<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[LHHopencv的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/LHHopencv</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; LHHopencv]]></copyright><item><title><![CDATA[SSM笔记]]></title><link>https://blog.youkuaiyun.com/LHHopencv/article/details/80001119</link><guid>https://blog.youkuaiyun.com/LHHopencv/article/details/80001119</guid><author>LHHopencv</author><pubDate>Fri, 20 Apr 2018 13:55:41 +0800</pubDate><description><![CDATA[进入数据库：&quot;D:\Program Files (x86)\mysql-5.7.20-winx64\bin\mysql.exe&quot; -u root -p admin展示数据库：show databases;创建数据库：CREATE DATABASE tmall_ssm DEFAULT CHARACTER SET utf8;导入数据库：&quot;D:\Program Files (x86)\mysql-5.7...]]></description><category></category></item><item><title><![CDATA[tiny-dnn配置运行]]></title><link>https://blog.youkuaiyun.com/LHHopencv/article/details/79286207</link><guid>https://blog.youkuaiyun.com/LHHopencv/article/details/79286207</guid><author>LHHopencv</author><pubDate>Thu, 08 Feb 2018 09:47:57 +0800</pubDate><description><![CDATA[tiny-dnn是一个轻量级的CNN（卷积神经网络），不需要各种依赖和GPU，由三千多行C++代码完成。适配android平台的话，感觉这个比较好做一点。下载地址：https://github.com/tiny-dnn/tiny-dnn新建一个cpp项目，在属性的C/C++，附加包含目录，在这把tiny-dnn的根目录加进来，导入头文件。之后新建一个cpp文件，把tiny-dnn\examples...]]></description><category></category></item><item><title><![CDATA[语义分割与实例分割的区别]]></title><link>https://blog.youkuaiyun.com/LHHopencv/article/details/79020077</link><guid>https://blog.youkuaiyun.com/LHHopencv/article/details/79020077</guid><author>LHHopencv</author><pubDate>Wed, 10 Jan 2018 09:56:45 +0800</pubDate><description><![CDATA[目前的分割任务主要有两种： 
（1）像素级别的语义分割 
（2）实例分割

这个有意思，什么叫实例分割呢？它与语义分割有什么区别与联系呢？ 
顾名思义，像素级别的语义分割，对图像中的每个像素都划分出对应的类别，即实现像素级别的分类； 
而类的具体对象，即为实例，那么实例分割不但要进行像素级别的分类，还需在具体的类别基础上区别开不同的实例。比如说图像有多个人甲、乙、丙，那边他们的语义分割]]></description><category></category></item><item><title><![CDATA[opencv_tutorial_code学习——pointPolygonTest计算点到轮廓的距离]]></title><link>https://blog.youkuaiyun.com/LHHopencv/article/details/78923626</link><guid>https://blog.youkuaiyun.com/LHHopencv/article/details/78923626</guid><author>LHHopencv</author><pubDate>Thu, 28 Dec 2017 16:40:59 +0800</pubDate><description><![CDATA[tutorial_code\ShapeDescriptors\pointPolygonTest_demo.cpp




double pointPolygonTest(InputArray contour,
 Point2f pt, bool measureDist)

参数： contour            ---输入轮廓

            pt       ]]></description><category></category></item><item><title><![CDATA[opencv_tutorial_code学习——moments()中心距&面积&边长]]></title><link>https://blog.youkuaiyun.com/LHHopencv/article/details/78923278</link><guid>https://blog.youkuaiyun.com/LHHopencv/article/details/78923278</guid><author>LHHopencv</author><pubDate>Thu, 28 Dec 2017 16:25:14 +0800</pubDate><description><![CDATA[tutorial_code\ShapeDescriptors\moments_demo.cpp
步骤：


1、灰度化

2、滤波

3、Canny边缘检测

4、画轮廓 findContours（）

5、画中心距
第一步：
vector mu(contours.size() );
for( size_t i = 0; i 
{ mu[i] = moments(]]></description><category></category></item><item><title><![CDATA[opencv_tutorial_code学习——画凸包convexHull（）]]></title><link>https://blog.youkuaiyun.com/LHHopencv/article/details/78921767</link><guid>https://blog.youkuaiyun.com/LHHopencv/article/details/78921767</guid><author>LHHopencv</author><pubDate>Thu, 28 Dec 2017 14:35:07 +0800</pubDate><description><![CDATA[tutorial_code\ShapeDescriptors\hull_demo.cpp

