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目前这个 OpenGL ES 极简教程的更新暂时告一段落，说是教程，其实自知有种吹嘘的味道，很荣幸获得了阮一峰老师的推荐。
因为在工作中频繁使用 OpenGL ES 做一些特效、滤镜之类的效果，加上平时学到的的知识点也比较细碎，就想着去系统地学习下 OpenGL ES 相关开发知识，并将学习过程记录下来。
另外，这个 Android OpenGL ES 极简教程主要是为音视频开发做准备的。
一些同学反映，学习这个教程感觉有点门槛。其实，就是需要懂一些 Android 开发和 C/C++ 开发知识。
该教程分为基础篇和应用篇，基础篇主要是讲解 GLES 3.0 的主要核心知识点，而应用篇主要是利用基础篇的知识实现一些常见的特效和功能。
基础篇
基于FFmpeg解码和OpenGL ES渲染的Android视频播放
前言在我的博文中，我们在Android平台上，实现了通过FFmpeg在native（C/C++）层进行视频解码，并通过SurfaceView（基于ANativeWindow）实现了图像的显示。实际上，我们也可以通过OpenGL来进行硬件的渲染工作，减少CPU的消耗，提高整个视频播放的性能。实现需求只演示视频解码和显示，不进行音频解码，也不做音视频同步，每两帧视频之间通过固定延时来间隔，所以视频播放时存在偏慢和偏快的问题；
openGL ES进阶教程（三）用openGL实现动态壁纸，就是这么简单
网上动态壁纸的实现教程有很多，但是用openGL实现动态壁纸的教程却是寥寥无几，那么今天就带领大家学习用openGL实现动态壁纸，起一个抛砖引玉吧。在之前曾写过一篇 ffmpeg/camera实现最近很火的视频壁纸，相机壁纸 ，动态壁纸的实现套路已经写了，今天就不追述了。老规矩，先看效果图让大家有个概念：大家也看出来了，没错，是基于上篇openGL ES进阶教程（二）之全景图片 实现的。下面就具体说
Android平台的FFmpeg和OpenGL ES视频解码与播放指南
视频播放作为移动应用中的常见功能，其背后隐藏着一系列复杂的技术。在Android平台上，视频播放技术涉及到数据的解析、解码、渲染以及最终的显示等多个层面。本章将对这些核心技术进行概述，为后续深入学习各个关键技术模块奠定基础。首先，视频播放技术的核心之一是多媒体框架的选择，如FFmpeg，它是一个强大的跨平台视频处理库，能够处理几乎所有音视频格式。在Android中集成FFmpeg，可以让开发者在应用中支持广泛的编解码格式，增强应用的兼容性。
StarRocks FE启动异常
服务器断电，StarRocks FE 启动失败。
Jetbrains IDE 配置上Gemini Code 插件，让你的的编程高效起飞～
Gemini Code Assist是 Google 家产品，基座模型为最新的Gemini 2.5 Pro。本篇实践了在 WebStrom 中Gemini Code Assist插件的高效编程。
慕课软件质量保证与测试(习题集)
慕课金陵科技学院.软件质量保证与测试.期末复习.习题汇总0 总目录1 绪论1.1 软件质量保证与测试的产生与发展1.2 软件缺陷,软件错误,软件故障1.3 软件质量保证与测试的意义,原则和挑战1.4 单元测试1.5 课后作业2 软件测试策略2.1 软件测试的模型,过程和生命周期2.2 软件测试的方法和技术2.3 单元测试2.4 课后作业3 黑盒测试3.1 黑盒测试概述3.2 等价类划分测试3.3 边界值测试3.4 错误推测法3.5 判定表驱动法3.6 因果图法3.7 场景法3.8 正交实验法3.9 单元测试
单窗算法的地表温度反演：谷歌地球引擎GEE代码
该论文基于Statistical Mono-Window（SMW）算法，对地表温度加以求解。其中，简单地说，SMW算法即是通过简单的线性关系，对由单热红外波段所得的大气表观亮温与地表温度之间的经验关系加以表示，从而计算得到LST。
宏基因组分析|从Binning到MAG优化的全流程实战指南
本文提供了一份宏基因组分析从Binning到MAG优化的全流程实战指南。详细解析了宏基因组分箱（Binning）的核心原理与必要性，并手把手指导如何利用Bowtie2、MetaBAT2和CheckM等工具构建分箱流水线，进行质量评估与筛选，最终获得高质量宏基因组组装基因组（MAG）。文章还分享了常见问题排查与进阶优化策略，为后续物种分类、功能注释等深度分析奠定坚实基础。
避坑指南：Qt无边框窗口拖动+阴影效果这些细节要注意
本文深入探讨了Qt无边框窗口开发中实现流畅拖动与阴影效果的关键细节与常见陷阱。针对高DPI屏幕适配、阴影闪烁、拖动卡顿及多显示器兼容性等核心难题，提供了从QGraphicsDropShadowEffect原理剖析到手动绘制优化、坐标系统精准处理及平台原生事件拦截的实战解决方案，帮助开发者构建稳定、美观的商业级应用界面。
用HFSS仿真平面线圈的电感量
平面线圈是指在平面上绕制而成的线圈，如PCB上的电感线圈、无线供电使用的金属丝绕制而成的线圈等。根据线圈的不同形状可将平面线圈分为方形线圈，六角形线圈、八角形线圈、螺旋原型线圈等。
如何利用air780e与stm32c8c6进行通信实现给手机发送一条英文短信的功能？
🏆 本文收录于《全栈Bug调优(实战版)》专栏，致力于分享我在项目实战过程中遇到的各类Bug及其原因，并提供切实有效的解决方案。无论你是初学者还是经验丰富的开发者，本文将为你指引出一条更高效的Bug修复之路，助你早日登顶，迈向财富自由的梦想🚀！同时，欢迎大家关注、收藏、订阅本专栏，更多精彩内容正在持续更新中。让我们一起进步，Up！Up！Up！ 备注： 部分问题/难题源自互联网，经过精心筛选和整理，结合数位十多年大厂实战经验资深大佬经验总结所得，数条可行方案供所需之人参考。
jre9下载 jdk9
(Java SE Runtime Environment 9.0) 9.0u175 官方最新版 【x86|x64】
计算机控制技术及应用
《计算机控制技术及应用》通过总结作者的科研成果和吸收国内外的先进理论、方法和技术，以计算机控制系统的网络化、开放化、智能化和集成化发展趋势为主线索，主要介绍计算机控制系统的基本概念和计算机控制系统的典型形式，计算机控制系统的数学描述与基本分析方法，以及数字PID控制器、最少拍控制系统、达林算法、状态变量设计法等设计方法及其各种改进算法。重点突出计算机控制系统输入输出通道接口的设计方法和典型控制系统的设计案例与实现方法等。
深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[](https://bitdeli.com/free "Bitdeli Badge")
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