基于OpenCV的PCB缺损判断研究

本人模式识别小硕一枚，目前帝都某校研一在读。寒假自己用opencv做了一个对PCB板的好坏的检测，拿出来和大家一起学习讨论，这篇博文也是我在CSDN上发表的第一篇文章，欢迎各路大神指导。

基本思想是通过定焦的工业摄像头，对放置于卡槽中的PCB进行拍摄并取ROI，与标准的PCB图片进行模板匹配，两者二值化后相减并中值滤波，在缺损处用红色矩形标出，最后只命名输出缺损PCB图片。

为了给大家更好的演示，我将程序改为直接读取图片。

程序如下：
（可能有些杂乱，本人水平还需提高）

/********************************************************************************/    /*     * Copyright(c)2016     * ALL rights  reserved.     *     * file name:  Lab_Identification_Program.cpp     * file description:         *     * Abstract:     * current version:2.0     * author: L T     * date: 2016.2.3     *     * replacement version:     * original author:     * date:     *//********************************************************************************/#include<opencv2/opencv.hpp>#include<iostream>using namespace cv;//------------------------------------------------------//  全局变量//------------------------------------------------------Mat frame, grayimage, StandardPhoto, WaitJudgePhoto, g_resultImage, srcImage;Mat img,dst,edge,out,mask,out1,out2,out3,out4;Mat img_result,img_result1,img_result2,Wback,WbackClone,WbackClone1;int ConditionJudgment;int g_nMatchMethod = 5;#define pic01 "AFTER2.jpg"  //需要比对的图#define pic02 "PRO5.jpg"#define WINDOW_NAME1 "【原始图片】"        //为窗口标题定义的宏 #define WINDOW_NAME2 "【匹配窗口】" //------------------------------------------------------//  二值化（需先灰度化）//------------------------------------------------------void Binarization(){    threshold(grayimage, out, 90, 255, 0);//THRESH_BINARY = 0    threshold(StandardPhoto, out1, 90, 255, 0);//将标准图二值化    threshold(WaitJudgePhoto, out2, 90, 255, 0);}//------------------------------------------------------//  读入图片//------------------------------------------------------void ReadPic(){    StandardPhoto = imread( pic02, 0 );//完美标准图 (灰度图)    WaitJudgePhoto = imread( pic01, 0 );//读入“待判断”图片 (灰度图)    out3 = imread( pic01, 1 );//再读入一张无修改图    mask = imread( pic01, 0 );//mask图必须读灰度图    Wback = imread("WhiteBack02.jpg",1);//同分辨率白底板    WbackClone = Wback.clone();    WbackClone1 = Wback.clone();}//------------------------------------------------------//  模板匹配//------------------------------------------------------void TempMatch(){    out1.copyTo( srcImage );    int resultImage_cols =  out1.cols - out2.cols + 1;    int resultImage_rows = out1.rows - out2.rows + 1;    g_resultImage.create( resultImage_cols, resultImage_rows, CV_32FC1 );    matchTemplate( out1, out2, g_resultImage, g_nMatchMethod );    normalize( g_resultImage, g_resultImage, 0, 1, NORM_MINMAX, -1, Mat() );    double minValue; double maxValue; Point minLocation; Point maxLocation;    Point matchLocation;    minMaxLoc( g_resultImage, &minValue, &maxValue, &minLocation, &maxLocation, Mat() );    if( g_nMatchMethod  == CV_TM_SQDIFF || g_nMatchMethod == CV_TM_SQDIFF_NORMED )    { matchLocation = minLocation; }    else    { matchLocation = maxLocation; }    rectangle( out1, matchLocation, Point( matchLocation.x + out2.cols , matchLocation.y + out2.rows ), Scalar(0,0,255), 2, 8, 0 );    rectangle( g_resultImage, matchLocation, Point( matchLocation.x + out2.cols , matchLocation.y + out2.rows ), Scalar(0,0,255), 2, 8, 0 );    Mat imageROI = out1(Rect(matchLocation.x,matchLocation.y,out2.cols,out2.rows));    out2.copyTo(imageROI,mask);    Mat imageROI1 = WbackClone(Rect(matchLocation.x,matchLocation.y,out3.cols,out3.rows));    out3.copyTo(imageROI1,mask);    