From 7ddd217123b8ae5dfbe8930565851a87e67ebd10 Mon Sep 17 00:00:00 2001 From: keyeslll Date: Wed, 22 Feb 2023 11:56:10 +0800 Subject: [PATCH] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E9=A1=B9=E7=9B=AE=E6=96=87?= =?UTF-8?q?=E4=BB=B6=E3=80=82?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- NiirsLinux.sln | 22 + NiirsLinux/NiirsLinux.vcxproj | 85 + NiirsLinux/NiirsLinux.vcxproj.filters | 53 + NiirsLinux/main.cpp | 28 + NiirsLinux/niirs.cpp | 3229 +++++++++++++++++++++++++ NiirsLinux/niirs.h | 199 ++ NiirsLinux/niirs.qrc | 4 + NiirsLinux/niirs.ui | 680 ++++++ 8 files changed, 4300 insertions(+) create mode 100644 NiirsLinux.sln create mode 100644 NiirsLinux/NiirsLinux.vcxproj create mode 100644 NiirsLinux/NiirsLinux.vcxproj.filters create mode 100644 NiirsLinux/main.cpp create mode 100644 NiirsLinux/niirs.cpp create mode 100644 NiirsLinux/niirs.h create mode 100644 NiirsLinux/niirs.qrc create mode 100644 NiirsLinux/niirs.ui diff --git a/NiirsLinux.sln b/NiirsLinux.sln new file mode 100644 index 0000000..9e865ae --- /dev/null +++ b/NiirsLinux.sln @@ -0,0 +1,22 @@ + +Microsoft Visual Studio Solution File, Format Version 12.00 +# Visual Studio Version 16 +VisualStudioVersion = 16.0.33027.164 +MinimumVisualStudioVersion = 10.0.40219.1 +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "NiirsLinux", "NiirsLinux\NiirsLinux.vcxproj", "{22B7E044-B017-466B-985E-D3FDF6435C2E}" +EndProject +Global + GlobalSection(SolutionConfigurationPlatforms) = preSolution + Release|x64 = Release|x64 + EndGlobalSection + GlobalSection(ProjectConfigurationPlatforms) = postSolution + {22B7E044-B017-466B-985E-D3FDF6435C2E}.Release|x64.ActiveCfg = Release|x64 + {22B7E044-B017-466B-985E-D3FDF6435C2E}.Release|x64.Build.0 = Release|x64 + EndGlobalSection + GlobalSection(SolutionProperties) = preSolution + HideSolutionNode = FALSE + EndGlobalSection + GlobalSection(ExtensibilityGlobals) = postSolution + SolutionGuid = {4D31A63B-64E0-4A37-A0E6-6AD47E2C0284} + EndGlobalSection +EndGlobal diff --git a/NiirsLinux/NiirsLinux.vcxproj b/NiirsLinux/NiirsLinux.vcxproj new file mode 100644 index 0000000..02c57ea --- /dev/null +++ b/NiirsLinux/NiirsLinux.vcxproj @@ -0,0 +1,85 @@ + + + + + Release + x64 + + + + {22B7E044-B017-466B-985E-D3FDF6435C2E} + QtVS_v304 + 10.0.22000.0 + $(MSBuildProjectDirectory)\QtMsBuild + + + + Application + v142 + + + + + + + Qt5.11.2 + core;xml;gui;widgets;printsupport + release + + + + + + + + + + + + + D:\qgis\osgeo4w\include;D:\qgis\osgeo4w\apps\Qt5\include;D:\OpenCV\opencv455\include;D:\OpenCV\opencv455\include\opencv2;D:\qgis\osgeo4w\apps\Qt5\include\QtXml;$(IncludePath) + D:\qgis\osgeo4w\lib;D:\qgis\osgeo4w\apps\Qt5\lib;D:\OpenCV\opencv455\x64\vc16\lib;$(LibraryPath) + + + + opencv_core455.lib;opencv_imgcodecs455.lib;opencv_imgproc455.lib;gdal_i.lib;%(AdditionalDependencies) + + + true + + + + + true + true + EditAndContinue + Disabled + + + Windows + true + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/NiirsLinux/NiirsLinux.vcxproj.filters b/NiirsLinux/NiirsLinux.vcxproj.filters new file mode 100644 index 0000000..2dabaa6 --- /dev/null +++ b/NiirsLinux/NiirsLinux.vcxproj.filters @@ -0,0 +1,53 @@ + + + + + {4FC737F1-C7A5-4376-A066-2A32D752A2FF} + qml;cpp;c;cc;cxx;def;odl;idl;hpj;bat;asm;asmx + + + {93995380-89BD-4b04-88EB-625FBE52EBFB} + h;hh;hpp;hxx;hm;inl;inc;xsd + + + {67DA6AB6-F800-4c08-8B7A-83BB121AAD01} + qrc;rc;ico;cur;bmp;dlg;rc2;rct;bin;rgs;gif;jpg;jpeg;jpe;resx;tiff;tif;png;wav;mfcribbon-ms + + + {99349809-55BA-4b9d-BF79-8FDBB0286EB3} + ui + + + {639EADAA-A684-42e4-A9AD-28FC9BCB8F7C} + ts + + + + + Source Files + + + Source Files + + + + + Form Files + + + + + Resource Files + + + + + Header Files + + + + + Form Files + + + \ No newline at end of file diff --git a/NiirsLinux/main.cpp b/NiirsLinux/main.cpp new file mode 100644 index 0000000..d7fc870 --- /dev/null +++ b/NiirsLinux/main.cpp @@ -0,0 +1,28 @@ +#include "niirs.h" +#include +#include +#include + +int main(int argc, char *argv[]) +{ + QString xmlInterface = ""; + + for (int i = 0; i < argc; i++) + { + QString temp = QString::fromLocal8Bit(argv[i]); + if (temp == "--xml" && i + 1 < argc) + { + xmlInterface = argv[i + 1]; + break; + } + } + + QApplication a(argc, argv); + QStyle* style = QStyleFactory::create("Fusion"); + a.setStyle(style); + + NIIRS w; + w.setXmlInterface(xmlInterface); + w.show(); + return a.exec(); +} diff --git a/NiirsLinux/niirs.cpp b/NiirsLinux/niirs.cpp new file mode 100644 index 0000000..a84ae4d --- /dev/null +++ b/NiirsLinux/niirs.cpp @@ -0,0 +1,3229 @@ +#include "niirs.h" + +NIIRS::NIIRS(QWidget* parent) + : QMainWindow(parent) +{ + ui.setupUi(this); + connect(ui.radioVisible, &QRadioButton::clicked, this, &NIIRS::onRadioBtn_Visi); + connect(ui.radioMidInfrared, &QRadioButton::clicked, this, &NIIRS::onRadioBtn_Infra); + connect(ui.radioLonInfrared, &QRadioButton::clicked, this, &NIIRS::onRadioBtn_Infra); + + ui.textFormula->setText(QString::fromLocal8Bit("可见光数据计算公式:\n") + + "NIIRS = 10.251 - a*lgGSD + b*lgRER - 0.656*H - 0.334*(G/SNR) | RER>=0.9:a=3.32,b=1.559;RER<0.9:a=3.16,b=2.817"); + + connect(ui.pbtCalculate, &QPushButton::clicked, this, &NIIRS::calculateNiirsScore); + connect(ui.pbtObjectivePara, &QPushButton::clicked, this, &NIIRS::calculateObjectivePara); + //connect(ui.pbtObjectivePara, &QPushButton::clicked, this, &NIIRS::calculateEvaluationImage); + //connect(ui.pbtRER, &QPushButton::clicked, this, &NIIRS::calculateRER); + + + ui.lineEdit_GSDX->setText("1.0"); + ui.lineEdit_GSDY->setText("1.0"); + //ui.lineEdit_RERX->setText("0.986"); + //ui.lineEdit_RERY->setText("0.965"); + //ui.lineEdit_MTFC_H->setText("0.9"); + + + ui.lineEdit_RER->setText("0.986"); + ui.lineEdit_H->setText("0.9"); + + ui.lineEdit_MTFC_G->setText("1"); + ui.lineEdit_SNR->setText("20"); + ui.lineEdit_PIQE->setText("0.986"); + + QString inputImgPath = mXmlImgPath; + GDALAllRegister(); + CPLSetConfigOption("GDAL_FILENAME_IS_UTF8", "NO"); + string tiffEdgePath = inputImgPath.toStdString(); + GDALDataset* poDataset; + poDataset = (GDALDataset*)GDALOpen(tiffEdgePath.c_str(), GA_ReadOnly); + if (NULL != poDataset) + { + for (int i = 0; i < poDataset->GetRasterCount(); i++) + ui.comboBoxBand->addItem(QString("band:%1").arg(i + 1)); + } + GDALClose(poDataset); +} + +QStringList NIIRS::getAllFiles(QString path, QString fileType) +{ + // 判断路径是否存在 + QDir dir(path); + if (!dir.exists()) + { + QMessageBox::warning(this, "warning", "no such path:\n" + path); + return QStringList(); + } + + dir.setFilter(QDir::Files | QDir::NoSymLinks); + QFileInfoList infoList = dir.entryInfoList(); + + int fileCount = infoList.count(); + if (fileCount <= 0) + { + QMessageBox::warning(this, "warning", "No files in folder:\n" + path); + return QStringList(); + } + + QStringList files; + for (int i = 0; i < fileCount; i++) + { + QFileInfo fileInfo = infoList.at(i); + QString suffix = fileInfo.suffix(); + if (QString::compare(suffix, fileType, Qt::CaseInsensitive) == 0) + { + QString absoluteFilePath = fileInfo.absoluteFilePath(); + files.append(absoluteFilePath); + } + } + return files; +} + +void NIIRS::onRadioBtn_Visi() +{ + ui.textFormula->setText(QString::fromLocal8Bit("可见光数据计算公式:\n") + + "NIIRS = 10.251 - a*lgGSD + b*lgRER - 0.656*H - 0.334*(G/SNR) | RER>=0.9:a=3.32,b=1.559;RER<0.9:a=3.16,b=2.817"); +} +void NIIRS::onRadioBtn_Infra() +{ + if (ui.radioMidInfrared->isChecked()) + ui.textFormula->setText(QString::fromLocal8Bit("中红外数据计算公式:\n") + + "NIIRS = 10.751 - a*lgGSD + b*lgRER - 0.656*H - 0.334*(G/SNR) | RER>=0.9:a=3.32,b=1.559;RER<0.9:a=3.16,b=2.817"); + else if (ui.radioLonInfrared->isChecked()) + ui.textFormula->setText(QString::fromLocal8Bit("长波红外数据计算公式:\n") + + "NIIRS = 10.751 - a*lgGSD + b*lgRER - 0.656*H - 0.334*(G/SNR) | RER>=0.9:a=3.32,b=1.559;RER<0.9:a=3.16,b=2.817"); +} + +void NIIRS::on_pbtOpenImg_clicked() +{ + QString openImg = QFileDialog::getOpenFileName(this, "", "", "*.tif *.tiff"); + if (!openImg.isEmpty()) + ui.lineEdit_img->setText(openImg); + else + ui.lineEdit_INFO->setText(QString::fromLocal8Bit("未打开数据")); + //QString openImg = QFileDialog::getExistingDirectory(this, "", ""); + //if (!openImg.isEmpty()) + // ui.lineEdit_img->setText(openImg); + //else + // ui.lineEdit_INFO->setText(QString::fromLocal8Bit("未打开路径")); +} + +//void NIIRS::on_pbtOpenEdgeX_clicked() +//{ +// QString edgeX = QFileDialog::getOpenFileName(this, "", "", "*.tif *.tiff"); +// if (!edgeX.isEmpty()) +// ui.lineEdit_EdgeX->setText(edgeX); +// else +// ui.lineEdit_INFO->setText(QString::fromLocal8Bit("未打开数据")); +// //QString edgeX= QFileDialog::getOpenFileName(this, "", "", "*.jpg"); +// //if (!edgeX.isEmpty()) +// // ui.lineEdit_EdgeX->setText(edgeX); +// //else +// // ui.lineEdit_INFO->setText(QString::fromLocal8Bit("未打开数据")); +//} + +//void NIIRS::on_pbtOpenEdgeY_clicked() +//{ +// QString edgeY = QFileDialog::getOpenFileName(this, "", "", "*.tif *.tiff"); +// if (!edgeY.isEmpty()) +// ui.lineEdit_EdgeY->setText(edgeY); +// else +// ui.lineEdit_INFO->setText(QString::fromLocal8Bit("未打开数据")); +// //QString edgeY = QFileDialog::getOpenFileName(this, "", "", "*.jpg"); +// //if (!edgeY.isEmpty()) +// // ui.lineEdit_EdgeY->setText(edgeY); +// //else +// // ui.lineEdit_INFO->setText(QString::fromLocal8Bit("未打开数据")); +//} + +int NIIRS::getImageInfo(const char* srcImgPath, struct ImgInfo* imgInfo) +{ + GDALAllRegister(); + CPLSetConfigOption("GDAL_FILENAME_IS_UTF8", "NO"); //中文路径 + GDALDataset* hSrcDS; + hSrcDS = (GDALDataset*)GDALOpen(srcImgPath, GA_ReadOnly); + + if (NULL == hSrcDS) + { + GDALClose(hSrcDS); + return -1; + } + + GDALRasterBand* rasterBand; + rasterBand = hSrcDS->GetRasterBand(1); + + imgInfo->dataType = rasterBand->GetRasterDataType(); + imgInfo->bandCount = hSrcDS->GetRasterCount(); + imgInfo->xSize = hSrcDS->GetRasterXSize(); + imgInfo->ySize = hSrcDS->GetRasterYSize(); + imgInfo->xOffset = 0; + imgInfo->yOffset = 0; + + strcpy(imgInfo->projWkt, hSrcDS->GetProjectionRef()); + hSrcDS->GetGeoTransform(imgInfo->geoTransform); + imgInfo->noData = rasterBand->GetNoDataValue(); + + GDALClose(hSrcDS); + return 0; +} + +int NIIRS::readTiffFile(const char* srcImgPath, ImgInfo imgInfo, void* imgArray, int bandIndex) +{ + GDALAllRegister(); + CPLSetConfigOption("GDAL_FILENAME_IS_UTF8", "NO"); //中文路径 + GDALDataset* poSrcDS = (GDALDataset*)GDALOpen(srcImgPath, GA_ReadOnly); + + //返回错误信息 + int errInfo = 0; + + if (NULL == poSrcDS) + { + GDALClose(poSrcDS); + return -1; + } + + //输入波段索引大于数据波段数 + if (bandIndex > imgInfo.bandCount) + { + GDALClose(poSrcDS); + return -1; + } + //读取全部波段 + if (0 == bandIndex) + { + errInfo = poSrcDS->RasterIO(GF_Read, imgInfo.xOffset, imgInfo.yOffset, imgInfo.xSize, imgInfo.ySize, + imgArray, imgInfo.xSize, imgInfo.ySize, imgInfo.dataType, imgInfo.bandCount, 0, 0, 0, 0); + } + //读取指定波段 + else + { + GDALRasterBand* poBand = poSrcDS->GetRasterBand(bandIndex); + errInfo = poBand->RasterIO(GF_Read, imgInfo.xOffset, imgInfo.yOffset, imgInfo.xSize, imgInfo.ySize, + imgArray, imgInfo.xSize, imgInfo.ySize, imgInfo.dataType, 0, 0); + } + GDALClose((GDALDatasetH)poSrcDS); + return errInfo; +} + +void NIIRS::initTableWidget(int rowNum, int colNum) +{ + ui.tableWidget->clear(); + ui.tableWidget->setColumnCount(colNum); + ui.tableWidget->setRowCount(rowNum); + QStringList header; + for (int i = 0; i < colNum; i++) + header << "band " + QString::number(i + 1); + //header << "band 1" << "band 2" << "band 3" << "band 4"; + ui.tableWidget->setHorizontalHeaderLabels(header); + header.clear(); + header << QString::fromLocal8Bit("亮度") << QString::fromLocal8Bit("标准差") << QString::fromLocal8Bit("对比度") << QString::fromLocal8Bit("熵") + << QString::fromLocal8Bit("信噪比") << QString::fromLocal8Bit("陡度") << QString::fromLocal8Bit("清晰度") << QString::fromLocal8Bit("灰度最大值") + << QString::fromLocal8Bit("灰度最小值") << QString::fromLocal8Bit("PIQE") + << QString::fromLocal8Bit("0°二阶矩") << QString::fromLocal8Bit("90°二阶矩") + << QString::fromLocal8Bit("45°二阶矩") << QString::fromLocal8Bit("135°二阶矩") + << QString::fromLocal8Bit("RER"); + ui.tableWidget->setVerticalHeaderLabels(header); + + ui.tableWidget->setVerticalScrollMode(QAbstractItemView::ScrollPerPixel); + ui.tableWidget->setHorizontalScrollMode(QAbstractItemView::ScrollPerPixel); + +} + +void NIIRS::setTableWidgetValue(double val, int row, int col) +{ + ui.tableWidget->setItem(row, col, new QTableWidgetItem(QString::number(val, 10, 6))); +} + +void NIIRS::setXmlInterface(QString strXmlInterface) +{ + mXmlInterface = strXmlInterface; + + //qDebug() << "setXmlInterface:" << mXmlInterface; + std::cout << "setXmlInterface:" << mXmlInterface.toStdString(); + + //解析xml + if (mXmlInterface != "") + { + QFile fileXML(mXmlInterface); + if (fileXML.open(QIODevice::ReadOnly)) + { + QDomDocument dom; + QString errorStr; + int errorLine = 0; + int errorCol = 0; + if (!dom.setContent(&fileXML, true, &errorStr, &errorLine, &errorCol)) + { + ui.lineEdit_INFO->setText(errorStr + ", line: " + QString::number(errorLine) + ", col: " + QString::number(errorCol)); + fileXML.close(); + return; + } + else + { + QDomElement root = dom.documentElement(); + QDomNode current = root; + QStack stack; + //遍历XML节点 + while (1) + { + if (!current.isNull()) + { + //查找并读取XML中参数 + QDomElement element = current.toElement(); + if (element.tagName() == "InputImg") + mXmlImgPath = element.firstChild().toCharacterData().data(); + else if (element.tagName() == "TopLeftX") + mXmlTopLeftX = element.firstChild().toCharacterData().data(); + else if (element.tagName() == "TopLeftY") + mXmlTopLeftY = element.firstChild().toCharacterData().data(); + else if (element.tagName() == "BottomRightX") + mXmlBottomRightX = element.firstChild().toCharacterData().data(); + else if (element.tagName() == "BottomRightY") + mXmlBottomRightY = element.firstChild().toCharacterData().data(); + else if (element.tagName() == "ProductDir") + mXmlProductDir = element.firstChild().toCharacterData().data(); + else if (element.tagName() == "ProductResultXML") + mResultXmlDir = element.firstChild().toCharacterData().data(); + + stack.push(current); + current = current.firstChild(); + } + else if (!stack.count()) + break; + else + current = stack.pop().nextSibling(); + } + } + fileXML.close(); + } + } + if (mXmlImgPath != "") + ui.lineEdit_img->setText(mXmlImgPath); + + qDebug() << mXmlImgPath; + qDebug() << mXmlTopLeftX; + qDebug() << mXmlTopLeftY; + qDebug() << mXmlBottomRightX; + qDebug() << mXmlBottomRightY; + qDebug() << mXmlProductDir; + + ui.lineEditLTx->setText(mXmlTopLeftX); + ui.lineEditLTy->setText(mXmlTopLeftY); + ui.lineEditRBx->setText(mXmlBottomRightX); + ui.lineEditRBy->setText(mXmlBottomRightY); + +} + +void NIIRS::calculateObjectivePara() +{ + ui.lineEdit_INFO->clear(); + ui.tableWidget->clear(); + ui.tableWidget->setColumnCount(0); + ui.tableWidget->setRowCount(0); + + GDALAllRegister(); + CPLSetConfigOption("GDAL_FILENAME_IS_UTF8", "NO"); //中文路径 + //读取tiff + QString evaluateImg = ui.lineEdit_img->text(); + if (evaluateImg.isEmpty()) + { + //QMessageBox::warning(this, "warning", "The image to be evaluated is not opened"); + ui.lineEdit_INFO->setText(QString::fromLocal8Bit("未打开待评估的图像")); + return; + } + + string tiffPath = evaluateImg.toStdString(); + GDALDataset* poDataset; + poDataset = (GDALDataset*)GDALOpen(tiffPath.c_str(), GA_ReadOnly); + if (NULL == poDataset) + { + GDALClose(poDataset); + return; + } + GDALRasterBand* pRasterBand; + GDALRasterBand* pClipBand; + vector pBuf; + + //存储各波段参数 + vectorvecGrayMax; + vectorvecGrayMin; + + vectorvecLight; + vectorvecStdDev; + vectorvecContrastRadio; + vectorvecSNR; + vectorvecGradient; + vectorvecDefinition; + vectorvecPIQE; + vectorvecEntropy; + vector>vecASM; + vectortemp; + + //poDataset->GetMetadata(); + + GDALRasterBand* pTestBand = poDataset->GetRasterBand(1); + GDALDataType dataType = pTestBand->GetRasterDataType(); + + if (dataType != GDT_UInt16)// && dataType != GDT_Byte + { + ui.lineEdit_INFO->setText(QString::fromLocal8Bit("输入影像数据类型无法用于计算客观参数")); + return; + } + //qDebug()<<"Dataset data type:GDT_UInt16"; + int bandCount = poDataset->GetRasterCount(); + int iwidth = pTestBand->GetXSize(); + int iheight = pTestBand->GetYSize(); + + // 根据裁切范围确定裁切后的图像宽高 +// int topLeftX = mXmlTopLeftX.toInt(); +// int topLeftY = mXmlTopLeftY.toInt(); +// int bottomRightX = mXmlBottomRightX.toInt(); +// int bottomRightY = mXmlBottomRightY.toInt(); + + int topLeftX = ui.lineEditLTx->text().toInt(); + int topLeftY = ui.lineEditLTy->text().toInt(); + int bottomRightX = ui.lineEditRBx->text().toInt(); + int bottomRightY = ui.lineEditRBy->text().toInt(); + + int clipWidth = bottomRightX - topLeftX; + int clipHeight = bottomRightY - topLeftY; + + bool isCliped = false; + + //逐波段计算参数 + for (int i = 0; i < bandCount; i++) + { + unsigned short* bandBuf = new unsigned short[iwidth * iheight]; + unsigned short* bandBufClip = new unsigned short[clipWidth * clipHeight]; + + vecASM.push_back(temp); + pRasterBand = poDataset->GetRasterBand(i + 1); + + //在当前文件夹创建单波段裁剪图 + QString tempTiffPath = QCoreApplication::applicationDirPath(); + tempTiffPath = tempTiffPath + "/test.tiff"; + + //计算1.亮度、2.标准差，5.信噪比=Mean/StdDev + double bandMin = 0; + double bandMax = 0; + double bandMean = 0; + double bandStdDev = 0; + + //clipWidth = 0; + //判断是否裁剪 + if (clipWidth > 0 && clipHeight > 0) + { + isCliped = true; + + //解析波段 + if (pRasterBand->RasterIO(GF_Read, topLeftX, topLeftY, clipWidth, clipHeight, bandBufClip, clipWidth, clipHeight, dataType, 0, 0) != 0) + { + ui.lineEdit_INFO->setText(QString::fromLocal8Bit("解析裁剪数据出现错误, 波段:") + QString::number(i + 1)); + continue; + } + pBuf.push_back(bandBufClip); + + //计算1.亮度，2.标准差，5.信噪比 + GDALDriver* poDriver = GetGDALDriverManager()->GetDriverByName("GTIFF"); + GDALDataset* clipDataset = poDriver->Create(tempTiffPath.toStdString().c_str(), clipWidth, clipHeight, 1, dataType, 0); + pClipBand = clipDataset->GetRasterBand(1); + pClipBand->RasterIO(GF_Write, 0, 0, clipWidth, clipHeight, bandBufClip, clipWidth, clipHeight, dataType, 0, 0); + + pClipBand->ComputeStatistics(false, &bandMin, &bandMax, &bandMean, &bandStdDev, NULL, NULL); + vecLight.push_back(bandMean); + vecStdDev.push_back(bandStdDev); + vecSNR.push_back(bandMean / bandStdDev); + + vecGrayMax.push_back(bandMax); + vecGrayMin.push_back(bandMin); + + //4.计算图像熵 + //std::cout<<"calculateTiffEntropy bandMax: "<RasterIO(GF_Read, 0, 0, iwidth, iheight, bandBuf, iwidth, iheight, dataType, 0, 0) != 0) + { + ui.lineEdit_INFO->setText(QString::fromLocal8Bit("输入数据解析错误, 波段:") + QString::number(i + 1)); + continue; + } + pBuf.push_back(bandBuf); + + //计算1.亮度，2.标准差，5.信噪比 + + pRasterBand->ComputeStatistics(false, &bandMin, &bandMax, &bandMean, &bandStdDev, NULL, NULL); + vecLight.push_back(bandMean); + vecStdDev.push_back(bandStdDev); + vecSNR.push_back(bandMean / bandStdDev); + + vecGrayMax.push_back(bandMax); + vecGrayMin.push_back(bandMin); + + //4.计算图像熵 + std::cout << "calculateTiffEntropy bandMax: " << bandMax; + double tiffEntropy = calculateTiffEntropy(pBuf[i], iwidth * iheight, bandMax); + vecEntropy.push_back(tiffEntropy); + + } + + + //数组转到mat，判断是否是裁剪过的 + cv::Mat imageBand; + int imgW, imgH; + if (isCliped) + { + imgW = clipWidth; + imgH = clipHeight; + } + else + { + imgW = iwidth; + imgH = iheight; + } + if (dataType == GDT_Byte) + imageBand = cv::Mat(imgH, imgW, CV_8UC1, pBuf[i]); + else if (dataType == GDT_UInt16) + imageBand = cv::Mat(imgH, imgW, CV_16UC1, pBuf[i]); + else + { + ui.lineEdit_INFO->setText(QString::fromLocal8Bit("输入影像数据类型无法用于计算客观参数")); + continue; + } + //else if(dataType == GDT_UInt32) + // imageBand = cv::Mat(iheigth, iwidth, CV_32SC1, pBuf[i]); + + + //3.计算对比度 + double contrastRadio = calculateContrastRatio(imageBand); + vecContrastRadio.push_back(contrastRadio); + + //6.计算陡度 + double sobel = gradient_Sobel(imageBand); + vecGradient.push_back(sobel); + + //7.计算laplation + double laplation = definition_Laplacian(imageBand); + vecDefinition.push_back(laplation); + + //计算角二阶矩、图像熵 + cv::Mat dst_0degree, dst_90degree, dst_45degree, dst_135degree; + get_GLCM_0deg(imageBand, dst_0degree); + get_GLCM_90deg(imageBand, dst_90degree); + get_GLCM_45deg(imageBand, dst_45degree); + get_GLCM_135deg(imageBand, dst_135degree); + //qDebug() << "--calculate asm band" << i + 1; + //vecASM.push_back(temp); + //qDebug() << "-----0 degree-----"; + vecASM[i].push_back(calculateASM(dst_0degree)); + //qDebug() << "-----90 degree-----"; + vecASM[i].push_back(calculateASM(dst_90degree)); + //qDebug() << "-----45 degree-----"; + vecASM[i].push_back(calculateASM(dst_45degree)); + //qDebug() << "-----135 degree-----"; + vecASM[i].push_back(calculateASM(dst_135degree)); + + //9.计算PIQE + double piqe = calculatePIQEScore(imageBand); + vecPIQE.push_back(piqe); + + delete[] bandBufClip; + delete[] bandBuf; + } + + initTableWidget(14, bandCount); + /* + * 亮度 + * 标准差 + * 对比度 + * 熵 + * 信噪比 + * 陡度 + * 清晰度 + * PIQE + * 0°二阶矩,90°二阶矩,45°二阶矩,135°二阶矩 + * --RER-- + */ + int currentBandCount = ui.comboBoxBand->currentIndex(); + ui.lineEdit_SNR->setText(QString::number(vecSNR[currentBandCount], 10, 4)); + ui.lineEdit_PIQE->setText(QString::number(vecPIQE[currentBandCount], 10, 4)); + + imgLight = vecLight[currentBandCount]; + imgStdDev = vecStdDev[currentBandCount]; + imgContrastRadio = vecContrastRadio[currentBandCount]; + imgEntropy = vecEntropy[currentBandCount]; + imgSNR = vecSNR[currentBandCount]; + imgGradient = vecGradient[currentBandCount]; + imgDefinition = vecDefinition[currentBandCount]; + imgASM = (vecASM[0][currentBandCount] + vecASM[currentBandCount][1] + vecASM[currentBandCount][2] + vecASM[currentBandCount][3]) / 4; + imgPIQE = vecPIQE[currentBandCount]; + + for (int i = 0; i < bandCount; i++) + { + setTableWidgetValue(vecLight[i], 0, i); + setTableWidgetValue(vecStdDev[i], 1, i); + setTableWidgetValue(vecContrastRadio[i], 2, i); + setTableWidgetValue(vecEntropy[i], 3, i); + setTableWidgetValue(vecSNR[i], 4, i); + setTableWidgetValue(vecGradient[i], 5, i); + setTableWidgetValue(vecDefinition[i], 6, i); + setTableWidgetValue(vecGrayMax[i], 7, i); + setTableWidgetValue(vecGrayMin[i], 8, i); + setTableWidgetValue(vecPIQE[i], 9, i); + setTableWidgetValue(vecASM[i][0], 10, i); + setTableWidgetValue(vecASM[i][1], 11, i); + setTableWidgetValue(vecASM[i][2], 12, i); + setTableWidgetValue(vecASM[i][3], 13, i); + } + + //qDebug() << "----------------------------------------------------------\n"; + //释放内存 + GDALClose(poDataset); + + QString outTxt; + //在输出文件夹生成txt + if (mXmlProductDir != "") + { + QDir productDir(mXmlProductDir); + if (!productDir.exists()) + { + ui.lineEdit_INFO->setText(QString::fromLocal8Bit("product.txt not exists")); + return; + } + outTxt = productDir.absoluteFilePath("product.txt"); + //qDebug() << "product.txt: " << outTxt; + QFile txtFile(outTxt); + if (!txtFile.open(QIODevice::WriteOnly | QIODevice::Text | QIODevice::Truncate))//Truncate:覆盖文本///Append:在文本最后追加内容 + return; + QDateTime time = QDateTime::currentDateTime(); + QString currentTime = time.toString("yyyyMMddhhmmss"); + QTextStream stream(&txtFile); + stream << QString::fromLocal8Bit("日期时间: %1\n").arg(currentTime); + stream << QString::fromLocal8Bit("文件路径: %1\n").arg(evaluateImg); + if (isCliped) { + stream << QString::fromLocal8Bit("图像宽度: %1, ").arg(clipWidth); + stream << QString::fromLocal8Bit("高度: %1\n").arg(clipHeight); + } + else { + stream << QString::fromLocal8Bit("图像宽度: %1, ").arg(iwidth); + stream << QString::fromLocal8Bit("高度: %1\n").arg(iheight); + } + for (int i = 0; i < bandCount; i++) + { + stream << QString::fromLocal8Bit("波段: %1\n").arg(i + 1); + + stream << QString::fromLocal8Bit(" 图像灰度最大值: %1\n").arg(vecGrayMax[i]); + stream << QString::fromLocal8Bit(" 图像灰度最小值: %1\n").arg(vecGrayMin[i]); + stream << QString::fromLocal8Bit(" 图像亮 度: %1\n").arg(vecLight[i]); + stream << QString::fromLocal8Bit(" 图像标准差: %1\n").arg(vecStdDev[i]); + stream << QString::fromLocal8Bit(" 图像对比度: %1\n").arg(vecContrastRadio[i]); + stream << QString::fromLocal8Bit(" 图像熵: %1\n").arg(vecEntropy[i]); + stream << QString::fromLocal8Bit(" 图像信噪比: %1\n").arg(vecSNR[i]); + stream << QString::fromLocal8Bit(" 图像陡 度: %1\n").arg(vecGradient[i]); + stream << QString::fromLocal8Bit(" 图像清晰度: %1\n").arg(vecDefinition[i]); + stream << QString::fromLocal8Bit(" 图像PIQE: %1\n").arg(vecPIQE[i]); + stream << QString::fromLocal8Bit(" 图像0°二阶矩: %1\n").arg(vecASM[i][0]); + stream << QString::fromLocal8Bit(" 图像90°二阶矩: %1\n").arg(vecASM[i][1]); + stream << QString::fromLocal8Bit(" 图像45°二阶矩: %1\n").arg(vecASM[i][2]); + stream << QString::fromLocal8Bit(" 图像135°二阶矩: %1\n").arg(vecASM[i][3]); + } + stream << "\n"; + txtFile.close(); + ui.lineEdit_INFO->setText("result files save at:" + outTxt); + } + else + { + ui.lineEdit_INFO->setText(QString::fromLocal8Bit("product.txt输出文件夹为空或不存在")); + } +} + +//void NIIRS::calculateEvaluationImage() +//{ +// QString evaluateImgPath = ui.lineEdit_img->text(); +// // QStringList evaluateFiles = getAllFiles(evaluateImgPath, "BMP"); +// // if (evaluateFiles.size() == 0) +// // { +// // ui.lineEdit_INFO->setText("No BMP files in folder:" + evaluateImgPath); +// // return; +// // } +// // QString textFileName = "D:\\A_testdata\\niirs_test\\tid2008\\a_distorted_images.txt"; +// // QFile file(textFileName); +// // if (!file.open(QFile::Append | QFile::Text)) +// // { +// // QMessageBox::warning(this, "warning", "Please select the correct file"); +// // return; +// // } +// // QTextStream textStream(&file); +// // textStream << "imgName,imgLight,imgStdDev,imgContrastRadio,imgEntropy,imgSNR,imgGradient,imgDefinition,imgASM,imgPIQE,\n"; +// // for (int i = 0; i < evaluateFiles.size(); i++) +// // { +// // cv::Mat evaluateImg = cv::imread(evaluateFiles[i].toStdString()); +// cv::Mat evaluateImg = cv::imread(evaluateImgPath.toStdString()); +// cv::Mat imgGray; +// if (evaluateImg.channels() == 3) +// cv::cvtColor(evaluateImg, imgGray, CV_BGR2GRAY); +// else +// imgGray = evaluateImg; +// +// cv::Scalar meanScalar, stdDevScalar; +// cv::meanStdDev(imgGray, meanScalar, stdDevScalar); +// QVectorparas; +// double imgLight = meanScalar.val[0]; +// double imgStdDev = stdDevScalar.val[0]; +// double imgContrastRadio = calculateContrastRatio(imgGray); +// double imgEntropy = calculateEntropy(imgGray); +// double imgSNR = calculateSNR(imgGray); +// double imgSobel = gradient_Sobel(imgGray); +// double imgLaplacian = definition_Laplacian(imgGray); +// paras.append(imgLight); +// paras.append(imgStdDev); +// paras.append(imgContrastRadio); +// paras.append(imgEntropy); +// paras.append(imgSNR); +// paras.append(imgSobel); +// paras.append(imgLaplacian); +// +// cv::Mat dst_0, dst_90, dst_45, dst_135; +// get_GLCM_0deg(imgGray, dst_0); +// get_GLCM_90deg(imgGray, dst_90); +// get_GLCM_45deg(imgGray, dst_45); +// get_GLCM_135deg(imgGray, dst_135); +// double degree_0 = calculateASM(dst_0); +// double degree_90 = calculateASM(dst_90); +// double degree_45 = calculateASM(dst_45); +// double degree_135 = calculateASM(dst_135); +// double imgASM = (degree_0 + degree_90 + degree_45 + degree_135) / 4.0; +// double imgPIQE = calculatePIQEScore(imgGray); +// paras.append(imgASM); +// paras.append(imgPIQE); +// +// // QFileInfo fileInfo(evaluateFiles[i]); +// // QString imgName = fileInfo.fileName(); +// // textStream << imgName<<","; +// // for (int j = 0; j < paras.size(); j++) +// // textStream << QString::number(paras[j], 10, 6)<<","; +// // textStream << "\n"; +// // } +// // file.close(); +// // qDebug() << "calculateEvaluationImage done"; +//} + +double NIIRS::calculateNiirsScore() +{ + calculateRER(); + + QString textGSDX = ui.lineEdit_GSDX->text(); + QString textGSDY = ui.lineEdit_GSDY->text(); + double GSDX, GSDY; + QString textRER = ui.lineEdit_RER->text(); + QString text_H = ui.lineEdit_H->text(); + double RER, MTFC_H; + QString textMTFC_G = ui.lineEdit_MTFC_G->text(); + double MTFC_G; + QString textSNR = ui.lineEdit_SNR->text(); + QString textPIQE = ui.lineEdit_PIQE->text(); + double SNR, PIQE; + //1.检查GSD输入是否正确 + if (textGSDX.toDouble() <= 0.0 || textGSDY.toDouble() <= 0.0) + { + ui.lineEdit_INFO->setText(QString::fromLocal8Bit("检查GSD输入")); + return 0.0; + } + else + { + GSDX = textGSDX.toDouble(); + GSDY = textGSDY.toDouble(); + ui.lineEdit_INFO->clear(); + } + //2.检查RER输入是否正确 + if (textRER.toDouble() == 0.0) + { + ui.lineEdit_INFO->setText(QString::fromLocal8Bit("请输入RER数据")); + return 0.0; + } + else + { + RER = textRER.toDouble(); + + ui.lineEdit_INFO->clear(); + } + //3.