/*! \file ImageCompareModel.cpp

	Copyright (C) 2014 Hangzhou Leaper.
  		
	Created by bang.jin at 2017/02/04.

*/

#include "ImageCompareModel.h"
#include "CVUtils.h"
#include "MultiScaleImageCompareModel.h"
#include <fstream>
#include <iostream>
//#define DEBUG_VIEW_INTERNAL_MAT
#define IMGCMP_STR_NAME "name"
#define IMGCMP_STR_IMAGE_COMPARE_MODEL "imageCompareModel"
#define IMGCMP_STR_ALIGN_BASE_IMAGE "alignBaseImage"
#define IMGCMP_STR_COMPARE_BASE_IMAGE "compareBaseImage"
#define IMGCMP_STR_WEIGHT_MAT "weightMat"
#define IMGCMP_STR_MATCH_VAL_SCALE "matchValScale"
#define IMGCMP_STR_TARGET_MEAN_VAL "targetMeanVal"
#define IMGCMP_STR_TARGET_STDDEV_VAL "targetStddevVal"
#define IMGCMP_STR_REPEAT_NUM "repeatNum"
#define IMGCMP_STR_TRUE_SAMPLE_DIS_MEAN "trueSampleDisMean"
#define IMGCMP_STR_TRUE_SAMPLE_DIS_STDDEV "trueSampleDisStddev"
#define IMGCMP_STR_TRUE_SAMPLE_DIS_MIN "trueSampleDisMin"
#define IMGCMP_STR_TRUE_SAMPLE_DIS_MAX "trueSampleDisMax"
#define IMGCMP_STR_DIS_THRE "disThre"
#define IMGCMP_STR_FALSE_SAMPLE_MIN_DIS "falseSampleMinDis"
#define IMGCMP_STR_TRAIN_DIS "trainDis"
#define IMGCMP_STR_SMALLEST_MATCH_DIS "falseMinDis"
#define IMGCMP_STR_AVER_DIAMETER "averageDiameter"
#define IMGCMP_STR_INSIDE_WEIGHT "insideWeightimage"
#define IMGCMP_STR_INSIDE_AVE_IMAGE "insideBaseimage"
#define IMGCMP_STR_INSIDE_COMP_AVE_IMAGE "insideCompareBaseImage"
#define IMGCMP_STR_INSIDE_RADIUS       "insideRadius"
#define IMGCMP_STR_INSIDE_TEMPL         "innerTempl"

#define IMGCMP_CACHE_MAX_SIZE 100

#define MIN_CIRCLE_RADII 5
#define MAX_CIRCLE_RADII 110
int ImageCompareModel::m_parallelFlag = 0;
cv::Point2f ImageCompareModel::refineCircleCen(const Mat& img, Point2f cen)
{
    int r = 2;
    vector<double> vec;
    double minDiff = DBL_MAX;
    Point2f bestCenter;
    vector<float> bestDftVec;
    for (float y = cen.y - r; y <= cen.y + r; y += 0.5)
    {
        for (float x = cen.x - r; x <= cen.x + r; x += 0.5)
        {
            std::cout << "newCenter: " << x << "-" << y << ", ";

            Point2f newCenter(x, y);

            vector<float> vec;
            selfRotationSimilarityFeature(img, vec, newCenter);

            vector<float> dftVec;
            dft(vec, dftVec);

// no need to code for plotting in visual studio later than 2017,
// install the ArrayPlotter extension to see the data distribution
#if (_MSC_VER < 1910) // vs2017
			CvPlot::plot<float>("refineCenter", &(vec[0]), vec.size());
#endif

            float diffVal = sum<float>(vec.begin(), vec.end());

            vec.push_back(diffVal);

            if (minDiff > diffVal)
            {
                minDiff = diffVal;
                bestCenter = newCenter;
                bestDftVec = dftVec;
            }

            std::cout << "diff: " << diffVal << ", "
                " minDiff: " << minDiff << ", "
                " bestCenter: " << bestCenter.x << "-" << bestCenter.y <<
                std::endl;
        }
    }

    return bestCenter;
}

cv::Mat ImageCompareModel::genWeightImage(const Mat& img, Point2f center,
    float innerR /*= -1*/, float outterR /*= -1*/)
{
    if (innerR == -1)
    {
        // default is 30
        innerR = img.rows*0.175;
    }
    if (outterR == -1)
    {
        // default is max radius - 10
        outterR = img.rows*0.475;
    }

    Mat weightImg;
    weightImg.create(img.size(), CV_32FC1);
    weightImg.setTo(0);

    uchar* pData = weightImg.data;
    for (int y = 0; y < weightImg.rows; y++)
    {
        float* pFData = (float*)weightImg.row(y).data;
        for (int x = 0; x < weightImg.cols; x++)
        {
            float& val = pFData[x];
            float dx = center.x - x;
            float dy = center.y - y;
            float r = sqrt(dx*dx + dy*dy);
            if (r > outterR || r < innerR)
            {
                val = 0;
            }
            else
            {
                val = min(outterR - r, r - innerR);
            }
        }
    }

    return weightImg;
}

void ImageCompareModel::selfRotationSimilarityFeature(
    const Mat& img, vector<float>& vec, Point2f center /*= Point2f(-1, -1)*/)
{
    float angleStep = 1.0;
    if (center.x < 0 || center.y < 0)
    {
        center = Point2f(img.cols / 2.0, img.rows / 2.0);
    }    

    double bestVal = DBL_MAX;
    Mat bestRImg;
    Mat fImg;
    img.convertTo(fImg, CV_32FC1);

    Mat weightImg = genWeightImage(img, center);
    fImg = fImg.mul(weightImg);

    double sumVal = sum(weightImg).val[0];

    Mat baseImg;
    {
        Mat t = getRotationMatrix2D(center, 1.0, 1.0);
        Mat rImg;
        warpAffine(fImg, rImg, t, fImg.size(), CV_INTER_CUBIC);
        t = getRotationMatrix2D(center, -1.0, 1.0);
        warpAffine(rImg, baseImg, t, rImg.size(), CV_INTER_CUBIC);
    }

#ifdef DEBUG_VIEW_INTERNAL_MAT
    Mat viewBaseImg = baseImg / 255.0/25.0;
    Mat viewfImg = fImg / 255.0/25.0;
#endif

    for (float a = 0; a <= 360.0; a += angleStep)
    {
        Mat t = getRotationMatrix2D(center, a, 1.0);
        Mat rImg;
        warpAffine(fImg, rImg, t, fImg.size(), CV_INTER_CUBIC);
        
        Mat diffImg = baseImg - rImg;
        //diffImg = diffImg.mul(weightImg);
        double diffVal = norm(diffImg)/sumVal;
        vec.push_back(diffVal);
    }
}

cv::Mat ImageCompareModel::genMask(const Mat& img, Point2f center,
    float innerR /*= -1*/, float outterR /*= -1*/, int type /*= CV_32FC1*/)
{
    Mat mask(img.size(), CV_8UC1);
    mask.setTo(0);
    if (innerR == -1)
    {
        // default is 30
        innerR = img.rows*0.178;
		//innerR = img.rows;
    }
    if (outterR == -1)
    {
        // default is max radius - 10
        outterR = img.rows*0.425;
		//outterR = img.rows;
    }
    circle(mask, center, outterR, Scalar(255), -1);
    circle(mask, center, innerR, Scalar(0), -1);
    if (type != CV_8UC1)
    {
        converToType(mask, type);
        mask /= 255;
    }
    return mask;
}

void ImageCompareModel::genMask()
{
    m32fMaskImg = genMask(mAlignBaseImg, Point2f(mAlignBaseImg.cols / 2.0, mAlignBaseImg.rows / 2.0));
    m8uMaskImg = genMask(mAlignBaseImg, Point2f(mAlignBaseImg.cols / 2.0, mAlignBaseImg.rows / 2.0),
        -1.0, -1.0, CV_8UC1);
	m32fInsideMaskImg = genInsideMask(mInSideBaseImg, Point2f(mInSideBaseImg.cols / 2.0, mInSideBaseImg.rows / 2.0));
	m8uInsideMaskImg = genInsideMask(mInSideBaseImg, Point2f(mInSideBaseImg.cols / 2.0, mInSideBaseImg.rows / 2.0),
		-1.0, -1.0, CV_8UC1);
}

void ImageCompareModel::printInfo()
{
    std::cout << IMGCMP_STR_IMAGE_COMPARE_MODEL << ": ";
    std::cout << std::endl;

    printProperty(1, IMGCMP_STR_NAME, mName);

    printIndent(1);
    std::cout << IMGCMP_STR_ALIGN_BASE_IMAGE << ": ";
    std::cout << std::endl;
    printMatInfo(mAlignBaseImg, 2);
    std::cout << std::endl;

    printIndent(1);
    std::cout << IMGCMP_STR_WEIGHT_MAT << ": ";
    std::cout << std::endl;
    printMatInfo(mWeightMat, 2);
    std::cout << std::endl;

    printProperty(1, IMGCMP_STR_MATCH_VAL_SCALE, mMatchValScale);
    printProperty(1, IMGCMP_STR_TARGET_MEAN_VAL, mTargetMeanVal);
    printProperty(1, IMGCMP_STR_TARGET_STDDEV_VAL, mTargetStddevVal);
    printProperty(1, IMGCMP_STR_REPEAT_NUM, mRepeatNum);
    printProperty(1, IMGCMP_STR_DIS_THRE, mDisThre);
    printProperty(1, IMGCMP_STR_TRUE_SAMPLE_DIS_STDDEV, mTrueSampleDisStddev);
    printProperty(1, IMGCMP_STR_TRUE_SAMPLE_DIS_MEAN, mTrueSampleDisMean);
    printProperty(1, IMGCMP_STR_TRUE_SAMPLE_DIS_MAX, mTrueSampleDisMax);
    printProperty(1, IMGCMP_STR_TRUE_SAMPLE_DIS_MIN, mTrueSampleDisMin);

    printProperty(1, "filePath", mFilePath);
    printProperty(1, "isEnableCache", mIsEnableCache);
}

void ImageCompareModel::saveImages(string dirPath)
{
    imwrite(dirPath + "/base.png", mAlignBaseImg);
    imwrite(dirPath + "/weight.png", mWeightMat);
}

bool ImageCompareModel::save2file(string filePath)
{
    FileStorage fs(filePath, FileStorage::WRITE);
    if (!fs.isOpened())
    {
        return false;
    }

	fs << IMGCMP_STR_NAME << mName <<
		IMGCMP_STR_ALIGN_BASE_IMAGE << mAlignBaseImg <<
		IMGCMP_STR_COMPARE_BASE_IMAGE << mCompareBaseImg <<
		IMGCMP_STR_WEIGHT_MAT << mWeightMat <<
		IMGCMP_STR_INSIDE_AVE_IMAGE << mInSideBaseImg <<
		IMGCMP_STR_INSIDE_COMP_AVE_IMAGE << mInsideCompareBaseImg <<
		IMGCMP_STR_INSIDE_WEIGHT << mInsideWeightMat <<
		IMGCMP_STR_INSIDE_TEMPL << innerTempl <<
		IMGCMP_STR_MATCH_VAL_SCALE << mMatchValScale <<
		IMGCMP_STR_TARGET_MEAN_VAL << mTargetMeanVal <<
		IMGCMP_STR_TARGET_STDDEV_VAL << mTargetStddevVal <<
		IMGCMP_STR_REPEAT_NUM << mRepeatNum <<
		IMGCMP_STR_TRUE_SAMPLE_DIS_MEAN << mTrueSampleDisMean <<
		IMGCMP_STR_TRUE_SAMPLE_DIS_STDDEV << mTrueSampleDisStddev <<
		IMGCMP_STR_TRUE_SAMPLE_DIS_MIN << mTrueSampleDisMin <<
		IMGCMP_STR_TRUE_SAMPLE_DIS_MAX << mTrueSampleDisMax <<
		IMGCMP_STR_FALSE_SAMPLE_MIN_DIS << getFalseSampleMinDis() <<
		IMGCMP_STR_DIS_THRE << mDisThre <<
		IMGCMP_STR_TRAIN_DIS << mDisMat <<
		IMGCMP_STR_AVER_DIAMETER << meanDiameter <<
		IMGCMP_STR_INSIDE_RADIUS << rInner;

    setFilePath(filePath);

    return true;
}

bool ImageCompareModel::readFromFile(string filePath)
{
    FileStorage fs(filePath, FileStorage::READ);
    if (!fs.isOpened())
    {
        return false;
    }

    fs[IMGCMP_STR_ALIGN_BASE_IMAGE] >> mAlignBaseImg;
    fs[IMGCMP_STR_COMPARE_BASE_IMAGE] >> mCompareBaseImg;
    fs[IMGCMP_STR_WEIGHT_MAT] >> mWeightMat;
	fs[IMGCMP_STR_INSIDE_AVE_IMAGE] >> mInSideBaseImg;
	fs[IMGCMP_STR_INSIDE_COMP_AVE_IMAGE] >> mInsideCompareBaseImg;
	fs[IMGCMP_STR_INSIDE_WEIGHT] >> mInsideWeightMat;
	fs[IMGCMP_STR_INSIDE_TEMPL] >> innerTempl;
    mMatchValScale = (double)fs[IMGCMP_STR_MATCH_VAL_SCALE];

