c#实现包裹扣面单的几种方式

爱迪图蕃 發表於 2026-1-15 10:46:00

无论是跨境电商还是制造业分拣设备，在包裹流转出入库的场景，为了保证包裹分拣计划和测量数据绑定真实性，经常会遇到面单扣取的需求，下面我就通过两种实现原理来实现这一功能。
    一:OpenCVSharp 通过面单轮廓/颜色/边缘等组合检测实现
    二:通过OCR识别面单内容,根据所有切割点坐标点最小外界矩形来定位面单位置(扣面单的场景需求是看清面单内容，当然想要扣取完整面单图片，可以添加面单尺寸,规则信息等维度计算或者直接用第三种方式)
    三:YOLO+Labelme标定工具，通过模型训练定位扣取(这个抽时间单独展开一篇解释)
方式一:OpencvSharp 通过轮廓/颜色/边缘检测
　　这种方式对于包裹和面单颜色有明显差异的场景很友好，对于包裹颜色和面单颜色接近的效果一般（建议考虑第二种方式），虽然可以根据面单样式或者文字聚集密度等多重维度来组合分析，但是过于复杂，并且定制化程度很高，废话少说，先看看效果:
 
        原图：
　　<img src="https://img2024.cnblogs.com/blog/116076/202601/116076-20260114185329554-1784692153.png" alt="333" width="439" height="328" loading="lazy">
 
  　 通过显示增强后的效果图:
       <img src="https://img2024.cnblogs.com/blog/116076/202601/116076-20260114185355689-1585898524.png" alt="zsvvyv" width="367" height="536" loading="lazy">
<img src="https://img2024.cnblogs.com/blog/116076/202509/116076-20250924180326264-1688793031.png" alt="116076-20250310125032345-1793233350" width="519" height="244" loading="lazy">
废话少说，附上核心代码:
<div class="cnblogs_code">
<pre>staticvoidProcessSingleImage(string imagePath)
{
if (!File.Exists(imagePath))
{
 Console.WriteLine("文件不存在!");
 Console.ReadKey();
return;
}

try
{
 Console.WriteLine($"处理: {Path.GetFileName(imagePath)}");

var stopwatch = Stopwatch.StartNew();

// 检测面单
var results = _detector.DetectLabels(imagePath);

 stopwatch.Stop();
 Console.WriteLine($"检测耗时: {stopwatch.ElapsedMilliseconds}ms");
 Console.WriteLine($"找到 {results.Count} 个面单区域");

if (results.Count == 0)
 {
 Console.WriteLine("未检测到面单!");
 Console.ReadKey();
return;
 }

// 显示结果
foreach (var result in results)
 {
 Console.WriteLine($"- {result.DetectionMethod}: 置信度 {result.Confidence:F2}, " +
$"位置 [{result.BoundingBox.X}, {result.BoundingBox.Y}, " +
$"{result.BoundingBox.Width}, {result.BoundingBox.Height}]");
 }

// 创建输出目录
var outputDir = _config.OutputDirectory;
if (!Directory.Exists(outputDir))
 Directory.CreateDirectory(outputDir);

var baseName = Path.GetFileNameWithoutExtension(imagePath);

// 保存可视化结果
if (_config.SaveVisualized)
 {
using (var original = new Bitmap(imagePath))
 {
 Bitmap bitResult = ImageProcessor.DrawBoundingBoxesSafe(original, results);

var visPath = Path.Combine(outputDir, $"{baseName}_detected.png");
 ImageProcessor.SaveImage(bitResult, visPath);
 Console.WriteLine($"可视化结果已保存: {visPath}");
 }
 }

// 保存抠图结果 
if (_config.SaveCropped)
 {
using (var mat = Cv2.ImRead(imagePath))
 {
for (int i = 0; i < results.Count; i++)
 {
var cropped = _detector.CropLabel(mat, results.BoundingBox);
if (cropped != null)
 {
// 图像增强
 _detector.EnhanceImage(ref cropped);

var cropPath = Path.Combine(outputDir, $"{baseName}_label_{i + 1}.png");
 Console.WriteLine(cropped);

