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Commit ce080e8c 由 张士柳
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1 个父辈 ee8a6917
内嵌 并排
正在显示 3 个修改的文件 包含 149 行增加 和 27 行删除
eyemLib-Sharp/EyemLib.cs
...@@ -861,7 +861,7 @@ namespace eyemLib_Sharp ...@@ -861,7 +861,7 @@ namespace eyemLib_Sharp
//flag = eyemImageAbs(image1, ref tpDstImg); //flag = eyemImageAbs(image1, ref tpDstImg);
//flag = eyemAllMindImpl(image); flag = eyemAllMindImpl(image);
#region Test Blob #region Test Blob
...@@ -1073,19 +1073,19 @@ namespace eyemLib_Sharp ...@@ -1073,19 +1073,19 @@ namespace eyemLib_Sharp
//"IP_SMALL_PARTS","IP_LARGE_PARTS","IP_LONG_PARTS","" //"IP_SMALL_PARTS","IP_LARGE_PARTS","IP_LONG_PARTS",""
//eyemCountObject(image, tpRoi, file.Replace(".png", ""), 35, 0, 100, 5, ref pNumObj, out tpDstImg); //eyemCountObject(image, tpRoi, file.Replace(".png", ""), 35, 0, 100, 5, ref pNumObj, out tpDstImg);
//eyemCountObjectIrregularParts(image, tpRoi, file.Replace(".png", ""), 0.1, "", 100, 7, ref pNumObj, out tpDstImg); //eyemCountObjectIrregularParts(image, tpRoi, file.Replace(".png", ""), 0.1, "", 100, 7, ref pNumObj, out tpDstImg);
eyemCountObjectE(image, tpRoi, fileName, ref pNumObj, out tpDstImg); //eyemCountObjectE(image, tpRoi, fileName, ref pNumObj, out tpDstImg);
//eyemCountObjectIrregularPartsE(image, tpRoi, file.Replace(".png", ""), tpModels[0], hModelID, ref pNumObj, out tpDstImg); //eyemCountObjectIrregularPartsE(image, tpRoi, file.Replace(".png", ""), tpModels[0], hModelID, ref pNumObj, out tpDstImg);
//eyemCountObjectIrregularPartsE(image, tpRoi, file.Replace(".png", ""), "D:\\模板文件\\" + /*file.Replace(".png", ".tpl")*/"74d571ed-9fd4-4959-85dd-3195261e4b48.tpl", ref pNumObj, out tpDstImg); //eyemCountObjectIrregularPartsE(image, tpRoi, file.Replace(".png", ""), "D:\\模板文件\\" + /*file.Replace(".png", ".tpl")*/"74d571ed-9fd4-4959-85dd-3195261e4b48.tpl", ref pNumObj, out tpDstImg);
//移除模板 //移除模板
//flag = eyemRemoveModelByName(hModelID, "D:\\模板文件及图像\\df871193-6632-48f9-abfe-540c3fc49c3f.tpl"); //flag = eyemRemoveModelByName(hModelID, "D:\\模板文件及图像\\df871193-6632-48f9-abfe-540c3fc49c3f.tpl");
Bitmap bitmap = eyemCvtToBitmap(tpDstImg); //Bitmap bitmap = eyemCvtToBitmap(tpDstImg);
if (bitmap != null) //if (bitmap != null)
{ //{
bitmap.Save(System.Windows.Forms.Application.StartupPath + "\\ResOut\\" + file); // bitmap.Save(System.Windows.Forms.Application.StartupPath + "\\ResOut\\" + file);
} //}
////<解码测试 ////<解码测试
//int ipNum; EyemBarCode* tpResults; //int ipNum; EyemBarCode* tpResults;
......
