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using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.IO;
using System.Linq;
using System.Text;
using System.Threading;
using System.Threading.Tasks;
using System.Windows;
using System.Windows.Controls;
using System.Windows.Data;
using System.Windows.Documents;
using System.Windows.Input;
using System.Windows.Media;
using System.Windows.Media.Imaging;
using System.Windows.Navigation;
using System.Windows.Threading;

namespace Tango.MachineStudio.Updater
{
    /// <summary>
    /// Interaction logic for MainWindow.xaml
    /// </summary>
    public partial class MainWindow : Window
    {
        private String _sourceFolder = AppDomain.CurrentDomain.BaseDirectory;
        private String _msProcessName = "Tango.MachineStudio.UI";
        private String _appPath = String.Join(" ", App.StartupArgs);

        public MainWindow()
        {
            if (!Directory.Exists(_appPath))
            {
                ShowError("This update utility can only be executed by Machine Studio.");
                Environment.Exit(0);
            }

            InitializeComponent();
            ContentRendered += MainWindow_ContentRendered;
        }

        private void MainWindow_ContentRendered(object sender, EventArgs e)
        {
            Update();
        }

        private void Update()
        {
            EnsureMachineStudioIsDown();
            RemoveOldDLLFiles();
            ReplaceFiles();
            StartMachineStudio();
            Environmentpre { line-height: 125%; }
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
//  By downloading, copying, installing or using the software you agree to this license.
//  If you do not agree to this license, do not download, install,
//  copy or use the software.
//
//
//                           License Agreement
//                For Open Source Computer Vision Library
//
// Copyright (C) 2008-2012, Willow Garage Inc., 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"

namespace cv
{
namespace xfeatures2d
{

/*!
 The "Star" Detector.

 The class implements the keypoint detector introduced by K. Konolige.
 */
class StarDetectorImpl : public StarDetector
{
public:
    //! the full constructor
    StarDetectorImpl(int _maxSize=45, int _responseThreshold=30,
                         int _lineThresholdProjected=10,
                         int _lineThresholdBinarized=8,
                         int _suppressNonmaxSize=5);

    void detect( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() );

protected:
    int maxSize;
    int responseThreshold;
    int lineThresholdProjected;
    int lineThresholdBinarized;
    int suppressNonmaxSize;
};

Ptr<StarDetector> StarDetector::create(int _maxSize,
                                       int _responseThreshold,
                                       int _lineThresholdProjected,
                                       int _lineThresholdBinarized,
                                       int _suppressNonmaxSize)
{
    return makePtr<StarDetectorImpl>(_maxSize, _responseThreshold,
                                     _lineThresholdProjected,
                                     _lineThresholdBinarized,
                                     _suppressNonmaxSize);
}


template <typename inMatType, typename outMatType> static void
computeIntegralImages( const Mat& matI, Mat& matS, Mat& matT, Mat& _FT,
                       int iiType )
{
    int x, y, rows = matI.rows, cols = matI.cols;

    matS.create(rows + 1, cols + 1, iiType );
    matT.create(rows + 1, cols + 1, iiType );
    _FT.create(rows + 1, cols + 1, iiType );

    const inMatType* I = matI.ptr<inMatType>();

    outMatType *S = matS.ptr<outMatType>();
    outMatType *T = matT.ptr<outMatType>();
    outMatType *FT = _FT.ptr<outMatType>();

    int istep = (int)(matI.step/matI.elemSize());
    int step = (int)(matS.step/matS.elemSize());

