OpenCV——识别各省份地图轮廓

好久没有发OpenCV的博客了,最近想到了一个识别地图轮廓的方案,就写来试试。(识别中国的28个省份地图轮廓,不考虑直辖市)

首先,我的基本思路是  用最小的矩形将地图的轮廓圈出来,可以根据长方形的长宽比判断,也可将其缩放至特定的大小,计算其轮廓上的像素个数来判断。

缺点:用摄像头读取图片时,使用这种方法会有一些误差。

也可以ANN训练识别,但是这样做效率低。

 

step 1. 读取图片、处理图像

Mat src = imread("12.jpg");
Mat grayImage;    
cvtColor(src, grayImage, CV_BGR2GRAY);
threshold(grayImage, grayImage, 48, 255, CV_THRESH_BINARY);
imshow("grayImage", grayImage);

问题来了,处理图片后的grayImage根本无法显示,结果为一张灰色的图片。

最后发现,因为大意,程序的最后没有加 cvWaitKey(0); 这句话,因此图片无法显示。

step 2. 寻找轮廓并画出

#include <opencv2/opencv.hpp>  
#include <iostream>
using namespace cv;
using namespace std;

int main()
{
    Mat src = imread("timg.jpg");
    Mat grayImage, dstImage;
    src.copyTo(dstImage);

    int g_nStructElementSize = 1; //结构元素(内核矩阵)的尺寸 
    //获取自定义核  
    Mat element = getStructuringElement(MORPH_RECT,
        Size(2 * g_nStructElementSize + 1, 2 * g_nStructElementSize + 1),
        Point(g_nStructElementSize, g_nStructElementSize));
    erode(src, src, element);
    cvtColor(src, grayImage, CV_BGR2GRAY);
    threshold(grayImage, grayImage, 48, 255, CV_THRESH_BINARY);
    imshow("2dst", grayImage);

    vector<vector<Point>> contours;
    vector<Vec4i> hierarchy;
    findContours(grayImage, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
    int i = 0;
    Point2f pp[5][4];
    vector<vector<Point>>::iterator It;
    Rect rect[10];
    for (It = contours.begin(); It < contours.end(); It++){                        //画出包围轮廓的最小矩形
        Point2f vertex[4];
        rect[i] = boundingRect(*It);
        vertex[0] = rect[i].tl();                                                           //矩阵左上角的点
        vertex[1].x = (float)rect[i].tl().x, vertex[1].y = (float)rect[i].br().y;           //矩阵左下方的点
        vertex[2] = rect[i].br();                                                           //矩阵右下角的点
        vertex[3].x = (float)rect[i].br().x, vertex[3].y = (float)rect[i].tl().y;           //矩阵右上方的点

        for (int j = 0; j < 4; j++)
            line(dstImage, vertex[j], vertex[(j + 1) % 4], Scalar(0, 0, 255), 1);
    }
    imshow("dst", dstImage);
    cvWaitKey(0);
    return 0;
}

结果发现根本找不到轮廓,最后发现原来是threshold函数参数设置错误,参数应如下:

threshold(grayImage, grayImage,48,255, THRESH_BINARY_INV);

同时由于地图边框线太细,应当先腐蚀图像,再二值化:

    int g_nStructElementSize = 1; //结构元素(内核矩阵)的尺寸 
    ///获取自定义核  
    Mat element = getStructuringElement(MORPH_RECT,
        Size(2 * g_nStructElementSize + 1, 2 * g_nStructElementSize + 1),
        Point(g_nStructElementSize, g_nStructElementSize));
    erode(src, src, element);

step 3. 收集地图的数据后,用10个省得数据来检测

最后在不断的探索中,发现有两个数据可以作为一个地图的特征,即轮廓长宽比和轮廓面积与图片的像素数之比。

最后的代码如下:


#include <opencv2/opencv.hpp>  
#include <iostream>
#include <math.h>
using namespace cv;
using namespace std;

