这篇文章主要介绍了C++ OpenCV如何实现银行卡号识别功能，具有一定借鉴价值，感兴趣的朋友可以参考下，希望大家阅读完这篇文章之后大有收获，下面让小编带着大家一起了解一下。
一、获取模板图像
如图所示，这是我们的模板图像。我们需要将上面的字符一一切割出来保存，以便进行后续的字符匹配环节。先进行图像灰度、阈值等操作进行轮廓提取，这里就不再细说。这里我想说的是，由于经过轮廓检索，提取出来的字符并不是按（0、1、2…7、8、9）顺序排列，所以，在这里我自定义了一个Card结构体，用于图像排序。具体请看源码。
1.1 功能效果
如图为顺序切割出来的模板字符。
1.2 功能源码
bool Get_Template(Mat temp, vector<Card>&Card_Temp){ //图像预处理 Mat gray; cvtColor(temp, gray, COLOR_BGR2GRAY); Mat thresh; threshold(gray, thresh, 0, 255, THRESH_BINARY_INV|THRESH_OTSU); //轮廓检测 vector <vector<Point>> contours; findContours(thresh, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE); for (int i = 0; i < contours.size(); i++) { Rect rect = boundingRect(contours[i]); double ratio = double(rect.width) / double(rect.height); //筛选出字符轮廓 if (ratio > 0.5 && ratio < 1) { Mat roi = temp(rect); //将字符扣出，放入Card_Temp容器备用 Card_Temp.push_back({ roi ,rect }); } } if (Card_Temp.empty())return false; //进行字符排序，使其按（0、1、2...7、8、9）顺序排序 for (int i = 0; i < Card_Temp.size()-1; i++) { for (int j = 0; j < Card_Temp.size() - 1 - i; j++) { if (Card_Temp[j].rect.x > Card_Temp[j + 1].rect.x) { Card temp = Card_Temp[j]; Card_Temp[j] = Card_Temp[j + 1]; Card_Temp[j + 1] = temp; } } } return true;}二、银行卡号定位
如图所示，这是本案例需要识别的银行卡。从图中可以看出，我们需要将银行卡号切割出来首先得将卡号分为4个小块切割，之后再需要将每一小块上的字符切割。接下来一步步看是如何操作的。
2.1 将银行卡号切割成四块
首先第一步得先进行图像预处理，通过灰度、二值化、形态学等操作提取出卡号轮廓。这里的图像预处理需要根据图像特征自行确定，并不是所有的步骤都是必须的，我们最终的目的是为了定位银行卡号所在轮廓位置。这里我使用的是二值化、以及形态学闭操作。
//形态学操作、以便找到银行卡号区域轮廓 Mat gray; cvtColor(src, gray, COLOR_BGR2GRAY); Mat gaussian; GaussianBlur(gray, gaussian, Size(3, 3), 0); Mat thresh; threshold(gaussian, thresh, 0, 255, THRESH_BINARY | THRESH_OTSU); Mat close; Mat kernel2 = getStructuringElement(MORPH_RECT, Size(15, 5)); morphologyEx(thresh, close, MORPH_CLOSE, kernel2);经过灰度、阈值、形态学操作后的图像如下图所示。我们已经将银行卡号分为四个小矩形块，接下来只需通过轮廓查找、筛选就可以扣出这四个ROI区域了。
vector<vector<Point>>contours; findContours(close, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE); for (int i = 0; i < contours.size(); i++) { //通过面积、长宽比筛选出银行卡号区域 double area = contourArea(contours[i]); if (area > 800 && area < 1400) { Rect rect = boundingRect(contours[i]); float ratio = double(rect.width) / double(rect.height); if (ratio > 2.8 && ratio < 3.1) { Mat ROI = src(rect); Block_ROI.push_back({ ROI ,rect }); } } }
同理，我们需要将切割下来的小块按照它原来的顺序存储。
for (int i = 0; i < Block_ROI.size()-1; i++) { for (int j = 0; j < Block_ROI.size() - 1 - i; j++) { if (Block_ROI[j].rect.x > Block_ROI[j + 1].rect.x) { Card temp = Block_ROI[j]; Block_ROI[j] = Block_ROI[j + 1]; Block_ROI[j + 1] = temp; } } }1.1 功能效果
1.2 功能源码
bool Cut_Block(Mat src, vector<Card>&Block_ROI){ //形态学操作、以便找到银行卡号区域轮廓 Mat gray; cvtColor(src, gray, COLOR_BGR2GRAY); Mat gaussian; GaussianBlur(gray, gaussian, Size(3, 3), 0); Mat thresh; threshold(gaussian, thresh, 0, 255, THRESH_BINARY | THRESH_OTSU); Mat close; Mat kernel2 = getStructuringElement(MORPH_RECT, Size(15, 5)); morphologyEx(thresh, close, MORPH_CLOSE, kernel2); vector<vector<Point>>contours; findContours(close, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE); for (int i = 0; i < contours.size(); i++) { //通过面积、长宽比筛选出银行卡号区域 double area = contourArea(contours[i]); if (area > 800 && area < 1400) { Rect rect = boundingRect(contours[i]); float ratio = double(rect.width) / double(rect.height); if (ratio > 2.8 && ratio < 3.1) { //rectangle(src, rect, Scalar(0, 255, 0), 2); Mat ROI = src(rect); Block_ROI.push_back({ ROI ,rect }); } } } if (Block_ROI.size()!