OpenCV每日函数 图像过滤模块 (15) morphologyEx高级形态学函数

        执行高级形态变换。函数 cv::morphologyEx 可以使用腐蚀和膨胀作为基本操作来执行高级形态变换。任何操作都可以就地完成。 在多通道图像的情况下，每个通道都是独立处理的。       

        支持的操作如下表

cv::morphologyEx (InputArray src, OutputArray dst, int op, InputArray kernel, Point anchor=Point(-1,-1), int iterations=1, int borderType=BORDER_CONSTANT, const Scalar &borderValue=morphologyDefaultBorderValue())

opencvmodulesimgprocsrcmorph.dispatch.cpp

void morphologyEx( InputArray _src, OutputArray _dst, int op,
                       InputArray _kernel, Point anchor, int iterations,
                       int borderType, const Scalar& borderValue )
{
    CV_INSTRUMENT_REGION();

    CV_Assert(!_src.empty());

    Mat kernel = _kernel.getMat();
    if (kernel.empty())
    {
        kernel = getStructuringElement(MORPH_RECT, Size(3,3), Point(1,1));
    }
#ifdef HAVE_OPENCL
    Size ksize = kernel.size();
    anchor = normalizeAnchor(anchor, ksize);

    CV_OCL_RUN(_dst.isUMat() && _src.dims() <= 2 && _src.channels() <= 4 &&
        anchor.x == ksize.width >> 1 && anchor.y == ksize.height >> 1 &&
        borderType == cv::BORDER_CONSTANT && borderValue == morphologyDefaultBorderValue(),
        ocl_morphologyEx(_src, _dst, op, kernel, anchor, iterations, borderType, borderValue))
#endif

    Mat src = _src.getMat(), temp;
    _dst.create(src.size(), src.type());
    Mat dst = _dst.getMat();

#if !IPP_DISABLE_MORPH_ADV
    //CV_IPP_RUN_FAST(ipp_morphologyEx(op, src, dst, kernel, anchor, iterations, borderType, borderValue));
#endif

    switch( op )
    {
    case MORPH_ERODE:
        erode( src, dst, kernel, anchor, iterations, borderType, borderValue );
        break;
    case MORPH_DILATE:
        dilate( src, dst, kernel, anchor, iterations, borderType, borderValue );
        break;
    case MORPH_OPEN:
        erode( src, dst, kernel, anchor, iterations, borderType, borderValue );
        dilate( dst, dst, kernel, anchor, iterations, borderType, borderValue );
        break;
    case MORPH_CLOSE:
        dilate( src, dst, kernel, anchor, iterations, borderType, borderValue );
        erode( dst, dst, kernel, anchor, iterations, borderType, borderValue );
        break;
    case MORPH_GRADIENT:
        erode( src, temp, kernel, anchor, iterations, borderType, borderValue );
        dilate( src, dst, kernel, anchor, iterations, borderType, borderValue );
        dst -= temp;
        break;
    case MORPH_TOPHAT:
        if( src.data != dst.data )
            temp = dst;
        erode( src, temp, kernel, anchor, iterations, borderType, borderValue );
        dilate( temp, temp, kernel, anchor, iterations, borderType, borderValue );
        dst = src - temp;
        break;
    case MORPH_BLACKHAT:
        if( src.data != dst.data )
            temp = dst;
        dilate( src, temp, kernel, anchor, iterations, borderType, borderValue );
        erode( temp, temp, kernel, anchor, iterations, borderType, borderValue );
        dst = temp - src;
        break;
    case MORPH_HITMISS:
        CV_Assert(src.type() == CV_8UC1);
        if(countNonZero(kernel) <=0)
        {
            src.copyTo(dst);
            break;
        }
        {
            Mat k1, k2, e1, e2;
            k1 = (kernel == 1);
            k2 = (kernel == -1);

            if (countNonZero(k1) <= 0)
                e1 = Mat(src.size(), src.type(), Scalar(255));
            else
                erode(src, e1, k1, anchor, iterations, borderType, borderValue);

            if (countNonZero(k2) <= 0)
                e2 = Mat(src.size(), src.type(), Scalar(255));
            else
            {
                Mat src_complement;
                bitwise_not(src, src_complement);
                erode(src_complement, e2, k2, anchor, iterations, borderType, borderValue);
            }
            dst = e1 & e2;
        }
        break;
    default:
        CV_Error( CV_StsBadArg, "unknown morphological operation" );
    }
}

数字图像处理 图像形态学处理_坐望云起的博客-CSDN博客_数字图像处理凸壳形态学一词通常表示生物学的一个分支，涉及动物和植物的形式和结构。 在这里在数学形态学的上下文中使用相同的词作为提取图像组件的工具，这些组件在区域形状的表示和描述中很有用，例如边界、骨架和凸包。 我们还对用于预处理或后处理的形态技术感兴趣，例如形态过滤、细化和修剪。形态学技术用称为结构元素的小形状或模板探测图像。https://blog.csdn.net/bashendixie5/article/details/108333356Opencv学习笔记 形态学重建(python)_坐望云起的博客-CSDN博客_形态学开重建在形态学梯度图像的基础上，利用形态学的开闭重建运算对梯度图像进行重建，在保留重要区域伦敦的同时去除细节和噪声。import numpy as npimport cv2 as cvfrom matplotlib import pyplot as pltimg = cv.imread("C:/Users/xiaomao/Desktop/test.jpg")# 二值化imgray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)imgray[imgray p9位置，该算法考虑p1点邻域的实际情况，以便决定是否删除p1点。假设我们处理的为二值图像，背景为黑色，值为0，要细化的前景物体像素值为1。https://skydance.blog.csdn.net/article/details/108333244