OpenCV3.0 Examples学习笔记（5）-distrans.cpp

这个系列的目的是通过对OpenCV示例，进一步了解OpenCV函数的使用，不涉及具体原理。

目录

简介

Example运行截图

Example分析

Example代码

简介

本文记录了对OpenCV示例distrans.cpp的分析。

资料地址:http://docs.opencv.org/3.0.0/d4/d11/distrans_8cpp-example.html

首先这个示例，主要讲述了distanceTransform函数的使用。distanceTransform函数用于计算二值图像中所有像素力其最近的值为0像素的近似距离。这个函数非常有用，

可以用于细化轮廓或者寻找物体质心。

整个示例可以总结为

（1）二值化

（2）调用distanceTransform函数

（3）根据distanceTransform函数计算结果，绘制距离变换图像（图像的值表示距离）。

Example截图

原图

处理结果

参数

二值化阈值：edgeThresh=100

distanceTransform参数：

distanceType = DIST_L1

maskSize = DIST_MASK_5

Example分析

示例主要涉及distanceTransform函数的使用。

如果对于distanceTransform算法本身感兴趣可以传送至Distance Transform  距离变换。
如果想进一步了解distanceTransform函数的使用，可以传送至Opencv距离变换distanceTransform应用——细化字符轮廓&&查找物体质心。

distanceTransform

计算二值图像，任意点到最近背景点的距离，一般为非零点到最近零点的距离。

distanceTransform函数计算二值图像中所有像素力其最近的值为0像素的近似距离。显然如果像素本身为0，则其距离显然也为0。

函数原型：

void distanceTransform( InputArray src, OutputArray dst,

                                     int distanceType, int maskSize, int dstType=CV_32F);

参数说明：

src：源图像，需要采用二值化后的8位灰度图像

dst：目标图像，可以是8位或32位浮点，尺寸和src相同

distanceType：计算距离的方式，具体如下

    DIST_USER    = -1,  //!< User defined distance

    DIST_L1      = 1,   //!< distance = |x1-x2| + |y1-y2|

    DIST_L2      = 2,   //!< the simple euclidean distance

    DIST_C       = 3,   //!< distance = max(|x1-x2|,|y1-y2|)

    DIST_L12     = 4,   //!< L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1))

    DIST_FAIR    = 5,   //!< distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998

    DIST_WELSCH  = 6,   //!< distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846

