Skip to content

张士柳 / eyemLib

转到一个项目
切换导航栏
切换导航栏固定状态
Commit da2526be 由 张士柳
选项

无

1 个父辈 94b11f53
内嵌 并排
正在显示 13 个修改的文件 包含 321 行增加 和 120 行删除
eyemLib-Sharp/EyemLib.cs
......@@ -7,6 +7,7 @@ using System.Drawing;
using System.Drawing.Imaging;
using System.Threading;
using System.IO;
using System.Linq;
namespace eyemLib_Sharp
{
......@@ -991,7 +992,7 @@ namespace eyemLib_Sharp
/// <param name="detectorModelPath">模型文件</param>
/// <returns></returns>
[DllImport("eyemLib.dll", CharSet = CharSet.None, CallingConvention = CallingConvention.Cdecl)]
private static extern int eyemInitNNDetector(string detectorConfigPath, string detectorModelPath);
private static extern int eyemInitNNDetector(string detectorConfigPath, string detectorModelPath, int iNetSizew, int iNetSizeh);
/// <summary>
/// 目标检测器
......@@ -999,7 +1000,12 @@ namespace eyemLib_Sharp
/// <param name="tpImage">输入图像</param>
/// <returns></returns>
[DllImport("eyemLib.dll", CharSet = CharSet.None, CallingConvention = CallingConvention.Cdecl)]
private static extern int eyemNNDetector(EyemImage tpImage, out int ipNum, ref BboxContainer container);
private static extern int eyemNNDetector(EyemImage tpImage, out int ipNum, ref BboxContainer container, out EyemImage tpDstImg);
[DllImport("eyemLib.dll", CharSet = CharSet.None, CallingConvention = CallingConvention.Cdecl)]
private static extern int eyemInitClassifier(string classifierConfigPath, string classifierModelPath, int ntype);
[DllImport("eyemLib.dll", CharSet = CharSet.None, CallingConvention = CallingConvention.Cdecl)]
private static extern int eyemClassifier(EyemImage tpImage);
#endregion
#region 模板匹配
......@@ -1114,6 +1120,8 @@ namespace eyemLib_Sharp
OnNewLogCallback += new TCallBack(EyemLib_OnNewLogCallback);
eyemInitNNDataCodeModel(".\\darknet\\detect-tiny.cfg", ".\\darknet\\detect-tiny.weights", ".\\darknet\\sr.prototxt", ".\\darknet\\sr.caffemodel");
eyemInitClassifier("D:\\detect-tiny-tray.cfg", "D:\\detect-tiny-tray.weights", 0);
}
public static void Free()
......@@ -1136,6 +1144,24 @@ namespace eyemLib_Sharp
#endregion
//list元素随机打乱
public static void RandomShuffle<T>(List<T> list)
{
List<T> cache = new List<T>();
int currentIndex;
while (list.Count > 0)
{
Random rd = new Random(unchecked((int)DateTime.Now.Ticks));
currentIndex = rd.Next(0, list.Count);
cache.Add(list[currentIndex]);
list.RemoveAt(currentIndex);
}
for (int i = 0; i < cache.Count; i++)
{
list.Add(cache[i]);
}
}
public static void eyemReadImageTool(string fileName)
{
EyemImage image;
......@@ -1151,6 +1177,35 @@ namespace eyemLib_Sharp
sw.Restart();
string file = fileName.Split(new string[] { "\\" }, StringSplitOptions.RemoveEmptyEntries)[2];
//string[] fileNames = Directory.GetFiles(@"D:\训练数据集\点料机料盘识别\val_images", "*.png", SearchOption.AllDirectories);
//List<string> random_shuffle = new List<string>(fileNames);
////随机打乱顺序
//RandomShuffle(random_shuffle);
//bool HaveDuplicates = random_shuffle.GroupBy(i => i).Where(g => g.Count() > 1).Count() >= 1;
//StreamWriter swr = new StreamWriter("D:\\val.txt", true);
//foreach (var item in random_shuffle)
//{
// string[] items = item.Split(new string[] { "\\" }, StringSplitOptions.RemoveEmptyEntries);
// swr.WriteLine("data/val_images/" + items[items.Length - 1]);
// swr.Flush();
//}
//return;
//eyemRenameFile(@"D:\训练数据集\20211021");
//return;
//flag = eyemInitNNDetector(".\\darknet\\cifar_resnet50.cfg", ".\\darknet\\cifar_resnet50.weights");
//if (flag == 0)
//{
// int ipNum;
// BboxContainer bboxes = new BboxContainer();
// eyemNNDetector(image, out ipNum, ref bboxes, out tpDstImg);
//}
//eyemImageFree(ref tpDstImg);
//eyemImageFree(ref image);
//return;
//UnmanagedBitmap umBitmap = new UnmanagedBitmap("D:\\算法测试图像\\circle_plate_04.png");
//return;
......@@ -1170,8 +1225,16 @@ namespace eyemLib_Sharp
//EyemHSVModel tpHsvModel = new EyemHSVModel();
//tpHsvModel.dpRangeL = new double[] { 100, 43, 46 }; tpHsvModel.dpRangeU = new double[] { 124, 255, 255 };
//tpHsvModel.dpRangeLExt = new double[] { 0, 0, 0 }; tpHsvModel.dpRangeUExt = new double[] { 0, 0, 0 };
//eyemClassifier(image);
//flag = eyemLibImpl(image, out tpDstImg);
//Bitmap bitmap = eyemCvtToBitmap(tpDstImg);
//if (bitmap != null)
//{
// bitmap.Save("D:\\ResOut\\" + file);
//}
//eyemImageFree(ref tpDstImg);
//eyemImageFree(ref image);
//return;
//EyemImage templ, search;
//flag = eyemImageRead("D://批量测试图像//template.png", -1, out templ);
......@@ -1392,11 +1455,8 @@ namespace eyemLib_Sharp
tpRoi.iHeight = image.iHeight - 100;
//flag = eyemMulFuncTool(image, tpRoi, "__func1", 65, 75, ref tpCircle, out tpDstImg);