cvtColor(WbackClone,out4,CV_BGR2GRAY);//灰度化    threshold(out4, WbackClone, 90, 255, 0);//二值化    Mat img2 = imread(pic01);    Mat imageROI2 = WbackClone1(Rect(matchLocation.x,matchLocation.y,out3.cols,out3.rows));    out3.copyTo(imageROI2,mask);    imshow("【彩色ROI位置待判断图】",WbackClone1);}//------------------------------------------------------//  缺损判断//------------------------------------------------------void DefectJudgment(){    int rowNumber = img_result.rows;//行数    int colcolNumber = img_result.cols;//列数    int colNumber = img_result.cols*img_result.channels();  //列数 x 通道数=每一行元素的个数    ConditionJudgment=1;    for(int i = 0; i < rowNumber && ConditionJudgment; i++)  //行循环    {          uchar* data = img_result.ptr<uchar>(i);  //获取第i行的首地址        for(int j = 0;j < colNumber;j++)   //列循环        {               // ---------【开始处理每个像素】-------------                 //data[j] = data[j]/div*div + div/2;              if(data[j]==255)//如果白色            {                //imshow("【效果图】Canny边缘检测", grayImage);                imwrite("有问题的PCB.jpg",WbackClone1);                ConditionJudgment=0;                break;            }            // ----------【处理结束】---------------------        }  //行处理结束    }  }//------------------------------------------------------//  主程序//------------------------------------------------------int main(){    //CallCamera();//调用摄像头    namedWindow("【滤波前二值化效果】", 2);    namedWindow("【滤波后缺损二值化显示】", 2);    namedWindow("【对拍摄图缺损位置进行标注】", 2);    namedWindow("【彩色ROI位置待判断图】", 2);    ReadPic();//读入图片    Binarization();//二值化    TempMatch();//模板匹配    subtract(srcImage,WbackClone,img_result1);//相减    subtract(WbackClone,srcImage,img_result2);    medianBlur(img_result1,img_result,3);//小噪点使用中值滤波  或  erode + dilate方案   3    medianBlur(img_result2,img_result2,3);    //GaussianBlur(img_result1,img_result,Size(3,3),0,0);//高斯滤波    //adaptiveThreshold(img, dst, 255, ADAPTIVE_THRESH_MEAN_C, THRESH_BINARY,3,5);    add(img_result,img_result2,img_result);//两幅互减图叠加    Mat ele = getStructuringElement(MORPH_RECT, Size(5,5));    dilate(img_result,img_result,ele);    Mat threshold_output;    vector<vector<Point>> contours;    vector<Vec4i> hierarchy;    threshold( img_result, threshold_output, 50, 255, THRESH_BINARY );//二值化    findContours( threshold_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );    vector<vector<Point> > contours_poly( contours.size() );    vector<Rect> boundRect( contours.size() );    for( unsigned int i = 0; i < contours.size(); i++ )    {         approxPolyDP( Mat(contours[i]), contours_poly[i], 3, true );        boundRect[i] = boundingRect( Mat(contours_poly[i]) );        //minEnclosingCircle( contours_poly[i], center[i], radius[i] );    }    for( int unsigned i = 0; i<contours.size( ); i++ )    {        Scalar color = Scalar( 0, 0, 255 );        //rectangle( drawing, boundRect[i].tl(), boundRect[i].br(), color, 2, 8, 0 );//效果图上绘制矩形        rectangle( WbackClone1, boundRect[i].tl(), boundRect[i].br(), color, 2, 8, 0 );//原图上绘制矩形    }    DefectJudgment();//缺损判断    imshow( "【滤波前二值化效果】", img_result1 );    imshow( "【滤波后缺损二值化显示】", img_result );    imshow( "【对拍摄图缺损位置进行标注】", WbackClone1 );    waitKey(0);}

效果如下图所示：

【完好无损的PCB】

【有缺损的PCB】（焊盘缺失或缺损）

【处理后未滤波的图片】

【滤波后图像】

【缺损判断标记】（图上红色矩形框）

程序中Wback是读入的一张纯白色底板图片，这个底板分辨率和标准模板图片一致，为了在模板匹配后相减时防止检测图片因拍摄或放PCB板入卡槽时，位置的改变而处理的。

还有太多的东西需要学习，欢迎大神前辈们指教。