为方便，只有源码，请自行安装包，请注意：OpenCV-Python-contirb 3.4.216 不要用4.-几的。代码中图片路径改一下运行即可

opencv_python-2.4.13.5-cp27-cp27m-win_amd64.whl。。。。

I'm glad you're here. It's about time we talked about machine learning. Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. As a subfield of data science, machine learning enables computers to learn through experience: to make predictions about the future using collected data from the past. And the amount of data to be analyzed is enormous! Current estimates put the daily amount of produced data at 2.5 exabytes (or roughly 1 billion gigabytes). Can you believe it? This would be enough data to fill up 10 million blu-ray discs, or amount to 90 years of HD video. In order to deal with this vast amount of data, companies such as Google, Amazon, Microsoft, and Facebook have been heavily investing in the development of data science platforms that allow us to benefit from machine learning wherever we go—scaling from your mobile phone application all the way to supercomputers connected through the cloud.

python上面的opencv程序开发方便快捷，而且借助python的向量库和数学库性能也不差，很适合做验证和测试。这里提供一个比较入门的中文教程。

opencv_python-3.4.0.12-cp36-cp36m-manylinux1_x86_64.whl python opencv

在图像处理的过程中，我们可能会遇到这样的一个场景，想要截取图像的某个区域，但是，又无法知道区域在图片的具体位置，无法通过正常的截图手段进行操作。所以，根据这一需求，我们应该想到在交互状态下去操作图像，也就是可以通过我们的鼠标进行选定某个区域，然后再进行截图操作。

基于python3 opencv 视频采集，采用ＴＣＰ协议进行双路视频传输，以及双显示界面实现

一个python调用opencv实现人脸识别的小DEMO，值得尝试

代码说明 执行顺序： （1）avi2img.m 将视频转化为单张图片 （2）mark_rect.py 将视频分成多个帧，在每一帧中将运动对象用矩形标注出来，并生成mask图，将mask图保存到“mask”目录下。 （3）merge.py 拼接图片，将视频的原始帧图片和各个帧的遮罩层进行拼接，获得拼接后的图片，将新生成的图片保存到“new”目录下。 （4）binaryzation.py 将图片做二值化处理，将新生成的图片保存到“last”目录下。 （5）img2avi.m 将多张图片转化为单个视频

压缩包包含自定义训练器的全过程，也有代码和理论的解释，使用Python-Opencv轻松搞定任意物体识别！！！