检查MTFC_H输入是否正确 + if (text_H.toDouble() < 0.0 || text_H.toDouble() > 1.90) + { + ui.lineEdit_INFO->setText(QString::fromLocal8Bit("检查几何过冲H输入")); + return 0.0; + } + else + { + MTFC_H = text_H.toDouble(); + ui.lineEdit_INFO->clear(); + } + //4.检查MTFC_G输入是否正确 + if (textMTFC_G.toDouble() < 1.0 || textMTFC_G.toDouble() > 19.0) + { + ui.lineEdit_INFO->setText(QString::fromLocal8Bit("检查噪声增益G输入")); + return 0.0; + } + else + { + MTFC_G = textMTFC_G.toDouble(); + ui.lineEdit_INFO->clear(); + } + //5.检查SNR输入是否正确 + if (textSNR.toDouble() < 0.0 || textSNR.toDouble() > 130.0) + { + ui.lineEdit_INFO->setText(QString::fromLocal8Bit("检查信噪比SNR输入")); + return 0.0; + } + else + { + SNR = textSNR.toDouble(); + ui.lineEdit_INFO->clear(); + } + //6.检查PIQE输入是否正确 + if (textPIQE.toDouble() == 0.0) + { + ui.lineEdit_INFO->setText(QString::fromLocal8Bit("检查PIQE")); + return 0.0; + } + else + { + PIQE = textPIQE.toDouble(); + ui.lineEdit_INFO->clear(); + } + + double NIIRS = 0.0; + double formulaPara = 0.0; + double GSD_inch; + if (ui.radioVisible->isChecked()) + formulaPara = 10.251; + else if (ui.radioMidInfrared->isChecked() || ui.radioLonInfrared->isChecked()) + formulaPara = 10.751; + //需要将米转换到英寸 + GSD_inch = sqrtf(GSDX * GSDY) * 39.3700787402; + + //RER>=0.9:a=3.32,b=1.559;RER<0.9:a=3.16,b=2.817 + double para_a = RER >= 0.9 ? 3.32 : 3.16; + double para_b = RER >= 0.9 ? 1.559 : 2.817; + //qDebug() << "--a:" << a << ", b:" << b << ",para:" << para; + //可见10.251 - a*lgGSD + b*lgRER - 0.656*H - 0.334*(G/SNR) + //红外10.751 - a*lgGSD + b*lgRER - 0.656*H - 0.334*(G/SNR) + NIIRS = formulaPara - (para_a * log10(GSD_inch)) + (para_b * log10(RER)) - (0.656 * MTFC_H) - (0.334 * (MTFC_G / SNR)); + + ui.lineEdit_NIIRS->setText(QString::number(NIIRS, 10, 4)); + + + + + QString outXMLPath = mResultXmlDir; + std::cout << outXMLPath.toStdString() << std::endl; + if (outXMLPath == "") + QMessageBox::information(this, tr("information"), tr("The Product Dir is Empty")); + else + { + //outXMLPath = outXMLPath + "/result.xml"; + //ui.lineEdit_INFO->setText(outXMLPath); + std::cout << outXMLPath.toStdString() << std::endl; + QFile file(outXMLPath); + if (!file.open(QIODevice::WriteOnly | QIODevice::Truncate)) + {//之前的内容被清空 + qDebug() << QStringLiteral("打开") << outXMLPath << QStringLiteral("文件失败！"); + return 0.0; + } + QDomDocument doc; + QDomProcessingInstruction processInstruction = doc.createProcessingInstruction("xml", "version=\"1.0\" encoding=\"UTF-8\""); + doc.appendChild(processInstruction); + + //根节点 + QDomElement root = doc.createElement("Root"); + doc.appendChild(root); + + { + //节点1 Metadata + QDomElement paraMetadata = doc.createElement("Metadata"); + root.appendChild(paraMetadata); + + QDomElement paraMeta1 = doc.createElement("Path"); + QDomText paraMeta1Text = doc.createTextNode(ui.lineEdit_img->text()); + paraMeta1.appendChild(paraMeta1Text); + paraMetadata.appendChild(paraMeta1); + + QDomElement paraMeta2 = doc.createElement("Time"); + QDomText paraMeta2Text = doc.createTextNode("2023.02.14"); + paraMeta2.appendChild(paraMeta2Text); + paraMetadata.appendChild(paraMeta2); + + QDomElement paraMetaLTx = doc.createElement("LeftTopX"); + QDomText paraMetaLTxText = doc.createTextNode(mXmlTopLeftX); + paraMetaLTx.appendChild(paraMetaLTxText); + paraMetadata.appendChild(paraMetaLTx); + + QDomElement paraMetaLTy = doc.createElement("LeftTopY"); + QDomText paraMetaLTyText = doc.createTextNode(mXmlTopLeftY); + paraMetaLTy.appendChild(paraMetaLTyText); + paraMetadata.appendChild(paraMetaLTy); + + QDomElement paraMetaRBx = doc.createElement("RightBottomX"); + QDomText paraMetaRBxText = doc.createTextNode(mXmlBottomRightX); + paraMetaRBx.appendChild(paraMetaRBxText); + paraMetadata.appendChild(paraMetaRBx); + + QDomElement paraMetaRBy = doc.createElement("RightBottomY"); + QDomText paraMetaRByText = doc.createTextNode(mXmlBottomRightY); + paraMetaRBy.appendChild(paraMetaRByText); + paraMetadata.appendChild(paraMetaRBy); + } + { + //节点2 NIIRS参数及结果 + QDomElement paraResultNiirs = doc.createElement("NIIRS"); + root.appendChild(paraResultNiirs); + + QDomElement paraNiirsPara = doc.createElement("Parameters"); + paraResultNiirs.appendChild(paraNiirsPara); + + QDomElement paraGSDX = doc.createElement("GSDX"); + QDomText paraGSDXText = doc.createTextNode(ui.lineEdit_GSDX->text()); + paraGSDX.appendChild(paraGSDXText); + paraNiirsPara.appendChild(paraGSDX); + + QDomElement paraGSDY = doc.createElement("GSDY"); + QDomText paraGSDYText = doc.createTextNode(ui.lineEdit_GSDY->text()); + paraGSDY.appendChild(paraGSDYText); + paraNiirsPara.appendChild(paraGSDY); + + QDomElement paraRER = doc.createElement("RER"); + QDomText paraRERTest = doc.createTextNode(ui.lineEdit_RER->text()); + paraRER.appendChild(paraRERTest); + paraNiirsPara.appendChild(paraRER); + + QDomElement paraH = doc.createElement("H"); + QDomText paraHText = doc.createTextNode(ui.lineEdit_H->text()); + paraH.appendChild(paraHText); + paraNiirsPara.appendChild(paraH); + + QDomElement paraG = doc.createElement("NoiseGain"); + QDomText paraGText = doc.createTextNode(ui.lineEdit_MTFC_G->text()); + paraG.appendChild(paraGText); + paraNiirsPara.appendChild(paraG); + + QDomElement paraSNR = doc.createElement("SNR"); + QDomText paraSNRText = doc.createTextNode(ui.lineEdit_SNR->text()); + paraSNR.appendChild(paraSNRText); + paraNiirsPara.appendChild(paraSNR); + + QDomElement paraNiirsPIQE = doc.createElement("PIQE"); + QDomText paraNiirsPIQEText = doc.createTextNode(ui.lineEdit_PIQE->text()); + paraNiirsPIQE.appendChild(paraNiirsPIQEText); + paraNiirsPara.appendChild(paraNiirsPIQE); + + + + QDomElement paraNiirsScore = doc.createElement("Result"); + paraResultNiirs.appendChild(paraNiirsScore); + + QDomElement paraTiffType = doc.createElement("Type"); + QDomText paraTiffTypeText; + if (ui.radioVisible->isChecked()) + paraTiffTypeText = doc.createTextNode("Visible"); + else if (ui.radioMidInfrared->isChecked()) + paraTiffTypeText = doc.createTextNode("MiddleInfrared"); + else if (ui.radioLonInfrared->isChecked()) + paraTiffTypeText = doc.createTextNode("LongWaveInfrared"); + paraTiffType.appendChild(paraTiffTypeText); + paraNiirsScore.appendChild(paraTiffType); + + QDomElement paraScore = doc.createElement("NiirsScore"); + QDomText paraScoreText = doc.createTextNode(ui.lineEdit_NIIRS->text()); + paraScore.appendChild(paraScoreText); + paraNiirsScore.appendChild(paraScore); + } + { + //节点3 十个客观参数 + QDomElement paraObjectiveParameter = doc.createElement("ObjectiveParameter"); + root.appendChild(paraObjectiveParameter); + + QDomElement paraLight = doc.createElement("ImageLight"); + QDomText paraLightText = doc.createTextNode(QString::number(imgLight, 10, 6)); + paraLight.appendChild(paraLightText); + paraObjectiveParameter.appendChild(paraLight); + + QDomElement paraStdDev = doc.createElement("ImageStdDev"); + QDomText paraStdDevText = doc.createTextNode(QString::number(imgStdDev, 10, 6)); + paraStdDev.appendChild(paraStdDevText); + paraObjectiveParameter.appendChild(paraStdDev); + + QDomElement paraContrastRadio = doc.createElement("ImageContrastRadio"); + QDomText paraContrastRadioText = doc.createTextNode(QString::number(imgContrastRadio, 10, 6)); + paraContrastRadio.appendChild(paraContrastRadioText); + paraObjectiveParameter.appendChild(paraContrastRadio); + + QDomElement paraEntropy = doc.createElement("ImageEntropy"); + QDomText paraEntropyText = doc.createTextNode(QString::number(imgEntropy, 10, 6)); + paraEntropy.appendChild(paraEntropyText); + paraObjectiveParameter.appendChild(paraEntropy); + + QDomElement paraSNR = doc.createElement("ImageSNR"); + QDomText paraSNRText = doc.createTextNode(QString::number(imgSNR, 10, 6)); + paraSNR.appendChild(paraSNRText); + paraObjectiveParameter.appendChild(paraSNR); + + QDomElement paraRER = doc.createElement("ImageRER"); + QDomText paraRERText = doc.createTextNode(QString::number(imgRER, 10, 6)); + paraRER.appendChild(paraRERText); + paraObjectiveParameter.appendChild(paraRER); + + QDomElement paraGradient = doc.createElement("ImageGradient"); + QDomText paraGradientText = doc.createTextNode(QString::number(imgGradient, 10, 6)); + paraGradient.appendChild(paraGradientText); + paraObjectiveParameter.appendChild(paraGradient); + + QDomElement paraDefinition = doc.createElement("ImageDefinition"); + QDomText paraDefinitionText = doc.createTextNode(QString::number(imgDefinition, 10, 6)); + paraDefinition.appendChild(paraDefinitionText); + paraObjectiveParameter.appendChild(paraDefinition); + + QDomElement paraASM = doc.createElement("ImageASM"); + QDomText paraASMText = doc.createTextNode(QString::number(imgASM, 10, 6)); + paraASM.appendChild(paraASMText); + paraObjectiveParameter.appendChild(paraASM); + + QDomElement paraPIQE = doc.createElement("ImagePIQE"); + QDomText paraPIQEText = doc.createTextNode(QString::number(imgPIQE, 10, 6)); + paraPIQE.appendChild(paraPIQEText); + paraObjectiveParameter.appendChild(paraPIQE); + } + QTextStream outFile(&file); + doc.save(outFile, 4);//缩进4格 + file.close(); + + ui.lineEdit_INFO->setText("result files save at:" + outXMLPath); + } + + return NIIRS; +} + +double NIIRS::calculateRER() +{ + ui.lineEdit_INFO->clear(); + + GDALAllRegister(); + CPLSetConfigOption("GDAL_FILENAME_IS_UTF8", "NO"); + + //QString inEdgeImg = mXmlImgPath; + QString inEdgeImg = ui.lineEdit_img->text(); + //ui.lineEdit_INFO->setText(inEdgeImg); + //double imgRERValue = 0.0, imgHValue = 0.0; + cv::Mat imgTarget; + + if (inEdgeImg == "") + { + ui.lineEdit_INFO->setText(QString::fromLocal8Bit("无刃边数据")); + return 0.0; + } + + string tiffEdgePath = inEdgeImg.toStdString(); + GDALDataset* poDataset; + poDataset = (GDALDataset*)GDALOpen(tiffEdgePath.c_str(), GA_ReadOnly); + if (poDataset == nullptr) + { + GDALClose(poDataset); + ui.lineEdit_INFO->setText(QString::fromLocal8Bit("刃边图像无法解析")); + return 0.0; + } + + //GDALRasterBand* rasterBand = poDataset->GetRasterBand(1); + int currentBand = ui.comboBoxBand->currentIndex(); + GDALRasterBand* rasterBand = poDataset->GetRasterBand(currentBand + 1); + GDALDataType dataType = rasterBand->GetRasterDataType(); + int inImgWidth = rasterBand->GetXSize(); + int inImgHeight = rasterBand->GetYSize(); + + //int topLeftX = mXmlTopLeftX.toInt(); + //int topLeftY = mXmlTopLeftY.toInt(); + //int bottomRightX = mXmlBottomRightX.toInt(); + //int bottomRightY = mXmlBottomRightY.toInt(); + + int topLeftX = ui.lineEditLTx->text().toInt(); + int topLeftY = ui.lineEditLTy->text().toInt(); + int bottomRightX = ui.lineEditRBx->text().toInt(); + int bottomRightY = ui.lineEditRBy->text().toInt(); + + int clipWidth = bottomRightX - topLeftX; + int clipHeight = bottomRightY - topLeftY; + if (clipWidth == 0 && clipHeight == 0) + { + clipWidth = inImgWidth; + clipHeight = inImgHeight; + } + + //读取x方向刃边图 + if (dataType == GDT_Byte) + { + unsigned char* bandBuf = new unsigned char[clipWidth * clipHeight]; + if (rasterBand->RasterIO(GF_Read, topLeftX, topLeftY, clipWidth, clipHeight, bandBuf, clipWidth, clipHeight, dataType, 0, 0) != 0) + { + ui.lineEdit_INFO->setText(QString::fromLocal8Bit("输入数据解析错误")); + return -1.0; + } + imgTarget = cv::Mat(clipHeight, clipWidth, CV_8UC1, bandBuf); + } + else if (dataType == GDT_UInt16) + { + unsigned short* bandBuf = new unsigned short[clipWidth * clipHeight]; + + if (rasterBand->RasterIO(GF_Read, topLeftX, topLeftY, clipWidth, clipHeight, bandBuf, clipWidth, clipHeight, dataType, 0, 0) != 0) + { + ui.lineEdit_INFO->setText(QString::fromLocal8Bit("输入数据解析错误")); + return -1.0; + } + cv::Mat img = cv::Mat(clipHeight, clipWidth, CV_16UC1, bandBuf); + double min, max; + cv::minMaxLoc(img, &min, &max); + img.convertTo(imgTarget, CV_8UC1, 255.0 / max, 0); + } + else + { + ui.lineEdit_INFO->setText(QString::fromLocal8Bit("输入影像数据类型无法用于RER计算")); + return -1.0; + } + GDALClose(poDataset); + + //根据两点，计算斜率k，判断为x方向或y方向 + //double slopeK = 0.0; + //QPairedgeVals; + //if (slopeK >= -1 && slopeK <= 1) + // edgeVals = calculateERx(imgTarget); + //else + // edgeVals = calculateERy(imgTarget); + //imgRERValue = edgeVals.first; + //imgHValue = edgeVals.second; + //ui.lineEdit_RER->setText(QString::number(imgRERValue, 10, 4)); + //ui.lineEdit_H->setText(QString::number(imgHValue, 10, 4)); + cv::Mat imgGray, imgGrayX4; + if (imgTarget.channels() == 3) + cv::cvtColor(imgTarget, imgGray, CV_BGR2GRAY); + else + imgGray = imgTarget; + + cv::pyrUp(imgGray, imgGrayX4); + cv::pyrUp(imgGrayX4, imgGrayX4);//重采样为原来4倍，用于1/4像元大小的超采样 + cv::Mat imgThres; + cv::threshold(imgGrayX4, imgThres, 130, 255, cv::THRESH_BINARY); + QString thresholdPath = mXmlProductDir + "/threshold.bmp"; + cv::imwrite(thresholdPath.toStdString().c_str(), imgThres); + + cv::Mat imgCanny; + cv::Canny(imgThres, imgCanny, 100, 200);//canny边缘提取 + QString cannyPath = mXmlProductDir + "/canny.bmp"; + cv::imwrite(cannyPath.toStdString().c_str(), imgCanny); + + vectorR_lines; + //cv::HoughLinesP(imgCanny, R_lines, 1, CV_PI / 180, 100, 25, 100);//提取直线 + cv::HoughLinesP(imgCanny, R_lines, 1, CV_PI / 180, 50, 50, 10);//提取直线 + + QMapmap_rer_h;//key RER, value H + + if (R_lines.size() == 0) + { + //qDebug() << "find no line in edge x"; + QMessageBox::information(this, "information", "find no edge in image\n" + inEdgeImg); + //ui.lineEdit_INFO->setText(QString::fromLocal8Bit("无法从输入数据中提取刃边")); + + return 0.0; + } + double maxRER = 0.0; + + omp_set_num_threads(8); +#pragma omp parallel for + for (int z = 0; z < R_lines.size(); z++) + { + cv::Vec4i p = R_lines[z]; + + cv::Point pointA = cv::Point(p[0], p[1]); + cv::Point pointB = cv::Point(p[2], p[3]); + + QPairrer_h = calculate_rer_h(imgGrayX4, pointA, pointB); + if (rer_h.first > maxRER) + { + mRerEdgePointA = pointA; + mRerEdgePointB = pointB; + } + map_rer_h.insert(rer_h.first, rer_h.second); + } + + QString edgeOutPath = mXmlProductDir + "/edges.bmp"; + QString outTxt; + //在输出文件夹生成txt + QDir productDir(mXmlProductDir); + outTxt = productDir.absoluteFilePath("edges.txt"); + QFile txtFile(outTxt); + txtFile.open(QIODevice::WriteOnly | QIODevice::Text | QIODevice::Truncate);//Truncate:覆盖文本///Append:在文本最后追加内容 + QTextStream stream(&txtFile); + stream << QString("max rer edge: LTx:%1, LTy:%2; RBx:%3, RBy:%4\n") + .arg(mRerEdgePointA.x).arg(mRerEdgePointA.y).arg(mRerEdgePointB.x).arg(mRerEdgePointB.y); + cv::cvtColor(imgGrayX4, imgGrayX4, CV_GRAY2BGR); + for (int z = 0; z < R_lines.size(); z++) + { + cv::Vec4i p = R_lines[z]; + + cv::Point pointA = cv::Point(p[0], p[1]); + cv::Point pointB = cv::Point(p[2], p[3]); + + cv::line(imgGrayX4, pointA, pointB, cv::Scalar(255, 255, 0)); + stream << QString("edge:%1, LTx:%2, LTy:%3; RBx:%4, RBy:%5\n").arg(z).arg(p[0]).arg(p[1]).arg(p[2]).arg(p[3]); + } + stream << "\n"; + txtFile.close(); + + cv::imwrite(edgeOutPath.toStdString().c_str(), imgGrayX4); + + maxRER = map_rer_h.lastKey(); + double maxH = map_rer_h.value(maxRER); + + ui.lineEdit_RER->setText(QString::number(maxRER, 10, 4)); + ui.lineEdit_H->setText(QString::number(maxH, 10, 4)); + + imgRER = maxRER; + + return maxRER; + + + ///////////////////////////////////////////////////////////////////////////////////////////// + + + //QString pathTargetX = mEdgeXPath; + //QString pathTargetY = mEdgeYPath; + ////cv::Mat imgTargetX, imgTargetY; + //if (pathTargetX == "" || pathTargetY == "") + //{ + // ui.lineEdit_INFO->setText(QString::fromLocal8Bit("未打开刃边数据")); + // return 0.0; + //} + + ////GDALAllRegister(); + ////CPLSetConfigOption("GDAL_FILENAME_IS_UTF8", "NO"); + //string tiffXPath = pathTargetX.toStdString(); + //string tiffYPath = pathTargetY.toStdString(); + //GDALDataset* poDatasetX; + //GDALDataset* poDatasetY; + //poDatasetX = (GDALDataset*)GDALOpen(tiffXPath.c_str(), GA_ReadOnly); + //poDatasetY = (GDALDataset*)GDALOpen(tiffYPath.c_str(), GA_ReadOnly); + //if (NULL == poDatasetX) + //{ + // GDALClose(poDatasetX); + // GDALClose(poDatasetY); + // ui.lineEdit_INFO->setText(QString::fromLocal8Bit("X方向刃边图像无法解析")); + // return -1.0; + //} + //if (NULL == poDatasetY) + //{ + // GDALClose(poDatasetX); + // GDALClose(poDatasetY); + // ui.lineEdit_INFO->setText(QString::fromLocal8Bit("Y方向刃边图像无法解析")); + // return -1.0; + //} + //GDALRasterBand* rasterXBand = poDatasetX->GetRasterBand(1); + //GDALRasterBand* rasterYBand = poDatasetY->GetRasterBand(1); + //GDALDataType dataTypeX = rasterXBand->GetRasterDataType(); + //GDALDataType dataTypeY = rasterYBand->GetRasterDataType(); + //int xwidth = rasterXBand->GetXSize(); + //int xheight = rasterXBand->GetYSize(); + //int ywidth = rasterYBand->GetXSize(); + //int yheight = rasterYBand->GetYSize(); + + //cv::Mat imgTargetX, imgTargetY; + ////读取x方向刃边图 + //if (dataTypeX == GDT_Byte) + //{ + // unsigned char* bandBuf = new unsigned char[xwidth * xheight]; + // if (rasterXBand->RasterIO(GF_Read, 0, 0, xwidth, xheight, bandBuf, xwidth, xheight, dataTypeX, 0, 0) != 0) + // { + // ui.lineEdit_INFO->setText(QString::fromLocal8Bit("输入数据解析错误")); + // return -1.0; + // } + // imgTargetX = cv::Mat(xheight, xwidth, CV_8UC1, bandBuf); + //} + //else if (dataTypeX == GDT_UInt16) + //{ + // unsigned short* bandBuf = new unsigned short[xwidth * xheight]; + // if (rasterXBand->RasterIO(GF_Read, 0, 0, xwidth, xheight, bandBuf, xwidth, xheight, dataTypeX, 0, 0) != 0) + // { + // ui.lineEdit_INFO->setText(QString::fromLocal8Bit("输入数据解析错误")); + // return -1.0; + // } + // cv::Mat img = cv::Mat(xheight, xwidth, CV_16UC1, bandBuf); + // double min, max; + // cv::minMaxLoc(img, &min, &max); + // img.convertTo(imgTargetX, CV_8UC1, 255.0 / max, 0); + // //cv::imwrite("D:\\A_testdata\\niirs_test\\imgTargetX.png", imgTargetX); + //} + //else + //{ + // ui.lineEdit_INFO->setText(QString::fromLocal8Bit("输入影像数据类型无法用于RER计算")); + // return -1.0; + //} + ////读取y方向刃边图 + //if (dataTypeY == GDT_Byte) + //{ + // unsigned char* bandBuf = new unsigned char[ywidth * yheight]; + // if (rasterYBand->RasterIO(GF_Read, 0, 0, ywidth, yheight, bandBuf, ywidth, yheight, dataTypeY, 0, 0) != 0) + // { + // ui.lineEdit_INFO->setText(QString::fromLocal8Bit("输入数据解析错误")); + // return -1.0; + // } + // imgTargetY = cv::Mat(yheight, ywidth, CV_8UC1, bandBuf); + //} + //else if (dataTypeY == GDT_UInt16) + //{ + // unsigned short* bandBuf = new unsigned short[ywidth * yheight]; + // if (rasterYBand->RasterIO(GF_Read, 0, 0, ywidth, yheight, bandBuf, ywidth, yheight, dataTypeY, 0, 0) != 0) + // { + // ui.lineEdit_INFO->setText(QString::fromLocal8Bit("输入数据解析错误")); + // return -1.0; + // } + // cv::Mat img = cv::Mat(yheight, ywidth, CV_16UC1, bandBuf); + // double min, max; + // cv::minMaxLoc(img, &min, &max); + // img.convertTo(imgTargetY, CV_8UC1, 255.0 / max, 0); + // //cv::imwrite("D:\\A_testdata\\niirs_test\\imgTargetY.png", imgTargetY); + //} + //else + //{ + // ui.lineEdit_INFO->setText(QString::fromLocal8Bit("输入影像数据类型无法用于RER计算")); + // return -1.0; + //} + + //double imgRER, imgH; + //QPairedgeXVals = calculateERx(imgTargetX); + //imgRER = edgeXVals.first; + //imgH = edgeXVals.second; + //ui.lineEdit_RER->setText(QString::number(imgRER, 10, 4)); + //ui.lineEdit_H->setText(QString::number(imgH, 10, 4)); + + //qDebug() << "--6.img RER: " << imgRER; + //qDebug() << "----img H:" << imgH; + + //GDALClose(poDatasetX); + //GDALClose(poDatasetY); + + //return imgRER; +} + +double NIIRS::calculateMeanStdDev(cv::Mat img) +{ + GDALAllRegister(); + CPLSetConfigOption("GDAL_FILENAME_IS_UTF8", "NO"); //中文路径 + //gdal读取待评价影像，逐波段计算平均值计算亮度 + QString evaluateImg = ui.lineEdit_img->text(); + if (evaluateImg.isEmpty()) + { + QMessageBox::warning(this, "warning", "The image to be evaluated is not opened"); + return -1; + } + string tiffPath = evaluateImg.toStdString(); + GDALDataset* hSrcDS; + hSrcDS = (GDALDataset*)GDALOpen(tiffPath.c_str(), GA_ReadOnly); + if (NULL == hSrcDS) + { + GDALClose(hSrcDS); + return -1; + } + GDALRasterBand* rasterBand; + qDebug() << "--Img Light&StdDev"; + for (int i = 0; i < hSrcDS->GetRasterCount(); i++) + { + rasterBand = hSrcDS->GetRasterBand(i + 1); + double bandMin = 0; + double bandMax = 0; + double bandMean = 0; + double bandStdDev = 0; + rasterBand->ComputeStatistics(false, &bandMin, &bandMax, &bandMean, &bandStdDev, NULL, NULL); + qDebug() << "---band:" << i + 1 << endl << "----Light:" << bandMean << endl << "----StdDev:" << bandStdDev; + } + delete hSrcDS; + return 0.0; +} + +//float NIIRS::calculateStdDev(cv::Mat img) +//{ +// //转为灰度图 +// cv::Mat imgGray; +// if (img.channels() == 3) +// cv::cvtColor(img, imgGray, CV_BGR2GRAY); +// else +// imgGray = img; +// +// cv::Scalar meanScalar, stdDevScalar; +// cv::meanStdDev(imgGray, meanScalar, stdDevScalar); +// +// float imgStdDev = stdDevScalar.val[0]; +// return imgStdDev; +//} + +double NIIRS::calculateTiffEntropy(unsigned short* bandBuf, int bufSize, int maxVal) +{ + //获取最大值，赋值temp，用于计算每级亮度累加值，使用vector + //1.计算每个亮度累加值 + //遍历数组，每个像元的亮度使对应vector位置的数量+1 + QVectortemp(maxVal + 1); + std::cout << "max:" << maxVal + 1 << std::endl; + for (int i = 0; i < bufSize; i++) + { + auto pos = bandBuf[i]; + int tempAddr = pos / 1; + temp[tempAddr] = temp[tempAddr] + 1; + } + + //2.计算每个亮度的概率 + for (int i = 0; i < temp.size(); i++) + temp[i] = temp[i] / double(bufSize); + //3.计算图像信息熵 + float result = 0; + for (int i = 0; i < temp.size(); i++) + { + if (temp[i] == 0.0) + result = result; + else + result = result - temp[i] * (std::log(temp[i]) / std::log(2.0));//公式 + } + + return result; +} + +//double NIIRS::calculateEntropy(cv::Mat img) +//{ +// double temp[256] = { 0.0 }; +// +// //计算每个像素的累积值 +// for (int m = 0; m < img.rows; m++) +// { +// const uchar* t = img.ptr(m); +// for (int n = 0; n < img.cols; n++) +// { +// int i = t[n]; +// temp[i] = temp[i] + 1; +// } +// } +// //计算每个像素的概率 +// for (int i = 0; i < 256; i++) +// { +// temp[i] = temp[i] / (img.rows * img.cols); +// } +// float result = 0; +// //计算图像信息熵 +// for (int i = 0; i < 256; i++) +// { +// if (temp[i] == 0.0) +// result = result; +// else +// result = result - temp[i] * (log(temp[i]) / log(2.0)); +// } +// +// return result; +//} + +double NIIRS::calculateContrastRatio(cv::Mat img) +{ + cv::Mat imgGray; + if (img.channels() == 3) + cv::cvtColor(img, imgGray, CV_BGR2GRAY); + else + imgGray = img; + + cv::Mat imgExtend; + cv::copyMakeBorder(imgGray, imgExtend, 1, 1, 1, 1, cv::BORDER_REPLICATE);//以边缘值四周各扩充1像元 + imgExtend = imgExtend / 1.0; + + int row = imgExtend.rows; + int col = imgExtend.cols; + double bb = 0.0; + for (int i = 1; i < row - 1; i++) + { + for (int j = 1; j < col - 1; j++) + { + double center = imgExtend.at(i, j); + bb += pow(center - imgExtend.at(i, j - 1), 2) + pow(center - imgExtend.at(i, j + 1), 2) + + pow(center - imgExtend.at(i - 1, j), 2) + pow(center - imgExtend.at(i + 1, j), 2); + } + } + double cr = bb / (4 * (row - 2) * (col - 2) + 3 * (2 * (row - 2) + 2 * (col - 2)) + 2 * 4); + return cr; +} + +//double NIIRS::calculateSNR(cv::Mat img) +//{ +// cv::Mat imgGray; +// if (img.channels() == 3) +// cv::cvtColor(img, imgGray, CV_BGR2GRAY); +// else +// imgGray = img; +// +// cv::Scalar meanScalar, stdDevScalar; +// cv::meanStdDev(imgGray, meanScalar, stdDevScalar); +// double snr = meanScalar.val[0] / stdDevScalar.val[0]; +// return snr; +//} + +//float NIIRS::SFRCalculation(cv::Mat& ROI, double gamma) +//{ +// if (ROI.empty()) +// { +// std::cerr << "Open the ROI image error" << std::endl; +// return 0; +// } +// int height = ROI.rows, width = ROI.cols; +// //进行伽马解码 +// de_Gamma(ROI, gamma); +// int i, j; +// double slope = 0, intercept = 0; +// //中心质心偏移Center centroid offset +// double CCoffset = 0; +// std::vector y_shifts(height); +// //计算图像中心的质心和质心之间的偏移 +// std::vector Cen_Shifts = CentroidFind(ROI, y_shifts, &CCoffset); +// if (Cen_Shifts.empty()) +// return 0; +// //斜边拟合，线性回归 +// SLR(Cen_Shifts, y_shifts, &intercept, &slope); +// //将数据行数截断为最大斜率周期，该周期将具有整数个完整相位旋转 +// ReduceRows(slope, &height); +// //将CCoffset更新为图像的原始中点与计算的参考中点之间的偏移 +// CCoffset = CCoffset + 0.5 + intercept - width / 2; +// //进行过采样 +// int SamplingLen = width * 4; +// std::vector OverSamplingData = OverSampling(ROI, slope, CCoffset, height, width, &SamplingLen); +// //利用汉明窗对数据两侧的纹波信号进行滤波 +// OverSamplingData = HammingWindows(OverSamplingData, SamplingLen); +// //离散傅里叶变换 +// DFT(OverSamplingData, SamplingLen); +// width = int(SamplingLen / 4); +// double maxData = 0; +// for (i = 0; i < SamplingLen; ++i) +// { +// if (OverSamplingData[i] != 0) +// { +// maxData = OverSamplingData[i]; +// break; +// } +// } +// for (int i = 0; i < SamplingLen; ++i) +// OverSamplingData[i] /= maxData; +// vector >vec_mtf; +// for (i = 0; i <= width; ++i) +// { +// double frequency = (double)i / width; +// pairmtf_single; +// mtf_single.first = frequency; +// mtf_single.second = OverSamplingData[i]; +// vec_mtf.push_back(mtf_single); +// //mtf_file << frequency << "," << OverSamplingData[i] << "\n"; +// } +// +// +// return 0.0; +//} + +//void NIIRS::DFT(std::vector& data, int size) +//{ +// int i, j; +// std::complex* arr = new std::complex[size]; +// for (i = 0; i < size; ++i) +// { +// arr[i] = std::complex(data[i], 0); +// } +// +// for (i = 0; i < size / 2.0; ++i) +// { +// std::complex temp = 0; +// for (j = 0; j < size; ++j) +// { +// double w = 2 * 3.1415 * i * j / size; +// std::complex deg(cos(w), -sin(w)); +// temp += arr[j] * deg; +// } +// data[i] = sqrt(temp.real() * temp.real() + temp.imag() * temp.imag()); +// } +//} + +//std::vector NIIRS::HammingWindows(std::vector& deSampling, int SamplingLen) +//{ +// int i, j; +// std::vector tempData(SamplingLen); +// +// //We want to shift the peak data to the center of line spread function data +// //Because we will do the hamming window later, this will keep the important data away from filtering +// //In case there are two peaks, we use two variable to record the peak data position +// int L_location = -1, R_location = -1; +// +// double SamplingMax = 0; +// for (i = 0; i < SamplingLen; ++i) +// { +// if (fabs(deSampling[i]) > fabs(SamplingMax)) +// SamplingMax = deSampling[i]; +// } +// +// for (i = 0; i < SamplingLen; ++i) +// { +// if (deSampling[i] == SamplingMax) +// { +// if (L_location < 0) { L_location = i; } +// R_location = i; +// } +// } +// +// //the shift amount +// int PeakOffset = (R_location + L_location) / 2 - SamplingLen / 2; +// +// if (PeakOffset) +// { +// for (i = 0; i < SamplingLen; ++i) +// { +// int newIndex = i - PeakOffset; +// if (newIndex >= 0 && newIndex < SamplingLen) { tempData[newIndex] = deSampling[i]; } +// } +// } +// else +// { +// for (i = 0; i < SamplingLen; ++i) { tempData[i] = deSampling[i]; } +// } +// +// //do the hamming window filtering +// for (int i = 0; i < SamplingLen; ++i) +// { +// tempData[i] = tempData[i] * (0.54 - 0.46 * cos(2 * CV_PI * i / (SamplingLen - 1))); +// } +// +// return tempData; +//} + +//std::vector NIIRS::OverSampling(cv::Mat& Src, double slope, double CCoffset, int height, int width, int* SamplingLen) +//{ +// std::vector RowShifts(height, 0); +// +// int i, j, k; +// int halfY = height >> 1; +// +// //calculate the pixel shift of each row to align the centroid of each row as close as possible +// for (i = 0; i < height; ++i) { RowShifts[i] = (double)(i - halfY) / slope + CCoffset; } +// +// //DataMap -> to record the index of pixel after shift +// //Datas -> to record the original pixel value +// std::vector DataMap(height * width, 0); +// std::vector Datas(height * width, 0); +// +// for (i = 0, k = 0; i < height; ++i) +// { +// int baseindex = width * i; +// for (j = 0; j < width; ++j) +// { +// DataMap[baseindex + j] = j - RowShifts[i]; +// Datas[baseindex + j] = int(Src.at(i, j)); +// } +// } +// +// std::vector SamplingBar(*SamplingLen, 0); +// std::vector MappingCount(*SamplingLen, 0); +// +// //Start to mapping the original data to 4x sampling data and record the count of each pixel in 4x sampling data +// for (i = 0; i < height * width; ++i) +// { +// int MappingIndex = static_cast(4 * DataMap[i]); +// if (MappingIndex >= 0 && MappingIndex < *SamplingLen) +// { +// SamplingBar[MappingIndex] = SamplingBar[MappingIndex] + Datas[i]; +// MappingCount[MappingIndex]++; +// } +// } +// +// //average the pixel value in 4x sampling data, if the pixel value in pixel is zero, copy the value of close pixel +// for (i = 0; i < *SamplingLen; ++i) { +// j = 0; +// k = 1; +// if (MappingCount[i] == 0) { +// +// if (i == 0) { +// while (!j) +// { +// if (MappingCount[i + k] != 0) +// { +// SamplingBar[i] = SamplingBar[i + k] / ((double)MappingCount[i + k]); +// j = 1; +// } +// else ++k; +// } +// } +// else { +// while (!j && ((i - k) >= 0)) +// { +// if (MappingCount[i - k] != 0) +// { +// SamplingBar[i] = SamplingBar[i - k]; /* Don't divide by counts since it already happened in previous iteration */ +// j = 1; +// } +// else ++k; +// } +// if ((i - k) < 0) +// { +// k = 1; +// while (!j) +// { +// if (MappingCount[i + k] != 0) +// { +// SamplingBar[i] = SamplingBar[i + k] / ((double)MappingCount[i + k]); +// j = 1; +// } +// else ++k; +// } +// } +// } +// } +// else +// SamplingBar[i] = (SamplingBar[i]) / ((double)MappingCount[i]); +// } +// +// // reduce the length of sampling data +// // because the datas at the edge are only matters, we truncate the data close to the two side of sampling data +// // which has very small contribution to the result +// +// //truncating the data smaller than 10% and bigger than 90% of original length +// int originalSamplingLen = *SamplingLen; +// *SamplingLen = *SamplingLen * 0.8; +// +// std::vector deSampling(*SamplingLen, 0); +// //derivative sampling data(which is ESF) to get the line spread function +// for (i = originalSamplingLen * 0.1, j = 1; i < originalSamplingLen, j < *SamplingLen; ++i, ++j) +// { +// deSampling[j] = SamplingBar[i + 1] - SamplingBar[i]; +// } +// +// return deSampling; +//} + +//void NIIRS::ReduceRows(double slope, int* ImgHeight) +//{ +// double tempSlope = fabs(slope); +// int cycs = (*ImgHeight) / tempSlope; +// if (tempSlope * cycs < *ImgHeight) { *ImgHeight = tempSlope * cycs; } +//} + +//void NIIRS::SLR(std::vector& Cen_Shifts, std::vector& y_shifts, double* a, double* b) +//{ +// //a -> intercept, b->slope of slanted edge +// +// int y_size = y_shifts.size(); +// //using simple linear regression to solve equation and get slope and intercept +// double xsquare = 0, xavg = 0, yavg = 0; +// int i; +// for (i = 0; i < y_size; ++i) +// { +// yavg += y_shifts[i]; +// xavg += Cen_Shifts[i]; +// } +// xavg /= (double)y_size; +// yavg /= (double)y_size; +// +// //simple linear regession +// for (i = 0; i < y_size; ++i) +// { +// double temp = (Cen_Shifts[i] - xavg); +// *b += temp * (y_shifts[i] - yavg); +// xsquare += temp * temp; +// } +// +// *b /= xsquare; +// *a = yavg - (*b) * xavg; +//} + +//std::vector NIIRS::CentroidFind(cv::Mat& Src, std::vector& y_shifts, double* CCoffset) +//{ +// std::vector Cen_Shifts(Src.rows); +// int i, j, height = Src.rows, width = Src.cols; +// +// cv::Mat tempSrc(Src.size(), CV_8UC1); +// +// //Do the bilaterFilter on template ROI image to make sure we can find the slanted edge more acurrate +// cv::bilateralFilter(Src, tempSrc, 3, 200, 200); +// +// // calculate the centroid of every row in ROI +// // centroid formuld: Total moments/Total amount +// for (i = 0; i < height; ++i) +// { +// double molecule = 0, denominator = 0, temp = 0; +// uchar* tempSrcPtr = tempSrc.ptr(i); +// for (j = 0; j < width - 1; ++j) +// { +// temp = (double)tempSrcPtr[j + 1] - (double)tempSrcPtr[j]; +// molecule += temp * j; +// denominator += temp; +// } +// Cen_Shifts[i] = molecule / denominator; +// } +// +// //Eliminate the noise far from slant edge(+/- 10 pixels) +// tempSrc = Src.clone(); +// cv::GaussianBlur(Src, Src, cv::Size(3, 3), 0); +// for (i = 0; i < height; ++i) +// { +// uchar* SrcPtr = Src.ptr(i); +// uchar* tempPtr = tempSrc.ptr(i); +// for (j = int(Cen_Shifts[i]) - 10; j < int(Cen_Shifts[i]) + 10; ++j) +// { +// SrcPtr[j] = tempPtr[j]; +// } +// } +// +// // check whether the edge in image is too close to the image corners +// if (Cen_Shifts[0] < 2.0 || width - Cen_Shifts[0] < 2.0) +// { +// std::cerr << "The edge in ROI is too close to the image corners" << std::endl; +// return {}; +// } +// +// if (Cen_Shifts[height - 1] < 2 || width - Cen_Shifts[height - 1] < 2) +// { +// std::cerr << "The edge in ROI is too close to the image corners" << std::endl; +// return {}; +// } +// +// int half_ySize = height / 2; +// int CC = Cen_Shifts[half_ySize]; //Centroid of image center +// *CCoffset = CC; +// for (i = 0; i < height; ++i) +// { +// Cen_Shifts[i] -= CC; //Calculate the Shifts between Centroids and Centroid of image center +// y_shifts[i] = i - half_ySize; //Calculate the shifts between height of image center and each row +// } +// +// return Cen_Shifts; +//} + +//void NIIRS::de_Gamma(cv::Mat& Src, double gamma) +//{ +// if (Src.channels() != 1) { return; } +// +// for (int i = 0; i < Src.rows; ++i) +// { +// uchar* SrcP = Src.ptr(i); +// for (int j = 0; j < Src.cols; ++j) +// { +// SrcP[j] = 255 * (pow((double)SrcP[j] / 255, 1 / gamma)); +// } +// } +//} + +QPair NIIRS::calculate_rer_h(cv::Mat img, cv::Point pointA, cv::Point pointB) +{ + QPairRER_H = QPair(); + cv::Point pointLeft, pointRight; + pointLeft = pointA; + pointRight = pointB; + if (pointLeft.x > pointRight.x) + { + cv::Point temp = pointLeft; + pointLeft = pointRight; + pointRight = temp; + } + + //pair中:vector 存放所有相同距离时像元值，之后求平均 + //pair中:int 表示离刃边距离 + QMap>qmapESF; + vectorvecTemp; + vecTemp.push_back(127); + for (int i = -20; i < 22; i++) + qmapESF.insert(i, vecTemp); + + + cv::Mat mask; + cv::Point pt1, pt2; + double k = (double(pointRight.y) - pointLeft.y) / (double(pointRight.x) - pointLeft.x); + if (k < -1 || k > 1) + { + cv::Point pointBottom, pointTop; + pointBottom = pointA; + pointTop = pointB; + + if (pointBottom.y > pointTop.y) + { + cv::Point temp = pointBottom; + pointBottom = pointTop; + pointTop = temp; + } + + int rangeL = 0, rangeT = 0, rangeR = 0, rangeB = 0; + int cutImgTopx = 0, cutImgTopy = 0, cutImgBotx = 0, cutImgBoty = 0; + if (pointBottom.x == pointTop.x) + { + rangeL = pointBottom.x - 20; + rangeR = pointBottom.x + 20 + 4; + rangeT = pointTop.y; + rangeB = pointTop.y; + + cutImgTopx = 20; + cutImgTopy = 0; + cutImgBotx = pointTop.x - pointBottom.x + 20; + cutImgBoty = pointTop.y - pointBottom.y; + } + else if (k > 0) + { + double k2 = 1 / k; + int deltay = 20, deltax; + while (25 > pow((k * deltay), 2) + pow(double(deltay), 2)) + deltay = deltay + 20; + deltax = ceil(deltay / k2); + + rangeL = pointBottom.x - deltax; + rangeR = pointTop.x + deltax; + rangeT = pointTop.y + deltay + 4; + rangeB = pointBottom.y - deltay; + + cutImgTopx = pointBottom.x - rangeL; + cutImgTopy = pointBottom.y - rangeB; + cutImgBotx = pointTop.x - rangeL; + cutImgBoty = pointTop.y - rangeB; + } + else//k<0 + { + double k2 = 1 / k; + int deltay = 20, deltax; + while (25 > pow((k * deltay), 2) + pow(double(deltay), 2)) + deltay = deltay + 20; + deltax = ceil(deltay / -k2); + //qDebug() << deltay << deltay / k2 << "->" << deltax; + rangeL = pointTop.x - deltax; + rangeR = pointBottom.x + deltax; + rangeB = pointBottom.y - deltay; + rangeT = pointTop.y + deltay + 4; + + cutImgBotx = pointTop.x - rangeL; + cutImgBoty = pointTop.y - rangeB; + cutImgTopx = pointBottom.x - rangeL; + cutImgTopy = pointBottom.y - rangeB; + } + + if (rangeL - rangeR == 0) + return RER_H; + if (rangeL < 0) + rangeL = 0; + if (rangeR > img.cols) + rangeR = img.cols; + if (rangeT > img.rows) + rangeT = img.rows; + if (rangeB < 0) + rangeB = 0; + //qDebug() << QString("Y rangeB:%1,rangeT:%2; rangeL:%3,rangeR:%4").arg(rangeB).arg(rangeT).arg(rangeL).arg(rangeR); + //QString outBmpPath = outPath + "/" + filename; + cv::Mat miniEdgeX = img(cv::Range(rangeB, rangeT), cv::Range(rangeL, rangeR)); + cv::Mat evalueImg; + miniEdgeX.copyTo(evalueImg); + + pt1 = cv::Point(cutImgTopx, cutImgTopy); + pt2 = cv::Point(cutImgBotx, cutImgBoty); + cv::Mat temp(evalueImg.rows, evalueImg.cols, evalueImg.type(), cv::Scalar(0)); + mask = temp; + int rows = evalueImg.rows; + int cols = evalueImg.cols; + for (int row = 0; row < rows; row++) + { + for (int col = 0; col < cols; col++) + { + //当前采样点 + cv::Point samplingPoint(col, row); + //用于计算当前点到刃边距离 + arraysampling_to_edge = { samplingPoint, pt1, pt2 }; + //求当前点到刃边的垂足，获取垂足的col，用于比较确认dis的正负 + cv::Point2d samplingPointFoot = calculateFootPoint(sampling_to_edge); + if (samplingPointFoot.y < pt1.y || samplingPointFoot.y > pt2.y) + continue; + //采样点距离刃边的距离 + int dis = pointDistance(samplingPointFoot, samplingPoint); + if (dis > 21) + continue; + mask.at(row, col) = evalueImg.at(row, col); + + if (samplingPointFoot.x > col) + dis = 0 - dis; + + // int value = evalueImg.at(row, col); + //存入vecESF中 + //遍历vecESF，寻找相同距离的值 + // if (qmapESF.contains(dis)) + // { + vectorpixValues = qmapESF.value(dis); + pixValues.push_back(evalueImg.at(row, col)); + qmapESF.insert(dis, pixValues); + // } + // else + // { + // vectorpixValues; + // pixValues.push_back(evalueImg.at(row, col)); + // qmapESF.insert(dis, pixValues); + // } + } + } + } + else + { + int rangeL = 0, rangeT = 0, rangeR = 0, rangeB = 0; + int cutImgLTx = 0, cutImgLTy = 0, cutImgRBx = 0, cutImgRBy = 0; + if (k == 0) + { + rangeL = pointLeft.x; + rangeR = pointRight.x; + rangeT = pointRight.y + 20 + 4; + rangeB = pointLeft.y - 20; + + cutImgLTx = pointLeft.x - pointLeft.x; + cutImgLTy = pointLeft.y + 20 - pointLeft.y; + cutImgRBx = pointRight.x - pointLeft.x; + cutImgRBy = pointRight.y + 20 - pointLeft.y; + } + else if (k > 0) + { + double k2 = 1 / k; + int deltaY = 20, deltaX; + while (25 > pow((k * deltaY), 2) + pow(double(deltaY), 2)) + deltaY = deltaY + 20; + deltaX = ceil(deltaY / k2); + + rangeL = pointLeft.x - deltaX; + rangeR = pointRight.x + deltaX; + rangeT = pointRight.y + deltaY + 4; + rangeB = pointLeft.y - deltaY; + + cutImgLTx = pointLeft.x - rangeL; + cutImgLTy = pointLeft.y - rangeB; + cutImgRBx = pointRight.x - rangeL; + cutImgRBy = pointRight.y - rangeB; + } + else//k<0 + { + double k2 = 1 / k; + int deltay = 20, deltax; + while (25 > pow((k * deltay), 2) + pow(double(deltay), 2)) + deltay = deltay + 20; + deltax = ceil(deltay / -k2); + rangeL = pointLeft.x - deltax; + rangeR = pointRight.x + deltax; + rangeB = pointRight.y - deltay; + rangeT = pointLeft.y + deltay + 4; + + cutImgLTx = pointLeft.x - rangeL; + cutImgLTy = pointLeft.y - rangeB; + cutImgRBx = pointRight.x - rangeL; + cutImgRBy = pointRight.y - rangeB; + } + if (rangeL - rangeR == 0) + return RER_H; + if (rangeL < 0) + rangeL = 0; + if (rangeB < 0) + rangeB = 0; + if (rangeT > img.rows) + rangeT = img.rows; + if (rangeR > img.cols) + rangeR = img.cols; + //qDebug() << QString("rangeB:%1,rangeT:%2; rangeL:%3,rangeR:%4").arg(rangeB).arg(rangeT).arg(rangeL).arg(rangeR); + + cv::Mat miniEdgeX = img(cv::Range(rangeB, rangeT), cv::Range(rangeL, rangeR)); + cv::Mat evalueImg; + miniEdgeX.copyTo(evalueImg); + + pt1 = cv::Point(cutImgLTx, cutImgLTy); + pt2 = cv::Point(cutImgRBx, cutImgRBy); + cv::Mat temp(evalueImg.rows, evalueImg.cols, evalueImg.type(), cv::Scalar(0)); + mask = temp; + + int rows = evalueImg.rows; + int cols = evalueImg.cols; + for (int row = 0; row < rows; row++) + { + for (int col = 0; col < cols; col++) + { + //当前采样点 + cv::Point samplingPoint(col, row); + //用于计算当前点到刃边距离 + arraysampling_to_edge = { samplingPoint, pt1, pt2 }; + //求当前点到刃边的垂足，获取垂足的col，用于比较确认dis的正负 + cv::Point2d samplingPointFoot = calculateFootPoint(sampling_to_edge); + if (samplingPointFoot.x < pt1.x || samplingPointFoot.x > pt2.x) + continue; + //采样点距离刃边的距离 + int dis = pointDistance(samplingPointFoot, samplingPoint); + if (dis > 21) + continue; + mask.at(row, col) = evalueImg.at(row, col); + + if (samplingPointFoot.y > row) + dis = 0 - dis; + + // int value = evalueImg.at(row, col); + //存入vecESF中 + //遍历vecESF，寻找相同距离的值 + // if (qmapESF.contains(dis)) + // { + vectorpixValues = qmapESF.value(dis); + pixValues.push_back(evalueImg.at(row, col)); + qmapESF.insert(dis, pixValues); + // } + // else + // { + // vectorpixValues; + // pixValues.push_back(evalueImg.at(row, col)); + // qmapESF.insert(dis, pixValues); + // } + } + } + + } + //cv::line(mask, pt1, pt2, cv::Scalar(0, 0, 0)); + //vecESF中存的值求平均得ESF + QMapESF; + QMap>::const_iterator it = qmapESF.constBegin(); + while (it != qmapESF.constEnd()) + { + if (it.value().size() == 1) + ESF.insert(it.key(), it.value().at(0)); + else + { + vectorsamdisValue = it.value(); + int sum = 0; + int mean = 0; + for (int valuesize = 0; valuesize < samdisValue.size(); valuesize++) + sum = sum + samdisValue[valuesize]; + int sameSize = samdisValue.size(); + mean = sum / samdisValue.size(); + ESF.insert(it.key(), mean); + } + ++it; + } + QMap::const_iterator it2 = ESF.constBegin(); + int min = it2.value(); + int max = it2.value(); + while (it2 != ESF.constEnd()) + { + if (min > it2.value()) + min = it2.value(); + if (max < it2.value()) + max = it2.value(); + ++it2; + } + int edgeValuePdot5 = ESF.value(2); + int edgeValueNdot5 = ESF.value(-2); + double ER_p05 = (double(edgeValuePdot5) - min) / (double(max) - min); + double ER_n05 = (double(edgeValueNdot5) - min) / (double(max) - min); + double resultRER = ER_p05 - ER_n05; + int edgeValue125 = ESF.value(5); + if (resultRER < 0) + { + edgeValue125 = ESF.value(-5); + resultRER = abs(resultRER); + } + double resultH = (double(edgeValue125) - min) / (double(max) - min); + + RER_H.first = resultRER; + RER_H.second = resultH; + + if (resultRER > 0.55) + { + cv::line(mask, pt1, pt2, cv::Scalar(0, 0, 0)); + qDebug() << "resultRER:" << resultRER << "resultH:" << resultH; + } + + + return RER_H; +} + +cv::Point2d NIIRS::calculateFootPoint(array& ps) +{ + cv::Point2d p(0.0, 0.0); // 垂足 + if (ps.at(1).x == ps.at(2).x) // 线与x轴垂直 + { + p.x = ps.at(1).x; + p.y = ps.at(0).y; + } + else if (ps.at(1).y == ps.at(2).y) // 线与y轴垂直 + { + p.x = ps.at(0).x; + p.y = ps.at(1).y; + } + else // 线与x轴，y轴都不垂直 + { + double a1 = double(ps.at(1).y) - double(ps.at(2).y); + double b1 = double(ps.at(2).x) - double(ps.at(1).x); + double c1 = double(ps.at(1).x) * double(ps.at(2).y) - double(ps.at(2).x) * float(ps.at(1).y); + //pFoot.x = (B * B * pA.x - A * B * pA.y - A * C) / (A * A + B * B); + //pFoot.y = (A * A * pA.y - A * B * pA.x - B * C) / (A * A + B * B); + double footx = (b1 * b1 * double(ps.at(0).x) - a1 * b1 * double(ps.at(0).y) - a1 * c1) / (a1 * a1 + b1 * b1); + double footy = (a1 * a1 * double(ps.at(0).y) - a1 * b1 * double(ps.at(0).x) - b1 * c1) / (a1 * a1 + b1 * b1); + //p.x = (b1 * b1 * ps.at(0).x - a1 * b1 * ps.at(0).y - a1 * c1) / (a1 * a1 + b1 * b1); + //p.y = (a1 * a1 * ps.at(0).y - a1 * b1 * ps.at(0).x - b1 * c1) / (a1 * a1 + b1 * b1); + p.x = footx; + p.y = footy; + } + return p; +} + +QPair NIIRS::calculateERx(cv::Mat