OpenCvSharp (C# OpenCV)实现纺织物缺陷检测-＞脏污、油渍、线条破损(详细步骤 + 源码)

导读

本文将介绍使用OpenCV实现纺织物缺陷检测(脏污、油渍、线条破损缺陷)的详细步骤 + 代码。（来源公众号：OpenCV与AI深度学习）

OpenCV实现纺织物缺陷检测效果

背景介绍

    机器视觉应用场景中缺陷检测的应用是非常广泛的，通常涉及各个行业、各种缺陷类型。今天我们要介绍的是纺织物的缺陷检测，缺陷类型包含脏污、油渍、线条破损三种，这三种缺陷与LCD屏幕检测的缺陷很相似，处理方法也可借鉴。

详细实现步骤请参考下面文章：

​​实战 | OpenCV实现纺织物缺陷检测-＞脏污、油渍、线条破损(详细步骤 + Python/C++源码)_Color Space的博客-CSDN博客​​

下面给出对应的OpenCvSharp实现代码与效果：

脏污缺陷

    脏污缺陷图片如下：

OpenCvSharp脏污检测代码：

/// <summary>
/// 脏污缺陷检测
/// </summary>
/// <param name="img">测试图像</param>
/// <returns>结果图</returns> //也可设置bool类型表示OK或NG
static Mat DirtyDetection(Mat img)
{
  Mat result = img.Clone();
  Mat gray = new Mat();
  Cv2.CvtColor(img, gray, ColorConversionCodes.BGR2GRAY);
  Cv2.GaussianBlur(gray, gray, new Size(7, 7), 0);
  Cv2.Canny(gray, gray, 10, 30);
  Point[][] contours; //轮廓查找结果变量
  HierarchyIndex[] hierarchy; //轮廓拓扑结构变量

  Cv2.FindContours(gray, out contours, out hierarchy, RetrievalModes.External,
                  ContourApproximationModes.ApproxNone);
  //Console.WriteLine("contour_size = {0}", contours.Length); //输出轮廓个数

  for(int i = 0; i < contours.Length; i++)
  {
    double length = Cv2.ArcLength(contours[i], true);
    if (length >= 1)
      Cv2.DrawContours(result, contours, i, new Scalar(0, 0, 255), 2);
  }
  return result;
}

检测结果：

油污缺陷

    油污缺陷图片如下：

OpenCvSharp油污检测代码： 

/// <summary>
/// 油污缺陷检测
/// </summary>
/// <param name="img">测试图像</param>
/// <returns>结果图</returns> //也可设置bool类型表示OK或NG
static Mat OilDetection(Mat img)
{
  Mat result = img.Clone();
  Mat imgLab = new Mat();
  Cv2.CvtColor(img, imgLab, ColorConversionCodes.BGR2Lab);
  Mat[] labArray = Cv2.Split(imgLab); //L, a, b
  Mat blur = new Mat();
  Mat thres = new Mat();
  Cv2.GaussianBlur(labArray[2], blur, new Size(3, 3), 0); //b通道
  Cv2.Threshold(blur, thres, 130, 255, ThresholdTypes.Binary);
  Mat element = Cv2.GetStructuringElement(MorphShapes.Rect, new Size(3, 3), new Point(-1, -1));
  Cv2.MorphologyEx(thres, thres, MorphTypes.Open, element, new Point(-1, -1), 1,
                   BorderTypes.Default, new Scalar());

  Point[][] contours; //轮廓查找结果变量
  HierarchyIndex[] hierarchy; //轮廓拓扑结构变量

  Cv2.FindContours(thres, out contours, out hierarchy, RetrievalModes.External,
                  ContourApproximationModes.ApproxNone);
  //Console.WriteLine("contour_size = {0}", contours.Length); //输出轮廓个数

  for (int i = 0; i < contours.Length; i++)
  {
    double area = Cv2.ContourArea(contours[i]);
    if (area >= 50)
      Cv2.DrawContours(result, contours, i, new Scalar(0, 0, 255), 2);
  }
  return result;
}

检测结果： 

线条破损缺陷

    线条破损缺陷图片如下：

OpenCvSharp线条破损检测代码：  

/// <summary>
/// 线条破损缺陷检测
/// </summary>
/// <param name="img">测试图像</param>
/// <returns>结果图</returns> //也可设置bool类型表示OK或NG
static Mat LineDefectDetection(Mat img)
{
  Mat result = img.Clone();
  Mat imgLab = new Mat();
  Cv2.CvtColor(img, imgLab, ColorConversionCodes.BGR2Lab);
  Mat[] labArray = Cv2.Split(imgLab); //L, a, b
  Mat blur = new Mat();
  Mat edged = new Mat();
  Cv2.GaussianBlur(labArray[2], blur, new Size(3, 3), 0); //b通道
  Cv2.Canny(blur, edged, 5, 10);
  Point[][] contours; //轮廓查找结果变量
  HierarchyIndex[] hierarchy; //轮廓拓扑结构变量

