├── .gitignore ├── ASCIIArt ├── input.jpg ├── output.jpg ├── javatext.ttf ├── README.md └── CharTransfer.py ├── MouseClick ├── GUI.png ├── README.md └── MouseClick.py ├── Translator ├── result1.png ├── result2.png └── README.md ├── FractalPainting ├── demo │ ├── demo.png │ ├── julia.png │ ├── koch.gif │ ├── Julia_im.gif │ ├── Julia_real.gif │ ├── Mandelbrot.gif │ ├── Mandelbrot.png │ ├── leaf_black.png │ └── leaf_white.png ├── src │ ├── theme │ │ ├── dark │ │ │ ├── card.png │ │ │ ├── empty.png │ │ │ ├── arrow-down.png │ │ │ ├── arrow-up.png │ │ │ ├── check-rest.png │ │ │ ├── entry-rest.png │ │ │ ├── notebook.png │ │ │ ├── radio-rest.png │ │ │ ├── scroll-up.png │ │ │ ├── separator.png │ │ │ ├── sizegrip.png │ │ │ ├── tab-hover.png │ │ │ ├── tab-rest.png │ │ │ ├── arrow-right.png │ │ │ ├── button-hover.png │ │ │ ├── button-rest.png │ │ │ ├── check-hover.png │ │ │ ├── entry-focus.png │ │ │ ├── entry-hover.png │ │ │ ├── radio-hover.png │ │ │ ├── scroll-down.png │ │ │ ├── scroll-left.png │ │ │ ├── scroll-right.png │ │ │ ├── tab-selected.png │ │ │ ├── button-disabled.png │ │ │ ├── button-pressed.png │ │ │ ├── check-disabled.png │ │ │ ├── check-pressed.png │ │ │ ├── check-tri-hover.png │ │ │ ├── check-tri-rest.png │ │ │ ├── entry-disabled.png │ │ │ ├── entry-invalid.png │ │ │ ├── notebook-border.png │ │ │ ├── radio-disabled.png │ │ │ ├── radio-pressed.png │ │ │ ├── switch-off-rest.png │ │ │ ├── switch-on-hover.png │ │ │ ├── switch-on-rest.png │ │ │ ├── check-tri-pressed.png │ │ │ ├── check-unsel-hover.png │ │ │ ├── check-unsel-rest.png │ │ │ ├── progress-pbar-hor.png │ │ │ ├── radio-unsel-hover.png │ │ │ ├── radio-unsel-rest.png │ │ │ ├── scale-thumb-hover.png │ │ │ ├── scale-thumb-rest.png │ │ │ ├── scale-trough-hor.png │ │ │ ├── scale-trough-vert.png │ │ │ ├── scroll-hor-thumb.png │ │ │ ├── scroll-hor-trough.png │ │ │ ├── scroll-vert-thumb.png │ │ │ ├── switch-off-hover.png │ │ │ ├── switch-on-pressed.png │ │ │ ├── treeheading-hover.png │ │ │ ├── treeheading-rest.png │ │ │ ├── button-accent-hover.png │ │ │ ├── button-accent-rest.png │ │ │ ├── check-tri-disabled.png │ │ │ ├── check-unsel-disabled.png │ │ │ ├── check-unsel-pressed.png │ │ │ ├── progress-pbar-vert.png │ │ │ ├── progress-trough-hor.png │ │ │ ├── progress-trough-vert.png │ │ │ ├── radio-unsel-disabled.png │ │ │ ├── radio-unsel-pressed.png │ │ │ ├── scale-thumb-disabled.png │ │ │ ├── scale-thumb-pressed.png │ │ │ ├── scroll-vert-trough.png │ │ │ ├── switch-off-disabled.png │ │ │ ├── switch-off-pressed.png │ │ │ ├── switch-on-disabled.png │ │ │ ├── treeheading-pressed.png │ │ │ ├── button-accent-disabled.png │ │ │ └── button-accent-pressed.png │ │ └── light │ │ │ ├── card.png │ │ │ ├── empty.png │ │ │ ├── arrow-up.png │ │ │ ├── notebook.png │ │ │ ├── scroll-up.png │ │ │ ├── separator.png │ │ │ ├── sizegrip.png │ │ │ ├── tab-hover.png │ │ │ ├── tab-rest.png │ │ │ ├── arrow-down.png │ │ │ ├── arrow-right.png │ │ │ ├── button-rest.png │ │ │ ├── check-hover.png │ │ │ ├── check-rest.png │ │ │ ├── entry-focus.png │ │ │ ├── entry-hover.png │ │ │ ├── entry-rest.png │ │ │ ├── radio-hover.png │ │ │ ├── radio-rest.png │ │ │ ├── scroll-down.png │ │ │ ├── scroll-left.png │ │ │ ├── button-hover.png │ │ │ ├── button-pressed.png │ │ │ ├── check-disabled.png │ │ │ ├── check-pressed.png │ │ │ ├── check-tri-rest.png │ │ │ ├── entry-disabled.png │ │ │ ├── entry-invalid.png │ │ │ ├── radio-disabled.png │ │ │ ├── radio-pressed.png │ │ │ ├── scroll-right.png │ │ │ ├── switch-on-rest.png │ │ │ ├── tab-selected.png │ │ │ ├── button-disabled.png │ │ │ ├── check-tri-hover.png │ │ │ ├── check-unsel-rest.png │ │ │ ├── notebook-border.png │ │ │ ├── radio-unsel-rest.png │ │ │ ├── scale-thumb-rest.png │ │ │ ├── scale-trough-hor.png │ │ │ ├── scroll-hor-thumb.png │ │ │ ├── switch-off-hover.png │ │ │ ├── switch-off-rest.png │ │ │ ├── switch-on-hover.png │ │ │ ├── treeheading-rest.png │ │ │ ├── button-accent-hover.png │ │ │ ├── button-accent-rest.png │ │ │ ├── check-tri-disabled.png │ │ │ ├── check-tri-pressed.png │ │ │ ├── check-unsel-hover.png │ │ │ ├── check-unsel-pressed.png │ │ │ ├── progress-pbar-hor.png │ │ │ ├── progress-pbar-vert.png │ │ │ ├── progress-trough-hor.png │ │ │ ├── radio-unsel-hover.png │ │ │ ├── radio-unsel-pressed.png │ │ │ ├── scale-thumb-hover.png │ │ │ ├── scale-thumb-pressed.png │ │ │ ├── scale-trough-vert.png │ │ │ ├── scroll-hor-trough.png │ │ │ ├── scroll-vert-thumb.png │ │ │ ├── scroll-vert-trough.png │ │ │ ├── switch-off-disabled.png │ │ │ ├── switch-off-pressed.png │ │ │ ├── switch-on-disabled.png │ │ │ ├── switch-on-pressed.png │ │ │ ├── treeheading-hover.png │ │ │ ├── treeheading-pressed.png │ │ │ ├── button-accent-pressed.png │ │ │ ├── check-unsel-disabled.png │ │ │ ├── progress-trough-vert.png │ │ │ ├── radio-unsel-disabled.png │ │ │ ├── scale-thumb-disabled.png │ │ │ └── button-accent-disabled.png │ ├── theme.tcl │ └── FunctionScripts.md ├── test │ ├── TestLeaf.py │ ├── TestKoch.py │ ├── TestMandelbrot.py │ └── TestJulia.py └── README.md ├── MySQLClient ├── figure │ └── module.png ├── main.py ├── src │ ├── Message.py │ ├── Login.py │ ├── Database.py │ ├── MainWindow.py │ └── SQL.py ├── README.md └── ui │ ├── PyQt_UI │ ├── mainwindow.ui │ └── login.ui │ ├── UiMainWindow.py │ └── UiLogin.py ├── PCAFaceRecognize ├── dataset │ ├── test.pt │ ├── train.pt │ ├── images │ │ ├── Barack_Obama_2.jpg │ │ ├── Barack_Obama_5.jpg │ │ ├── Barack_Obama_6.jpg │ │ ├── Barack_Obama_7.jpg │ │ ├── Barack_Obama_9.jpg │ │ ├── Daniel_Craig_1.jpg │ │ ├── Daniel_Craig_4.jpg │ │ ├── Daniel_Craig_7.jpg │ │ ├── Daniel_Craig_8.jpg │ │ ├── Daniel_Craig_9.jpg │ │ ├── Alyssa_Milano_1.jpg │ │ ├── Alyssa_Milano_11.jpg │ │ ├── Alyssa_Milano_12.jpg │ │ ├── Alyssa_Milano_13.jpg │ │ ├── Alyssa_Milano_18.jpg │ │ ├── Alyssa_Milano_20.jpg │ │ ├── Alyssa_Milano_21.jpg │ │ ├── Alyssa_Milano_23.jpg │ │ ├── Alyssa_Milano_25.jpg │ │ ├── Alyssa_Milano_31.jpg │ │ ├── Alyssa_Milano_33.jpg │ │ ├── Alyssa_Milano_42.jpg │ │ ├── Alyssa_Milano_43.jpg │ │ ├── Alyssa_Milano_48.jpg │ │ ├── Alyssa_Milano_50.jpg │ │ ├── Alyssa_Milano_52.jpg │ │ ├── Alyssa_Milano_53.jpg │ │ ├── Alyssa_Milano_54.jpg │ │ ├── Alyssa_Milano_56.jpg │ │ ├── Alyssa_Milano_58.jpg │ │ ├── Alyssa_Milano_6.jpg │ │ ├── Alyssa_Milano_8.jpg │ │ ├── Alyssa_Milano_86.jpg │ │ ├── Alyssa_Milano_87.jpg │ │ ├── Alyssa_Milano_88.jpg │ │ ├── Alyssa_Milano_9.jpg │ │ ├── Alyssa_Milano_95.jpg │ │ ├── Alyssa_Milano_97.jpg │ │ ├── Barack_Obama_10.jpg │ │ ├── Barack_Obama_100.jpg │ │ ├── Barack_Obama_11.jpg │ │ ├── Barack_Obama_116.jpg │ │ ├── Barack_Obama_12.jpg │ │ ├── Barack_Obama_13.jpg │ │ ├── Barack_Obama_139.jpg │ │ ├── Barack_Obama_14.jpg │ │ ├── Barack_Obama_141.jpg │ │ ├── Barack_Obama_146.jpg │ │ ├── Barack_Obama_147.jpg │ │ ├── Barack_Obama_148.jpg │ │ ├── Barack_Obama_15.jpg │ │ ├── Barack_Obama_16.jpg │ │ ├── Barack_Obama_161.jpg │ │ ├── Barack_Obama_17.jpg │ │ ├── Barack_Obama_173.jpg │ │ ├── Barack_Obama_189.jpg │ │ ├── Barack_Obama_197.jpg │ │ ├── Barack_Obama_205.jpg │ │ ├── Barack_Obama_216.jpg │ │ ├── Barack_Obama_225.jpg │ │ ├── Barack_Obama_235.jpg │ │ ├── Barack_Obama_236.jpg │ │ ├── Barack_Obama_245.jpg │ │ ├── Barack_Obama_246.jpg │ │ ├── Barack_Obama_247.jpg │ │ ├── Barack_Obama_250.jpg │ │ ├── Barack_Obama_251.jpg │ │ ├── Barack_Obama_252.jpg │ │ ├── Barack_Obama_253.jpg │ │ ├── Barack_Obama_258.jpg │ │ ├── Barack_Obama_259.jpg │ │ ├── Barack_Obama_271.jpg │ │ ├── Barack_Obama_289.jpg │ │ ├── Barack_Obama_63.jpg │ │ ├── Barack_Obama_71.jpg │ │ ├── Barack_Obama_88.jpg │ │ ├── Barack_Obama_89.jpg │ │ ├── Barack_Obama_90.jpg │ │ ├── Barack_Obama_92.jpg │ │ ├── Barack_Obama_96.jpg │ │ ├── Barack_Obama_99.jpg │ │ ├── Daniel_Craig_10.jpg │ │ ├── Daniel_Craig_120.jpg │ │ ├── Daniel_Craig_126.jpg │ │ ├── Daniel_Craig_128.jpg │ │ ├── Daniel_Craig_137.jpg │ │ ├── Daniel_Craig_147.jpg │ │ ├── Daniel_Craig_148.jpg │ │ ├── Daniel_Craig_149.jpg │ │ ├── Daniel_Craig_153.jpg │ │ ├── Daniel_Craig_159.jpg │ │ ├── Daniel_Craig_16.jpg │ │ ├── Daniel_Craig_160.jpg │ │ ├── Daniel_Craig_165.jpg │ │ ├── Daniel_Craig_167.jpg │ │ ├── Daniel_Craig_169.jpg │ │ ├── Daniel_Craig_170.jpg │ │ ├── Daniel_Craig_182.jpg │ │ ├── Daniel_Craig_194.jpg │ │ ├── Daniel_Craig_220.jpg │ │ ├── Daniel_Craig_226.jpg │ │ ├── Daniel_Craig_261.jpg │ │ ├── Daniel_Craig_269.jpg │ │ ├── Daniel_Craig_27.jpg │ │ ├── Daniel_Craig_309.jpg │ │ ├── Daniel_Craig_310.jpg │ │ ├── Daniel_Craig_41.jpg │ │ ├── Daniel_Craig_47.jpg │ │ ├── Daniel_Craig_50.jpg │ │ ├── Daniel_Craig_55.jpg │ │ ├── Daniel_Craig_56.jpg │ │ ├── Daniel_Craig_57.jpg │ │ ├── Daniel_Craig_72.jpg │ │ ├── Daniel_Craig_73.jpg │ │ ├── Daniel_Craig_75.jpg │ │ ├── Daniel_Craig_76.jpg │ │ ├── Daniel_Craig_77.jpg │ │ ├── Daniel_Craig_78.jpg │ │ ├── Daniel_Craig_80.jpg │ │ ├── Daniel_Craig_81.jpg │ │ ├── Daniel_Craig_83.jpg │ │ ├── Daniel_Craig_84.jpg │ │ ├── Daniel_Craig_85.jpg │ │ ├── Daniel_Craig_86.jpg │ │ ├── Alyssa_Milano_114.jpg │ │ ├── Alyssa_Milano_116.jpg │ │ ├── Alyssa_Milano_117.jpg │ │ ├── Alyssa_Milano_125.jpg │ │ ├── Alyssa_Milano_136.jpg │ │ ├── Alyssa_Milano_154.jpg │ │ ├── Alyssa_Milano_174.jpg │ │ ├── Alyssa_Milano_175.jpg │ │ ├── Alyssa_Milano_176.jpg │ │ ├── Alyssa_Milano_179.jpg │ │ ├── Alyssa_Milano_180.jpg │ │ ├── Alyssa_Milano_181.jpg │ │ ├── Alyssa_Milano_185.jpg │ │ ├── Alyssa_Milano_186.jpg │ │ ├── Alyssa_Milano_187.jpg │ │ ├── Alyssa_Milano_193.jpg │ │ ├── Alyssa_Milano_195.jpg │ │ ├── Alyssa_Milano_210.jpg │ │ ├── Alyssa_Milano_219.jpg │ │ └── Alyssa_Milano_235.jpg │ ├── split.py │ └── download.py ├── figs │ ├── eigenface_1.jpg │ ├── eigenface_2.jpg │ ├── eigenface_5.jpg │ ├── eigenvalues.jpg │ ├── eigenface_10.jpg │ ├── eigenface_100.jpg │ ├── eigenface_20.jpg │ ├── eigenface_50.jpg │ ├── eigenface_80.jpg │ ├── ori_Barack_Obama.jpg │ ├── ori_Daniel_Craig.jpg │ ├── ori_Alyssa_Milano.jpg │ ├── reconstruct_Barack_Obama_1.jpg │ ├── reconstruct_Barack_Obama_2.jpg │ ├── reconstruct_Barack_Obama_5.jpg │ ├── reconstruct_Daniel_Craig_1.jpg │ ├── reconstruct_Daniel_Craig_2.jpg │ ├── reconstruct_Daniel_Craig_5.jpg │ ├── reconstruct_Alyssa_Milano_1.jpg │ ├── reconstruct_Alyssa_Milano_10.jpg │ ├── reconstruct_Alyssa_Milano_100.jpg │ ├── reconstruct_Alyssa_Milano_2.jpg │ ├── reconstruct_Alyssa_Milano_20.jpg │ ├── reconstruct_Alyssa_Milano_5.jpg │ ├── reconstruct_Alyssa_Milano_50.jpg │ ├── reconstruct_Alyssa_Milano_80.jpg │ ├── reconstruct_Barack_Obama_10.jpg │ ├── reconstruct_Barack_Obama_100.jpg │ ├── reconstruct_Barack_Obama_20.jpg │ ├── reconstruct_Barack_Obama_50.jpg │ ├── reconstruct_Barack_Obama_80.jpg │ ├── reconstruct_Daniel_Craig_10.jpg │ ├── reconstruct_Daniel_Craig_100.jpg │ ├── reconstruct_Daniel_Craig_20.jpg │ ├── reconstruct_Daniel_Craig_50.jpg │ ├── reconstruct_Daniel_Craig_80.jpg │ ├── reconstruct_mean_Barack_Obama.jpg │ ├── reconstruct_mean_Daniel_Craig.jpg │ └── reconstruct_mean_Alyssa_Milano.jpg ├── README.md └── predict.py ├── NeuralStyleTransfer ├── images │ ├── result.jpg │ ├── style.jpg │ ├── content.jpg │ └── pytorch.png └── README.md ├── 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rights reserved. 4 | # ---------------------------------------------------------- 5 | from src.MainWindow import MainWindow 6 | from PyQt5.QtWidgets import QApplication 7 | 8 | 9 | if __name__ == "__main__": 10 | import sys 11 | app = QApplication(sys.argv) 12 | w = MainWindow() 13 | sys.exit(app.exec_()) 14 | -------------------------------------------------------------------------------- /FractalPainting/test/TestLeaf.py: -------------------------------------------------------------------------------- 1 | # ---------------------------------------------------------- 2 | # coding=utf-8 3 | # Copyright © 2021 Komorebi660 All rights reserved. 4 | # THIS FILE IS USED TO TEST PAINTING LEAF 5 | # ---------------------------------------------------------- 6 | from src.DrawFractal import * 7 | 8 | pl.figure(figsize=(6, 8)) 9 | draw_Leaf(150000) # 绘制分形树叶 10 | pl.gcf().patch.set_facecolor("black") # 设置背景为黑色 11 | pl.savefig("./leaf_black.png", dpi=1024) # 保存 12 | print("Done!") 13 | pl.show() 14 | -------------------------------------------------------------------------------- /FractalPainting/test/TestKoch.py: -------------------------------------------------------------------------------- 1 | # ---------------------------------------------------------- 2 | # coding=utf-8 3 | # Copyright © 2021 Komorebi660 All rights reserved. 4 | # THIS FILE IS USED TO TEST PAINTING KOCH SNOWFLAKE 5 | # ---------------------------------------------------------- 6 | import matplotlib.animation as animation 7 | from src.DrawFractal import * 8 | 9 | # 绘制科赫雪花的动图 10 | figure = pl.figure() 11 | iter = np.arange(1, 8, 1) 12 | 13 | # 绘制动图并保存 14 | anims = animation.FuncAnimation( 15 | fig=figure, func=draw_Koch, frames=iter, interval=500, repeat=True) 16 | anims.save('./Koch.gif') 17 | print("Done!") 18 | pl.show() 19 | -------------------------------------------------------------------------------- /FractalPainting/test/TestMandelbrot.py: -------------------------------------------------------------------------------- 1 | # ---------------------------------------------------------- 2 | # coding=utf-8 3 | # Copyright © 2021 Komorebi660 All rights reserved. 