=4)return false; for (int i = 0; i < Block_ROI.size()-1; i++) { for (int j = 0; j < Block_ROI.size() - 1 - i; j++) { if (Block_ROI[j].rect.x > Block_ROI[j + 1].rect.x) { Card temp = Block_ROI[j]; Block_ROI[j] = Block_ROI[j + 1]; Block_ROI[j + 1] = temp; } } } //for (int i = 0; i < Block_ROI.size(); i++) //{ // imshow(to_string(i), Block_ROI[i].mat); // waitKey(0); //} return true;}2.2 字符切割
由步骤2.1，我们已经将银行卡号定位，且顺序切割成四个小块。接下来，我们只需要将他们依次的将字符切割下来就可以了。其实切割字符跟上面的切割小方块是差不多的，这里就不再多说了。在这里我着重要说明的是，切割出来的字符相对于银行卡所在位置。
由步骤2.1，我们顺序切割出来四个小方块。以其中一个小方块为例，当时我们存储了rect变量，它表示该小方块相对于图像起点（X，Y），宽W，高H。而步骤2.2我们需要做的就是将这个小方块的字符切割出来，那么每一个字符相对于小方块所在位置为起点（x,y），宽w，高h。所以，这些字符相当于银行卡所在位置就是起点（X+x，Y+y），宽 (w），高（h）。具体请细看源码。也比较简单容易理解。
//循环上面切割出来的四个小块，将上面的字符一一切割出来。 for (int i = 0; i < Block_ROI.size(); i++) { Mat roi_gray; cvtColor(Block_ROI[i].mat, roi_gray, COLOR_BGR2GRAY); Mat roi_thresh; threshold(roi_gray, roi_thresh, 0, 255, THRESH_BINARY|THRESH_OTSU); vector <vector<Point>> contours; findContours(roi_thresh, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE); for (int j = 0; j < contours.size(); j++) { Rect rect = boundingRect(contours[j]); //字符相对于银行卡所在的位置 Rect roi_rect(rect.x + Block_ROI[i].rect.x, rect.y + Block_ROI[i].rect.y, rect.width, rect.height); Mat r_roi = Block_ROI[i].mat(rect); Slice_ROI.push_back({ r_roi ,roi_rect }); } }同样，在这里我们也需要将切割出来的字符顺序排序。即银行卡上的号码是怎样排序的，我们就需要怎样排序保存
for (int i = 0; i < Slice_ROI.size() - 1; i++) { for (int j = 0; j < Slice_ROI.size() - 1 - i; j++) { if (Slice_ROI[j].rect.x > Slice_ROI[j + 1].rect.x) { Card temp = Slice_ROI[j]; Slice_ROI[j] = Slice_ROI[j + 1]; Slice_ROI[j + 1] = temp; } } }2.1 功能效果
如图为顺序切割出来的字符
2.2 功能源码
bool Cut_Slice(vector<Card>&Block_ROI,vector<Card>&Slice_ROI){ //循环上面切割出来的四个小块，将上面的字符一一切割出来。 for (int i = 0; i < Block_ROI.size(); i++) { Mat roi_gray; cvtColor(Block_ROI[i].mat, roi_gray, COLOR_BGR2GRAY); Mat roi_thresh; threshold(roi_gray, roi_thresh, 0, 255, THRESH_BINARY|THRESH_OTSU); vector <vector<Point>> contours; findContours(roi_thresh, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE); for (int j = 0; j < contours.size(); j++) { Rect rect = boundingRect(contours[j]); //字符相对于银行卡所在的位置 Rect roi_rect(rect.x + Block_ROI[i].rect.x, rect.y + Block_ROI[i].rect.y, rect.width, rect.height); Mat r_roi = Block_ROI[i].mat(rect); Slice_ROI.push_back({ r_roi ,roi_rect }); } } if (Slice_ROI.size() != 16) return false; for (int i = 0; i < Slice_ROI.size() - 1; i++) { for (int j = 0; j < Slice_ROI.size() - 1 - i; j++) { if (Slice_ROI[j].rect.x > Slice_ROI[j + 1].rect.x) { Card temp = Slice_ROI[j]; Slice_ROI[j] = Slice_ROI[j + 1]; Slice_ROI[j + 1] = temp; } } } //for (int i = 0; i < Slice_ROI.size(); i++) //{ // imshow(to_string(i), Slice_ROI[i].mat); // waitKey(0); //} return true;}三、字符识别
