avplayer源代码
一直以来, 在多媒体播放器这块, 即使目前有许多开源的播放器项目, 但要写一个播放器仍然是件非常困难的事, 如果在windows上你有可能需要熟悉DShow, 另外的话, 你需要学习一堆开源项目(比如FFmpeg, MPC, VLC, Mplayer), 而且多数都是基于linux, 在windows上学习起来很不容易, 然而这些开源项目对于一些希望快速实现自己播放器, 就显得很困难.
因此, 我创建了这个项目, 致力于以最简单的方法实现自己的播放器, 并提供一个可以很方便使用的接口.
目前, 在这个代码中, 主要链接到FFmpeg来进行解码, 并将其改造成一个通用的播放器框架. 在这个框架中能够接受各种数据的读入, 可以很方便的封装自己的数据读取模块, 也可以很方便的定制自己的视频渲染模块和音频播放模块, 你只需要参考其中的实现即可.
另外在当前的实现中, 因为个人精力实在有限, 所以借鉴了一些开源项目的代码(如Mplayer), 并且该代码主要基于windows平台, 有移植到linux等其它平台的打算. 所以, 我希望有朋友能参与到这个项目中一起研究和学习, 并完成这个目标.
在 https://github.com/Jackarain/avplayer/downloads 中有已经编译好的exe可供测试, 测试命令如下:
avplayer.exe test.mp4
或者
avplayer.exe http://edstream.googlecode.com/files/stream.m3u8
或者
avplayer.exe gq.flv.torrent
gq.flv.torrent 是从优酷找的一个超清视频文件做的种子.
整个项目分为几个模块:
1. avcore 这只是一个包装类, 实现windows上的窗口创建以及消息响应, 比如: 右击暂停/开始, 单击按屏幕宽百分比seek, F2全屏切换.
在这个模块中, avplayer类是外部接口类, 具体实现在player_impl类中. 在player_impl类中, 初始化各模块结构指针由下面几个函数实现,
void init_file_source(media_source *ms);
void init_audio(audio_render *ao);
void init_video(video_render *vo);
你可以根据自己的需求来修改这些函数实现.
media_source是一个提供数据访问的结构, 包含一些函数指针, 如果你需要重新从其它地方读取数据进来提供给播放器, 那么你可以参照这个结构体, 实现这些函数并指向它, 就可以获得从你指定的地方读取数据来进行播放, 这里实现了一个从文件读取数据播放的dll.
audio_render是用于播放音频的结构, 同样包含了一些函数指针, 只要实现这些函数指针, 就可以改变音频输出, 比如把音频数据写到文件, 这里实现了2种方式输出音频, 一种是使用dsound来输出音频, 另一种是使用waveout来输出音频.
video_render是用于渲染视频的结构, 原理同上, 需要注意的是render_one_frame的data是YUV420格式.
2. libav 这是一个基于ffmpeg的播放框架, 该框架不包含视频渲染和音频播放以及数据读取, 若需要可以实现上述结构体各函数即可, 这个框架中主要完成了使用ffmpeg解码, 以及音视频同步, seek操作处理, 并提供一些基本的控制播放器的函数(在avplay.h中定义).
3. audio 是一个音频播放输出模块实现, 主要实现了audio_render中那几个函数指针. 在这个模块中实现了2套音频输出dsound和waveout.
4. video 是一个视频渲染模块的实现, 实现了video_render中的那几个函数指针. 在这个模块中, 实现了3套视频渲染输出d3d, ddraw, opengl这些方式(还有gdi方式渲染没有添加到video_out.h中).
5. avplayer 是一个使用avcore实现一个简单播放器的示范, 如果要创建一个播放器, 只需要avplayer.h, 然后使用创建一个avplayer对象, 就可以创建一个播放器, 关于如何使用avcore的具体细节可以参考avplayer/main.cpp.
最后, 任何方式(包括商业或非商业)修改或引用该项目的全部或部分源码(或dll), 请在你的软件中或产品文档中必须清楚的声明引用了该项目, 并注明url(https://github.com/Jackarain/avplayer), 你可以偷偷的不这样做, 但这样做是令人欣赏的做法.
最后本程序作者不承担使用该程序所带来的任何问题并拥有一切解释权.
mail: jack.wgm@gmail.com
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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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