 ImageProcessor.SaveImage(cropped, cropPath);
 Console.WriteLine($"抠图已保存: {cropPath}");

 cropped.Dispose();
 }
 }
 }
 }

// 保存检测结果到JSON
 SaveResultsToJson(results, Path.Combine(outputDir, $"{baseName}_results.json"));

 Console.WriteLine("\n处理完成! 按任意键继续...");
}
catch (Exception ex)
{
 Console.WriteLine($"处理失败: {ex.Message}");
}
}</pre>
</div>
通过轮廓检测、颜色检测和边缘检测三种方式组合定位面单位置
<div class="cnblogs_code">
<pre>public List<DetectionResult> DetectLabels(string imagePath)
{
var results = new List<DetectionResult>();

using (var mat = Cv2.ImRead(imagePath, OpenCvSharp.ImreadModes.Color))
 {
if (mat.Empty())
thrownew FileNotFoundException($"无法加载图像: {imagePath}");

// 方法1: 轮廓检测
var contourResults = DetectByContours(mat);
 results.AddRange(contourResults);

// 方法2: 颜色检测
var colorResults = DetectByColor(mat);
 results.AddRange(colorResults);

// 方法3: 边缘检测
var edgeResults = DetectByEdges(mat);
 results.AddRange(edgeResults);
 }

// 合并和筛选结果
return FilterResults(results);
}</pre>
</div>
轮廓检测
<div class="cnblogs_code">
<pre>private List<DetectionResult> DetectByContours(OpenCvSharp.Mat src)
{
var results = new List<DetectionResult>();
using (var gray = new OpenCvSharp.Mat())
using (var binary = new OpenCvSharp.Mat())
 {
 Cv2.CvtColor(src, gray, ColorConversionCodes.BGR2GRAY);

// 二值化
 Cv2.Threshold(gray, binary, 0, 255, ThresholdTypes.Binary | ThresholdTypes.Otsu);

// 形态学操作
var kernel = Cv2.GetStructuringElement(MorphShapes.Rect, new OpenCvSharp.Size(3, 3));
 Cv2.MorphologyEx(binary, binary, MorphTypes.Close, kernel);

// 查找轮廓
 Cv2.FindContours(binary, outvar contours, outvar hierarchy,
 RetrievalModes.External, ContourApproximationModes.ApproxSimple);

foreach (var contour in contours)
 {
var area = Cv2.ContourArea(contour);
if (area < _minArea || area > _maxArea)
continue;

 Console.WriteLine($"面积：{area}");
var rect = Cv2.BoundingRect(contour);

// 计算宽高比
var aspectRatio = (double)rect.Width / rect.Height;

// 面单通常为矩形，宽高比在一定范围内
if (aspectRatio > 0.5 && aspectRatio < 3.0)
 {
// 计算矩形度
var rectArea = rect.Width * rect.Height;
var rectangularity = area / rectArea;
Console.WriteLine(rectangularity);
if (rectangularity > 0.55)
 {
 results.Add(new DetectionResult
 {
 BoundingBox = rect.ToRectangle(),
 Confidence = rectangularity,
 DetectionMethod = "Contour"
 });
 }
 }
 }
 }

return results;
}</pre>
</div>
2.颜色检测
<div class="cnblogs_code">
<pre>private List<DetectionResult> DetectByColor(OpenCvSharp.Mat src)
{
var results = new List<DetectionResult>();

using (var hsv = new OpenCvSharp.Mat())
using (var mask = new OpenCvSharp.Mat())
 {
// 转换到HSV色彩空间
 Cv2.CvtColor(src, hsv, ColorConversionCodes.BGR2HSV);

// 定义白色/浅色范围
var lowerWhite1 = new Scalar(0, 0, 200);
var upperWhite1 = new Scalar(180, 30, 255);
var lowerWhite2 = new Scalar(0, 0, 180);
var upperWhite2 = new Scalar(180, 80, 255);

using (var mask1 = new OpenCvSharp.Mat())
using (var mask2 = new OpenCvSharp.Mat())
 {
 Cv2.InRange(hsv, lowerWhite1, upperWhite1, mask1);
 Cv2.InRange(hsv, lowerWhite2, upperWhite2, mask2);
 Cv2.BitwiseOr(mask1, mask2, mask);
 }