    for( x = 0; x <= cols; x++ 
n>] = FT[x] = 0; S += step; T += step; FT += step; S[0] = T[0] = 0; FT[0] = I[0]; for( x = 1; x < cols; x++ ) { S[x] = S[x-1] + I[x-1]; T[x] = I[x-1]; FT[x] = I[x] + I[x-1]; } S[cols] = S[cols-1] + I[cols-1]; T[cols] = FT[cols] = I[cols-1]; for( y = 2; y <= rows; y++ ) { I += istep, S += step, T += step, FT += step; S[0] = S[-step]; S[1] = S[-step+1] + I[0]; T[0] = T[-step + 1]; T[1] = FT[0] = T[-step + 2] + I[-istep] + I[0]; FT[1] = FT[-step + 2] + I[-istep] + I[1] + I[0]; for( x = 2; x < cols; x++ ) { S[x] = S[x - 1] + S[-step + x] - S[-step + x - 1] + I[x - 1]; T[x] = T[-step + x - 1] + T[-step + x + 1] - T[-step*2 + x] + I[-istep + x - 1] + I[x - 1]; FT[x] = FT[-step + x - 1] + FT[-step + x + 1] - FT[-step*2 + x] + I[x] + I[x-1]; } S[cols] = S[cols - 1] + S[-step + cols] - S[-step + cols - 1] + I[cols - 1]; T[cols] = FT[cols] = T[-step + cols - 1] + I[-istep + cols - 1] + I[cols - 1]; } } template <typename iiMatType> static int StarDetectorComputeResponses( const Mat& img, Mat& responses, Mat& sizes, int maxSize, int iiType ) { const int MAX_PATTERN = 17; static const int sizes0[] = {1, 2, 3, 4, 6, 8, 11, 12, 16, 22, 23, 32, 45, 46, 64, 90, 128, -1}; static const int pairs[12][2] = {{1, 0}, {3, 1}, {4, 2}, {5, 3}, {7, 4}, {8, 5}, {9, 6}, {11, 8}, {13, 10}, {14, 11}, {15, 12}, {16, 14}}; const int MAX_PAIR = sizeof(pairs)/sizeof(pairs[0]); float invSizes[MAX_PATTERN][2]; int sizes1[MAX_PATTERN]; #if CV_SSE2 __m128 invSizes4[MAX_PATTERN][2]; __m128 sizes1_4[MAX_PATTERN]; union { int i; float f; } absmask; absmask.i = 0x7fffffff; volatile bool useSIMD = cv::checkHardwareSupport(CV_CPU_SSE2) && iiType == CV_32S; #endif struct StarFeature { int area; iiMatType* p[8]; }; StarFeature f[MAX_PATTERN]; Mat sum, tilted, flatTilted; int y, rows = img.rows, cols = img.cols; int border, npatterns=0, maxIdx=0; responses.create( img.size(), CV_32F ); sizes.create( img.size(), CV_16S ); while( npatterns < MAX_PAIR && ! ( sizes0[pairs[npatterns][0]] >= maxSize || sizes0[pairs[npatterns+1][0]] + sizes0[pairs[npatterns+1][0]]/2 >= std::min(rows, cols) ) ) { ++npatterns; } if (npatterns-1 < MAX_PAIR) ++npatterns; maxIdx = pairs[npatterns-1][0]; // Create the integral image appropriate for our type & usage if ( img.type() == CV_8U ) computeIntegralImages<uchar, iiMatType>( img, sum, tilted, flatTilted, iiType ); else if ( img.type() == CV_8S ) computeIntegralImages<char, iiMatType>( img, sum, tilted, flatTilted, iiType ); else if ( img.type() == CV_16U ) computeIntegralImages<ushort, iiMatType>( img, sum, tilted, flatTilted, iiType ); else if ( img.type() == CV_16S ) computeIntegralImages<short, iiMatType>( img, sum, tilted, flatTilted, iiType ); else CV_Error( Error::StsUnsupportedFormat, "" ); int step = (int)(sum.step/sum.elemSize()); for(int i = 0; i <= maxIdx; i++ ) { int ur_size = sizes0[i], t_size = sizes0[i] + sizes0[i]/2; int ur_area = (2*ur_size + 1)*(2*ur_size + 1); int t_area = t_size*t_size + (t_size + 1)*(t_size + 1); f[i].p[0] = sum.ptr<iiMatType>() + (ur_size + 1)*step + ur_size + 1; f[i].p[1] = sum.ptr<iiMatType>() - ur_size*step + ur_size + 1; f[i].p[2] = sum.ptr<iiMatType>() + (ur_size + 1)*step - ur_size; f[i].p[3] = sum.ptr<iiMatType>() - ur_size*step - ur_size; f[i].p[4] = tilted.ptr<iiMatType>() + (t_size + 1)*step + 1; f[i].p[5] = flatTilted.ptr<iiMatType>() - t_size; f[i].p[6] = flatTilted.ptr<iiMatType>() + t_size + 1; f[i].p[7] = tilted.ptr<iiMatType>() - t_size*step + 1; f[i].area = ur_area + t_area; sizes1[i] = sizes0[i]; } // negate end points of the size range // for a faster rejection of very small or very large features in non-maxima suppression. sizes1[0] = -sizes1[0]; sizes1[1] = -sizes1[1]; sizes1[maxIdx] = -sizes1[maxIdx]; border = sizes0[maxIdx] + sizes0[maxIdx]/2; for(int i = 0; i < npatterns; i++ ) { int innerArea = f[pairs[i][1]].area; int outerArea = f[pairs[i][0]].area - innerArea; invSizes[i][0] = 1.f/outerArea; invSizes[i][1] = 1.f/innerArea; } #if CV_SSE2 if( useSIMD ) { for(int i = 0; i < npatterns; i++ ) { _mm_store_ps((float*)&invSizes4[i][0], _mm_set1_ps(invSizes[i][0])); _mm_store_ps((float*)&invSizes4[i][1], _mm_set1_ps(invSizes[i][1])); } for(int i = 0; i <= maxIdx; i++ ) _mm_store_ps((float*)&sizes1_4[i], _mm_set1_ps((float)sizes1[i])); } #endif for( y = 0; y < border; y++ ) { float* r_ptr = responses.ptr<float>(y); float* r_ptr2 = responses.ptr<float>(rows - 1 - y); short* s_ptr = sizes.ptr<short>(y); short* s_ptr2 = sizes.ptr<short>(rows - 1 - y); memset( r_ptr, 0, cols*sizeof(r_ptr[0])); memset( r_ptr2, 0, cols*sizeof(r_ptr2[0])); memset( s_ptr, 0, cols*sizeof(s_ptr[0])); memset( s_ptr2, 0, cols*sizeof(s_ptr2[0])); } for( y = border; y < rows - border; y++ ) { int x = border; float* r_ptr = responses.ptr<float>(y); short* s_ptr = sizes.ptr<short>(y); memset( r_ptr, 0, border*sizeof(r_ptr[0])); memset( s_ptr, 0, border*sizeof(s_ptr[0])); memset( r_ptr + cols - border, 0, border*sizeof(r_ptr[0])); memset( s_ptr + cols - border, 0, border*sizeof(s_ptr[0])); #if CV_SSE2 if( useSIMD ) { __m128 absmask4 = _mm_set1_ps(absmask.f); for( ; x <= cols - border - 4; x += 4 ) { int ofs = y*step + x; __m128 vals[MAX_PATTERN]; __m128 bestResponse = _mm_setzero_ps(); __m128 bestSize = _mm_setzero_ps(); for(int i = 0; i <= maxIdx; i++ ) { const iiMatType** p = (const iiMatType**)&f[i].p[0]; __m128i r0 = _mm_sub_epi32(_mm_loadu_si128((const __m128i*)(p[0]+ofs)), _mm_loadu_si128((const __m128i*)(p[1]+ofs))); __m128i r1 = _mm_sub_epi32(_mm_loadu_si128((const __m128i*)(p[3]+ofs)), _mm_loadu_si128((const __m128i*)(p[2]+ofs))); __m128i r2 = _mm_sub_epi32(_mm_loadu_si128((const __m128i*)(p[4]+ofs)), _mm_loadu_si128((const __m128i*)(p[5]+ofs))); __m128i r3 = _mm_sub_epi32(_mm_loadu_si128((const __m128i*)(p[7]+ofs)), _mm_loadu_si128((const __m128i*)(p[6]+ofs))); r0 = _mm_add_epi32(_mm_add_epi32(r0,r1), _mm_add_epi32(r2,r3)); _mm_store_ps((float*)&vals[i], _mm_cvtepi32_ps(r0)); } for(int i = 0; i < npatterns; i++ ) { __m128 inner_sum = vals[pairs[i][1]]; __m128 outer_sum = _mm_sub_ps(vals[pairs[i][0]], inner_sum); __m128 