<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[xiaotingting_的博客]]></title><description><![CDATA[不爱读书写作的科研工作者不是一个合格的小可爱]]></description><link>https://blog.youkuaiyun.com/xiaotingting_</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; xiaotingting_]]></copyright><item><title><![CDATA[pandasVSexcel 课程笔记 6]]></title><link>https://blog.youkuaiyun.com/xiaotingting_/article/details/106313454</link><guid>https://blog.youkuaiyun.com/xiaotingting_/article/details/106313454</guid><author>xiaotingting_</author><pubDate>Sun, 24 May 2020 13:11:47 +0800</pubDate><description><![CDATA[B站课程的学习笔记，含部分自己补充知识，课程链接：https://www.bilibili.com/video/av36643275/?p=3

函数填充、计算列

1、对列操作，操作符重载



2、利用for循环，单元格对单元格操作



3、.apply(函数名) 或 .apply(lambda 表达式)

（1）.apply(函数名)





（2）.apply(lambda 表达式)






...]]></description><category></category></item><item><title><![CDATA[pandasVSexcel 课程笔记 4-5]]></title><link>https://blog.youkuaiyun.com/xiaotingting_/article/details/104468211</link><guid>https://blog.youkuaiyun.com/xiaotingting_/article/details/104468211</guid><author>xiaotingting_</author><pubDate>Sun, 23 Feb 2020 21:49:04 +0800</pubDate><description><![CDATA[B站课程的学习笔记，含部分自己补充知识，课程链接：https://www.bilibili.com/video/av36643275/?p=3

1、读取excel中特定区域的内容









2、填充ID列，数值

（1）ID列的类型为series数据类型(在series类型中填充数据):



（2）利用for循环填充数据，注意ID列为空时默认为float型数据，故在读入excel时根...]]></description><category></category></item><item><title><![CDATA[pandasVSexcel 课程笔记  1-3]]></title><link>https://blog.youkuaiyun.com/xiaotingting_/article/details/104451976</link><guid>https://blog.youkuaiyun.com/xiaotingting_/article/details/104451976</guid><author>xiaotingting_</author><pubDate>Sat, 22 Feb 2020 22:29:35 +0800</pubDate><description><![CDATA[B站课程的学习笔记，含部分自己补充知识，课程链接：https://www.bilibili.com/video/av36643275/?p=3


第一课：文件中含自动生成的索引：

1、在读入文件时指定索引列

df = df.read_excel('...',index_col='..')

2、读入后，另写一行设置索引

df = df.set_index('...')



第二课：文件...]]></description><category></category></item><item><title><![CDATA[python制作excel分表（按照某一列分类拆分）（一）]]></title><link>https://blog.youkuaiyun.com/xiaotingting_/article/details/104292518</link><guid>https://blog.youkuaiyun.com/xiaotingting_/article/details/104292518</guid><author>xiaotingting_</author><pubDate>Sat, 15 Feb 2020 22:14:31 +0800</pubDate><description><![CDATA[#!/usr/bin/env python
# -*- coding: UTF-8 -*-
#-*-coding:gb2312 -*-
import sys
reload(sys)
sys.setdefaultencoding('utf8')