步骤：

1、灰度化

2、滤波

3、二值化

4、画轮廓 findContours（）

5、画最小包围旋转矩形和最小包围椭圆

第一步：

vector >hull( contours.size() );

for( size_t i = 0; i 

{   ]]></description><category></category></item><item><title><![CDATA[opencv_tutorial_code学习——画最小包围旋转矩形&画最小包围椭圆]]></title><link>https://blog.youkuaiyun.com/LHHopencv/article/details/78921040</link><guid>https://blog.youkuaiyun.com/LHHopencv/article/details/78921040</guid><author>LHHopencv</author><pubDate>Thu, 28 Dec 2017 13:50:24 +0800</pubDate><description><![CDATA[tutorial_code\ShapeDescriptors\generalContours_demo2.cpp
步骤：
1、灰度化
2、滤波
3、二值化
4、画轮廓 findContours（）
5、画最小包围旋转矩形和最小包围椭圆
vector minRect( contours.size() );
vector minEllipse( contours.size() );
]]></description><category></category></item><item><title><![CDATA[opencv_tutorial_code学习——findContours&drawContours&画最小包围矩形&画最小包围圆]]></title><link>https://blog.youkuaiyun.com/LHHopencv/article/details/78919934</link><guid>https://blog.youkuaiyun.com/LHHopencv/article/details/78919934</guid><author>LHHopencv</author><pubDate>Thu, 28 Dec 2017 11:19:41 +0800</pubDate><description><![CDATA[tutorial_code\ShapeDescriptors\generalContours_demo1.cpp
步骤：
1、灰度化
2、滤波
3、二值化
4、画轮廓
findContours( threshold_output, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(0, 0) );
for( size]]></description><category></category></item><item><title><![CDATA[opencv_tutorial_code学习——canny边缘检测后findContours]]></title><link>https://blog.youkuaiyun.com/LHHopencv/article/details/78919240</link><guid>https://blog.youkuaiyun.com/LHHopencv/article/details/78919240</guid><author>LHHopencv</author><pubDate>Thu, 28 Dec 2017 10:30:49 +0800</pubDate><description><![CDATA[tutorial_code\ShapeDescriptors\findContours_demo.cpp
步骤：
1、灰度化
2、滤波
3、canny边缘检测
4、findContours(canny_output, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(0, 0) );
5、
Mat drawing =]]></description><category></category></item><item><title><![CDATA[小甲鱼视频课程作业——创建一个简单的类]]></title><link>https://blog.youkuaiyun.com/LHHopencv/article/details/78913621</link><guid>https://blog.youkuaiyun.com/LHHopencv/article/details/78913621</guid><author>LHHopencv</author><pubDate>Wed, 27 Dec 2017 16:25:03 +0800</pubDate><description><![CDATA[BF.h
#pragma once

class BF {
private:	
	int height;
	int weight;
	int beauty;
	int salary;
public:	
	void cook(float time);
	void wash(float time);
	int makeMoney(float time);
	BF();
};

BF.cpp
#i]]></description><category></category></item><item><title><![CDATA[opencv_tutorial_code学习——VideoWriter生成视频流]]></title><link>https://blog.youkuaiyun.com/LHHopencv/article/details/78913021</link><guid>https://blog.youkuaiyun.com/LHHopencv/article/details/78913021</guid><author>LHHopencv</author><pubDate>Wed, 27 Dec 2017 15:43:50 +0800</pubDate><description><![CDATA[tutorial_code\videoio\video-input-psnr-ssim\video-input-psnr-ssim.cpp
tutorial_code\videoio\video-write\video-write.cpp



参考见博客：http://blog.youkuaiyun.com/zkl99999/article/details/51146363]]></description><category></category></item><item><title><![CDATA[训练神经网络中最基本的三个概念：Epoch, Batch, Iteration]]></title><link>https://blog.youkuaiyun.com/LHHopencv/article/details/78912256</link><guid>https://blog.youkuaiyun.com/LHHopencv/article/details/78912256</guid><author>LHHopencv</author><pubDate>Wed, 27 Dec 2017 14:54:02 +0800</pubDate><description><![CDATA[转载地址：https://zhuanlan.zhihu.com/p/29409502

原作者：Michael Yuan

作者主页：https://www.zhihu.com/people/mikeyuan

今天让我们来总结下训练神经网络中最最基础的三个概念：Epoch, Batch, Iteration。




1. 名词解释





2. 换算关系
]]></description><category></category></item><item><title><![CDATA[opencv小程序练习——createBackgroundSubtractorMOG2()实现跟踪]]></title><link>https://blog.youkuaiyun.com/LHHopencv/article/details/78909766</link><guid>https://blog.youkuaiyun.com/LHHopencv/article/details/78909766</guid><author>LHHopencv</author><pubDate>Wed, 27 Dec 2017 10:52:14 +0800</pubDate><description><![CDATA[思想：
使用createBackgroundSubtractorMOG2()使用运动物体背景分割
使用findContours()查找轮廓并画出
实现一定感官上的跟踪功能
代码如下：
#include
#include
#include

using namespace cv;
using namespace std;