img) +{ + QPairER_H; + + cv::Mat imgGray, imgGrayX4; + if (img.channels() == 3) + cv::cvtColor(img, imgGray, CV_BGR2GRAY); + else + imgGray = img; + //重采样为原来4倍，用于1/4像元大小的超采样 + cv::pyrUp(imgGray, imgGrayX4); + cv::pyrUp(imgGrayX4, imgGrayX4); + + ////高斯滤波 + //cv::Mat imgGaussBlur; + //cv::GaussianBlur(imgGray, imgGaussBlur, cv::Size(3, 3), 3, 3); + //cv::imshow("imgGaussBlur", imgGaussBlur); + + cv::Mat imgThres; + cv::threshold(imgGray, imgThres, 127, 255, cv::THRESH_BINARY); + //cv::imwrite("D:\\A_testdata\\niirs_test\\imgThresx.png", imgThres); + //cv::imshow("imgThres", imgThres); + //cv::waitKey(1); + + //canny边缘提取 + cv::Mat imgCanny; + cv::Canny(imgThres, imgCanny, 100, 200); + //cv::imshow("imgCanny", imgCanny); + //cv::waitKey(1); + + vectorR_lines; + cv::HoughLinesP(imgCanny, R_lines, 1, CV_PI / 180, 10, 25, 100); + //获取所有直线的端点坐标，寻找两两之间距离最大的作为直线端点pt1、pt2 + //for (size_t i = 0; i < R_lines.size(); i++) + //{ + // cv::Vec4i l = R_lines[i]; + // cv::line(imgGrayX4, cv::Point(l[0] * 4, l[1] * 4), cv::Point(l[2] * 4, l[3] * 4), cv::Scalar(255 / (i+1)), 3); + //} + //cv::imshow("imgGrayX4", imgGrayX4); + //return 0.0; + + vectortop_points; + vectorbot_points; + if (R_lines.size() == 0) + { + qDebug() << "find no line in edge x"; + ui.lineEdit_INFO->setText(QString::fromLocal8Bit("无法从输入数据中提取刃边")); + return ER_H; + } + for (size_t z = 0; z < R_lines.size(); z++) + { + cv::Vec4i p = R_lines[z]; + //cout << "line x0: " << cv::Point(p[0], p[1]) <<", x1: "<< cv::Point(p[2], p[3]) << std::endl; + top_points.push_back(cv::Point(p[0], p[1])); + bot_points.push_back(cv::Point(p[2], p[3])); + } + //用于存放点之间距离 + cv::Mat img_dis(top_points.size(), bot_points.size(), CV_32FC1, cv::Scalar(0)); + for (int row = 0; row < top_points.size(); row++) + { + cv::Vec* data_Ptr = img_dis.ptr>(row); + for (int col = 0; col < bot_points.size(); col++) + { + if (row == col) + data_Ptr[col] = 0.0; + else + data_Ptr[col] = pointDistance(top_points[row], bot_points[col]); + } + } + double minv, maxv; + cv::Point pt_min, pt_max; + cv::minMaxLoc(img_dis, &minv, &maxv, &pt_min, &pt_max); + //std::cout << "img_dis = " << std::endl << img_dis << std::endl; + //std::cout << "maxv = " << maxv << std::endl; + //std::cout << "idx_max = " << pt_max << std::endl; + + //刃边端点 + cv::Point pt1 = cv::Point(top_points[pt_max.y].x * 4, top_points[pt_max.y].y * 4); + cv::Point pt2 = cv::Point(bot_points[pt_max.x].x * 4, bot_points[pt_max.x].y * 4); + //std::cout << "Edge End Point: " << pt1 << ", " << pt2 << std::endl; + + cv::Mat masksrc(imgCanny.rows * 4, imgCanny.cols * 4, imgCanny.type(), cv::Scalar(0)); + int rows = masksrc.rows; + int cols = masksrc.cols; + //cout << "row:" << rows << ", col:" << cols << std::endl; + + //cv::line(masksrc, pt1, pt2, cv::Scalar(255), 1, 8); + //cv::imshow("xxxedge", masksrc); + //cv::waitKey(1); + //cv::imwrite("D:\\A_testdata\\niirs_test\\imgLinex.png", masksrc); + + //pair中:vector 存放所有相同距离时像元值，之后求平均 + //pair中:int 表示离刃边距离 + vector>>vecESF; + + for (int row = 0; row < rows; row++) + { + for (int col = 0; col < cols; col++) + { + //当前采样点 + cv::Point samplingPoint(col, row); + //用于计算当前点到刃边距离 + arraysampling_to_edge = { samplingPoint, pt1, pt2 }; + //采样点距离刃边的距离 + int dis = 0; + //cout << "mask at row:" << row << ",col:" << col; + //求当前点到刃边的垂足，获取垂足的col，用于比较确认dis的正负 + cv::Point samplingPointFoot = calculate_foot_point(sampling_to_edge); + //cout << ",edge foot at row:" << samplingPointFoot.y << ",col:" << samplingPointFoot.x; + if (samplingPointFoot.x < col) + { + //dis = calculate_distance(sampling_to_edge); + dis = pointDistance(samplingPointFoot, samplingPoint); + } + else + { + //dis = 0 - calculate_distance(sampling_to_edge); + dis = 0 - pointDistance(samplingPointFoot, samplingPoint); + } + int value = imgGrayX4.at(row, col); + //cout << ",distance to edge:" << dis << ",value:" + // << value << std::endl; + pair>pix_dis_value; + //存入vecESF中 + if (vecESF.size() == 0)//vecESF大小为零，直接放入 + { + pix_dis_value.first = dis; + pix_dis_value.second.push_back(imgGrayX4.at(row, col)); + vecESF.push_back(pix_dis_value); + } + else//vecESF大小不为零，查找有无距离相同的点存入 + { + //遍历vecESF，寻找相同距离的值 + for (int vecsize = 0; vecsize < vecESF.size(); vecsize++) + { + if (vecESF[vecsize].first == dis)//有，存入 + { + vecESF[vecsize].second.push_back(imgGrayX4.at(row, col)); + break; + } + if (vecsize == vecESF.size() - 1)//遍历至最后找不到，新建存入 + { + pix_dis_value.first = dis; + pix_dis_value.second.push_back(imgGrayX4.at(row, col)); + vecESF.push_back(pix_dis_value); + break;//上一行vecESF.size+1,break防止无限循环 + } + } + } + } + } + //cout << "vecESF.size():" << vecESF.size() << std::endl; + //vecESF中存的值求平均得ESF + vector>ESF; + //求ESF + for (int vecsize = 0; vecsize < vecESF.size(); vecsize++) + { + if (vecESF[vecsize].second.size() == 1) + { + pairsamedisESF(vecESF[vecsize].first, vecESF[vecsize].second[0]); + ESF.push_back(samedisESF); + } + else + { + vectorsamdisValue = vecESF[vecsize].second; + int sum = 0; + int mean = 0; + for (int valuesize = 0; valuesize < samdisValue.size(); valuesize++) + { + sum = sum + samdisValue[valuesize]; + } + int sameSize = samdisValue.size(); + mean = sum / samdisValue.size(); + pairsamedisESF(vecESF[vecsize].first, mean); + ESF.push_back(samedisESF); + } + } + //遍历ESF，距离大于0，求平均，距离小于0，求平均 + pairDN_max_min; + vectorpositive_side, negative_side; + // 距离刃边+0.5 距离刃边-0.5 距离刃边+0.5 + double edgeValue_p05, edgeValue_n05, edgeValue_p1dot25; + for (int i = 0; i < ESF.size(); i++) + { + if (ESF[i].first > 0) + positive_side.push_back(ESF[i].second); + else if (ESF[i].first < 0) + negative_side.push_back(ESF[i].second); + if (ESF[i].first == 2) + edgeValue_p05 = ESF[i].second; + if (ESF[i].first == -2) + edgeValue_n05 = ESF[i].second; + if (ESF[i].first == 5 && ESF[i].second > 0) + edgeValue_p1dot25 = abs(ESF[i].second); + } + double sumpos = accumulate(begin(positive_side), end(positive_side), 0.0); + double sumneg = accumulate(begin(negative_side), end(negative_side), 0.0); + double mean_positive = sumpos / positive_side.size(); + double mean_negative = sumneg / negative_side.size(); + + DN_max_min.first = mean_positive > mean_negative ? mean_positive : mean_negative; + DN_max_min.second = mean_positive < mean_negative ? mean_positive : mean_negative; + //cout << "lager side:" << DN_max_min.first << ",small side:" << DN_max_min.second << std::endl; + //edgeValue_n05 + //edgeValue_p05 + + double ER_p05 = (edgeValue_p05 - DN_max_min.second) / (DN_max_min.first - DN_max_min.second); + double ER_n05 = (edgeValue_n05 - DN_max_min.second) / (DN_max_min.first - DN_max_min.second); + double Hx = double(edgeValue_p1dot25 - DN_max_min.second) / double(DN_max_min.first - DN_max_min.second); + double ER = ER_p05 - ER_n05; + ER_H.first = ER; + ER_H.second = Hx; + return ER_H; + + /*-----------------------------------------------------------------------------------------------------------*/ + + ////ESF求斜率求LSF + ////在ESF最后扩展一个用于求斜率 + //pairtempend(ESF[ESF.size()].first + 1, ESF[ESF.size()].second); + //ESF.push_back(tempend); + //for (int i = 0; i < ESF.size() - 1; i++)//遍历至倒数第二位 + //{ + // pairtemp; + // temp.first = ESF[i].first; + // temp.second = (ESF[i].second - ESF[i + 1].second) / (ESF[i].first - ESF[i + 1].first); + // LSF.push_back(temp); + //} + //vectorvec_LSF[2]; + //for (int vecsize = 0; vecsize < LSF.size(); vecsize++) + //{ + // vec_LSF[0].push_back(LSF[vecsize].first); + // vec_LSF[1].push_back(LSF[vecsize].second); + //} + ////一行 + ////cv::Mat mat_LSF = cv::Mat::zeros(1, vec_LSF[1].size(), CV_32F);// = cv::Mat(vec_LSF) + ////for (int i = 0; i < vec_LSF[1].size(); i++) + //// mat_LSF.at(0, i) = float(vec_LSF[1][i]); + ////cv::imwrite("mat_LSF.png", mat_LSF); + ////std::cout <<"mat_LSF channels: " << mat_LSF.channels() << ", size(width x height): " << mat_LSF.size() << std::endl; + //cv::Mat fft_real, fft_imag, fft_complex;//复数实部，复数虚部，完整复数 + //fft_real = cv::Mat::zeros(1, vec_LSF[1].size(), CV_32F); + //fft_imag = cv::Mat::zeros(1, vec_LSF[1].size(), CV_32F); + //for (int i = 0; i < vec_LSF[1].size(); i++) + //{ + // fft_real.at(0, i) = float(vec_LSF[1][i]); + // fft_imag.at(0, i) = 0; + //} + //std::cout << "1.fft_real channels: " << fft_real.channels() << ", size(width x height): " << fft_real.size() << std::endl; + //std::cout << "1.fft_imag channels: " << fft_imag.channels() << ", size(width x height): " << fft_imag.size() << std::endl; + //vectorvec_complex; + //vec_complex.push_back(fft_real); + //vec_complex.push_back(fft_imag); + //cv::merge(vec_complex, fft_complex); + //cv::dft(fft_complex, fft_complex); + //cv::Mat twoChannels[2]; + ////重新拆分出实部虚部 + //cv::split(fft_complex, twoChannels); + ////std::cout << "----2.fft_real----" << std::endl << twoChannels[0] << std::endl + //// << "channels: " << twoChannels[0].channels() << ", size(width x height): " << twoChannels[0].size() << std::endl; + ////std::cout << "----2.fft_imag----" << std::endl << twoChannels[1] << std::endl + //// << "channels: " << twoChannels[1].channels() << ", size(width x height): " << twoChannels[1].size() << std::endl; + //cv::Mat matMTF = cv::Mat::zeros(1, vec_LSF[1].size(), CV_32F); + //for (int i = 0; i < vec_LSF[1].size(); i++) + //{ + // matMTF.at(0, i) + // = sqrt(float(twoChannels[0].at(0, i)) * float(twoChannels[0].at(0, i)) + // + float(twoChannels[1].at(0, i)) * float(twoChannels[1].at(0, i))); + //} + //std::cout << "matMTF" << std::endl << matMTF << std::endl + // << "channels: " << matMTF.channels() << ", size(width x height): " << matMTF.size() << std::endl; + ////暂时取前1/2作为mtf，xy轴归一化，做积分算ER + //cout << "--------------------------------------------" << std::endl; + //return 0.0; +} + +QPair NIIRS::calculateERy(cv::Mat img) +{ + QPairER_H; + + cv::Mat imgGray, imgGrayX4; + if (img.channels() == 3) + cv::cvtColor(img, imgGray, CV_BGR2GRAY); + else + imgGray = img; + //重采样为原来4倍，用于1/4像元大小的超采样 + cv::pyrUp(imgGray, imgGrayX4); + cv::pyrUp(imgGrayX4, imgGrayX4); + //cv::imshow("yyyimgGrayX4", imgGrayX4); + + ////高斯滤波 + //cv::Mat imgGaussBlur; + //cv::GaussianBlur(imgGray, imgGaussBlur, cv::Size(3, 3), 3, 3); + cv::Mat imgThres; + cv::threshold(imgGray, imgThres, 127, 255, cv::THRESH_BINARY); + //cv::imwrite("D:\\A_testdata\\niirs_test\\imgGrayy.png", imgThres); + //cv::imshow("imgThresy", imgThres); + //cv::waitKey(1); + + //canny边缘提取 + cv::Mat imgCanny; + cv::Canny(imgThres, imgCanny, 100, 200); + //cv::imshow("imgCannyy", imgCanny); + //cv::waitKey(1); + + vectorR_lines; + cv::HoughLinesP(imgCanny, R_lines, 1, CV_PI / 180, 10, 25, 100); + //获取所有直线的端点坐标，寻找两两之间距离最大的作为直线端点pt1、pt2 + vectortop_points; + vectorbot_points; + if (R_lines.size() == 0) + { + qDebug() << "find no line in edge y"; + ui.lineEdit_INFO->setText(QString::fromLocal8Bit("无法从输入数据中提取刃边")); + return ER_H; + } + for (size_t z = 0; z < R_lines.size(); z++) + { + cv::Vec4i p = R_lines[z]; + //cout << "line x0: " << cv::Point(p[0], p[1]) << ", x1: " << cv::Point(p[2], p[3]) << std::endl; + top_points.push_back(cv::Point(p[0], p[1])); + bot_points.push_back(cv::Point(p[2], p[3])); + } + //用于存放点之间距离 + cv::Mat img_dis(top_points.size(), bot_points.size(), CV_32FC1, cv::Scalar(0)); + for (int row = 0; row < top_points.size(); row++) + { + cv::Vec* data_Ptr = img_dis.ptr>(row); + for (int col = 0; col < bot_points.size(); col++) + { + if (row == col) + data_Ptr[col] = 0.0; + else + data_Ptr[col] = pointDistance(top_points[row], bot_points[col]); + } + } + double minv, maxv; + cv::Point pt_min, pt_max; + cv::minMaxLoc(img_dis, &minv, &maxv, &pt_min, &pt_max); + //std::cout << "img_dis = " << std::endl << img_dis << std::endl; + //std::cout << "maxv = " << maxv << std::endl; + //std::cout << "idx_max = " << pt_max << std::endl; + + //刃边端点 + cv::Point pt1 = cv::Point(top_points[pt_max.y].x * 4, top_points[pt_max.y].y * 4); + cv::Point pt2 = cv::Point(bot_points[pt_max.x].x * 4, bot_points[pt_max.x].y * 4); + //std::cout << "Edge End Point: " << pt1 << ", " << pt2 << std::endl; + + cv::Mat masksrc(imgCanny.rows * 4, imgCanny.cols * 4, imgCanny.type(), cv::Scalar(0)); + int rows = masksrc.rows; + int cols = masksrc.cols; + //cout << "row:" << rows << ", col:" << cols << std::endl; + + cv::line(masksrc, pt1, pt2, cv::Scalar(255), 1, 8); + //cv::imshow("yyyedge", masksrc); + //cv::imwrite("D:\\A_testdata\\niirs_test\\imgLiney.png", masksrc); + + //pair中:vector 存放所有相同距离时像元值，之后求平均 + //pair中:int 表示离刃边距离 + vector>>vecESF; + + for (int row = 0; row < rows; row++) + { + for (int col = 0; col < cols; col++) + { + //当前采样点 + cv::Point samplingPoint(col, row); + //用于计算当前点到刃边距离 + arraysampling_to_edge = { samplingPoint, pt1, pt2 }; + //采样点距离刃边的距离 + int dis = 0; + //cout << "mask at row:" << row << ",col:" << col; + //求当前点到刃边的垂足，获取垂足的col，用于比较确认dis的正负 + cv::Point samplingPointFoot = calculate_foot_point(sampling_to_edge); + //cout << ",edge foot at row:" << samplingPointFoot.y << ",col:" << samplingPointFoot.x; + if (samplingPointFoot.x < col) + { + //dis = calculate_distance(sampling_to_edge); + dis = pointDistance(samplingPointFoot, samplingPoint); + } + else + { + //float a1 = float(sampling_to_edge.at(1).y) - float(sampling_to_edge.at(2).y); + //float b1 = float(sampling_to_edge.at(2).x) - float(sampling_to_edge.at(1).x); + //float c1 = float(sampling_to_edge.at(1).x) * float(sampling_to_edge.at(2).y) + // - float(sampling_to_edge.at(2).x) * float(sampling_to_edge.at(1).y); + //dis = abs(a1 * sampling_to_edge.at(0).x + b1 * sampling_to_edge.at(0).y + c1) + // / sqrt(a1 * a1 + b1 * b1); + dis = 0 - pointDistance(samplingPointFoot, samplingPoint); + } + int value = imgGrayX4.at(row, col); + //cout << ",distance to edge:" << dis << ",value:" + // << value << std::endl; + pair>pix_dis_value; + //存入vecESF中 + if (vecESF.size() == 0)//vecESF大小为零，直接放入 + { + pix_dis_value.first = dis; + pix_dis_value.second.push_back(imgGrayX4.at(row, col)); + vecESF.push_back(pix_dis_value); + } + else//vecESF大小不为零，查找有无距离相同的点存入 + { + //遍历vecESF，寻找相同距离的值 + for (int vecsize = 0; vecsize < vecESF.size(); vecsize++) + { + if (vecESF[vecsize].first == dis)//有，存入 + { + vecESF[vecsize].second.push_back(imgGrayX4.at(row, col)); + break; + } + if (vecsize == vecESF.size() - 1)//遍历至最后找不到，新建存入 + { + pix_dis_value.first = dis; + pix_dis_value.second.push_back(imgGrayX4.at(row, col)); + vecESF.push_back(pix_dis_value); + break;//上一行vecESF.size+1,break防止无限循环 + } + } + } + } + } + //cout << "vecESF.size():" << vecESF.size() << std::endl; + //vecESF中存的值求平均得ESF + vector>ESF; + //ESF求斜率得LSF + vector>LSF; + //求ESF + for (int vecsize = 0; vecsize < vecESF.size(); vecsize++) + { + if (vecESF[vecsize].second.size() == 1) + { + pairsamedisESF(vecESF[vecsize].first, vecESF[vecsize].second[0]); + ESF.push_back(samedisESF); + } + else + { + vectorsamdisValue = vecESF[vecsize].second; + int sum = 0; + int mean = 0; + for (int valuesize = 0; valuesize < samdisValue.size(); valuesize++) + { + sum = sum + samdisValue[valuesize]; + } + int sameSize = samdisValue.size(); + mean = sum / samdisValue.size(); + pairsamedisESF(vecESF[vecsize].first, mean); + ESF.push_back(samedisESF); + } + } + //遍历ESF，距离大于0，求平均，距离小于0，求平均 + pairDN_max_min; + vectorpositive_side, negative_side; + // 距离刃边+0.5 距离刃边-0.5 + double edgeValue_p05, edgeValue_n05, edgeValue_p1dot25; + for (int i = 0; i < ESF.size(); i++) + { + //cout << ESF[vecSize].first << ",," << ESF[vecSize].second << std::endl; + if (ESF[i].first > 0) + positive_side.push_back(ESF[i].second); + else if (ESF[i].first < 0) + negative_side.push_back(ESF[i].second); + if (ESF[i].first == 2) + edgeValue_p05 = ESF[i].second; + if (ESF[i].first == -2) + edgeValue_n05 = ESF[i].second; + if (ESF[i].first == 5 && ESF[i].second > 0) + edgeValue_p1dot25 = abs(ESF[i].second); + } + double sumpos = accumulate(begin(positive_side), end(positive_side), 0.0); + double sumneg = accumulate(begin(negative_side), end(negative_side), 0.0); + double mean_positive = sumpos / positive_side.size(); + double mean_negative = sumneg / negative_side.size(); + + DN_max_min.first = mean_positive > mean_negative ? mean_positive : mean_negative; + DN_max_min.second = mean_positive < mean_negative ? mean_positive : mean_negative; + //cout << "lager side:" << DN_max_min.first << ",small side:" << DN_max_min.second << std::endl; + //edgeValue_n05 + //edgeValue_p05 + double ER_p05 = (edgeValue_p05 - DN_max_min.second) / (DN_max_min.first - DN_max_min.second); + double ER_n05 = (edgeValue_n05 - DN_max_min.second) / (DN_max_min.first - DN_max_min.second); + double Hx = double(edgeValue_p1dot25 - DN_max_min.second) / double(DN_max_min.first - DN_max_min.second); + double ER = ER_p05 - ER_n05; + ER_H.first = ER; + ER_H.second = Hx; + return ER_H; +} + +float NIIRS::pointDistance(cv::Point pt1, cv::Point pt2) +{ + //float dis = sqrtf(((float)pt1.x - (float)pt2.x) * ((float)pt1.x - (float)pt2.x) + // + ((float)pt1.y - (float)pt2.y) * ((float)pt1.y - (float)pt2.y)); + float dis = sqrt((float(pt1.x) - float(pt2.x)) * (float(pt1.x) - float(pt2.x)) + + (float(pt1.y) - float(pt2.y)) * (float(pt1.y) - float(pt2.y))); + //std::cout << "pt1:" << pt1 << "pt2" << pt2 << "pointDistance:" << dis << std::endl; + return dis; +} + +//int NIIRS::calculate_distance(array& ps) +//{ +// int d = 0;// 距离 +// int a1 = -(ps.at(2).y - ps.at(1).y); +// int b1 = ps.at(2).x - ps.at(1).x; +// int c1 = (ps.at(2).y - ps.at(1).y) * ps.at(1).x - (ps.at(2).x - ps.at(1).x) * ps.at(1).y; +// d = abs(a1 * ps.at(0).x + b1 * ps.at(0).y + c1) / sqrt(a1 * a1 + b1 * b1); +// //allnum++; +// //if (allnum % 100 == 0) +// // cout << "calculate_distance:" << d << ",at:" << allnum << std::endl; +// return d; +//} + +cv::Point NIIRS::calculate_foot_point(array& ps) +{ + cv::Point p(0, 0); // 垂足 + if (ps.at(1).x == ps.at(2).x) // 线与x轴垂直 + { + p.x = ps.at(1).x; + p.y = ps.at(0).y; + } + else if (ps.at(1).y == ps.at(2).y) // 线与y轴垂直 + { + p.x = ps.at(0).x; + p.y = ps.at(1).y; + } + else // 线与x轴，y轴都不垂直 + { + float a1 = float(ps.at(1).y) - float(ps.at(2).y); + float b1 = float(ps.at(2).x) - float(ps.at(1).x); + float c1 = float(ps.at(1).x) * float(ps.at(2).y) - float(ps.at(2).x) * float(ps.at(1).y); + //pFoot.x = (B * B * pA.x - A * B * pA.y - A * C) / (A * A + B * B); + //pFoot.y = (A * A * pA.y - A * B * pA.x - B * C) / (A * A + B * B); + float footx = (b1 * b1 * float(ps.at(0).x) - a1 * b1 * float(ps.at(0).y) - a1 * c1) / (a1 * a1 + b1 * b1); + float footy = (a1 * a1 * float(ps.at(0).y) - a1 * b1 * float(ps.at(0).x) - b1 * c1) / (a1 * a1 + b1 * b1); + //p.x = (b1 * b1 * ps.at(0).x - a1 * b1 * ps.at(0).y - a1 * c1) / (a1 * a1 + b1 * b1); + //p.y = (a1 * a1 * ps.at(0).y - a1 * b1 * ps.at(0).x - b1 * c1) / (a1 * a1 + b1 * b1); + p.x = footx; + p.y = footy; + } + return p; +} + +//cv::Point NIIRS::line_intersection(array& line1, array& line2) +//{ +// cv::Point inter; +// +// double ka, kb; +// // line1 y2 line1 y1 line1 x2 line1 x1 +// //ka = (double)(LineA[3] - LineA[1]) / (double)(LineA[2] - LineA[0]); //求出LineA斜率 +// // line2 y2 line2 y1 line2 x2 line2 x1 +// //kb = (double)(LineB[3] - LineB[1]) / (double)(LineB[2] - LineB[0]); //求出LineB斜率 +// +// // line1 x1 line1 y1 line2 x1 line2 y1 +// //inter.x = (ka * LineA[0] - LineA[1] - kb * LineB[0] + LineB[1]) / (ka - kb); +// // line1 x1 line2 x1 line2 y1 line1 y1 +// //inter.y = (ka * kb * (LineA[0] - LineB[0]) + ka * LineB[1] - kb * LineA[1]) / (ka - kb); +// ka = (double(line1[1].y) - double(line1[0].y)) / (double(line1[1].x) - double(line1[0].x)); //求出LineA斜率 +// kb = (double(line2[1].y) - double(line2[0].y)) / (double(line2[1].x) - double(line2[0].x)); //求出LineB斜率 +// +// inter.x = (ka * line1[0].x - line1[0].y - kb * line2[0].x + line2[0].y) / (ka - kb); +// inter.y = (ka * kb * (double(line1[0].x) - double(line2[0].x)) + ka * line2[0].y - kb * line1[0].y) / (ka - kb); +// +// return inter; +//} + +//cv::Point NIIRS::point_k_edge_intersection(cv::Point pt, double k, array& line) +//{ +// cv::Point inter; +// // 根据点斜式求直线 +// // 根据直线是水平或垂直，求两线交点 +// //cout << "point_k_edge_intersection point: " << pt << std::endl; +// if (line[0].x == line[1].x)//x相等，相当于x已知，求y = k * (x - x1) + y1 +// { +// //cout << "line[0].x == line[1].x: " << line[0] << "," << line[1] << std::endl; +// float interpoint = k * (double(line[0].x) - double(pt.x)) + double(pt.y); +// inter.x = line[0].x; +// inter.y = round(interpoint); +// } +// else if (line[0].y == line[1].y)//y相等，相当于y已知，求x = (y - y1) / k + x1 +// { +// //cout << "line[0].y == line[1].y: " << line[0] << "," << line[1] << std::endl; +// float interpoint = (double(line[0].y) - double(pt.y)) / k + double(pt.x); +// inter.x = round(interpoint); +// inter.y = line[0].y; +// } +// //cout << "intersection: " << inter << std::endl; +// +// return inter; +//} + +//基于sobel算子求梯度、陡度 +double NIIRS::gradient_Sobel(cv::Mat img) +{ + //assert(img.empty()); + cv::Mat gray_img, sobel_x, abs_x, sobel_y, abs_y, sobel_mean; + if (img.channels() == 3) + cv::cvtColor(img, gray_img, CV_BGR2GRAY); + else + gray_img = img; + //分别计算x、y方向梯度 + cv::Sobel(gray_img, sobel_x, CV_32FC1, 1, 0); + cv::Sobel(gray_img, sobel_y, CV_32FC1, 0, 1); + + //cv::multiply(sobel_x, sobel_x, sobel_x); + //cv::multiply(sobel_y, sobel_y, sobel_y); + //cv::Mat sqrt_mat = sobel_x + sobel_y; + //cv::sqrt(sqrt_mat, sobel_mean); + cv::convertScaleAbs(sobel_x, abs_x); + cv::convertScaleAbs(sobel_y, abs_y); + cv::addWeighted(abs_x, 0.5, abs_y, 0.5, 0, sobel_mean); + + double mean = cv::mean(sobel_mean)[0]; + //cv::resize(sobel_mean, sobel_mean, cv::Size(600, 400)); + + return mean; +} +//基于Laplacian算子求二阶导，求清晰度 +double NIIRS::definition_Laplacian(cv::Mat img) +{ + cv::Mat gray_img, lap_image; + if (img.channels() == 3) + cv::cvtColor(img, gray_img, CV_BGR2GRAY); + else + gray_img = img; + + cv::Laplacian(gray_img, lap_image, CV_32FC1); + lap_image = cv::abs(lap_image); + + return cv::mean(lap_image)[0]; +} + +void NIIRS::get_GLCM_0deg(cv::Mat& input, cv::Mat& dst) +{ + cv::Mat src; + input.copyTo(src); + src.convertTo(src, CV_32S); + int height = src.rows; + int width = src.cols; + int max_gray_level = 0; + for (int j = 0; j < height; j++)//寻找像素灰度最大值 + { + int* srcdata = src.ptr(j); + for (int i = 0; i < width; i++) + { + if (srcdata[i] > max_gray_level) + { + max_gray_level = srcdata[i]; + } + } + } + max_gray_level++;//像素灰度最大值加1即为该矩阵所拥有的灰度级数 + dst.create(max_gray_level, max_gray_level, CV_32SC1); + dst = cv::Scalar::all(0); + for (int i = 0; i < height; i++) + { + int* srcdata = src.ptr(i); + for (int j = 0; j < width - 1; j++) + { + int rows = srcdata[j]; + int cols = srcdata[j + 1]; + dst.ptr(rows)[cols]++; + } + } +} + +void NIIRS::get_GLCM_90deg(cv::Mat& input, cv::Mat& dst) +{ + cv::Mat src; + input.copyTo(src); + src.convertTo(src, CV_32S); + int height = src.rows; + int width = src.cols; + int max_gray_level = 0; + for (int j = 0; j < height; j++) + { + int* srcdata = src.ptr(j); + for (int i = 0; i < width; i++) + { + if (srcdata[i] > max_gray_level) + { + max_gray_level = srcdata[i]; + } + } + } + max_gray_level++; + dst.create(max_gray_level, max_gray_level, CV_32SC1); + dst = cv::Scalar::all(0); + for (int i = 0; i < height - 1; i++) + { + int* srcdata = src.ptr(i); + int* srcdata1 = src.ptr(i + 1); + for (int j = 0; j < width; j++) + { + int rows = srcdata[j]; + int cols = srcdata1[j]; + dst.ptr(rows)[cols]++; + } + } +} + +void NIIRS::get_GLCM_45deg(cv::Mat& input, cv::Mat& dst) +{ + cv::Mat src; + input.copyTo(src); + src.convertTo(src, CV_32S); + int height = src.rows; + int width = src.cols; + int max_gray_level = 0; + for (int j = 0; j < height; j++) + { + int* srcdata = src.ptr(j); + for (int i = 0; i < width; i++) + { + if (srcdata[i] > max_gray_level) + { + max_gray_level = srcdata[i]; + } + } + } + max_gray_level++; + dst.create(max_gray_level, max_gray_level, CV_32SC1); + dst = cv::Scalar::all(0); + for (int i = 0; i < height - 1; i++) + { + int* srcdata = src.ptr(i); + int* srcdata1 = src.ptr(i + 1); + for (int j = 0; j < width - 1; j++) + { + int rows = srcdata[j]; + int cols = srcdata1[j + 1]; + dst.ptr(rows)[cols]++; + } + } +} + +void NIIRS::get_GLCM_135deg(cv::Mat& input, cv::Mat& dst) +{ + cv::Mat src; + input.copyTo(src); + src.convertTo(src, CV_32S); + int height = src.rows; + int width = src.cols; + int max_gray_level = 0; + for (int j = 0; j < height; j++) + { + int* srcdata = src.ptr(j); + for (int i = 0; i < width; i++) + { + if (srcdata[i] > max_gray_level) + { + max_gray_level = srcdata[i]; + } + } + } + max_gray_level++; + dst.create(max_gray_level, max_gray_level, CV_32SC1); + dst = cv::Scalar::all(0); + for (int i = 0; i < height - 1; i++) + { + int* srcdata = src.ptr(i); + int* srcdata1 = src.ptr(i + 1); + for (int j = 1; j < width; j++) + { + int rows = srcdata[j]; + int cols = srcdata1[j - 1]; + dst.ptr(rows)[cols]++; + } + } +} + +double NIIRS::calculateASM(cv::Mat& src) +{ + //cv::Mat src; + //cv::cvtColor(input, src, CV_BGR2GRAY);//转为灰度图 + + int height = src.rows; + int width = src.cols; + int total = 0; + for (int i = 0; i < height; i++) + { + int* srcdata = src.ptr(i); + for (int j = 0; j < width; j++) + { + total += srcdata[j];//求图像所有像素的灰度值的和 + } + } + + cv::Mat copy; + copy.create(height, width, CV_64FC1); + for (int i = 0; i < height; i++) + { + int* srcdata = src.ptr(i); + double* copydata = copy.ptr(i); + for (int j = 0; j < width; j++) + { + copydata[j] = (double)srcdata[j] / (double)total;//图像每一个像素的的值除以像素总和 + } + } + //纹理特征的因子 + double Asm = 0.0, Eng = 0.0, Con = 0.0; + for (int i = 0; i < height; i++) + { + double* srcdata = copy.ptr(i); + for (int j = 0; j < width; j++) + { + Asm += srcdata[j] * srcdata[j];//能量 + if (srcdata[j] > 0) + Eng -= srcdata[j] * log(srcdata[j]);//熵//+0.0000001 + Con += (double)(i - j) * (double)(i - j) * srcdata[j];//对比度 + //Idm += srcdata[j] / (1 + (double)(i - j) * (double)(i - j));//逆差矩 + } + } + //qDebug() << "--1. Angular Sec Moment" << Asm; + //qDebug() << "--2. Entropy-----------" << Eng; + //qDebug() << "--3. contranst radio---" << Con; + + return Asm; +} + +double NIIRS::calculatePIQEScore(cv::Mat& img) +{ + int blockSize = 16; + double activityThreshold = 0.1; + double blockImpairedThreshold = 0.1; + int windowSize = 6; + int nSegments = blockSize - windowSize + 1; + double distBlockScores = 0.0; + double NHSA = 0.0; + double c = 1.0;//常数 + + cv::Mat gray_img; + if (img.channels() == 3) + cv::cvtColor(img, gray_img, CV_BGR2GRAY); + else + gray_img = img; + //cv::Mat copy = gray_img; + //cv::resize(copy, copy, cv::Size(960, 540)); + //cv::imshow("gray_img", copy); + + gray_img.convertTo(gray_img, CV_64FC1); + int rows = gray_img.rows; + int columns = gray_img.cols; + int rowsPad = rows % blockSize; + int columnsPad = columns % blockSize; + //qDebug() << "rowsPad:" << rowsPad << "columnsPad:" << columnsPad; + bool isPadded = false; + if (rowsPad > 0 || columnsPad > 0) + { + if (rowsPad > 0) + rowsPad = blockSize - rowsPad; + if (columnsPad > 0) + columnsPad = blockSize - columnsPad; + isPadded = true; + //扩充图像，向右向下扩充，使图像行列能够被block整除，[rowsPad向下 columnsPad向右] + cv::copyMakeBorder(gray_img, gray_img, 0, rowsPad, 0, columnsPad, cv::BORDER_REPLICATE); + //rows = gray_img.rows; + //columns = gray_img.cols; + //rowsPad = rows % blockSize; + //columnsPad = columns % blockSize; + //qDebug() << "rowsPad:" << rowsPad << "columnsPad:" << columnsPad; + } + cv::Mat muGaussfilt, gray_Dot_gray, sigmaGaussfilt; + cv::GaussianBlur(gray_img, muGaussfilt, cv::Size(7, 7), 7 / 6, 7 / 6); + //sigma = sqrt(abs(imgaussfilt(ipImage.*ipImage, 7 / 6, 'FilterSize', 7, 'Padding', 'replicate') - mu.*mu)); + //1.原图与自己点乘gray_Dot_gray=gray_img.*gray_img + //2.点乘结果进行高斯滤波GaussianBlur(gray_Dot_gray, gray_Dot_gray, cv::Size(7, 7), 7 / 6, 7 / 6) + //3.sqrt(abs(gray_Dot_gray- muGaussfilt.*muGaussfilt)) + //1.原图与自己点乘 + gray_Dot_gray = gray_img.mul(gray_img); + //2.