Mat result;
const double cha = 0.02;   //可接受范围的误差

bool compare(double a, double b)
{
    if (abs(a - b) < cha){
        return true;
    }
    return false;
}

bool result_output(double rate1,double rate2)
{
    if (compare(rate1, (double)172 / 96) && compare(rate2, 0.171524)){
        cout << "陕西省" << endl;
        return true;
    }
    if (compare(rate1, (double)172 / 143) && compare(rate2, 0.270173)){
        cout << "安徽省" << endl;
        return true;
    }
    if (compare(rate1, (double)154 / 123) && compare(rate2, 0.230148)){
        cout << "福建省" << endl;
        return true;
    }
    if (compare(rate1, (double)170 / 190) && compare(rate2, 0.132584)){
        cout << "甘肃省" << endl;
        return true;
    }
    if (compare(rate1, (double)155 / 208) && compare(rate2, 0.200146)){
        cout << "广东省" << endl;
        return true;
    }
    if (compare(rate1, (double)129 / 180) && compare(rate2, 0.22718)){
        cout <<"广西壮族自治区" << endl;
        return true;
    }
    if (compare(rate1, (double)118 / 145) && compare(rate2, 0.219451)){
        cout << "贵州省" << endl;
        return true;
    }
    if (compare(rate1, (double)77 / 96) && compare(rate2, 0.196616)){
        cout << "海南省" << endl;
        return true;
    }
    if (compare(rate1, (double)162 / 119) && compare(rate2, 0.247134)){
        cout << "河北省" << endl;
        return true;
    }
    if (compare(rate1, (double)125 / 135) && compare(rate2, 0.176323)){
        cout << "河南省" << endl;
        return true;
    }
    cout << "无法检测" << endl;
    return false;
}
int main()
{
    Mat src = imread("1.jpg");
    Mat grayImage, dstImage;
    src.copyTo(dstImage);

    int g_nStructElementSize = 1; //结构元素(内核矩阵)的尺寸 
    ///获取自定义核  
    Mat element = getStructuringElement(MORPH_RECT,
        Size(2 * g_nStructElementSize + 1, 2 * g_nStructElementSize + 1),
        Point(g_nStructElementSize, g_nStructElementSize));
    erode(src, src, element);
    cvtColor(src, grayImage, CV_BGR2GRAY);
    blur(grayImage, grayImage, Size(3, 3));
    threshold(grayImage, grayImage,48,255, THRESH_BINARY_INV);
    grayImage.copyTo(result);

    vector< vector<Point> > contours;
    vector<Vec4i> hierarchy;
    findContours(grayImage, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
    drawContours(dstImage, contours, -1, (255, 255, 255));
    vector<Point> point = contours[0];

    Rect rect = boundingRect(point);
    Point2f vertex[4];
    vertex[0] = rect.tl();
    vertex[1].x = (float)rect.tl().x, vertex[1].y = (float)rect.br().y;
    vertex[2] = rect.br();
    vertex[3].x = (float)rect.br().x, vertex[3].y = (float)rect.tl().y; 
    for (int j = 0; j < 4; j++)
        line(dstImage, vertex[j], vertex[(j + 1) % 4], Scalar(0, 0, 255), 1);
    int x = rect.x, y = rect.y;
    int h = rect.height, w = rect.width;
    double rate = (double)h / w;
    cout << "height:" << h << endl;
    cout << "width:" << w << endl;
    cout << "h / w:" << rate << endl;
    double area = contourArea(point, false);
    double sum = grayImage.cols * grayImage.rows;
    cout << "面积:" << area << endl;
    cout << "面积比:" << area / sum << endl;
    
    imshow("show", dstImage);
    result_output(rate, area / sum);
    
    cvWaitKey(0);
    return 0;
}

 最后发现一个问题,由于需要通过摄像头检测地图,图片可能会有一定角度的倾斜,因此应将Rect换成RotatedRect。

    RotatedRect  rect = minAreaRect(point);
    Point2f vertex[4];
    rect.points(vertex);
    for (int j = 0; j < 4; j++)
        line(dstImage, vertex[j], vertex[(j + 1) % 4], Scalar(0, 0, 255), 1);

    int h = rect.size.height, w = rect.size.width;

 

posted @ 2017-04-16 21:40  Not-Bad  阅读(4152)  评论(0编辑  收藏  举报