4 | # THIS FILE IS USED TO TEST PAINTING MANDELBROT SET 5 | # ---------------------------------------------------------- 6 | from src.DrawFractal import * 7 | 8 | # 绘制曼德勃罗集 9 | pl.subplot(231) 10 | draw_Mandelbrot(256, -0.5, 0, 1.5) 11 | for i in range(2, 7): 12 | pl.subplot(230+i) 13 | draw_Mandelbrot(256, 0.27322626, 0.595153338, 0.2**(i-1)) 14 | pl.subplots_adjust(0.02, 0, 0.98, 1, 0.02, 0) 15 | # 保存 16 | pl.savefig("./mandelbrot.png", dpi=2048) 17 | print("Done!") 18 | pl.show() 19 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 基于Pyhton的小程序 2 | 3 | 本仓库代码基于的环境如下： 4 | 5 | - `Windows 10`及以上版本 6 | - `Python 3.9.5`及以上版本 7 | 8 | ## 内容简介 9 | 10 | - [图片转字符画](./ASCIIArt/) 11 | - [分形图绘制](./FractalPainting/) 12 | - [鼠标连点器](./MouseClick/) 13 | - [神经网络图像风格迁移](./NeuralStyleTransfer/) 14 | - [在线翻译中英翻译器](./Translator/) 15 | - [中国科学技术大学自动健康打卡脚本](./USTC/) 16 | - [`Verilog Testbench`自动生成器](./VerilogTestbenchGen/) 17 | - [`MySQL`客户端框架](./MySQLClient/) 18 | - [跳动心脏动图绘制](./HeartBeat/) 19 | - [`PCA`降维人脸识别](./PCAFaceRecognize/) 20 | 21 | ## 代码使用 22 | 23 | 下载源码到本地: 24 | 25 | ``` 26 | git clone git@github.com:Komorebi660/PythonCode.git 27 | ``` 28 | 29 | 进入任意子文件夹, 按照其中的`README`安装好第三方库后使用`Python`命令即可运行。 -------------------------------------------------------------------------------- /MouseClick/README.md: -------------------------------------------------------------------------------- 1 | # 简易鼠标连点器 2 | 3 | **代码介绍见[此](https://zhuanlan.zhihu.com/p/413878810)** 4 | 5 | ## 安装依赖库 6 | 7 | 安装代码所需库： 8 | 9 | ``` 10 | pip install pynput 11 | ``` 12 | 13 | `threading`、`time`、`tkinter`库为`Python`自带库，无需手动安装。 14 | 15 | Linux环境可能没有安装`tkinter`库，输入： 16 | 17 | ``` 18 | sudo apt-get install python-tk 19 | 或 20 | sudo apt-get install python3-tk 21 | ``` 22 | 23 | 安装`tkinter` 24 | 25 | ## 使用示例 26 | 27 | 直接运行代码: 28 | 29 |
30 | 31 |
32 | 33 | 在上述`GUI`界面中设置点击鼠标左键或右键(`Mouse Button`)、点击时间间隔(`Time Interval`)，然后点击`START`开始监听，通过`F8`来控制连点的开始与结束，通过`ESC`来控制监听的结束。 34 | 35 | 在`Windows`环境下，你还可以将其打包为`.exe`文件，这样即使电脑不含`Python`也能运行，方法如下： 36 | 37 | 安装`pyinstaller`: 38 | 39 | ``` 40 | pip install pyinstaller 41 | ``` 42 | 43 | 由于版本兼容问题，需要将pynput降级至1.6.8: 44 | 45 | ``` 46 | pip install pynput==1.6.8 47 | ``` 48 | 49 | 然后在源码所在目录执行： 50 | 51 | ``` 52 | pyinstaller -F -w MouseClick.py 53 | ``` 54 | 55 | 即可生成`.exe`文件，位于`/dist`目录下 -------------------------------------------------------------------------------- /FractalPainting/test/TestJulia.py: -------------------------------------------------------------------------------- 1 | # ---------------------------------------------------------- 2 | # coding=utf-8 3 | # Copyright © 2023 Komorebi660 All rights reserved. 4 | # THIS FILE IS USED TO TEST PAINTING JULIA SET 5 | # ---------------------------------------------------------- 6 | from src.DrawFractal import * 7 | 8 | # 绘制朱丽叶集 9 | C = [0.279, 0.400, 0.484, 0.544, 0.590, 0.626] 10 | 11 | 12 | def draw_Julia_(n, c=0.400): 13 | julia = get_Julia(-1.5, 1.5, -1.5, 1.5, c, 1024, n) 14 | pl.imshow(julia, cmap=cm.jet, extent=[-1.5, 1.5, -1.5, 1.5]) 15 | pl.gca().set_axis_off() 16 | # 去除白边 17 | pl.gca().xaxis.set_major_locator(pl.NullLocator()) 18 | pl.gca().yaxis.set_major_locator(pl.NullLocator()) 19 | pl.subplots_adjust(top=1, bottom=0, left=0, right=1, hspace=0, wspace=0) 20 | 21 | 22 | for i in range(1, 7): 23 | pl.subplot(230+i) 24 | draw_Julia_(i+1, C[i-1]) 25 | 26 | # 保存 27 | pl.savefig("./julia.png", bbox_inches='tight', dpi=2048, pad_inches=0.0) 28 | print("Done!") 29 | pl.show() 30 | -------------------------------------------------------------------------------- /MySQLClient/src/Message.py: -------------------------------------------------------------------------------- 1 | # ---------------------------------------------------------- 2 | # -*- coding: UTF-8 -*- 3 | # Copyright © 2023 Komorebi660 All rights reserved. 4 | # ---------------------------------------------------------- 5 | from PyQt5.QtWidgets import QMessageBox 6 | 7 | 8 | def information(self, alarm): 9 | self.alarm = alarm 10 | QMessageBox.information(self, '提示', self.alarm, 11 | QMessageBox.Ok, QMessageBox.Ok) 12 | 13 | 14 | def critical(self, alarm): 15 | self.alarm = alarm 16 | QMessageBox.critical(self, '错误', self.alarm, 17 | QMessageBox.Ok, QMessageBox.Ok) 18 | 19 | 20 | def warning(self, alarm): 21 | self.alarm = alarm 22 | QMessageBox.warning(self, '警告', self.alarm, 23 | QMessageBox.Ok, QMessageBox.Ok) 24 | 25 | 26 | # for testing 27 | if __name__ == "__main__": 28 | from PyQt5.QtWidgets import QApplication 29 | from PyQt5.QtWidgets import QDialog 30 | import sys 31 | 32 | app = QApplication(sys.argv) 33 | w = QDialog() 34 | 35 | information(w, "test") 36 | critical(w, "test") 37 | warning(w, "test") 38 | 39 | sys.exit(app.exec_()) 40 | -------------------------------------------------------------------------------- /Translator/README.md: -------------------------------------------------------------------------------- 1 | # 基于百度翻译的本地化中英翻译器 2 | 3 | ## 代码使用 4 | 5 | 安装依赖库： 6 | 7 | ``` 8 | pip install requests 9 | pip install js2py 10 | ``` 11 | 12 | Linux环境可能没有安装`tkinter`库，输入： 13 | 14 | ``` 15 | sudo apt-get install python-tk 16 | 或 17 | sudo apt-get install python3-tk 18 | ``` 19 | 20 | 即可安装`tkinter`. 