Analysing cinema is a time-consuming process. In the cinematography domain alone, there's a lot of factors to consider, such as shot scale, shot composition, camera movement, color, lighting, etc. Whatever you shoot is in some way influenced by what you've watched. There's only so much one can watch, and even lesser that one can analyse thoroughly.
This is where neural networks offer ample promise. They can recognise patterns in images that weren't possible until less than a decade ago, thus offering an unimaginable speed up in analysing cinema. I've developed a neural network that focuses on one fundamental element of visual grammar: shot types. It's capable of recognising 6 unique shot types, and is ~91% accurate. The pretrained model, validation dataset (the set of images used to determine its accuracy), code used to train the network, and some more code to classify your own images is freely available.