美文网首页
形态学滤波

形态学滤波

作者: 轻歌若雪 | 来源:发表于2018-03-12 13:05 被阅读0次

getStructuringElement

@brief Returns a structuring element of the specified size and shape for morphological operations.
The function constructs and returns the structuring element that can be further passed to cv::erode,
cv::dilate or cv::morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as
the structuring element.
@param shape Element shape that could be one of cv::MorphShapes
@param ksize Size of the structuring element.
@param anchor Anchor position within the element. The default value \f$(-1, -1)\f$ means that the
anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor
position. In other cases the anchor just regulates how much the result of the morphological
operation is shifted.

cv::Mat cv::getStructuringElement(int shape, Size ksize, Point anchor)
{
    int i, j;
    int r = 0, c = 0;
    double inv_r2 = 0;

    CV_Assert( shape == MORPH_RECT || shape == MORPH_CROSS || shape == MORPH_ELLIPSE );

    anchor = normalizeAnchor(anchor, ksize);

    if( ksize == Size(1,1) )
        shape = MORPH_RECT;

    if( shape == MORPH_ELLIPSE )
    {
        r = ksize.height/2;
        c = ksize.width/2;
        inv_r2 = r ? 1./((double)r*r) : 0;
    }

    Mat elem(ksize, CV_8U);

    for( i = 0; i < ksize.height; i++ )
    {
        uchar* ptr = elem.ptr(i);
        int j1 = 0, j2 = 0;

        if( shape == MORPH_RECT || (shape == MORPH_CROSS && i == anchor.y) )
            j2 = ksize.width;
        else if( shape == MORPH_CROSS )
            j1 = anchor.x, j2 = j1 + 1;
        else
        {
            int dy = i - r;
            if( std::abs(dy) <= r )
            {
                int dx = saturate_cast<int>(c*std::sqrt((r*r - dy*dy)*inv_r2));
                j1 = std::max( c - dx, 0 );
                j2 = std::min( c + dx + 1, ksize.width );
            }
        }

        for( j = 0; j < j1; j++ )
            ptr[j] = 0;
        for( ; j < j2; j++ )
            ptr[j] = 1;
        for( ; j < ksize.width; j++ )
            ptr[j] = 0;
    }

    return elem;
}

erode

#ifndef FBC_CV_ERODE_HPP_  
#define FBC_CV_ERODE_HPP_  

/* reference: include/opencv2/imgproc.hpp 
              modules/imgproc/src/morph.cpp 
*/  

#include <typeinfo>  
#include "core/mat.hpp"  
#include "imgproc.hpp"  
#include "filterengine.hpp"  
#include "core/core.hpp"  
#include "morph.hpp"  

namespace fbc {  

// Erodes an image by using a specific structuring element  
// \f[\texttt{ dst } (x, y) = \min _{ (x',y') : \, \texttt{ element } (x',y') \ne0 } \texttt{ src } (x + x',y+y')\f]  
// In case of multi - channel images, each channel is processed independently.  
// Erosion can be applied several ( iterations ) times.  
// support type: uchar/float, multi-channels  
template<typename _Tp, int chs>  
int erode(const Mat_<_Tp, chs>& src, Mat_<_Tp, chs>& dst, Mat_<uchar, 1>& kernel,  
    Point anchor = Point(-1, -1), int iterations = 1, int borderType = BORDER_CONSTANT, const Scalar& borderValue = Scalar::all(DBL_MAX))  
{  
    FBC_Assert(typeid(uchar).name() == typeid(_Tp).name() || typeid(float).name() == typeid(_Tp).name()); // uchar || float  
    if (dst.empty()) {  
        dst = Mat_<_Tp, chs>(src.rows, src.cols);  
    } else {  
        FBC_Assert(src.rows == dst.rows && src.cols == dst.cols);  
    }  

