OPENCV条形码定位与识别

#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; }