去雨算法的PYTHON实现， Rain streaks can severely degrade the visibility, which causes many current computer vision algorithms fail to work. So it is necessary to remove the rain from images. We propose a novel deep network architecture based on deep convolutional and recurrent neural networks for single image deraining. As contextual information is very important for rain removal, we first adopt the dilated convolutional neural network to acquire large receptive field. To better fit the rain removal task, we also modify the network. In heavy rain, rain streaks have various directions and shapes, which can be regarded as the accumulation of multiple rain streak layers. We assign different alpha-values to various rain streak layers according to the intensity and transparency by incorporating the squeeze-and-excitation block. Since rain streak layers overlap with each other, it is not easy to remove the rain in one stage. So we further decompose the rain removal into multiple stages. Recurrent neural network is incorporated to preserve the useful information in previous stages and benefit the rain removal in later stages. We conduct extensive experiments on both synthetic and real-world datasets. Our proposed method outperforms the state-of-the-art approaches under all evaluation metrics.

老外做的Python版LIMS（实验室信息管理系统），功能完备，完整源码。

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航空公司客户价值大数据分析全部的源代码，使用python编写，含数据部分

本代码为NSGA2的python版，论文原文复现结果，实验文档和matlab见之前的资源。

MTCNN的python实现，实现人脸检测。同时可以训练检测其他物体

基于scheme版，内容大致相同，顺序与部分细节有调整，可与scheme对照读