First of all, my test scenario is “ Identify QR codes printed on paper ”, Look for the QR code in the scanning results and identify it , Instead of just having the camera scan the QR code .

zbar and zxing It's all about yourself github On clone Of c++ Source code / Interface dll, All are default parameters

Let's talk about the conclusion : We took thousands of pictures , The accuracy of the two libraries is similar ( Because of the diversity of images and scenes , The exact accuracy figures don't make any sense ), however zbar It's a lot faster , Probably zxing Of 4-5 times . In fact, the accuracy of the two databases is not very satisfactory , It's not recognizable if it's a little fuzzy , Even some non blurred images can't be recognized . by comparison , The recognition effect of wechat is against the sky , You can tell how to toss , It makes me curious .

Later, I tried to improve the recognition effect , Let's take a look at the recognition principle of QR code , It's too complicated , Do not know how to start . So we try to improve the image preprocessing , As a result, the recognition effect is greatly improved by using a binary addition operation , It makes me wonder why developers didn't do such a simple preprocessing ? And then went on to optimize , It is found that the threshold of binarization is very important for the recognition of two-dimensional code ,badcase Usually because the threshold is not appropriate , So at the expense of performance , Try different thresholds many times in the recognizer , Finally, the recognition result is satisfactory , The accuracy is from 90% Up to 99.8% about , Most of the printing is not clear badcase It's all settled , The code is as follows :

#include <iostream>
#include <include\zbar.h>
#include "opencv/cv.h"
#include "opencv/highgui.h" using namespace std;
using namespace cv;
using namespace zbar; //zbar Interface
string ZbarDecoder(Mat img)
string result;
ImageScanner scanner;
const void *raw = (&img)->data;
// configure the reader
scanner.set_config(ZBAR_QRCODE, ZBAR_CFG_ENABLE, );
// wrap image data
Image image(img.cols, img.rows, "Y800", raw, img.cols * img.rows);
// scan the image for barcodes
int n = scanner.scan(image);
// extract results
result = image.symbol_begin()->get_data();
image.set_data(NULL, );
return result;
} // Recognize binary image , If it fails, open operation is used for secondary identification
string GetQRInBinImg(Mat binImg)
string result = ZbarDecoder(binImg);
Mat openImg;
Mat element = getStructuringElement(MORPH_RECT, Size(, ));
morphologyEx(binImg, openImg, MORPH_OPEN, element);
result = ZbarDecoder(openImg);
return result;
} //main function
string GetQR(Mat img)
Mat binImg;
// stay otsu On the basis of binary results , Increasing the threshold , Fuzzy image recognition
int thre = threshold(img, binImg, , , cv::THRESH_OTSU);
string result;
while(result.empty() && thre<)
threshold(img, binImg, thre, , cv::THRESH_BINARY);
result = GetQRInBinImg(binImg);
thre += ;// The threshold step size is set to 20, The greater the step length , The lower the recognition rate , The faster the speed.
return result;

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