Using my movies fingerprint and movies dominant colors scripts, and after I parallelized some of the color-processing routines, I decided to take on a larger project involving a set of movies from the same studio. I’ve watched a lot of Disney animated films, so I took the list of their animation studios films and processed them all individually.

Brief Description

First, I generated all the independent fingerprints with their titles (using the movies fingerprint algorithm with the movies dominant colors upgrades). The “fingerprint” was generated by re-scaling the movies to a size of 640:370 and extracted 3600 frames from each video file (distributed uniformly in time). From these still frames, the dominant color was calculated from a number of 5 total clusters per frame.

Some examples look like this (click to enlarge):

Then I used the image-grid python package to assemble them together:

I still want to make a slight tweak to the routines so that it allows the use of a clustering algorithm that doesn’t pre-require the number of clusters as an input (such as agglomerative clustering).

Code Repo

As this is a use case of previous coding exercises, please have a look at the original posts (movies fingerprint algorithm, movies dominant colors, and color palette extractor) for the descriptions of how the code works.

• Repository: Github repo
• Dependencies: opencv-python, ffmpeg-python, Pillow, numpy, scikit-learn, matplotlib, ffmpeg