检测对象中的Opencv行

时间:2013-01-06 16:28:31

标签: opencv

我有下面的图片。我想检测将这个对象分成两部分的行。哪种方式最好?我已经尝试过霍夫变换,但有时候物体不够大,无法检测到。任何想法?

谢谢!

enter image description here

1 个答案:

答案 0 :(得分:29)

通常,Hough变换用于线检测。

但如果它不适合你,拟合线也是一个不错的选择。

检查OpenCV fitline 功能,了解更多详情和参数。

既然你已经尝试过hough行,我将使用OpenCV-Python在这里展示拟合线:

# Load image, convert to grayscale, threshold and find contours
img = cv2.imread('lail.png')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray,127,255,cv2.THRESH_BINARY)
contours,hier = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
cnt = contours[0]

# then apply fitline() function
[vx,vy,x,y] = cv2.fitLine(cnt,cv2.cv.CV_DIST_L2,0,0.01,0.01)

# Now find two extreme points on the line to draw line
lefty = int((-x*vy/vx) + y)
righty = int(((gray.shape[1]-x)*vy/vx)+y)

#Finally draw the line
cv2.line(img,(gray.shape[1]-1,righty),(0,lefty),255,2)
cv2.imshow('img',img)
cv2.waitKey(0)
cv2.destroyAllWindows()

以下是我得到的结果:

enter image description here

enter image description here

编辑:

如果要找到将对象分成两部分的线,首先找到拟合线,然后找到与其垂直的直线。

为此,在cv2.fitLine()函数下添加以下代码:

nx,ny = 1,-vx/vy
mag = np.sqrt((1+ny**2))
vx,vy = nx/mag,ny/mag

以下是我得到的结果:

enter image description here

enter image description here

希望它有所帮助!!!

更新:

以下是您请求的第一个案例的Python代码的C ++代码。代码对我来说很好。输出与上面给出的相同:

#include <iostream>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/core/core.hpp>
#include <opencv/cv.h>

using namespace std;
using namespace cv;

int main()
{
    cv::Mat img, gray,thresh;
    vector<vector<Point>> contours;
    Vec4f lines;

    img = cv::imread("line.png");
    cv::cvtColor(img,gray,cv::COLOR_BGR2GRAY);
    cv::threshold(gray,thresh,127,255,cv::THRESH_BINARY);
    cv::findContours(thresh,contours,cv::RETR_LIST,cv::CHAIN_APPROX_SIMPLE);
    cv::fitLine(Mat(contours[0]),lines,2,0,0.01,0.01);

    //lefty = int((-x*vy/vx) + y)
    //righty = int(((gray.shape[1]-x)*vy/vx)+y)
    int lefty = (-lines[2]*lines[1]/lines[0])+lines[3];
    int righty = ((gray.cols-lines[2])*lines[1]/lines[0])+lines[3];

    cv::line(img,Point(gray.cols-1,righty),Point(0,lefty),Scalar(255,0,0),2);

    cv::imshow("img",img);
    cv::waitKey(0);
    cv::destroyAllWindows();
}