21 | 22 | 接下来，进入源码`BaiduTranslator.py`，修改参数(**必选**)： 23 | 24 | - **USER_AGENT**：位于`Request Headers`中的`User-Agent`; 25 | - **COOKIE**：位于`Request Headers`中的`Cookie`; 26 | - **TOKEN**：位于`From Data`中的`token`. 27 | 28 | 进入[百度翻译](https://fanyi.baidu.com/), 按`F12`打开浏览器的开发者工具，在`Request URL`为`https://fanyi.baidu.com/v2transapi`处可以获取上述参数. 29 | 30 | **注意：最新的百度翻译可能需要登录百度账号才可以使用，建议先登录百度账号再抓取有关内容。** 31 | 32 | ## 使用示例 33 | 34 | 若运行时出现： 35 | 36 | ``` 37 | ERROR, Can not Translate now! 38 | 或 39 | 出现异常，暂时无法翻译! 40 | ``` 41 | 42 | 原因是翻译结果没有正常返回，可能的原因是参数设置错误，正常的运行结果如下： 43 | 44 |
45 | 46 |
47 | 48 |
49 | 50 |
51 | 52 | 在`Windows`环境下，你还可以将其打包为`.exe`文件，这样即使电脑不含`Python`也能运行，方法如下： 53 | 54 | 安装`pyinstaller`: 55 | 56 | ``` 57 | pip install pyinstaller 58 | ``` 59 | 60 | 在源码所在目录执行： 61 | 62 | ``` 63 | pyinstaller -F -w BaiduTranslator.py 64 | ``` 65 | 66 | 即可生成`.exe`文件，位于`/dist`目录下 -------------------------------------------------------------------------------- /USTC/README.md: -------------------------------------------------------------------------------- 1 | # 肥科自动健康打卡脚本 2 | 3 | ## 安装依赖库 4 | 5 | 安装代码所需库： 6 | 7 | ``` 8 | pip install requests 9 | ``` 10 | 11 | ## 修改参数 12 | 13 | 接下来，进入源码文件`USTC.py`，修改如下参数(**必选**): 14 | 15 | - **USR**：学号 16 | - **PWD**：密码 17 | - **LOCATION**：所在校区 18 | - **DORM_BUILDING**：寝室楼号 19 | - **DORM**：寝室门牌号 20 | - **EMERGENCY_CONTACT**：紧急联系人姓名 21 | - **RELATIONSHIP**：本人与紧急联系人关系 22 | - **PHONE_NUMBER**：紧急联系人电话 23 | 24 | 然后运行代码，可能会出现以下几种情况： 25 | 26 | 若出现 27 | 28 | ``` 29 | 上报成功，最近一次上报是*分钟*秒之前，请每日按时打卡 30 | ``` 31 | 32 | 则表明成功打卡、报备。 33 | 34 | 若出现 35 | 36 | ``` 37 | UserName or Password ERROR! 38 | ``` 39 | 40 | 则表明用户名或密码输入错误，请检查参数是否修改正确。 41 | 42 | 若出现 43 | 44 | ``` 45 | Health Check-in ERROR! 46 | ``` 47 | 48 | 则表明上报或报备失败，原因可能是打卡网站进行了更新，需要重新修改代码。 49 | 50 | ## 上传至`Vlab`定时执行 51 | 52 | 以`Ubuntu20.04`版本的`Vlab`为例，要实现自动运行脚本需要进行如下设置： 53 | 54 | **第一步：上传源码** 55 | 56 | 将源码上传至`Vlab`虚拟机，你可以使用`ftp`传输。 57 | 58 | **第二步：编辑`crontab`** 59 | 60 | ``` 61 | sudo crontab -e 62 | ``` 63 | 64 | 使用`vim`进行设置，设置格式如下： 65 | 66 | ``` 67 | * * * * * command 68 | 分 时 日 月 周 命令 69 | ``` 70 | 71 | 例如： 72 | ``` 73 | * 21 * * * /usr/bin/python3 /home/ubuntu/USTC.py 74 | ``` 75 | 76 | 就是每天`21：00`执行位于`/home/ubuntu/`下的`USTC.py`文件 77 | 78 | **第三步：重启`cron`服务** 79 | 80 | ``` 81 | service cron restart 82 | ``` 83 | 84 | 接下来就能解放双手自动打卡了~ -------------------------------------------------------------------------------- /VerilogTestbenchGen/module_port.txt: -------------------------------------------------------------------------------- 1 | module AXI_Top( 2 | input clk, 3 | input rstn, 4 | //I-Cache 5 | input i_rd_req, 6 | input [2:0] i_rd_type, 7 | input [31:0] i_rd_addr, 8 | output i_rd_rdy, 9 | output i_ret_valid, 10 | output i_ret_last, 11 | output [31:0] i_ret_data, 12 | input i_wr_req, 13 | input [2:0] i_wr_type, 14 | input [31:0] i_wr_addr, 15 | input [3:0] i_wr_wstrb, 16 | input [127:0] i_wr_data, 17 | output i_wr_rdy, 18 | //D-Cache 19 | input d_rd_req, 20 | input [2:0] d_rd_type, 21 | input [31:0] d_rd_addr, 22 | output d_rd_rdy, 23 | output d_ret_valid, 24 | output d_ret_last, 25 | output [31:0] d_ret_data, 26 | input d_wr_req, 27 | input [2:0] d_wr_type, 28 | input [31:0] d_wr_addr, 29 | input [3:0] d_wr_wstrb, 30 | input [127:0] d_wr_data, 31 | output d_wr_rdy, 32 | //UART signal 33 | output TxD, 34 | input RxD, 35 | output interrupt, 36 | //VGA signal 37 | output [11:0] vrgb, 38 | output hs, 39 | output vs 40 | ); -------------------------------------------------------------------------------- /ASCIIArt/README.md: -------------------------------------------------------------------------------- 1 | # ASCII Art 图片转字符画 2 | 3 | ## ASCII Art 简介 4 | 5 | 字符画艺术`(ASCII Arts)`，又名“文字图”、“字符画”、“文字画”，这种主要依靠电脑表现的艺术形式是指使用电脑字符(主要是`ASCII`)来表达图片，最早于`1982`年美国卡内基梅隆大学出现。 6 | 7 | 最简单的字符画艺术是颜文字`(emoticon)`，也就是用简单的字符表达表情符号，它的出现是由早期互联网环境决定的。早期的网络交流只局限于使用文字和聊天软件内置的有限的图形表情，交流双方看到的是屏幕上的文字而非对方的面部，因此在交流过程中无法通过观察对方的面部表情读取对方情绪，这种视觉感受上的缺陷在某种程度上阻碍了沟通的深入。`Emoticon`的出现，改善了这种视觉感受的单一状况。由字符组成的简洁的面部表情形象，虽简单却又生动形象，弥补了颜文网络沟通中无法读取对方表情的视觉缺陷，使得交流可以更人性化和具有趣味性。 8 | 9 | 更进一步，我们可以把任意一张图片转化为以字符形式呈现，其实现原理是利用图片每个像素点的灰度信息，将其映射至不同的字符，从而完成图片的转化。 10 | 11 | ## 代码使用 12 | 13 | 直接输入命令： 14 | 15 | ``` 16 | python CharTransfer.py [output image path] 17 | ``` 18 | 19 | 即可运行代码，其中，`input image path`是输入图片的路径；`output image path`是结果的存储路径，这个参数是可选的，若不指明，则最终的结果将不会存储。 20 | 21 | 你还可以在`CharTransfer.py`中修改以下内容： 22 | 23 | - **MAX_SIZE**: 输入图片压缩尺寸，默认`800` 24 | - **TYPEFACE_PATH**: 字符文件路径，默认为仓库自带的`javatext.ttf` 25 | 26 | 以调整图片的最终效果。 27 | 28 | 运行中还会打印代码运行的进度信息，如： 29 | 30 | ``` 31 | Transfering: 87%|███████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 494/565 [00:16<00:02, 29.52it/s] 32 | ``` 33 | 34 | ## 效果展示 35 | 36 | 使用仓库中的测试图片，结果如下： 37 | 38 | - **输入图片** 39 | 40 |