response = _mm_sub_ps(_mm_mul_ps(inner_sum, invSizes4[i][1]), _mm_mul_ps(outer_sum, invSizes4[i][0])); __m128 swapmask = _mm_cmpgt_ps(_mm_and_ps(response,absmask4), _mm_and_ps(bestResponse,absmask4)); bestResponse = _mm_xor_ps(bestResponse, _mm_and_ps(_mm_xor_ps(response,bestResponse), swapmask)); bestSize = _mm_xor_ps(bestSize, _mm_and_ps(_mm_xor_ps(sizes1_4[pairs[i][0]], bestSize), swapmask)); } _mm_storeu_ps(r_ptr + x, bestResponse); _mm_storel_epi64((__m128i*)(s_ptr + x), _mm_packs_epi32(_mm_cvtps_epi32(bestSize),_mm_setzero_si128())); } } #endif for( ; x < cols - border; x++ ) { int ofs = y*step + x; int vals[MAX_PATTERN]; float bestResponse = 0; int bestSize = 0; for(int i = 0; i <= maxIdx; i++ ) { const iiMatType** p = (const iiMatType**)&f[i].p[0]; vals[i] = (int)(p[0][ofs] - p[1][ofs] - p[2][ofs] + p[3][ofs] + p[4][ofs] - p[5][ofs] - p[6][ofs] + p[7][ofs]); } for(int i = 0; i < npatterns; i++ ) { int inner_sum = vals[pairs[i][1]]; int outer_sum = vals[pairs[i][0]] - inner_sum; float response = inner_sum*invSizes[i][1] - outer_sum*invSizes[i][0]; if( fabs(response) > fabs(bestResponse) ) { bestResponse = response; bestSize = sizes1[pairs[i][0]]; } } r_ptr[x] = bestResponse; s_ptr[x] = (short)bestSize; } } return border; } static bool StarDetectorSuppressLines( const Mat& responses, const Mat& sizes, Point pt, int lineThresholdProjected, int lineThresholdBinarized ) { const float* r_ptr = responses.ptr<float>(); int rstep = (int)(responses.step/sizeof(r_ptr[0])); const short* s_ptr = sizes.ptr<short>(); int sstep = (int)(sizes.step/sizeof(s_ptr[0])); int sz = s_ptr[pt.y*sstep + pt.x]; int x, y, delta = sz/4, radius = delta*4; float Lxx = 0, Lyy = 0, Lxy = 0; int Lxxb = 0, Lyyb = 0, Lxyb = 0; for( y = pt.y - radius; y <= pt.y + radius; y += delta ) for( x = pt.x - radius; x <= pt.x + radius; x += delta ) { float Lx = r_ptr[y*rstep + x + 1] - r_ptr[y*rstep + x - 1]; float Ly = r_ptr[(y+1)*rstep + x] - r_ptr[(y-1)*rstep + x]; Lxx += Lx*Lx; Lyy += Ly*Ly; Lxy += Lx*Ly; } if( (Lxx + Lyy)*(Lxx + Lyy) >= lineThresholdProjected*(Lxx*Lyy - Lxy*Lxy) ) return true; for( y = pt.y - radius; y <= pt.y + radius; y += delta ) for( x = pt.x - radius; x <= pt.x + radius; x += delta ) { int Lxb = (s_ptr[y*sstep + x + 1] == sz) - (s_ptr[y*sstep + x - 1] == sz); int Lyb = (s_ptr[(y+1)*sstep + x] == sz) - (s_ptr[(y-1)*sstep + x] == sz); Lxxb += Lxb * Lxb; Lyyb += Lyb * Lyb; Lxyb += Lxb * Lyb; } if( (Lxxb + Lyyb)*(Lxxb + Lyyb) >= lineThresholdBinarized*(Lxxb*Lyyb - Lxyb*Lxyb) ) return true; return false; } static void StarDetectorSuppressNonmax( const Mat& responses, const Mat& sizes, std::vector<KeyPoint>& keypoints, int border, int responseThreshold, int lineThresholdProjected, int lineThresholdBinarized, int suppressNonmaxSize ) { int x, y, x1, y1, delta = suppressNonmaxSize/2; int rows = responses.rows, cols = responses.cols; const float* r_ptr = responses.ptr<float>(); int rstep = (int)(responses.step/sizeof(r_ptr[0])); const short* s_ptr = sizes.ptr<short>(); int sstep = (int)(sizes.step/sizeof(s_ptr[0])); short featureSize = 0; for( y = border; y < rows - border; y += delta+1 ) for( x = border; x < cols - border; x += delta+1 ) { float maxResponse = (float)responseThreshold; float minResponse = (float)-responseThreshold; Point maxPt(-1, -1), minPt(-1, -1); int tileEndY = MIN(y + delta, rows - border - 1); int tileEndX = MIN(x + delta, cols - border - 1); for( y1 = y; y1 <= tileEndY; y1++ ) for( x1 = x; x1 <= tileEndX; x1++ ) { float val = r_ptr[y1*rstep + x1]; if( maxResponse < val ) { maxResponse = val; maxPt = Point(x1, y1); } else if( minResponse > val ) { minResponse = val; minPt = Point(x1, y1); } } if( maxPt.x >= 0 ) { for( y1 = maxPt.y - delta; y1 <= maxPt.y + delta; y1++ ) for( x1 = maxPt.x - delta; x1 <= maxPt.x + delta; x1++ ) { float val = r_ptr[y1*rstep + x1]; if( val >= maxResponse && (y1 != maxPt.y || x1 != maxPt.x)) goto skip_max; } if( (featureSize = s_ptr[maxPt.y*sstep + maxPt.x]) >= 4 && !StarDetectorSuppressLines( responses, sizes, maxPt, lineThresholdProjected, lineThresholdBinarized )) { KeyPoint kpt((float)maxPt.x, (float)maxPt.y, featureSize, -1, maxResponse); keypoints.push_back(kpt); } } skip_max: if( minPt.x >= 0 ) { for( y1 = minPt.y - delta; y1 <= minPt.y + delta; y1++ ) for( x1 = minPt.x - delta; x1 <= minPt.x + delta; x1++ ) { float val = r_ptr[y1*rstep + x1]; if( val <= minResponse && (y1 != minPt.y || x1 != minPt.x)) goto skip_min; } if( (featureSize = s_ptr[minPt.y*sstep + minPt.x]) >= 4 && !StarDetectorSuppressLines( responses, sizes, minPt, lineThresholdProjected, lineThresholdBinarized)) { KeyPoint kpt((float)minPt.x, (float)minPt.y, featureSize, -1, maxResponse); keypoints.push_back(kpt); } } skip_min: ; } } StarDetectorImpl::StarDetectorImpl(int _maxSize, int _responseThreshold, int _lineThresholdProjected, int _lineThresholdBinarized, int _suppressNonmaxSize) : maxSize(_maxSize), responseThreshold(_responseThreshold), lineThresholdProjected(_lineThresholdProjected), lineThresholdBinarized(_lineThresholdBinarized), suppressNonmaxSize(_suppressNonmaxSize) {} void StarDetectorImpl::detect( InputArray _image, std::vector<KeyPoint>& keypoints, InputArray _mask ) { Mat image = _image.getMat(), mask = _mask.getMat(), grayImage = image; if( image.empty() ) { keypoints.clear(); return; } if( image.channels() > 1 ) cvtColor( image, grayImage, COLOR_BGR2GRAY ); Mat responses, sizes; int border; // Use 32-bit integers if we won't overflow in the integral image if ((grayImage.depth() == CV_8U || grayImage.depth() == CV_8S) && (int)grayImage.total() < 8388608 ) // 8388608 = 2 ^ (32 - 8(bit depth) - 1(sign bit)) border = StarDetectorComputeResponses<int>( grayImage, responses, sizes, maxSize, CV_32S ); else border = StarDetectorComputeResponses<double>( grayImage, responses, sizes, maxSize, CV_64F ); keypoints.clear(); if( border >= 0 ) StarDetectorSuppressNonmax( responses, sizes, keypoints, border, responseThreshold, lineThresholdProjected, lineThresholdBinarized, suppressNonmaxSize ); KeyPointsFilter::runByPixelsMask( keypoints, mask ); } } }