video_sampler 0.10.2

Video Sampler -- sample frames from a video file

video-sampler

Video sampler allows you to efficiently sample video frames and summarize the videos. Currently, it uses keyframe decoding, frame interval gating and perceptual hashing to reduce duplicated samples.

Use case: video data collection for machine learning, video summarisation, video frame analysis.

Table of Contents

• video-sampler
  • Table of Contents
  • Documentation
  • Features
  • Installation and Usage
    • Basic usage
      • YT-DLP integration plugin
        • Extra YT-DLP options
      • OpenAI summary
      • API examples
    • Advanced usage
      • Gating
      • CLIP-based gating comparison
      • Blur gating
  • Benchmarks
  • Benchmark videos
  • Flit commands
    • Build
    • Install
    • Publish
  • 🛡 License
  • 📃 Citation

Documentation

Documentation is available at https://lemurpwned.github.io/video-sampler/.

Features

• Direct sampling methods:
  • hash - uses perceptual hashing to reduce duplicated samples
  • entropy - uses entropy to reduce duplicated samples (work in progress)
  • gzip - uses gzip compressed size to reduce duplicated samples (work in progress)
  • buffer - uses sliding buffer to reduce duplicated samples
  • grid - uses grid sampling to reduce duplicated samples
• Gating methods (modifications on top of direct sampling methods):
  • clip - uses CLIP to filter out frames that do not contain the specified objects
  • blur - uses blur detection to filter out frames that are too blurry
• Language capture:
  • Keyword capture from subtitles
• Integrations
  • YTDLP integration -- streams directly from yt-dlp queries, playlists or single videos
  • OpenAI multimodal models integration for video summaries

Installation and Usage

python3 -m pip install -U video_sampler

If you intend to use all the integrations, you need can with all dependencies:

python3 -m pip install -U video_sampler[all]

Available extras are:

• yt-dlp - for YT-DLP integration
• clip - for CLIP models integration
• language - for language capture
• all - for all dependencies
• dev - for development dependencies

To see all available options, run:

python3 -m video_sampler --help

or simply

video_sampler --help

Basic usage

python3 -m video_sampler hash FatCat.mp4 ./dataset-frames/ --hash-size 3 --buffer-size 20

YT-DLP integration plugin

Before using please consult the ToS of the website you are scraping from -- use responsibly and for research purposes. To use the YT-DLP integration, you need to install yt-dlp first (see yt-dlp). Then, you simply add --yt-dlp to the command, and it changes the meaning of the video_path argument.

• to search

video_sampler hash "ytsearch:cute cats" ./folder-frames/ \
  --hash-size 3 --buffer-size 20 --ytdlp

• to sample a single video

video_sampler hash "https://www.youtube.com/watch?v=W86cTIoMv2U" ./folder-frames/ \
    --hash-size 3 --buffer-size 20 --ytdlp

• to sample a playlist

video_sampler hash "https://www.youtube.com/watch?v=GbpP3Sxp-1U&list=PLFezMcAw96RGvTTTbdKrqew9seO2ZGRmk" ./folder-frames/ \
  --hash-size 3 --buffer-size 20 --ytdlp

• segment based on the keyword extraction

video_sampler hash "https://www.youtube.com/watch?v=GbpP3Sxp-1U&list=PLFezMcAw96RGvTTTbdKrqew9seO2ZGRmk" ./folder-frames/ \
  --hash-size 3 --buffer-size 20 --ytdlp --keywords "cat,dog,another keyword,test keyword"

The videos are never directly downloaded, only streamed, so you can use it to sample videos from the internet without downloading them first.

Extra YT-DLP options

You can pass extra options to yt-dlp by using the -yt-extra-args flag. For example:

this will only sample videos uploaded before 2019-01-01:

... --ytdlp --yt-extra-args '--datebefore 20190101'

or this will only sample videos uploaded after 2019-01-01:

... --ytdlp --yt-extra-args '--dateafter 20190101'

or this will skip all shorts:

... --ytdlp --yt-extra-args '--match-filter "original_url!*=/shorts/ & url!*=/shorts/"

OpenAI summary

To use the OpenAI multimodal models integration, you need to install openai first pip install openai. Then, you simply add --summary-interval to the command and the url.

In the example, I'm using llamafile LLAVA model to summarize the video every 50 frames. If you want to use the OpenAI multimodal models, you need to export OPENAI_API_KEY=your_api_key first. The format should also work with default OpenAI stuff.

