OPENCV条形码检测与识别

#include <opencv2/opencv.hpp>
#include <iostream>
#include <zbar.h>

using namespace cv;
using namespace std;
using namespace zbar;

int main(int argc,char* argv[])
{
    char fileNameString[100];
    char windowNameString[50];
    char resultFileNameSring[100];
    Mat srcImage,grayImage,blurImage,thresholdImage,gradientXImage,gradientYImage,gradientImage,morphImage;
    for (int fileCount = 1;fileCount < 8;fileCount++)
    {
        sprintf(fileNameString,"F:\\opencv\\条形码检测与识别\\barcode_0%d.jpg",fileCount);
        sprintf(windowNameString,"result 0%d",fileCount);
        sprintf(resultFileNameSring,"F:\\opencv\\条形码检测与识别\\barcodeResult_0%d.jpg",fileCount);
        //读取图像
        srcImage = imread(fileNameString);
        if(srcImage.empty())
        {
            cout<<"image file read error"<<endl;

            return -1;
        }
        //图像转换为灰度图像
        if(srcImage.channels() == 3)
        {
            cvtColor(srcImage,grayImage,CV_RGB2GRAY);
        }
        else
        {
            grayImage = srcImage.clone();
        }
        //建立图像的梯度幅值
        Scharr(grayImage,gradientXImage,CV_32F,1,0);
        Scharr(grayImage,gradientYImage,CV_32F,0,1);
        //因为我们需要的条形码在需要X方向水平,所以更多的关注X方向的梯度幅值,而省略掉Y方向的梯度幅值
        subtract(gradientXImage,gradientYImage,gradientImage);
        //归一化为八位图像
        convertScaleAbs(gradientImage,gradientImage);
        //看看得到的梯度图像是什么样子
        //imshow(windowNameString,gradientImage);
        //对图片进行相应的模糊化,使一些噪点消除
        blur(gradientImage,blurImage,Size(9,9));
        //模糊化以后进行阈值化,得到到对应的黑白二值化图像,二值化的阈值可以根据实际情况调整
        threshold(blurImage,thresholdImage,210,255,THRESH_BINARY);
        //看看二值化图像
        //imshow(windowNameString,thresholdImage);
        //二值化以后的图像,条形码之间的黑白没有连接起来,就要进行形态学运算,消除缝隙,相当于小型的黑洞,选择闭运算
        //因为是长条之间的缝隙,所以需要选择宽度大于长度
        Mat kernel = getStructuringElement(MORPH_RECT,Size(21,7));
        morphologyEx(thresholdImage,morphImage,MORPH_CLOSE,kernel);
        //看看形态学操作以后的图像
        //imshow(windowNameString,morphImage);
        //现在要让条形码区域连接在一起,所以选择膨胀腐蚀,而且为了保持图形大小基本不变,应该使用相同次数的膨胀腐蚀
        //先腐蚀,让其他区域的亮的地方变少最好是消除,然后膨胀回来,消除干扰,迭代次数根据实际情况选择
        erode(morphImage, morphImage, getStructuringElement(MORPH_RECT, Size(3,3)),Point(-1,-1),4);
        dilate(morphImage, morphImage, getStructuringElement(MORPH_RECT, Size(3,3)),Point(-1,-1),4);
        //看看形态学操作以后的图像
        //imshow(windowNameString,morphImage);
        vector<vector<Point2i>>contours;
        vector<float>contourArea;
        //接下来对目标轮廓进行查找,目标是为了计算图像面积
        findContours(morphImage,contours,RETR_EXTERNAL,CHAIN_APPROX_SIMPLE);
        //计算轮廓的面积并且存放
        for(int i = 0; i < contours.size();i++)
        {
            contourArea.push_back(cv::contourArea(contours[i]));
        }
        //找出面积最大的轮廓
        double maxValue;Point maxLoc;
        minMaxLoc(contourArea, NULL,&maxValue,NULL,&maxLoc);
        //计算面积最大的轮廓的最小的外包矩形
        RotatedRect minRect = minAreaRect(contours[maxLoc.x]);
        //为了防止找错,要检查这个矩形的偏斜角度不能超标
        //如果超标,那就是没找到
        if(minRect.angle<2.0)
        {
            //找到了矩形的角度,但是这是一个旋转矩形,所以还要重新获得一个外包最小矩形
            Rect myRect = boundingRect(contours[maxLoc.x]);
            //把这个矩形在源图像中画出来
            //rectangle(srcImage,myRect,Scalar(0,255,255),3,LINE_AA);
            //看看显示效果,找的对不对
            //imshow(windowNameString,srcImage);
            //将扫描的图像裁剪下来,并保存为相应的结果,保留一些X方向的边界,所以对rect进行一定的扩张
             myRect.x= myRect.x - (myRect.width/20);
             myRect.width = myRect.width*1.1;
            Mat resultImage = Mat(srcImage,myRect);
            if(!imwrite(resultFileNameSring,resultImage))
            {
                cout<<"file save error!"<<endl;
                return -2;
            }
        }
    }
    //检测到了之后进行条形码识别
    FileStorage file("F:\\opencv\\条形码检测与识别\\result.xml",FileStorage::WRITE);
    for (int fileCount = 1;fileCount < 8;fileCount++)
    {
        sprintf(resultFileNameSring,"F:\\opencv\\条形码检测与识别\\barcodeResult_0%d.jpg",fileCount);
        sprintf(windowNameString,"result 0%d",fileCount);
        Mat result = imread(resultFileNameSring);
        if(!result.empty())
        {
            //现在开始识别
            cvtColor(result,grayImage,CV_RGB2GRAY);
            int width = grayImage.cols;   // extract dimensions
            int height = grayImage.rows;
            Image image(width,height,"Y800",grayImage.data,width*height);
            ImageScanner scanner;
            scanner.set_config(ZBAR_NONE,ZBAR_CFG_ENABLE,1);
            int n = scanner.scan(image);
            for (Image::SymbolIterator symbol = image.symbol_begin(); symbol != image.symbol_end();++symbol)
            {
                cout <<"pic name:\t"<<resultFileNameSring<<endl<<"code type:\t"<<symbol->get_type_name()<<endl<<                    "decode string:\t"<<symbol->get_data()<<endl;
                image.set_data(NULL,0);
                //xml文件写入

            }
        }
    }
    waitKey(0);
    return 1;
}

http://download.csdn.net/detail/dengrengong/9461797