Advanced pipelines for video datasets
Project description
Bumblebee
Bumblebee provides high level components to construct training pipelines for videos conveniently.
Install
pip install eyecu_bumblebee
Motivation
Everything should be made as simple as possible, but no simpler. - Albert Einstein
Our Websites
Examples
A pipeline with basic elements
from bumblebee import *
if __name__ == "__main__":
VIDEO_PATH = "/path/to/video.mp4"
# Create a source
file_stream = sources.FileStream(VIDEO_PATH)
# Add an effect
goto = effects.GoTo(file_stream)
END_OF_VIDEO = file_stream.get_duration()
goto(END_OF_VIDEO)
# Create a dataset
single_frame = datasets.SingleFrame(file_stream)
last_frame = single_frame.read()
Using Manager API
from bumblebee import *
if __name__ == "__main__":
# Create a training manager
manager = managers.BinaryClassification(
["path/to/video_dir","path/to/another_dir"],
["path/to/labels"]
)
number_of_epochs = 300
for epoch,(frame_no,frame,prob) in manager(number_of_epochs):
# Use data stuff
...
Read limited section of video
from bumblebee import *
from
if __name__ == "__main__":
VIDEO_PATH = "/path/to/video.mp4"
start_frame = 35
end_frame = 40
file_stream = sources.FileStream(VIDEO_PATH)
limited_stream = effects.Start(file_stream,start_frame)
limited_stream = effects.End(limited_stream,end_frame)
single_frame = datasets.SingleFrame(file_stream)
for frame in single_frame:
...
Team
This project is currently developed and maintained by ovuruska.
License
Bumblebee has MIT license. You can find further details in LICENSE.
Project details
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