使用使用OpenCV获取图片连通域数量获取图片连通域数量,并用不同颜色标记函并用不同颜色标记函
主要介绍了使用OpenCV获取图片连通域数量,并用不同颜色标记函,具有很好的参考价值,希望对大家有所帮
助。一起跟随小编过来看看吧
一,原图和效果图一,原图和效果图
二,代码二,代码
//#########################产生随机颜色#########################
cv::Scalar icvprGetRandomColor()
{
uchar r = 255 * (rand() / (1.0 + RAND_MAX));
uchar g = 255 * (rand() / (1.0 + RAND_MAX));
uchar b = 255 * (rand() / (1.0 + RAND_MAX));
return cv::Scalar(b, g, r);
}
//#########################产生随机颜色#########################
//########################种子填充法)#########################
void ConnectedCountBySeedFill(const cv::Mat& _binImg, cv::Mat& _lableImg, int &iConnectedAreaCount)
{
//拓宽1个像素的原因是:如果连通域在边缘,运行此函数会异常崩溃,所以需要在周围加一圈0值,确保连通域不在边上
//==========图像周围拓宽1个像素============================================
int top, bottom; //【添加边界后的图像尺寸】
int leftImage, rightImage;
int borderType = BORDER_CONSTANT; //BORDER_REPLICATE
//【初始化参数】
top = (int)(1); bottom = (int)(1);
leftImage = (int)(1); rightImage = (int)(1);
Mat _binImg2, _binImg3;
_binImg.copyTo(_binImg2);
//初始化参数value
Scalar value(0); //填充值
//创建图像边界
copyMakeBorder(_binImg2, _binImg3, top, bottom, leftImage, rightImage, borderType, value);
//==========图像周围拓宽1个像素============================================
// connected component analysis (4-component)
// use seed filling algorithm
// 1. begin with a foreground pixel and push its foreground neighbors into a stack;
// 2. pop the top pixel on the stack and label it with the same label until the stack is empty
//
// foreground pixel: _binImg(x,y) = 1
// background pixel: _binImg(x,y) = 0
if (_binImg3.empty() ||
_binImg3.type() != CV_8UC1)
{
return;
}
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