    FileNode fn = fs[IMGCMP_STR_TARGET_MEAN_VAL];
    if (!fn.empty())
    {
         mTargetMeanVal = (int)fn;
    }

    fn = fs[IMGCMP_STR_TARGET_STDDEV_VAL];
    if (!fn.empty())
    {
        mTargetStddevVal = (int)fn;
    }

    fn = fs[IMGCMP_STR_REPEAT_NUM];
    if (!fn.empty())
    {
        mRepeatNum = (int)fn;
    }
    else
    {
        //mRepeatNum = computeRepeatNum();
    }

    fn = fs[IMGCMP_STR_NAME];
    if (!fn.empty())
    {
        mName = (string)fn;
    }

    fn = fs[IMGCMP_STR_TRUE_SAMPLE_DIS_MEAN];
    if (!fn.empty())
    {
        mTrueSampleDisMean = (double)fn;
    }
    fn = fs[IMGCMP_STR_TRUE_SAMPLE_DIS_STDDEV];
    if (!fn.empty())
    {
        mTrueSampleDisStddev = (double)fn;
    }
    fn = fs[IMGCMP_STR_TRUE_SAMPLE_DIS_MIN];
    if (!fn.empty())
    {
        mTrueSampleDisMin = (double)fn;
    }
    fn = fs[IMGCMP_STR_TRUE_SAMPLE_DIS_MAX];
    if (!fn.empty())
    {
        mTrueSampleDisMax = (double)fn;
    }
    fn = fs[IMGCMP_STR_FALSE_SAMPLE_MIN_DIS];
    if (!fn.empty())
    {
        setFalseSampleMinDis((double)fn);
    }
    fn = fs[IMGCMP_STR_DIS_THRE];
    if (!fn.empty())
    {
        mDisThre = (double)fn;
    }
	
	fn = fs[IMGCMP_STR_AVER_DIAMETER];
	if (!fn.empty())
	{
		meanDiameter = (int)fn;
	}

	fn = fs[IMGCMP_STR_INSIDE_RADIUS];
	if (!fn.empty())
	{
		rInner = (float)fn;
	}

    setFilePath(filePath);

    genMask();

    return true;
}

void ImageCompareModel::preProcessImage(Mat& img, Mat &insideImg) const
{
    preProcessImage(img, m8uMaskImg, mTargetMeanVal, mTargetStddevVal, mHighlightsThreshold);
	if (insideImg.empty())
	{
		return;
	}
	preProcessImage(insideImg, m8uInsideMaskImg, mTargetMeanVal, mTargetStddevVal, mHighlightsThreshold);
}

void ImageCompareModel::preProcessImage(Mat& img, const Mat& mask, double tarMean, 
    double tarStddev, int highlightsThreshold) const
{
    if (img.channels() > 1)
    {
        img = getFirstChannel(img);
    }
    if (img.type() != CV_32FC1)
    {
        converToType(img, CV_32FC1);
    }

    Mat gaussImg;
    GaussianBlur(img, gaussImg, Size(3, 3), 5.0);
    img = gaussImg;

    Mat dilatedMask;
    dilate(mask, dilatedMask, Mat::ones(Size(3, 3), CV_32FC1));

    Mat hightlightsMask = img < highlightsThreshold;
    Mat imgMask = hightlightsMask & dilatedMask;

    Scalar meanScalar, stddevScalar;
    meanStdDev(img, meanScalar, stddevScalar, imgMask);
    img = (img - meanScalar.val[0]) * tarStddev / stddevScalar.val[0] + tarMean;

    converToType(imgMask, CV_32FC1);
    imgMask /= 255.0;
    Mat imgNorm = cocentricNorm(img, Point2f(img.cols / 2.0, img.rows / 2.0), 
        imgMask, 125);
#ifdef DEBUG_VIEW_INTERNAL_MAT
    Mat vImgNorm = imgNorm / 255.0;
#endif
    img = imgNorm;
}

void ImageCompareModel::preProcessImage(Mat& img, const Mat& mask,
    const Mat& weightMat, double dstMean, double dstStddev, int highlightsThreshold) const
{
    if (img.channels() > 1)
    {
        img = getFirstChannel(img);
    }
    if (img.type() != CV_32FC1)
    {
        converToType(img, CV_32FC1);
    }

    Mat gaussImg;
    GaussianBlur(img, gaussImg, Size(3, 3), 5.0);
    img = gaussImg;

    Mat hightlightsMask = img < highlightsThreshold;
    Mat imgMask = hightlightsMask & mask & (weightMat > 0);

    Scalar meanScalar, stddevScalar;
    meanStdDev(img, meanScalar, stddevScalar, imgMask);
	img = (img - meanScalar.val[0]) * dstStddev / stddevScalar.val[0] + dstMean;
	//img.setTo(0, img < 0);
    converToType(hightlightsMask, CV_32FC1);
	//converToType(imgMask, CV_32FC1);
    Mat imgNorm = cocentricNorm(img, Point2f(img.cols / 2.0, img.rows / 2.0), 
		weightMat.mul(hightlightsMask), 125);
#ifdef DEBUG_VIEW_INTERNAL_MAT
    Mat vImgNorm = imgNorm / 255.0;
#endif
    img = imgNorm;
}

void ImageCompareModel::preProcessImage0(Mat& img) const
{

}

double ImageCompareModel::compare(Mat img0, Mat img1, Mat* pRImg0, Mat* pRImg1)
{
    if (img0.size() != img1.size() && img0.size() != mAlignBaseImg.size())
    {
        return -1;
    }

    double bestMatchVal = DBL_MAX;
    int bestAngle = -1;
    Mat bestImg1;
    double bestDD = 0;

    double w = 0;

    Point2f center((float)img1.cols / 2.0, (float)img1.rows / 2.0);
    Mat rImg0 = rotateMatch(img0).mBestRImg;
    Mat rImg1 = rotateMatch(img1).mBestRImg;
    Mat dMat = rImg0 - rImg1;
    converToType(dMat, CV_32FC1);
    dMat = dMat.mul(mWeightMat);

    if (pRImg0)
    {
        *pRImg0 = rImg0;
    }
    if (pRImg1)
    {
        *pRImg1 = rImg1;
    }

    return genMatchValue(dMat);
}

double ImageCompareModel::compare(Mat srcImage, Mat* pRImg /*= NULL*/, int levelNum /*= 1*/,
	bool isFilterSize /*= true*/, int flag, double md_diameter, double md_height)
{
    if (mIsEnableCache)
    {
		auto cacheIter = mDisCache.find(srcImage.data);
        if (cacheIter != mDisCache.end())
        {
            return cacheIter->second;
        }
    } 
/*	CircleDetector cd;
	cd.setAlgoType(CircleDetector::PeakCircle);
	cd.setEdgeWidth(3);
	//cd.setPolarity(Polarity::White2Black);
	cd.setPolarity(Polarity::Either);
	cd.setFindBy(FindBy::Best);
	//cd.setFindBy(FindBy::Last);
	cd.setRadii(MIN_CIRCLE_RADII, MAX_CIRCLE_RADII);
	cd.setACThres(5);
	Vec3f bestCircle;
	cd.detectBest(srcImage, Point2f(srcImage.cols / 2, srcImage.rows / 2), bestCircle, nullptr);
	
#ifdef DEBUG_VIEW_INTERNAL_MAT
	vector<Mat> vecs;
	vecs.push_back(srcImage);
	vecs.push_back(srcImage);
	vecs.push_back(srcImage);
	Mat rr;
	merge(vecs, rr);

	circle(rr, Point2f(bestCircle[0], bestCircle[1]), bestCircle[2], Scalar(255, 0, 0), 2);
#endif

	if (bestCircle[2] < 3 || bestCircle[0] == 0 || bestCircle[1] == 0)
		return DBL_MAX;
		*/
// 	int x_Axis = bestCircle[0] - MAX_CIRCLE_RADII;
// 	int y_Axis = bestCircle[1] - MAX_CIRCLE_RADII;

	int x_Axis = srcImage.cols / 2 - MAX_CIRCLE_RADII;
	int y_Axis = srcImage.rows / 2 - MAX_CIRCLE_RADII;

	int r_Axis = 2 * MAX_CIRCLE_RADII;

	if (x_Axis <= 0 || y_Axis <= 0)
		return DBL_MAX;
	if (r_Axis <= 0)
		return DBL_MAX;
	if (r_Axis >= srcImage.cols || r_Axis >= srcImage.rows)
		return DBL_MAX;
// 	if (srcImage.cols < (bestCircle[0] + MAX_CIRCLE_RADII) || srcImage.rows < (bestCircle[1] + MAX_CIRCLE_RADII))
// 		return DBL_MAX;

	if (srcImage.cols < (srcImage.cols / 2 + MAX_CIRCLE_RADII) || srcImage.rows < (srcImage.rows / 2 + MAX_CIRCLE_RADII))
	 	return DBL_MAX;

	Rect rect(x_Axis, y_Axis, r_Axis, r_Axis);
	Mat srcCenterMat;
	srcImage(rect).copyTo(srcCenterMat);
	Mat img, centerMat;
	resizeMat(srcImage, img);
	resizeMat(srcCenterMat, centerMat);
	Mat rawImg = img.clone();
	int nRadiusDiff = 15;
	if (mAlignBaseImg.size() != img.size() || mInSideBaseImg.size() !=centerMat.size())
    {
		if (isFilterSize \
			&& abs(mAlignBaseImg.size().width - img.size().width) > nRadiusDiff \
			|| abs(mInSideBaseImg.size().width - centerMat.size().width) > 3)
        {
			return DBL_MAX;
		}
		resize(img, img, mAlignBaseImg.size());
		resize(centerMat, centerMat, mInSideBaseImg.size());
    }
	
	Mat matchImg = img.clone();
	Mat matchInsideImg = centerMat.clone();
	
    preProcessImage(matchImg, matchInsideImg);

    if (!mpMultiScaleModel)
    {
        initMultiScaleModel();
    }
	m_parallelFlag = 0;
    RotateMatchResult rmr = rotateMatch(matchImg, levelNum);
    Mat rImg = rmr.mBestRImg;
	m_parallelFlag = 1;
	RotateMatchResult rmmInside = rotateMatch(matchInsideImg, 1);
	Mat rInsideImg = rmmInside.mBestRImg;
    if (rImg.empty() || rInsideImg.empty())
    {
        return DBL_MAX;
    }

#ifdef DEBUG_VIEW_INTERNAL_MAT
    Mat vRImg = rImg / 255.0;
	Mat vInsideMat = rInsideImg / 255.0;
#endif

    double bestAngle = rmr.mBestAngle;
    Mat cmpImg = rotateImage(img, Point2f(img.cols / 2.0, img.rows / 2.0),
        bestAngle);

