier* classifier,
|
||||
nodes[1]->threshold,
|
||||
nodes[0]->threshold));
|
||||
|
||||
__m256 offset = _mm256_set_ps(calc_sum(nodes[7]->feature.rect[0], p_offset),
|
||||
calc_sum(nodes[6]->feature.rect[0], p_offset),
|
||||
calc_sum(nodes[5]->feature.rect[0], p_offset),
|
||||
calc_sum(nodes[4]->feature.rect[0], p_offset),
|
||||
calc_sum(nodes[3]->feature.rect[0], p_offset),
|
||||
calc_sum(nodes[2]->feature.rect[0], p_offset),
|
||||
calc_sum(nodes[1]->feature.rect[0], p_offset),
|
||||
calc_sum(nodes[0]->feature.rect[0], p_offset));
|
||||
__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,
|
||||
@ -683,14 +689,14 @@ double icvEvalHidHaarClassifierAVX( CvHidHaarClassifier* classifier,
|
||||
|
||||
__m256 sum = _mm256_mul_ps(offset, weight);
|
||||
|
||||
offset = _mm256_set_ps(calc_sum(nodes[7]->feature.rect[1], p_offset),
|
||||
calc_sum(nodes[6]->feature.rect[1], p_offset),
|
||||
calc_sum(nodes[5]->feature.rect[1], p_offset),
|
||||
calc_sum(nodes[4]->feature.rect[1], p_offset),
|
||||
calc_sum(nodes[3]->feature.rect[1], p_offset),
|
||||
calc_sum(nodes[2]->feature.rect[1], p_offset),
|
||||
calc_sum(nodes[1]->feature.rect[1], p_offset),
|
||||
calc_sum(nodes[0]->feature.rect[1], p_offset));
|
||||
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,
|
||||
@ -704,21 +710,21 @@ double icvEvalHidHaarClassifierAVX( CvHidHaarClassifier* classifier,
|
||||
sum = _mm256_add_ps(sum, _mm256_mul_ps(offset, weight));
|
||||
|
||||
if( nodes[0]->feature.rect[2].p0 )
|
||||
tmp[0] = calc_sum(nodes[0]->feature.rect[2], p_offset) * nodes[0]->feature.rect[2].weight;
|
||||
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_sum(nodes[1]->feature.rect[2], p_offset) * nodes[1]->feature.rect[2].weight;
|
||||
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_sum(nodes[2]->feature.rect[2], p_offset) * nodes[2]->feature.rect[2].weight;
|
||||
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_sum(nodes[3]->feature.rect[2], p_offset) * nodes[3]->feature.rect[2].weight;
|
||||
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_sum(nodes[4]->feature.rect[2], p_offset) * nodes[4]->feature.rect[2].weight;
|
||||
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_sum(nodes[5]->feature.rect[2], p_offset) * nodes[5]->feature.rect[2].weight;
|
||||
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_sum(nodes[6]->feature.rect[2], p_offset) * nodes[6]->feature.rect[2].weight;
|
||||
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_sum(nodes[7]->feature.rect[2], p_offset) * nodes[7]->feature.rect[2].weight;
|
||||
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));
|
||||
|
||||
@ -918,7 +924,7 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
|
||||
classifiers[7] = cascade->stage_classifier[i].classifier + j + 7;
|
||||
nodes[7] = classifiers[7]->node;
|
||||
|
||||
__m256 t = _mm256_set1_ps(variance_norm_factor);
|
||||
__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,
|
||||
@ -928,14 +934,14 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
|
||||
nodes[1]->threshold,
|
||||
nodes[0]->threshold));
|
||||
|
||||
__m256 offset = _mm256_set_ps(calc_sum(nodes[7]->feature.rect[0], p_offset),
|
||||
calc_sum(nodes[6]->feature.rect[0], p_offset),
|
||||
calc_sum(nodes[5]->feature.rect[0], p_offset),
|
||||
calc_sum(nodes[4]->feature.rect[0], p_offset),
|
||||
calc_sum(nodes[3]->feature.rect[0], p_offset),
|
||||
calc_sum(nodes[2]->feature.rect[0], p_offset),
|
||||
calc_sum(nodes[1]->feature.rect[0], p_offset),
|
||||
calc_sum(nodes[0]->feature.rect[0], p_offset));
|
||||
__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,
|
||||
@ -948,14 +954,14 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
|
||||
|
||||
__m256 sum = _mm256_mul_ps(offset, weight);
|
||||
|
||||
offset = _mm256_set_ps(calc_sum(nodes[7]->feature.rect[1], p_offset),
|
||||
calc_sum(nodes[6]->feature.rect[1], p_offset),
|
||||
calc_sum(nodes[5]->feature.rect[1], p_offset),
|
||||
calc_sum(nodes[4]->feature.rect[1], p_offset),
|
||||
calc_sum(nodes[3]->feature.rect[1], p_offset),
|
||||
calc_sum(nodes[2]->feature.rect[1], p_offset),
|
||||
calc_sum(nodes[1]->feature.rect[1], p_offset),
|
||||
calc_sum(nodes[0]->feature.rect[1], p_offset));
|
||||
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,
|
||||
@ -1023,7 +1029,7 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
|
||||
classifiers[7] = cascade->stage_classifier[i].classifier + j + 7;
|
||||
nodes[7] = classifiers[7]->node;
|
||||
|
||||
__m256 t = _mm256_set1_ps(variance_norm_factor);
|
