MFC去掉窗口右上方最大化最小化关闭按钮

去掉MFC的MDI程序中的浮动工具条上标题栏中的“关闭”按钮 <br />去掉MFC的MDI程序中的浮动工具条上标题栏中的“关闭”按钮:<br /><br /><br /><br />由于工具栏也是一个对话框窗口,我们可以利用 CControlBar 基类的m_pDockBar成员来调用GetParent()以获取工具条的父窗口。从而改变父窗口的式样——屏蔽系统菜单——来达到我们的目的——去掉“关闭”按钮。<br />(摘自http://www.vckbase.com/vckbase/default.aspx)<br /> <br />(1)如果想在视图CMFC_fr 阅读详情

MFC去掉窗口右上方最大化最小化关闭按钮 

简介:

最大化、最小化和关闭按钮是窗口中最主要的元素。首先要说明,说他们是按钮其实是不准确的,按钮是一种窗口,而这三个组件根本就不是窗口,而是一个窗口常见的组成部分。出于习惯的原因,这里还是称呼他们为按钮。

这三个按钮是否显示似乎只能在窗口创建是决定。首先,要显示这三个按钮,窗口必须有标题条和系统菜单,没有标题条和系统菜单的窗口是不能显示这三个按钮的,也就是说,要想有这三个按钮,创建窗口时必须有WS_CAPTION和WS_SYSMENU风格。当窗口创建时有风格WS_MAXIMIZEBOX时,窗口显示最大化按钮,当有风格WS_MINIMIZEBOX时,窗口显示最小化按钮,而只要有标题条并且有系统菜单,关闭按钮就必然会显示出来。</

ModifyStyle()调用不起作用 现象:在使用WTL做CListBox子类化时,使用 ModifyStyle(0, LBS_OWNERDRAWFIXED)不起作用; 原因:并不是所有的风格都可以动态利用ModifyStyle/ModifyStyleEx()函数增加和去除,有些风格比如 LBS_HASSTRINGS | LBS_OWNERDRAWFIXED| LBS_OWNERDRAWVARIABLE 就只能在创建窗口的时候指定( 阅读详情

相关推荐

Q窗口操作函数(窗口最大化,全屏,隐藏最大化最小化按钮)

Qt窗口中的一些小技术总结   //Qt主窗口没有最小化,最大化按钮且最大化显示  int main(int argc, char *argv[])  {  QApplication a(argc, argv);  TestQtForWinCE w;  w.setWindowFlags(w.windowFlags()&amp; ~Qt::WindowMaximizeButtonHin...

qimo601的专栏 5666

MFC窗体美化技巧:去掉最大化、最小化和关闭按钮的三种方法

本文详细介绍了MFC窗体美化中隐藏最大化、最小化和关闭按钮的三种实用方法,包括API级控制、资源编辑器方案和高级自定义绘制技术。这些技巧特别适用于金融终端、医疗影像系统等需要全屏沉浸式体验的专业场景,帮助开发者实现界面深度定制。

v6b7n8m9q0的博客 793

Qt窗口操作函数(最大化,全屏,隐藏最大化,最小化)

Qt窗口中的一些小技术总结   //Qt主窗口没有最小化,最大化按钮且最大化显示  int main(int argc, char *argv[])  {  QApplication a(argc, argv);  TestQtForWinCE w;  w.setWindowFlags(w.windowFlags()& ~Qt::WindowMaximizeButtonHint& ~

软件工程 2430

MFC窗体美化技巧:去掉最大化最小化关闭按钮的5种方法对比

本文详细解析了MFC窗体美化中隐藏最大化、最小化和关闭按钮的5种实用方法,包括直接样式修改、非客户区绘制拦截、子类化技术等。通过对比各方案的性能、兼容性和开发复杂度,帮助开发者选择最适合的界面定制方案,特别适用于信息展示系统和工业控制界面开发。

e3f4g5的博客 182

VC 窗口最大化、最小化、关闭按钮变灰色、禁用或无效实例.rar

VC 6.0 禁止窗口最大化、最小化、关闭按钮变灰色、禁用或无效的实例,禁止用户通过窗体右上角的最大化、最小化、关闭按钮关闭窗口、最小化或最大化窗口,也就是不让全屏显示,一般情况下不这样,不过有时候对于非常规窗口,根据用途,窗口最大化可能会影响其它功能使用,所以通过本例介绍的方法可将其禁用或无效,变成灰色的不可点击。通过系统菜单命令,你可以选择针对的操作。

【具身算法】VLA算法-Diffusion Policy

《扩散策略:多模态机器人控制的新方法》摘要 本文提出了一种基于扩散模型的机器人控制策略(DiffusionPolicy),用于解决视觉运动控制中的多模态行为、高维动作空间和训练稳定性问题。该方法利用去噪扩散概率模型(DDPM)生成动作序列,通过迭代优化处理复杂动作分布,显著提升了性能。实验表明,在15项机器人操作任务中,扩散策略平均性能提升46.9%,有效解决了传统显式/隐式策略的局限性。其核心优势在于:1)优雅建模多模态动作分布;2)适应高维动作空间;3)训练过程稳定。未来工作将探索与强化学习的结合及计算

puzhinan7781的博客 1389

别再用MounRiver了!在VSCode里用PlatformIO搞定CH32V208开发(附完整配置流程)

本文详细介绍了如何在VSCode中使用PlatformIO进行CH32V208开发,替代传统的MounRiver Studio。通过完整的配置流程和优化技巧,开发者可以构建高效、现代化的RISC-V开发环境,显著提升编译速度和开发效率。

weixin_28711397的博客 532

RAG项目简历包装指南:从“流水线工人”到“问题解决者”的跃迁(附代码与思路)

本文代码已在Python 3.11 + CUDA 12.3环境测试,部分功能需LangChain 0.2+支持。

Conan_0728的博客 1885

OpenHarmony 5.1 RK3568 开发板烧录:从源码编译到镜像写入的 6 步实战

本文详细解析了OpenHarmony 5.1在RK3568开发板上的全链路开发流程,包括环境搭建、源码编译、镜像烧录等6个关键步骤。通过实战指南和避坑技巧,帮助开发者快速掌握OpenHarmony系统的烧录与定制能力,提升物联网设备开发效率。

weixin_33722405的博客 343

华为手环实时心率数据导出到UWP项目中

hello大家好呀O(∩_∩)O最近在做毕设 其中有个小功能要用到实时心率数据。查找网上的资料发现只有小米手环的实时心率数据导出但是没有华为手环的,我试了华为手环但是并不能行得通哈哈哈。现在分享一下华为手环实时心率数据导出的代码。

apple_63713858的博客 1581

从零搭建 C# + YOLO 工业视觉系统:从模型训练到上位机部署(工业级落地)

做工业质检、自动化生产线的同学都懂:一套能用的工业视觉系统,既要“检测准”,又要“界面易操作”,还要“部署稳”。之前帮某电子厂做PCB缺陷检测,一开始用Python+YOLO做检测核心,QT做上位机,结果踩了无数坑:Python推理速度波动大,QT和Python跨语言集成繁琐,数据传递容易卡顿;后来换成C# + YOLO的组合,直接打通了“模型训练-上位机开发-工业部署”全流程——C#的WinForms/WPF开发上位机高效,工业相机SDK支持完善;

这里只有干货:从 Modbus 通信到 MES 对接,从 YOLO 训练到工控机部署,带你搞定工业软件开发全流程。 737

华为OD机考双机位C卷 - 打印机队列 (Java & Python& JS & C/C++ & GO )

华为OD机考双机位C卷 - 打印机队列

算法大师! 4619

C语言试题四之计算并输出3到n之间所有素数的平方根之和

1、题目 请编写函数function,它的功能是:计算并输出3到n之间所有素数的平方根之和 例子:若我们main函数从键盘给n输入5之后,我们需要输出结果为3.968119 输入值n的要求:正整数n, 要求小于100。 2、部分函数说明 C 库函数 double sqrt(double x) 返回 x 的平方根 3、源代码实现 #include <stdio.h> #...

码莎拉蒂 7232

廖雪峰Python教程[完整版]

廖雪峰的python教程整理的,已经排好版了,而且还是完整的,适合小白

IEM模型,地表粗糙度模拟

利用微波反演土壤水分时候,利用IEM模型可以实现对随机地表的模拟

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');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

华为的C语言编程规范.pdf

华为技术有限公司内部技术规范。本规范制定了编写C语言程序的基本原则、规则和建议。从代码的清晰、简洁、可测试、安全、程序效率、可移植各个方面对C语言编程作出了具体指导。

上一篇: double的最大值
下一篇: MFC对话框不响应标题栏双击
死神在世
博客等级 码龄14年 233粉丝 102原创
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付:10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值