    // remove highlights
    Mat hightlightsMask = cmpImg < mHighlightsThreshold;
    converToType(hightlightsMask, CV_32FC1);
    hightlightsMask /= 255.0;

    Mat unifiedMask = m32fMaskImg.mul(hightlightsMask).mul(mWeightMat);
	//Mat ii = mWeightMat / 255.0;
    preProcessImage(cmpImg, m8uMaskImg, mWeightMat, mTargetMeanVal,
        mTargetStddevVal, mHighlightsThreshold);

    cmpImg.setTo(0, cmpImg < 0);

    converToType(cmpImg, CV_32FC1);
    normSectors_tarImg(cmpImg, unifiedMask, imgCen(cmpImg), 1,
        mCompareBaseImg);

	double bestInsideAngle = rmmInside.mBestAngle;
	Mat camInsideMat = rotateImage(centerMat, Point2f(centerMat.cols / 2.0, centerMat.rows / 2.0),
		bestInsideAngle);

//	float offset = 5;
//	float startX = mAlignBaseImg.cols / 2.0 - rInner - offset;
//	float startY = mAlignBaseImg.rows / 2.0 - rInner - offset;
//	Rect rect(startX, startY, 2*(rInner + offset), 2*(rInner + offset));
//	Mat innerPartMat = camInsideMat(rect);
//	Mat rstMat;
//	cv::matchTemplate(innerTempl, innerPartMat, rstMat, CV_TM_SQDIFF_NORMED);
//	double minVal;
//	Point minLoc;
//	minMaxLoc(rstMat, &minVal, NULL, &minLoc, NULL);
//#ifdef DEBUG_VIEW_INTERNAL_MAT
//	Mat test1 = innerPartMat.clone();
//	Mat test2 = innerPartMat.clone();
//	vector<Mat> imgs;
//	imgs.push_back(test2);
//	imgs.push_back(test1);
//	imgs.push_back(innerPartMat);
//	Mat rst;
//	merge(imgs, rst);
//	cv::rectangle(rst, Rect(minLoc.x, minLoc.y, innerTempl.cols, innerTempl.rows), Scalar(0, 0, 255));
//#endif
//	//debug
//
	Mat highLightInsideMask = camInsideMat < mHighlightsThreshold;
	converToType(highLightInsideMask, CV_32FC1);
	highLightInsideMask /= 255.0;
	Mat unifiedInsideMask = m32fInsideMaskImg.mul(highLightInsideMask).mul(mInsideWeightMat);
	preProcessImage(camInsideMat, m8uInsideMaskImg, mInsideWeightMat, mTargetMeanVal,
		mTargetStddevVal, mHighlightsThreshold);
	Mat vcamInside = camInsideMat / 255.0;
	camInsideMat.setTo(0, camInsideMat < 0);
	//mInsideCompareBaseImg.setTo(0, mInsideCompareBaseImg < 0);
	normSectors_tarImg(camInsideMat, unifiedInsideMask, imgCen(camInsideMat), 1, mInsideCompareBaseImg);
	//
	mInsideCompareBaseImg.setTo(0, mInsideCompareBaseImg < 0);
#ifdef DEBUG_VIEW_INTERNAL_MAT
    Mat vRotatedImg0 = cmpImg / 255.0;
	Mat vrotatedInsideImage = camInsideMat / 255.0;
	cv::imwrite("F:\\temp\\insideAfterSector.png", camInsideMat);
#endif

#ifdef DEBUG_VIEW_INTERNAL_MAT
    Mat vRotatedImg1 = mCompareBaseImg / 255.0;
	Mat vrotatedInsideBase = mInsideCompareBaseImg / 255.0;
	cv::imwrite("F:\\temp\\insideAfterSectorCompareBase.png", mInsideCompareBaseImg);
#endif

    cmpImg.setTo(0, cmpImg < 10);
	//camInsideMat.setTo(0, camInsideMat < 10);
    if (pRImg)
    {
        *pRImg = cmpImg;
    }
	m_parallelFlag = 0;
    double ret = genMatchValue(cmpImg, mCompareBaseImg, unifiedMask, flag, rawImg.rows, md_diameter, md_height);//得出相似值

	m_parallelFlag = 1;
	double retInside = genMatchValue(camInsideMat, mInsideCompareBaseImg, unifiedInsideMask, 0, rawImg.rows, md_diameter, md_height);//得出相似值

	ret = 0.6*ret + 0.4*retInside;

	if (ret > mDisThre)
	{
		return DBL_MAX;
	}

	if (mIsEnableCache)
    {
        mDisCache[img.data] = ret;
        if (mDisCache.size() > IMGCMP_CACHE_MAX_SIZE)
        {
            mDisCache.clear();
        }
    }
    return ret;
}

void ImageCompareModel::train(const vector<Mat>& vec)
{
#ifdef _DEBUG
    std::cout << getName() << std::endl;
#endif _DEBUG
	if (0 == vec.size()) {
		return;
	}

// 	CircleDetector cd;
// 	cd.setAlgoType(CircleDetector::PeakCircle);
// 	cd.setEdgeWidth(3);
// 	//cd.setPolarity(Polarity::White2Black);
// 	cd.setPolarity(Polarity::Either);
// 	cd.setFindBy(FindBy::Best);
// 	//cd.setFindBy(FindBy::Last);
// 	cd.setRadii(MIN_CIRCLE_RADII, MAX_CIRCLE_RADII);
// 	cd.setACThres(5);
 	vector<Mat> centerMatVec;
 	vector<Mat> tmpVec;
	for (int i = 0; i < vec.size(); i++)
	{
		Mat originalMat = vec[i];
		Vec3f bestCircle;
		Point2f p = Point2f(originalMat.cols / 2.0, originalMat.rows / 2.0);
//		cd.detectBest(originalMat, Point2f(originalMat.cols / 2.0, originalMat.rows / 2.0), bestCircle, nullptr);
#ifdef DEBUG_VIEW_INTERNAL_MAT
		Mat img1 = vec.front().clone();
		Mat img2 = vec.front().clone();
		vector<Mat> vecs;
		vecs.push_back(originalMat);
		vecs.push_back(originalMat);
		vecs.push_back(originalMat);
		Mat rr;
		merge(vecs, rr);

		circle(rr, Point2f(bestCircle[0], bestCircle[1]), bestCircle[2], Scalar(255, 0, 0), 2);
		vector<Mat> imgVec;
		resizeVecMat(vec, imgVec);
		vector<Mat> vecss;
		vecss.push_back(originalMat);
		vecss.push_back(originalMat);
		vecss.push_back(originalMat);
		Mat rrr;
		merge(vecss, rrr);

	//	circle(rrr, Point2f(bestCircle[0] / COLS_SCALE, bestCircle[1] / COLS_SCALE), rInner, Scalar(255, 0, 0), 1);
#endif // DEBUG_VIEW_INTERNAL_MAT
		
		//bob edit 
// 		if (bestCircle[2] > 0.00000001)
// 		{
// 			float startX = bestCircle[0] - MAX_CIRCLE_RADII;//找最大圆外径，固定直径
// 			float startY = bestCircle[1] - MAX_CIRCLE_RADII;
// 			Rect rect(startX, startY, MAX_CIRCLE_RADII * 2, MAX_CIRCLE_RADII * 2);
// 			Mat origianlCenterMat;
// 			originalMat(rect).copyTo(origianlCenterMat);
// 			centerMatVec.push_back(origianlCenterMat);
// 			tmpVec.push_back(vec[i]);
// 		}
// 		else
// 		{
// 			int a = 0;
// 		}

		float startX = originalMat.cols / 2.0 - MAX_CIRCLE_RADII;//找最大圆外径，固定直径
		float startY = originalMat.rows / 2.0 - MAX_CIRCLE_RADII;
		Rect rect(startX, startY, MAX_CIRCLE_RADII * 2, MAX_CIRCLE_RADII * 2);
		Mat origianlCenterMat;
		originalMat(rect).copyTo(origianlCenterMat);
		centerMatVec.push_back(origianlCenterMat);
		tmpVec.push_back(vec[i]);

		/*bob end edit*/

// 		if (bestCircle[2] > 0.00000001)
// 		{
// 			float startX = bestCircle[0] - bestCircle[2];
// 			float startY = bestCircle[1] - bestCircle[2];
// 			Rect rect(startX, startY, bestCircle[2]*2, bestCircle[2]*2);
// 			Mat origianlCenterMat;
// 			originalMat(rect).copyTo(origianlCenterMat);
// 			centerMatVec.push_back(origianlCenterMat);
// 		}
// 		else
// 		{
// 
// 		}
	}
	vector<Mat> scaledCenterVec;
	resizeVecMat(centerMatVec, scaledCenterVec);
	if (scaledCenterVec.size() <= 0)
		return;
	if (tmpVec.size() <= 0)
		return;

	vector<Mat> imgVec;
	resizeVecMat(tmpVec, imgVec);
    mAlignBaseImg = imgVec.front();
	mInSideBaseImg = scaledCenterVec.front();
    genMask();
	//Mat baseTempl = imgVec.front();
	//float realR = rInner + 2;
	//float hf_width = sqrt(realR*realR / 2.0);
	//float startx = baseTempl.cols / 2 - hf_width;
	//float starty = baseTempl.rows / 2 - hf_width;
	//Rect rect(startx, starty, 2*hf_width, 2*hf_width);
	//innerTempl = baseTempl(rect);
	preProcessImage(mAlignBaseImg, mInSideBaseImg);//预处理 光照均衡
    Mat sumOutsideMat = Mat::zeros(mAlignBaseImg.size(), CV_32FC1);
	Mat sumInsideMat = Mat::zeros(mInSideBaseImg.size(), CV_32FC1);
	Mat minOutsideMat(mAlignBaseImg.size(), mAlignBaseImg.type());
	Mat minInsideMat(mInSideBaseImg.size(), mInSideBaseImg.type());
	minOutsideMat.setTo(FLT_MAX);
	minInsideMat.setTo(FLT_MAX);
    vector<Mat> rImgVec;
	vector<Mat> rInsideImgVec;
    vector<RotateMatchResult> rmrVec;
	vector<RotateMatchResult> rmrVecInside;
    vector<Mat> resizedImgVec;
	vector<Mat> resizedCenterVec;
	//vector<Mat> resizedWarp;
	vector<int> diametersVec;
    for (int i = 0; i < imgVec.size(); ++i)
    {
		diametersVec.push_back(imgVec[i].rows);
        Mat img = imgVec[i].clone();
        if (img.size() != mAlignBaseImg.size())
        {
            Mat resizedImg;
            resize(img, resizedImg, mAlignBaseImg.size());
			//resize(warpImage, warpImage, mAlignBaseImg.size());
            img = resizedImg;
        }

		Mat centerMat = scaledCenterVec[i];
		if (centerMat.size() != mInSideBaseImg.size())
		{
			Mat resizedCenterMat;
			resize(centerMat, resizedCenterMat, minInsideMat.size());
			centerMat = resizedCenterMat;
		}
        resizedImgVec.push_back(img);
		resizedCenterVec.push_back(centerMat);
		//resizedWarp.push_back(warpImage);
		//Mat insideImg = img.clone();
		preProcessImage(img, centerMat);
		m_parallelFlag = 0;
        RotateMatchResult rmr = rotateMatch(img);//旋转匹配  输出匹配完成的角度及图片
        rmrVec.push_back(rmr);
		m_parallelFlag = 1;
		RotateMatchResult rmrInside = rotateMatch(centerMat);
		rmrVecInside.push_back(rmrInside);

        Mat rImg = rmr.mBestRImg;
        //rImgVec.push_back(rImg);

		Mat rInsideImg = rmrInside.mBestRImg;
		rInsideImgVec.push_back(rInsideImg);
        //minMat = min(minMat, rImg);
		minOutsideMat = min(minOutsideMat, rImg);//生成权重图  去毛刺
        sumOutsideMat += rImg;

		minInsideMat = min(minInsideMat, rInsideImg);
		sumInsideMat += rInsideImg;

