[人工智能]OpenTLD编译

由于OpenTLD依赖的OpenCV库版本是2.X，所以对应高版本的OpenCV编译会存在类似下列问题：
‘vector’ was not declared in this scope
don’t have “PatchGenerator” with OpenTLD(arthurv)
针对这些问题，本质是OpenCV版本的不对应。在https://www.cnblogs.com/happyamyhope/p/10694057.html有对这个问题提出解决方法，但不够完整。
S1：在OpenTLD-master/include文件夹建立patchgenerator.h文件

gedit patchgenerator.h

输入以下内容：

//#pragma once
#ifndef PATCH_GENERATOR_H
#define PATCH_GENERATOR_H
#include "opencv2/opencv.hpp"

namespace cv{
class CV_EXPORTS PatchGenerator
//class PatchGenerator
{
public:
    PatchGenerator();
    
    PatchGenerator(double _backgroundMin, double _backgroundMax,
    double _noiseRange, bool _randomBlur=true,
    double _lambdaMin=0.6, double _lambdaMax=1.5,
    double _thetaMin=-CV_PI, double _thetaMax=CV_PI,
    double _phiMin=-CV_PI, double _phiMax=CV_PI );
    
    void operator()(const Mat& image, Point2f pt, Mat& patch, Size patchSize, RNG& rng) const;
    
    void operator()(const Mat& image, const Mat& transform, Mat& patch,
    Size patchSize, RNG& rng) const;
    
    void warpWholeImage(const Mat& image, Mat& matT, Mat& buf,
    CV_OUT Mat& warped, int border, RNG& rng) const;
    
    void generateRandomTransform(Point2f srcCenter, Point2f dstCenter,
    CV_OUT Mat& transform, RNG& rng,
    bool inverse=false) const;
    
    void setAffineParam(double lambda, double theta, double phi);
    
    double backgroundMin, backgroundMax;
    double noiseRange;
    bool randomBlur;
    double lambdaMin, lambdaMax;
    double thetaMin, thetaMax;
    double phiMin, phiMax;
};

};
#endif

S2:在TLD.h文件中加入：

#include <patchgenerator.h>

S3:在LKTracker.h文件中加入：

#include <vector>

S4：在在OpenTLD-master/src文件夹建立patchgenerator.cpp文件，输入：

#include "opencv2/opencv.hpp"
#include "patchgenerator.h"
namespace cv
{

    const int progressBarSize = 50;
    
     Patch Generator //
    
    static const double DEFAULT_BACKGROUND_MIN = 0;
    static const double DEFAULT_BACKGROUND_MAX = 256;
    static const double DEFAULT_NOISE_RANGE = 5;
    static const double DEFAULT_LAMBDA_MIN = 0.6;
    static const double DEFAULT_LAMBDA_MAX = 1.5;
    static const double DEFAULT_THETA_MIN = -CV_PI;
    static const double DEFAULT_THETA_MAX = CV_PI;
    static const double DEFAULT_PHI_MIN = -CV_PI;
    static const double DEFAULT_PHI_MAX = CV_PI;
    
    PatchGenerator::PatchGenerator()
    : backgroundMin(DEFAULT_BACKGROUND_MIN), backgroundMax(DEFAULT_BACKGROUND_MAX),
    noiseRange(DEFAULT_NOISE_RANGE), randomBlur(true), lambdaMin(DEFAULT_LAMBDA_MIN),
    lambdaMax(DEFAULT_LAMBDA_MAX), thetaMin(DEFAULT_THETA_MIN),
    thetaMax(DEFAULT_THETA_MAX), phiMin(DEFAULT_PHI_MIN),
    phiMax(DEFAULT_PHI_MAX)
    {
    }
    
