合工大苍穹战队视觉组培训Day8——视觉，目标识别
2024-06-10 09:17:09
学习目标：
- 目标识别
学习内容：
车牌OCR
学习时间：
- 2022年7月26日到2022年7月27日
学习产出：
因为之前用FPGA和基于python的cv做过车牌识别（大创），感觉战队C++用的多，这次用C++实现。
之前做的代码我已经开源、上传到我的仓库了，欢迎大家交流，不过没有完善好，会逐步推进的。
py链接,FPGA的还需要完善，目前对0和8的识别不是太行。
下面是战队的任务，就不过多讲解了。
头文件：Global.h
#ifdef GLOBALextern int flag_1;
extern bool flag;
extern bool specialFlag;
extern int captureRead
extern string carPlate;
extern char test[10];extern struct stu1
{char number;Mat image;double matchDegree;
};
extern struct stu
{Mat image;double matchDegree;
};#endifCPP文件也是唯一的一个：
#include <opencv2/opencv.hpp>
#include<opencv2/imgproc/imgproc.hpp>
#include<opencv2/highgui/highgui.hpp>
#include"Global.h"
#include <windows.h>
#include <string>using namespace std;
using namespace cv;void fillHole(const Mat srcBw, Mat &dstBw); //填补算法
Mat cutOne(Mat cutImage); //边框切割算法
void CharCut(Mat srcImage); //单个字符切割算法
Mat Location(Mat srcImage); //图像识别算法
void SingleCharCut(Mat doubleImage, int k1, int k2);
void ShowChar();
void MatchProvince();
void MatchNumber();
void readProvince();
void readNumber();
void VideoShow(Mat videoImage);
void GetStringSize(HDC hDC, const char* str, int* w, int* h);
void putTextZH(Mat &dst, const char* str, Point org, Scalar color, int fontSize, const char* fn, bool italic, bool underline);int flag_1; //判断是否倾斜，需不需要二次定位车牌
bool flag; //判断提取是否成功
bool specialFlag = false; //针对嵌套车牌
int captureRead = 0;
int videoFlag = 0;
string carPlateProvince = " ";
string carPlate = " ";
char test[10];
vector<Mat> singleChar; //字符图片容器int main(int argc, char *argv[])
{//计时开始double time0 = static_cast<double>(getTickCount());//视频操作VideoCapture capture("1.mp4");Mat srcImage;Mat theFirst;int singleCharLength;//读取字符图片readProvince();readNumber();while (1) {capture >> srcImage;try {if (!srcImage.data) { printf("视频识别结束 \n"); return 0; }if (srcImage.rows >= srcImage.cols){resize(srcImage, srcImage, Size(640, 640 * srcImage.rows / srcImage.cols));}else{resize(srcImage, srcImage, Size(400 * srcImage.cols / srcImage.rows, 400));}//车牌定位theFirst = Location(srcImage);if (flag){if (flag_1 == 1) //旋转后要再次定位去上下杂边{theFirst = Location(theFirst);flag_1 = 0;}}if (flag){//车牌切割(切割上下边，去除干扰)theFirst = cutOne(theFirst);//单个字符切割CharCut(theFirst);singleCharLength = singleChar.size();printf("采取字符轮廓数 %d\n", singleCharLength);ShowChar();if (singleCharLength >= 7){MatchProvince();MatchNumber();}singleChar.clear();}}catch (Exception e) {cout << "Standard ecxeption : " << e.what() << " \n" << endl;}if (waitKey(30) >= 0)break;//延时30ms}//imwrite("match\\xxxxxx.bmp", singleChar[2]);time0 = ((double)getTickCount() - time0) / getTickFrequency();cout << "运行时间" << time0 << "秒" << endl;waitKey(0);
}void fillHole(const Mat srcBw, Mat &dstBw)
{Size imageSize = srcBw.size();Mat Temp = Mat::zeros(imageSize.height + 2, imageSize.width + 2, srcBw.type());//延展图像srcBw.copyTo(Temp(Range(1, imageSize.height + 1), Range(1, imageSize.width + 1)));cv::floodFill(Temp, Point(0, 0), Scalar(255));Mat cutImg;//裁剪延展的图像Temp(Range(1, imageSize.height + 1), Range(1, imageSize.width + 1)).copyTo(cutImg);dstBw = srcBw | (~cutImg);
}Mat Location(Mat srcImage)