#ifdef DEBUG_VIEW_INTERNAL_MAT
        Mat viewRImg = rImg / 255.0;
        Mat viewRInsideImg = rInsideImg / 255.0;
		Mat viewImage = minOutsideMat / 255.0;
		Mat viewInside = minInsideMat / 255.0;
#endif
    }
	meanDiameter = mean(diametersVec)[0];

    Mat avgMat = sumOutsideMat / imgVec.size();
    mAlignBaseImg = avgMat;

	Mat avgInsideMat = sumInsideMat / imgVec.size();
	mInSideBaseImg = avgInsideMat;
#ifdef DEBUG_VIEW_INTERNAL_MAT
    Mat vAvgMat = avgMat / 255.0;
	Mat vAvgInsideMat = avgInsideMat / 255.0;
#endif
    //mWeightMat = minMat;
 	mWeightMat = minOutsideMat;
    mWeightMat /= 255.0;
	mInsideWeightMat = minInsideMat;
	mInsideWeightMat /= 255.0;
    /*luffy_base::luffy_imageProc::*/meanvarnorm(mWeightMat, mWeightMat,//对权重进行 归一化
        mTargetMeanVal, 150, m8uMaskImg);
    mWeightMat.setTo(0, mWeightMat < 10);
	Scalar meanScalar, stddevScalar;
	meanStdDev(mInsideWeightMat, meanScalar, stddevScalar, m8uInsideMaskImg);
	mInsideWeightMat = mInsideWeightMat* (127 / meanScalar.val[0]);
	//luffy_base::luffy_imageProc::meanvarnorm(mInsideWeightMat, mInsideWeightMat,
	//	mTargetMeanVal, 50, m8uInsideMaskImg);
	mInsideWeightMat.setTo(0, mInsideWeightMat < 0);

    {
        if(mRepeatNum <= 0) 
        {
            mRepeatNum = computeRepeatNum();
        }
        selfRotateMin(mWeightMat, mWeightMat, mRepeatNum);
    }

#ifdef DEBUG_VIEW_INTERNAL_MAT
    Mat vWeightMat = mWeightMat / 255.0;
    Mat vBaseMat = mAlignBaseImg / 255.0;
   // Mat vMinMat = minMat / 255.0;
	Mat vMinMat = minOutsideMat / 255.0;

	Mat vInsideWeight = mInsideWeightMat / 255.0;
	Mat vInsidebaseMat = mInSideBaseImg / 255.0;
	Mat vMinInsideMat = minInsideMat / 255.0;

#endif

    sumOutsideMat.setTo(0);
	sumInsideMat.setTo(0);
    for (int i = 0; i < rmrVec.size(); ++i)
    {
        double bestAngle = rmrVec[i].mBestAngle;
        Mat img = resizedImgVec[i];
        Mat rotatedImg = rotateImage(img, Point2f(img.cols/2.0, img.rows/2.0), 
            bestAngle);

		preProcessImage(rotatedImg, m8uMaskImg, mWeightMat, mTargetMeanVal,
			mTargetStddevVal, mHighlightsThreshold);

		double bestInsidePart = rmrVecInside[i].mBestAngle;
		//Mat i_Mat = resizedImgVec[i];
		Mat centerMat = resizedCenterVec[i];
		Mat rotatedInsideMat = rotateImage(centerMat, Point2f(centerMat.cols / 2.0, centerMat.rows / 2.0), bestInsidePart);
     
		preProcessImage(rotatedInsideMat, m8uInsideMaskImg, mInsideWeightMat, mTargetMeanVal,
			mTargetStddevVal, mHighlightsThreshold);

        sumOutsideMat += rotatedImg;
		//rotatedInsideMat.setTo(0, rotatedInsideMat < 0);
		sumInsideMat += rotatedInsideMat;
    }
    mCompareBaseImg = sumOutsideMat / resizedImgVec.size();//得到最终匹配图
	mInsideCompareBaseImg = sumInsideMat / resizedImgVec.size();
#ifdef DEBUG_VIEW_INTERNAL_MAT
    Mat vCompareBaseImg = mCompareBaseImg / 255.0;
    vWeightMat = mWeightMat / 255.0;
	Mat vcompareInsideBaseImg = mInsideCompareBaseImg / 255.0;
	vInsideWeight = mInsideWeightMat / 255.0;
	//cv::imwrite("F:\\temp\\insideWeight.png", mInsideWeightMat);
	//cv::imwrite("F:\\temp\\insideBase.png", mInSideBaseImg);
	//cv::imwrite("F:\\temp\\insideCompareBase.png", mInsideCompareBaseImg);
#endif
   
	trueSampleWeightRecon(resizedImgVec, resizedCenterVec, rmrVec, rmrVecInside);
	mWeightMat = falseReConMat.clone();
	mInsideWeightMat = insideWeightRecon.clone();
}

void ImageCompareModel::trueSampleWeightRecon(const vector<Mat>& resizedVec,
	const vector<Mat>& resizedCenterVec,
	vector<RotateMatchResult> rmrVec,
	vector<RotateMatchResult> rmrVecInside)
{
	falseReConMat = mWeightMat.clone();
	insideWeightRecon = mInsideWeightMat.clone();
	for (int i = 0; i < resizedVec.size(); i++)
	{
		const Mat &img = resizedVec[i];
		double bestAngle = rmrVec[i].mBestAngle;
		//preProcessImage(processedImg, insideMatchImg);
		Mat rotatedImg = rotateImage(img, Point2f(img.cols / 2.0, img.rows / 2.0),
			bestAngle);

		Mat hightlightsMask = rotatedImg < mHighlightsThreshold;
		converToType(hightlightsMask, CV_32FC1);
		hightlightsMask /= 255.0;

		Mat unifiedMask = m32fMaskImg.mul(hightlightsMask).mul(mWeightMat);

		preProcessImage(rotatedImg, m8uMaskImg, mWeightMat, mTargetMeanVal,
			mTargetStddevVal, mHighlightsThreshold);

		rotatedImg.setTo(0, rotatedImg < 0);
		converToType(rotatedImg, CV_32FC1);
		normSectors_tarImg(rotatedImg, unifiedMask, imgCen(rotatedImg), 1, mCompareBaseImg);

#ifdef DEBUG_VIEW_INTERNAL_MAT
		Mat vRotatedImg1 = rotatedImg / 255.0;
#endif
		rotatedImg.setTo(0, rotatedImg < 10);

		////// FOR INSIDE CIRCLE
		double insideBestAngle = rmrVecInside[i].mBestAngle;
		const Mat & centerMat = resizedCenterVec[i];
		Mat rotatedInsideImg = rotateImage(centerMat, Point2f(centerMat.cols / 2.0, centerMat.rows / 2.0),
			insideBestAngle);

		Mat highLightInsideMask = rotatedInsideImg < mHighlightsThreshold;
		converToType(highLightInsideMask, CV_32FC1);
		highLightInsideMask /= 255.0;
		Mat unifiedInsideMask = m32fInsideMaskImg.mul(highLightInsideMask).mul(mInsideWeightMat);
		preProcessImage(rotatedInsideImg, m8uInsideMaskImg, mInsideWeightMat, mTargetMeanVal,
			mTargetStddevVal, mHighlightsThreshold);
		Mat vcamInside = rotatedInsideImg / 255.0;
		rotatedInsideImg.setTo(0, rotatedInsideImg < 0);
		//mInsideCompareBaseImg.setTo(0, mInsideCompareBaseImg < 0);
		normSectors_tarImg(rotatedInsideImg, unifiedInsideMask, imgCen(rotatedInsideImg), 1, mInsideCompareBaseImg);
		trueWeightRecon(rotatedImg, mCompareBaseImg, rotatedInsideImg, mInsideCompareBaseImg);
	}
}

void ImageCompareModel::trueWeightRecon(const Mat& rImg, const Mat& baseImg,
	const Mat& insideRimg, const Mat& insideBaseImg)
{
	Mat dMat = abs(rImg - baseImg);
#ifdef DEBUG_VIEW_INTERNAL_MAT
	Mat tt = dMat / 255.0;
	Mat m1 = rImg / 255.0;
	Mat b1 = baseImg / 255.0;
#endif // DEBUG_VIEW_INTERNAL_MAT

	//Mat mData = dMat.mul(m32fMaskImg).mul(mWeightMat);
	Mat mData;
	dMat.copyTo(mData, dMat > 1000);
	
	double outterVal = sum(mData).val[0];
	if (outterVal != 0)
	{
		mData = minMaxNorm(mData, 0, 255.0);
	}


	Mat i_dMat = abs(insideRimg - insideBaseImg);
#ifdef DEBUG_VIEW_INTERNAL_MAT
	Mat i_tt = i_dMat / 255.0;
	Mat i_m1 = insideRimg / 255.0;
	Mat i_b1 = insideBaseImg / 255.0;
	Mat i_maskR = i_m1.mul(m32fInsideMaskImg);
	Mat i_masB = i_b1.mul(m32fInsideMaskImg);
	
#endif // DEBUG_VIEW_INTERNAL_MAT
	Mat i_mData;
	i_mData.setTo(0);
	//Mat test = i_dMat / 255;
	//Mat imgtest = i_dMat.mul(m32fInsideMaskImg) / 255;
	Mat innerDmat = i_dMat.mul(m32fInsideMaskImg).mul(mInsideWeightMat);
	innerDmat = minMaxNorm(innerDmat, 0, 255.0);

	weightMapping(mData, 255.0, innerDmat, 255.0);
}

void ImageCompareModel::weightMapping(const Mat& mData, double maxVal, const Mat& i_mData, double i_maxVal)
{
	for (int i = 0; i < mData.rows; i++)
	{
		float* pData = (float*)mData.row(i).data;
		float*pWeight = (float*)falseReConMat.row(i).data;
		
		for (int j = 0; j < mData.cols; j++)
		{
			double pixVal = pData[j];
			
			if (pixVal != 0)
			{
				double affineCoeff = descendFunction(pixVal, maxVal);
				pWeight[j] *= affineCoeff;
			}
			
		


		}
	}


	for (int i = 0; i < i_mData.rows; i++)
	{
		float* i_pData = (float*)i_mData.row(i).data;
		float*i_pWeight = (float*)insideWeightRecon.row(i).data;

		for (int j = 0; j < i_mData.cols; j++)
		{
			double i_pixVal = i_pData[j];

			if (i_pixVal != 0)
			{
				double affineCoeff = descendFunction(i_pixVal, i_maxVal);
				i_pWeight[j] *= affineCoeff;
			}
		}
	}
}
double ImageCompareModel::descendFunction(double pixVal, double maxVal)
{
	return (-1.0 / maxVal)*pixVal + 1.0;
	//return -1 * pow(pixVal, 2) + 1;
}


void ImageCompareModel::calculateAllParams(const vector<Mat>& imgVec)
{
	mDisThre = DBL_MAX;
	Mat disMat(1, imgVec.size(), CV_64FC1);
	for (int i = 0; i < imgVec.size(); ++i)
	{
		const Mat& img = imgVec[i];
		double dis = compare(img, NULL, 1, false, 0);
		disMat.at<double>(i) = dis;
		//disMat.copyTo(mDisMat);
	}

	if (disMat.cols == 1)
	{
		mTrueSampleDisStddev = 0;
		mTrueSampleDisMean = disMat.at<double>(0);
	}
	else
	{
		Scalar m, s;
		meanStdDev(disMat, m, s);
		mTrueSampleDisStddev = s.val[0];
		mTrueSampleDisMean = m.val[0];
	}
	disMat.copyTo(mDisMat);
	minMaxIdx(disMat, &mTrueSampleDisMin, &mTrueSampleDisMax);
}