from openpyxl import load_workbook
import pandas as pd
import numpy as n...]]></description><category></category></item><item><title><![CDATA[检测标记最大联通区域后使用grabcut分割]]></title><link>https://blog.youkuaiyun.com/xiaotingting_/article/details/79854043</link><guid>https://blog.youkuaiyun.com/xiaotingting_/article/details/79854043</guid><author>xiaotingting_</author><pubDate>Sun, 29 Apr 2018 20:24:21 +0800</pubDate><description><![CDATA[用图像分割之最大连通域算法得到最大轮廓矩形框，再使用grabcut分割：#include&amp;lt;opencv2\opencv.hpp&amp;gt;  #include&amp;lt;cmath&amp;gt;    using namespace cv;  using namespace std;    int main()  {      Mat srcImage = imread( &quot;test_image.png&quot;...]]></description><category></category></item><item><title><![CDATA[grabcut图像分割（2）]]></title><link>https://blog.youkuaiyun.com/xiaotingting_/article/details/79602421</link><guid>https://blog.youkuaiyun.com/xiaotingting_/article/details/79602421</guid><author>xiaotingting_</author><pubDate>Sun, 18 Mar 2018 17:34:13 +0800</pubDate><description><![CDATA[  grabcut分割算法简单且效果较好，初步决定将其应用到项目中，为了省去其人机交互的部分考虑到图像的显著性，查看的基本方法大多涉及机器学习。由于我们的项目待处理图像较为规整，于是想到每幅图片均提取相同的矩形区域，为grabcut算法提供先验像素。具体代码如下：#include&quot;highgui.h&quot;  #include&quot;cv.h&quot;  using namespace cv;    Rect sel...]]></description><category></category></item><item><title><![CDATA[grabcut 图像目标分割（1）]]></title><link>https://blog.youkuaiyun.com/xiaotingting_/article/details/79432381</link><guid>https://blog.youkuaiyun.com/xiaotingting_/article/details/79432381</guid><author>xiaotingting_</author><pubDate>Sat, 03 Mar 2018 19:48:00 +0800</pubDate><description><![CDATA[代码：#include&amp;lt;opencv2/opencv.hpp&amp;gt;bool selectObject = false;cv::Point origin;cv::Rect selection;cv::Mat src;cv::Mat srcMarks;cv::Mat result;cv::Mat foreground;void GrabCutSegment(); void onMouse(in...]]></description><category></category></item><item><title><![CDATA[霍夫变换 检测直线]]></title><link>https://blog.youkuaiyun.com/xiaotingting_/article/details/78642005</link><guid>https://blog.youkuaiyun.com/xiaotingting_/article/details/78642005</guid><author>xiaotingting_</author><pubDate>Mon, 27 Nov 2017 09:51:55 +0800</pubDate><description><![CDATA[#include &quot;opencv2/core/core.hpp&quot;  
#include &quot;opencv2/highgui/highgui.hpp&quot;  
#include &quot;opencv2/imgproc/imgproc.hpp&quot;  
  
#include   
using namespace cv;  
using namespace std;  
  
int main( in]]></description><category></category></item><item><title><![CDATA[opencv学习笔记—8，阈值操作]]></title><link>https://blog.youkuaiyun.com/xiaotingting_/article/details/78057845</link><guid>https://blog.youkuaiyun.com/xiaotingting_/article/details/78057845</guid><author>xiaotingting_</author><pubDate>Thu, 21 Sep 2017 22:44:20 +0800</pubDate><description><![CDATA[#include
#include
#include


using  namespace  cv;
Mat img,gray_img,output_img;


int threshold_vlaue=127;
int threshold_max=255;


void Threshold_Demo(int,void*);
int main(int argc,char]]></description><category></category></item><item><title><![CDATA[opencv学习笔记-7，laplance边缘检测]]></title><link>https://blog.youkuaiyun.com/xiaotingting_/article/details/78044085</link><guid>https://blog.youkuaiyun.com/xiaotingting_/article/details/78044085</guid><author>xiaotingting_</author><pubDate>Wed, 20 Sep 2017 18:41:05 +0800</pubDate><description><![CDATA[一、laplance算子
在二阶导数的时候，最大变化处的值为零即边缘是零值。通过二阶导数计算，依据此理论我们可以计算图像二阶导数，提取边缘。


 
Laplacian函数定义如下：
void cv::Laplacian  (   InputArray      src,
        OutputArray     dst,
        int     ddepth,
    ]]></description><category></category></item><item><title><![CDATA[opencv—学习笔记6，sobel边缘检测]]></title><link>https://blog.youkuaiyun.com/xiaotingting_/article/details/78035513</link><guid>https://blog.youkuaiyun.com/xiaotingting_/article/details/78035513</guid><author>xiaotingting_</author><pubDate>Tue, 19 Sep 2017 22:43:25 +0800</pubDate><description><![CDATA[一、sobel算子
1、是离散微分算子，用来计算图像灰度的近似梯度


代码：
#include
#include
#include


using  namespace  cv;
using namespace std;