{//判断变量重赋值flag = false;//用于旋转车牌int imageWidth, imageHeight; //输入图像的长和宽imageWidth = srcImage.rows; //获取图片的宽imageHeight = srcImage.cols; //获取图像的长//!!!!!!!!!!!!!!!!!!!Mat blueROI = srcImage.clone();cvtColor(blueROI, blueROI, CV_BGR2HSV);//namedWindow("hsv图");//imshow("hsv图", blueROI);//中值滤波操作medianBlur(blueROI, blueROI, 3);//namedWindow("medianBlur图");//imshow("medianBlur图", blueROI);//将蓝色区域二值化inRange(blueROI, Scalar(100, 130, 50), Scalar(124, 255, 255), blueROI);//namedWindow("blue图");//imshow("blue图", blueROI);Mat element1 = getStructuringElement(MORPH_RECT, Size(2, 2)); //size（）对速度有影响morphologyEx(blueROI, blueROI, MORPH_OPEN, element1);//namedWindow("0次K运算后图像");//imshow("0次K运算后图像", blueROI);Mat element0 = getStructuringElement(MORPH_ELLIPSE, Size(10, 10)); //size（）对速度有影响morphologyEx(blueROI, blueROI, MORPH_CLOSE, element0);//namedWindow("0次闭运算后图像");//imshow("0次闭运算后图像", blueROI);vector<vector<Point>> contours;findContours(blueROI, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);int cnt = contours.size();cout << "number of contours " << cnt << endl; //打印轮廓个数if (cnt == 0){if (!flag) //在视频中显示{cout << "图中无车牌 " << endl;//namedWindow("提取车牌结果图");//imshow("提取车牌结果图", srcImage); //显示最终结果图VideoShow(srcImage);return srcImage;}}double area;double longside, temp, shortside, long2short;float angle = 0;Rect rect;RotatedRect box; //可旋转的矩形盒子Point2f vertex[4]; //四个顶点Mat image = srcImage.clone(); //为后来显示做准备Mat rgbCutImg; //车牌裁剪图//box.points(vertex); //获取矩形四个顶点坐标//length=arcLength(contour[i]); //获取轮廓周长//area=contourArea(contour[i]); //获取轮廓面积//angle=box.angle; //得到车牌倾斜角度for (int i = 0; i < cnt; i++){area = contourArea(contours[i]); //获取轮廓面积if (area > 600 && area < 15000) //矩形区域面积大小判断{rect = boundingRect(contours[i]); //计算矩形边界box = minAreaRect(contours[i]); //获取轮廓的矩形box.points(vertex); //获取矩形四个顶点坐标angle = box.angle; //得到车牌倾斜角度longside = sqrt(pow(vertex[1].x - vertex[0].x, 2) + pow(vertex[1].y - vertex[0].y, 2));shortside = sqrt(pow(vertex[2].x - vertex[1].x, 2) + pow(vertex[2].y - vertex[1].y, 2));if (shortside > longside) //短轴大于长轴，交换数据{temp = longside;longside = shortside;shortside = temp;cout << "交换" << endl;}elseangle += 90;long2short = longside / shortside;if (long2short > 1.5 && long2short < 4.5){flag = true;for (int i = 0; i < 4; ++i) //划线框出车牌区域line(image, vertex[i], vertex[((i + 1) % 4) ? (i + 1) : 0], Scalar(0, 255, 0), 1, CV_AA);if (!flag_1) //在视频中显示{printf("提取成功\n");/*namedWindow("提取车牌结果图");imshow("提取车牌结果图", image); */ //显示最终结果图VideoShow(image);}rgbCutImg = srcImage(rect);//namedWindow("车牌图");//imshow("车牌图", rgbCutImg);//裁剪出车牌 break; //退出循环，以免容器中变量变换}}}cout << "倾斜角度：" << angle << endl;if (flag && fabs(angle) > 0.8) {flag_1 = 1;Mat RotractImg(imageWidth, imageHeight, CV_8UC1, Scalar(0, 0, 0)); Point2f center = box.center; //获取车牌中心坐标Mat M2 = getRotationMatrix2D(center, angle, 1); //计算旋转加缩放的变换矩阵 warpAffine(srcImage, RotractImg, M2, srcImage.size(), 1, 0, Scalar(0)); //namedWindow("倾斜矫正后图片",0);//imshow("倾斜矫正后图片", RotractImg);rgbCutImg = RotractImg(rect); //截取车牌彩色照片//namedWindow("矫正后车牌照");//imshow("矫正后车牌照", rgbCutImg);/*cout << "矩形中心:" << box.center.x << "," << box.center.y << endl;*/return rgbCutImg;}if (flag == false) {printf("提取失败\n"); //后期加边缘检测法识别if (!flag_1) //在视频中显示{/*namedWindow("提取车牌结果图");imshow("提取车牌结果图", image); */ //显示最终结果图VideoShow(image);}}return rgbCutImg;
}/注意嵌套车牌/emmmm这个模型需要优化一下
Mat cutOne(Mat cutImage)
{//打印车牌长宽try {/*cout << " cutImage.rows : " << cutImage.rows << endl;cout << " cutImage.cols : " << cutImage.cols << endl;*/if(cutImage.rows >= cutImage.cols)resize(cutImage, cutImage, Size(320, 320 * cutImage.rows / cutImage.cols));}catch (Exception e){resize(cutImage, cutImage, Size(320, 100));}/*namedWindow("Resize车牌图");imshow("Resize车牌图", cutImage);*/int height = cutImage.rows;cout << "\tHeight:" << height << "\tWidth:" << 320 << endl;if (height < 86){printf("嵌套车牌\n");specialFlag = true;}Mat whiteROI = cutImage.clone();if (specialFlag){cvtColor(whiteROI, whiteROI, CV_BGR2HSV);//将白色区域二值化//inRange(whiteROI, Scalar(0, 0, 0), Scalar(130, 50, 245), whiteROI); //增大 S 即饱和度可以使hsv白色检测范围更大inRange(whiteROI, Scalar(0, 0, 0), Scalar(180, 