openCV 2.4.10 bwlabel - 连接组件
这里是matlab的原始代码:
Here is the original code from matlab:
% Calculate each separated object area
cDist=regionprops(bwImg, 'Area');
cDist=[cDist.Area];
% Label each object
[bwImgLabeled, ~]=bwlabel(bwImg);
% Calculate min and max object size based on assumptions on the color
% checker size
maxLabelSize = prod(size(imageData)./[4 6]);
minLabelSize = prod(size(imageData)./[4 6]./10);
% Find label indices for objects that are too large or too small
remInd = find(cDist > maxLabelSize);
remInd = [remInd find(cDist < minLabelSize)];
% Remove over/undersized objects
for n=1:length(remInd)
ri = bwImgLabeled == remInd(n);
bwImgLabeled(ri) = 0;
这是我使用 openCV 的代码
Here is my code using openCV
//regionprops(bwImg, 'Area');
// cDist=[cDist.Area]
//cv::FileStorage file("C:\Users\gdarmon\Desktop\gili.txt", cv::FileStorage::WRITE);
//
//file << dst;
dst.convertTo(dst,CV_8U);
cv::vector<cv::vector<cv::Point> > contours;
cv::vector<cv::Vec4i> hierarchy;
cv::findContours(dst,contours,hierarchy,CV_RETR_CCOMP, CV_CHAIN_APPROX_NONE);
std::vector<cv::Moments> mu(contours.size());
for (int i = 0; i < contours.size(); i++)
{
mu[i] = cv::moments(contours[i],false);
}
vector<cv::Point2f> mc( contours.size() );
for( int i = 0; i < contours.size(); i++ )
{
mc[i] = cv::Point2f( mu[i].m10/mu[i].m00 , mu[i].m01/mu[i].m00 );
}
自从现在我有了轮廓，我想使用 bwlabel 函数
1. 我认为标记是为了连接 4-8 个对象.你能解释一下标签实际上是什么吗?我愿意为任何链接付费.
2.OpenCV中的连接组件在这篇文章中，有些人在谈论 CVblob，有些人在谈论 opecv 的 cvContourArea，您能解释一下区别吗?什么更适合我的用例?
Since now ihave the contours I would like to user bwlabel function
1. i have figured that labeling is done in order to get connected 4-8 objects.
can you please explain what is labeling actually is? I would aapriciate any link.
2.connected components in OpenCV
in this article some people are talking about CVblob and some about opecv's cvContourArea, can you explain the difference. and what will be better suited for my use case?
更新:这是我尝试使用 cvBlobs
Update: here is what I have tried using cvBlobs
IplImage* img_bw = new IplImage(dst);
CBlobResult blobs;
CBlob *currentBlob;
blobs = CBlobResult(img_bw, NULL, 0);
// Exclude all white blobs smaller than the given value (80)
// The bigger the last parameter, the bigger the blobs need
// to be for inclusion
blobs.Filter( blobs,
B_EXCLUDE,
CBlobGetArea(),
B_LESS,
80 );
// Get the number of blobs discovered
int num_blobs = blobs.GetNumBlobs();
// Display the filtered blobs
IplImage* filtered = cvCreateImage( cvGetSize( img_bw ),
IPL_DEPTH_8U,
3 );
cvMerge( img_bw, img_bw, img_bw, NULL, filtered );
for ( int i = 0; i < num_blobs; i++ )
{
currentBlob = blobs.GetBlob( i );
currentBlob->FillBlob( filtered, CV_RGB(255,0,0));
}
// Display the input / output windows and images
cvNamedWindow( "input" );
cvNamedWindow( "output" );
cvShowImage("input", img_bw );
cvShowImage("output", filtered);
cv::waitKey(0);
/*% Calculate min and max object size based on assumptions on the color
% checker size
maxLabelSize = prod(size(imageData)./[4 6]);
minLabelSize = prod(size(imageData)./[4 6]./10);*/
double maxLabelSize = (dst.rows/4.0) * (dst.cols/6.0);
double minLabelSize = ((dst.rows/40.0) * (dst.cols/60.0));
推荐答案
- 我认为标记是为了连接 4-8 个对象.你能解释一下标签实际上是什么吗?我愿意为任何链接付费.