    DIST_HUBER   = 7    //!< distance = |x|<c ? x^2/2 : c(|x|-c/2), c=1.345

maskSize：掩膜的尺寸，具体如下：

    DIST_MASK_3       = 3, //!< mask=3

    DIST_MASK_5       = 5, //!< mask=5

    DIST_MASK_PRECISE = 0  //!<

    PS:当distanceType = DIST_L1或DIST_C时，maskSize强制为3（设为5也没用）

dstType：目标图像的类型，默认为CV_32F；

Example代码

#include <opencv2/core/utility.hpp>#include "opencv2/imgproc.hpp"#include "opencv2/imgcodecs.hpp"#include "opencv2/highgui.hpp"#include <stdio.h>using namespace std;using namespace cv;int maskSize0 = DIST_MASK_5;int voronoiType = -1;int edgeThresh = 100;int distType0 = DIST_L1;// The output and temporary imagesMat gray;// threshold trackbar callbackstatic void onTrackbar( int, void* ){    static const Scalar colors[] =    {        Scalar(0,0,0),        Scalar(255,0,0),        Scalar(255,128,0),        Scalar(255,255,0),        Scalar(0,255,0),        Scalar(0,128,255),        Scalar(0,255,255),        Scalar(0,0,255),        Scalar(255,0,255)    };    int maskSize = voronoiType >= 0 ? DIST_MASK_5 : maskSize0;    int distType = voronoiType >= 0 ? DIST_L2 : distType0;    Mat edge = gray >= edgeThresh, dist, labels, dist8u;    if( voronoiType < 0 )        distanceTransform( edge, dist, distType, maskSize );    else        distanceTransform( edge, dist, labels, distType, maskSize, voronoiType );    if( voronoiType < 0 )    {        // begin "painting" the distance transform result        dist *= 5000;        pow(dist, 0.5, dist);        Mat dist32s, dist8u1, dist8u2;        dist.convertTo(dist32s, CV_32S, 1, 0.5);        dist32s &= Scalar::all(255);        dist32s.convertTo(dist8u1, CV_8U, 1, 0);        dist32s *= -1;        dist32s += Scalar::all(255);        dist32s.convertTo(dist8u2, CV_8U);        Mat planes[] = {dist8u1, dist8u2, dist8u2};        merge(planes, 3, dist8u);    }    else    {        dist8u.create(labels.size(), CV_8UC3);        for( int i = 0; i < labels.rows; i++ )        {            const int* ll = (const int*)labels.ptr(i);            const float* dd = (const float*)dist.ptr(i);            uchar* d = (uchar*)dist8u.ptr(i);            for( int j = 0; j < labels.cols; j++ )            {                int idx = ll[j] == 0 || dd[j] == 0 ? 0 : (ll[j]-1)%8 + 1;                float scale = 1.f/(1 + dd[j]*dd[j]*0.0004f);                int b = cvRound(colors[idx][0]*scale);                int g = cvRound(colors[idx][1]*scale);                int r = cvRound(colors[idx][2]*scale);                d[j*3] = (uchar)b;                d[j*3+1] = (uchar)g;                d[j*3+2] = (uchar)r;            }        }    }    imshow("Distance Map", dist8u );}static void help(){    printf("\nProgram to demonstrate the use of the distance transform function between edge images.\n"            "Usage:\n"            "./distrans [image_name -- default image is ../data/stuff.jpg]\n"            "\nHot keys: \n"            "\tESC - quit the program\n"            "\tC - use C/Inf metric\n"            "\tL1 - use L1 metric\n"            "\tL2 - use L2 metric\n"            "\t3 - use 3x3 mask\n"            "\t5 - use 5x5 mask\n"            "\t0 - use precise distance transform\n"            "\tv - switch to Voronoi diagram mode\n"            "\tp - switch to pixel-based Voronoi diagram mode\n"            "\tSPACE - loop through all the modes\n\n");}const char* keys ={    "{@image |../data/stuff.jpg|input image file}"};int main( int argc, const char** argv ){    help();    CommandLineParser parser(argc, argv, keys);    string filename = parser.get<string>(0);    gray = imread(filename.c_str(), 0);    if(gray.empty())    {        printf("Cannot read image file: %s\n", filename.c_str());        help();        return -1;    }    namedWindow("Distance Map", 1);    createTrackbar("Brightness Threshold", "Distance Map", &edgeThresh, 255, onTrackbar, 0);    for(;;)    {        // Call to update the view        onTrackbar(0, 0);        int c = waitKey(0) & 255;        if( c == 27 )            break;        if( c == 'c' || c == 'C' || c == '1' || c == '2' ||            c == '3' || c == '5' || c == '0' )            voronoiType = -1;        if( c == 'c' || c == 'C' )            distType0 = DIST_C;        else if( c == '1' )            distType0 = DIST_L1;        else if( c == '2' )            distType0 = DIST_L2;        else if( c == '3' )            maskSize0 = DIST_MASK_3;        else if( c == '5' )            maskSize0 = DIST_MASK_5;        else if( c == '0' )            maskSize0 = DIST_MASK_PRECISE;        else if( c == 'v' )            voronoiType = 0;        else if( c == 'p' )            voronoiType = 1;        else if( c == ' ' )        {            if( voronoiType == 0 )                voronoiType = 1;            else if( voronoiType == 1 )            {                voronoiType = -1;                maskSize0 = DIST_MASK_3;                distType0 = DIST_C;            }            else if( distType0 == DIST_C )                distType0 = DIST_L1;            else if( distType0 == DIST_L1 )                distType0 = DIST_L2;            else if( maskSize0 == DIST_MASK_3 )                maskSize0 = DIST_MASK_5;            else if( maskSize0 == DIST_MASK_5 )                maskSize0 = DIST_MASK_PRECISE;            else if( maskSize0 == DIST_MASK_PRECISE )                voronoiType = 0;        }    }    return 0;}

参考资料：

1.《基于distanceTransform-距离变换的区域中心提取》

2.《Distance Transform 距离变换》

3.《Distance Transform  距离变换》

4.《Opencv距离变换distanceTransform应用——细化字符轮廓&&查找物体质心》

5.《基于距离变换的新型骨架提取方法》