AutoVMAF Preprocessing

Python script that analyzes a video (local file or hls-stream) via a combination of motion and sharpness to determine the most suitable section to be used for VMAF analysis.

The output is a time-code of the frame that contains the most motion and finer details. This can for example be confetti, particles, tree leaves, etc.

Example of a processed frame: The tool converts motion pixels to white and everything else to black pixels. The percentage of white pixels in the frame and the overall sharpness of the original frame (how much that is in focus) is then calculated per frame and then compared to the next frame and so on.

Installation

The tool has a dependency on openCV for python, which should be installed using pip (or possibly pip3 or similar):

pip3 install opencv-python

Usage

Example:

from .src.analyze import video_analyzer

threshold = 4 # Motion threshold (higher = more motion required) (default: 25 max: 255)
timecode = video_analyzer(file_path="video.mp4", threshold=threshold)

print(timecode) # HH:MM:SS:FF

# To run the script with the debug option to show the processed frames:
timecode = video_analyzer(file_path="video.mp4", threshold=threshold, debug_video=True)

print(timecode) # HH:MM:SS:FF

An example CLI have also been provided that prints the timecode to the console.

usage: cli.py [-h] [-f FILE] [-d] [-t THRESHOLD]

required arguments:
  -f FILE, --file FILE  path to the video file

optional arguments:
  -d, --debug           show the current processed frame
  -t THRESHOLD, --threshold THRESHOLD
                        threshold for motion detection, default: 25 max: 255

Example:

python cli.py -f video.mp4 -t 4

# To run the script with the debug option to show the processed frames:
python cli.py -f video.mp4 -t 4 -d

About Eyevinn Technology

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