点乘结果进行高斯滤波 + cv::GaussianBlur(gray_Dot_gray, gray_Dot_gray, cv::Size(7, 7), 7 / 6, 7 / 6); + cv::Mat absDiff, imnorm; + //3.1 + cv::absdiff(gray_Dot_gray, muGaussfilt.mul(muGaussfilt), absDiff); + //3.2 + cv::sqrt(absDiff, sigmaGaussfilt); + //imnorm = (ipImage - mu). / (sigma + 1); + imnorm = (gray_img - muGaussfilt) / (sigmaGaussfilt + 1); + + //NoticeableArtifactsMask = false(size(imnorm, 1), size(imnorm, 2)); + //NoiseMask = false(size(imnorm, 1), size(imnorm, 2)); + //ActivityMask = false(size(imnorm, 1), size(imnorm, 2)); + //qDebug() << imnorm.rows << "," << imnorm.cols << ","<< imnorm.rows - blockSize * 2<<"," + // << "rows%blockSize" << imnorm.rows % blockSize << "cols%blockSize" << imnorm.cols % blockSize; + for (int i = 0; i < imnorm.rows; i = i + blockSize) + { + for (int j = 0; j < imnorm.cols; j = j + blockSize) + { + int WNDC = 0, WNC = 0; + cv::Mat Block = imnorm(cv::Rect(j, i, blockSize, blockSize)); + cv::Scalar meanScalar, stdDevScalar; + cv::meanStdDev(Block, meanScalar, stdDevScalar); + double blockStdDev = stdDevScalar.val[0]; + double blockVar = stdDevScalar.val[0] * stdDevScalar.val[0]; + if (blockVar > activityThreshold) + { + double WHSA = 1.0; + NHSA = NHSA + 1; + //qDebug() << i << j << "var > activityThreshold"<< blockVar; + + bool blockImpaired = noticeDistCriterion(Block, nSegments, blockSize - 1, windowSize, + blockImpairedThreshold, blockSize); + if (blockImpaired) + WNDC = 1; + double blockBeta = noiseCriterion(Block, blockSize - 1, blockVar, blockStdDev); + if (blockStdDev > 2 * blockBeta) + WNC = 1; + distBlockScores = distBlockScores + WHSA * WNDC * (1 - blockVar) + WHSA * WNC * (blockVar); + } + } + } + //cv::resize(imnorm, imnorm, cv::Size(960, 540)); + //cv::imshow("imnorm", imnorm); + double Score = ((distBlockScores + c) / (c + NHSA));//* 100 + + return Score; +} + +bool NIIRS::noticeDistCriterion(cv::Mat Block, int nSegments, int blockSize_1, + int windowSize, float blockImpairedThreshold, int blockSize) +{ + cv::Mat topEdge, segTopEdge; + topEdge = Block.row(0); + segTopEdge = segmentEdge(topEdge, nSegments, blockSize_1, windowSize); + + cv::Mat bottomEdge, segBottomEdge; + bottomEdge = Block.row(15); + segBottomEdge = segmentEdge(bottomEdge, nSegments, blockSize_1, windowSize); + + cv::Mat leftEdge, segLeftEdge; + leftEdge = Block.col(0).t();//并转置 + segLeftEdge = segmentEdge(leftEdge, nSegments, blockSize_1, windowSize); + + cv::Mat rightEdge, segRightEdge; + rightEdge = Block.col(15).t();//并转置 + segRightEdge = segmentEdge(rightEdge, nSegments, blockSize_1, windowSize); + + bool blockImpaired = false; + //按行求标准差 [6col×11row] + //std::cout << segTopEdge.rows<< std::endl; + cv::Scalar meanScalar, stdDevScalar; + cv::Mat segRowTop, segRowBottom, segRowLeft, segRowRight; + vectorstdTop, stdBottom, stdLeft, stdRight; + for (int row = 0; row < segTopEdge.rows; row++) + { + //获取行 + segRowTop = segTopEdge.row(row); + segRowBottom = segBottomEdge.row(row); + segRowLeft = segLeftEdge.row(row); + segRowRight = segRightEdge.row(row); + //求StdDev，并存在vector中 + cv::meanStdDev(segRowTop, meanScalar, stdDevScalar); + stdTop.push_back(stdDevScalar.val[0]); + cv::meanStdDev(segRowBottom, meanScalar, stdDevScalar); + stdBottom.push_back(stdDevScalar.val[0]); + cv::meanStdDev(segRowLeft, meanScalar, stdDevScalar); + stdLeft.push_back(stdDevScalar.val[0]); + cv::meanStdDev(segRowRight, meanScalar, stdDevScalar); + stdRight.push_back(stdDevScalar.val[0]); + } + for (int vecsize = 0; vecsize < stdTop.size(); vecsize++) + { + if (stdTop[vecsize] < blockImpairedThreshold || + stdBottom[vecsize] < blockImpairedThreshold || + stdLeft[vecsize] < blockImpairedThreshold || + stdRight[vecsize] < blockImpairedThreshold) + { + blockImpaired = true; + break; + } + } + return blockImpaired; +} + +cv::Mat NIIRS::segmentEdge(cv::Mat blockEdge, int nSegments, int blockSize_1, int windowSize) +{ + cv::Mat segments(nSegments, windowSize, CV_64FC1);//空 + //matlab: + //for i = 1:nSegments + // segments(i, :) = blockEdge(i : windowSize); + // if (windowSize <= (blockSize + 1)) + // windowSize = windowSize + 1; + // end + //end + for (int row = 0; row < segments.rows; row++) + { + for (int col = 0; col < segments.cols; col++) + { + segments.at(row, col) = blockEdge.at(0, col + row); + } + } + //std::cout << "blockEdge" << std::endl << blockEdge << std::endl; + //std::cout << "segments" << std::endl << segments << std::endl; + return segments; +} + +float NIIRS::noiseCriterion(cv::Mat Block, int blockSize_1, float blockVar, float blockStdDev) +{ + float blockBeta = 0.0, cenSurDev = 0.0; + cenSurDev = centerSurDev(Block, blockSize_1); + blockBeta = (abs(blockStdDev - cenSurDev)) / (max(blockStdDev, cenSurDev)); + + return blockBeta; +} + +float NIIRS::centerSurDev(cv::Mat Block, int blockSize_1) +{ + int center1 = (blockSize_1 + 1) / 2; + int center2 = center1 + 1; + + cv::Mat center(16, 2, CV_64FC1), surround(16, 14, CV_64FC1); + + int surround_col = 0; + for (int col = 0; col < Block.cols; col++) + { + if (col == center1 - 1)//center col1 + { + for (int row = 0; row < Block.rows; row++) + center.at(row, 0) = Block.at(row, col); + } + else if (col == center2 - 1)//center col2 + { + for (int row = 0; row < Block.rows; row++) + center.at(row, 1) = Block.at(row, col); + } + else//surround + { + for (int row = 0; row < Block.rows; row++) + surround.at(row, surround_col) = Block.at(row, col); + surround_col = surround_col + 1; + } + } + //std::cout << "Block" << std::endl << Block << std::endl + // << "center" << std::endl << center << std::endl + // << "surround" << std::endl << surround << std::endl; + cv::Scalar meanScalar, stdDevScalar; + float stdDevCenter, stdDevSurround; + cv::meanStdDev(center, meanScalar, stdDevScalar); + stdDevCenter = stdDevScalar.val[0]; + cv::meanStdDev(surround, meanScalar, stdDevScalar); + stdDevSurround = stdDevScalar.val[0]; + float cenSurDev = stdDevCenter / stdDevSurround; + + return cenSurDev; +} + +double NIIRS::calculateMTFC_G(cv::Mat kernel) +{ + double sqr_sum = 0.0; + cv::Mat floatkernel = kernel; + floatkernel.convertTo(floatkernel, CV_64FC1); + + for (int row = 0; row < floatkernel.rows; row++) + for (int col = 0; col < floatkernel.cols; col++) + sqr_sum = sqr_sum + floatkernel.at(row, col) * floatkernel.at(row, col); + + double MTFC_G = sqrt(sqr_sum); + + return MTFC_G; +} diff --git a/NiirsLinux/niirs.h b/NiirsLinux/niirs.h new file mode 100644 index 0000000..8b28355 --- /dev/null +++ b/NiirsLinux/niirs.h @@ -0,0 +1,199 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "ui_niirs.h" + +#include +#include +#include + +#include +#include +//#include +//#include + +#include "gdal_priv.h" + +using namespace std; + +struct ImgInfo +{ + GDALDataType dataType; //标示数据类型 + int bandCount; //数据波段数目 + int xSize; //数据在x方向上的尺寸 + int ySize; //数据在y方向上的尺寸 + int xOffset; //x方向上的偏移； + int yOffset; //y方向上的偏移； + char projWkt[1024]; //数据的投影方式 + double geoTransform[6]; //数据的仿射坐标 + double noData; //数据的背景值 +}; + +class NIIRS : public QMainWindow +{ + Q_OBJECT + +public: + NIIRS(QWidget* parent = Q_NULLPTR); + + void setXmlInterface(QString strXmlInterface); + + /* 计算NIIRS + * @return NIIRS */ + double calculateNiirsScore(); + //计算10个客观参数 + void calculateObjectivePara(); + //void calculateEvaluationImage(); + double calculateRER(); + + /* + * @brief 获取图像基础信息并保存至结构体 + * @param srcImgPath 输入文件路径 + * @param imgInfo 结构体，保存获取的tiff文件信息 + * @return 返回错误信息 */ + int getImageInfo(const char* srcImgPath, struct ImgInfo* imgInfo); + + /* + * @brief 按波段读取tiff + * @param srcImgPath 输入文件路径 + * @param imgInfo 结构体，存储tiff文件信息 + * @param imgArray 读取的tiff数组 + * @param bandIndex 要读取的波段，bandIndex=0,则读取所有波段 + * @return 返回错误信息 */ + int readTiffFile(const char* srcImgPath, ImgInfo imgInfo, void* imgArray, int bandIndex); + + void initTableWidget(int rowNum, int colNum); + void setTableWidgetValue(double val, int row, int col); + + /* + * @brief 遍历文件夹下文件 + * @param path 输入文件路径 + * @param fileType 文件格式 + * @return 文件路径absoluteFilePath,返回空值时为存在错误 */ + QStringList getAllFiles(QString path, QString fileType); + +public slots: + void onRadioBtn_Visi(); + void onRadioBtn_Infra(); + + void on_pbtOpenImg_clicked(); + //void on_pbtOpenEdgeX_clicked(); + //void on_pbtOpenEdgeY_clicked(); + +private: + Ui::NIIRSClass ui; + + /* 计算噪声增益 + * @param kernel MTFC所使用的滤波器 + * @return MTFC_G噪声增益 */ + double calculateMTFC_G(cv::Mat kernel); + ////计算信噪比，mean/StdDev + //double calculateSNR(cv::Mat img); + + /* 计算数字图像亮度 + * @return 灰度图片平均亮度 */ + double calculateMeanStdDev(cv::Mat img); + ////计算图像标准差方差 + //float calculateStdDev(cv::Mat img); + //计算图像对比度 + double calculateContrastRatio(cv::Mat img); + ////计算信息熵 + //double calculateEntropy(cv::Mat img); + /* + * @brief 计算单波段的图像熵 + * @param imgArray tiff数组 + * @param maxVal tiff波段中的最大值 + * @return 该波段的图像熵 */ + double calculateTiffEntropy(unsigned short* bandBuf, int bufSize, int maxVal); + //计算相对边缘相应 +// double calculateERx(cv::Mat img); +// double calculateERy(cv::Mat img); + //return pair.first:ER, pair.second:H + QPair calculateERx(cv::Mat img); + QPair calculateERy(cv::Mat img); + + QPair calculate_rer_h(cv::Mat img, cv::Point pointA, cv::Point pointB); + + cv::Point2d calculateFootPoint(array& ps); + //float SFRCalculation(cv::Mat& ROI, double gamma); + //void de_Gamma(cv::Mat& Src, double gamma); + //std::vector CentroidFind(cv::Mat& Src, std::vector& y_shifts, double* CCoffset); + //void SLR(std::vector& Cen_Shifts, std::vector& y_shifts, double* a, double* b); + //void ReduceRows(double slope, int* ImgHeight); + //std::vector OverSampling(cv::Mat& Src, double slope, double CCoffset, int height, int width, int* SamplingLen); + //std::vector HammingWindows(std::vector& deSampling, int SamplingLen); + //void DFT(std::vector& data, int size); + + + //陡度 + //基于sobel算子，计算平均梯度 + double gradient_Sobel(cv::Mat img); + //基于Laplacian算子，拉普拉斯导数，评价清晰度 + double definition_Laplacian(cv::Mat img); + //四个方向的角二阶矩 + void get_GLCM_0deg(cv::Mat& input, cv::Mat& dst); + void get_GLCM_90deg(cv::Mat& input, cv::Mat& dst); + void get_GLCM_45deg(cv::Mat& input, cv::Mat& dst); + void get_GLCM_135deg(cv::Mat& input, cv::Mat& dst); + //计算二阶矩的特征值 + double calculateASM(cv::Mat& src); + //PIQE + double calculatePIQEScore(cv::Mat& img); + bool noticeDistCriterion(cv::Mat Block, int nSegments, int blockSize_1, + int windowSize, float blockImpairedThreshold, int blockSize); + cv::Mat segmentEdge(cv::Mat blockEdge, int nSegments, int blockSize_1, int windowSize); + float noiseCriterion(cv::Mat Block, int blockSize_1, float blockVar, float blockStdDev); + float centerSurDev(cv::Mat Block, int blockSize_1); + + // 计算垂足的坐标，array：点、直线两端点 + cv::Point calculate_foot_point(array& ps); + // 计算两点距离 + float pointDistance(cv::Point pt1, cv::Point pt2); + //// 计算两直线交点 + //cv::Point line_intersection(array& line1, array& line2); + //// 根据一条线的点pt、斜率k、另一条水平或垂直的直线的端点，求交点 + //cv::Point point_k_edge_intersection(cv::Point pt, double k, array& line); + //// 计算点到直线距离 + //int calculate_distance(array& ps); + + bool jpegTest = false; + //int mBandCount = 0; + + QString mXmlInterface = ""; + + //QString mXmlImgPath = "/home/testdata/19data/VALID-ALIGACE/JB19-2_MSS_000811113_MMB04_001_01_001_003_L1/JB19-2_MSS_000811113_MMB04_001_01_001_003_L1.tiff"; + QString mXmlImgPath = "";//D:\\A_testdata\\niirs_test\\data\\GF2_mask1024.tif + QString mXmlTopLeftX = "0";//311,336 + QString mXmlTopLeftY = "0";//324,293 + QString mXmlBottomRightX = "0";//335,348 + QString mXmlBottomRightY = "0";//337,311 + + QString mXmlProductDir = ""; + QString mResultXmlDir = ""; + + cv::Point mRerEdgePointA; + cv::Point mRerEdgePointB; + + + double imgLight = 0.0; //亮度 + double imgStdDev = 0.0; //标准差 + double imgContrastRadio = 0.0; //对比度 + double imgEntropy = 0.0; //熵 + double imgSNR = 0.0; //信噪比 + double imgGradient = 0.0; //陡度 + double imgDefinition = 0.0; //清晰度 + double imgASM = 0.0; //角二阶矩 + double imgPIQE = 0.0; //PIQE + + double imgRER = 0.986; //相对边缘响应 + +}; diff --git a/NiirsLinux/niirs.qrc b/NiirsLinux/niirs.qrc new file mode 100644 index 0000000..a4d4aa6 --- /dev/null +++ b/NiirsLinux/niirs.qrc @@ -0,0 +1,4 @@ + + + + diff --git a/NiirsLinux/niirs.ui b/NiirsLinux/niirs.ui new file mode 100644 index 0000000..bbf83e7 --- /dev/null +++ b/NiirsLinux/niirs.ui @@ -0,0 +1,680 @@ + + + NIIRSClass + + + + 0 + 0 + 780 + 520 + + + + + 780 + 520 + + + + + 16777215 + 16777215 + + + + NIIRS + + + + + + + + + + 0 + 0 + + + + 客观参数计算 + + + + + + Qt::Horizontal + + + QSizePolicy::Fixed + + + + 40 + 20 + + + + + + + + + + + 0 + 0 + + + + + 16777215 + 23 + + + + 左上角X坐标 + + + + + + + + 60 + 16777215 + + + + 浏览 + + + + + + + + + + + + + 0 + 0 + + + + + 16777215 + 23 + + + + 左上角Y坐标 + + + + + + + + + + + + + 0 + 0 + + + + + 16777215 + 23 + + + + 待评价影像 + + + + + + + + + + + + + 0 + 0 + + + + + 16777215 + 23 + + + + 右下角Y坐标 + + + + + + + + + + + + + 0 + 0 + + + + + 16777215 + 23 + + + + 右下角X坐标 + + + + + + + + + + + + Qt::Horizontal + + + + 40 + 20 + + + + + + + + + 0 + 0 + + + + + + + + Qt::Horizontal + + + + 40 + 20 + + + + + + + + + 90 + 0 + + + + + 90 + 16777215 + + + + 计算客观参数 + + + + + + + + + + + + + 0 + 20 + + + + + + + + 异常信息: + + + + + + + + + + 0 + 0 + + + + + 340 + 16777215 + + + + NIIRS计算 + + + + + + + + NIIRS等级计算结果: + + + + + + + + 0 + 20 + + + + + + + + 计算 + + + + + + + + + 调制传递函数补偿(MTFC)影像参数 + + + + + + + + 噪 声 增 益G + + + Qt::AlignLeading|Qt::AlignLeft|Qt::AlignVCenter + + + + + + + + 0 + 20 + + + + + + + + + + + + + 信噪比(SNR) + + + + + + + + + 0 + 20 + + + + + + + + SNR + + + + + + + + + + + + 分辨率(GSD) + + + + + + + + GSD_X + + + + + + + + 0 + 20 + + + + + + + + 米 + + + + + + + GSD_Y + + + + + + + + 0 + 20 + + + + + + + + 米 + + + + + + + + + + + + 边缘响应及过冲 + + + + + + + + + 0 + 20 + + + + + 60 + 16777215 + + + + + + + + + 0 + 20 + + + + + 60 + 16777215 + + + + + + + + + 40 + 16777215 + + + + 过冲H + + + + + + + + 40 + 16777215 + + + + RER + + + + + + + + + + + + 参数(PIQE) + + + + + + + + PIQE + + + + + + + + 0 + 20 + + + + + + + + + + + + + + + 可见光数据 + + + true + + + buttonGroup + + + + + + + 中红外数据 + + + buttonGroup + + + + + + + 长波红外数据 + + + buttonGroup + + + + + + + + + + + + 16777215 + 23 + + + + 计算公式: + + + + + + + + 0 + 60 + + + + + 16777215 + 150 + + + + <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0//EN" "http://www.w3.org/TR/REC-html40/strict.dtd"> +<html><head><meta name="qrichtext" content="1" /><style type="text/css"> +p, li { white-space: pre-wrap; } +</style></head><body style=" font-family:'SimSun'; font-size:9pt; font-weight:400; font-style:normal;"> +<p style="-qt-paragraph-type:empty; margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><br /></p></body></html> + + + + + + + + + + 60 + 16777215 + + + + 波段选择: + + + + + + + + 0 + 0 + + + + + + + + + + + + + + + + + + + lineEdit_img + pbtOpenImg + radioVisible + radioMidInfrared + textFormula + lineEdit_GSDX + lineEdit_GSDY + lineEdit_RER + lineEdit_H + lineEdit_MTFC_G + lineEdit_SNR + lineEdit_PIQE + lineEdit_NIIRS + pbtCalculate + lineEdit_INFO + + + + + + +