||||
__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,
|
||||
@ -1034,14 +1040,14 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
|
||||
nodes[1]->threshold,
|
||||
nodes[0]->threshold));
|
||||
|
||||
__m256 offset = _mm256_set_ps(calc_sum(nodes[7]->feature.rect[0], p_offset),
|
||||
calc_sum(nodes[6]->feature.rect[0], p_offset),
|
||||
calc_sum(nodes[5]->feature.rect[0], p_offset),
|
||||
calc_sum(nodes[4]->feature.rect[0], p_offset),
|
||||
calc_sum(nodes[3]->feature.rect[0], p_offset),
|
||||
calc_sum(nodes[2]->feature.rect[0], p_offset),
|
||||
calc_sum(nodes[1]->feature.rect[0], p_offset),
|
||||
calc_sum(nodes[0]->feature.rect[0], p_offset));
|
||||
__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,
|
||||
@ -1054,14 +1060,14 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
|
||||
|
||||
__m256 sum = _mm256_mul_ps(offset, weight);
|
||||
|
||||
offset = _mm256_set_ps(calc_sum(nodes[7]->feature.rect[1], p_offset),
|
||||
calc_sum(nodes[6]->feature.rect[1], p_offset),
|
||||
calc_sum(nodes[5]->feature.rect[1], p_offset),
|
||||
calc_sum(nodes[4]->feature.rect[1], p_offset),
|
||||
calc_sum(nodes[3]->feature.rect[1], p_offset),
|
||||
calc_sum(nodes[2]->feature.rect[1], p_offset),
|
||||
calc_sum(nodes[1]->feature.rect[1], p_offset),
|
||||
calc_sum(nodes[0]->feature.rect[1], p_offset));
|
||||
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,
|
||||
@ -1075,21 +1081,21 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
|
||||
sum = _mm256_add_ps(sum, _mm256_mul_ps(offset, weight));
|
||||
|
||||
if( nodes[0]->feature.rect[2].p0 )
|
||||
tmp[0] = calc_sum(nodes[0]->feature.rect[2],p_offset) * nodes[0]->feature.rect[2].weight;
|
||||
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_sum(nodes[1]->feature.rect[2],p_offset) * nodes[1]->feature.rect[2].weight;
|
||||
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_sum(nodes[2]->feature.rect[2],p_offset) * nodes[2]->feature.rect[2].weight;
|
||||
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_sum(nodes[3]->feature.rect[2],p_offset) * nodes[3]->feature.rect[2].weight;
|
||||
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_sum(nodes[4]->feature.rect[2],p_offset) * nodes[4]->feature.rect[2].weight;
|
||||
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_sum(nodes[5]->feature.rect[2],p_offset) * nodes[5]->feature.rect[2].weight;
|
||||
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_sum(nodes[6]->feature.rect[2],p_offset) * nodes[6]->feature.rect[2].weight;
|
||||
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_sum(nodes[7]->feature.rect[2],p_offset) * nodes[7]->feature.rect[2].weight;
|
||||
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));
|
||||
|
||||
|
||||
@ -628,7 +628,7 @@ bool DpSeamFinder::getSeamTips(int comp1, int comp2, Point &p1, Point &p2)
|
||||
{
|
||||
for (int j = i+1; j < nlabels; ++j)
|
||||
{
|
||||
double size1 = points[i].size(), size2 = points[j].size();
|
||||
double size1 = static_cast<double>(points[i].size()), size2 = static_cast<double>(points[j].size());
|
||||
double cx1 = cvRound(sum[i].x / size1), cy1 = cvRound(sum[i].y / size1);
|
||||
double cx2 = cvRound(sum[j].x / size2), cy2 = cvRound(sum[j].y / size1);
|
||||
|
||||
@ -648,7 +648,7 @@ bool DpSeamFinder::getSeamTips(int comp1, int comp2, Point &p1, Point &p2)
|
||||
|
||||
for (int i = 0; i < 2; ++i)
|
||||
{
|
||||
double size = points[idx[i]].size();
|
||||
double size = static_cast<double>(points[idx[i]].size());
|
||||
double cx = cvRound(sum[idx[i]].x / size);
|
||||
double cy = cvRound(sum[idx[i]].y / size);
|
||||
|
||||
@ -1036,7 +1036,7 @@ void DpSeamFinder::updateLabelsUsingSeam(
|
||||
|
||||
for (map<int, int>::iterator itr = connect2.begin(); itr != connect2.end(); ++itr)
|
||||
{
|
||||
double len = contours_[comp1].size();
|
||||
double len = static_cast<double>(contours_[comp1].size());
|
||||
isAdjComp[itr->first] = itr->second / len > 0.05 && connectOther.find(itr->first)->second / len < 0.1;
|
||||
}
|
||||
|
||||
|
||||
@ -6352,7 +6352,9 @@ namespace internal {
|
||||
|
||||
// Valid only for fast death tests. Indicates the code is running in the
|
||||
// child process of a fast style death test.
|
||||
# if !GTEST_OS_WINDOWS
|
||||
static bool g_in_fast_death_test_child = false;
|
||||
# endif
|
||||
|
||||
// Returns a Boolean value indicating whether the caller is currently
|
||||
// executing in the context of the death test child process. Tools such as
|
||||
|
||||
Loading…
Reference in New Issue
Block a user