    
    PatchGenerator::PatchGenerator(double _backgroundMin, double _backgroundMax,
    double _noiseRange, bool _randomBlur,
    double _lambdaMin, double _lambdaMax,
    double _thetaMin, double _thetaMax,
    double _phiMin, double _phiMax )
    : backgroundMin(_backgroundMin), backgroundMax(_backgroundMax),
    noiseRange(_noiseRange), randomBlur(_randomBlur),
    lambdaMin(_lambdaMin), lambdaMax(_lambdaMax),
    thetaMin(_thetaMin), thetaMax(_thetaMax),
    phiMin(_phiMin), phiMax(_phiMax)
    {
    }
    
    
    void PatchGenerator::generateRandomTransform(Point2f srcCenter, Point2f dstCenter,
    Mat& transform, RNG& rng, bool inverse) const
    {
        double lambda1 = rng.uniform(lambdaMin, lambdaMax);
        double lambda2 = rng.uniform(lambdaMin, lambdaMax);
        double theta = rng.uniform(thetaMin, thetaMax);
        double phi = rng.uniform(phiMin, phiMax);
        
        // Calculate random parameterized affine transformation A,
        // A = T(patch center) * R(theta) * R(phi)' *
        // S(lambda1, lambda2) * R(phi) * T(-pt)
        double st = sin(theta);
        double ct = cos(theta);
        double sp = sin(phi);
        double cp = cos(phi);
        double c2p = cp*cp;
        double s2p = sp*sp;
        
        double A = lambda1*c2p + lambda2*s2p;
        double B = (lambda2 - lambda1)*sp*cp;
        double C = lambda1*s2p + lambda2*c2p;
        
        double Ax_plus_By = A*srcCenter.x + B*srcCenter.y;
        double Bx_plus_Cy = B*srcCenter.x + C*srcCenter.y;
        
        transform.create(2, 3, CV_64F);
        Mat_<double>& T = (Mat_<double>&)transform;
        T(0,0) = A*ct - B*st;
        T(0,1) = B*ct - C*st;
        T(0,2) = -ct*Ax_plus_By + st*Bx_plus_Cy + dstCenter.x;
        T(1,0) = A*st + B*ct;
        T(1,1) = B*st + C*ct;
        T(1,2) = -st*Ax_plus_By - ct*Bx_plus_Cy + dstCenter.y;
        
        if( inverse ) invertAffineTransform(T, T);
    }
    
    
    void PatchGenerator::operator ()(const Mat& image, Point2f pt, Mat& patch, Size patchSize, RNG& rng) const
    {
        double buffer[6];
        Mat_<double> T(2, 3, buffer);
        
        generateRandomTransform(pt, Point2f((patchSize.width-1)*0.5f, (patchSize.height-1)*0.5f), T, rng);
        (*this)(image, T, patch, patchSize, rng);
    }
    
    
    void PatchGenerator::operator ()(const Mat& image, const Mat& T,
    Mat& patch, Size patchSize, RNG& rng) const
    {
        patch.create( patchSize, image.type() );
        if( backgroundMin != backgroundMax )
        {
            rng.fill(patch, RNG::UNIFORM, Scalar::all(backgroundMin), Scalar::all(backgroundMax));
            warpAffine(image, patch, T, patchSize, INTER_LINEAR, BORDER_TRANSPARENT);
        }
        else
        warpAffine(image, patch, T, patchSize, INTER_LINEAR, BORDER_CONSTANT, Scalar::all(backgroundMin));
        
        int ksize = randomBlur ? (unsigned)rng % 9 - 5 : 0;
        if( ksize > 0 )
        {
            ksize = ksize*2 + 1;
            GaussianBlur(patch, patch, Size(ksize, ksize), 0, 0);
        }
        
        if( noiseRange > 0 )
        {
            AutoBuffer<uchar> _noiseBuf( patchSize.width*patchSize.height*image.elemSize() );
            Mat noise(patchSize, image.type(), (uchar*)_noiseBuf);
            int delta = image.depth() == CV_8U ? 128 : image.depth() == CV_16U ? 32768 : 0;
            rng.fill(noise, RNG::NORMAL, Scalar::all(delta), Scalar::all(noiseRange));
            if( backgroundMin != backgroundMax ) addWeighted(patch, 1, noise, 1, -delta, patch);
            else
            {
                for( int i = 0; i <patchSize.height; i++ )
                {
                    uchar* prow = patch.ptr<uchar>(i);
                    const uchar* nrow = noise.ptr<uchar>(i);
                    for( int j = 0; j <patchSize.width; j++ )
                    if( prow[j] != backgroundMin )
                        prow[j] = saturate_cast<uchar>(prow[j] + nrow[j] - delta);
                }
            }
        }
    }
    