  Cv2.FindContours(edged, out contours, out hierarchy, RetrievalModes.External,
                  ContourApproximationModes.ApproxNone);
  //Console.WriteLine("contour_size = {0}", contours.Length); //输出轮廓个数

  for (int i = 0; i < contours.Length; i++)
  {
    double length = Cv2.ArcLength(contours[i], true);
    if (length >= 10)
      Cv2.DrawContours(result, contours, i, new Scalar(0, 0, 255), 2);
  }
  return result;
}

检测结果：  

 完整源码：

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;

using OpenCvSharp;

namespace Defects_Detection
{
    class Program
    {
        /// <summary>
        /// 脏污缺陷检测
        /// </summary>
        /// <param name="img">测试图像</param>
        /// <returns>结果图</returns> //也可设置bool类型表示OK或NG
        static Mat DirtyDetection(Mat img)
        {
            Mat result = img.Clone();
            Mat gray = new Mat();
            Cv2.CvtColor(img, gray, ColorConversionCodes.BGR2GRAY);
            Cv2.GaussianBlur(gray, gray, new Size(7, 7), 0);
            Cv2.Canny(gray, gray, 10, 30);
            Point[][] contours; //轮廓查找结果变量
            HierarchyIndex[] hierarchy; //轮廓拓扑结构变量

            Cv2.FindContours(gray, out contours, out hierarchy, RetrievalModes.External,
                                            ContourApproximationModes.ApproxNone);
            //Console.WriteLine("contour_size = {0}", contours.Length); //输出轮廓个数

            for(int i = 0; i < contours.Length; i++)
            {
                double length = Cv2.ArcLength(contours[i], true);
                if (length >= 1)
                    Cv2.DrawContours(result, contours, i, new Scalar(0, 0, 255), 2);
            }
            return result;
        }

        /// <summary>
        /// 油污缺陷检测
        /// </summary>
        /// <param name="img">测试图像</param>
        /// <returns>结果图</returns> //也可设置bool类型表示OK或NG
        static Mat OilDetection(Mat img)
        {
            Mat result = img.Clone();
            Mat imgLab = new Mat();
            Cv2.CvtColor(img, imgLab, ColorConversionCodes.BGR2Lab);
            Mat[] labArray = Cv2.Split(imgLab); //L, a, b
            Mat blur = new Mat();
            Mat thres = new Mat();
            Cv2.GaussianBlur(labArray[2], blur, new Size(3, 3), 0); //b通道
            Cv2.Threshold(blur, thres, 130, 255, ThresholdTypes.Binary);
            Mat element = Cv2.GetStructuringElement(MorphShapes.Rect, new Size(3, 3), new Point(-1, -1));
            Cv2.MorphologyEx(thres, thres, MorphTypes.Open, element, new Point(-1, -1), 1,
                                             BorderTypes.Default, new Scalar());

            Point[][] contours; //轮廓查找结果变量
            HierarchyIndex[] hierarchy; //轮廓拓扑结构变量

            Cv2.FindContours(thres, out contours, out hierarchy, RetrievalModes.External,
                                            ContourApproximationModes.ApproxNone);
            //Console.WriteLine("contour_size = {0}", contours.Length); //输出轮廓个数

            for (int i = 0; i < contours.Length; i++)
            {
                double area = Cv2.ContourArea(contours[i]);
                if (area >= 50)
                    Cv2.DrawContours(result, contours, i, new Scalar(0, 0, 255), 2);
            }
            return result;
        }

        /// <summary>
        /// 线条破损缺陷检测
        /// </summary>
        /// <param name="img">测试图像</param>
        /// <returns>结果图</returns> //也可设置bool类型表示OK或NG
        static Mat LineDefectDetection(Mat img)
        {
            Mat result = img.Clone();
            Mat imgLab = new Mat();
            Cv2.CvtColor(img, imgLab, ColorConversionCodes.BGR2Lab);
            Mat[] labArray = Cv2.Split(imgLab); //L, a, b
            Mat blur = new Mat();
            Mat edged = new Mat();
            Cv2.GaussianBlur(labArray[2], blur, new Size(3, 3), 0); //b通道
            Cv2.Canny(blur, edged, 5, 10);
            Point[][] contours; //轮廓查找结果变量
            HierarchyIndex[] hierarchy; //轮廓拓扑结构变量

            Cv2.FindContours(edged, out contours, out hierarchy, RetrievalModes.External,
                                            ContourApproximationModes.ApproxNone);
            //Console.WriteLine("contour_size = {0}", contours.Length); //输出轮廓个数

            for (int i = 0; i < contours.Length; i++)
            {
                double length = Cv2.ArcLength(contours[i], true);
                if (length >= 10)
                    Cv2.DrawContours(result, contours, i, new Scalar(0, 0, 255), 2);
            }
            return result;
        }

        static void Main(string[] args)
        {
            Mat img = Cv2.ImRead("Line.jpg");
            if(img.Empty())
            {
                Console.WriteLine("read image failed, try again!\n");
                return;
            }
            Cv2.ImShow("src", img);
            //Mat result = DirtyDetection(img); //脏污缺陷检测
            //Mat result = OilDetection(img); //油渍缺陷检测
            Mat result = LineDefectDetection(img); //油渍缺陷检测
            Cv2.ImShow("result", result);
            Cv2.WaitKey();
        }
    }
}