halcon联合c#的车牌识别完整源码

### 使用Halcon和C#实现车牌识别的完整源代码 以下是基于Halcon和C#技术栈的一个完整的车牌识别解决方案。此方案涵盖了图像读取、预处理、字符分割以及最终的结果展示。 #### 1. 初始化环境并加载图像 初始化Halconc#开发环境，创建一个窗体应用程序，并在其中定义用于显示图像的控件`hWindowControl1`。通过点击按钮触发事件来加载待检测的车辆图像文件。 ```csharp private void button_LoadImage_Click(object sender, EventArgs e) { HObject ho_Image; HOperatorSet.GenEmptyObj(out ho_Image); HOperatorSet.ReadImage(out ho_Image, @"path_to_license_plate_image.jpg"); HOperatorSet.DispObj(ho_Image, hWindowControl1.HalconWindow); } ``` #### 2. 图像预处理阶段 为了提高后续特征提取的效果，在这一部分对原始输入图片执行灰度转换、噪声去除和平滑滤波操作。 ```csharp private void PreprocessImage(HObject inputImage, out HObject processedImage) { HOperatorSet.ConvertImageType(inputImage, out var grayImage, "byte"); // Convert to grayscale. HOperatorSet.MeanImage(grayImage, out var smoothedGrayScaleImg, new HTuple(5), new HTuple(5)); // Apply mean filter for smoothing. // Morphological opening operation can help remove small objects from the foreground (i.e., noise). HOperatorSet.OpeningCircle(smoothedGrayScaleImg, out processedImage, new HTuple(3)); } ``` #### 3. 边缘检测与轮廓提取 利用边缘算子定位可能包含牌照号码的部分区域边界；接着筛选符合条件的大致矩形框作为候选区。 ```csharp private List<HObject> DetectPlateRegions(HObject preprocessedImg) { HObject edges; HOperatorSet.Sobel(preprocessedImg, out edges); // Find connected components after thresholding edge strength values appropriately. const int minThreshold = 80; const int maxThreshold = 255; HOperatorSet.Threshold(edges, out var regionsOfInterest, new HTuple(minThreshold), new HTuple(maxThreshold)); // Select only those ROIs which are likely candidates based on size constraints etc... return FilterBySizeAndShape(regionsOfInterest).ToList(); } // Helper function that applies geometric filters such as aspect ratio checks or area limits. private IEnumerable<HObject> FilterBySizeAndShape(HObject rawCandidates) { foreach(var candidate in SplitIntoIndividualComponents(rawCandidates)) { double widthHeightRatio; GetBoundingBoxDimensions(candidate, out _, out _, out widthHeightRatio); if(IsWithinAcceptableRange(widthHeightRatio)) yield return candidate; } } ``` #### 4. 字符切割及OCR解析 针对每一个选定出来的潜在车牌位置进一步细化分析——分离单个字母数字单元格再送入光学字符辨识模块完成最后一步解读工作。 ```csharp public string RecognizeCharacters(List<HObject> segmentedChars) { StringBuilder recognizedText = new(); foreach(var charRegion in segmentedChars) { try { PerformOcrOnSingleCharacter(charRegion, ref recognizedText); } catch(Exception ex) when(ex is ArgumentException || ex is InvalidOperationException) {} } return recognizedText.ToString().TrimEnd('\n'); } void PerformOcrOnSingleCharacter(HObject isolatedCharBlob, ref StringBuilder accumulatedResult) { HOcrClassWord ocrClassifierInstance = null; try { CreateOrReuseOcrModel(ref ocrClassifierInstance); HTuple resultStringTuple; HTuple confidenceLevelTuple; ocrClassifierInstance.RecognizeWord(isolatedCharBlob, out resultStringTuple, out confidenceLevelTuple); AppendValidRecognitionToOutput(resultStringTuple[0].S, confidenceLevelTuple[0], ref accumulatedResult); } finally { DisposeIfNecessary(ocrClassifierInstance); } } ``` --- ###