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方法一：通过imageio库和skimage库
1. 安装环境：
pip install imageio
pip install skimage
这时候会报错Please install the `scikit-image` package (instead of `skimage`)
所以按照提示操作即可：
pip install scikit-image
环境安装成功。
2.通过python安装ffmpge（用来解码视频流）
import imageio
imageio.plugins.ffmpeg.download()
这时候会提示：
Imageio: 'ffmpeg.linux64' was not found on your computer;
Rust 与 C/C++：Rust 比 C/C++ 更好还是只是“技能问题”？
对于许多嵌入式系统开发人员而言，的崛起似乎掀起了一股不小的浪潮，特别是在相较于传统的语言，Rust的“内存安全”优势让人眼前一亮。然而，是否可以将Rust作为C/C++的替代品？或者说，Rust是否真的比C/C++更好，抑或它只是一个“技能问题”呢？在嵌入式开发的领域，我观察到，尽管Rust在一些高性能、内存安全敏感的场景中受到了广泛推崇，但它在实际应用中的普及度，特别是在嵌入式领域，依然有限。今天，我会从多个角度探讨这个问题，并且分享一些对于Rust、C/C++以及嵌入式系统开发的个人见解。
Python爬虫之爬取并下载哔哩哔哩视频
它可以爬取并下载视频文件和音频文件！亲自使用过，太好用了。
Phantom Omni的力与力矩操作
*本说明为对Phantom Omni的力与力矩操作 ****************虚拟夹具的力读取与控制************************ 1、首先力的读取要声明【全局变量】 hduVector3Dd F;//三维矢量力（x,y,z三个方向的力） 2、对力的控制可以采用配置函数Set()与Get()函数； 3、对力觉的控制或读取操作需要放到【回调帧】开始与结束之间，如例子
ESP32-C61 eFuse控制器深度解析：寄存器架构与安全烧写实践
eFuse（电子熔丝）是嵌入式芯片实现硬件级不可逆配置与信任根构建的核心物理机制，其本质是通过电学编程固化关键安全参数。原理上依赖熔丝单元的单向导通/断开特性，结合Reed-Solomon纠错、多级写保护和时序可控编程，保障数据在宽温域与工艺偏差下的高可靠性。技术价值在于为Secure Boot、Flash加密、密钥隔离等安全功能提供可信锚点，支撑从启动到运行的全链路防护。典型应用场景涵盖物联网设备量产烧录、安全启动使能、MAC地址固化及抗DPA密码运算配置。本文聚焦ESP32-C61平台，深入解析其eFu
告别CPU空转！用STC8的SPI+DMA高效驱动上百颗WS2812B灯珠
本文详细解析了如何利用STC8单片机的SPI+DMA外设高效驱动WS2812B全彩灯珠，解决传统软件延时驱动方式在长灯带应用中的性能瓶颈。通过硬件方案设计、数据格式转换和DMA传输配置，实现高达400Hz的刷新率和低于5%的CPU占用率，适用于大规模灯光项目。
QGis二次开发 —— 2、第一个程序 - 加载栅格tif与矢量shp文件（附源码）
QGis二次开发 —— 2、第一个程序 - 加载栅格tif与矢量shp文件（附源码）
【异常】OpenClaw LLM请求超时异常LLM request timed out.的技术分析与解决方案
LLM请求超时是OpenClaw工程化落地中最常见的链路稳定性问题，排查处置需遵循标准化的分级流程。绝大多数场景可通过重试、调整超时配置、优化模型选型快速解决；针对顽固型异常，可通过全链路日志拆解超时阶段，精准定位根因并完成针对性修复，最终保障OpenClaw业务链路的稳定运行。
海波龙（Hyperion，现属 Oracle）合并报表以 HFM（Hyperion Financial Management）为核心系统，方法论核心是以控制为基础确定合并范围，通过维度建模统一数据口径
摘要：海波龙合并报表系统(HFM)采用"规则驱动、维度建模"的方法论，通过四个核心环节实现自动化合并：确定合并范围、数据标准化、自动抵销调整及报表生成。系统支持多会计准则，以控制为基础划分合并方法(完全合并/权益法/成本法)，通过维度建模统一数据口径，自动处理内部交易抵销、外币折算等复杂操作。业务操作流程涵盖数据采集、转换、抵销调整到报表生成全过程，关键控制点包括内部交易匹配、股权变动处理等。最终输出合规合并报表及完整审计线索，满足监管与管理需求。(150字)
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
WINCC7.3破解文件
WINCC7.3硬件够破解文件，有需要的下载！WINCC7.3硬件够破解文件，有需要的下载！
Statistics and Machine Learning Toolbox
Statistics and Machine Learning Toolbox（带书签），MATLAB统计学习工具
LinePhone3.2.7(android端最新版本) sip客户端
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