mirror of
https://github.com/opencv/opencv.git
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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
|
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ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4267 /wd4305 /wd4306) # vs2008 Win64
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ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4703) # vs2012
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ocv_warnings_disable(CMAKE_C_FLAGS /wd4267 /wd4244 /wd4018)
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if(UNIX AND (CMAKE_COMPILER_IS_GNUCXX OR CV_ICC))
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set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fPIC")
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endif()
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@ -806,6 +806,7 @@ struct Mutex::Impl
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int refcount;
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};
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#ifndef __GNUC__
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int _interlockedExchangeAdd(int* addr, int delta)
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{
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#if defined _MSC_VER && _MSC_VER >= 1500
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@ -814,6 +815,7 @@ int _interlockedExchangeAdd(int* addr, int delta)
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return (int)InterlockedExchangeAdd((long volatile*)addr, delta);
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#endif
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}
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#endif // __GNUC__
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#elif defined __APPLE__
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@ -44,6 +44,10 @@ The references are:
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#include "precomp.hpp"
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#include "fast_score.hpp"
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#if defined _MSC_VER
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# pragma warning( disable : 4127)
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#endif
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namespace cv
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{
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@ -120,10 +120,14 @@ PERF_TEST_P( TestWarpPerspectiveNear_t, WarpPerspectiveNear,
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resize(src, src, size);
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int shift = src.cols*0.04;
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Mat srcVertices = (Mat_<Vec2f>(1, 4) << Vec2f(0, 0), Vec2f(size.width-1, 0),
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Vec2f(size.width-1, size.height-1), Vec2f(0, size.height-1));
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Mat dstVertices = (Mat_<Vec2f>(1, 4) << Vec2f(0, shift), Vec2f(size.width-shift/2, 0),
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Vec2f(size.width-shift, size.height-shift), Vec2f(shift/2, size.height-1));
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Mat srcVertices = (Mat_<Vec2f>(1, 4) << Vec2f(0, 0),
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Vec2f(static_cast<float>(size.width-1), 0),
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Vec2f(static_cast<float>(size.width-1), static_cast<float>(size.height-1)),
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Vec2f(0, static_cast<float>(size.height-1)));
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Mat dstVertices = (Mat_<Vec2f>(1, 4) << Vec2f(0, static_cast<float>(shift)),
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Vec2f(static_cast<float>(size.width-shift/2), 0),
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Vec2f(static_cast<float>(size.width-shift), static_cast<float>(size.height-shift)),
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Vec2f(static_cast<float>(shift/2), static_cast<float>(size.height-1)));
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Mat warpMat = getPerspectiveTransform(srcVertices, dstVertices);
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Mat dst(size, type);
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@ -1,176 +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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// Intel License Agreement
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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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||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
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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
|
||||
// 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,
|
||||
// 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
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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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#include "precomp.hpp"
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/*======================= KALMAN FILTER AS TRACKER =========================*/
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/* State vector is (x,y,w,h,dx,dy,dw,dh). */
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/* Measurement is (x,y,w,h) */
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/* Dynamic matrix A: */
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const float A8[] = { 1, 0, 0, 0, 1, 0, 0, 0,
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0, 1, 0, 0, 0, 1, 0, 0,
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0, 0, 1, 0, 0, 0, 1, 0,
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0, 0, 0, 1, 0, 0, 0, 1,
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0, 0, 0, 0, 1, 0, 0, 0,
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0, 0, 0, 0, 0, 1, 0, 0,
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0, 0, 0, 0, 0, 0, 1, 0,
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0, 0, 0, 0, 0, 0, 0, 1};
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/* Measurement matrix H: */
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const float H8[] = { 1, 0, 0, 0, 0, 0, 0, 0,
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0, 1, 0, 0, 0, 0, 0, 0,
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0, 0, 1, 0, 0, 0, 0, 0,
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0, 0, 0, 1, 0, 0, 0, 0};
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/* Matices for zero size velocity: */