100, 245), whiteROI);//namedWindow("specialFlagwhiteROI图");//imshow("specialFlagwhiteROI图", whiteROI);}else{GaussianBlur(whiteROI, whiteROI, Size(3, 3), 0, 0);/*namedWindow("GaussianBlur车牌图");imshow("GaussianBlur车牌图", whiteROI);*/cvtColor(whiteROI, whiteROI, CV_BGR2HSV);//medianBlur(whiteROI, whiteROI, 3);//namedWindow("Src_medianBlur图");//imshow("Src_medianBlur图", whiteROI);//将白色区域二值化//inRange(whiteROI, Scalar(0, 0, 10), Scalar(180, 30, 255), whiteROI); //增大 S 即饱和度可以使hsv白色检测范围更大inRange(whiteROI, Scalar(0, 0, 10), Scalar(180, 120, 255), whiteROI);//namedWindow("whiteROI图");//imshow("whiteROI图", whiteROI);}/*Mat element0 = getStructuringElement(MORPH_ELLIPSE, Size(4, 4)); //size（）对速度有影响morphologyEx(whiteROI, whiteROI, MORPH_OPEN, element0);namedWindow("OPEN图");imshow("OPEN图", whiteROI);*/Mat dstImage = cutImage.clone();vector<vector<Point>> contours;findContours(whiteROI, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);drawContours(dstImage, contours, -1, Scalar(0, 0, 255), 1);//namedWindow("疑似字符轮廓识别图");//imshow("疑似字符轮廓识别图", dstImage);inRange(dstImage, Scalar(0, 0, 255), Scalar(0, 0, 255), dstImage);//namedWindow("字符大轮廓图");//imshow("字符大轮廓图", dstImage);/*fillHole(dstImage, dstImage);namedWindow("填补轮廓后图");imshow("填补轮廓后图", dstImage);*/int row1 = 2;int row2 = dstImage.rows;int rowMax = dstImage.rows - 1; //开区间，防止越界int colMax = dstImage.cols - 1; //开区间，防止越界int addFirst = 10;int addFirst0 = 0;int addFirst1 = 0;int addFirst2 = 0;//测中间像素//dstImage.at<uchar>(rowMax-1, colMax-1);//cout << "Width:" << j << endl;int addFirstTemp = addFirst; //第一次用时已经改变数值，容易忽略！！！！！uchar* data;//裁剪上下边。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。//上边for (int i = 2; i < rowMax / 3; i++, addFirst1 = 0) // 6 刚刚好{data = dstImage.ptr<uchar>(i);for (int j = 2; j < colMax; j++){if (data[j] == 255){addFirst1++;}}if (addFirst1 < addFirst) //筛选最小值所在行{row1 = i;addFirst = addFirst1 + 3;//cout << "行头" << row1 << endl;//flag_x = 1;}}//下边for (int i = rowMax - 2; i > rowMax - rowMax / 4; i--, addFirst2 = 0) // 6 刚刚好{data = dstImage.ptr<uchar>(i);for (int j = 2; j < colMax; j++){if (data[j] == 255){addFirst2++;}}if (addFirst2 < addFirstTemp) //筛选最小值所在行{row2 = i;addFirstTemp = addFirst2 + 3;//cout << "行底" << row2 << endl;//flag_y = 1;}}int orow;orow = row2 - row1;Mat w_image;Mat rgb_w_image;w_image = dstImage(Rect(0, row1, colMax, orow));rgb_w_image = cutImage(Rect(0, row1, colMax, orow));//namedWindow("裁剪上下图");//imshow("裁剪上下图", w_image);int rowMax_ALT = w_image.rows - 1; //开区间，防止越界(注意，裁剪完上下后要重新写行和宽，因为行和宽已经改变)int colMax_ALT = w_image.cols - 1; //开区间，防止越界（注意，裁剪完上下后要重新写行和宽，因为行和宽已经改变）int col_1 = 2;int col_2 = w_image.cols;int add = 2;int add1 = 0;int add2 = 0;int addTemp = add; //第一次用时已经改变数值，容易忽略！！！！！//裁剪左右边。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。//左边//for (int i = 0; i < colMax_ALT / 18; i++, add1 = 0) // 刚刚好//{// for (int j = 2; j < rowMax_ALT; j++)// {// data = dstImage.ptr<uchar>(j);// if (data[i] == 255)// {// add1++;// }// }// if (add1 < add) //筛选最小值所在列// {// col_1 = i;// add = add1 + 1;// }//}//右边if (specialFlag){for (int i = colMax_ALT; i > colMax_ALT - colMax_ALT / 18; i--, add2 = 0) // 刚刚好{for (int j = 2; j < rowMax_ALT; j++){data = dstImage.ptr<uchar>(j);if (data[i] == 255){add2++;}}if (add2 < addTemp) //筛选最小值所在列{col_2 = i;addTemp = add2 + 1;//cout << "行底" << row2 << endl;}}}int o_col;o_col = col_2 - col_1;Mat H_image;H_image = w_image(Rect(col_1, 0, o_col, rowMax_ALT));rgb_w_image = rgb_w_image(Rect(col_1, 0, o_col, rowMax_ALT));//namedWindow("再裁剪左右图");//imshow("再裁剪左右图", H_image);//namedWindow("裁剪后彩图");//imshow("裁剪后彩图", rgb_w_image);return rgb_w_image;
