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PyQt5基础控件详解之QInputDialog使用方法
在PyQt5中，QInputDialog是常用的一种输入对话框，它可以用于获取用户输入数据，比如字符串、整数、浮点数等数据类型。下面我们就来了解一下QInputDialog的基本使用方法。
1.导入模块
from PyQt5.QtWidgets import QApplication,QInputDialog,QWidget
2.创建应用程序对象
app = QApplication(sys.argv)
3.创建主窗口对象
widget PyQt5 基础教程之输入控件
在实际开发中，我们可以根据具体的需求来选择合适的输入控件，并为其绑定相应的信号和槽函数，从而实现更为复杂的交互功能。输入控件可以让用户输入数据，包括文本、数字、日期等等，我们可以通过信号来获取输入控件中的值，从而进行下一步操作。QLineEdit 是最基本也是最常见的输入控件，它可以让用户输入单行文本，如用户名、密码等。QSpinBox 是一个可以输入数字的控件，它可以让用户通过拖动或直接输入的方式快速输入数字。QTextEdit 是一个可以多行输入的控件，它可以让用户输入大段文本，如文章、日记等。
PyQt5 输入对话框（QInputDialog）详解
是 PyQt5 中用于快速获取用户简单输入的标准对话框类。它提供了一个预配置的对话框，用于从用户获取各种类型的简单输入，是快速用户交互的理想选择。输入对话框的主要功能获取文本输入获取整数输入获取浮点数输入从列表中选择项目提供多功能的输入验证理论要点特性自定义表单使用场景简单、单一的输入复杂、多个字段的输入开发速度快速，几行代码较慢，需要更多代码自定义性有限完全可定制用户体验标准、一致可以完全定制验证基本验证复杂验证逻辑理论要点。
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NRF52832调试方法1——HardFault_Handler问题定位
一、遇到问题 keil debug模式下，直接点击全速运行（Run）发现程序卡死，点击stop按钮后，停到了HardFault_Handler位置，如下图所示。 此时使用navigate backwards（蓝色左箭头）是无效的，找不到程序卡在哪里。 二、解决办法 此时首先观察register窗口 若Stack为MSP，则复制R13（SP）的值到memory观察窗的地址搜索栏。每一行显示连续的28个地址上的存储信息，每个地址上存有一个字节。 从第21个位置起的连续四个字节是LR的值，
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手把手教你配置UFS的PSA状态机：WRITE/UNMAP命令实战与LBA映射避雷
本文详细解析了UFS PSA状态机的配置流程，重点探讨了在Pre-soldering状态下WRITE命令的原子性操作原则与单次写入LBA的重要性。通过实战代码示例，指导开发者如何正确初始化PSA环境、切换状态机，并深入剖析了使用UNMAP命令清理LBA映射时的常见陷阱与避雷指南，确保量产数据安全与良率。
【PSINS工具箱】159联邦滤波器代码优化与注释、运行结果、文字性解释。15维状态量、9维观测量，联邦滤波与集中式滤波对比。完整代码可订阅后查看
基于工具箱的联邦滤波代码，15维状态量、9为观测量
【银河麒麟V10】【桌面】【服务器】系统启动问题
1、如果之前运行好好的，重启后系统报错，一般80%是因为fstab的问题，回想下之前有没有添加过外接存储，修改过/etc/fstab，如果是的话一般是因为存储异常（比如网络存储无法连接，本地存储路径变化）导致系统开机自检失败，导致无法正常启动； 处理方式：进入单用户（单用户进入的几种方式上面有专门文章介绍），修改/etc/fstab，将新增的内容暂时注释掉，然后重启系统，能正常进入系统后再分析失败原因。 ...
后摄模块辐射超标整改
某行车记录仪在EMI测试中出现辐射超标问题，通过模块剥离法定位到后摄模块的Ethernet数据线为噪声源。深入检查发现TYPE-C延长线屏蔽层未有效接地，形成辐射泄漏通道。整改措施包括加强屏蔽层360°接地、使用导电布改善连接处屏蔽等，最终使辐射降至EN55032 Class B限值以内。案例揭示了高速信号延长线中屏蔽连续性的重要性，建议设计阶段就重视连接器屏蔽设计和共模干扰抑制。
【K8S系列】Kubernetes 中 Pod 无法通过 Service 名称访问服务的 DNS 解析失败问题【已解决】
在 Kubernetes 中，Service 提供了一种稳定的方式，通过名称访问一组 Pod。当其他 Pod 无法通过 Service 名称访问服务，并且出现 DNS 解析失败时，通常会导致应用无法正常工作。本文将详细分析此问题的常见原因及其解决方案。
win10 跳过U8环境检测的IE WebControls.rar
win10 安装u8的时候 提示IE WebControls 不通过，这个工具可以欺骗u8的检查。 跳过U8环境检测的IE WebControls。提示导入失败后，重新检测通过。
Probability and Statistics - 4th edition
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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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
C# WinForm 基于DirectShow.dll编写的程序，可边预览边保存视频，音频和图片
基于微软的DirectShow.dll的二次开发1.自动识别连接的摄像头跟麦克风且可以对其进行选择（默认选中第一个）2.可录制wmv和avi两种格式的视频并且都带有音频，avi可以录制的分辨率和帧数进行选择，wmv默认进行了淸晰度优化如不喜欢可去除代码中有相关提示3.可在预览点开之后进行拍照，也可边录制视频边拍照4.添加了视频和音频的压缩功能（酌情使用）这次二次开发发现C#对DirectShow的相关信息太少，这次做完也不敢藏私共享出来，还望大家指正
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