41 | 42 |
43 | 44 | - **输出图片** 45 | 46 |
47 | 48 |
-------------------------------------------------------------------------------- /PCAFaceRecognize/dataset/split.py: -------------------------------------------------------------------------------- 1 | import os 2 | from PIL import Image 3 | import numpy as np 4 | import pickle 5 | import pandas as pd 6 | 7 | 8 | train_data = {"vector": [], "label": []} 9 | test_data = {"vector": [], "label": []} 10 | train_count = {"Alyssa_Milano": 0, "Barack_Obama": 0, "Daniel_Craig": 0} 11 | 12 | for filename in os.listdir("./images/"): 13 | name = filename.split("_")[0] + "_" + filename.split("_")[1] 14 | file_path = os.path.join("./images/", filename) 15 | image = Image.open(file_path).convert("L") # gray image 16 | image = image.resize((64, 64)) 17 | image_array = np.array(image, dtype=np.uint8) 18 | vector = image_array.flatten() 19 | # 3*12=36 images for training, 12 images for testing 20 | if train_count[name] < 36: 21 | train_data["vector"].append(vector) 22 | train_data["label"].append(name) 23 | train_count[name] += 1 24 | else: 25 | test_data["vector"].append(vector) 26 | test_data["label"].append(name) 27 | 28 | pickle.dump(train_data, open("train.pt", "wb")) 29 | pickle.dump(test_data, open("test.pt", "wb")) 30 | 31 | with open("train.pt", "rb") as f: 32 | train_data = pickle.load(f) 33 | train_data = pd.DataFrame(train_data) 34 | print(train_data.describe()) 35 | with open("test.pt", "rb") as f: 36 | test_data = pickle.load(f) 37 | test_data = pd.DataFrame(test_data) 38 | print(test_data.describe()) 39 | -------------------------------------------------------------------------------- /MySQLClient/src/Login.py: -------------------------------------------------------------------------------- 1 | # ---------------------------------------------------------- 2 | # -*- coding: UTF-8 -*- 3 | # Copyright © 2023 Komorebi660 All rights reserved. 4 | # ---------------------------------------------------------- 5 | from ui.UiLogin import Ui_LoginDialog 6 | from PyQt5.QtWidgets import QDialog 7 | from src.Database import Database 8 | from src.Message import critical 9 | 10 | 11 | class LoginDialog(QDialog): 12 | ui = None 13 | parent = None 14 | 15 | def __init__(self, parent) -> None: 16 | super().__init__(parent) 17 | # 设置UI 18 | self.ui = Ui_LoginDialog() 19 | self.ui.setupUi(self) 20 | self.ui.ipaddr.setText('127.0.0.1') 21 | self.ui.database.setText('test') 22 | self.ui.username.setText('root') 23 | # 设置父界面 24 | self.parent = parent 25 | # 绑定登陆按键 26 | self.ui.LoginBtn.clicked.connect(self.login) 27 | self.show() 28 | 29 | def login(self): 30 | ipaddr = self.ui.ipaddr.text() 31 | dbname = self.ui.database.text() 32 | username = self.ui.username.text() 33 | password = self.ui.password.text() 34 | 35 | db = Database() 36 | db.login(username, password, ipaddr, dbname) 37 | 38 | if db.isConnected(): 39 | #information(self, "登陆成功") 40 | self.parent.db = db 41 | self.close() 42 | else: 43 | critical(self, "登陆失败" + str(db.getError())) 44 | -------------------------------------------------------------------------------- /PCAFaceRecognize/dataset/download.py: -------------------------------------------------------------------------------- 1 | import os 2 | import requests 3 | from PIL import Image 4 | from io import BytesIO 5 | 6 | 7 | if not os.path.exists('./images'): 8 | os.mkdir('./images') 9 | 10 | name_num = {'Alyssa_Milano': 0, 'Barack_Obama': 0, 'Daniel_Craig': 0} 11 | 12 | with open('dev_urls.txt', 'r') as f: 13 | _ = f.readline() 14 | _ = f.readline() 15 | for line in f.readlines(): 16 | # split line by \t 17 | line = line.strip().split('\t') 18 | name = str(line[0]).replace(' ', '_') 19 | index = int(line[1]) 20 | url = str(line[2]) 21 | x0,y0,x1,y1 = line[3].split(',') 22 | x0,y0,x1,y1 = int(x0),int(y0),int(x1),int(y1) 23 | 24 | if name not in name_num.keys(): 