void ImageCompareModel::computeDisThre(const vector<Mat>& imgVec, double* pMinDis, double md_diameter, double md_height)
{
    if (imgVec.empty())
    {
        return;
    }
    mDisThre = DBL_MAX;
    vector<double> disVec;
	std::map<double, cv::Mat> imgs;
    for_each(imgVec.begin(), imgVec.end(), [&](Mat img)
    {
        double dis = compare(img, NULL, 3, false, 1, md_diameter, md_height);
        disVec.push_back(dis);
		imgs[dis] = img;
    });
    double minDis = *(min_element(disVec.begin(), disVec.end()));
    setFalseSampleMinDis(minDis);

    if (pMinDis)
    {
        *pMinDis = minDis;
    }
    if (minDis > mTrueSampleDisMax)
    {
        mDisThre = mTrueSampleDisMax*0.3 + minDis*0.7;
		//mDisThre = mTrueSampleDisMax*0.15 + minDis*0.85 + mTrueSampleDisStddev;
		//mDisThre = minDis;
        if (mDisThre > mTrueSampleDisMax*1.8)
        {
            mDisThre = mTrueSampleDisMax*1.8;
        }
    }
    else
    {
        mDisThre = DBL_MAX;
        std::cout << "max distance in training > min distance in testing" << std::endl;
    }
}

void ImageCompareModel::genUniformSepIdx(int num, int startIdx, int endIdx, vector<int>& cenVec)
{
    float step = (endIdx - startIdx) / (num);
    for (int i = 0; i < num; i++)
    {
        cenVec.push_back(floorf(step*i + startIdx));
    }
}

void ImageCompareModel::genAngleRanges(vector<int> cenVec, vector<Range>& rangeVec, int localRange)
{
    rangeVec.clear();
    for_each(cenVec.begin(), cenVec.end(), [&](int cen)
    {
        Range r;
        r.start = cen - localRange;
        r.end = cen + localRange + 1;
        rangeVec.push_back(r);
    });
}

void ImageCompareModel::genCandidateAngleRanges(vector<float> disVec,
    float angleStep, vector<Range>& rangeVec)
{
    auto minDisIter = min_element(disVec.begin(), disVec.end());
    int startIdx = minDisIter - disVec.begin();

    rotate(disVec.begin(), minDisIter, disVec.end());

    Mat disVecMat(1, disVec.size(), CV_32FC1, &(disVec[0]));
    Mat normDisVecMat;
    /*luffy_base::luffy_imageProc::*/meanvarnorm(disVecMat, normDisVecMat, 0, 1.0);

    set<unsigned int> canNums;
    genCandidateRepeatNums(canNums);
    vector<int> cenVec;
    int repeatNum = recognizeLowerExtremes(normDisVecMat, canNums, 5, &cenVec);
#ifdef _TEST
    if (repeatNum <= 0)
    {
        std::cout << "repeatNum < 0" << std::endl;
        //waitKey();
    }
#endif
    if (repeatNum < 0)
    {
        rangeVec.clear();
        return;
        cenVec.clear();
        cenVec.push_back(disVec.size() / 2);
        genAngleRanges(cenVec, rangeVec, disVec.size() / 2);
    }
    else
    {
        if (cenVec.size() >= 2 && diffAngleRaw(cenVec.front(), cenVec.back()) < 2)
        {
            cenVec.pop_back();
        }

        for_each(cenVec.begin(), cenVec.end(), [&](int& cen)
        {
            cen += startIdx;
            cen *= angleStep;
            cen = normAngle(cen);
        });
        for (auto i = cenVec.begin(); i != cenVec.end(); ++i)
        {
            if (*i < startIdx*angleStep)
            {
                rotate(cenVec.begin(), i, cenVec.end());
                break;
            }
        }
#ifdef _TEST
        for (int i = 0; i < cenVec.size() - 1; ++i)
        {
            float curVal = cenVec[i];
            float nxtVal = cenVec[i + 1];
            if (nxtVal <= curVal)
            {
                std::cout << "assert nxtVal <= curVal failed" << std::endl;
                waitKey();
            }
        }
#endif
        genAngleRanges(cenVec, rangeVec, 10);
    }
    
}

void ImageCompareModel::genCandidateAngleRanges(const Mat& disMat,
    float angleStep, vector<Range>& rangeVec, float rangeScale /*= 1*/)
{
    for (int i = 0; i < disMat.rows; ++i)
    {
        Mat disVec = disMat.row(i);
        double minVal, maxVal;
        Point minPt, maxPt;
        minMaxLoc(disVec, &minVal, &maxVal, &minPt, &maxPt);
        Range &r = rangeVec[i];
        float cenAngle = r.start + angleStep*minPt.x;
        float angleRangeWidth = r.end - r.start;

        r.start = floorf(cenAngle - angleRangeWidth * 0.5 * rangeScale);
        r.end = floorf(cenAngle + angleRangeWidth * 0.5 * rangeScale);
    }
}

void ImageCompareModel::selfRotateMin(const Mat& img, Mat& weightMat, int repeatNum)
{
    Point2f cen(img.cols / 2.0, img.rows / 2.0);
	float angleStep = 360.0 / repeatNum;
	Mat dSumMat = Mat::ones(img.size(), img.type()) * 255;
	//Mat dSumMat = Mat::ones(img.size(), img.type());
    for (int i = 1; i < repeatNum; ++i)
    {
        Mat rotatedImg = rotateImage(img, cen, i*angleStep);
        Mat dilatedImg;
        dilate(rotatedImg, dilatedImg, Mat::ones(3, 3, CV_32FC1));
        dSumMat = min(dSumMat, dilatedImg);
    }
    weightMat = dSumMat;
}

double ImageCompareModel::genMatchValue(const Mat& dMat) const
{
    double v = norm(dMat, NORM_L2);
    
    double ret = scaleMatchValue(v);

    return ret;
}

double ImageCompareModel::genMatchValue(const Mat& rImg, const Mat& baseImg,
	const Mat& maskImg, int flag, int diameter, double md_diameter, double md_height) const
{
    Mat dMat = rImg - baseImg;
	//Mat testMask = maskImg.mul(m32fMaskImg);
    dMat = abs(dMat.mul(maskImg));
	//dMat = abs(dMat.mul(testMask));
#ifdef DEBUG_VIEW_INTERNAL_MAT
	//Mat vRImg = rImg.mul(m32fMaskImg) / 255.0;
    Mat vDMat0 = abs(dMat) / 255.0;
	//Mat vBase = baseImg.mul(m32fMaskImg) / 255.0;
    Mat vDMat = abs(dMat) / 255.0 / 255.0;

	//Mat vinsideImg = rImg.mul(m32fInsideMaskImg) / 255.0;
	//Mat vinsideBase = baseImg.mul(m32fInsideMaskImg) / 255.0;

   // Mat vMask = maskImg / 255.0;
	//Mat vMask = testMask / 255.0;
#endif
	Mat minmaxScaleMat;
	Mat mMask;
	if (m_parallelFlag != 1)
	{
		minmaxScaleMat = minMaxNorm(dMat, 0, 255, m8uMaskImg);
		converToType(minmaxScaleMat, CV_8UC1);
		mMask = lowerMajorityMask(minmaxScaleMat, m8uMaskImg, 0.98);
		printLog("myLog.txt", "finish outside mMask Part");
	}
	else
	{
		minmaxScaleMat = minMaxNorm(dMat, 0, 255, m8uInsideMaskImg);
		converToType(minmaxScaleMat, CV_8UC1);
		mMask = lowerMajorityMask(minmaxScaleMat, m8uInsideMaskImg, 0.97);
		printLog("myLog.txt", "finish inside mMask Part");
	}
 
    converToType(mMask, CV_32FC1);
    mMask /= 255.0;

    dMat = dMat.mul(mMask);

#ifdef DEBUG_VIEW_INTERNAL_MAT
    Mat vDMat1 = abs(dMat) / 255.0 / 255.0;
#endif
	Mat vii = mMask.mul(maskImg);
	Mat ii = mMask.mul(maskImg) / 255.0;
    double s = sum(mMask.mul(maskImg)).val[0];
    double ret = genMatchValue(dMat) / s;

	if (flag == 1)
	{
		double matchedVal = ret;
		ret = penltyCoeff(matchedVal, meanDiameter, diameter, md_diameter, md_height);//做惩罚
		printf("the matched value %f", ret);
	}
	printLog("myLog.txt", "finish detect, return val");
        return ret;
}

double ImageCompareModel::scaleMatchValue(double val) const
{
    return val*mMatchValScale;
}


ImageCompareModel* ImageCompareModel::scale(float s)
{
    Mat baseImage;
    resize(mAlignBaseImg, baseImage, Size(), s, s, INTER_CUBIC);
    Mat weightMat;
    resize(mWeightMat, weightMat, Size(), s, s, INTER_CUBIC);

    ImageCompareModel* pRet = new ImageCompareModel;
    *pRet = *this;
    pRet->setBaseImg(baseImage);
    pRet->setWeightMat(weightMat);

    //pRet->genMask();
	pRet->genOutterMask();

//     Mat mask8uImg;
//     resize(m8uMaskImg, mask8uImg, Size(), s, s, INTER_NEAREST);
//     Mat mask32fImg;
//     resize(m32fMaskImg, mask32fImg, Size(), s, s, INTER_NEAREST);
// 
//     pRet->set8uMaskImg(mask8uImg);
//     pRet->set32fMaskImg(mask32fImg);

    return pRet;
}

ImageCompareModel::RotateMatchResult ImageCompareModel::rotateMatch(const Mat& img, int levelNum /*= 1*/, float angleStep /*= 1.0*/, 
    float startAngle /*= 0*/, float endAngle /*= 360*/) const
{
#ifdef _TEST
    static int idx = 0;
    std::cout << idx << " ";
#endif
    RotateData* pData = new RotateData;
    vector<Range> angleRangeVec;
    if (levelNum > 1)
    {
        MultiScaleImage msi;
        msi.setLevelNum(3);
        msi.setBaseLevel(img);
        msi.genMultiScale();
        ImageCompareData imgCmpData(&msi);
        imgCmpData.setStartAngle(startAngle);
        imgCmpData.setEndAngle(endAngle);

        mpMultiScaleModel->proc(&imgCmpData);

        *pData = *imgCmpData.getRotateData();
    }
    else
    {
        rotateMatchData(img, pData, angleStep, startAngle, endAngle);
    }

#ifdef _TEST
    if(1)
    {
        //if (idx == 15)
        {
            RotateData* pTestData = new RotateData;
            rotateMatchData(img, pTestData, angleStep, startAngle, endAngle);

// no need to code for plotting in visual studio later than 2017,
// install the ArrayPlotter extension to see the data distribution
#if (_MSC_VER < 1910) // vs2017
            stringstream ss;
            ss << "multi_scale_comp_plot";
            CvPlot::clear(ss.str());
            CvPlot::plot<float>(ss.str(), &(pTestData->mDisValVec[0]),
                pTestData->mDisValVec.size());
#endif

            float baseAngle = pTestData->bestAngle();
            float curAngle = pData->bestAngle();
            std::cout << baseAngle << " " << curAngle << "  " << baseAngle - curAngle << std::endl;
            delete pTestData;
            if (abs(diffAngleRaw(baseAngle, curAngle)) > 1)
            {
                waitKey();
            }
        }
        idx++;
    }
#endif

    RotateMatchResult rmr;
    
    if (pData->mDisValVec.empty())
    {
        delete pData;
        return rmr;
    }
    else
    {
        size_t bestIndex = min_element(pData->mDisValVec.begin(), pData->mDisValVec.end()) - pData->mDisValVec.begin();

        Mat ret = pData->mRImgVec[bestIndex];
        rmr.mBestRImg = ret;
        rmr.mBestAngle = pData->bestAngle();

        delete pData;

        return rmr;
    }    
}

void ImageCompareModel::rotateMatchData(const Mat& _img, RotateData* pData,
    float angleStep /*= 1.0*/, float startAngle /*= 0*/, float endAngle /*= 360*/) const
{
    //Mat img = _img.clone();
	if (_img.empty())
		return;
	Point2f center(_img.cols / 2.0, _img.rows / 2.0);
    int nNum = (endAngle - startAngle) / angleStep;
    RotateData& data = *pData;
    data.init(_img.clone(), center, angleStep, nNum);
    data.mStartAngle = startAngle;
    data.mEndAngle = endAngle;

    parallel_for_(Range(0, nNum), ImageCompareModelInvoker(this, pData));
}

void ImageCompareModel::rotateMatchData(const Mat& img, RotateData* pData,
    float angleStep /*= 1.0*/, const vector<Range>& angleRangeVec) const
{
    float minDis = FLT_MAX;
    RotateData bestData;
    for_each(angleRangeVec.begin(), angleRangeVec.end(), [&](const Range& r)
    {
        float startAngle = r.start;
        float endAngle = r.end;
        rotateMatchData(img, pData, angleStep, startAngle, endAngle);
        float localMinDis = *min_element(pData->mDisValVec.begin(), pData->mDisValVec.end());
        if (localMinDis < minDis)
        {
            minDis = localMinDis;
            bestData = *pData;
        }
    });
    *pData = bestData;
}

void ImageCompareModel::rotateMatchData(const Mat& img, RotateData* pData, 
    const vector<Range>& angleRangeVec, float angleStep, Mat& disMat) const
{
    if (angleRangeVec.empty())
    {
        return;
    }
    disMat.create(angleRangeVec.size(), angleRangeVec.front().size(), CV_32FC1);
    pData->mRImgVec.resize(angleRangeVec.size());
    pData->mDisValVec.resize(angleRangeVec.size(), FLT_MAX);
    double minDis = DBL_MAX;
    for (int i = 0; i < angleRangeVec.size(); ++i)
    {
        Range r = angleRangeVec[i];
        Mat disVec = disMat.row(i);
        RotateData rotateData;
        float startAngle = r.start;
        float endAngle = r.end;
        rotateMatchData(img, &rotateData, angleStep, startAngle, endAngle);
        std::copy(rotateData.mDisValVec.begin(), rotateData.mDisValVec.end(),
            (float*)disVec.data);
        double localMinDis, localMaxDis;
        Point minPt, maxPt;
        minMaxLoc(disVec, &localMinDis, &localMaxDis, &minPt, &maxPt);
        if (localMinDis < minDis)
        {
            minDis = localMinDis;
            *pData = rotateData;
        }
    }
}

void ImageCompareModel::genCandidateRepeatNums(set<unsigned int>& canNums)
{
    canNums.clear();

    for (int i = 4; i < 25; ++i)
    {
        canNums.insert(i);
    }
}

bool isValidExtremas(const Mat& vec, const vector<int>&  lessExtremaIdxVec,
    const vector<int>& moreExtremaIdxVec)
{
    float* pVec = (float*)vec.data;
    auto lessIdxIter = lessExtremaIdxVec.begin();
    auto moreIdxIter = moreExtremaIdxVec.begin();
    while (lessIdxIter != lessExtremaIdxVec.end())
    {
        auto leftLessIdxIter = lessIdxIter;
        auto leftMoreIdxIter = moreIdxIter;
        auto rightLessIdxIter = lessIdxIter;
        rightLessIdxIter++;
        if (rightLessIdxIter == lessExtremaIdxVec.end())
        {
            break;
        }
        auto rightMoreIdxIter = leftMoreIdxIter;
        while (*rightLessIdxIter > *rightMoreIdxIter)
        {
            rightMoreIdxIter++;
        }

        float leftLessExtrema = pVec[*leftLessIdxIter];
        float rightLessExtrema = pVec[*rightLessIdxIter];
        float localMaxVal = *std::max_element(pVec + *leftLessIdxIter, 
            pVec + *rightLessIdxIter + 1);
        float valRange = localMaxVal - min(leftLessExtrema, rightLessExtrema);
        float valTor = valRange*0.05;
        float slope = (rightLessExtrema - leftLessExtrema) / (*rightLessIdxIter - *leftLessIdxIter);

        leftMoreIdxIter++;
        while (leftMoreIdxIter != rightMoreIdxIter)
        {
            float interVal = leftLessExtrema + slope*(*leftMoreIdxIter - *leftLessIdxIter);
            float curExtremaVal = pVec[*leftMoreIdxIter];
            if (curExtremaVal < interVal + valTor)
            {
                return false;
            }
            leftMoreIdxIter++;
        }
        
        lessIdxIter++;
        moreIdxIter = rightMoreIdxIter;
    }

    return true;
}


int ImageCompareModel::recognizeLowerExtremes(const Mat& vec,
    set<unsigned int> canNums, int extremeRefineRange /*= 5*/, vector<int> *pExtremaIdxVec /*= 0*/)
{
    unsigned int size = vec.cols;
    set<unsigned int> validNs;
    float* pVec = (float*)vec.data;

    double vecMinVal, vecMaxVal;
    minMaxIdx(vec, &vecMinVal, &vecMaxVal);

// no need to code for plotting in visual studio later than 2017,
// install the ArrayPlotter extension to see the data distribution
#if (_MSC_VER < 1910) // vs2017
#ifdef DEBUG_VIEW_PLOT
    CvPlot::clear("recognizeLowerExtreams");
    CvPlot::plot<float>("recognizeLowerExtreams", (float*)vec.data, vec.cols);
#endif
#endif

    map<int, vector<int> > repeatNumLocalExtremaIdxMap;
    map<int, Mat> repeatNumLocalExtremaValMap;
    for (auto iter = canNums.rbegin(); iter != canNums.rend(); ++iter)
    {
        unsigned int n = *iter;

        Mat extremaValVec;
        vector<int> extremaIdxVec;
        uniformLocalMinExtremas(vec, extremaValVec, extremaIdxVec, 
            n, extremeRefineRange);

        float* pExtremaVecVec = (float*)extremaValVec.data;

        float curVal, nxtVal;
        bool isValid = true;

        Mat preLocalVec;
        
        for (unsigned int i = 0; i < extremaIdxVec.size() - 1; ++i)
        {
            unsigned int curIdx = extremaIdxVec[i];
            unsigned int nxtIdx = extremaIdxVec[i + 1];
            curVal = pVec[curIdx];
            nxtVal = pVec[nxtIdx];

            // no smaller value between two neighbor extremes
            if (anyInRange(pVec + curIdx + 1, pVec + nxtIdx, 
                -FLT_MAX, min(curVal, nxtVal)))
            {
                isValid = false;
                break;
            }

            if (!preLocalVec.empty())
            {
                Mat curLocalVec = vec.colRange(curIdx, nxtIdx + 1);
                if (curLocalVec.size() != preLocalVec.size())
                {
                    resize(curLocalVec, curLocalVec, preLocalVec.size());
                }
                float sim = compareHist(curLocalVec, preLocalVec, CV_COMP_CORREL);
                if (sim < 0)
                {
                    isValid = false;
                    break;
                }
            }

            preLocalVec = vec.colRange(curIdx, nxtIdx + 1);
        }
        if (!isValid)
        {
            continue;
        }
        
        set<int> nToBeErased;
        for_each(validNs.begin(), validNs.end(), [&](int preN)
        {
            if (preN % n != 0 || !isValid)
            {
                return;
            }
            vector<int> preExtremaIdxVec = repeatNumLocalExtremaIdxMap[preN];
            if (isValidExtremas(vec, extremaIdxVec, preExtremaIdxVec))
            {
                nToBeErased.insert(preN);
            }
            else
            {
                isValid = false;
            }
        });
        if (!isValid)
        {
            continue;
        }
        if (nToBeErased.size() == validNs.size())
        {
            validNs.clear();
        }
        else
        {
            for_each(nToBeErased.begin(), nToBeErased.end(), [&](int toBeErasedN)
            {
                validNs.erase(toBeErasedN);
            });
        }

        validNs.insert(n);
        repeatNumLocalExtremaIdxMap[n] = extremaIdxVec;
        repeatNumLocalExtremaValMap[n] = extremaValVec;
        if (pExtremaIdxVec)
        {
            *pExtremaIdxVec = extremaIdxVec;
        }
    }
    if (validNs.size() == 0)
    {
        return -1;
    }
    if (validNs.size() > 1)
    {
        return -2;
    }

    return *(validNs.begin());
}

int ImageCompareModel::recognizeRepeatedLocalExtremas(const Mat& vec,
    const set<unsigned int>& canNums, int refineRange /*= 5*/,
    vector<int> *pIdxVec /*= 0*/)
{
    assert(vec.type() == CV_32FC1);
    float* pVecData = (float*)vec.data;

    for_each(canNums.begin(), canNums.end(), [&](int n)
    {
        Mat localMinExtremaValVec;
        vector<int> localMinExtremaIdxVec;
        uniformLocalMinExtremas(vec, localMinExtremaValVec, localMinExtremaIdxVec,
            n, refineRange);

        float* pExtremaVecVec = (float*)localMinExtremaValVec.data;

        // get locally top 10% values

    });

    return -1;
}

int ImageCompareModel::computeRepeatNum(const Mat& _img)
{
    Mat img;
    cv::GaussianBlur(_img, img, Size(3, 3), 5.0);
    vector<float> vec;
    selfRotationSimilarityFeature(img, vec, Point2f(img.cols / 2.0, img.rows / 2.0));

#ifdef DEBUG_VIEW_INTERNAL_MAT
    Mat viewImg = img / 255.0 /255.0;
    Mat view_Img = _img / 255.0 /255.0;
#endif

    vector<float> normVec(vec);
    Mat vecMat(1, vec.size(), CV_32FC1, &(vec[0]));
    Mat normVecMat(1, normVec.size(), CV_32FC1, &(normVec[0])); 
    meanvarnorm(vecMat, normVecMat, 0, 1.0);

#ifdef DEBUG_VIEW_PLOT
    Mat dftVec;
    dft(normVec, dftVec);
    dftVec = abs(dftVec);

    Mat dVecMat = normVecMat.clone();
    genSobelImage(dVecMat);
    Mat dDftVec;
    dft(dVecMat, dDftVec);

    Mat ddVecMat = dVecMat.clone();
    genSobelImage(ddVecMat);
    Mat ddDftVec;
    dft(ddVecMat, ddDftVec);

// no need to code for plotting in visual studio later than 2017,
// install the ArrayPlotter extension to see the data distribution
#if (_MSC_VER < 1910) // vs2017
    CvPlot::plot<float>("normVec", (float*)&(normVec[0]), normVec.size());
    CvPlot::plot<float>("dVec", (float*)dVecMat.data, dVecMat.cols);
    CvPlot::plot<float>("ddVec", (float*)ddVecMat.data, ddVecMat.cols);
    CvPlot::plot<float>("ddft", (float*)dDftVec.data, 50);
    CvPlot::plot<float>("dft", (float*)dftVec.data, 50);
    CvPlot::plot<float>("dddft", (float*)ddDftVec.data, 50);
#endif

    waitKey();
#endif

    set<unsigned int> canNums;
    genCandidateRepeatNums(canNums);
    return recognizeLowerExtremes(normVecMat, canNums);
}

int ImageCompareModel::computeRepeatNum()
{
    if (mWeightMat.empty() || mAlignBaseImg.empty())
    {
        return 0;
    }

    Mat img = mAlignBaseImg.mul(mWeightMat);

#ifdef DEBUG_VIEW_INTERNAL_MAT
    Mat viewImg = img / 255.0 / 255.0;
#endif

    return computeRepeatNum(img);
}

void ImageCompareModel::initMultiScaleModel()
{
    if (mpMultiScaleModel)
    {
        delete mpMultiScaleModel;
    }
    mpMultiScaleModel = new MultiScaleImageCompareModel;
    mpMultiScaleModel->setLevelNum(3);
    mpMultiScaleModel->setBaseLevel(this);
    mpMultiScaleModel->genMultiScale();
}

void ImageCompareModelInvoker::operator()(const cv::Range& range) const
{
	int i0 = range.start;
	int i1 = range.end;
	assert(abs(i1 - i0) == 1);
	m_pModel->parallelDetect(i0, m_pData);
}

void ImageCompareModel::parallelDetect(int index, void *p) const
{
	RotateData *pData = (RotateData *)p;
	Mat t = getRotationMatrix2D(pData->mCenter, pData->angle(index), 1.0);
	Mat rImg;
	warpAffine(pData->mImgSrc, rImg, t, pData->mImgSrc.size());
    
