OpenCV3实现车牌识别(C++版)
本文实例为大家分享了OpenCV3实现车牌识别的具体代码，供大家参考，具体内容如下
车牌识别（基于OpenCV3.4.7+VS2017）
视频识别
蓝色车牌识别
视觉入坑的第一个Demo（注释很详细），因为本人之前拖延，一直没能写详细实现博客，先将代码贴出来供大家交流，个人认为精华部分在字符切割（直接用指针遍历像素加限制条件切割），车牌模板已上传，整个工程也已上传，后续完善各环节实现步骤详解。
头文件：Global.h
#ifdef GLOBAL
extern 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;
};
#endif
唯一的.cpp文件：PlateIdentify.cpp（说实话，这Demo挺 “C” 的）
#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;
}
else
angle += 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) //车牌过偏，转一下 偏移角度小时可不调用，后续找到合适范围再改进
{
flag_1 = 1;
Mat RotractImg(imageWidth, imageHeight, CV_8UC1, Scalar(0, 0, 0)); //倾斜矫正图片
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)); //进行倾斜矫正
//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;
}
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)
{
//!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!处理新型嵌套车牌!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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);
else
i = 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);
else
i = 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;
else
first[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");
}
//读取字母和数字模板
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;
else
second[x].matchDegree = 0;
//获取最大匹配度对应索引index
if (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);
else
putTextZH(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);
}
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持我们。
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在前几天写的一篇博文<如何从TensorFlow的mnist数据集导出手写体数字图片>中,我们介绍了如何通过TensorFlow将mnist手写体数字集导出到本地保存为bmp文件. 车牌识别在当今社会中广泛存在,其应用场景包括各类交通监控和停车场出入口收费系统,在自动驾驶中也得到一定应用,其原理也不难理解,故很适合作为图像处理+机器学习的入门案例. 现在我们不妨酝酿一个大胆的想法:在TensorFlow中通过卷积神经网络+mnist数字集实现车牌识别. 实际上车牌字符除了数字0-9,还有字母A
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TensorFlow车牌识别完整版代码（含车牌数据集）
在之前发布的一篇博文<MNIST数据集实现车牌识别--初步演示版>中,我们演示了如何使用TensorFlow进行车牌识别,但是,当时采用的数据集是MNIST数字手写体,只能分类0-9共10个数字,无法分类省份简称和字母,局限性较大,无实际意义. 经过图像定位分割处理,博主收集了相关省份简称和26个字母的图片数据集,结合前述博文中贴出的python+TensorFlow代码,实现了完整的车牌识别功能.本着分享精神,在此送上全部代码和车牌数据集. 车牌数据集下载地址(约4000张图片):tf_ca
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Python+Tensorflow+CNN实现车牌识别的示例代码
一.项目概述 本次项目目标是实现对自动生成的带有各种噪声的车牌识别.在噪声干扰情况下,车牌字符分割较困难,此次车牌识别是将车牌7个字符同时训练,字符包括31个省份简称.10个阿拉伯数字.24个英文字母('O'和'I'除外),共有65个类别,7个字符使用单独的loss函数进行训练. (运行环境:tensorflow1.14.0-GPU版) 二.生成车牌数据集 import os import cv2 as cv import numpy as np from math import * from
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python利用百度云接口实现车牌识别的示例
一个小需求---实现车牌识别. 目前有两个想法 1. 调云在线的接口或者使用SDK做开发(配置环境和编译第三方库很麻烦,当然使用python可以避免这些问题) 2. 自己实现车牌识别算法(复杂) 一开始准备使用百度云文字识别C++ SDK来做,发现需要准备curl.jsoncpp和OpenCV,并且curl和jsoncpp需要自己编译,很麻烦,所以换用了python来做,真的是顺畅简单. 1. 安装python环境(我用python3.7) python官网下载地址:https://www.py
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python 实现的车牌识别项目
车牌识别在高速公路中有着广泛的应用,比如我们常见的电子收费(ETC)系统和交通违章车辆的检测,除此之外像小区或地下车库门禁也会用到,基本上凡是需要对车辆进行身份检测的地方都会用到. 简介 车牌识别系统(Vehicle License Plate Recognition)是计算机视频图像识别技术在车辆牌照识别中的一种应用,通常一个车牌识别系统主要包括以下这四个部分: 车辆图像获取 车牌定位 车牌字符分割 车牌字符识别 我们再来看一下百科中对车牌识别技术的描述: 车牌识别技术要求能够将运动中的汽车牌
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python实现车牌识别的示例代码
某天回家之时,听到有个朋友说起他正在做一个车牌识别的项目 于是对其定位车牌的位置算法颇有兴趣,今日有空得以研究,事实上车牌识别算是比较成熟的技术了, 这里我只是简单实现. 我的思路为: 对图片进行一些预处理,包括灰度化.高斯平滑.中值滤波.Sobel算子边缘检测等等. 利用OpenCV对预处理后的图像进行轮廓查找,然后根据一些参数判断该轮廓是否为车牌轮廓. 效果如下: test1: test2 实现代码如下(对图像预处理(滤波器等)的原理比较简单,这里只是对一些函数进行调包): import c
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android车牌识别系统EasyPR使用详解
上篇文章介绍了身份证识别,现在我们来说说关于车牌识别. EasyPR是一个开源的中文车牌识别系统,gitHub地址 EasyPR有如下特点: 1. 它基于openCV这个开源库,这意味着所有它的代码都可以轻易的获取. 2. 它能够识别中文.例如车牌为苏EUK722的图片,它可以准确地输出std:string类型的"苏EUK722"的结果. 3. 它的识别率较高.目前情况下,字符识别已经可以达到90%以上的精度. 使用方法 package com.android.guocheng.eas
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python实现图片，视频人脸识别(dlib版)
图片人脸检测 #coding=utf-8 import cv2 import dlib path = "img/meinv.png" img = cv2.imread(path) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #人脸分类器 detector = dlib.get_frontal_face_detector() # 获取人脸检测器 predictor = dlib.shape_predictor( "C:\\Pytho
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python实现图像，视频人脸识别(opencv版)
图片人脸识别 import cv2 filepath = "img/xingye-1.png" img = cv2.imread(filepath) # 读取图片 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 转换灰色 # OpenCV人脸识别分类器 classifier = cv2.CascadeClassifier( "C:\Python36\Lib\site-packages\opencv-master\data\haar