25 | continue 26 | if name_num[name] > 100: 27 | continue 28 | 29 | file_name = f'./images/{name}_{index}.jpg' 30 | if os.path.exists(file_name): 31 | continue 32 | print(f'Downloading {url}') 33 | try: 34 | r = requests.get(url, timeout=10) 35 | image_data = BytesIO(r.content) 36 | image = Image.open(image_data) 37 | cropped_image = image.crop((x0, y0, x1, y1)) 38 | gray_image = cropped_image.convert('L') 39 | final_image = gray_image.resize((128, 128)) 40 | final_image.save(file_name) 41 | name_num[name] += 1 42 | except: 43 | print(f'Failed {url}') 44 | continue 45 | -------------------------------------------------------------------------------- /MySQLClient/README.md: -------------------------------------------------------------------------------- 1 | # MySQL客户端框架 2 | 3 | 这是一个`C/S`架构的`MySQL`客户端框架, 它实现了`MySQL`与`Python`的基本交互功能, 支持登录与展示表格信息, 可在此基础上进行扩展。 4 | 5 | 各个模块之间的连接图如下: 6 | 7 |
8 | 9 |
10 | 11 | ## Getting Started 12 | 13 | ### 安装MySQL Server 14 | 15 | 官网下载并安装: `https://dev.mysql.com/downloads/mysql/`, 安装完成后需要进行初始化(可能需要管理员权限): 16 | 17 | ```bash 18 | mysqld.exe --initialize --user=mysql --console 19 | ``` 20 | 21 | 在`MySQL`的安装目录下找到`my.ini`文件(没有则直接新建一个), 在此文件中添加如下内容: 22 | 23 | ```ini 24 | [mysqld] 25 | #设置3306端口号 26 | port=3306 27 | #设置MySQL的安装目录 28 | basedir= 29 | #设置MySQL数据库的数据存放目录 30 | datadir= 31 | #运行最大连接数 32 | max_connections=200 33 | #运行连接失败的次数。这也是为了防止有人从该主机试图攻击数据库系统 34 | max_connect_errors=10 35 | #服务端使用的字符集默认为utf-8 36 | character-set-server=utf8 37 | #设置可以读入读出文件 38 | secure_file_priv='' 39 | [mysql] 40 | #客户端使用的字符集默认为utf8 41 | default-character-set=utf8 42 | [client] 43 | #客户端默认端口号为3306 44 | port=3306 45 | ``` 46 | 47 | 保存后就可以启动`MySQL`服务了: 48 | 49 | ```bash 50 | mysqld.exe --install mysql --defaults-file="my.ini" 51 | net start mysql 52 | ``` 53 | 54 | 尝试登录`MySQL`服务器: 55 | 56 | ```bash 57 | mysql -u root -p 58 | ``` 59 | 60 | 若出现: 61 | 62 | ``` 63 | Welcome to the MySQL monitor. 64 | ``` 65 | 66 | 则表明成功登录。若要关闭连接, 请输入`exit`; 若要关闭`MySQL`服务, 请使用`net stop mysql`. 67 | 68 | ### 安装MySQL Client 69 | 70 | ```bash 71 | pip install mysqlclient 72 | ``` 73 | 74 | 这样在`Python`中就可以通过`import MySQLdb`来导入`Python--MySQL`接口模块。 75 | 76 | ### 运行 77 | 78 | ```bash 79 | python main.py 80 | ``` 81 | 82 | 在左上角选择登录后即可看到数据库中存在的三张表格数据(`Book`， `Reader`, `Borrow`), 这是`MainWindow.py`函数在登录成功后插入到数据库中的, 具体的SQL语句见`SQL.py`. 83 | 84 | ## Advanced 85 | 86 | 进一步我们可以在此框架上添加更多的功能, 如:数据的增、删、改、查等。该过程主要分为以下几步: 87 | 88 | ### Step 1 89 | 90 | 利用`Qt Designer`创建`ui`文件, 然后使用`pyuic5`将生成的`.ui`文件转变为`.py`文件(放在`ui`文件夹内), 如: 91 | 92 | ```bash 93 | pyuic5 -o ./ui/UiLogin.py ./ui/PyQt_UI/login.ui 94 | pyuic5 -o ./ui/UiMainWindow.py ./ui/PyQt_UI/mainwindow.ui 95 | ``` 96 | 97 | ### Step 2 98 | 99 | 编写相应功能的`SQL`代码并进行测试, 以验证其功能性。 100 | 101 | ### Step 3 102 | 103 | 在`src`文件夹中创建控制模块, 导入先前转换的`ui`文件, 并在控制模块中调用经过测试的`SQL`语句以实现相应的功能, 将结果以图形化的形式展现出来。 -------------------------------------------------------------------------------- /USTC/USTC.py: -------------------------------------------------------------------------------- 1 | # ---------------------------------------------------------- 2 | # coding=utf-8 3 | # Copyright © 2023 Komorebi660 All rights reserved. 4 | # ---------------------------------------------------------- 5 | 6 | import requests 7 | import re 8 | from requests.sessions import session 9 | requests.packages.urllib3.disable_warnings() 10 | 11 | # ------ 用户在运行前需更改的变量 ------ # 12 | USR = '' # 学号 13 | PWD = '' # 密码 14 | LOCATION = '' # 所在校区, 用中文表示 15 | DORM_BUILDING = '' # 寝室楼号 16 | DORM = '' # 寝室门牌号 17 | EMERGENCY_CONTACT = '' # 紧急联系人姓名 18 | RELATIONSHIP = '' # 本人与紧急联系人关系 19 | PHONE_NUMBER = '' # 紧急联系人电话 20 | # ------------------------------------ # 21 | 22 | 23 | # 需要在同一session下执行 24 | session = requests.Session() 25 | 26 | # Step1: 获取CAS_LT 27 | CAS_LT_url = 'https://passport.ustc.edu.cn/login?service=https%3A%2F%2Fweixine.ustc.edu.cn%2F2020%2Fcaslogin' 28 | CAS_LT_res = session.get(CAS_LT_url) 29 | CAS_LT_html = CAS_LT_res.content.decode() 30 | CAS_LT = re.findall('(?<=name="CAS_LT" value=")(.*?)(?=")', CAS_LT_html)[0] 31 | # print(CAS_LT) 32 | 33 | 34 | #Step2: 获取token 35 | login_url = 'https://passport.ustc.edu.cn/login' 36 | login_data = { 37 | 'model': 'uplogin.jsp', 38 | 'CAS_LT': CAS_LT, 39 | 'service': 'https://weixine.ustc.edu.cn/2020/caslogin', 40 | 'warn': '', 41 | 'showCode': '', 42 | 'username': USR, 43 | 'password': PWD, 44 | 'button': '' 45 | } 46 | login_res = session.post(login_url, data=login_data) 47 | login_html = login_res.content.decode('utf-8') 48 | token_temp = re.findall('(?