标签实际作用的最清晰演示是在 Matlab 文档中的 bwlabel.如果将原始矩阵 BW 与结果矩阵 L 进行比较，您将看到它采用二进制图像并为 1 的每个连接组分配唯一标签 的:
The clearest demonstration of what labeling actually does is in the Matlab documentation for bwlabel. If you compare the original matrix BW to the resulting matrix L, you'll see that it takes a binary image and assigns unique labels to each connected group of 1's:
L =
1 1 1 0 0 0 0 0
1 1 1 0 2 2 0 0
1 1 1 0 2 2 0 0
1 1 1 0 0 0 3 0
1 1 1 0 0 0 3 0
1 1 1 0 0 0 3 0
1 1 1 0 0 3 3 0
1 1 1 0 0 0 0 0
这里标记了三个组件.此示例查找 4 连通分量；如果一个像素位于当前像素的左侧、右侧、上方或下方，则该像素被视为连接到当前像素.8-连接的对象包括对角线，这将导致标签 2 和 3 合并为上面的矩阵，因为对象 2 的右下角和对象的顶部3 对角连接.连接组件标记算法在维基百科这里上有描述.
Here there are three components labeled. This example looks for 4-connected components; a pixel is considered to be connected to the current pixel if it is to the left, right, above or below it. 8-connected objects include the diagonals, which would result in labels 2 and 3 being merged for the matrix above since the lower-right corner of object 2 and the top of object 3 are diagonally connected. The connected component labeling algorithm is described on Wikipedia here.
2.openCV中的connected components 这篇文章有人在说CVblob，有人在说opecv的cvContourArea，能不能解释一下区别.什么更适合我的用例?
2.connected components in OpenCV in this article some people are talking about CVblob and some about opecv's cvContourArea, can you explain the difference. and what will be better suited for my use case?
OpenCV 3.0 已出测试版，有两个全新的方法:connectedComponents 和 connectedComponentsWithStats (文档).如果你想复制 Matlab 的 bwlabel，这是要走的路.
OpenCV 3.0 is out of beta and has two brand new methods: connectedComponents and connectedComponentsWithStats (documentation). If you're trying to replicate Matlab's bwlabel, this is the way to go.
我编写了一个测试程序来试用 connectedComponentsWithStats(完整代码如下)，以此作为我的测试图像:
I wrote a test program to try out connectedComponentsWithStats (complete code below) using this as my test image:
(实际上，这张图片从 800x600 缩小到 400x300，但生成它的代码包含在下面.)
(Actually, this image is reduced from 800x600 to 400x300, but the code to generate it is included below.)
我使用以下方法生成了标记图像:
I generated the labeled image using:
int nLabels = connectedComponentsWithStats(src, labels, stats, centroids, 8, CV_32S);
nLabels 中返回的值为 5.请记住，此方法将背景视为标签 0.
The value returned in nLabels is 5. Remember, that this method considers the background to be label 0.
要查看标记区域是什么，您可以将灰度值从 [0..nLabels-1] 放大到 [0..255]，或者您可以分配随机 RGB 值并创建彩色图像.对于这个测试，我只是打印了几个我知道在不同组件中的位置的值.
To see what the labeled areas are, you could scale up the grayscale values from [0..nLabels-1] to [0..255], or you could assign random RGB values and create a color image. For this test I just printed out the values at a couple of locations that I knew were in different components.