int[] ipReelNum = new int[4];
//flag = eyemAchvMatchMat(image, tpRoi, out tpDstImg);
//Bitmap bitmap = eyemCvtToBitmap(tpDstImg);
//if (bitmap != null)
//{
......@@ -1463,18 +1523,18 @@ namespace eyemLib_Sharp
//"IP_SMALL_PARTS","IP_LARGE_PARTS","IP_LONG_PARTS",,IP_SQUARE_PARTS
//eyemCountObject(image, tpRoi, file.Replace(".png", ""), ipReelNum, out tpDstImg);
eyemCountObjectIrregularParts(image, tpRoi, file.Replace(".png", ""), "IP_SQUARE_PARTS", ipReelNum, out tpDstImg);
//eyemCountObjectIrregularParts(image, tpRoi, file.Replace(".png", ""), "IP_LARGE_PARTS", ipReelNum, out tpDstImg);
//eyemCountObjectE(image, tpRoi, file.Replace(".png", ""), ipReelNum, out tpDstImg);
//eyemCountObjectIrregularPartsE(image, tpRoi, file.Replace(".png", ""), "D:\\模板文件\\" + "20210825095751-1.tpl", hModelID, ipReelNum, out tpDstImg);
//移除模板
//flag = eyemRemoveModelByName(hModelID, "D:\\模板文件及图像\\df871193-6632-48f9-abfe-540c3fc49c3f.tpl");
Bitmap bitmap = eyemCvtToBitmap(tpDstImg);
if (bitmap != null)
{
bitmap.Save(System.Windows.Forms.Application.StartupPath + "\\ResOut\\" + file);
}
//Bitmap bitmap = eyemCvtToBitmap(tpDstImg);
//if (bitmap != null)
//{
// bitmap.Save(System.Windows.Forms.Application.StartupPath + "\\ResOut\\" + file);
//}
//< 解码测试
//int ipNum; EyemBarCode* tpResults;
......
eyemLib-Sharp/Program.cs
......@@ -17,7 +17,7 @@ namespace eyemLib_Sharp
}
//初始化
EyemLib.Init();
string[] fileNames = Directory.GetFiles(@"D:\批量测试图像", "*.*", SearchOption.AllDirectories);
string[] fileNames = Directory.GetFiles(@"D:\批量测试图像\", "*.*", SearchOption.AllDirectories);
foreach (var item in fileNames)
{
EyemLib.eyemReadImageTool(item);
......
eyemLib/eyemCodeDetector.cpp
......@@ -33,7 +33,7 @@ CodeDetector::CodeDetector(const std::string & detector_config_path,
p = cv::makePtr<CodeDetector::Impl>();
if (!detector_config_path.empty() && !detector_model_path.empty()) {
p->detector_ = std::make_shared<YoloWrapper>();
p->detector_->init(detector_config_path, detector_model_path);
p->detector_->init(detector_config_path, detector_model_path, cv::Size(960, 960));
}
else {
p->detector_ = NULL;
......
eyemLib/eyemFit.cpp
......@@ -288,9 +288,9 @@ static void fitLine2D_wods(int iPtnNum, const EyemOcsDXY2D *taPoints, EyemOcsDAB
tpLine.dC = -(nx*px + ny*py);
}
int eyemFitLine(int iPtnNum, EyemOcsDXY *taPoint, int numToIgnore, EyemOcsDABC &tpLine)
int eyemFitLine(int iPtnNum, EyemOcsDXY *taPoint, int iNumToIgnore, EyemOcsDABC &tpLine)
{
if (iPtnNum - numToIgnore < 2 || iPtnNum < 2) {
if (iPtnNum - iNumToIgnore < 2 || iPtnNum < 2) {
return FUNC_CANNOT_CALC;
}
//初始化参数，或许可用RANSAC算法进一步提高鲁棒性,不过没必要
......@@ -313,7 +313,7 @@ int eyemFitLine(int iPtnNum, EyemOcsDXY *taPoint, int numToIgnore, EyemOcsDABC &
}
std::sort(taPoints.begin(), taPoints.end(), std::less<EyemOcsDXY2D>());
for (int i = (iPtnNum - numToIgnore); i < iPtnNum; i++) {
for (int i = (iPtnNum - iNumToIgnore); i < iPtnNum; i++) {
taPoints[i].bValid = false;
}
/* Run again... */
......@@ -325,7 +325,7 @@ int eyemFitLine(int iPtnNum, EyemOcsDXY *taPoint, int numToIgnore, EyemOcsDABC &
sr += r;
}
}
err = std::sqrt(sr / (double)(iPtnNum - numToIgnore));
err = std::sqrt(sr / (double)(iPtnNum - iNumToIgnore));
if (err < EPS)
break;
if (std::abs(err - min_err) < EPS)
......@@ -437,9 +437,9 @@ static void fitCircle2D_wods(int iPtnNum, const EyemOcsDXY2D *taPoints, EyemOcsD
tpCircle.dR = sqrt(pow(x.ptr<double_t>(0)[0], 2) + pow(x.ptr<double_t>(1)[0], 2) - 4.0*(x.ptr<double_t>(2)[0])) / 2.0;
}
int eyemFitCircle(int iPtnNum, EyemOcsDXY *taPoint, int numToIgnore, double &dRMS, EyemOcsDXYR &tpCircle)
int eyemFitCircle(int iPtnNum, EyemOcsDXY *taPoint, int iNumToIgnore, double &dRMS, EyemOcsDXYR &tpCircle)
{
if (iPtnNum - numToIgnore < 3 || iPtnNum < 3) {
if (iPtnNum - iNumToIgnore < 3 || iPtnNum < 3) {
return FUNC_CANNOT_CALC;
}
//初始化参数，或许可用RANSAC算法进一步提高鲁棒性,不过没必要
......@@ -463,7 +463,7 @@ int eyemFitCircle(int iPtnNum, EyemOcsDXY *taPoint, int numToIgnore, double &dRM
//排序
std::sort(taPoints.begin(), taPoints.end(), std::less<EyemOcsDXY2D>());
//忽略的点
for (int i = iPtnNum - numToIgnore; i < iPtnNum; i++) {
for (int i = iPtnNum - iNumToIgnore; i < iPtnNum; i++) {
taPoints[i].bValid = false;
}
fitCircle2D_wods(iPtnNum, &taPoints[0], tpCircle);
......@@ -474,7 +474,7 @@ int eyemFitCircle(int iPtnNum, EyemOcsDXY *taPoint, int numToIgnore, double &dRM
sr += r;
}
}
rms = std::sqrt(sr / (double)(iPtnNum - numToIgnore));
rms = std::sqrt(sr / (double)(iPtnNum - iNumToIgnore));
if (rms < 0.03 || std::abs((dRMS - rms)) < 1e-3) {
break;
}
......
