车牌检测+车牌定位完整程序

#include <opencv2/opencv.hpp> #include <opencv2/imgproc/imgproc.hpp> #include <iostream> #include <algorithm> #include <cmath> using namespace cv; using namespace std; Mat getHSVMask(Mat& srcImage) { int height = srcImage.rows; //原图的高度 int width = srcImage.cols; //原图的宽度 double h, s, v; uchar r, g, b; uchar max, min, delta; Mat hsvMask = Mat::zeros(srcImage.size(), CV_8UC1); for (int i = 0; i < height; i++) { for (int j = 0; j < width; j++) { b = srcImage.at<Vec3b>(i, j)[0]; g = srcImage.at<Vec3b>(i, j)[1]; r = srcImage.at<Vec3b>(i, j)[2]; max = (r>g) ? r : g; max = (max>b) ? max : b; min = (r<g) ? r : g; min = (min<b) ? min : b; delta = max - min; if (delta == 0) h = 0; else { if (max == r && g >= b) h = double(60 * (g - b)) / delta; if (max == r && g<b) h = double(60 * (g - b)) / delta + 360; if (max == g) h = double(60 * (b - r)) / delta + 120; if (max == b) h = double(60 * (r - g)) / delta + 240; } v = max; if (v == 0) s = 0; else s = double(delta) / max; if (h<0) h += 360; if (h <= 270 && h >= 180 && s>0.30) { hsvMask.at<uchar>(i, j) = 255; } if (h <= 270 && h >= 205 && s>0.48) { hsvMask.at<uchar>(i, j) = 255; } } } //imshow("hsvMask", hsvMask); waitKey(100); return hsvMask; } Mat getSuspendEdge(Mat& hsvMask, Mat& cannyImage) { int height = hsvMask.rows; int width = hsvMask.cols; Mat suspendEdge = Mat::zeros(hsvMask.size(), hsvMask.type()); Mat element_erode = getStructuringElement(cv::MORPH_ELLIPSE, Size(5, 5)); //腐蚀的参数 Mat element_dilate = getStructuringElement(cv::MORPH_ELLIPSE, Size(5, 5)); //膨胀的参数 //进行一次开操作消除杂碎点 开运算的作用是为了去掉细节 erode(hsvMask, hsvMask, element_erode); //腐蚀 dilate(hsvMask, hsvMask, element_dilate); //膨胀 //车牌疑似区域提取 for (int i = 1; i != height - 2; i++) { for (int j = 1; j != width - 2; j++) { Rect rct; rct.x = j - 1; rct.y = i - 1; rct.height = 3; rct.width = 3; if ((cannyImage.at<uchar>(i, j) == 255) && (cv::countNonZero(hsvMask(rct)) >= 3)) suspendEdge.at<uchar>(i, j) = 255; } } //imshow("suspendEdge", suspendEdge); waitKey(100); return suspendEdge; } Mat getFinalMask(Mat& suspendEdge) { Mat finalMask = suspendEdge.clone(); int height = finalMask.rows; int width = finalMask.cols; //再对得到的边缘进行一次闭运算，得到车牌的掩模 Mat element_erode = getStructuringElement(cv::MORPH_ELLIPSE, Size(0.025*width, 0.025*height)); Mat element_dilate = getStructuringElement(cv::MORPH_ELLIPSE, Size(0.025*width, 0.025*height)); dilate(finalMask, finalMask, element_dilate); //膨胀 erode(finalMask, finalMask, element_erode); //腐蚀 //imshow("finalMask", finalMask); waitKey(100); return finalMask; } Mat getArea(Mat& srcImage, Mat& finalMask) { int height = finalMask.rows; int width = finalMask.cols; vector<vector<Point> > contours; findContours(finalMask.clone(), contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE, Point(0, 0)); vector<Rect> candidates;//候选 int best_i = -1; int best_area = 0; double ratio_high = 0.3;//比例 double ratio_low = 0.1; for (int i = 0; i != contours.size(); i++)//轮廓上点的个数 { Rect rect = boundingRect(contours[i]);//计算轮廓的最小外接正矩形 int area = countNonZero(finalMask(rect));//对二值化图像执行countNonZero,可得到非零像素点数 double ratio = double(area) / rect.area();//非零像素点数占最小外接正矩形的比例 double wh_ratio = double(rect.width) / rect.height;//最小外接正矩形的长（width）宽(height)比 if (ratio > ratio_high && wh_ratio > 2 && wh_ratio < 5 && rect.height > 25 && rect.width > 75) { if (area > best_area) { best_area = area; best_i = i; } } else if (ratio > ratio_low && wh_ratio > 2 && wh_ratio < 5 && rect.height > 25 && rect.width > 75) { Mat element_dilate = getStructuringElement(cv::MORPH_ELLIPSE, Size(0.02*width, 0.02*height)); Mat tmp = finalMask.clone(); dilate(tmp, tmp, element_dilate); //膨胀 area = countNonZero(tmp(rect)); ratio = double(area) / rect.area(); if (ratio > ratio_high && wh_ratio > 2 && wh_ratio < 5 && rect.height > 25 && rect.width > 75) { if (area > best_area) { best_area = area; best_i = i; } } } } if (best_i < 0) cout << "no area found!" << endl; Rect rect = boundingRect(contours[best_i]); cout<<"left-top coordinates:("<<rect.x<<","<<rect.y<<") " <<"rect.width:"<< rect.width << " " <<"rect.height:"<< rect.height << endl; rect.x -= rect.width * 0.05; rect.y -= rect.height * 0.05; rect.width += rect.width * 0.1; rect.height += rect.height * 0.1; Mat Image = srcImage.clone(); cv::rectangle(Image, Rect(rect.x, rect.y, rect.width, rect.height), Scalar(0, 0, 255), 1, 1, 0); //画出车牌的位置 imshow("Image", Image); waitKey(5); Mat resultArea = srcImage(rect); resize(resultArea, resultArea, Size(200,70), 0, 0, CV_INTER_LINEAR); //imshow("resultArea", resultArea); waitKey(100); return resultArea; } int main(void) { char name[10]; for (int i = 1; i <= 10; i++) { sprintf(name, "%d.jpg", i); Mat srcImage = imread(name); //读取图片 resize(srcImage, srcImage, Size(800, 600), 0, 0, CV_INTER_LINEAR); //对图片的大小进行统一 //imshow("srcImage", srcImage); //显示读取的图片 Mat grayImage; //获取灰度图像 cvtColor(srcImage, grayImage, CV_RGB2GRAY); //通过cvtColor函数将RGB空间转换成灰度图 GaussianBlur(grayImage, grayImage, Size(3, 3), 0, 0, cv::BORDER_DEFAULT); //高斯滤波 Mat cannyImage; //通过Canny算子检测边缘 int edgeThresh = 50; Canny(grayImage, cannyImage, edgeThresh, edgeThresh * 3, 3); //imshow("cannyImage", cannyImage); waitKey(5); Mat hsvMask = getHSVMask(srcImage); //筛选符合HSV条件的区域，并进行一次开运算消除杂碎点 Mat suspendEdge = getSuspendEdge(hsvMask, cannyImage); Mat finalMask = getFinalMask(suspendEdge); Mat resultArea = getArea(srcImage, finalMask); } waitKey(); }