// 形态学操作
var kernel = Cv2.GetStructuringElement(MorphShapes.Rect, new OpenCvSharp.Size(5, 5));
 Cv2.MorphologyEx(mask, mask, MorphTypes.Close, kernel);
 Cv2.MorphologyEx(mask, mask, MorphTypes.Open, kernel);

// 查找轮廓
 Cv2.FindContours(mask, outvar contours, outvar hierarchy,
 RetrievalModes.External, ContourApproximationModes.ApproxSimple);

foreach (var contour in contours)
 {
var area = Cv2.ContourArea(contour);

if (area < _minArea || area > _maxArea)
continue;

var rect = Cv2.BoundingRect(contour);

// 计算颜色均匀度
var uniformity = CalculateColorUniformity(src, rect);

if (uniformity > _confidenceThreshold)
 {
 results.Add(new DetectionResult
 {
 BoundingBox = rect.ToRectangle(),
 Confidence = uniformity,
 DetectionMethod = "Color"
 });
 }
 }
 }

return results;
}
3.边缘检测

private List<DetectionResult> DetectByEdges(OpenCvSharp.Mat src)
{
var results = new List<DetectionResult>();

using (var gray = new OpenCvSharp.Mat())
using (var edges = new OpenCvSharp.Mat())
 {
 Cv2.CvtColor(src, gray, ColorConversionCodes.BGR2GRAY);

// 降噪
 Cv2.GaussianBlur(gray, gray, new OpenCvSharp.Size(5, 5), 1.5);

// 边缘检测
 Cv2.Canny(gray, edges, 50, 150);

// 膨胀
var kernel = Cv2.GetStructuringElement(MorphShapes.Rect, new OpenCvSharp.Size(3, 3));
 Cv2.Dilate(edges, edges, kernel, iterations: 2);

// 查找轮廓
 Cv2.FindContours(edges, outvar contours, outvar hierarchy,
 RetrievalModes.External, ContourApproximationModes.ApproxSimple);

foreach (var contour in contours)
 {
var area = Cv2.ContourArea(contour);

if (area < _minArea || area > _maxArea)
continue;

var rect = Cv2.BoundingRect(contour);

// 计算边缘密度
using (var roi = new OpenCvSharp.Mat(edges, rect))
 {
var totalPixels = roi.Rows * roi.Cols;
var edgePixels = Cv2.CountNonZero(roi);
var edgeDensity = (double)edgePixels / totalPixels;

if (edgeDensity > 0.1 && rect.Width > 100 && rect.Height > 100)
 {
 results.Add(new DetectionResult
 {
 BoundingBox = rect.ToRectangle(),
 Confidence = edgeDensity,
 DetectionMethod = "Edge"
 });
 }
 }
 }
 }

return results;
}</pre>
</div>
方式二:通过OCR识别面单内容,根据所有切割点坐标点最小外界矩形来定位面单位置
    OCR基础模型用的是SVTR-LCNet这个架构的网络模型，论文是公开的，我们在这个基础上做的复现与调优。话不多说，先看效果
    相机拍照原始包裹图片
    <img src="https://img2024.cnblogs.com/blog/116076/202601/116076-20260114185618097-2092867094.png" alt="ScreenShot_2026-01-14_185601_117" width="458" height="383" loading="lazy">
 
