《图像处理实例》 之 提取特殊背景的直线

目标：提取图像中的直线

直接运用霍夫变换行不通，需要经过形态学处理之后再霍夫提取！

自动化阈值OTSU算法：

形态学操作：

 这一步其实已经分离出我们需要的区域，可以用我们其他博客所说的方法进行操作--水漫算法，也可以用下面的霍夫变换！

霍夫变换：

水漫的好处是可以操作这个区域，霍夫的好处是可以得到这个位置！

上代码：

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

int Threshold_Value = 176;
const int Threshold_Max_value = 255;
const int Threshold_type_value = 3;
double MaxWidth = 0, MaxHeight = 0;//找最大的矩形边

RNG rng(12345);

Mat input_image, threshold_image, output_image, Middle_image;

void Threshold_Image_Bar(int, void *);

int main(int argc, char**argv)
{
    input_image = imread("1.jpg");
    if (input_image.data == NULL) {
        return -1; cout << "can't open image.../";
    }
    imshow("Sourse Image", input_image);
    blur(input_image, Middle_image,Size(3,3),Point(-1,-1),4);
    imshow("Blur Image", Middle_image);
    cvtColor(Middle_image, Middle_image,COLOR_RGB2GRAY);
    imshow("Gray Image", Middle_image);
    namedWindow("Threshold Image",1);
    createTrackbar("阈值调整", "Threshold Image",&Threshold_Value,255,Threshold_Image_Bar);
    Threshold_Image_Bar(0,0);
    waitKey(0);
    return 0;
}

void Threshold_Image_Bar(int, void *)
{
    threshold(Middle_image, threshold_image, 0, 255, THRESH_BINARY_INV | THRESH_OTSU);
    //Canny(threshold_image,threshold_image, Threshold_Value, Threshold_Value*3);
    //bitwise_not(threshold_image, threshold_image,Mat());
    imshow("Threshold Image", threshold_image);
    Mat kernel = getStructuringElement(MORPH_RECT,Size(threshold_image.cols/20,1),Point(-1,-1));
    erode(threshold_image,threshold_image,kernel);
    dilate(threshold_image, threshold_image,kernel);
    imshow("Erode Image", threshold_image);
    vector<Vec4i> tline;
    HoughLinesP(threshold_image,tline,1,CV_PI/360,5,0,0);
    Mat LineImage = Mat::zeros(threshold_image.size(), threshold_image.type());
    for (size_t i = 0; i < tline.size(); i++)
    {
        line(input_image, Point(tline[i][0],tline[i][1]), Point(tline[i][2],tline[i][3]), Scalar(200, 55, 205),3,8,0);
    }
    imshow("LineImage", input_image);
}

参考：贾老师opencv系列