简单的python程序

说明：  面部地标检测旨在定位给定特征点的面部图像的关键点，这些特征点通常遭受由任意姿势，不同面部表情和部分遮挡引起的变化。我们提出了一个粗到细的框架，它将堆叠的沙漏网络和显着区域的注意力细化联系起来，以实现稳健的面部对齐。为了实现这一目标，我们首先提出了一个多尺度区域学习模块（MSL）来分析不同面部区域的结构信息，并提取强烈的判别性深层特征。然后我们采用堆叠沙漏网络（SHN）进行热图回归和初始面部地标预测。(Facial landmark detection aims to locate keypoints for a facial image given feature points, which typically suffers from variations caused by arbitrary pose, diverse facial expressions, and partial occlusion. We put forward a coarse-to-fine framework which joints stacked hourglass network and salient region attention refinement for robust face alignment. In order to achieve this goal, we firstly put forward a multi-scale region learning module (MSL) to analyze the structure information at different facial region and extract strong discriminative deep feature. Then we employ stacked hourglass network (SHN) for heatmap regression and initial facial landmarks prediction.)

python开发的demo，实现将地理数据从高德坐标转换为wgs84坐标(Conversion of Goethe coordinates to WGS84 coordinates)

分类器的性能比较与调优: 使用scikit-learn 包中的tree，贝叶斯，knn，对数据进行模型训练，尽量了解其原理及运用。 使用不同分析三种分类器在实验中的性能比较，分析它们的特点。 本实验采用的数据集为house与segment。(Performance comparison and optimization of classifiers: We use tree, Bayesian and KNN in scikit-learnpackage to train the data model and try to understand its principle and application. The performances of three classifiers are compared and their characteristics are analyzed. The data set used in this experiment is house and segment.)

clear case 的操作，如check in check out (clear case handle client check in check out )

说明：  用户注册 登录 修改密码 基础家电控制(User registration, login, password modification, basic household appliances control)

说明：  手动实现RNN神经循环网络，自动实现神经循环网络(Manual implementation of RNN neural network)

测试 代码文件的 ！！！！！！！！！！！！！！！！！！！！！

利用可见光图像训练编解码网络，然后将红外图像和可见光图像进行融合编码，最后通过解码将图像恢复出来，用此类方法进行图像融合借鉴了图像风格迁移的思想，将一部分红外图像的信息迁移进可见光图像中，使得原本在可见光图像中不可见的信息显现出来，该方法相比较于传统的图像融合，在指标参数上有一定的提高。

关于PYTHON编成语言的最新中文翻译书，有大量的代码，还增加讲解最新的功能(About PYTHON compiled the latest English language translation of the book, there are a lot of code, but also on the latest features to increase)

使用Python实现MOEA/D，对于分解部分可以更换其他分解函数；对于基因重组部分可以更换其他交叉变异算子(Using Python to implement MOEA/D, other decomposition functions can be replaced for the decomposition part, and other crossover mutation operators can be replaced for the gene recombination part.)