版权声明：共享知识，欢迎转载 https://blog.csdn.net/kakiebu/article/details/82967085
1. API
该函数有两个初始化API
C++: void distanceTransform(InputArray src, OutputArray dst, int distanceType, int maskSize)
C++: void distanceTransform(
InputArray src,
OutputArray dst,
OutputArray labels,
int distanceType,
int maskSize,
int labelType=DIST_LABEL_CCOMP )2. 参数说明
- src – 8-bit, 单通道（二值化）输入图片。
- dst – 输出结果中包含计算的距离，这是一个32-bit float 单通道的Mat类型数组，大小与输入图片相同。
- src – 8-bit, 单通道（二值化）输入图片。
- dst – 输出结果中包含计算的距离，这是一个32-bit float 单通道的Mat类型数组，大小与输入图片相同。
- distanceType – 计算距离的类型那个，可以是 CV_DIST_L1、CV_DIST_L2 、CV_DIST_C。
- maskSize – 距离变换掩码矩阵的大小，可以是
- 3（CV_DIST_L1、 CV_DIST_L2 、CV_DIST_C）
- 5（CV_DIST_L2 ）
- CV_DIST_MASK_PRECISE (这个只能在4参数的API中使用)
- labels – 可选的2D标签输出（离散 Voronoi 图），类型为 CV_32SC1 大小同输入图片。
- labelType – 输出标签的类型，这里有些两种。
- labelType==DIST_LABEL_CCOMP 将周围较近的白色像素点作为一个整体计算其到黑色边缘的距离
- labelType==DIST_LABEL_PIXEL 单独计算每个白色像素点到其黑色边缘的距离.
| distanceType | maskSize | a \ b \ c |
| CV_DIST_C | 3（3X3） | a = 1, b = 1 |
| CV_DIST_L1 | 3（3X3） | a = 1, b = 2 |
| CV_DIST_L2 | 3（3X3） | a=0.955, b=1.3693 |
| CV_DIST_L2 | 5（5X5） | a=1, b=1.4, c=2.1969 |
其中 a b c 含义：在这个函数中计算每个白色像素到黑色像素（0值像素）的最短距离，因此需要通过最短的移动方式找到这个点兵计算他们之间的值。通常来说移动有水平方向、竖直方向、对角方向、跳跃式几个移动方法。虽然计算距离的方法都是一些很基础的公式，但是这个这个掩码矩阵必须是对阵的（这里插一句，要求对是式为了卷积计算的过程可以使用加速算法），因此掩码矩阵上所有水平和竖直方向的变化量（原文是must have the same shift cost ），这里用 a 代表；对角方向的变化量用 b 代表；跳跃移动的变化量用 c 代表。CV_DIST_C、CV_DIST_L1、CV_DIST_L2（maskSize=5）的计算结果是精确的，CV_DIST_L2（maskSize=3）是一个快速计算方法。
3. 代码
我写的代码结果不明显
#include<stdio.h>
#include<opencv2/opencv.hpp>
using namespace cv;
using namespace std;
int main(int argc, char* argv[]){
Mat src = imread("10.jpg");
resize(src, src, Size(), 0.25, 0.25, 1);
imshow("src", src);
Mat bin;
cvtColor(src, bin, CV_BGR2GRAY);
threshold(bin, bin, 80, 255, CV_THRESH_BINARY);
imshow("bin", bin);
Mat Dist, Labels;
distanceTransform(bin, Dist, CV_DIST_L1, 3);
normalize(Dist, Dist, 0, 1, NORM_MINMAX);
imshow("dist1", Dist);
distanceTransform(bin, Dist, Labels, CV_DIST_L1, 3, DIST_LABEL_CCOMP);
normalize(Dist, Dist, 0, 1, NORM_MINMAX);
imshow("dist2", Dist);
imshow("labels2", Labels);
distanceTransform(bin, Dist, Labels, CV_DIST_L1, 3, DIST_LABEL_PIXEL);
normalize(Dist, Dist, 0, 1, NORM_MINMAX);
//normalize(Labels, Labels, 0, 255, NORM_MINMAX);
imshow("dist3", Dist);
imshow("labels3", Labels);
waitKey();
return 0;
}后来又找来OpenCV官方的示例代码：
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <stdio.h>
using namespace cv;
int maskSize0 = CV_DIST_MASK_5;
int voronoiType = -1;
int edgeThresh = 100;
int distType0 = CV_DIST_L1;
// The output and temporary images
Mat gray;
// threshold trackbar callback
static 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 ? CV_DIST_MASK_5 : maskSize0;
int distType = voronoiType >= 0 ? CV_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 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 =
{
"{1| |stuff.jpg|input image file}"
};
int main( int argc, const char** argv )
{
help();
CommandLineParser parser(argc, argv, keys);
string filename = parser.get<string>("1");
//gray = imread("stuff.jpg", 0);
gray = imread("10.jpg", 0);
resize(gray, gray, Size(), 0.25, 0.25, 1);
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 = CV_DIST_C;
else if( c == '1' )
distType0 = CV_DIST_L1;
else if( c == '2' )
distType0 = CV_DIST_L2;
else if( c == '3' )
maskSize0 = CV_DIST_MASK_3;
else if( c == '5' )
maskSize0 = CV_DIST_MASK_5;
else if( c == '0' )
maskSize0 = CV_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 = CV_DIST_MASK_3;
distType0 = CV_DIST_C;
}
else if( distType0 == CV_DIST_C )
distType0 = CV_DIST_L1;
else if( distType0 == CV_DIST_L1 )
distType0 = CV_DIST_L2;
else if( maskSize0 == CV_DIST_MASK_3 )
maskSize0 = CV_DIST_MASK_5;
else if( maskSize0 == CV_DIST_MASK_5 )
maskSize0 = CV_DIST_MASK_PRECISE;
else if( maskSize0 == CV_DIST_MASK_PRECISE )
voronoiType = 0;
}
}
return 0;
}
