python 目标检测

SAR processing library

说明：  利用深度学习进行遥感图像场景分类 这里我们对NWPU-RESISC45数据集的场景图像进行分类 我们将卷积神经网络应用于图像分类。我们从头开始训练数据集。此外，还应用了预先训练的VGG16 abd ResNet50进行迁移学习。(Scene Classification of Remote Sensing Images Using Deep Learning Here we classify scene images from NWPU-RESISC45 dataset We apply convolutional neural network to image classification. We start training data sets from scratch. In addition, a pre-trained VGG16 abd ResNet50 is used for migration learning.)

Python的学习手册，有需要的可以看看(Python Learning manual for people to get more knowledge about this language.)

使用python脚本，实现了下载糗事百科上的图片的功能。定义了500页，怎么也够用了吧。程序主题简单，主要是用到了sgmllib模块和urllib2模块。压缩包内包括了几张运行下载的图片。

说明：  Python入门学习，利用蚁群算法解决多旅行商问题，适合入门者学习(freshman for python learning.. how to use ant colony algorithm to solve TSP problem)

使用Python写的坦克大战，回忆童年的经典游戏，

SVM: 一种分类器，采用最大化分类间隔进行优化参数。 关于这个分类器两点比较重要： 1）SMO优化算法需要掌握， 可以具体参看两篇文章，John Platt的文章 以及“Improvements to Platt s SMO algorithm for SVM Classifier Design” 2）核函数的使用，如何将核函数使用到SVM中，核函数就是空间转换的函数， 说白了就是距离计算函数，如何将同类之间的距离计算的比较近，如何将低维空间转换到易于分类的高维空间。 我自己看书写的程序，采用Python实现，注释比较多(SVM: a classification, a classification intervals to maximize the use of optimization parameters. About this classifier is more important points:     1) SMO optimization algorithms need to know, you can see the specific two articles, John Platt article         And "Improvements to Platt s SMO algorithm for SVM Classifier Design"     2) the use of nuclear function, how to use the SVM kernel function, the kernel function is a function space conversion, That white is the distance calculation function, how relatively close distance between similar calculations, how to convert low-dimensional space to a high-dimensional space is easy classification. I see myself writing program, using Python, notes more)

可以批量生成图片缩略图，可以自定义缩略图大小等。源作者：djhui (Photos can be batch generated thumbnails, you can customize the thumbnail size. Source Author: djhui)

说明：  通过PYTHON编写的爬虫程序，爬取Geoq的人口密度数据(The population density data of Geoq is crawled by a crawler program written in PYTHON)

说明：  读取数据、绘制频率直方图、*计算偏度和峰度*、正态性检验*、求经验分布函数值*、核分布估计**、核分布估计**、*求Copula中参数的估计值**、绘制Copula的密度函数和分布函数图******、求Kendall秩相关系数和Spearman秩相关系数*******、模型评价(My English is poor i hope you can understand from the chinese introdunction thinks)