eyemLib/eyemLib.h
......@@ -490,10 +490,10 @@ extern "C" {
#endif
// 函数接口
EXPORTS int eyemFitLine(int iPtnNum, EyemOcsDXY *taPoint, int numToIgnore, EyemOcsDABC &tpLine);
EXPORTS int eyemFitLine(int iPtnNum, EyemOcsDXY *taPoint, int iNumToIgnore, EyemOcsDABC &tpLine);
EXPORTS int eyemRobustFitLine(int iPtnNum, EyemOcsDXY *taPoint, int iCalcMode, double dRobustCoef, EyemOcsDABC &tpLine);
EXPORTS int eyemFitLineRANSAC(int iPtnNum, EyemOcsDXY *taPoint, double dClippingFactor, EyemOcsDABC &tpLine);
EXPORTS int eyemFitCircle(int iPtnNum, EyemOcsDXY *taPoint, int numToIgnore, double &dRMS, EyemOcsDXYR &tpCircle);
EXPORTS int eyemFitCircle(int iPtnNum, EyemOcsDXY *taPoint, int iNumToIgnore, double &dRMS, EyemOcsDXYR &tpCircle);
EXPORTS int eyemRobustFitCircle(int iPtnNum, EyemOcsDXY *taPoint, int iCalcMode, double dRobustCoef, EyemOcsDXYR &tpCircle);
EXPORTS int eyemFitRTMatrix(int iPtnNum, EyemOcsDXY *taPointA, EyemOcsDXY *taPointB, EyemRigidMatrix &dpResult);
EXPORTS int eyemRobustFitPlane(int iPtnNum, EyemOcsDXYZ *taPoint, int iCalcMode, double dRobustCoef, EyemOcsDABCD &tpPlane);
......@@ -867,8 +867,11 @@ extern "C" {
#endif
// 函数接口
EXPORTS int eyemInitNNDetector(const char *detectorConfigPath, const char *detectorModelPath);
EXPORTS int eyemNNDetector(EyemImage tpImage, int *ipNum, BboxContainer &container);
EXPORTS int eyemInitNNDetector(const char *detectorConfigPath, const char *detectorModelPath, int iNetSizew, int iNetSizeh);
EXPORTS int eyemNNDetector(EyemImage tpImage, int *ipNum, BboxContainer &container, EyemImage *tpDstImg);
EXPORTS int eyemInitClassifier(const char *classifierConfigPath, const char *classifierModelPath, int ntype);
EXPORTS int eyemClassifier(EyemImage tpImage);
#ifdef __cplusplus
}
......