Mat src,dst,gray,x_gray,y_gray;
int main(int argc,char**argv)
{
src=i]]></description><category></category></item><item><title><![CDATA[opencv学习笔记-5，绘制线条、矩形、圆、椭圆，添加文字]]></title><link>https://blog.youkuaiyun.com/xiaotingting_/article/details/75536426</link><guid>https://blog.youkuaiyun.com/xiaotingting_/article/details/75536426</guid><author>xiaotingting_</author><pubDate>Thu, 20 Jul 2017 16:07:18 +0800</pubDate><description><![CDATA[一、代码
#include
#include
#include




using  namespace  cv;
Mat img;
void Mylines();
void Myrectangle();
void Mycircle();
void Myellipse();


int main(int argc,char**argv)
{ 
 img=imre]]></description><category></category></item><item><title><![CDATA[qt 将.ui文件生成.h或.cpp文件]]></title><link>https://blog.youkuaiyun.com/xiaotingting_/article/details/73823438</link><guid>https://blog.youkuaiyun.com/xiaotingting_/article/details/73823438</guid><author>xiaotingting_</author><pubDate>Tue, 27 Jun 2017 20:31:57 +0800</pubDate><description><![CDATA[生成步骤：
1、将.ui文件复制到与uic.exe同一文件夹中
2、在计算机开始中打开cmd
3、输入一下命令：
cd ***(文件夹位置路径)
uic ***.ui -o ***.h (生成.h文件)
uic ***.ui -o ***.cpp(生成.cpp文件)
4、生成的文件会保存在uic.exe所在文件夹中]]></description><category></category></item><item><title><![CDATA[opencv学习笔记-4、调整图像亮度和对比度]]></title><link>https://blog.youkuaiyun.com/xiaotingting_/article/details/73251567</link><guid>https://blog.youkuaiyun.com/xiaotingting_/article/details/73251567</guid><author>xiaotingting_</author><pubDate>Wed, 14 Jun 2017 19:42:48 +0800</pubDate><description><![CDATA[Mat new_image = Mat::zero(image.size(), image.type);//创建一张跟原图大小和类型一致的空白图像、像素初始为0。


image.at(i,j)：取出灰度图像中i行j列的点。

image.at(i,j)[k]：取出彩色图像中i行j列第k通道的颜色点。其中uchar,Vec3b都是图像像素值的类型，不要对Vec3b这种类型感觉害怕，其实在]]></description><category></category></item><item><title><![CDATA[opencv学习笔记-3，图像混合]]></title><link>https://blog.youkuaiyun.com/xiaotingting_/article/details/73194683</link><guid>https://blog.youkuaiyun.com/xiaotingting_/article/details/73194683</guid><author>xiaotingting_</author><pubDate>Tue, 13 Jun 2017 19:57:11 +0800</pubDate><description><![CDATA[一、原理
线性混合操作：


这里α 表示两种图片的融合比例，这个g(x) 表示 融合图片中的像素点，f0(x) 和 f1(x) 分别表示背景和前景图片中的像素点。



二、API
C++: void addWeighted(InputArray src1, double alpha, InputArray src2, double beta, double gamma,Out]]></description><category></category></item><item><title><![CDATA[opencv学习笔记-2，图像颜色空间转换]]></title><link>https://blog.youkuaiyun.com/xiaotingting_/article/details/72953524</link><guid>https://blog.youkuaiyun.com/xiaotingting_/article/details/72953524</guid><author>xiaotingting_</author><pubDate>Fri, 09 Jun 2017 09:19:56 +0800</pubDate><description><![CDATA[一、代码
#include
#include
#include
using  namespace  cv;
using  namespace std;

int main(int argc,char**argv)
{
Mat img=imread("1.jpg");
namedWindow("my picture",CV_WINDOW_AUTOSIZE);
imshow("m]]></description><category></category></item><item><title><![CDATA[opencv学习笔记-1，存储并打开图片]]></title><link>https://blog.youkuaiyun.com/xiaotingting_/article/details/72903436</link><guid>https://blog.youkuaiyun.com/xiaotingting_/article/details/72903436</guid><author>xiaotingting_</author><pubDate>Wed, 07 Jun 2017 20:14:29 +0800</pubDate><description><![CDATA[opencv学习笔记，小白入门，多多交流，一起进步~]]></description><category></category></item></channel></rss>