        OCR识别切割效果(根据识别文字角度自动校正)
　　<img src="https://img2024.cnblogs.com/blog/116076/202601/116076-20260114185658596-1401802679.png" alt="640" loading="lazy">
　　定位到每个识别内容的矩形坐标，获取所有当前图片所有切割矩形的最小外接矩形，然后裁切，就可以得到包含所有面单内容的图片
　　<img src="https://img2024.cnblogs.com/blog/116076/202601/116076-20260114185730759-1515244179.png" alt="640 (1)" width="840" height="547" loading="lazy">
　　抠面单效果(实际会比面单小，但是满足客户需求，包含了所有面单内容)
　　<img src="https://img2024.cnblogs.com/blog/116076/202601/116076-20260114185752661-1398249190.png" alt="640 (2)" width="849" height="536" loading="lazy">
　　<img src="https://img2024.cnblogs.com/blog/116076/202509/116076-20250924180326264-1688793031.png" alt="116076-20250310125032345-1793233350" loading="lazy">
　　废话不多说，附上代码
　　
<div class="cnblogs_code">
<pre>///<summary>
/// 返回面单图片
///</summary>
///<param name="errorMsg">异常信息</param>
///<param name="IsEnhanceImage">面单是否增强</param>
///<param name="IsSaveLocl">是否本地保存</param>
///<returns></returns>
public Bitmap GetLabelImageByBitmap(outstring errorMsg, bool IsEnhanceImage = true, bool IsSaveLocl = true)
{
Bitmap croppedImage = null;
errorMsg = string.Empty;
try
{
 if (!File.Exists(imagePath))
 {
 ShellLine.WriteLine($"请确保 {imagePath} 存在");
 errorMsg = $"请确保 {imagePath} 存在";
 returnnew Bitmap(10, 10);
 }
 //图片目录
 string imageDir = Path.GetDirectoryName(debugImagePath);
 if (Directory.Exists(imageDir))
 {
 Directory.CreateDirectory(imageDir);
 }
 Bitmap bitmap1 = new Bitmap(imagePath);
 var rr = oCR.GetOCRDataStr(bitmap1, debugImagePath);

 // 读取JSON文件
 string jsonFilePath = imageDir + "\\content.json";

 if (!File.Exists(jsonFilePath))
 {
 errorMsg = $"未找到JSON文件，请确保 {jsonFilePath} 存在";
 ShellLine.WriteLine($"未找到JSON文件，请确保 {jsonFilePath} 存在");
 returnnew Bitmap(imagePath);
 }

 string preRotatedImage = imageDir + "\\preRotatedImg.jpg";
 if (!File.Exists(preRotatedImage))
 {
 errorMsg = $"未找到面单文件，请确保包裹面单清晰且存在";
 ShellLine.WriteLine($"未找到面单文件，请确保包裹面单清晰且存在");
 returnnew Bitmap(imagePath);
 }

 // 解析矩形数据并计算最小外接矩形
 List<Rectangle> rectangles = ParseRectanglesFromJson(jsonFilePath);
 if (rectangles.Count == 0)
 {
 errorMsg = "未在JSON文件中找到有效的矩形数据";
 ShellLine.WriteLine("未在JSON文件中找到有效的矩形数据");
 returnnew Bitmap(imagePath);
 }

 Rectangle boundingRect = CalculateBoundingRectangle(rectangles);
 ShellLine.WriteLine($"最小外接矩形: X={boundingRect.X}, Y={boundingRect.Y}, Width={boundingRect.Width}, Height={boundingRect.Height}");
 ShellLine.WriteLine($"包含 {rectangles.Count} 个元素");

 // 加载图片并进行裁剪
 using (Bitmap originalImage = new Bitmap(preRotatedImage))
 {
 // 确保矩形在图片范围内
 Rectangle safeRect = GetSafeRectangle(boundingRect, originalImage);
 // 裁剪图片
 croppedImage = CropImage(originalImage, safeRect);
 if (IsEnhanceImage)
 {
 // 增强显示 
 EnhanceImage(ref croppedImage);
 }
 if (IsSaveLocl)
 {
 // 保存结果
 string outputPath = Path.Combine(
 Path.GetDirectoryName(preRotatedImage),
 Path.GetFileNameWithoutExtension(preRotatedImage) + "_cropped_enhanced.jpg");