eyemLib/eyemLib.rc
此文件类型无法预览
eyemLib/eyemLib.vcxproj
......@@ -74,8 +74,8 @@
</PropertyGroup>
<PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">
<LinkIncremental>true</LinkIncremental>
<IncludePath>D:\opencv420\build\include;D:\opencv420\build\include\opencv2;D:\tbb2017_20170604oss\include;D:\zxing-cpp-master\core\src;D:\zxing-cpp-master\opencv\src;$(IncludePath)</IncludePath>
<LibraryPath>D:\opencv420\build\x64\vc14\lib;D:\tbb2017_20170604oss\lib\intel64\vc14;D:\zxing-cpp-master\build\Debug;$(LibraryPath)</LibraryPath>
<IncludePath>D:\opencv420\build\include;D:\opencv420\build\include\opencv2;D:\tbb2017_20170604oss\include;D:\zxing-cpp-master\core\src;D:\zxing-cpp-master\opencv\src;D:\darknet\include;D:\3rdparty\pthreads\include;$(IncludePath)</IncludePath>
<LibraryPath>D:\opencv420\build\x64\vc14\lib;D:\tbb2017_20170604oss\lib\intel64\vc14;D:\zxing-cpp-master\build\Debug;D:\darknet\lib;D:\3rdparty\pthreads\lib;$(LibraryPath)</LibraryPath>
<TargetExt>.dll</TargetExt>
</PropertyGroup>
<PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">
......@@ -83,8 +83,8 @@
</PropertyGroup>
<PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|x64'">
<LinkIncremental>false</LinkIncremental>
<IncludePath>D:\opencv420\build\include;D:\opencv420\build\include\opencv2;D:\tbb2017_20170604oss\include;D:\zxing-cpp-master\core\src;D:\zxing-cpp-master\opencv\src;$(IncludePath)</IncludePath>
<LibraryPath>D:\opencv420\build\x64\vc14\lib;D:\tbb2017_20170604oss\lib\intel64\vc14;D:\zxing-cpp-master\build\Release;$(LibraryPath)</LibraryPath>
<IncludePath>D:\opencv420\build\include;D:\opencv420\build\include\opencv2;D:\tbb2017_20170604oss\include;D:\zxing-cpp-master\core\src;D:\zxing-cpp-master\opencv\src;D:\darknet\include;D:\3rdparty\pthreads\include;$(IncludePath)</IncludePath>
<LibraryPath>D:\opencv420\build\x64\vc14\lib;D:\tbb2017_20170604oss\lib\intel64\vc14;D:\zxing-cpp-master\build\Release;D:\darknet\lib;D:\3rdparty\pthreads\lib;$(LibraryPath)</LibraryPath>
<TargetExt>.dll</TargetExt>
</PropertyGroup>
<ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'">
......@@ -111,7 +111,7 @@
</PrecompiledHeader>
<WarningLevel>Level3</WarningLevel>
<Optimization>Disabled</Optimization>
<PreprocessorDefinitions>_DEBUG;_CONSOLE;_CRT_SECURE_NO_WARNINGS;%(PreprocessorDefinitions)</PreprocessorDefinitions>
<PreprocessorDefinitions>_DEBUG;_CONSOLE;_CRT_SECURE_NO_WARNINGS;HAVE_STRUCT_TIMESPEC;%(PreprocessorDefinitions)</PreprocessorDefinitions>
<SDLCheck>true</SDLCheck>
<RuntimeLibrary>MultiThreadedDebugDLL</RuntimeLibrary>
<OpenMPSupport>true</OpenMPSupport>
......@@ -119,7 +119,7 @@
<Link>
<SubSystem>Console</SubSystem>
<GenerateDebugInformation>true</GenerateDebugInformation>
<AdditionalDependencies>libdmtx.lib;libzxing-debug.lib;libdecoded.lib;%(AdditionalDependencies)</AdditionalDependencies>
<AdditionalDependencies>libdmtx.lib;libzxing-debug.lib;libdecoded.lib;darknetd.lib;pthreadVC2.lib;%(AdditionalDependencies)</AdditionalDependencies>
</Link>
</ItemDefinitionGroup>
<ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">
......@@ -148,7 +148,7 @@
<Optimization>MaxSpeed</Optimization>
<FunctionLevelLinking>true</FunctionLevelLinking>
<IntrinsicFunctions>true</IntrinsicFunctions>
<PreprocessorDefinitions>NDEBUG;_CONSOLE;_CRT_SECURE_NO_WARNINGS;%(PreprocessorDefinitions)</PreprocessorDefinitions>
<PreprocessorDefinitions>NDEBUG;_CONSOLE;_CRT_SECURE_NO_WARNINGS;HAVE_STRUCT_TIMESPEC;%(PreprocessorDefinitions)</PreprocessorDefinitions>
<SDLCheck>true</SDLCheck>
<RuntimeLibrary>MultiThreadedDLL</RuntimeLibrary>
<OpenMPSupport>false</OpenMPSupport>
......@@ -161,7 +161,7 @@
<EnableCOMDATFolding>true</EnableCOMDATFolding>
<OptimizeReferences>true</OptimizeReferences>
<GenerateDebugInformation>true</GenerateDebugInformation>
<AdditionalDependencies>libdmtx.lib;libzxing.lib;libdecode.lib;%(AdditionalDependencies)</AdditionalDependencies>
<AdditionalDependencies>libdmtx.lib;libzxing.lib;libdecode.lib;darknet.lib;pthreadVC2.lib;%(AdditionalDependencies)</AdditionalDependencies>
</Link>
</ItemDefinitionGroup>
<ItemGroup>
......
