在这里,我正在尝试获取相等大小的块,以便为OCR应用程序准备好数字
第一次尝试通过按固定步长移动的小代码,在某些位置由于数字之间的空格而跳得很高,主要问题是最后5位,有时是2个数字,然后是3个数字,有时是3个数字,然后是空格,然后是2个数字,如果5个数字很大,最后可能是5个数字
第二次尝试使用FindContour,当它找到对象时,我调整矩形的大小以适合它,但问题是它没有按从左到右或相反的顺序给我数字。
那么我该如何处理呢?
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);
}}
这是原始图像:
发布于 2015-09-04 04:58:56
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;
}
我使用自适应阈值来做阈值处理,你不需要自己估计亮度。
https://stackoverflow.com/questions/32262667
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