    Size ksize = !kernel.empty() ? kernel.size() : Size(3, 3);  
    anchor = normalizeAnchor(anchor, ksize);  

    if (iterations == 0 || kernel.rows * kernel.cols == 1) {  
        src.copyTo(dst);  
        return 0;  
    }  

    if (kernel.empty()) {  
        kernel = Mat_<uchar, 1>(1 + iterations * 2, 1 + iterations * 2);  
        getStructuringElement(kernel, MORPH_RECT, Size(1 + iterations * 2, 1 + iterations * 2));  
        anchor = Point(iterations, iterations);  
        iterations = 1;  
    } else if (iterations > 1 && countNonZero(kernel) == kernel.rows * kernel.cols) {  
        anchor = Point(anchor.x*iterations, anchor.y*iterations);  
        kernel = Mat_<uchar, 1>(ksize.height + (iterations - 1)*(ksize.height - 1), ksize.width + (iterations - 1)*(ksize.width - 1));  
        getStructuringElement(kernel, MORPH_RECT,  
            Size(ksize.width + (iterations - 1)*(ksize.width - 1), ksize.height + (iterations - 1)*(ksize.height - 1)), anchor);  
        iterations = 1;  
    }  

    anchor = normalizeAnchor(anchor, kernel.size());  

    Ptr<BaseRowFilter> rowFilter;  
    Ptr<BaseColumnFilter> columnFilter;  
    Ptr<BaseFilter> filter2D;  

    if (countNonZero(kernel) == kernel.rows*kernel.cols) {  
        // rectangular structuring element  
        rowFilter = getMorphologyRowFilter<_Tp, chs>(0, kernel.cols, anchor.x);  
        columnFilter = getMorphologyColumnFilter<_Tp, chs>(0, kernel.rows, anchor.y);  
    } else {  
        filter2D = getMorphologyFilter<_Tp, chs>(0, kernel, anchor);  
    }  

    Scalar borderValue_ = borderValue;  
    if (borderType == BORDER_CONSTANT && borderValue_ == Scalar::all(DBL_MAX)) {  
        if (sizeof(_Tp) == 1) // CV_8U  
            borderValue_ = Scalar::all((double)UCHAR_MAX);  
        else // CV_32F  
            borderValue_ = Scalar::all((double)FLT_MAX);  
    }  

    Ptr<FilterEngine<_Tp, _Tp, _Tp, chs, chs, chs>> f = makePtr<FilterEngine<_Tp, _Tp, _Tp, chs, chs, chs>>(filter2D, rowFilter, columnFilter, borderType, borderType, borderValue_);  
    f->apply(src, dst);  
    for (int i = 1; i < iterations; i++)  
        f->apply(dst, dst);  

    return 0;  
}  

} // namespace fbc  

#endif // FBC_CV_ERODE_HPP_  

dilate

#ifndef FBC_CV_DILATE_HPP_  
#define FBC_CV_DILATE_HPP_  

/* reference: include/opencv2/imgproc.hpp 
              modules/imgproc/src/morph.cpp 
*/  

#include <typeinfo>  
#include "core/mat.hpp"  
#include "imgproc.hpp"  
#include "filterengine.hpp"  
#include "core/core.hpp"  
#include "morph.hpp"  

namespace fbc {  

// Dilates an image by using a specific structuring element  
// \f[\texttt{dst} (x,y) =  \max _{(x',y'):  \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f]  
// In case of multi - channel images, each channel is processed independently.  
// support type: uchar/float, multi-channels  
template<typename _Tp, int chs>  
int dilate(const Mat_<_Tp, chs>& src, Mat_<_Tp, chs>& dst, Mat_<uchar, 1>& kernel,  
    Point anchor = Point(-1, -1), int iterations = 1, int borderType = BORDER_CONSTANT, const Scalar& borderValue = Scalar::all(DBL_MAX))  
{  
    FBC_Assert(typeid(uchar).name() == typeid(_Tp).name() || typeid(float).name() == typeid(_Tp).name()); // uchar || float  
    if (dst.empty()) {  
        dst = Mat_<_Tp, chs>(src.rows, src.cols);  
    } else {  
        FBC_Assert(src.rows == dst.rows && src.cols == dst.cols);  
    }  