}void CharCut(Mat srcImage)
{resize(srcImage, srcImage, Size(320, 320 * srcImage.rows / srcImage.cols));//namedWindow("Resize车牌图");//imshow("Resize车牌图", srcImage);GaussianBlur(srcImage, srcImage, Size(3, 3), 0, 0);/*namedWindow("GaussianBlur车牌图");imshow("GaussianBlur车牌图", srcImage);
*/medianBlur(srcImage, srcImage, 3);//namedWindow("Src_medianBlur图");//imshow("Src_medianBlur图", srcImage);cvtColor(srcImage, srcImage, CV_BGR2HSV);//将白色区域二值化Mat doubleImage;//inRange(srcImage, Scalar(0, 0, 10), Scalar(180, 75, 255), doubleImage); //增大 S 即饱和度可以使hsv白色检测范围更大inRange(srcImage, Scalar(0, 0, 0), Scalar(180, 125, 245), doubleImage);namedWindow("doubleImage图");imshow("doubleImage图", doubleImage);int colTemp = 0;int rowMax = doubleImage.rows;int colMax = doubleImage.cols;int addFirst = 0;int add = 0;int k1 = 0;int k2;int kTemp = 0;int times = 0;int oneCutEnd = 0;float t = 1.0;uchar* data;cout << "Test: " << specialFlag << endl;//针对嵌套车牌处理if (specialFlag){for (int i = 2; i < colMax; i++, addFirst = 0, add = 0){for (int j = rowMax / 10.8; j < rowMax - rowMax / (10.8*t); j++){data = doubleImage.ptr<uchar>(j);if (data[i - 1] == 255){addFirst++; //统计前一列}}for (int j = rowMax / 10.8; j < rowMax - rowMax / (10.8*t); j++){data = doubleImage.ptr<uchar>(j);if (data[i] == 255){add++; //统计后一列}}//省份字符分开切割if (!times){if (!oneCutEnd && (!addFirst && add))k1 = i - 1;if (addFirst && !add){k2 = i;oneCutEnd = 1;if (k2 - k1 > colMax / 11.25){times = 1;if (k2 - k1 < colMax / 5.625)SingleCharCut(doubleImage, k1, k2);elsei = 2;}}} //切割其他字符else {if (!addFirst && add)k1 = i - 1;if (addFirst && !add){k2 = i;if (k2 - k1 > colMax / 32){if (k2 - k1 < colMax / 5.625)SingleCharCut(doubleImage, k1, k2);else //针对嵌套车牌下部连接过靠上{i = k1;t -= 0.1;}}else{ //!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!处理中间分割点与‘ 1 ’!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!for (int a = k1; a <= k2; a++){data = doubleImage.ptr<uchar>(rowMax / 5);if (data[a] == 255)kTemp++;}if (kTemp > 0)SingleCharCut(doubleImage, k1, k2);kTemp = 0;}}}}k2 = colMax;if (k2 - k1 > colMax / 32)SingleCharCut(doubleImage, k1, k2);specialFlag = false;}else {for (int i = 2; i < colMax; i++, addFirst = 0, add = 0){for (int j = rowMax / 12.8; j < rowMax - rowMax / 12.8; j++){data = doubleImage.ptr<uchar>(j);if (data[i - 1] == 255){addFirst++;}}for (int j = rowMax / 12.8; j < rowMax - rowMax / 12.8; j++){data = doubleImage.ptr<uchar>(j);if (data[i] == 255){add++;}}if (!times){if (!oneCutEnd && (!addFirst && add))k1 = i - 1;if (addFirst && !add){k2 = i;oneCutEnd = 1;if (k2 - k1 > colMax / 11.25){times = 1;if (k2 - k1 < colMax / 5.625)SingleCharCut(doubleImage, k1, k2);elsei = 2;}}}else {if (!addFirst && add)k1 = i - 1;if (addFirst && !add){k2 = i;if (k2 - k1 > colMax / 32)SingleCharCut(doubleImage, k1, k2);else{ //!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!处理中间分割点与‘ 1 ’!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!for (int a = k1; a <= k2; a++){data = doubleImage.ptr<uchar>(rowMax / 5);if (data[a] == 255)kTemp++;}if (kTemp > 0)SingleCharCut(doubleImage, k1, k2);kTemp = 0;}}}}}
}void SingleCharCut(Mat doubleImage, int k1, int k2)
