Question

这里我试图找出等大小的块来为OCR应用程序准备好数字

第一次尝试通过固定步骤移动的小代码，而在某些位置它由于数字之间的空间而跳得很高，主要问题是最后5位数，有时它们是2个数字，空间则是3个数字，有时它们是3个数字，空格然后是2个数字，最后如果5个数字很大，它们可能是5个数字

第二次尝试我使用了FindContour，当它找到对象我调整矩形大小以适应它但问题是它没有按顺序从左到右或相反给我数字。

那我怎么处理呢？

第一次尝试：

void DetectEqualRectangles(Mat image){
 resize(image,image,Size(810,52));
int k=0;
for(int i=0;i<14;i++){
    rectangle(image,Point(k,0),Point(45+k,52),Scalar(0,0,255),1,8,0);
    imshow("1",image);
    waitKey(0);
    if(i==0){k+=70;}
    else if(i==2){k+=71;}
    else if(i==4){k+=75;}
    else if(i==6){k+=78;}
    else if(i==8){k+=76;}
    else{k+=50;}
}}

第二次尝试：

void DetectUsingContours(Mat image){
resize(image,image,Size(810,52));
Mat gray;int BrightnessIndicator=0;
cvtColor(image,gray,CV_BGR2GRAY);

GaussianBlur(gray,gray,Size(5,5),3,0); // applying a gaussianBlur
BrightnessIndicator=EstimateBrighteness(image); // getting the approximate value for the brightness

cout<<BrightnessIndicator<<endl;
threshold(gray,gray,BrightnessIndicator-33,255,CV_THRESH_BINARY_INV); //thresholding
imshow("s",gray);

vector< vector<Point> > Contour;
findContours(gray,Contour,CV_RETR_EXTERNAL,CV_CHAIN_APPROX_SIMPLE);     //finding outer contours
cout<<Contour.size();
for(int i=0;i<Contour.size();i++){

   Rect bounding = boundingRect(Contour[i]);  // draw a rectangle
   if(bounding.x>15 && bounding.x<image.cols-50){bounding.x-=15;bounding.width=50;}
   else if(bounding.x>image.cols-50){bounding.x=image.cols-40;bounding.width=40;}
   else{bounding.x=0;bounding.width=50;}


    bounding.y-=bounding.y;
    bounding.height=image.rows;
  //  rectangle(image,bounding,Scalar(0,255,0),1,8,0);

    Mat CroppedImage=image(bounding);
    stringstream ss;
    ss<<"C:\\Users\\cdc\\Desktop\\GSC\\ExtractingNumbers\\"<<i<<".jpg";
    imwrite(ss.str(),CroppedImage);
    imshow("5",image);
    imshow("23",CroppedImage);
    waitKey(0);
}}

这里是原始图片：

Answer 1

只需按std :: sort

对结果进行排序

#include <opencv2/core.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>

#include <algorithm>
#include <iostream>
#include <sstream>

using namespace cv;
using namespace std;

void DetectUsingContours(Mat &image)
{
    resize(image,image,Size(810,52));
    Mat gray;
    cvtColor(image,gray,CV_BGR2GRAY);

    GaussianBlur(gray,gray,Size(5,5),3,0); // applying a gaussianBlur
    threshold(gray, gray,0, 255,
              CV_THRESH_BINARY_INV | CV_THRESH_OTSU); //thresholding
    imshow("s",gray);

    vector< vector<Point> > Contour;
    findContours(gray,Contour,CV_RETR_EXTERNAL,CV_CHAIN_APPROX_SIMPLE);     //finding outer contours
    cout<<Contour.size();
    std::vector<cv::Rect> rects;
    for(size_t i=0;i<Contour.size();i++){

        Rect bounding = boundingRect(Contour[i]);  // draw a rectangle
        if(bounding.x>15 && bounding.x<image.cols-50){bounding.x-=15;bounding.width=50;}
        else if(bounding.x>image.cols-50){bounding.x=image.cols-40;bounding.width=40;}
        else{bounding.x=0;bounding.width=50;}

        bounding.y-=bounding.y;
        bounding.height=image.rows;
        rects.emplace_back(bounding);
    }

    auto func = [](cv::Rect const &lhs, cv::Rect const &rhs)
    {
      return lhs.x < rhs.x;
    };
    std::sort(std::begin(rects), std::end(rects), func);
    for(size_t i = 0; i != rects.size(); ++i){
        Mat CroppedImage=image(rects[i]);
        stringstream ss;
        ss<<"C:/Users/cdc/Desktop/GSC/ExtractingNumbers/"<<i<<".jpg";
        imwrite(ss.str(),CroppedImage);
        imshow("5",image);
        imshow("23",CroppedImage);
        waitKey(0);
    }
}

int main()
{
    DetectUsingContours(cv::imread("tVVEl.jpg"));

    return 0;
}

我使用自适应阈值进行阈值处理，您不需要自己估算亮度。

从图像中获取数字

1 个答案: