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上一篇文章中讲述了在哪里绘图，怎么开始绘制，怎么跟绘制设备绑定、解绑等基础知识，今天就来点实操的，下面坐好了，开始阅读吧。
下图是《C++ GUI Programming with Qt 4》这本书中列出的关于QPainter常用的画图方法，基本上都是以draw开头，对应的图片很直观地说明的每种方法的效果，并且很多方法都进行了重载，方便我们使用，非常地友好，下面我们对这些接口做一些详细的介绍。
Qt的坐标系都是以左上角为起点（0, 0），右为X轴正向，下为Y轴正向，如下图所示，我们使用QPainter绘图的时候也是使用该坐标系进行绘制，这里区别于数学中使用的笛卡尔坐标系，X轴向右为正，Y轴向上为正。且每个部件的绘制都是独立的坐标系，父子部件之间的坐标系互不干扰。
4.2 QPainter的绘图接口介绍（中）
上篇文章中我们举例介绍了这几个接口的用法，本篇文章继续讲解绘图的接口。
【QT】QPainter基本绘图
本文介绍QPainter基本绘图的基础知识。
Qt坐标系转换（绘制逻辑坐标系）
本资源为Qt绘图基础，世界坐标系转换为逻辑坐标系。世界坐标系原点在视图左上角，本例子通过世界坐标转换，将坐标原点定位在视图中央，Y轴向上，X轴向右，并绘制坐标轴，基于逻辑坐标系下的绘图，可将转换关系函数取消生效，对比世界坐标系下的绘图。 重写PainterEvent函数： void QtPixPainter::paintEvent(QPaintEvent* event) { QPainter painter(this); // 反走样 painter.setRenderHint(QPainter::Antialiasing, true); //物理坐标系与逻辑坐标系的转换，如果不转换，下面的绘图都是在世界坐标系下 setWorldTransform(painter); // 其他一些绘制矩形，多边形的例子，经过上面转换，都是在逻辑坐标系下 drawRectScale(painter); //draw_shearRect(painter); //利用rotate()函数进行比例变换，实现缩放效果 //draw_rotate_act(painter); //draw_by_save_restore(painter); //transform_draw_SinX(painter); transform_draw(painter); local_drawConvexPolygon(painter); } // 将世界坐标(原点左上角)转换为逻辑坐标（原点在屏幕中间） QPointF QtPixPainter::mapToScene(const QPointF& point) { QTransform transMatrix = _transform.inverted(); //翻转矩阵？ return transMatrix.map(point); //将点piont映射到transMatrix定义的坐标系中来 } // 将鼠标的逻辑位置返回并以标签形式展示 void QtPixPainter::mouseMoveEvent(QMouseEvent* event) { QString msg; QPointF mouse_po = mapToScene(event->pos()); //总是返回屏幕物理坐标系 double x = mouse_po.x(); // 总是返回屏幕物理坐标系 double y = mouse_po.y(); QString str = "(" + QString::number(x) + "," + QString::number(y) + ")"; //qDebug()<<"world x = "<pos().x()<<",world y = "<pos().y(); m_mouse_lable->setText(str); }
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牛耕式路径全覆盖算法，也称为牛耕算法或者蚂蚁算法，是一种用于解决路径规划问题的启发式算法。这个算法的灵感来自于牛在耕田时的行为。在这个算法中，假设有一块田地需要耕作，牛从田地的某个角落开始行走，走过的路径会被标记。牛会优先选择尚未经过的路径，当所有的路径都走过后，算法停止。在路径规划问题中，田地可以看作是一个由节点和边构成的图。每个节点代表一个位置，每条边代表两个节点之间的连接。路径全覆盖问题是找出一组路径，使得每个节点都被至少一条路径经过。牛耕式路径全覆盖算法的基本思想是以启发式的方式逐步构建路径。
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十一、增强现实
那都是n年前的老版本了，新版本完全没有这个问题，我就是纠结这个问题，搭建若干环境，又是vmware虚拟机，又是msys2，又是anaconda，又是找机器，折腾了几天，结果通通不是。以一张实际卡片为锚点，通过笔记本的摄像头，在显示器上将一个计算机内的3d物体显示在卡片上，类似于电影上看到的那种全息投影技术；如果你是想在windows环境下开发linux运行程序，那么CygWin/gcc是你的不二之选。而如果你想开发的是windows运行程序，并且追求速度，那么二者相比而言，MinGW是更好的选择。
Windows 11 安装 Miniconda + Mamba，配置国内源
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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！
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]); 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