eyemLib-Sharp/Program.cs
...@@ -17,25 +17,26 @@ namespace eyemLib_Sharp ...@@ -17,25 +17,26 @@ namespace eyemLib_Sharp
} }
//初始化 //初始化
//EyemLib.Init(); 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);
for (int j = 0; j < 1; j++)
{
ParallelOptions po = new ParallelOptions();
po.MaxDegreeOfParallelism = 1;
Parallel.ForEach(fileNames, po, fn =>
{
EyemLib.eyemReadImageTool(fn);
});
Console.Write("请按任意键继续。。。");
}
//foreach (var item in fileNames) //for (int j = 0; j < 1; j++)
//{ //{
// EyemLib.eyemReadImageTool(item); // ParallelOptions po = new ParallelOptions();
// po.MaxDegreeOfParallelism = 1;
// Parallel.ForEach(fileNames, po, fn =>
// {
// EyemLib.eyemReadImageTool(fn);
// });
// Console.Write("请按任意键继续。。。");
//} //}
foreach (var item in fileNames)
{
EyemLib.eyemReadImageTool(item);
}
//for (int i = 0; i < 1; i++) //for (int i = 0; i < 1; i++)
//{ //{
// EyemLib.eyemTestVideoCapture("D:\\插件完成检测\\视频\\cap5.mp4"); // EyemLib.eyemTestVideoCapture("D:\\插件完成检测\\视频\\cap5.mp4");
...@@ -69,7 +70,7 @@ namespace eyemLib_Sharp ...@@ -69,7 +70,7 @@ namespace eyemLib_Sharp
//EyemLib.eyemReleaseModelE(ref hModelID); //EyemLib.eyemReleaseModelE(ref hModelID);
//EyemLib.Free(); EyemLib.Free();
Console.Write("请按任意键继续。。。"); Console.Write("请按任意键继续。。。");
Console.ReadKey(); Console.ReadKey();
......
eyemLib/eyemMisc.cpp
...@@ -5970,6 +5970,74 @@ int eyemAOIForTSAV(EyemImage tpRefImg, EyemImage tpNextImg, EyemRect3 *tpRois, i ...@@ -5970,6 +5970,74 @@ int eyemAOIForTSAV(EyemImage tpRefImg, EyemImage tpNextImg, EyemRect3 *tpRois, i
return FUNC_OK; return FUNC_OK;
} }
////绘制预测边界框
//static void drawPred(int classId, float conf, int left, int top, int right, int bottom, cv::Mat& frame) {
// //绘制边界框
// cv::rectangle(frame, cv::Point(left, top), cv::Point(right, bottom), cv::Scalar(255, 178, 50), 3);
//
// std::string label = cv::format("%.2f", conf);
// if (!classes.empty()) {
// CV_Assert(classId < (int)classes.size());
// label = classes[classId] + ":" + label;//边框上的类别标签与置信度
// }
// //绘制边界框上的标签
// int baseLine;
// cv::Size labelSize = cv::getTextSize(label, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
// top = std::max(top, labelSize.height);
// cv::rectangle(frame, cv::Point(left, top - round(1.5*labelSize.height)), cv::Point(left + round(1.5*labelSize.width), top + baseLine), cv::Scalar(255, 255, 255), cv::FILLED);
// cv::putText(frame, label, cv::Point(left, top), cv::FONT_HERSHEY_SIMPLEX, 0.75, cv::Scalar(0, 0, 0), 1);
//}
//移除低置信度边界框
static void postprocess(cv::Mat& frame, const std::vector<cv::Mat>& outs) {
std::vector<int> classIds;//储存识别类的索引
std::vector<float> confidences;//储存置信度
std::vector<cv::Rect> boxes;//储存边框
for (size_t i = 0; i < outs.size(); i++) {
//从网络输出中扫描所有边界框
//保留高置信度选框
cv::Mat tm = outs[i];
//目标数据data:x,y,w,h为百分比，x,y为目标中心点坐标
float* data = (float*)outs[i].data;
for (int j = 0; j < outs[i].rows; j++, data += outs[i].cols) {
cv::Mat scores = outs[i].row(j).colRange(5, outs[i].cols);
cv::Point classIdPoint;
double confidence;//置信度
//取得最大分数值与索引
cv::minMaxLoc(scores, 0, &confidence, 0, &classIdPoint);
if (confidence > 0.5) {
int centerX = (int)(data[0] * frame.cols);
