Comments (3)
You may want to read SAD (Sum of absolute difference)
from katna.
You may want to read SAD (Sum of absolute difference)
Here is your wiki script :
" Frame extraction and selection criteria for key-frame extraction
Frame that are sufficiently different from previous ones using absolute differences in LUV color-space
Brightness score filtering of extracted frames
Entropy/contrast score filtering of extracted frames
K-Means Clustering of frames using image histogram
Selection of best frame from clusters based on and variance of Laplacian (image blur detection)"
I think "Frame that are sufficiently different from previous ones" is the most important step to follow. But I don't understand why it can generate good key frames. And why the following cluster process and blur detection should be added? Is there any paper to refer?
from katna.
Closing the question as it is not an issue
from katna.
Related Issues (20)
- ValueError: Input vector needs to be bigger than window size. HOT 2
- Memory leak while calling "get_key_frames_from_video" function in a for loop HOT 15
- Facing out of memory error HOT 6
- memory problem HOT 5
- train? HOT 1
- Conversion failed HOT 4
- About extract_video_keyframes only return 4 frames HOT 2
- sorting of the output key frame images HOT 10
- pip3 install katan installs a different python package from pypi HOT 1
- Why do I have to set no_of_frames? HOT 4
- 'Video' object has no attribute 'extract_frames_as_images' HOT 5
- return key frames with the exact frame id in the original video frame sequence HOT 2
- Timestamp support for extracted frames HOT 1
- Timing of keyframe extraction
- Create AUR Package
- Images from video has weird sequence.
- It takes a long time to extract video keyframes..
- How to output specified image size only? HOT 3
- TypeError: exceptions must derive from BaseException HOT 5
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from katna.