3.1.读取文件
如图所示，为模板图像对应的label。我们需要读取文件，进行匹配。
bool ReadData(string filename, vector<int>&label){ fstream fin; fin.open(filename, ios::in); if (!fin.is_open()) { cout << "can not open the file!" << endl; return false; } int data[10] = { 0 }; for (int i = 0; i < 10; i++) { fin >> data[i]; } fin.close(); for (int i = 0; i < 10; i++) { label.push_back(data[i]); } return true;}3.2.字符匹配
在这里，我的思路是：使用一个for循环，将我们切割出来的字符与现有的模板一一进行匹配。使用的算法是图像模板匹配matchTemplate。具体用法请大家自行查找相关资料。具体请看源码
3.3.功能源码
bool Template_Matching(vector<Card>&Card_Temp, vector<Card>&Block_ROI, vector<Card>&Slice_ROI, vector<int>&result_index){ for (int i = 0; i < Slice_ROI.size(); i++) { //将字符resize成合适大小，利于识别 resize(Slice_ROI[i].mat, Slice_ROI[i].mat, Size(60, 80), 1, 1, INTER_LINEAR); Mat gray; cvtColor(Slice_ROI[i].mat, gray, COLOR_BGR2GRAY); int maxIndex = 0; double Max = 0.0; for (int j = 0; j < Card_Temp.size(); j++) { resize(Card_Temp[j].mat, Card_Temp[j].mat, Size(60, 80), 1, 1, INTER_LINEAR); Mat temp_gray; cvtColor(Card_Temp[j].mat, temp_gray, COLOR_BGR2GRAY); //进行模板匹配，识别数字 Mat result; matchTemplate(gray, temp_gray, result, TM_SQDIFF_NORMED); double minVal, maxVal; Point minLoc, maxLoc; minMaxLoc(result, &minVal, &maxVal, &minLoc, &maxLoc); //得分最大的视为匹配结果 if (maxVal > Max) { Max = maxVal; maxIndex = j; //匹配结果 } } result_index.push_back(maxIndex);//将匹配结果进行保存 } if (result_index.size() != 16)return false; return true;}四、效果显示
4.1 功能源码
bool Show_Result(Mat src, vector<Card>&Block_ROI, vector<Card>&Slice_ROI, vector<int>&result_index){ //读取label标签 vector<int>label; if (!ReadData("label.txt", label))return false; //将匹配结果进行显示 for (int i = 0; i < Block_ROI.size(); i++) { rectangle(src, Rect(Block_ROI[i].rect.tl(), Block_ROI[i].rect.br()), Scalar(0, 255, 0), 2); } for (int i = 0; i < Slice_ROI.size(); i++) { cout << label[result_index[i]] << " "; putText(src, to_string(label[result_index[i]]), Point(Slice_ROI[i].rect.tl()), FONT_HERSHEY_SIMPLEX, 1, Scalar(0, 0, 255), 2); } imshow("Demo", src); waitKey(0); destroyAllWindows(); return true;}4.2 效果显示
如图所示，为本案例最终的效果展示。
五、源码
5.1 hpp文件
#pragma once#include<opencv2/opencv.hpp>#include<iostream>struct Card{cv::Mat mat;cv::Rect rect;};//获取模板图像bool Get_Template(cv::Mat temp, std::vector<Card>&Card_Temp);//将银行卡卡号部分切成四块bool Cut_Block(cv::Mat src, std::vector<Card>&Block_ROI);//将每一块数字区域切分出单独数字bool Cut_Slice(std::vector<Card>&Block_ROI, std::vector<Card>&Slice_ROI);//将数字与模板进行模板匹配bool Template_Matching(std::vector<Card>&Card_Temp, std::vector<Card>&Block_ROI,std::vector<Card>&Slice_ROI,std::vector<int>&result_index);//显示最终结果bool Show_Result(cv::Mat src,std::vector<Card>&Block_ROI, std::vector<Card>&Slice_ROI,std::vector<int>&result_index);5.2 cpp文件
