检测瓶坯上的缺陷

问题描述

我正在做一个项目，以检测瓶坯上的缺陷。原则上，我已经准备好一切，但是我的算法对噪声和其他失真的抵抗力很差，这使它的有效性受到怀疑。你能告诉我如何改善它吗？预先感谢您的帮助：）附言我用C ++编写，但是我很了解Python代码，因此您可以使用任何适合您的语言。

@H_404_2@

#include "widget.h"

using namespace cv;
using namespace std;

Point cntr;

Mat main_part,fg,bg,preform,bin,M,rotated,ROI;

double getorientation(const vector<Point> &pts)
{
    //Construct a buffer used by the pca analysis
    int sz = static_cast<int>(pts.size());
    Mat data_pts = Mat(sz,2,CV_64F);
    for (int i = 0; i < data_pts.rows; i++)
    {
        data_pts.at<double>(i,0) = pts[i].x;
        data_pts.at<double>(i,1) = pts[i].y;
    }
    //Perform PCA analysis
    PCA pca_analysis(data_pts,Mat(),PCA::DATA_AS_ROW);
    //Store the center of the object
    cntr = Point(static_cast<int>(pca_analysis.mean.at<double>(0,0)),static_cast<int>(pca_analysis.mean.at<double>(0,1)));
    //Store the eigenvalues and eigenvectors
    vector<Point2d> eigen_vecs(2);
    vector<double> eigen_val(2);
    for (int i = 0; i < 2; i++)
    {
        eigen_vecs[i] = Point2d(pca_analysis.eigenvectors.at<double>(i,0),pca_analysis.eigenvectors.at<double>(i,1));
        eigen_val[i] = pca_analysis.eigenvalues.at<double>(i);
    }
    double angle = atan2(eigen_vecs[0].y,eigen_vecs[0].x); // orientation in radians
    return angle;
}

// Using for find preform
int max_contour_area(vector<vector<Point>> &v) {
    int max_area = 0;
    for(size_t i = 0; i < v.size(); i++) {
        if(v[max_area].size() < v[i].size())
            max_area = (int)i;
    }
    return max_area;
}

int main()
{
    fg = imread("C:\\Users\\Roman\\Pictures\\pf6.bmp",IMREAD_GRAYSCALE),bg = imread("C:\\Users\\Roman\\Pictures\\background.bmp",IMREAD_GRAYSCALE);

    vector<vector<Point>> contours,correct_contours;
    vector<Vec4i> hierarchy;

    resize(fg,Size(),0.3,INTER_AREA);
    resize(bg,INTER_AREA);

    absdiff(fg,preform);

    threshold(preform,10,255,THRESH_BINARY);
    findContours(bin,contours,hierarchy,RETR_LIST,CHAIN_APPROX_NONE);

    int max_area = max_contour_area(contours);
    RotatedRect rBox = minAreaRect(contours[max_area]);
    double angle_pca = getorientation(contours[max_area]) * (180 / CV_PI);

    if(rBox.size.width < rBox.size.height) swap(rBox.size.height,rBox.size.width);

    M = getRotationMatrix2D(cntr,angle_pca,1.0);
    // perform the affine transformation
    warpAffine(fg,fg.size(),INTER_CUBIC,BORDER_DEFAULT);
    // crop the resulting image
    getRectSubPix(rotated,rBox.size,cntr,ROI);

    if(ROI.cols < ROI.rows)
        rotate(ROI,ROI,ROTATE_90_Clockwise);

    threshold(ROI,250,THRESH_BINARY_INV);
    findContours(bin,THRESH_BINARY_INV);

    max_area = max_contour_area(contours);

    Moments mnt = moments(contours[max_area]);
    Point center_mass(mnt.m10/mnt.m00,mnt.m01/mnt.m00);
    Point p1(contours[max_area][0].x,p2(contours[max_area][0].x,ROI.rows-1);

    // Using for find rectangle into preform
    for(size_t i = 0; i < contours[max_area].size(); i++) {
        if(contours[max_area][i].y == 0)
            p1.x = p1.x < contours[max_area][i].x ? contours[max_area][i].x : p1.x;
        if(contours[max_area][i].y == ROI.rows-1)
            p2.x = p2.x < contours[max_area][i].x ? contours[max_area][i].x : p2.x;
    }

    if((p1.x & p2.x) > ROI.cols/2) {
        p1.x = ROI.cols * 0.79;
        p2.x = ROI.cols * 0.05;
    }
    else {
        p1.x = ROI.cols * 0.17;
        p2.x = ROI.cols * 0.91;
    }

    p1.y += ROI.rows * 0.2;
    p2.y -= ROI.rows * 0.2;

    Rect rect_main(p1,p2);
    main_part = ROI(rect_main);

    adaptiveThreshold(main_part,ADAPTIVE_THRESH_GAUSSIAN_C,THRESH_BINARY_INV,21,3);

    findContours(bin,CHAIN_APPROX_NONE);

    // deleting too large or too small contours
    for(uint i = (uint)contours.size()-1; i != 0; --i) {
        double area = contourArea(contours[i]);
        if ((area >= 30) && (area <= 70))
            correct_contours.push_back(contours[i]);
    }

    Mat defect;

    main_part.convertTo(main_part,COLOR_GRAY2RGB);
    ROI.convertTo(ROI,COLOR_GRAY2RGB);

    // Drawing contours
    vector<vector<Point>> contours_poly( correct_contours.size() );
    for( size_t i = 0; i < correct_contours.size(); i++ )
        approxpolyDP( correct_contours[i],contours_poly[i],1,true );    
    for( size_t i = 0; i< correct_contours.size(); i++ )
        drawContours( main_part,correct_contours,(int)i,Scalar(255,255) );

    imshow("ROI",ROI);
    imshow("main",main_part);

    if(waitKey() == 27) waitKey(0);

    return EXIT_SUCCESS;
}

源图像： background pf1.bmp

程序结果： preform central part threshold preform result

所以，that`s我想要得到的东西

解决方法

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c++computer-vision opencv pca python