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/* Dynamic matrix A: */
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const float A6[] = { 1, 0, 0, 0, 1, 0,
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0, 1, 0, 0, 0, 1,
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0, 0, 1, 0, 0, 0,
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0, 0, 0, 1, 0, 0,
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0, 0, 0, 0, 1, 0,
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0, 0, 0, 0, 0, 1};
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/* Measurement matrix H: */
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const float H6[] = { 1, 0, 0, 0, 0, 0,
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0, 1, 0, 0, 0, 0,
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0, 0, 1, 0, 0, 0,
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0, 0, 0, 1, 0, 0};
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#define STATE_NUM 6
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#define A A6
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#define H H6
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class CvBlobTrackerOneKalman:public CvBlobTrackerOne
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{
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private:
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CvBlob m_Blob;
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CvKalman* m_pKalman;
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int m_Frame;
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public:
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CvBlobTrackerOneKalman()
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{
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m_Frame = 0;
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m_pKalman = cvCreateKalman(STATE_NUM,4);
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memcpy( m_pKalman->transition_matrix->data.fl, A, sizeof(A));
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memcpy( m_pKalman->measurement_matrix->data.fl, H, sizeof(H));
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cvSetIdentity( m_pKalman->process_noise_cov, cvRealScalar(1e-5) );
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cvSetIdentity( m_pKalman->measurement_noise_cov, cvRealScalar(1e-1) );
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// CV_MAT_ELEM(*m_pKalman->measurement_noise_cov, float, 2,2) *= (float)pow(20,2);
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// CV_MAT_ELEM(*m_pKalman->measurement_noise_cov, float, 3,3) *= (float)pow(20,2);
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cvSetIdentity( m_pKalman->error_cov_post, cvRealScalar(1));
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cvZero(m_pKalman->state_post);
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cvZero(m_pKalman->state_pre);
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SetModuleName("Kalman");
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}
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~CvBlobTrackerOneKalman()
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{
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cvReleaseKalman(&m_pKalman);
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}
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virtual void Init(CvBlob* pBlob, IplImage* /*pImg*/, IplImage* /*pImgFG*/ = NULL)
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{
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m_Blob = pBlob[0];
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m_pKalman->state_post->data.fl[0] = CV_BLOB_X(pBlob);
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m_pKalman->state_post->data.fl[1] = CV_BLOB_Y(pBlob);
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m_pKalman->state_post->data.fl[2] = CV_BLOB_WX(pBlob);
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m_pKalman->state_post->data.fl[3] = CV_BLOB_WY(pBlob);
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}
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virtual CvBlob* Process(CvBlob* pBlob, IplImage* /*pImg*/, IplImage* /*pImgFG*/ = NULL)
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{
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CvBlob* pBlobRes = &m_Blob;
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float Z[4];
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CvMat Zmat = cvMat(4,1,CV_32F,Z);
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m_Blob = pBlob[0];
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if(m_Frame < 2)
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{ /* First call: */
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m_pKalman->state_post->data.fl[0+4] = CV_BLOB_X(pBlob)-m_pKalman->state_post->data.fl[0];
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m_pKalman->state_post->data.fl[1+4] = CV_BLOB_Y(pBlob)-m_pKalman->state_post->data.fl[1];
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if(m_pKalman->DP>6)
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{
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m_pKalman->state_post->data.fl[2+4] = CV_BLOB_WX(pBlob)-m_pKalman->state_post->data.fl[2];
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m_pKalman->state_post->data.fl[3+4] = CV_BLOB_WY(pBlob)-m_pKalman->state_post->data.fl[3];
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}
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m_pKalman->state_post->data.fl[0] = CV_BLOB_X(pBlob);
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m_pKalman->state_post->data.fl[1] = CV_BLOB_Y(pBlob);
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m_pKalman->state_post->data.fl[2] = CV_BLOB_WX(pBlob);
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m_pKalman->state_post->data.fl[3] = CV_BLOB_WY(pBlob);
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memcpy(m_pKalman->state_pre->data.fl,m_pKalman->state_post->data.fl,sizeof(float)*STATE_NUM);
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}
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else
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{ /* Another call: */
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Z[0] = CV_BLOB_X(pBlob);
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Z[1] = CV_BLOB_Y(pBlob);
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Z[2] = CV_BLOB_WX(pBlob);
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Z[3] = CV_BLOB_WY(pBlob);
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cvKalmanCorrect(m_pKalman,&Zmat);
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cvKalmanPredict(m_pKalman,0);