{//printf("k1 = %d ,k2 = %d\n", k1, k2);int rowMax = doubleImage.rows;Mat image;image = doubleImage(Rect(k1, 0, k2 - k1, rowMax));int row1 = 0;int row2 = image.rows;rowMax = image.rows - 1; //开区间，防止越界int colMax = image.cols; //开区间，防止越界int addFirst = 2;int addFirst1 = 0;int addFirst2 = 0;uchar* data;//测中间像素//image.at<uchar>(rowMax-1, colMax-1);//cout << "Width:" << j << endl;int addFirstTemp = addFirst; //第一次用时已经改变数值，容易忽略！！！！！//裁剪上下边。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。//上边for (int i = 0; i < rowMax / 4; i++, addFirst1 = 0) // 6 刚刚好{data = image.ptr<uchar>(i);for (int j = 0; j < colMax; j++){if (data[j] == 255){addFirst1++;}}if (addFirst1 < addFirst) //筛选最小值所在行{row1 = i;addFirst = addFirst1 + 1;}}//下边for (int i = rowMax; i > rowMax - rowMax / 4; i--, addFirst2 = 0) // 6 刚刚好{data = image.ptr<uchar>(i);for (int j = 2; j < colMax; j++){if (data[j] == 255){addFirst2++;}}if (addFirst2 < addFirstTemp) //筛选最小值所在行{row2 = i;addFirstTemp = addFirst2 + 1;}}int orow;orow = row2 - row1;Mat w_image;w_image = image(Rect(0, row1, colMax, orow));singleChar.push_back(w_image);}void ShowChar()
{int length = singleChar.size();for (int i = 0; i < length; i++) {resize(singleChar[i], singleChar[i], Size(20, 40)); //字符图像归一化//namedWindow(to_string(i) + "图");//imshow(to_string(i) + "图", singleChar[i]);}
}//读取省份模板
struct stu
{Mat image;double matchDegree;
};
struct stu first[35];
void readProvince()
{int i = 0;//读取字符{first[i].image = imread("match\\zw1.bmp", 0);i++;first[i].image = imread("match\\zw2.bmp", 0);i++;first[i].image = imread("match\\zw3.bmp", 0);i++;first[i].image = imread("match\\zw4.bmp", 0);i++;first[i].image = imread("match\\zw5.bmp", 0);i++;first[i].image = imread("match\\zw6.bmp", 0);i++;first[i].image = imread("match\\zw7.bmp", 0);i++;first[i].image = imread("match\\zw8.bmp", 0);i++;first[i].image = imread("match\\zw9.bmp", 0);i++;first[i].image = imread("match\\zw10.bmp", 0);i++;first[i].image = imread("match\\zw11.bmp", 0);i++;first[i].image = imread("match\\zw12.bmp", 0);i++;first[i].image = imread("match\\zw13.bmp", 0);i++;first[i].image = imread("match\\zw14.bmp", 0);i++;first[i].image = imread("match\\zw15.bmp", 0);i++;first[i].image = imread("match\\zw16.bmp", 0);i++;first[i].image = imread("match\\zw17.bmp", 0);i++;first[i].image = imread("match\\zw18.bmp", 0);i++;first[i].image = imread("match\\zw19.bmp", 0);i++;first[i].image = imread("match\\zw20.bmp", 0);i++;first[i].image = imread("match\\zw21.bmp", 0);i++;first[i].image = imread("match\\zw22.bmp", 0);i++;first[i].image = imread("match\\zw23.bmp", 0);i++;first[i].image = imread("match\\zw24.bmp", 0);i++;first[i].image = imread("match\\zw25.bmp", 0);i++;first[i].image = imread("match\\zw26.bmp", 0);i++;first[i].image = imread("match\\zw27.bmp", 0);i++;first[i].image = imread("match\\zw28.bmp", 0);i++;first[i].image = imread("match\\zw29.bmp", 0);i++;first[i].image = imread("match\\zw30.bmp", 0);i++;first[i].image = imread("match\\zw31.bmp", 0);i++;first[i].image = imread("match\\zw32.bmp", 0);i++;first[i].image = imread("match\\zw33.bmp", 0);i++;first[i].image = imread("match\\zw34.bmp", 0);i++;first[i].image = imread("match\\zw35.bmp", 0);}}//识别省份字符
void MatchProvince()
{int rowMax = 40;int colMax = 20;int add = 0;int addTemp = 0;Mat absCutImage;double temp;int index = 0;uchar* data;for (int i = 0; i < rowMax; i++){data = singleChar[0].ptr<uchar>(i);for (int j = 0; j < colMax; j++){if (data[j] == 255){add++;}}}int firstLength = end(first) - begin(first);//printf("数组长度1 %d\n",firstLength);for (int x = 0; x < firstLength; x++, addTemp = 0){absCutImage = abs(first[x].image - singleChar[0]);for (int i = 0; i < rowMax; i++){data = absCutImage.ptr<uchar>(i);for (int j = 0; j < colMax; j++){if (data[j] == 255){addTemp++;}}}temp = 1.0 - 1.0*addTemp / add;if (temp <= 1)first[x].matchDegree = temp;elsefirst[x].matchDegree = 0;if (x > 0 && first[x].matchDegree > first[index].matchDegree)index = x;}/*absCutImage = abs(singleChar[0] - first[index].image);namedWindow("省份图片相减后图" + to_string(0));imshow("省份图片相减后图" + to_string(0), absCutImage);*/printf("省份字符最大匹配度： %lf\n", first[index].matchDegree);switch (index) {case 0:printf("藏");carPlateProvince += "藏";break;case 1:printf("川");carPlateProvince += "川";break;case 2:printf("鄂");carPlateProvince += "鄂";break;case 3:printf("甘");carPlateProvince += "甘";break;case 4:printf("赣");carPlateProvince += "赣";break;case 5:printf("贵");carPlateProvince += "贵";break;case 6:printf("桂");carPlateProvince += "桂";break;case 7:printf("黑");carPlateProvince += "黑";break;case 8:printf("泸");carPlateProvince += "泸";break;case 9:printf("吉");carPlateProvince += "吉";break;case 10:printf("翼");carPlateProvince += "翼";break;case 11:printf("津");carPlateProvince += "津";break;case 12:printf("晋");carPlateProvince += "晋";break;case 13:printf("京");carPlateProvince += "京";break;case 14:printf("辽");carPlateProvince += "辽";break;case 15:printf("鲁");carPlateProvince += "鲁";break;case 16:printf("蒙");carPlateProvince += "蒙";break;case 17:printf("闽");carPlateProvince += "闽";break;case 18:printf("宁");carPlateProvince += "宁";break;case 19:printf("青");carPlateProvince += "青";break;case 20:printf("琼");carPlateProvince += "琼";break;case 21:printf("陕");carPlateProvince += "陕";break;case 22:printf("苏");carPlateProvince += "苏";break;case 23:printf("皖");carPlateProvince += "皖";break;case 24:printf("湘");carPlateProvince += "湘";break;case 25:printf("新");carPlateProvince += "新";break;case 26:printf("渝");carPlateProvince += "渝";break;case 27:printf("豫");carPlateProvince += "豫";break;case 28:printf("粤");carPlateProvince += "粤";break;case 29:printf("云");carPlateProvince += "云";break;case 30:printf("浙");carPlateProvince += "浙";break;case 31:printf("湘");carPlateProvince += "湘";break;case 32:printf("湘");carPlateProvince += "湘";break;case 33:printf("鲁");carPlateProvince += "鲁";break;case 34:printf("粤");carPlateProvince += "粤";break;}printf("\n");
}读取模板文件
struct stu1
{char number;Mat image;double matchDegree;
};
struct stu1 second[49];
void readNumber()
{for (int i = 0; i < 10; i++) {second[i].image = imread("match\\" + to_string(i) + ".bmp", 0);second[i].number = 48 + i;}//读取字符{int i = 10;second[i].image = imread("match\\6a.bmp", 0);second[i].number = '6';i++;second[i].image = imread("match\\3a.bmp", 0);second[i].number = '3';i++;second[i].image = imread("match\\P1.bmp", 0);second[i].number = 'P';i++;second[i].image = imread("match\\8b.bmp", 0);second[i].number = '8';i++;second[i].image = imread("match\\K1.bmp", 0);second[i].number = 'K';i++;second[i].image = imread("match\\9a.bmp", 0);second[i].number = '9';i++;second[i].image = imread("match\\B2.bmp", 0);second[i].number = 'B';i++;second[i].image = imread("match\\G1.bmp", 0);second[i].number = 'G';i++;second[i].image = imread("match\\T1.bmp", 0);second[i].number = 'T';i++;second[i].image = imread("match\\B1.bmp", 0);second[i].number = 'B';i++;second[i].image = imread("match\\8a.bmp", 0);second[i].number = '8';i++;second[i].image = imread("match\\1a.bmp", 0);second[i].number = '1';i++;second[i].image = imread("match\\7a.bmp", 0);second[i].number = '7';i++;second[i].image = imread("match\\D1.bmp", 0);second[i].number = 'D';i++;second[i].image = imread("match\\0a.bmp", 0);second[i].number = '0';i++;second[i].image = imread("match\\A.bmp", 0);second[i].number = 'A';i++;second[i].image = imread("match\\B.bmp", 0);second[i].number = 'B';i++;second[i].image = imread("match\\C.bmp", 0);second[i].number = 'C';i++;second[i].image = imread("match\\D.bmp", 0);second[i].number = 'D';i++;second[i].image = imread("match\\E.bmp", 0);second[i].number = 'E';i++;second[i].image = imread("match\\F.bmp", 0);second[i].number = 'F';i++;second[i].image = imread("match\\G.bmp", 0);second[i].number = 'G';i++;second[i].image = imread("match\\H.bmp", 0);second[i].number = 'H';i++;second[i].image = imread("match\\J.bmp", 0);second[i].number = 'J';i++;second[i].image = imread("match\\K.bmp", 0);second[i].number = 'K';i++;second[i].image = imread("match\\L.bmp", 0);second[i].number = 'L';i++;second[i].image = imread("match\\M.bmp", 0);second[i].number = 'M';i++;second[i].image = imread("match\\N.bmp", 0);second[i].number = 'N';i++;second[i].image = imread("match\\P.bmp", 0);second[i].number = 'P';i++;second[i].image = imread("match\\Q.bmp", 0);second[i].number = 'Q';i++;second[i].image = imread("match\\R.bmp", 0);second[i].number = 'R';i++;second[i].image = imread("match\\S.bmp", 0);second[i].number = 'S';i++;second[i].image = imread("match\\T.bmp", 0);second[i].number = 'T';i++;second[i].image = imread("match\\U.bmp", 0);second[i].number = 'U';i++;second[i].image = imread("match\\V.bmp", 0);second[i].number = 'V';i++;second[i].image = imread("match\\W.bmp", 0);second[i].number = 'W';i++;second[i].image = imread("match\\X.bmp", 0);second[i].number = 'X';i++;second[i].image = imread("match\\Y.bmp", 0);second[i].number = 'Y';i++;second[i].image = imread("match\\Z.bmp", 0);second[i].number = 'Z';}}//识别其他字符
void MatchNumber()
{int rowMax = 40;int colMax = 20;int add = 0;int addTemp = 0;Mat absCutImage;double temp;int index = 0;int length = singleChar.size();int secondLength = end(second) - begin(second);//printf("数组长度2 %d \n", secondLength);uchar* data;int q = 0;for (int y = 1; y < length; y++, add = 0, index = 0){if (y > 6) //防止多读break;//统计要读取字符的白色像素总值for (int i = 0; i < rowMax; i++){data = singleChar[y].ptr<uchar>(i);for (int j = 0; j < colMax; j++){if (data[j] == 255){add++;}}}//逐个字符识别for (int x = 0; x < secondLength; x++, addTemp = 0){absCutImage = abs(singleChar[y] - second[x].image);//统计相减之后的图像白色像素总值for (int i = 0; i < rowMax; i++){data = absCutImage.ptr<uchar>(i);for (int j = 0; j < colMax; j++){if (data[j] == 255){addTemp++;}}}temp = 1.0 - 1.0*addTemp / add;if (temp <= 1 && temp > 0)second[x].matchDegree = temp;elsesecond[x].matchDegree = 0;//获取最大匹配度对应索引indexif (x > 0 && second[x].matchDegree > second[index].matchDegree)index = x;}absCutImage = abs(singleChar[y] - second[index].image);/* namedWindow("图片相减后图"+to_string(y));imshow("图片相减后图" + to_string(y), absCutImage);*/printf("最大匹配度： %lf\n", second[index].matchDegree);printf("对应字符： %c\n", second[index].number);test[q] = second[index].number;//printf("\ntest11111 %c\n", test[q]);q++;}test[q] = '\0';//printf("\ntest22222 %c\n", test[q-1]);//cout<< "xxxxxxxxxxxxxx"<<carPlate<<endl;
}void VideoShow(Mat videoImage)
{/*if(videoFlag==0)*///carPlate = "京A J9846";carPlate += test;carPlateProvince += carPlate;/*carPlate.copy(test,strlen(test));*/cout << carPlateProvince << endl;cout << carPlateProvince.length() << endl;if(carPlateProvince.length()<10)putTextZH(videoImage, "Not Plate!", Point(490, 20), Scalar(0, 0, 255), 30, "Arial", false, false);elseputTextZH(videoImage, carPlateProvince.c_str(), Point(490, 20), Scalar(0, 0, 255), 30, "Arial", false, false);/*else if(videoFlag==1)*/namedWindow("提取车牌结果图");imshow("提取车牌结果图", videoImage);carPlateProvince = " ";carPlate = " ";
}void GetStringSize(HDC hDC, const char* str, int* w, int* h)
{SIZE size;GetTextExtentPoint32A(hDC, str, strlen(str), &size);if (w != 0) *w = size.cx;if (h != 0) *h = size.cy;
}void putTextZH(Mat &dst, const char* str, Point org, Scalar color, int fontSize, const char* fn, bool italic, bool underline)
