墨衍会员
·
AI 创作全网分发
墨衍智能分发
本文由作者通过墨衍一键同步至各平台
1分发平台
1.7k累计阅读
1.8k平均单平台
已同步平台
优快云
微信公众号
微博
知乎
掘金
百家号
博客园
抖音
小红书
你的文章也可以这样分发
墨衍会员 ¥399起/年，写一次发全网
我也要
注意：本文为收费专栏文章，对应免费专栏文章为《moviepy音视频剪辑：与time时间线相关的变换函数freeze_region、make_loopable、speedx、time_mirror、time_symmetrize介绍》
在《moviepy音视频剪辑：moviepy中的剪辑基类Clip详解》介绍了剪辑基类的fl、fl_time、fx方法，在《moviepy音视频剪辑：视频剪辑基类VideoClip的属性及方法详解》介绍了fl_image和subfx方法，在《Python+moviepy音视频剪辑：视频帧
Moviepy时间变换time_mirror再遇‘OSError: MoviePy error: failed to read the first frame
为了解决这个问题，我们可以尝试使用不同的视频编解码器进行视频文件的读取。我们可以尝试使用其他的编解码器，如ImageMagick或OpenCV。请注意，本文提供的示例代码和解决方法仅供参考，并不能保证适用于所有情况。如果问题仍然存在，可能需要进一步检查视频文件的格式、路径等问题，并确保相关依赖库已正确安装。在使用Moviepy库进行视频处理时，有时候我们需要对视频进行时间变换，比如将视频镜像翻转。通过修改编解码器的设置，我们可以尝试使用不同的工具进行视频文件的读取，以解决。函数可以帮助我们实现这一功能。
Moviepy时间变换time_mirror再遇‘OSError: MoviePy error: failed to read the first frame of video file‘解决示例代码
一、引言 在《https://blog.youkuaiyun.com/LaoYuanPython/article/details/106478711 moviepy音视频剪辑：使用fl_time报错OSError: MoviePy error: failed to read the first frame of video file》老猿介绍了使用fl_time进行时间变换遇到的OSError报错，并给出了解决办法，在付费专栏文章《https://blog.youkuaiyun.com/LaoYuanPython/article/
Python中的time_symmetrize介绍及示例代码
函数内部通过调用datetime模块中的now()函数获取当前时间，并计算给定时间点与当前时间的时间差，以秒为单位返回时间差。time_symmetrize函数的作用是将给定的时间点对称化为与当前时间点的相对时间。例如，如果给定的时间是过去的时间，time_symmetrize函数将返回一个负数，表示该时间点在当前时间之前的时间差。如果给定的时间是未来的时间，函数将返回一个正数，表示该时间点在当前时间之后的时间差。请注意，time_symmetrize函数返回的时间差是一个浮点数，表示时间差的秒数。
Time Symmetrize是一种在物理学和数学中常用的技术，用于通过时间对称性在不同的时间点上重建物理过程
接下来，我们使用time_symmetrize()函数来对原始函数进行时间对称化处理。Time Symmetrize是一种在物理学和数学中常用的技术，用于通过时间对称性在不同的时间点上重建物理过程。在Python中，我们可以使用SymPy库中的time_symmetrize()函数来实现这一技术。通过这个简单的示例，我们可以看到时间对称化是一个很有用的技术，可用于使不同时间点上的物理过程产生关联。time_symmetrize()函数接受两个参数：一个表示要重构的时间范围，另一个表示要操作的函数。
moviepy音视频剪辑实战：加载与输出
本文还有配套的精品资源，点击获取 简介：moviepy是一个模块化工具，用于在Python中处理音视频文件，包括创建、编辑和操作功能。本教程将详细指导如何使用moviepy进行音视频的加载、剪辑、合成和输出操作。通过实例代码和最佳实践，学习moviepy将帮助编程爱好者和专业人士在音视频项目中实现高效的编辑工作。 1. moviepy库简介和核心功能 mov...
