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.
The tool has a dependency on openCV for python, which should be installed using pip (or possibly pip3 or similar):
pip3 install opencv-python
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:FFAn 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 -dEyevinn Technology is an independent consultant firm specialized in video and streaming. Independent in a way that we are not commercially tied to any platform or technology vendor.
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