eyemLib/eyemMisc.cpp
......@@ -2228,8 +2228,10 @@ int eyemCountObjectIrregularParts(EyemImage tpImage, EyemRect tpRoi, const char
//最小外包矩形
cv::Point2f _pts[4];
ta.RBox.points(_pts);
//已做标记(TODO:考虑增加判断哪些是起点哪些不是)
if (trackMat.ptr<uint8_t>(cvRound(startCenter.y))[cvRound(startCenter.x)] == 255) {
//已做标记(TODO:考虑增加判断哪些是起点哪些不是,20211018增加为四联通判断当前起点是否追踪过)
if (trackMat.ptr<uint8_t>(cvRound(startCenter.y))[cvRound(startCenter.x)] == 255 || trackMat.ptr<uint8_t>(cvRound(startCenter.y))[cvRound(startCenter.x) - 1] == 255 ||
trackMat.ptr<uint8_t>(cvRound(startCenter.y) - 1)[cvRound(startCenter.x)] == 255 || trackMat.ptr<uint8_t>(cvRound(startCenter.y))[cvRound(startCenter.x) + 1] == 255 ||
trackMat.ptr<uint8_t>(cvRound(startCenter.y) + 1)[cvRound(startCenter.x)] == 255) {
continue;
}
//获取模板图像(是否每次起点都计算模板，如果元件变形过大是否还会有用？)
......@@ -3320,8 +3322,10 @@ int eyemCountObjectIrregularParts(EyemImage tpImage, EyemRect tpRoi, const char
//最小外包矩形
cv::Point2f _pts[4];
ta.RBox.points(_pts);
//已做标记(TODO:考虑增加判断哪些是起点哪些不是)
if (trackMat.ptr<uint8_t>(cvRound(startCenter.y))[cvRound(startCenter.x)] == 255) {
//已做标记(TODO:考虑增加判断哪些是起点哪些不是,20211018增加为四联通判断当前起点是否追踪过)
if (trackMat.ptr<uint8_t>(cvRound(startCenter.y))[cvRound(startCenter.x)] == 255 || trackMat.ptr<uint8_t>(cvRound(startCenter.y))[cvRound(startCenter.x) - 1] == 255 ||
trackMat.ptr<uint8_t>(cvRound(startCenter.y) - 1)[cvRound(startCenter.x)] == 255 || trackMat.ptr<uint8_t>(cvRound(startCenter.y))[cvRound(startCenter.x) + 1] == 255 ||
trackMat.ptr<uint8_t>(cvRound(startCenter.y) + 1)[cvRound(startCenter.x)] == 255) {
continue;
}
//获取模板图像(是否每次起点都计算模板，如果元件变形过大是否还会有用？)
......@@ -5045,8 +5049,10 @@ int eyemCountObjectE(EyemImage tpImage, EyemRect tpRoi, const char *fileName, in
//最小外包矩形
cv::Point2f _pts[4];
ta.RBox.points(_pts);
//已做标记(TODO:考虑增加判断哪些是起点哪些不是)
if (trackMat.ptr<uint8_t>(cvRound(startCenter.y))[cvRound(startCenter.x)] == 255) {
//已做标记(TODO:考虑增加判断哪些是起点哪些不是,20211018增加为四联通判断当前起点是否追踪过)
if (trackMat.ptr<uint8_t>(cvRound(startCenter.y))[cvRound(startCenter.x)] == 255 || trackMat.ptr<uint8_t>(cvRound(startCenter.y))[cvRound(startCenter.x) - 1] == 255 ||
trackMat.ptr<uint8_t>(cvRound(startCenter.y) - 1)[cvRound(startCenter.x)] == 255 || trackMat.ptr<uint8_t>(cvRound(startCenter.y))[cvRound(startCenter.x) + 1] == 255 ||
trackMat.ptr<uint8_t>(cvRound(startCenter.y) + 1)[cvRound(startCenter.x)] == 255) {
continue;
}
//获取模板图像(是否每次起点都计算模板，如果元件变形过大是否还会有用？)
......@@ -5926,7 +5932,6 @@ int eyemCountObjectE(EyemImage tpImage, EyemRect tpRoi, const char *fileName, in
int eyemCountObjectIrregularPartsE(EyemImage tpImage, EyemRect tpRoi, const char *fileName, const char *ccTplName, IntPtr hModelID, int *ipReelNum, EyemImage *tpDstImg)
{
cv::Mat src = cv::Mat(tpImage.iHeight, tpImage.iWidth, MAKETYPE(tpImage.iDepth, tpImage.iChannels), tpImage.vpImage).clone();
//判断文件是否存在
if (src.empty() || NULL == hModelID) {
return FUNC_IMAGE_NOT_EXIST;
......@@ -7479,9 +7484,7 @@ int eyemReleaseModel(IntPtr &hModelID)
{
if (NULL == hModelID)
return FUNC_OK;
std::vector<EyemModelID> *tpModelID = reinterpret_cast<std::vector<EyemModelID>*>(hModelID);
for (std::vector<EyemModelID>::iterator it = tpModelID->begin(); it != tpModelID->end(); ++it) {
EyemModelID *modelID = &(*it);
......@@ -7494,11 +7497,9 @@ int eyemReleaseModel(IntPtr &hModelID)
}
//清空容器
tpModelID->clear();
//释放容器
delete tpModelID;
tpModelID = NULL; hModelID = NULL;
return FUNC_OK;
}
......@@ -7596,7 +7597,6 @@ int eyemMarkerTracing(EyemImage tpImage, EyemHSVModel tpHSVModel, EyemOcsFXYR *t
cv::blur(image, image, cv::Size(5, 5));
//用于显示
cv::Mat cc = image.clone();
//转hsv空间
cv::Mat imgGray;
cv::cvtColor(image, imgGray, cv::COLOR_BGR2HSV);
......@@ -7615,7 +7615,7 @@ int eyemMarkerTracing(EyemImage tpImage, EyemHSVModel tpHSVModel, EyemOcsFXYR *t
cv::bitwise_or(mask1, mask2, maskj);
//去掉干扰
cv::morphologyEx(maskj, maskj, cv::MORPH_OPEN, cv::getStructuringElement(cv::MORPH_RECT, cv::Size(3, 3)));
cv::morphologyEx(maskj, maskj, cv::MORPH_OPEN, cv::getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(5, 5)));
