使用 findContours 时如何避免检测到图像帧

Posted 2023-02-21

【中文标题】使用 findContours 时如何避免检测到图像帧

【英文标题】：How to avoid detecting image frame when using findContours

【发布时间】：2015-03-29 13:46:24

【问题描述】：

在使用findContours (OpenCV) 时如何避免检测到图像的边框？直到我找到OpenCV findContours allways finds two contours for every object 并实现了那个答案，我并没有始终如一地检测到内部对象（对象线被分成几部分），但现在我每次都检测到图像帧。

图像是从底部看到的四旋翼无人机；我正在使用一系列图片来“训练”对象检测。为此，我需要确保我能够始终如一地获得 UAV 对象。我想我可以反转颜色，但这似乎是一种肮脏的技巧。

图像首先是findContours 之前的输入图像，以及生成的轮廓。我有七张测试图像，所有七张都有一个框架和无人机。 hu 时刻非常相似（正如预期的那样）。

用于查找轮廓/对象并计算 hu 矩的代码（C++11，相当混乱）：

#include <opencv/cv.h>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <iostream>
#include <fstream>
#include <string>

using namespace cv;
using namespace std;

#define EROSION_SIZE 1
#define ERODE_CANNY_PREP_ITERATIONS 5

int main() 
    Mat image, canny_output, element, padded;
    RNG rng(12345);
    int numbers[] = 195, 223, 260, 295, 331, 368, 396;
    string pre = "/home/alrekr/Pictures/UAS/hu-images/frame_";
    string middle = "_threshold";
    string post = ".png";
    string filename = "";
    vector<vector<Point>> contours;
    vector<Vec4i> hierarchy;
    ofstream fout("/home/alrekr/Pictures/UAS/hu-data/hu.dat");
    element = getStructuringElement(MORPH_RECT,
            Size(2*EROSION_SIZE + 1, 2*EROSION_SIZE+1),
            Point(EROSION_SIZE, EROSION_SIZE));
    namedWindow("Window", CV_WINDOW_AUTOSIZE);
    for (int i : numbers) 
        filename = pre + to_string(i) + middle + post;
        image = imread(filename, CV_LOAD_IMAGE_GRAYSCALE);
        erode(image, image, element, Point(-1,-1), ERODE_CANNY_PREP_ITERATIONS);
        imwrite("/home/alrekr/Pictures/UAS/hu-data/prep_for_canny_" + to_string(i) + ".png", image);
    findContours(image, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
        vector<Moments> mu(contours.size());
        if(contours.size() < 1) 
            cout << "No contours found" << endl;
         else 
            cout << "Contours found: " << contours.size() << endl;
        
        vector<Point2f> mc(contours.size());
        for(int j = 0; j < (int)contours.size(); j++) 
            mc[j] = Point2f(mu[j].m10/mu[j].m00 , mu[j].m01/mu[j].m00);
        
        Mat drawing = Mat::zeros(image.size(), CV_8UC3);
        for(int j = 0; j < (int)contours.size(); j++) 
            Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255));
            drawContours(drawing, contours, j, color, 2, 8, hierarchy, 0, Point());
            imshow("Window", drawing);
            waitKey(0);
        
        imwrite("/home/alrekr/Pictures/UAS/hu-data/cannied_" + to_string(i) + ".png", drawing);
        fout << "Frame " << i << "\n";
        for(int j = 0; j < (int)contours.size(); j++) 
            mu[j] = moments(contours[j]);
            double hu[7];
            HuMoments(mu[j], hu);
            fout << "Object " << to_string(j) << "\n";
            fout << hu[0] << "\n";
            fout << hu[1] << "\n";
            fout << hu[2] << "\n";
            fout << hu[3] << "\n";
            fout << hu[4] << "\n";
            fout << hu[5] << "\n";
            fout << hu[6] << "\n";
        
    
    fout.close();
    return 0;

【参考方案1】：

函数cv::findContours 描述由1 组成的区域的轮廓。不过，您感兴趣的区域是黑色的。

所以解决方案很简单。在检测轮廓之前反转输入图像：

image = 255 - image;

以下是我从您上面的示例中得出的代码示例：

#include <opencv2/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>

#include <iostream>
#include <string>

#define EROSION_SIZE 1
#define ERODE_CANNY_PREP_ITERATIONS 5

int main( int argc, char ** argv )

    // Display the version of the linked OpenCV library.
    std::cout << "Using OpenCV " << CV_VERSION_MAJOR << "." << CV_VERSION_MINOR << ".";
    std::cout << CV_VERSION_REVISION << CV_VERSION_STATUS << std::endl;

    // Load the input file.
    std::string filename = std::string( argv[ 1 ] );
    cv::Mat image = imread( filename, cv::IMREAD_GRAYSCALE );

    // Invert the image so the area of the UAV is filled with 1's. This is necessary since
    // cv::findContours describes the boundary of areas consisting of 1's.
    image = 255 - image;

    // Detect contours.
    std::vector< std::vector< cv::Point> > contours;
    std::vector< cv::Vec4i > hierarchy;

    cv::findContours( image, contours, hierarchy, cv::RETR_TREE, cv::CHAIN_APPROX_SIMPLE );
    std::cout << "Contours found: " << contours.size() << std::endl;

    // Display and save the results.
    cv::RNG rng( 12345 );
    cv::Mat contourImage = cv::Mat::zeros( image.size(), CV_8UC3);

    for( size_t j = 0; j <  contours.size(); j++ )
    
        cv::Scalar color( rng.uniform( 0, 255 ), rng.uniform( 0,255 ), rng.uniform( 0, 255 ) );
        cv::drawContours( contourImage, contours, j, color, 2, 8, hierarchy, 0, cv::Point() );
    

//  cv::imwrite( "contours.png", contourImage );

    cv::imshow( "contours", contourImage );
    cv::waitKey( 0 );

    return 0;

控制台输出如下：

$ ./a.out gvlGK.png 
Using OpenCV 3.0.0-beta
Contours found: 1

得到的轮廓图像是这样的：

【讨论】：

所以我提到的“肮脏的黑客”是要走的路。我在findContours 之前插入了image=255-image，它按规定工作。

【参考方案2】：

另一种解决方案是：-

找到轮廓的边界矩形

x,y,w,h = cv2.boundingRect(c)

例如比较图像的大小和边界矩形的大小

cnt_size=w*h
if(abs(cnt_size-img_size<=ERROR_THRESHOLD):
    ##discard this contour 

【参考方案3】：

如果你有白色背景，首先使用 THRESH_BINARY_INV 类型反转它，然后使用轮廓。

    image = imread(filename, CV_LOAD_IMAGE_GRAYSCALE);
    threshold(image,image,100,255,THRESH_BINARY_INV);
    findContours( image, contours, hierarchy, cv::RETR_TREE, cv::CHAIN_APPROX_SIMPLE );

这只会返回您需要的轮廓。