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cvMatMulAdd(m_pKalman->measurement_matrix, m_pKalman->state_pre, NULL, &Zmat);
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CV_BLOB_X(pBlobRes) = Z[0];
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CV_BLOB_Y(pBlobRes) = Z[1];
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CV_BLOB_WX(pBlobRes) = Z[2];
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CV_BLOB_WY(pBlobRes) = Z[3];
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}
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m_Frame++;
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return pBlobRes;
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}
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virtual void Release()
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{
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delete this;
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}
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}; /* class CvBlobTrackerOneKalman */
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#if 0
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static CvBlobTrackerOne* cvCreateModuleBlobTrackerOneKalman()
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{
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return (CvBlobTrackerOne*) new CvBlobTrackerOneKalman;
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}
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CvBlobTracker* cvCreateBlobTrackerKalman()
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{
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return cvCreateBlobTrackerList(cvCreateModuleBlobTrackerOneKalman);
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}
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#endif
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@ -54,6 +54,9 @@
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#if CV_AVX
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# define CV_HAAR_USE_AVX 1
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# if defined _MSC_VER
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# pragma warning( disable : 4752 )
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# endif
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#else
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# if CV_SSE2 || CV_SSE3
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# define CV_HAAR_USE_SSE 1
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@ -412,6 +415,9 @@ icvCreateHidHaarClassifierCascade( CvHaarClassifierCascade* cascade )
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#define calc_sum(rect,offset) \
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((rect).p0[offset] - (rect).p1[offset] - (rect).p2[offset] + (rect).p3[offset])
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#define calc_sumf(rect,offset) \
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static_cast<float>((rect).p0[offset] - (rect).p1[offset] - (rect).p2[offset] + (rect).p3[offset])
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CV_IMPL void
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cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* _cascade,
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@ -652,7 +658,7 @@ double icvEvalHidHaarClassifierAVX( CvHidHaarClassifier* classifier,
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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(variance_norm_factor);
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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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@ -663,14 +669,14 @@ double icvEvalHidHaarClassifierAVX( CvHidHaarClassifier* classifier,
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nodes[1]->threshold,
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nodes[0]->threshold));
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__m256 offset = _mm256_set_ps(calc_sum(nodes[7]->feature.rect[0], p_offset),
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calc_sum(nodes[6]->feature.rect[0], p_offset),
|
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calc_sum(nodes[5]->feature.rect[0], p_offset),
|
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calc_sum(nodes[4]->feature.rect[0], p_offset),
|
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calc_sum(nodes[3]->feature.rect[0], p_offset),
|
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calc_sum(nodes[2]->feature.rect[0], p_offset),
|
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calc_sum(nodes[1]->feature.rect[0], p_offset),
|
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calc_sum(nodes[0]->feature.rect[0], p_offset));
|
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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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|
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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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@ -683,14 +689,14 @@ double icvEvalHidHaarClassifierAVX( CvHidHaarClassifier* classifier,
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|
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__m256 sum = _mm256_mul_ps(offset, weight);
|
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|
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offset = _mm256_set_ps(calc_sum(nodes[7]->feature.rect[1], p_offset),
|
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calc_sum(nodes[6]->feature.rect[1], p_offset),
|
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calc_sum(nodes[5]->feature.rect[1], p_offset),
|
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calc_sum(nodes[4]->feature.rect[1], p_offset),
|
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calc_sum(nodes[3]->feature.rect[1], p_offset),
|
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calc_sum(nodes[2]->feature.rect[1], p_offset),
|
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calc_sum(nodes[1]->feature.rect[1], p_offset),
|
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calc_sum(nodes[0]->feature.rect[1], p_offset));
|
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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,
|
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nodes[6]->feature.rect[1].weight,
|
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@ -704,21 +710,21 @@ double icvEvalHidHaarClassifierAVX( CvHidHaarClassifier* classifier,
|
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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;
|
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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