//测试用，局部用RBG分割
cv::Mat labels, stats, centroids;
......@@ -7625,7 +7625,7 @@ int eyemMarkerTracing(EyemImage tpImage, EyemHSVModel tpHSVModel, EyemOcsFXYR *t
for (int i = 1; i < nccomps; i++) {
colors[i] = 255;
double minSize = cv::min(stats.ptr<int>(i)[cv::CC_STAT_WIDTH], stats.ptr<int>(i)[cv::CC_STAT_HEIGHT]); double dRate = (double)stats.ptr<int>(i)[cv::CC_STAT_WIDTH] / (double)stats.ptr<int>(i)[cv::CC_STAT_HEIGHT];
if (minSize < 24 || !(dRate > 0.75&&dRate < 1.25))
if (minSize < 20 || !(dRate > 0.75&&dRate < 1.25))
{
colors[i] = 0;
}
......@@ -7659,7 +7659,7 @@ int eyemMarkerTracing(EyemImage tpImage, EyemHSVModel tpHSVModel, EyemOcsFXYR *t
cv::approxPolyDP(cv::Mat(contour), approx, arcL*0.01, true);
if (approx.size() > 5) {
cv::Rect bbox = cv::boundingRect(contour);
if (MIN(bbox.width, bbox.height) > 24 && ((float)bbox.width / (float)bbox.height) > 0.75 && \
if (MIN(bbox.width, bbox.height) > 20 && ((float)bbox.width / (float)bbox.height) > 0.75 && \
((float)bbox.width / (float)bbox.height) < 1.25) {
//圆度
double afa = 4.0f*CV_PI*cv::contourArea(contour, false) / std::pow(arcL, 2);
......@@ -7924,6 +7924,7 @@ int eyemMulFuncTool(EyemImage tpImage, EyemRect tpRoi, const char *funcName, dou
}
#include "eyemStopwatch.h"
#include "yoloWrapper.h"
int eyemLibImpl(EyemImage tpImage, EyemImage *tpDstImg)
{
CV_Assert(NULL != tpImage.vpImage);
......@@ -7932,6 +7933,55 @@ int eyemLibImpl(EyemImage tpImage, EyemImage *tpDstImg)
if (image.empty())
return FUNC_IMAGE_NOT_EXIST;
if (image.channels() > 3) {
cv::cvtColor(image, image, cv::COLOR_BGRA2BGR);
}
cv::Mat output = image.clone();
#pragma region darknet.lib
//int topk = 5;
//DarkNet net;
//net.setPreferableParams(0.2, topk);
//if (net.init("D:/cifar_resnet50.cfg", "D:/cifar_resnet50.weights", 1) != 0)
//{
// std::cout << "net load failed" << std::endl;
// return FUNC_CANNOT_CALC;
//}
//net.forward(image);
////获取结果
//int predicts[5]; float confidence[5]; auto bbox = std::vector<cv::Rect>();
//net.getResult(predicts, confidence, &bbox);
#pragma endregion
#pragma region test new preprocess
////转单通道
//if (image.channels() != 1)
// cv::cvtColor(image, image, cv::COLOR_BGR2GRAY);
////图像裁剪
//cv::Mat src = image(cv::Rect(200, 200, image.cols - 400, image.rows - 400)).clone();
////image size
//int X = src.cols, Y = src.rows;
////用于去除黑色坏点干扰
//cv::Mat medBlur;
//cv::medianBlur(src, medBlur, 3);
////去除局部量斑影响（默认亮斑尺寸不会大于15个像素）
//cv::morphologyEx(medBlur, medBlur, cv::MORPH_ERODE, cv::getStructuringElement(cv::MORPH_RECT, cv::Size(15, 15)));
////图像增强
//double min, max;
//cv::minMaxLoc(medBlur, &min, &max, NULL);
//src.convertTo(src, CV_64FC1);
//src -= min;
//src /= (max - min);
//src *= 65535;
//src.convertTo(src, CV_16UC1);
//src.convertTo(output, CV_8UC1, 1 / 255.);
#pragma endregion
#pragma region clock test
//Stopwatch sw;
......@@ -7943,15 +7993,13 @@ int eyemLibImpl(EyemImage tpImage, EyemImage *tpDstImg)
#pragma region resize img
//const int minInputSize = 832;
//const int minInputSize = 128;
//float resizeRatio = (float)sqrt(image.cols * image.rows * 1.0 / (minInputSize * minInputSize));
//int target_width = cvRound((float)image.cols / resizeRatio);
//int target_height = cvRound((float)image.rows / resizeRatio);
//cv::Mat input;
//resize(image, input, cv::Size(image.cols / 2, image.rows / 2), 0, 0, cv::INTER_CUBIC);
//resize(image, output, cv::Size(target_width, target_height), 0, 0, cv::INTER_CUBIC);
//image = input;
#pragma endregion
......@@ -8095,6 +8143,19 @@ int eyemLibImpl(EyemImage tpImage, EyemImage *tpDstImg)
#pragma endregion
if (!output.empty()) {
//<输出结果图像
tpDstImg->iWidth = output.cols; tpDstImg->iHeight = output.rows; tpDstImg->iDepth = output.depth(); tpDstImg->iChannels = output.channels();
//内存尺寸
int _Size = tpDstImg->iWidth*tpDstImg->iHeight*tpDstImg->iChannels * sizeof(uint8_t);
//分配初始化内存
tpDstImg->vpImage = (uint8_t *)malloc(_Size);
if (NULL == tpDstImg->vpImage)
return FUNC_NOT_ENOUGH_MEM;
memset(tpDstImg->vpImage, 0, _Size);
//拷贝数据
memcpy(tpDstImg->vpImage, output.data, _Size);
}
return FUNC_OK;
}
......