{CV_Assert(dst.data != 0 && (dst.channels() == 1 || dst.channels() == 3));int x, y, r, b;if (org.x > dst.cols || org.y > dst.rows) return;x = org.x < 0 ? -org.x : 0;y = org.y < 0 ? -org.y : 0;LOGFONTA lf;lf.lfHeight = -fontSize;lf.lfWidth = 0;lf.lfEscapement = 0;lf.lfOrientation = 0;lf.lfWeight = 5;lf.lfItalic = italic; //斜体lf.lfUnderline = underline; //下划线lf.lfStrikeOut = 0;lf.lfCharSet = DEFAULT_CHARSET;lf.lfOutPrecision = 0;lf.lfClipPrecision = 0;lf.lfQuality = PROOF_QUALITY;lf.lfPitchAndFamily = 0;strcpy_s(lf.lfFaceName, fn);HFONT hf = CreateFontIndirectA(&lf);HDC hDC = CreateCompatibleDC(0);HFONT hOldFont = (HFONT)SelectObject(hDC, hf);int strBaseW = 0, strBaseH = 0;int singleRow = 0;char buf[1 << 12];strcpy_s(buf, str);char *bufT[1 << 12]; // 这个用于分隔字符串后剩余的字符，可能会超出。//处理多行{int nnh = 0;int cw, ch;const char* ln = strtok_s(buf, "\n", bufT);while (ln != 0){GetStringSize(hDC, ln, &cw, &ch);strBaseW = max(strBaseW, cw);strBaseH = max(strBaseH, ch);ln = strtok_s(0, "\n", bufT);nnh++;}singleRow = strBaseH;strBaseH *= nnh;}if (org.x + strBaseW < 0 || org.y + strBaseH < 0){SelectObject(hDC, hOldFont);DeleteObject(hf);DeleteObject(hDC);return;}r = org.x + strBaseW > dst.cols ? dst.cols - org.x - 1 : strBaseW - 1;b = org.y + strBaseH > dst.rows ? dst.rows - org.y - 1 : strBaseH - 1;org.x = org.x < 0 ? 0 : org.x;org.y = org.y < 0 ? 0 : org.y;BITMAPINFO bmp = { 0 };BITMAPINFOHEADER& bih = bmp.bmiHeader;int strDrawLineStep = strBaseW * 3 % 4 == 0 ? strBaseW * 3 : (strBaseW * 3 + 4 - ((strBaseW * 3) % 4));bih.biSize = sizeof(BITMAPINFOHEADER);bih.biWidth = strBaseW;bih.biHeight = strBaseH;bih.biPlanes = 1;bih.biBitCount = 24;bih.biCompression = BI_RGB;bih.biSizeImage = strBaseH * strDrawLineStep;bih.biClrUsed = 0;bih.biClrImportant = 0;void* pDibData = 0;HBITMAP hBmp = CreateDIBSection(hDC, &bmp, DIB_RGB_COLORS, &pDibData, 0, 0);CV_Assert(pDibData != 0);HBITMAP hOldBmp = (HBITMAP)SelectObject(hDC, hBmp);//color.val[2], color.val[1], color.val[0]SetTextColor(hDC, RGB(255, 255, 255));SetBkColor(hDC, 0);//SetStretchBltMode(hDC, COLORONCOLOR);strcpy_s(buf, str);const char* ln = strtok_s(buf, "\n", bufT);int outTextY = 0;while (ln != 0){TextOutA(hDC, 0, outTextY, ln, strlen(ln));outTextY += singleRow;ln = strtok_s(0, "\n", bufT);}uchar* dstData = (uchar*)dst.data;int dstStep = dst.step / sizeof(dstData[0]);unsigned char* pImg = (unsigned char*)dst.data + org.x * dst.channels() + org.y * dstStep;unsigned char* pStr = (unsigned char*)pDibData + x * 3;for (int tty = y; tty <= b; ++tty){unsigned char* subImg = pImg + (tty - y) * dstStep;unsigned char* subStr = pStr + (strBaseH - tty - 1) * strDrawLineStep;for (int ttx = x; ttx <= r; ++ttx){for (int n = 0; n < dst.channels(); ++n) {double vtxt = subStr[n] / 255.0;int cvv = vtxt * color.val[n] + (1 - vtxt) * subImg[n];subImg[n] = cvv > 255 ? 255 : (cvv < 0 ? 0 : cvv);}subStr += 3;subImg += dst.channels();}}SelectObject(hDC, hOldBmp);SelectObject(hDC, hOldFont);DeleteObject(hf);DeleteObject(hBmp);DeleteDC(hDC);
}最后是一个帧率显示：
我看到了一个opencv播放视频实时显示帧速（FPS），然后用他的方法进行函数封装。
void ShowFPS_Method_One(Mat &dstImage) {char str[20]; //存放字符串化的帧率double fps; //帧率double t_front, t_now; //用于中间计算t_front = (double)getTickCount(); //返回从操作系统启动到当前所经过的毫秒数t_now = ((double)getTickCount() - t_front) / getTickFrequency(); //getTickFrequency返回每秒的计时周期数fps = 1.0 / t_now;string FPSstring("FPS:");sprintf_s(str, "%.2f", fps);FPSstring += str;//在帧上显示"FPS:XXXX"putText(dstImage, FPSstring, Point(5, 20),CV_FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255, 255, 255));
}
代码还需要优化，字符切割用的指针遍历像素加限制条件切割，感觉思想可以用到FPGA上（大概吧），我接触计算机视觉不久而且主要是深度学习那方面，这个完整工程文件是我和我同学做的所以在询问他之后可能会上传。
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