    Size ksize = !kernel.empty() ? kernel.size() : Size(3, 3);  
    anchor = normalizeAnchor(anchor, ksize);  

    if (iterations == 0 || kernel.rows * kernel.cols == 1) {  
        src.copyTo(dst);  
        return 0;  
    }  

    if (kernel.empty()) {  
        kernel = Mat_<uchar, 1>(1 + iterations * 2, 1 + iterations * 2);  
        getStructuringElement(kernel, MORPH_RECT, Size(1 + iterations * 2, 1 + iterations * 2));  
        anchor = Point(iterations, iterations);  
        iterations = 1;  
    } else if (iterations > 1 && countNonZero(kernel) == kernel.rows * kernel.cols) {  
        anchor = Point(anchor.x*iterations, anchor.y*iterations);  
        kernel = Mat_<uchar, 1>(ksize.height + (iterations - 1)*(ksize.height - 1), ksize.width + (iterations - 1)*(ksize.width - 1));  
        getStructuringElement(kernel, MORPH_RECT,  
            Size(ksize.width + (iterations - 1)*(ksize.width - 1), ksize.height + (iterations - 1)*(ksize.height - 1)), anchor);  
        iterations = 1;  
    }  

    anchor = normalizeAnchor(anchor, kernel.size());  

    Ptr<BaseRowFilter> rowFilter;  
    Ptr<BaseColumnFilter> columnFilter;  
    Ptr<BaseFilter> filter2D;  

    if (countNonZero(kernel) == kernel.rows*kernel.cols) {  
        // rectangular structuring element  
        rowFilter = getMorphologyRowFilter<_Tp, chs>(1, kernel.cols, anchor.x);  
        columnFilter = getMorphologyColumnFilter<_Tp, chs>(1, kernel.rows, anchor.y);  
    } else {  
        filter2D = getMorphologyFilter<_Tp, chs>(1, kernel, anchor);  
    }  

    Scalar borderValue_ = borderValue;  
    if (borderType == BORDER_CONSTANT && borderValue_ == Scalar::all(DBL_MAX)) {  
        if (sizeof(_Tp) == 1) // CV_8U  
            borderValue_ = Scalar::all(0.);  
        else // CV_32F  
            borderValue_ = Scalar::all(-FLT_MAX);  
    }  

    Ptr<FilterEngine<_Tp, _Tp, _Tp, chs, chs, chs>> f = makePtr<FilterEngine<_Tp, _Tp, _Tp, chs, chs, chs>>(filter2D, rowFilter, columnFilter, borderType, borderType, borderValue_);  
    f->apply(src, dst);  
    for (int i = 1; i < iterations; i++)  
        f->apply(dst, dst);  

    return 0;  
}  

} // namespace fbc  

#endif // FBC_CV_DILATE_HPP_  

morphologyEx

#ifndef FBC_CV_MORPHOLOGYEX_HPP_  
#define FBC_CV_MORPHOLOGYEX_HPP_  

/* reference: include/opencv2/imgproc.hpp 
              modules/imgproc/src/morph.cpp 
*/  

#include <typeinfo>  
#include "erode.hpp"  
#include "dilate.hpp"  

namespace fbc {  

// perform advanced morphological transformations using an erosion and dilation as basic operations  
// In case of multi - channel images, each channel is processed independently.  
// morphologyEx can be applied several ( iterations ) times.  
// op ==> enum MorphTypes  
// support type: uchar/float, multi-channels  
template<typename _Tp, int chs>  
int morphologyEx(const Mat_<_Tp, chs>& src, Mat_<_Tp, chs>& dst, int op, const Mat_<uchar, 1>& kernel,  
    Point anchor = Point(-1, -1), int iterations = 1, int borderType = BORDER_CONSTANT, const Scalar& borderValue = Scalar::all(DBL_MAX))  
{  
    FBC_Assert(typeid(uchar).name() == typeid(_Tp).name() || typeid(float).name() == typeid(_Tp).name()); // uchar || float  
    if (dst.empty()) {  
        dst = Mat_<_Tp, chs>(src.rows, src.cols);  
    } else {  
        FBC_Assert(src.rows == dst.rows && src.cols == dst.cols);  
    }  

    Mat_<uchar, 1> kernel_ = kernel;  
    if (kernel_.empty()) {  
        kernel_ = Mat_<uchar, 1>(3, 3);  
        getStructuringElement(kernel_, MORPH_RECT, Size(3, 3), Point(1, 1));  
    }  