	//    need add insideImage base image;

    // ....
	Mat imgRes;
	if (m_parallelFlag!=1)
	{
		if (rImg.size() != mAlignBaseImg.size())
		{
			resize(rImg, rImg, mAlignBaseImg.size());
		}
		imgRes = rImg - mAlignBaseImg;
		if (!m32fMaskImg.empty())
		{
			if (m32fMaskImg.size() != imgRes.size())
				resize(imgRes, imgRes, m32fMaskImg.size());
			imgRes = imgRes.mul(m32fMaskImg);
			if (!mWeightMat.empty())
			{
				if (mWeightMat.size() != imgRes.size())
					resize(imgRes, imgRes, mWeightMat.size());
				imgRes.mul(mWeightMat);
			}
		}
	}
	else
	{
		if (rImg.size() != mInSideBaseImg.size())
		{
			resize(rImg, rImg, mInSideBaseImg.size());
		}

		imgRes = rImg - mInSideBaseImg;
		if (!m32fInsideMaskImg.empty())
		{
			if (m32fInsideMaskImg.size() != imgRes.size())
				resize(imgRes, imgRes, m32fInsideMaskImg.size());
			imgRes = imgRes.mul(m32fInsideMaskImg);
			if (!mInsideWeightMat.empty())
			{
				if (mInsideWeightMat.size() != imgRes.size())
					resize(imgRes, imgRes, mInsideWeightMat.size());
				imgRes.mul(mInsideWeightMat);
			}
		}
	}
	
    
#ifdef DEBUG_VIEW_INTERNAL_MAT
    Mat vRImg, vBaseImg;
    vRImg = rImg / 255.0;
    vBaseImg = mAlignBaseImg / 255.0;
    Mat vImgRes = imgRes / 255.0;
    Mat vWeightMat = mWeightMat / 255.0;
	Mat viewInsideMat = mInSideBaseImg / 255.0;
#endif    
	double val = 0;
	if(!imgRes.empty())
		val = norm(imgRes);

#ifdef DEBUG_VIEW_INTERNAL_MAT
    vImgRes = imgRes / 255.0;
#endif
	pData->mDisValVec[index] = val;
	pData->mRImgVec[index] = rImg;
}
double ImageCompareModel::filterTrainImage(const Mat&img)
{
	if (img.empty())
	{
		return DBL_MAX;
	}
	mDisThre = DBL_MAX;
	double dis = compare(img, NULL, 3, false);
	/*if (dis < mTrueSampleDisMax)
	{
		dis = mTrueSampleDisMax;
	}
	double absDiff = abs(dis - mTrueSampleDisMax);*/
	return dis;
}
void ImageCompareModel::weightOptimization(const vector<Mat>& falseSamples)
{
	if (falseSamples.size() <= 0)
		return;
	reConMat = mWeightMat.clone();
	vector<Mat> falseVec;
	resizeVecMat(falseSamples, falseVec);
	for (int i = 0; i < falseVec.size(); i++)
	{
		Mat falseSample = falseVec[i].clone();
		preWeightReconstruction(falseSample, reConMat);
	}
	mWeightMat = reConMat;
}

void ImageCompareModel::preWeightReconstruction(Mat img, Mat &reConImg)
{
	if (mAlignBaseImg.size().height <= 0 || mAlignBaseImg.size().width <= 0)
	{
		//mAlignBaseImg = img.clone();
		return;
	}
	if (mAlignBaseImg.size() != img.size())
	{
		resize(img, img, mAlignBaseImg.size());
	}

	Mat matchImg = img.clone();
	preProcessImage(matchImg, Mat());

	if (!mpMultiScaleModel)
	{
		initMultiScaleModel();
	}
	RotateMatchResult rmr = rotateMatch(matchImg, 1);
	Mat rImg = rmr.mBestRImg;

#ifdef DEBUG_VIEW_INTERNAL_MAT
	Mat ttt = mAlignBaseImg / 255.0;
	Mat vRImg = rImg / 255.0;
#endif

	double bestAngle = rmr.mBestAngle;
	Mat cmpImg = rotateImage(img, Point2f(img.cols / 2.0, img.rows / 2.0),
		bestAngle);

	// remove highlights
	Mat hightlightsMask = cmpImg < mHighlightsThreshold;
	converToType(hightlightsMask, CV_32FC1);
	hightlightsMask /= 255.0;

	Mat unifiedMask = m32fMaskImg.mul(hightlightsMask).mul(mWeightMat);

	preProcessImage(cmpImg, m8uMaskImg, mWeightMat, mTargetMeanVal,
		mTargetStddevVal, mHighlightsThreshold);

	cmpImg.setTo(0, cmpImg < 0);
	converToType(cmpImg, CV_32FC1);
	normSectors_tarImg(cmpImg, unifiedMask, imgCen(cmpImg), 1,
		mCompareBaseImg);

#ifdef DEBUG_VIEW_INTERNAL_MAT
	Mat vRotatedImg0 = cmpImg / 255.0;
#endif

#ifdef DEBUG_VIEW_INTERNAL_MAT
	Mat vRotatedImg1 = mCompareBaseImg / 255.0;
#endif

	cmpImg.setTo(0, cmpImg < 10);

	weightReconstruction(cmpImg, mCompareBaseImg, unifiedMask, reConImg);
	
}

void ImageCompareModel::weightReconstruction(const Mat& rImg, const Mat& baseImg, const Mat& maskImg, Mat &reConImg)
{
	Mat dMat = abs(rImg - baseImg);
	Mat test = dMat / 255.0;
	Mat norImg = Mat::ones(dMat.size(), dMat.type()) * 255;
	dMat = min(dMat, norImg);
	Mat w(dMat.size(), dMat.type(), Scalar::all(0));
	dMat.copyTo(w, dMat < 0.5);
	w = minMaxNorm(w, 0, 1.0);
	for (int i = 0; i < reConImg.rows; i++)
	{
		float* pData = (float*)reConImg.row(i).data;
		float* pWeight = (float*)w.row(i).data;
		for (int j = 0; j < reConImg.cols; j++)
		{
			if (pWeight[j]!=0)
			{
				//pData[j] *= pWeight[j];
				pWeight[j] = -1 * pow(pWeight[j] - 1, 2) + 1;
				pData[j] *= pWeight[j];
			}
		}
	}
}
double ImageCompareModel::penltyCoeff(double matchedVal, int diameterMean, int targetDiameter, double modelDiamter, double modelHeight) const
{
	int diff = abs(diameterMean - targetDiameter);
	double modelDiameterDiff = abs(modelDiamter - realWidth);
	double modelHeightDiff = abs(modelHeight - realHeight);
	
	if (diff >=10)
	{
		return matchedVal*(diff / 100.0 + 1) * (modelHeightDiff / 100.0 + 1) * (modelDiameterDiff / 100.0 + 1);
	}
	else
	{
		return matchedVal;
	}
}

cv::Mat ImageCompareModel::genInsideMask(const Mat& img, Point2f center, float innerR /*= -1*/, float outterR /*= -1*/, int type /*= CV_32FC1*/)
{
	Mat mask(img.size(), CV_8UC1);
	mask.setTo(0);
	if (innerR == -1)
	{
		// default is 30
		innerR = img.rows*0.178;
		//innerR = img.rows;
	}
	if (outterR == -1)
	{
		// default is max radius - 10
		outterR = img.rows*0.425;
	}
	//outterR = r;
	/*Mat img1 = img.clone();

	img1.convertTo(img1, CV_8UC1);
	float r = allocateInnerRadius(img1);
	rInner = r;*/
	circle(mask, center, outterR, Scalar(255), -1);
	circle(mask, center, innerR, Scalar(0), -1);
	if (type != CV_8UC1)
	{
		converToType(mask, type);
		mask /= 255;
	}
	return mask;
}

float ImageCompareModel::allocateInnerRadius(cv::Mat subImage)
{

//	//// find global center
//	const int &roughRadius = subImage.cols *0.08;
//	const Point center(subImage.cols / 2.0, subImage.rows / 2.0);
//	Rect rect(center.x - roughRadius, center.y - roughRadius, 2 * roughRadius, 2 * roughRadius);
//	Mat centerMat = subImage(rect);
//	Mat test = subImage(rect).clone();
//	genSobelImage(centerMat);
//	centerMat.convertTo(centerMat, CV_8UC1);
//	Mat bMat = centerMat > 100;
//	//cv::threshold(centerMat, bMat, 0, 255, CV_THRESH_OTSU);
//	Mat mask(bMat.size(), CV_8UC1);
//	mask.setTo(0);
//	//circle(mask, center, roughRadius, Scalar(255), -1);
//	//cv::Mat bImage = subImage.mul(mask / 255) > 100;
//	Mat dilatedImgBin;
//	Mat erodeMat;
//	dilate(bMat, dilatedImgBin, Mat::ones(3, 3, CV_32FC1));
//	erode(dilatedImgBin, erodeMat, Mat::ones(3, 3, CV_32FC1));
//	closeOper(erodeMat, Mat::ones(1, 3, CV_32FC1));
//	Mat vMat = erodeMat.clone();
//	vector<vector<Point>> contours;
//	findContours(erodeMat, contours, RETR_EXTERNAL, CHAIN_APPROX_NONE);
//	for (int i = 0; i < contours.size(); i++) {
//		const vector<Point> &pt = contours.at(i);
//		if (pt.size() < 10) {
//			continue;
//		}
//		Rect rt = boundingRect(pt);
//		if (rt.width < 5 || rt.height < 5) {
//			continue;
//		}
//		drawContours(mask, contours, i, Scalar::all(255), -1);
//	}
//
//	using namespace luffy_base;
//	Mat hit; vector<Point> pts;
//	luffy_hit::firstHit4Circle(mask, hit, pts, Point(mask.cols / 2, mask.rows / 2), 0, mask.cols / 2, 360, luffy_hit::emHitOut2In);
//	luffy_imageProc::RansacParam rs(0.01, 1, 100, 80, 100);//0421
//	vector<Point> pts2 = luffy_imageProc::fitModelbyRansac(pts, luffy_imageProc::emModelCircle, &rs);
//	float fRadius;
//	Point2f ptCenter;
//	bool bFind = luffy_imageProc::lsCircleFit(pts2, fRadius, ptCenter);
//#ifdef _DEBUG
//	Mat insideMat(mask.size(), mask.type(), Scalar::all(0));
//	int offset = 2;
//	cv::circle(insideMat, ptCenter, fRadius, Scalar(1), -1);
//	Mat ddst = insideMat.mul(test);
//#endif
//	const Point globalCenter(ptCenter.x + center.x - roughRadius, ptCenter.y + center.y - roughRadius);
//
//
//
//	////  find minR
//	cv::Mat binaryImg = subImage > 25;
//	const int innerRadius = binaryImg.rows*0.05;
//	const int outterRadius = binaryImg.cols*0.22;
//	Mat mmask(binaryImg.size(), CV_8UC1);
//	mmask.setTo(0);
//	circle(mmask, globalCenter, outterRadius, Scalar(1), -1);
//	Mat dilatedImgBin1;
//	Mat erodeMat1;
//	dilate(binaryImg, dilatedImgBin1, Mat::ones(9, 9, CV_32FC1));
//	erode(dilatedImgBin1, erodeMat1, Mat::ones(9, 9, CV_32FC1));
//	openOper(erodeMat1, Mat::ones(1, 13, CV_32FC1));
//	circle(erodeMat1, globalCenter, innerRadius, Scalar(255), -1);
//	Mat tarMat = mmask.mul(erodeMat1) > 0;
//
//	vector<Point> maxCon;
//	vector<vector<Point>> m_contours;
//	findContours(tarMat, m_contours, RETR_EXTERNAL, CHAIN_APPROX_NONE);
//	maxCon = m_contours.front();
//	for (const vector<Point>& contour : m_contours)
//	{
//		if (contour.size() > maxCon.size())
//		{
//			maxCon = contour;
//		}
//	}
//	float minDis = FLT_MAX;
//	Point initialPoint = maxCon.front();
//	for (int i = 0; i < maxCon.size(); i++)
//	{
//		const Point& p = maxCon[i];
//		float dis = pointDis(p, globalCenter);
//		if (dis < minDis)
//		{
//			minDis = dis;
//			initialPoint = p;
//		}
//	}
//#ifdef _DEBUG
//	Mat m_Mat(tarMat.size(), tarMat.type(), Scalar::all(0));
//	//int offset = 2;
//	cv::circle(m_Mat, globalCenter, minDis, Scalar(1), -1);
//	Mat d_dst = m_Mat.mul(subImage);
//#endif
//	vector<float> rst;
//	rst.resize(3);
//	rst[0] = minDis - 3;
//	rst[1] = globalCenter.x;
//	rst[2] = globalCenter.y;
//	return rst;
	if (subImage.size() != mAlignBaseImg.size())
	{
		resize(subImage, subImage, mAlignBaseImg.size());
	}