    void PatchGenerator::warpWholeImage(const Mat& image, Mat& matT, Mat& buf,
    Mat& warped, int border, RNG& rng) const
    {
        Mat_<double> T = matT;
        Rect roi(INT_MAX, INT_MAX, INT_MIN, INT_MIN);
        
        for( int k = 0; k <4; k++ )
        {
            Point2f pt0, pt1;
            pt0.x = (float)(k == 0 || k == 3 ? 0 : image.cols);
            pt0.y = (float)(k <2 ? 0 : image.rows);
            pt1.x = (float)(T(0,0)*pt0.x + T(0,1)*pt0.y + T(0,2));
            pt1.y = (float)(T(1,0)*pt0.x + T(1,1)*pt0.y + T(1,2));
            
            roi.x = std::min(roi.x, cvFloor(pt1.x));
            roi.y = std::min(roi.y, cvFloor(pt1.y));
            roi.width = std::max(roi.width, cvCeil(pt1.x));
            roi.height = std::max(roi.height, cvCeil(pt1.y));
        }
        
        roi.width -= roi.x - 1;
        roi.height -= roi.y - 1;
        int dx = border - roi.x;
        int dy = border - roi.y;
        
        if( (roi.width+border*2)*(roi.height+border*2) > buf.cols )
            buf.create(1, (roi.width+border*2)*(roi.height+border*2), image.type());
        
        warped = Mat(roi.height + border*2, roi.width + border*2,
        image.type(), buf.data);
        
        T(0,2) += dx;
        T(1,2) += dy;
        (*this)(image, T, warped, warped.size(), rng);
        
        if( T.data != matT.data ) T.convertTo(matT, matT.type());
    }
    
    
    // Params are assumed to be symmetrical: lambda w.r.t. 1, theta and phi w.r.t. 0
    void PatchGenerator::setAffineParam(double lambda, double theta, double phi)
    {
        lambdaMin = 1. - lambda;
        lambdaMax = 1. + lambda;
        thetaMin = -theta;
        thetaMax = theta;
        phiMin = -phi;
        phiMax = phi;
    }
};

S5：在LKTrancker.cpp中添加

using namespace std;

S6:修改编译文件CMakeLists如下：

#Set minimum version requered
cmake_minimum_required(VERSION 2.4.6)
#just to avoid the warning
if(COMMAND cmake_policy)
     cmake_policy(SET CMP0003 NEW)
endif(COMMAND cmake_policy)
#set project name
project(TLD)
#Append path to the module path
list(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR})
#OpenCV
find_package(OpenCV REQUIRED)
#set the default path for built executables to the "bin" directory
set(EXECUTABLE_OUTPUT_PATH ${PROJECT_SOURCE_DIR}/../bin)
#set the default path for built libraries to the "lib" directory
set(LIBRARY_OUTPUT_PATH ${PROJECT_SOURCE_DIR}/../lib)
#set the include directories
include_directories (${PROJECT_SOURCE_DIR}/../include	${OpenCV_INCLUDE_DIRS})
#libraries
add_library(tld_utils tld_utils.cpp)
add_library(LKTracker LKTracker.cpp)
add_library(ferNN FerNNClassifier.cpp)
add_library(tld TLD.cpp patchgenerator.cpp)
#executables
add_executable(run_tld run_tld.cpp)
#link the libraries
target_link_libraries(run_tld tld LKTracker ferNN tld_utils ${OpenCV_LIBS})
#set optimization level 
set(CMAKE_BUILD_TYPE Release)

S7: 安装教程编译：

mkdir build
cd build
cmake ../src/
make
cd ../bin/
./run_tld -p ../parameters.yml -s ../datasets/06_car/car.mpg

修改好的文件在此处，需要直接下载，编译