<=name="_token" value=")(.*?)(?=")', login_html) 49 | # 检查返回值是否正确 50 | if (len(token_temp) == 0): 51 | print("UserName or Password ERROR!") 52 | exit(0) 53 | _token = token_temp[0] 54 | # print(token) 55 | 56 | 57 | #Step3: 上报 58 | post_url = 'https://weixine.ustc.edu.cn/2020/daliy_report' 59 | post_data = { 60 | '_token': _token, 61 | 'juzhudi': LOCATION, 62 | 'dorm_building': DORM_BUILDING, 63 | 'dorm': DORM, 64 | 'body_condition': '1', 65 | 'body_condition_detail': '', 66 | 'now_status': '1', 67 | 'now_status_detail': '', 68 | 'has_fever': '0', 69 | 'last_touch_sars': '0', 70 | 'last_touch_sars_date': '', 71 | 'last_touch_sars_detail': '', 72 | 'is_danger': '0', 73 | 'is_goto_danger': '0', 74 | 'jinji_lxr': EMERGENCY_CONTACT, 75 | 'jinji_guanxi': RELATIONSHIP, 76 | 'jiji_mobile': PHONE_NUMBER, 77 | 'other_detail': '' 78 | } 79 | res = session.post(post_url, data=post_data) 80 | html = res.content.decode('utf-8') 81 | alert = re.findall('(?<=

)(.*?)(?=i!lI;:,\"^`'. ") 16 | 17 | 18 | # 将256灰度映射到70个字符上 19 | def get_char(r, b, g, alpha=256): 20 | # 透明则直接返回空格 21 | if alpha == 0: 22 | return ' ' 23 | length = len(ascii_char) 24 | gray = int(0.2126 * r + 0.7152 * g + 0.0722 * b) 25 | unit = (256.0 + 1)/length 26 | return ascii_char[int(gray/unit)] 27 | 28 | 29 | def CharTransfer(path): 30 | try: 31 | im = Image.open(path) 32 | # 调整图片至合适大小 33 | WIDTH, HEIGHT = im.size 34 | max = WIDTH if WIDTH > HEIGHT else HEIGHT 35 | if(max > MAX_SIZE): 36 | rate = MAX_SIZE/max 37 | WIDTH = int(WIDTH*rate) 38 | HEIGHT = int(HEIGHT*rate) 39 | im = im.resize((WIDTH, HEIGHT), Image.ANTIALIAS) 40 | # 创建新图片用来保存结果 41 | output = Image.new('RGB', (WIDTH*10, HEIGHT*10), (255, 255, 255)) 42 | output_draw = ImageDraw.Draw(output) 43 | font = ImageFont.truetype(TYPEFACE_PATH, 13) 44 | # 转化 45 | with tqdm(desc="Transfering", total=HEIGHT) as progress_bar: 46 | for i in range(HEIGHT): 47 | for j in range(WIDTH): 48 | ch = get_char(*im.getpixel((j, i))) 49 | output_draw.text((j*10, i*10), 50 | ch, 51 | fill=(0, 0, 0), 52 | font=font) 53 | progress_bar.update() 54 | return output 55 | except: 56 | print("ERROR: Can not Open Image!") 57 | exit(-1) 58 | 59 | 60 | input_image_path = None 61 | output_image_path = None 62 | argv = sys.argv[1:] 63 | if(len(argv) < 1): 64 | print("Too little parameters, the command should be like this:") 65 | print( 66 | "python CharTransfer.py [output image path]") 67 | exit(-1) 68 | elif(len(argv) > 2): 69 | print("Too much parameters, the command should be like this:") 70 | print( 71 | "python CharTransfer.py [output image path]") 72 | exit(-1) 73 | else: 74 | input_image_path = argv[0] 75 | if(len(argv) == 2): 76 | output_image_path = argv[1] 77 | 78 | output_image = CharTransfer(input_image_path) 79 | 80 | if(len(argv) == 1): 81 | output_image.show() 82 | else: 83 | output_image.save(output_image_path) 84 | print("Save Successfully!") 85 | output_image.show() 86 | -------------------------------------------------------------------------------- /NeuralStyleTransfer/README.md: -------------------------------------------------------------------------------- 1 | # 基于神经网络的图像风格迁移 2 | 3 | ## 目录结构 4 | 5 | ``` 6 | root 7 | │ 8 | ├─README.md #说明文档(本文件) 9 | │ 10 | ├─images #存储demo图片以及其它需要在README中使用的图片 11 | │ | content.jpg 12 | │ | result.jpg 13 | │ | style.jpg 14 | | ...... 15 | │ 16 | └─NeuralStyleTransfer.py #本项目源码 17 | ``` 18 | 19 | ## Nerual Style Transfer 介绍 20 | 21 | 我们认为画家的作品包含两部分内容——内容(`Content`)和风格(`Style`)，`Nerual Style Transfer`要做的事就是把图片的`Content`和`Style`剥离，然后重新组合，实现把一个图像的风格和另一个图像的内容相结合。 22 | 23 | 深度卷积网络通过层次化的表示，每一层建立在前一层的基础上，因此逐层学习到越来越高层，越来越抽象的特征。高层的网络丢弃掉了和识别物体无关的一些细节，它们更加关注图片的内容和而不是像素级的细节，我们称之为内容特征(`Content Features`)。而风格是与具体内容无关的一种作者个性化的特征，我们可以通过计算同一层神经网络的`Filter`的相关性来提取风格特征(`Style Features`)。 24 | 25 | 更详细的介绍参看下面两篇文章： 26 | 27 | - [Nerual Style Transfer](http://fancyerii.github.io/books/neural-style-transfer/) 28 | - [图像风格迁移(Neural Style)简史](https://zhuanlan.zhihu.com/p/26746283) 29 | 30 | ## 源码介绍 31 | 32 | 本代码利用训练好的`VGG16`神经网络，基于`Gatys`教授的论文[A Neural Algorithm of Artistic Style](https://arxiv.org/abs/1508.06576)，使用`Python`实现。 33 | 34 | 使用本代码需要安装以下库： 35 | 36 | - **matplotlib** 37 | - **pytorch** 38 | 39 | 其中`pytorch`库分为`CPU`和`GPU`版本，安装`GPU`版本前需要安装`CUDA`及`CUDNN`. 详情参见[pytorch 官网](https://pytorch.org/)，选择合适的版本，如下图所示： 40 | 41 |

42 | 43 |
44 | 45 | 由于`pytorch`较大，下载过程可能需要花费较长时间。 46 | 47 | 为了运行代码，你需要准备两张图片——内容图片和风格图片，然后输入： 48 | 49 | ``` 50 | python NeuralStyleTransfer.py