Mat frame;
Mat fgMaskMOG2;
Mat ma]]></description><category></category></item><item><title><![CDATA[opencv_tutorial_code学习——视频播放&createBackgroundSubtractorMOG2&npos]]></title><link>https://blog.youkuaiyun.com/LHHopencv/article/details/78903176</link><guid>https://blog.youkuaiyun.com/LHHopencv/article/details/78903176</guid><author>LHHopencv</author><pubDate>Tue, 26 Dec 2017 15:56:03 +0800</pubDate><description><![CDATA[tutorial_code\video\bg_sub.cpp


npos：http://blog.youkuaiyun.com/jiejinquanil/article/details/51789682


createBackgroundSubtractorMOG2官方说明http://opencv-python-tutroals.readthedocs.io/en/latest/py_tut]]></description><category></category></item><item><title><![CDATA[opencv_tutorial_code学习——matchTemplate模板匹配]]></title><link>https://blog.youkuaiyun.com/LHHopencv/article/details/78901760</link><guid>https://blog.youkuaiyun.com/LHHopencv/article/details/78901760</guid><author>LHHopencv</author><pubDate>Tue, 26 Dec 2017 14:16:06 +0800</pubDate><description><![CDATA[tutorial_code\Histograms_Matching\MatchTemplate_Demo.cpp

参考博客：http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/imgproc/histograms/template_matching/template_matching.html]]></description><category></category></item><item><title><![CDATA[opencv_tutorial_code学习——equalizeHist直方图均衡化]]></title><link>https://blog.youkuaiyun.com/LHHopencv/article/details/78899799</link><guid>https://blog.youkuaiyun.com/LHHopencv/article/details/78899799</guid><author>LHHopencv</author><pubDate>Tue, 26 Dec 2017 10:45:26 +0800</pubDate><description><![CDATA[tutorial_code\Histograms_Matching\EqualizeHist_Demo.cpp
步骤：
1、转化为灰度图
2、equalizeHist( src, dst )]]></description><category></category></item><item><title><![CDATA[opencv_tutorial_code学习——compareHist直方图比较]]></title><link>https://blog.youkuaiyun.com/LHHopencv/article/details/78899404</link><guid>https://blog.youkuaiyun.com/LHHopencv/article/details/78899404</guid><author>LHHopencv</author><pubDate>Tue, 26 Dec 2017 10:17:47 +0800</pubDate><description><![CDATA[tutorial_code\Histograms_Matching\compareHist_Demo.cpp
结果数值的意义：https://www.cnblogs.com/tornadomeet/archive/2012/05/03/2480824.html
compareHist（）的使用方法：http://blog.youkuaiyun.com/qq_23880193/article/details]]></description><category></category></item><item><title><![CDATA[色彩空间中的HSL、HSV、HSB有什么区别？]]></title><link>https://blog.youkuaiyun.com/LHHopencv/article/details/78894535</link><guid>https://blog.youkuaiyun.com/LHHopencv/article/details/78894535</guid><author>LHHopencv</author><pubDate>Mon, 25 Dec 2017 16:57:26 +0800</pubDate><description><![CDATA[作者：大蔚陈
链接：https://www.zhihu.com/question/22077462/answer/29483467
来源：知乎
著作权归作者所有。商业转载请联系作者获得授权，非商业转载请注明出处。


这是特别容易混淆的几个概念。
RGB 是对机器很友好的色彩模式，但并不够人性化，因为我们对色彩的认识往往是”什么颜色？鲜艳不鲜艳？亮还是暗？”。HSL 模式和 HSV(]]></description><category></category></item><item><title><![CDATA[opencv_tutorial_code学习——FloodFill漫水填充和物体选取]]></title><link>https://blog.youkuaiyun.com/LHHopencv/article/details/78894211</link><guid>https://blog.youkuaiyun.com/LHHopencv/article/details/78894211</guid><author>LHHopencv</author><pubDate>Mon, 25 Dec 2017 16:33:39 +0800</pubDate><description><![CDATA[tutorial_code\Histograms_Matching\calcBackProject_Demo2.cpp

见参考博客：http://blog.youkuaiyun.com/u011630458/article/details/44260119]]></description><category></category></item><item><title><![CDATA[opencv_tutorial_code学习——图像直方图]]></title><link>https://blog.youkuaiyun.com/LHHopencv/article/details/78892296</link><guid>https://blog.youkuaiyun.com/LHHopencv/article/details/78892296</guid><author>LHHopencv</author><pubDate>Mon, 25 Dec 2017 14:18:42 +0800</pubDate><description><![CDATA[void  mixChannels (constMat*  src , int  nsrc ,Mat*  dst , int  ndst , constint*  fromTo , size_t  npairs );
从输入中拷贝某通道到输出中特定的通道。
src——输入矩阵，可以为一个也可以为多个，但是矩阵必须有相同的大小和深度.
nsrcs——输入矩阵的个数。
dst——输出矩阵，可以]]></description><category></category></item></channel></rss>