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
200+累计阅读
252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
在本文中，我们将讨论如何使用Python编程语言批量制作模糊效果的短视频。我们将通过使用OpenCV库来实现视频处理功能。下面是一个简单的Python代码示例，可以帮助你开始处理视频并应用模糊效果。
首先，确保已经安装了OpenCV库。你可以使用以下命令通过pip安装：
pip install opencv-python
一旦安装完成，你可以使用以下代码来实现批量处理视频文件并应用模糊效果：
import cv2
import glob
# 定义模糊效果的函数
def apply_blur(frame):
# 使用高斯模糊对帧进行处理
blurred_frame Python实现高斯模糊算法（含完整源码）
在图像处理中，高斯模糊是一种常用的模糊滤镜算法，其主要原理是通过对图像进行卷积操作来减少噪点和细节，从而得到更加平滑的图像效果。在Python语言中，我们可以通过NumPy和OpenCV等第三方库来实现高斯模糊算法。最终我们可以得到一个高斯模糊后的图像，使之更加平滑，从而达到去噪点和保留细节的效果。的函数，它接收一张图像作为输入参数，并可选地指定内核大小(函数对输入图像进行卷积操作，最后返回模糊后的图像。库创建一个高斯卷积核，然后使用OpenCV库的。函数对其进行高斯模糊处理，并将结果保存到名为。
【信息科学与工程学】计算机科学与自动化——第六十三篇 人机交互之前端交互参数知识库02
每个分支下又细分了四个关键方向，并按照“研究对象/焦点、基本问题、核心理论/概念、研究方法、应用/目标、前沿/发展”的框架进行阐述，严格遵循了六级编码体系。在最终整合时，应建立唯一的学科分类编码表（例如，用两位数字或“ART”、“MED”、“ECO”等字母前缀），并确保所有下级编码（尺度、层次、类别、子类、具体项）的逻辑一致性。此回答中的编码为示例，实际系统需据此调整。控制过程（前馈、同期、反馈）、全面质量管理（TQM）、平衡计分卡（BSC）定量（调查、实验）、定性（访谈、民族志）、混合方法、大数据。
第四届“网鼎杯”网络安全大赛 - 青龙组
第四届“网鼎杯”网络安全大赛 - 青龙组
AXI VIP配置全指南：从报错到成功仿真的完整流程（Vivado 2023版）
本文详细解析了在Vivado 2023环境中配置AXI Verification IP（VIP）进行协议仿真的完整流程。针对常见的include和import报错问题，提供了具体的解决方案和代码示例，指导用户从IP核创建、参数化配置，到构建可工作的仿真测试平台，最终实现成功仿真。
汇川EASY系列之以太网通讯（MODBUS_TCP做从站）
汇川easy系列PLC做MODBUS_TCP从站，，ModbusTCP服务器默认开启，无需设置通信协议（即不需要配置），端口号为“502”。ModbusTCP从站最多支持31个ModbusTCP客户端（ModbusTCP主站）同时连接。
BUCK-BOOST升降压电路Multisim电路仿真分析
本文分析了三极管型BUCK-BOOST升降压开关电源的工作原理。该电路结合了BUCK降压和BOOST升压拓扑，通过控制三极管Q1的高频通断，使电感L1周期性储能和释能，实现输出电压可高于或低于输入电压且极性相反。详细阐述了Q1导通和关断阶段的工作过程，推导了输出电压与占空比的关系式Vo=-D/(1-D)*Vi，并分析了不同占空比下的工作模式（降压、升压）。通过Multisim仿真验证了理论分析，当D=25%时输出降压至1.6V，D=60%时输出升压至7.5V。电路具有升降压灵活、极性反转、效率高等特点，适用
VSS shielding线可以floating吗？
shielding vss不打孔，就相当于dummy metal，如果周围有其他干扰线，他们影响了这段dummy metal，这段dummy metal仍然会对目标线产生干扰，bug的是，工具知道这段dummy是vss，那么计算寄生的时候仍然把它当vss，就有可能噪声没有完全消除，而用户又看不见。而真正接地的线，相当于一直是0，就不会对目标线产生干扰，一般加shield是在route之前，这时候shield能尽量覆盖目标线，等到绕线后再加就会出现shield只覆盖到一部分目标线的情况。
主题045：四足机器人的运动仿真
四足机器人（Quadruped Robot）是模仿哺乳动物四肢运动设计的机器人系统，具有优异的地形适应性和运动稳定性。与双足机器人相比，四足机器人在静态和动态稳定性方面具有天然优势，能够在复杂地形（如楼梯、斜坡、碎石地面）上稳定行走，因此在野外探索、救援任务、军事应用等领域具有广阔的应用前景。本教程将介绍四足机器人的运动学建模、步态规划、稳定性分析和控制策略，并通过Python仿真验证相关理论。典型的四足机器人每条腿包含3个关节：常见的腿部构型包括：对于单腿，足部位置 pfoot\mathbf{p}_{fo
SAP Single Sign-On 全景指南：从传统 SAP SSO 到 SAP Secure Login Service for SAP GUI 的架构、场景与迁移实践
本文系统梳理了SAP生态下的单点登录技术方案，重点分析了SAP GUI的云化替代方案SAP Secure Login Service(SLS)。内容涵盖SNC、Kerberos/SPNEGO、SAML 2.0等核心协议的技术实现，对比了传统SAP SSO与新型SLS for SAP GUI的架构差异，并给出AS ABAP系统的具体实施路径。针对SAP GUI访问、浏览器访问Fiori/移动端等不同场景，提供了详细的协议选型建议和配置指南，帮助团队构建安全可靠且适应云时代的统一认证体系。
三相五柱式消弧线圈自动测控装置设计与实现
本文设计了一种基于STM32的三相五柱式消弧线圈自动测控装置。该装置采用信号注入法检测系统对地电容电流，通过PID自适应算法调节消弧线圈电感值，实现单相接地故障时的快速补偿。硬件系统包含信号采集、注入、控制执行等模块，软件采用Goertzel算法提取特征频率信号，结合增量式PID实现精确控制。实验结果表明，装置电容检测误差小于2%，调谐响应时间小于50ms，满足配电网对消弧线圈自动调谐的技术要求。
VC中sqlite的使用
在VC中对sqlite的使用，实现数据库大批数据的快速插入，读取数据库中的数据在ListCtrl中显示
efm32 入门.pdf
本PDF共计22页，介绍的是操作驱动API，下载与否自己斟酌。
Camera2Demo
在Camera2 的基础上进行封装，使得Camera2一堆初始化逻辑与Activity分离，降低耦合度，完成预览拍照并保存的操作
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")
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
订阅专栏 解锁全文
2680
扫一扫