 croppedImage.Save(outputPath, ImageFormat.Jpeg);
 ShellLine.WriteLine($"处理完成！结果已保存到: {outputPath}");
 }
 // 显示裁剪区域信息
 ShellLine.WriteLine($"\n裁剪区域信息:");
 ShellLine.WriteLine($"原始图片尺寸: {originalImage.Width}x{originalImage.Height}");
 ShellLine.WriteLine($"裁剪区域: {safeRect.X}, {safeRect.Y}, {safeRect.Width}x{safeRect.Height}");
 ShellLine.WriteLine($"增强后图片尺寸: {croppedImage.Width}x{croppedImage.Height}");
 return croppedImage;
 }
}
catch (Exception ex)
{
 errorMsg = $"处理过程中出现错误: {ex.Message}";
 ShellLine.WriteLine($"处理过程中出现错误: {ex.Message}");
 ShellLine.WriteLine($"堆栈跟踪: {ex.StackTrace}");
 returnnew Bitmap(imagePath);
}
finally
{
 // 释放资源
 croppedImage?.Dispose();
}
}</pre>
</div>
　　图片增强显示，有需要可以调用
<div class="cnblogs_code">
<pre>///<summary>
/// 图片增强显示
///</summary>
///<param name="image"></param>
publicvoidEnhanceImage(ref Bitmap image)
{
using (var mat = image.ToMat())
using (var lab = new OpenCvSharp.Mat())
{
 // 转换为Lab色彩空间
 Cv2.CvtColor(mat, lab, ColorConversionCodes.BGR2Lab);

 Cv2.Split(lab, outvar labChannels);

 // 对亮度通道进行直方图均衡化
 Cv2.EqualizeHist(labChannels[0], labChannels[0]);

 Cv2.Merge(labChannels, lab);
 Cv2.CvtColor(lab, mat, ColorConversionCodes.Lab2BGR);

 // 释放通道
 foreach (var channel in labChannels)
 channel.Dispose();

 // 更新图像
 image.Dispose();
 image = mat.ToBitmap();
}
}</pre>
</div>
　　获取包含所有切割字符的最小外接矩形
<div class="cnblogs_code">
<pre>// 计算包含所有矩形的最小外接矩形
static Rectangle CalculateBoundingRectangle(List<Rectangle> rectangles)
{
 if (rectangles.Count == 0)
 thrownew ArgumentException("矩形列表为空");

 int minX = int.MaxValue;
 int minY = int.MaxValue;
 int maxX = int.MinValue;
 int maxY = int.MinValue;

 foreach (Rectangle rect in rectangles)
 {
 minX = Math.Min(minX, rect.X);
 minY = Math.Min(minY, rect.Y);
 maxX = Math.Max(maxX, rect.X + rect.Width);
 maxY = Math.Max(maxY, rect.Y + rect.Height);
 }

 // 添加一些边距，使裁剪更美观
 int margin = 10;
 minX = Math.Max(0, minX - margin);
 minY = Math.Max(0, minY - margin);
 maxX = maxX + margin;
 maxY = maxY + margin;

 returnnew Rectangle(minX, minY, maxX - minX, maxY - minY);
}

// 确保矩形在图片范围内
static Rectangle GetSafeRectangle(Rectangle rect, Bitmap image)
{
 int x = Math.Max(0, Math.Min(rect.X, image.Width - 1));
 int y = Math.Max(0, Math.Min(rect.Y, image.Height - 1));
 int width = Math.Min(rect.Width, image.Width - x);
 int height = Math.Min(rect.Height, image.Height - y);

 returnnew Rectangle(x, y, width, height);
}

// 裁剪图片
static Bitmap CropImage(Bitmap source, Rectangle cropArea)
{
 Bitmap target = new Bitmap(cropArea.Width, cropArea.Height);

 using (Graphics g = Graphics.FromImage(target))
 {
 g.DrawImage(source, new Rectangle(0, 0, cropArea.Width, cropArea.Height),
 cropArea, GraphicsUnit.Pixel);
 }
 return target;
}</pre>
</div>
　　结束语
    　　感谢各位耐心查阅！  如果您有更好的想法欢迎一起交流，有不懂的也可以微信公众号联系博主，作者公众号会经常发一些实用的小工具和demo源码，需要的可以去看看！另外，如果觉得本篇博文对您或者身边朋友有帮助的，麻烦点个关注！赠人玫瑰，手留余香，您的支持就是我写作最大的动力，感谢您的关注，期待和您一起探讨！再会！
<img src="https://img2024.cnblogs.com/blog/116076/202509/116076-20250924180748850-573212949.gif" alt="640" loading="lazy">
 

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来源：https://www.cnblogs.com/axing/p/19483856

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c#实现包裹扣面单的几种方式