Free-form deformation自由变形

说明：  飞机大战，你还在等什么，赶快来游戏吧，这里需要你的激情(What are you waiting for in the air war? Come to the game now. You need your passion here.)

说明：  python简单验证判断nan，同时讨论python中nan的一些用法和问题，以及处理方法。(Python simply verifies and judges nan, and discusses some usage and problems of nan in python, as well as processing methods.)

利用栅格法建模，基于强化学习Qlearning算法实现路径规划，可以实时显示(Using raster method to model and Qlearning algorithm based on reinforcement learning to realize path planning, it can be displayed in real time.)

说明：  This is tensorflow example source code

通过视频文件库dib进行人脸关键点检测，当图片倾斜时，旋转图片为正方向，进行人脸检测，每次旋转的角度为60度，当旋转360度，还是无法检测时，则认为人脸不存在。

python flask advance

说明：  计算长方形产生的重力异常,理论计算和具体的实现方法.(Calculation of gravity anomaly caused by rectangle)

文件包含鸢尾花csv数据集以及鸢尾花项目代码的py文件以及txt文件(The file contains the iris CSV dataset and the PY file and the txt file of the iris project code.)

关于Python编程的压缩包，包含很多用Python编写的程序。(Python programming on the compressed package, including a lot of procedures written in Python.)

说明：  图像超分辨率技术源码； 基于深度学习的图像超分辨率重建的流程如下[2]： 1.首先找到一组原始图像Image1; 2.将这组图片降低分辨率为一组图像Image2 3.通过各种神经网络结构，将Image2超分辨率重建为Image3(Image3和Image1分辨率一样) 4.通过PSNR等方法比较Image1和Image3，验证超分辨率重建的效果，根据效果调节神经网络中的节点和参数 5.反复执行以上直到第4步的结果比较满意(Image super-resolution technology source code; The process of image super-resolution reconstruction based on deep learning is as follows [2]: 1. First find a set of original images image1; 2. Reduce the resolution of this group of pictures to a group of image2 3. Through various neural network structures, the super-resolution of image2 is reconstructed into image3 (the resolution of image3 is the same as that of image1) 4. Compare image1 and image3 by PSNR and other methods to verify the effect of super-resolution reconstruction, and adjust the nodes and parameters of neural network according to the effect 5. Repeat the above until the result of step 4 is satisfactory)