Python Crash Course is a fast-paced, thorough introduction to programming with Python that will have you writing programs, solving problems, and making things that work in no time. In the first half of the book, you'll learn about basic programming concepts, such as lists, dictionaries, classes, and loops, and practice writing clean and readable code with exercises for each topic. You'll also learn how to make your programs interactive and how to test your code safely before adding it to a project. In the second half of the book, you'll put your new knowledge into practice with three substantial projects: a Space Invaders-inspired arcade game, data visualizations with Python's super-handy libraries, and a simple web app you can deploy online. As you work through Python Crash Course, you'll learn how to: Use powerful Python libraries and tools, including matplotlib, NumPy, and Pygal Make 2D games that respond to keypresses and mouse clicks, and that grow more difficult as the game progresses Work with data to generate interactive visualizations Create and customize simple web apps and deploy them safely online Deal with mistakes and errors so you can solve your own programming problems If you've been thinking seriously about digging into programming, Python Crash Course will get you up to speed and have you writing real programs fast. Why wait any longer? Start your engines and code! Table of Contents Part I: Basics Chapter 1: Getting Started Chapter 2: Variables and Simple Data Types Chapter 3: Introducing Lists Chapter 4: Working with Lists Chapter 5: if Statements Chapter 6: Dictionaries Chapter 7: User Input and while Loops Chapter 8: Functions Chapter 9: Classes Chapter 10: Files and Exceptions Chapter 11: Testing Your Code Part II: Projects Project 1: Alien Invasion Project 2: Data Visualization Project 3: Web Applications Appendix A: Installing Python Appendix B: Text Editors Appendix C: Getting Help Appendix D: Using Git for Version Control

Machine Learning A Constraint-Based Approach 英文无水印pdf pdf所有页面使用FoxitReader和PDF-XChangeViewer测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 本资源转载自网络，如有侵权，请联系上传者或csdn删除

图像质量的提高是广泛的基于视觉的应用的基本过程。在不利环境下捕获的图像通常会降低信息内容、清晰度和色彩。 在改善图像的尝试中，非锐化掩蔽滤波器因其计算效率而成为有吸引力的候选者。 然而，过滤器容易受到超范围问题的影响，即像素大小超出允许范围。 如果在增强中使用非自适应过程，则此缺点尤其明显。 因此，在此提出一种自适应增益调整方法，其目的是在使图像清晰度和信息含量最大化的同时，使超范围像素的数量最小。 在这种方法中，色彩通过颜色通道拉伸得到改善，对比度通过边缘增强得到增强。 具体而言，构建了一个双曲正切函数，其尺度取决于原始图像强度和检测到的边缘，用于调整锐度增强的增益。

Fall_detection_by_gcn 跌倒检测演示的一些示例： 坠落事件发生后，红色矩形将闪烁。 它在gtx 1060 GPU上以6 fps的速度运行。 往前走 倒退 向左下落 向右下落 安装： 在之后，首先将openpose安装到您的计算机上。 安装 。 将“ / net”和“ Fall_detection_demo.py”复制到$ Openpose_path / python中。 python3 Fall_detection_demo.py来运行演示。

基于位置的动态 该库支持机械效果的基于物理的模拟。 在过去的几年中，基于位置的仿真方法已在图形界盛行。 与传统的仿真方法相比，这些方法基于准静态问题的解决方案直接计算每个仿真步骤中的位置变化。 因此，基于位置的方法是快速，稳定和可控制的，这使其非常适合在交互式环境中使用。 但是，这些方法通常不如基于力的方法准确，但仍具有视觉上的真实性。 因此，基于位置的模拟的主要应用领域是虚拟现实，计算机游戏以及电影和广告中的特殊效果。 PositionBasedDynamics库允许在基于物理的仿真中对多种类型的约束进行基于位置的处理。 该库使用 ， ， 和 （仅用于演示）。 包括所有外部依赖项。 此外，我们使用自己的库： 生成用于碰撞检测的三次有符号距离场 作者： ，许可证：麻省理工学院 消息 我们有关刚性杆基于位置的直接求解器的新使用了PositionBasedDynamics库。 您可

Lux Plus is the commercial branch of the open source Lux shader framework and brings Lux's advanced lighting features such as skin, translucent and anisotropic lighting to the deferred rendering pipeline — all without adding any additional data to the built in gbuffers. So unlike other solutions Lux Plus will not stress your memory bandwidth furthermore but uses a cleverly packed default gbuffer. Lux Plus has been successfully tested with DX11, DX9 and OpenGLCore on Win and Mac using nvidia GPUs and Unity 5.4.2, Unity 5.5.0b10, Unity 5.6.1., Unity 2017.2.0f3 and Unity 2018.1. and above. Lighting features - Fast approximated area lights - Diffuse fill lights - Deferred pre-integrated Skin Lighting and wrinkle maps - Deferred translucent Lighting - Deferred anisotropic Lighting* (read more) - Deferred lambert lighting - Diffuse scattering or fuzz lighting - Specular Anti Aliasing - Horizon Occlusion Surface features - Dynamic weather - Mix mapping - Parallax, parallax occlusion and tessellation - Double sided rendering Although Lux ships with a rather flexible standard shader it allows you write custom surface shaders which will take full advantage of all feature mentioned above. In order to make this as easy as possible Lux offers a bunch of predefined shader macros and ships with various example surface shaders including: - Tessellation - Geometry based refraction - Geometry based deferred decals Lux Plus and Image Effects As Lux Plus packs Unity’s built in GBuffer in a special way, image effects, which rely on data from the GBuffer, will most likely break. But Lux Plus ships with fixes for the Cinematic Image Effects (Lux 2.01 only), Unity's Post Processing Stack and Amplify Occlusion. (read more)

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数据融合matlab代码基于多视图深度学习的基于表面肌电图的手势识别 此回购包含我们关于sEMG的最新论文的代码：魏文涛，戴庆峰，黄永康，余杜，坎坎哈利，耿卫东。” 要求 CUDA兼容GPU Ubuntu> = 14.04或任何其他可以运行DockerLinux / Unix 用法 拉泊坞窗图片 我们已将docker映像上传到。 您可以使用命令行来提取docker映像，如下所示： docker pull zjucapg/semg:latest 使用下面的命令行进入Docker容器 nvidia-docker run -ti -v your_prodjectdir:/code your_featuredir:/feature your_imudir:/imu zjucapg/semg /bin/bash 数据集 本文使用的原始数据集包括11个类别，包括（ DB1-DB7 ）和（ 10mov4chUntargetedForearm，6mov8chUFS，10mov4chUF_AFEs，8mov16chLowerLimb ）。 在这项工作中，sEMG的手工功能被用作多视图深度学习的不同视图。