cout << "Show label values:" << endl;
// Middle of square at top-left
int component1Pixel = labels.at<int>(150,150);
cout << "pixel at(150,150) = " << component1Pixel << endl;
// Middle of rectangle at far right
int component2Pixel = labels.at<int>(300,550);
cout << "pixel at(300,550) = " << component2Pixel << endl << endl;
Show label values:
pixel at(150,150) = 1
pixel at(300,550) = 2
stats 是一个 5 x nLabels Mat，包含每个组件(包括背景)的left、top、width、height 和 area.对于这张图片:
The stats is a 5 x nLabels Mat containing left, top, width, height, and area for each component (including background). For this image:
stats:
(left,top,width,height,area)
[0, 0, 800, 600, 421697;
100, 100, 101, 101, 10201;
500, 150, 101, 301, 30401;
350, 246, 10, 10, 36;
225, 325, 151, 151, 17665]
您会注意到组件 0 是图像的完整宽度/高度.对所有区域求和，得到 480,000 = 800x600.前 4 个元素可用于创建边界矩形:
You'll notice that component 0 is the full width/height of the image. Summing all of the areas, you get 480,000 = 800x600. The first 4 elements can be used to create a bounding rectangle:
Rect(Point(left,top), Size(width,height))
centroids 是一个 2 x nLabels Mat，包含每个组件的质心的 x, y 坐标:
centroids is a 2 x nLabels Mat containing the x, y coordinates of the centroid of each component:
centroids:
(x, y)
[398.8575636060963, 298.8746232484461;
150, 150;
550, 300;
354.5, 250.5;
300, 400]
最后，在某些时候，您可能希望单独对其中一个组件进行进一步处理.在这里，我使用 compare 生成一个新的 Mat only2，它只包含来自 labels 标记为 2 的像素.
Finally, at some point you're probably going to want to do further processing on one of the components individually. Here I use compare to generate a new Mat only2 that only contains pixels from labels that labeled 2.
compare(labels, 2, only2, CMP_EQ);
compare 有助于将这些像素设置为新图像中的 255 值，以便您可以查看结果:
compare helpfully sets these pixels to a value of 255 in the new image so you can see the results:
完整代码如下:
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
using namespace std;
using namespace cv;
int main(int argc, const char * argv[]) {
// Create an image
const int color_white = 255;
Mat src = Mat::zeros(600, 800, CV_8UC1);
rectangle(src, Point(100, 100), Point(200, 200), color_white, CV_FILLED);
rectangle(src, Point(500, 150), Point(600, 450), color_white, CV_FILLED);
rectangle(src, Point(350,250), Point(359,251), color_white, CV_FILLED);
rectangle(src, Point(354,246), Point(355,255), color_white, CV_FILLED);
circle(src, Point(300, 400), 75, color_white, CV_FILLED);
imshow("Original", src);
// Get connected components and stats
const int connectivity_8 = 8;
Mat labels, stats, centroids;
int nLabels = connectedComponentsWithStats(src, labels, stats, centroids, connectivity_8, CV_32S);
cout << "Number of connected components = " << nLabels << endl << endl;
cout << "Show label values:" << endl;
int component1Pixel = labels.at<int>(150,150);
cout << "pixel at(150,150) = " << component1Pixel << endl;
int component2Pixel = labels.at<int>(300,550);
cout << "pixel at(300,550) = " << component2Pixel << endl << endl;
// Statistics
cout << "Show statistics and centroids:" << endl;
cout << "stats:" << endl << "(left,top,width,height,area)" << endl << stats << endl << endl;
cout << "centroids:" << endl << "(x, y)" << endl << centroids << endl << endl;
// Print individual stats for component 1 (component 0 is background)
cout << "Component 1 stats:" << endl;
cout << "CC_STAT_LEFT = " << stats.at<int>(1,CC_STAT_LEFT) << endl;
cout << "CC_STAT_TOP = " << stats.at<int>(1,CC_STAT_TOP) << endl;
cout << "CC_STAT_WIDTH = " << stats.at<int>(1,CC_STAT_WIDTH) << endl;
cout << "CC_STAT_HEIGHT = " << stats.at<int>(1,CC_STAT_HEIGHT) << endl;
cout << "CC_STAT_AREA = " << stats.at<int>(1,CC_STAT_AREA) << endl;
// Create image with only component 2
Mat only2;
compare(labels, 2, only2, CMP_EQ);
imshow("Component 2", only2);
waitKey(0);
}
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