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https://github.com/Eyevinn/autovmaf-preprocessing
Tool that analyses a video via a combination of motion and sharpness to determine the most suitable parts to be used for VMAF analysis. https://github.com/Eyevinn/autovmaf
https://github.com/Eyevinn/autovmaf-preprocessing
opencv tools video-processing vmaf
Last synced: 5 days ago
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Tool that analyses a video via a combination of motion and sharpness to determine the most suitable parts to be used for VMAF analysis. https://github.com/Eyevinn/autovmaf
- Host: GitHub
- URL: https://github.com/Eyevinn/autovmaf-preprocessing
- Owner: Eyevinn
- License: mit
- Created: 2022-07-14T09:27:03.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-09-02T19:05:22.000Z (about 2 years ago)
- Last Synced: 2024-08-02T20:45:40.260Z (3 months ago)
- Topics: opencv, tools, video-processing, vmaf
- Language: Python
- Homepage:
- Size: 474 KB
- Stars: 7
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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:
```python
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.
```text
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:
```bash
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
Eyevinn 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.
At Eyevinn, every software developer consultant has a dedicated budget reserved for open source development and contribution to the open source community. This give us room for innovation, team building and personal competence development. And also gives us as a company a way to contribute back to the open source community.
Want to know more about Eyevinn and how it is to work here. Contact us at [email protected]!