基于蚁群的分类算法

说明：  图片拼接 实现了多个图片的无缝拼接技术，完美无瑕(Picture stitching achieves seamless stitching technology of multiple pictures, perfect and flawless)

说明：  用于细胞检测的神经网络代码。 使用Unet进行边缘检测。(Neural network code for cell detection. Unet is used for edge detection.)

人脸68点标注，用于人类面部关键点检测，结合dlib(face landmark master)

NTP Amplification attack

用Python计算图像对不同地物分类精确度， 计算分类结果图与标准参考图的混淆矩阵， 对分类结果进行评价(Python was used to calculate the classification accuracy of images for different ground objects, calculate the confusion matrix between the classification result map and the standard reference map, and evaluate the classification results)

算法思想来自于网上资源，先使用图像边缘和车牌颜色定位车牌，再识别字符。车牌定位在predict方法中，为说明清楚，完成代码和测试后，加了很多注释，请参看源码。车牌字符识别也在predict方法中，请参看源码中的注释，需要说明的是，车牌字符识别使用的算法是opencv的SVM， opencv的SVM使用代码来自于opencv附带的sample，StatModel类和SVM类都是sample中的代码。SVM训练使用的训练样本来自于github上的EasyPR的c++版本。由于训练样本有限，你测试时会发现，车牌字符识别，可能存在误差，尤其是第一个中文字符出现的误差概率较大。(Bits in the predict method, for clarity, add a lot of comments after completing the code and testing, see the source code. License plate character recognition is also included in the predict method. Please refer to the comments in the source code. It needs to be noted that the algorithm used for license plate character recognition is SVM of opencv. The training sample used for SVM training comes from the c++ version of EasyPR on github. Due to limited training samples, you may find errors in license plate character recognition when testing, especially the error probability of the first Chinese character is large.)

在VS2013+openCV平台运行，只要对图像进行均衡化，像素均衡，零度均衡，试图像像素分布均匀，使图像更加清晰，细节突出

频繁项集挖掘算法FPGrowth用Python实现(Python implementation of frequent itemsets mining algorithm)

说明：  使用ARIMA-SVR组合预测的方式对数据进行预测，ARIMA与SVR分别对线性与非线性有较好的预测效果，因此通过SVR来预测ARIMA的残差使得预测结果更为精确(Arima-svr combined forecasting method is used to forecast the data. ARIMA and SVR have better prediction effect on linear and nonlinear respectively. Therefore, the residual of ARIMA predicted by SVR makes the prediction result more accurate)

基于python语言读取Micaps格式数据，包括地面、高空、格点数据等类型。(Read Micaps format data based on Python language)