Qt Coordinate System
Qt 坐标系统Qt 坐标系统 数学基础 Qt坐标系统 视口 平移变换 缩放变换 setWindow 旋转 总结数学基础 矩阵与线性变换坐标系变换Qt坐标系统 视口和OpenGL一样，视口确定了画面在窗口(QWidget)中显示的区域，Qt的绘图坐标会完全映射到视口(View port)上，就像图示一样。默认情况下，窗口和视口是重合的，视口的大小覆盖了整个窗口。和OpenGL一样，Qt的QPa
Aveva Marine C# 二次开发入门001
1# 引用 C:\AVEVA\Marine\OH12.1.SP4\Aveva.ApplicationFramework.dll C:\AVEVA\Marine\OH12.1.SP4\Aveva.ApplicationFramework.Presentation.dll 2# 引用命名空间， using Aveva.ApplicationFramework.Presentation;usi...
第三节: 串口通信(用CubeMX学习STM32)
用CubeMX学习STM32, 串口通信学习, 通过轮询方式和中断方式两种方式进行通信演示。 STM32F407zgt6
SpringBoot文件上传实战：MultipartFile重复读取的3种方法（附避坑指南）
本文详细介绍了SpringBoot中处理MultipartFile重复读取的3种实用方法，包括字节数组缓存法、临时文件法和流重置法，并提供了避坑指南和性能优化技巧。特别适合需要多次读取上传文件内容的开发者，帮助解决InputStream流只能读取一次的问题。
【WSL】window11 安装WSL2配置conda环境及mlatom环境搭建
写这篇文章之前自己安装过两次环境，可能每次都会产生碰到同样的问题，为了搭建适合自己的虚拟环境保证每一步的正确性，按顺序记录一下。
5.17华为OD机试真题 新系统 - 输出二叉树后序遍历结果 (Java/Py/C/C++/Js/Go)
5.17华为OD机试真题 新系统 - 输出二叉树后序遍历结果 (Java/Py/C/C++/Js/Go)
【FF-A】第四章 Concepts
SPM的责任分为两个组件：SPM调度器（SPMD）和SPM核心（SPMC）。这两个组件都可以访问整个物理地址空间，并且是受信任的计算基础的一部分。当无需区分这两个组件时，术语SPM被使用。这些组件的责任如下所示。SPMD 位于 EL3 中，以 AArch64 或 AArch32 执行状态运行。SPMC 可以与 SPMD 共存在 EL3 中，也可以位于相邻的异常级别，即 S-EL1 或 S-EL2。SPM 在启动时初始化 EL3 LSP，并在运行时促进它们与其他端点之间的通信。
收放卷伺服控制系统详细算法介绍(电子齿轮+张力PID卷绕轴控制功能块)
收放卷控制系统涉及的内容非常多，这里我们介绍全伺服系统利用电子齿轮指令实现主从轴的比例随动速度控制，收放卷控制算法介绍常用链接如下1、收放卷+排线控制收放卷+排线控制系统框图-优快云博客文章浏览阅读24次。1、收放卷前馈量计算FC收放卷前馈量计算FC(CODESYS ST源代码)-优快云博客文章浏览阅读16次。1、收放卷前馈量计算FC（博途PLC）：收放卷前馈量计算FC(梯形图+SCL代码)-优快云博客文章浏览阅读534次。2、收放卷前馈量计算FC。
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")
TMS_WebGMaps_1.9.8.0_Full_Source
TMS_WebGMaps_1.9.8.0_Full_Source 全源码 delphi cbuilder
jdk-8u144-linux-x64.tar.gz.
该资源为搭建Linux系统Java环境用，资源的后缀为tar.gz格式的压缩包
ET2012新版免狗免安装绿色软件
ET2012新版免狗免安祖昂绿色软件。，新界面，新视觉，
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
超级会员免费看
订阅专栏 解锁全文
463
扫一扫