eyemLib/eyemNNDetector.cpp
#include "eyemNNDetector.h"
#include "yoloWrapper.h"
class NNDetector::Impl {
public:
......@@ -12,12 +11,12 @@ public:
};
NNDetector::NNDetector(const std::string & detector_config_path,
const std::string & detector_model_path)
const std::string & detector_model_path, const cv::Size& input_size)
{
p = cv::makePtr<NNDetector::Impl>();
if (!detector_config_path.empty() && !detector_model_path.empty()) {
p->detector_ = std::make_shared<YoloWrapper>();
p->detector_->init(detector_config_path, detector_model_path);
p->detector_->init(detector_config_path, detector_model_path, input_size);
}
else {
p->detector_ = NULL;
......@@ -45,10 +44,10 @@ std::vector<cv::Rect> NNDetector::Impl::detect(const cv::Mat& img) {
return detector_->forward(img);
}
int eyemInitNNDetector(const char *detectorConfigPath, const char *detectorModelPath)
int eyemInitNNDetector(const char *detectorConfigPath, const char *detectorModelPath, int iNetSizew, int iNetSizeh)
{
try {
pNNDetector = cv::makePtr<NNDetector>(detectorConfigPath, detectorModelPath);
pNNDetector = cv::makePtr<NNDetector>(detectorConfigPath, detectorModelPath, cv::Size(iNetSizew, iNetSizeh));
}
catch (const std::exception& e) {
std::cout << e.what() << std::endl;
......@@ -57,7 +56,7 @@ int eyemInitNNDetector(const char *detectorConfigPath, const char *detectorModel
return FUNC_OK;
}
int eyemNNDetector(EyemImage tpImage, int *ipNum, BboxContainer &container)
int eyemNNDetector(EyemImage tpImage, int *ipNum, BboxContainer &container, EyemImage *tpDstImg)
{
cv::Mat src = cv::Mat(tpImage.iHeight, tpImage.iWidth, MAKETYPE(tpImage.iDepth, tpImage.iChannels), tpImage.vpImage).clone();
if (src.empty()) {
......@@ -69,6 +68,77 @@ int eyemNNDetector(EyemImage tpImage, int *ipNum, BboxContainer &container)
EyemRect bbox; bbox.iXs = target[i].x; bbox.iYs = target[i].y; bbox.iWidth = target[i].width; bbox.iHeight = target[i].height;
container.bboxes[i] = bbox;
}
//输出图像
cv::Mat input;
int incn = src.channels();
if (incn == 4) {
cv::cvtColor(src, input, cv::COLOR_BGRA2BGR);
}
else if (incn == 1) {
cv::cvtColor(src, input, cv::COLOR_GRAY2BGR);//根据配置支持三通道图像
}
else {
input = src;
}
for (int idx = 0; idx < target.size(); idx++) {
cv::Rect bbox = target[idx] & cv::Rect(0, 0, src.cols, src.rows);
//标签
std::string label = "TARGET";
int baseLine;
cv::Size labelSize = cv::getTextSize(label, cv::FONT_HERSHEY_SIMPLEX, 1.5, 2, &baseLine);
cv::Scalar pal(0, 255, 0);
cv::rectangle(input, bbox, pal);
cv::rectangle(input, cv::Point(bbox.x, bbox.y - labelSize.height - baseLine), cv::Point(bbox.x + labelSize.width, bbox.y), pal, cv::FILLED);
cv::putText(input, label, cv::Point(bbox.tl().x + 2, bbox.tl().y - baseLine + 2), cv::FONT_HERSHEY_SIMPLEX, 1.5, cv::Scalar(0, 0, 0), 2);
}
//<输出计数结果标记图像
tpDstImg->iWidth = input.cols; tpDstImg->iHeight = input.rows; tpDstImg->iDepth = input.depth(); tpDstImg->iChannels = input.channels();
//内存尺寸
int _Size = tpDstImg->iWidth*tpDstImg->iHeight*tpDstImg->iChannels * sizeof(uint8_t);
//分配初始化内存
tpDstImg->vpImage = (uint8_t *)malloc(_Size);
if (NULL == tpDstImg->vpImage)
return FUNC_NOT_ENOUGH_MEM;
memset(tpDstImg->vpImage, 0, _Size);
//拷贝数据
memcpy(tpDstImg->vpImage, input.data, _Size);
*ipNum = static_cast<int>(target.size());
return FUNC_OK;
}
int eyemInitClassifier(const char *classifierConfigPath, const char *classifierModelPath, int ntype)
{
try {
pClassifier = cv::makePtr<YoloDarknet>(classifierConfigPath, classifierModelPath, ntype);
}
catch (const std::exception& e) {
std::cout << e.what() << std::endl;
return FUNC_CANNOT_CALC;
}
return FUNC_OK;
}
int eyemClassifier(EyemImage tpImage)
{
cv::Mat src = cv::Mat(tpImage.iHeight, tpImage.iWidth, MAKETYPE(tpImage.iDepth, tpImage.iChannels), tpImage.vpImage).clone();
if (src.empty()) {
return FUNC_IMAGE_NOT_EXIST;
}
cv::Mat input;
int incn = src.channels();
if (incn == 4) {
cv::cvtColor(src, input, cv::COLOR_BGRA2BGR);
}
else if (incn == 1) {
cv::cvtColor(src, input, cv::COLOR_GRAY2BGR);//根据配置支持三通道图像
}
else {
input = src;
}
pClassifier->setInput(input, 0.4, 5);
auto predict = std::vector<int>(); auto confidence = std::vector<float>(); auto bbox = std::vector<cv::Rect>();
pClassifier->forward(predict, confidence, bbox);
std::cout << "run_end!" << std::endl;
return FUNC_OK;
}
eyemLib/eyemNNDetector.h
......@@ -6,11 +6,12 @@
#define __EYEMNNDETECTOR_H
#include "eyemLib.h"
#include "yoloWrapper.h"
class NNDetector {
public:
NNDetector(const std::string& detector_config_path = "",
const std::string& detector_model_path = "");