	cv::Mat binaryImg = subImage > 30;
	const int innerRadius = binaryImg.rows*0.05;
	const int outterRadius = binaryImg.cols*0.22;
	const Point center(binaryImg.cols / 2.0, binaryImg.rows / 2.0);
	Mat mask(binaryImg.size(), CV_8UC1);
	mask.setTo(0);
	circle(mask, center, outterRadius, Scalar(1), -1);
	Mat dilatedImgBin;
	Mat erodeMat;
	dilate(binaryImg, dilatedImgBin, Mat::ones(9, 9, CV_32FC1));
	erode(dilatedImgBin, erodeMat, Mat::ones(9, 9, CV_32FC1));
	openOper(erodeMat, Mat::ones(1, 13, CV_32FC1));
	circle(erodeMat, center, innerRadius, Scalar(255), -1);
	Mat tarMat = mask.mul(erodeMat) > 0;

	vector<Point> maxCon;
	vector<vector<Point>> contours;
	findContours(tarMat, contours, RETR_EXTERNAL, CHAIN_APPROX_NONE);
	maxCon = contours.front();
	for (const vector<Point>& contour : contours)
	{
		if (contour.size() > maxCon.size())
		{
			maxCon = contour;
		}
	}
	float minDis = FLT_MAX;
	Point initialPoint = maxCon.front();
	for (int i = 0; i < maxCon.size(); i++)
	{
		const Point& p = maxCon[i];
		float dis = pointDis(p, center);
		if (dis < minDis)
		{
			minDis = dis;
			initialPoint = p;
		}
	}
#ifdef _DEBUG
	Mat insideMat(tarMat.size(), tarMat.type(), Scalar::all(0));
	int offset = 2;
	cv::circle(insideMat, center, minDis - offset, Scalar(1), -1);
	Mat ddst = insideMat.mul(subImage);
#endif
	return minDis - 2;
}

float ImageCompareModel::allocateRadius(cv::Mat subImage)
{

//	//// find global center
//	const int &roughRadius = subImage.cols *0.08;
//	const Point center(subImage.cols / 2.0, subImage.rows / 2.0);
//	Rect rect(center.x - roughRadius, center.y - roughRadius, 2 * roughRadius, 2*roughRadius);
//	Mat centerMat = subImage(rect);
//	Mat test = subImage(rect).clone();
//	genSobelImage(centerMat);
//	centerMat.convertTo(centerMat, CV_8UC1);
//	Mat bMat = centerMat > 100;
//	//cv::threshold(centerMat, bMat, 0, 255, CV_THRESH_OTSU);
//	Mat mask(bMat.size(), CV_8UC1);
//	mask.setTo(0);
//	//circle(mask, center, roughRadius, Scalar(255), -1);
//	//cv::Mat bImage = subImage.mul(mask / 255) > 100;
//	Mat dilatedImgBin;
//	Mat erodeMat;
//	dilate(bMat, dilatedImgBin, Mat::ones(3, 3, CV_32FC1));
//	erode(dilatedImgBin, erodeMat, Mat::ones(3, 3, CV_32FC1));
//	closeOper(erodeMat, Mat::ones(1, 3, CV_32FC1));
//	Mat vMat = erodeMat.clone();
//	vector<vector<Point>> contours;
//	findContours(erodeMat, contours, RETR_EXTERNAL, CHAIN_APPROX_NONE);
//	for (int i = 0; i < contours.size(); i++) {
//		const vector<Point> &pt = contours.at(i);
//		if (pt.size() < 5) {
//			continue;
//		}
//		Rect rt = boundingRect(pt);
//		if (rt.width < 5 || rt.height < 5) {
//			continue;
//		}
//		drawContours(mask, contours, i, Scalar::all(255), -1);
//	}
//
//	using namespace luffy_base;
//	Mat hit; vector<Point> pts;
//	luffy_hit::firstHit4Circle(mask, hit, pts, Point(mask.cols / 2, mask.rows / 2), 0, mask.cols / 2, 360, luffy_hit::emHitOut2In);
//	luffy_imageProc::RansacParam rs(0.01, 1, 100, 80, 100);//0421
//	vector<Point> pts2 = luffy_imageProc::fitModelbyRansac(pts, luffy_imageProc::emModelCircle, &rs);
//	float fRadius;
//	Point2f ptCenter;
//	bool bFind = luffy_imageProc::lsCircleFit(pts2, fRadius, ptCenter);
//#ifdef _DEBUG
//	Mat insideMat(mask.size(), mask.type(), Scalar::all(0));
//	//int offset = 2;
//	cv::circle(insideMat, ptCenter, fRadius, Scalar(1), -1);
//	Mat ddst = insideMat.mul(test);
//#endif
//	const Point globalCenter(ptCenter.x + center.x - roughRadius, ptCenter.y + center.y - roughRadius);
//
//
//
//	////  find minR
//	cv::Mat binaryImg = subImage > 25;
//	const int innerRadius = binaryImg.rows*0.05;
//	const int outterRadius = binaryImg.cols*0.22;
//	Mat mmask(binaryImg.size(), CV_8UC1);
//	mmask.setTo(0);
//	circle(mmask, globalCenter, outterRadius, Scalar(1), -1);
//	Mat dilatedImgBin1;
//	Mat erodeMat1;
//	dilate(binaryImg, dilatedImgBin1, Mat::ones(9, 9, CV_32FC1));
//	erode(dilatedImgBin1, erodeMat1, Mat::ones(9, 9, CV_32FC1));
//	openOper(erodeMat1, Mat::ones(1, 13, CV_32FC1));
//	circle(erodeMat1, globalCenter, innerRadius, Scalar(255), -1);
//	Mat tarMat =mmask.mul(erodeMat1) > 0;
//
//	vector<Point> maxCon;
//	vector<vector<Point>> m_contours;
//	findContours(tarMat, m_contours, RETR_EXTERNAL, CHAIN_APPROX_NONE);
//	maxCon = m_contours.front();
//	for (const vector<Point>& contour : m_contours)
//	{
//		if (contour.size() > maxCon.size())
//		{
//			maxCon = contour;
//		}
//	}
//	float minDis = FLT_MAX;
//	Point initialPoint = maxCon.front();
//	for (int i = 0; i < maxCon.size(); i++)
//	{
//		const Point& p = maxCon[i];
//		float dis = pointDis(p, globalCenter);
//		if (dis < minDis)
//		{
//			minDis = dis;
//			initialPoint = p;
//		}
//	}
//#ifdef _DEBUG
//	Mat m_Mat(tarMat.size(), tarMat.type(), Scalar::all(0));
//	//int offset = 2;
//	cv::circle(m_Mat, globalCenter, minDis, Scalar(1), -1);
//	Mat d_dst = m_Mat.mul(subImage);
//#endif
//	//return minDis - 2;
//	vector<float> rst;
//	rst.resize(3);
//	rst[0] = minDis - 3;
//	rst[1] = globalCenter.x;
//	rst[2] = globalCenter.y;
//	return rst;

   if (subImage.size() != mAlignBaseImg.size())
   {
	  resize(subImage, subImage, mAlignBaseImg.size());
   }
	cv::Mat binaryImg = subImage > 30;
	const int innerRadius = binaryImg.rows*0.05;
	const int outterRadius = binaryImg.cols*0.22;
	const Point center(binaryImg.cols / 2.0, binaryImg.rows / 2.0);
	Mat mask(binaryImg.size(), CV_8UC1);
	mask.setTo(0);
	circle(mask, center, outterRadius, Scalar(1), -1);
	Mat dilatedImgBin;
	Mat erodeMat;
	dilate(binaryImg, dilatedImgBin, Mat::ones(9, 9, CV_32FC1));
	erode(dilatedImgBin, erodeMat, Mat::ones(9, 9, CV_32FC1));
	openOper(erodeMat, Mat::ones(1, 13, CV_32FC1));
	circle(erodeMat, center, innerRadius, Scalar(255), -1);
	Mat tarMat = mask.mul(erodeMat) > 0;

	vector<Point> maxCon;
	vector<vector<Point>> contours;
	findContours(tarMat, contours, RETR_EXTERNAL, CHAIN_APPROX_NONE);
	maxCon = contours.front();
	for (const vector<Point>& contour : contours)
	{
		if (contour.size() > maxCon.size())
		{
			maxCon = contour;
		}
	}
	float minDis = FLT_MAX;
	Point initialPoint = maxCon.front();
	for (int i = 0; i < maxCon.size(); i++)
	{
		const Point& p = maxCon[i];
		float dis = pointDis(p, center);
		if (dis < minDis)
		{
			minDis = dis;
			initialPoint = p;
		}
	}
	#ifdef _DEBUG
	Mat insideMat(tarMat.size(), tarMat.type(), Scalar::all(0));
	int offset = 2;
	cv::circle(insideMat, center, minDis - offset, Scalar(1), -1);
	Mat ddst = insideMat.mul(subImage);
	#endif
	return minDis - 2;
}

void ImageCompareModel::genOutterMask()
{
	m32fMaskImg = genMask(mAlignBaseImg, Point2f(mAlignBaseImg.cols / 2.0, mAlignBaseImg.rows / 2.0));
	m8uMaskImg = genMask(mAlignBaseImg, Point2f(mAlignBaseImg.cols / 2.0, mAlignBaseImg.rows / 2.0),
		-1.0, -1.0, CV_8UC1);
}

void ImageCompareModel::resizeVecMat(vector<Mat> srcVec, vector<Mat> &dstVec)
{
	if (srcVec.size() <= 0)
		return;
	dstVec.resize(srcVec.size());
	int nWidth = ((int)((float)srcVec.front().cols / COLS_SCALE / 4)) * 4;
	for (int i = 0; i < srcVec.size(); i++)
	{
		const Mat& mat = srcVec[i];
		Mat dstMat;
		cv::resize(mat, dstMat, cv::Size(nWidth, nWidth));
		dstVec[i] = dstMat;
	}
}

void ImageCompareModel::resizeMat(Mat src, Mat &dst)
{
	int nWidth = ((int)((float)src.cols / COLS_SCALE / 4)) * 4;
	cv::resize(src, dst, cv::Size(nWidth, nWidth));
}

void ImageCompareModel::printLog(string root, string log, double n) const
{
#ifdef DEBUG_VIEW_INTERNAL_MAT
	ofstream write;
	write.open(root, ios::app);
	time_t seconds = time(NULL);
	struct tm *p;
	p = localtime(&seconds);
	char strTime[100] = { 0 };
	sprintf(strTime, "%02d-%02d %02d:%02d:%02d:", 1 + p->tm_mon, p->tm_mday, p->tm_hour, p->tm_min, p->tm_sec);
	std::ostringstream str;
	str << log;
	if (n != 0)
	{
		str << n;
	}
	write << "time:" << string(strTime);
	write << str.str() << "\n";
	write.close();
#endif
}