int centerY = (int)(data[1] * frame.rows);
int width = (int)(data[2] * frame.cols);
int height = (int)(data[3] * frame.rows);
int left = centerX - width / 2;
int top = centerY - height / 2;
classIds.push_back(classIdPoint.x);
confidences.push_back((float)confidence);
boxes.push_back(cv::Rect(left, top, width, height));
}
}
}
//低置信度
std::vector<int> indices;//保存没有重叠边框的索引
//该函数用于抑制重叠边框
cv::dnn::NMSBoxes(boxes, confidences, 0.5, 0.4, indices);
for (size_t i = 0; i < indices.size(); i++) {
int idx = indices[i];
cv::Rect box = boxes[idx];
//drawPred(classIds[idx], confidences[idx], box.x, box.y,
//box.x + box.width, box.y + box.height, frame);
cv::rectangle(frame, box, cv::Scalar(0, 0, 255));
}
}
int eyemAllMindImpl(EyemImage tpImage) int eyemAllMindImpl(EyemImage tpImage)
{ {
cv::Mat image = cv::Mat(tpImage.iHeight, tpImage.iWidth, MAKETYPE(tpImage.iDepth, tpImage.iChannels), tpImage.vpImage).clone(); cv::Mat image = cv::Mat(tpImage.iHeight, tpImage.iWidth, MAKETYPE(tpImage.iDepth, tpImage.iChannels), tpImage.vpImage).clone();
...@@ -5997,14 +6065,67 @@ int eyemAllMindImpl(EyemImage tpImage) ...@@ -5997,14 +6065,67 @@ int eyemAllMindImpl(EyemImage tpImage)
#pragma endregion #pragma endregion
cv::Mat sum, sqsum, templ; //加载网络
cv::Scalar templMean, templSdv; cv::dnn::Net net = cv::dnn::readNet(".\\darknet\\detect.cfg", ".\\darknet\\detect.weights");
double *q0 = 0, *q1 = 0, *q2 = 0, *q3 = 0; net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
double templNorm = 0, templSum2 = 0; net.setPreferableBackend(cv::dnn::DNN_TARGET_CPU);
//获取输出层名称
auto layerNames = net.getLayerNames();
std::vector<int> outLayers = net.getUnconnectedOutLayers();
std::vector<std::string> outBlobNames(outLayers.size());
for (int i = 0; i < outLayers.size(); i++) {
outBlobNames[i] = layerNames[outLayers[i] - 1];
}
cv::integral(image, sum, sqsum, CV_64F); //预处理图像
cv::meanStdDev(templ, templMean, templSdv); cv::Mat blob;
cv::dnn::blobFromImage(image, blob, 1 / 255., cv::Size(416, 416));
//为网络输入新值
net.setInput(blob);
//获取预测结果
std::vector<cv::Mat> outputBlobs;
net.forward(outputBlobs, outBlobNames);
for (int n = 0; n < outputBlobs.size(); n++) {
for (int row = 0; row < outputBlobs[n].rows; row++) {
//置信度
cv::Mat prob = outputBlobs[n](cv::Range(row, row + 1), cv::Range(5, outputBlobs[n].cols));
double confidence;
cv::Point classIdPoint;
//取得最大分数值与索引
cv::minMaxLoc(prob, 0, &confidence, 0, &classIdPoint);
cv::Mat dt = outputBlobs[n];
cv::Mat x= outputBlobs[n](cv::Range(row, row + 1), cv::Range(0, 5));
//如果置信度大于阈值
if (confidence > 0.5) {
/*int centerX = (int)(data[0] * frame.cols);
int centerY = (int)(data[1] * frame.rows);
int width = (int)(data[2] * frame.cols);
int height = (int)(data[3] * frame.rows);
int left = centerX - width / 2;
int top = centerY - height / 2;*/
//classIds.push_back(classIdPoint.x);
//confidences.push_back((float)confidence);
//boxes.push_back(cv::Rect(left, top, width, height));
}
}
}
//cv::Mat showMat;
//cv::cvtColor(image)
//绘制结果
postprocess(image, outputBlobs);
return FUNC_OK; return FUNC_OK;
} }
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