NNDetector(const std::string& detector_config_path,
const std::string& detector_model_path, const cv::Size& input_size);
NNDetector() {};
std::vector<cv::Rect> detect(cv::InputArray img);
......@@ -22,4 +23,6 @@ protected:
cv::Ptr<NNDetector> pNNDetector;
cv::Ptr<YoloDarknet> pClassifier;
#endif/* __EYEMNNDETECTOR_H */
\ No newline at end of file
eyemLib/resnet.h deleted 100644 → 0
#pragma once
#ifndef __RESNET_LIB_H
#define __RESNET_LIB_H
#ifdef __DLLEXPORT
#define __DLL_EXP _declspec(dllexport) // 导出函数 - 生成dll文件时使用
#else
#define __DLL_EXP _declspec(dllimport) // 导入函数 -使用dll时使用
#endif // __DLLEXPORT
#include "ncnn/net.h"
#include "opencv2/opencv.hpp"
#include <vector>
#include <iostream>
#include<fstream>
#include <sstream>
#include <string>
#include <windows.h>
//typedef struct {
// void* vpImage; // 地址
// int iWidth; // 图像内存 x 方向大小
// int iHeight; // 图像内存 y 方向大小
// int iDepth; // 图像位深度（参见说明）
// int iChannels; // 图像通道数
//} EyemImage;
#ifndef MAKETYPE
#define MAKETYPE CV_MAKETYPE
#endif
//// 一般定义
//#define FUNC_OK 0 // 正常
//#define FUNC_NOT_ENOUGH_MEM -1 // 工作内存不足
//#define FUNC_ILLEGAL_ARGUMENT -2 // 参数不合适
//#define FUNC_IMAGE_NOT_EXIST -3 // 图像不存在
//#define FUNC_CANNOT_CALC -100 // 不可计算
//#define FUNC_CANNOT_USE -999 // 不可用
#ifdef __cplusplus
extern "C" {
#endif
__DLL_EXP void match_feature(const char* input_path, EyemImage image, char* part_number);
__DLL_EXP void enroll_feature(const char* input_path, const char* output_path);
#ifdef __cplusplus
}
#endif
#endif
eyemLib/yoloWrapper.cpp
#include "yoloWrapper.h"
int YoloWrapper::init(const std::string& detector_config_path,
const std::string& detector_model_path) {
const std::string& detector_model_path, const cv::Size& input_size) {
if (!detector_config_path.empty() && !detector_model_path.empty()) {
//加载并初始化网络
net_ = cv::dnn::readNet(detector_config_path, detector_model_path);
net_.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
net_.setPreferableBackend(cv::dnn::DNN_TARGET_CPU);
//设置输入尺寸
Size_ = cv::Size(input_size.width, input_size.height);
}
return 0;
}
......@@ -23,7 +25,7 @@ std::vector<cv::Rect> YoloWrapper::forward(cv::Mat img) {
}
//预处理图像
cv::Mat blob;
cv::dnn::blobFromImage(img, blob, 1.0 / 255.0, cv::Size(960, 960));
cv::dnn::blobFromImage(img, blob, 1.0 / 255.0, Size_);
//为网络输入新值
net_.setInput(blob);
......@@ -73,4 +75,42 @@ std::vector<cv::Rect> YoloWrapper::forward(cv::Mat img) {
points.push_back(bbox);
}
return points;
}
\ No newline at end of file
}
class YoloDarknet::Impl {
public:
Impl() {}
~Impl() {}
std::shared_ptr<DarkNet> net_;
};
YoloDarknet::YoloDarknet(const std::string& config_path, const std::string& model_path, const int ntype)
{
p = cv::makePtr<YoloDarknet::Impl>();
if (!config_path.empty() && !model_path.empty()) {
p->net_ = std::make_shared<DarkNet>();
p->net_->init(config_path, model_path, ntype);
}
else {
p->net_ = NULL;
}
}
void YoloDarknet::setInput(cv::Mat& img, float threshold, int topk) {
topk_ = topk;
p->net_->setPreferableParams(threshold, topk);
p->net_->forward(img);
}
void YoloDarknet::forward(std::vector<int> &outputPredict, std::vector<float> &outputConfidence, std::vector<cv::Rect> &outputBoxes)
{
auto bbox = std::vector<cv::Rect>();
int *predicts = new int[topk_]; float *confidence = new float[topk_];
p->net_->getResult(predicts, confidence, &bbox);
for (int top = 0; top < topk_; top++) outputPredict.push_back(predicts[top]), outputConfidence.push_back(confidence[top]);
for (auto&box : bbox) {
outputBoxes.push_back(box);
}
delete[] predicts; predicts = NULL; delete[] confidence; confidence = NULL;
}
eyemLib/yoloWrapper.h
......@@ -8,15 +8,34 @@
#include "opencv2/dnn.hpp"
#include "opencv2/imgproc.hpp"
#include <darknet.h>
#include <yolo_class.h>
class YoloWrapper
{
public:
YoloWrapper() {};
int init(const std::string& config_path, const std::string& model_path);
int init(const std::string& config_path, const std::string& model_path, const cv::Size& input_size);
std::vector<cv::Rect> forward(cv::Mat img);
private:
cv::Size Size_;
cv::dnn::Net net_;
};
class YoloDarknet
{
public:
YoloDarknet(const std::string& config_path = "", const std::string& model_path = "", const int ntype = 0);
YoloDarknet() {};
void setInput(cv::Mat& img, float threshold, int topk = 2);
void forward(std::vector<int> &outputPredict, std::vector<float> &outputConfidence, std::vector<cv::Rect> &outputBoxes);
private:
int topk_ = 0;
protected:
class Impl;
cv::Ptr<Impl> p;
};
#endif/* __YOLOWRAPPER_H */
\ No newline at end of file
支持 Markdown 格式
你添加了 0 人 到此讨论。请谨慎行事。
Finish editing this message first!