    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 CV_MOP_CLOSE: {  
            dilate(src, dst, kernel_, anchor, iterations, borderType, borderValue);  
            erode(dst, dst, kernel_, anchor, iterations, borderType, borderValue);  
            break;  
        }  
        case CV_MOP_GRADIENT: {  
            Mat_<_Tp, chs> temp(src.rows, src.cols);  
            erode(src, temp, kernel_, anchor, iterations, borderType, borderValue);  
            dilate(src, dst, kernel_, anchor, iterations, borderType, borderValue);  
            dst -= temp;  
            break;  
        }  
        case CV_MOP_TOPHAT: {  
            Mat_<_Tp, chs> temp(src.rows, src.cols);  
            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 CV_MOP_BLACKHAT: {  
            Mat_<_Tp, chs> temp(src.rows, src.cols);  
            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: {  
            FBC_Assert(typeid(uchar).name() == typeid(_Tp).name() && chs == 1);  
            Mat_<uchar, 1> k1 = (kernel_ == Mat_<uchar, 1>(kernel_.rows, kernel_.cols, Scalar::all(1)));  
            Mat_<uchar, 1> k2 = (kernel_ == Mat_<int, 1>(kernel_.rows, kernel_.cols, Scalar::all(-1)));  
            Mat_<_Tp, chs> e1, e2;  

            if (countNonZero(k1) <= 0)  
                e1 = src;  
            else  
                erode(src, e1, k1, anchor, iterations, borderType, borderValue);  
            if (countNonZero(k2) <= 0) {  
                e2 = src;  
            } else {  
                Mat_<_Tp, chs> src_complement;  
                bitwise_not(src, src_complement);  
                erode(src_complement, e2, k2, anchor, iterations, borderType, borderValue);  
            }  
            bitwise_and(e1, e2, dst);  
            break;  
        }  
        default:  
            FBC_Assert("unknown morphological operation");  
    }  

    return 0;  
}  

} // namespace fbc  

#endif // FBC_CV_MORPHOLOGYEX_HPP_  

相关文章

  • 形态学滤波

    形态学滤波基本操作 形态学本来是生物中的一个概念,但是对于图像处理来说,形态学指的是数学方面的形态学滤波,特别是对...

  • Opencv第七课--形态学滤波

    形态学滤波 形态学滤波包括腐蚀、膨胀、开运算、闭运算、形态学梯度、礼帽、黑帽下面针对这四种形态学操作,说明一下其原...

  • 第 5 章 用形态学运算变换图像

    本章包括以下内容: 用形态学滤波器腐蚀和膨胀图像; 用形态学滤波器开启和闭合图像; 在灰度图像中应用形态学运算; ...

  • OpenCV图像处理(七)图像滤波(2)

    1、形态学滤波 简单来说,形态学操作就是基于形状的一系列图像处理操作,最基本的形态学操作:膨胀、腐蚀。在图像处理中...

  • 形态学滤波

    getStructuringElement @brief Returns a structuring elemen...

  • 形态学滤波

    数字形态学是图像处理的基本理论,这里简单介绍一下基本的形态学运算,针对的是灰度图像,包括:腐蚀与膨胀,开闭运算,形...

  • 形态学滤波

    腐蚀与膨胀能实现多种多样的功能,主要如下: · 消除噪声 · 分割出独立的图像元素,在图像中连接相邻的元素 · 寻...

  • OpenCV-Python系列七:图像形态学

    如果说滤波是在大的尺度空间来处理,图像形态学则对消除局部噪声信号十分有用,其目的是剔除二值图像中的干扰,更好的保留...

  • OpenCV For iOS(六)方框、均值、高斯、中值、双边滤

    本节主要记录OpenCV 两类五种常见的滤波方式: 线性滤波:方框滤波、均值滤波、高斯滤波非线性滤波: 中值滤波、...

  • 灰度形态学基本运算

    上海交通大学 医学图像处理 与二值形态学相对应另一种形态学运算是灰度形态学。灰度形态学与二值形态学相比,不仅在图像...

网友评论

      本文标题:形态学滤波

      本文链接:https://www.haomeiwen.com/subject/tysnfftx.html