#include<iostream>#include"CardDectection.h"#include<fstream>using namespace std;using namespace cv;bool Get_Template(Mat temp, vector<Card>&Card_Temp){//图像预处理Mat gray;cvtColor(temp, gray, COLOR_BGR2GRAY);Mat thresh;threshold(gray, thresh, 0, 255, THRESH_BINARY_INV|THRESH_OTSU);//轮廓检测vector <vector<Point>> contours;findContours(thresh, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);for (int i = 0; i < contours.size(); i++){Rect rect = boundingRect(contours[i]);double ratio = double(rect.width) / double(rect.height);//筛选出字符轮廓if (ratio > 0.5 && ratio < 1){Mat roi = temp(rect); //将字符扣出，放入Card_Temp容器备用Card_Temp.push_back({ roi ,rect });}}if (Card_Temp.empty())return false;//进行字符排序，使其按（0、1、2...7、8、9）顺序排序for (int i = 0; i < Card_Temp.size()-1; i++){for (int j = 0; j < Card_Temp.size() - 1 - i; j++){if (Card_Temp[j].rect.x > Card_Temp[j + 1].rect.x){Card temp = Card_Temp[j];Card_Temp[j] = Card_Temp[j + 1];Card_Temp[j + 1] = temp;}}}//for (int i = 0; i < Card_Temp.size(); i++)//{//imshow(to_string(i), Card_Temp[i].mat);//waitKey(0);//}return true;}bool Cut_Block(Mat src, vector<Card>&Block_ROI){//形态学操作、以便找到银行卡号区域轮廓Mat gray;cvtColor(src, gray, COLOR_BGR2GRAY);Mat gaussian;GaussianBlur(gray, gaussian, Size(3, 3), 0);Mat thresh;threshold(gaussian, thresh, 0, 255, THRESH_BINARY | THRESH_OTSU);Mat close;Mat kernel2 = getStructuringElement(MORPH_RECT, Size(15, 5));morphologyEx(thresh, close, MORPH_CLOSE, kernel2);vector<vector<Point>>contours;findContours(close, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);for (int i = 0; i < contours.size(); i++){//通过面积、长宽比筛选出银行卡号区域double area = contourArea(contours[i]);if (area > 800 && area < 1400){Rect rect = boundingRect(contours[i]);float ratio = double(rect.width) / double(rect.height);if (ratio > 2.8 && ratio < 3.1){//rectangle(src, rect, Scalar(0, 255, 0), 2);Mat ROI = src(rect);Block_ROI.push_back({ ROI ,rect });}}}if (Block_ROI.size()!=4)return false;for (int i = 0; i < Block_ROI.size()-1; i++){for (int j = 0; j < Block_ROI.size() - 1 - i; j++){if (Block_ROI[j].rect.x > Block_ROI[j + 1].rect.x){Card temp = Block_ROI[j];Block_ROI[j] = Block_ROI[j + 1];Block_ROI[j + 1] = temp;}}}//for (int i = 0; i < Block_ROI.size(); i++)//{//imshow(to_string(i), Block_ROI[i].mat);//waitKey(0);//}return true;}bool Cut_Slice(vector<Card>&Block_ROI,vector<Card>&Slice_ROI){//循环上面切割出来的四个小块，将上面的字符一一切割出来。for (int i = 0; i < Block_ROI.size(); i++){Mat roi_gray;cvtColor(Block_ROI[i].mat, roi_gray, COLOR_BGR2GRAY);Mat roi_thresh;threshold(roi_gray, roi_thresh, 0, 255, THRESH_BINARY|THRESH_OTSU);vector <vector<Point>> contours;findContours(roi_thresh, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);for (int j = 0; j < contours.size(); j++){Rect rect = boundingRect(contours[j]);//字符相对于银行卡所在的位置Rect roi_rect(rect.x + Block_ROI[i].rect.x, rect.y + Block_ROI[i].rect.y, rect.width, rect.height);Mat r_roi = Block_ROI[i].mat(rect);Slice_ROI.push_back({ r_roi ,roi_rect });}}if (Slice_ROI.size() != 16) return false;for (int i = 0; i < Slice_ROI.size() - 1; i++){for (int j = 0; j < Slice_ROI.size() - 1 - i; j++){if (Slice_ROI[j].rect.x > Slice_ROI[j + 1].rect.x){Card temp = Slice_ROI[j];Slice_ROI[j] = Slice_ROI[j + 1];Slice_ROI[j + 1] = temp;}}}//for (int i = 0; i < Slice_ROI.size(); i++)//{//imshow(to_string(i), Slice_ROI[i].mat);//waitKey(0);//}return true;}bool ReadData(string filename, vector<int>&label){fstream fin;fin.open(filename, ios::in);if (!fin.is_open()){cout << "can not open the file!" << endl;return false;}int data[10] = { 0 };for (int i = 0; i < 10; i++){fin >> data[i];}fin.close();for (int i = 0; i < 10; i++){label.push_back(data[i]);}return true;}bool Template_Matching(vector<Card>&Card_Temp,vector<Card>&Block_ROI, vector<Card>&Slice_ROI,vector<int>&result_index){for (int i = 0; i < Slice_ROI.size(); i++){//将字符resize成合适大小，利于识别resize(Slice_ROI[i].mat, Slice_ROI[i].mat, Size(60, 80), 1, 1, INTER_LINEAR);Mat gray;cvtColor(Slice_ROI[i].mat, gray, COLOR_BGR2GRAY);int maxIndex = 0;double Max = 0.0;for (int j = 0; j < Card_Temp.size(); j++){resize(Card_Temp[j].mat, Card_Temp[j].mat, Size(60, 80), 1, 1, INTER_LINEAR);Mat temp_gray;cvtColor(Card_Temp[j].mat, temp_gray, COLOR_BGR2GRAY);//进行模板匹配，识别数字Mat result;matchTemplate(gray, temp_gray, result, TM_SQDIFF_NORMED);double minVal, maxVal;Point minLoc, maxLoc;minMaxLoc(result, &minVal, &maxVal, &minLoc, &maxLoc);//得分最大的视为匹配结果if (maxVal > Max){Max = maxVal;maxIndex = j; //匹配结果}}result_index.push_back(maxIndex);//将匹配结果进行保存}if (result_index.size() != 16)return false;return true;}bool Show_Result(Mat src, vector<Card>&Block_ROI,vector<Card>&Slice_ROI, vector<int>&result_index){//读取label标签vector<int>label;if (!ReadData("label.txt", label))return false;//将匹配结果进行显示for (int i = 0; i < Block_ROI.size(); i++){rectangle(src, Rect(Block_ROI[i].rect.tl(), Block_ROI[i].rect.br()), Scalar(0, 255, 0), 2);}for (int i = 0; i < Slice_ROI.size(); i++){cout << label[result_index[i]] << " ";putText(src, to_string(label[result_index[i]]), Point(Slice_ROI[i].rect.tl()), FONT_HERSHEY_SIMPLEX, 1, Scalar(0, 0, 255), 2);}imshow("Demo", src);waitKey(0);destroyAllWindows();return true;}5.3 main文件
#include<iostream>#include"CardDectection.h"using namespace std;using namespace cv;int main(){Mat src = imread("card.png"); //源图像 银行卡Mat temp = imread("number.png"); //模板图像if (src.empty() || temp.empty()){cout << "no image data !" << endl;system("pause");return -1;}vector<Card>Card_Temp;if (!Get_Template(temp, Card_Temp)){cout << "模板切割失败！" << endl;system("pause");return -1;}vector<Card>Block_ROI;if (Cut_Block(src, Block_ROI)){vector<Card>Slice_ROI;if (Cut_Slice(Block_ROI, Slice_ROI)){vector<int>result_index;if (Template_Matching(Card_Temp, Block_ROI, Slice_ROI, result_index)){Show_Result(src, Block_ROI, Slice_ROI, result_index);}else{cout << "识别失败！" << endl;system("pause");return -1;}}else{cout << "切片失败！" << endl;system("pause");return -1;}}else{cout << "切块失败！" << endl;system("pause");return -1;}system("pause");return 0;}感谢你能够认真阅读完这篇文章，希望小编分享的“C++ OpenCV如何实现银行卡号识别功能”这篇文章对大家有帮助，同时也希望大家多多支持编程网，关注编程网行业资讯频道，更多相关知识等着你来学习!