To replicate, run LLAVA model locally and set the summary-url to the address of the model. Specify the summary-interval to the minimal interval in seconds between frames that are to be summarised/described.

video_sampler hash ./videos/FatCat.mp4 ./output-frames/ --hash-size 3 --buffer-size 20 --summary-url "http://localhost:8080/completion" --summary-interval 50

Supported env in case you need those:

• OPENAI_API_KEY - OpenAI API key
• OPENAI_MODEL - OpenAI model name

Confirmed that you can make it work with e.g. LM Studio, but you need to adjust the summary-url to the correct address, e.g. it might be "http://localhost:8080/completions". Similar if you want to use the OpenAI API.

Some frames, based on the interval specified, will be summarised by the model and the result will saved in the ./output-frames/summaries.json folder. The frames that are summarised come after the sampling and gating process happens, and only those frames that pass both stages are viable for summarisation.

summaries.jsonl
---
{"time": 56.087, "summary": "A cat is walking through a field of tall grass, with its head down and ears back. The cat appears to be looking for something in the grass, possibly a mouse or another small creature. The field is covered in snow, adding a wintry atmosphere to the scene."}
{"time": 110.087, "summary": "A dog is walking in the snow, with its head down, possibly sniffing the ground. The dog is the main focus of the image, and it appears to be a small animal. The snowy landscape is visible in the background, creating a serene and cold atmosphere."}
{"time": 171.127, "summary": "The image features a group of animals, including a dog and a cat, standing on a beach near the ocean. The dog is positioned closer to the left side of the image, while the cat is located more towards the center. The scene is set against a beautiful backdrop of a blue sky and a vibrant green ocean. The animals appear to be enjoying their time on the beach, possibly taking a break from their daily activities."}

API examples

See examples in ./scripts.

Advanced usage

There are 3 sampling methods available:

• hash - uses perceptual hashing to reduce duplicated samples
• entropy - uses entropy to reduce duplicated samples (work in progress)
• gzip - uses gzip compressed size to reduce duplicated samples (work in progress)

To launch any of them you can run and substitute method-name with one of the above:

video_sampler buffer `method-name` ...other options

e.g.

video_sampler buffer entropy --buffer-size 20 ...

where buffer-size for entropy and gzip mean the top-k sliding buffer size. Sliding buffer also uses hashing to reduce duplicated samples.

Gating

Aside from basic sampling rules, you can also apply gating rules to the sampled frames, further reducing the number of frames. There are 3 gating methods available:

• pass - pass all frames
• clip - use CLIP to filter out frames that do not contain the specified objects
• blur - use blur detection to filter out frames that are too blurry

Here's a quick example of how to use clip:

python3 -m video_sampler clip ./videos ./scratch/clip --pos-samples "a cat" --neg-samples "empty background, a lemur"  --hash-size 4

CLIP-based gating comparison

Here's a brief comparison of the frames sampled with and without CLIP-based gating with the following config:

  gate_def = dict(
      type="clip",
      pos_samples=["a cat"],
      neg_samples=[
          "an empty background",
          "text on screen",
          "a forest with no animals",
      ],
      model_name="ViT-B-32",
      batch_size=32,
      pos_margin=0.2,
      neg_margin=0.3,
  )

Evidently, CLIP-based gating is able to filter out frames that do not contain a cat and in consequence, reduce the number of frames with plain background. It also thinks that a lemur is a cat, which is not entirely wrong as fluffy creatures go.

Pass gate (no gating)	CLIP gate	Grid

The effects of gating in numbers, for this particular set of examples (see produced vs gated columns). produced represents the number of frames sampled without gating, here after the perceptual hashing, while gated represents the number of frames sampled after gating.

video	buffer	gate	decoded	produced	gated
FatCat.mp4	grid	pass	179	31	31
SmolCat.mp4	grid	pass	118	24	24
HighLemurs.mp4	grid	pass	161	35	35
FatCat.mp4	hash	pass	179	101	101
SmolCat.mp4	hash	pass	118	61	61
HighLemurs.mp4	hash	pass	161	126	126
FatCat.mp4	hash	clip	179	101	73
SmolCat.mp4	hash	clip	118	61	31
HighLemurs.mp4	hash	clip	161	126	66

Blur gating

Helps a little with blurry videos. Adjust threshold and method (laplacian or fft) for best results. Some results from fft at threshold=20:

video	buffer	gate	decoded	produced	gated
MadLad.mp4	grid	pass	120	31	31
MadLad.mp4	hash	pass	120	110	110
MadLad.mp4	hash	blur	120	110	85

Benchmarks

Configuration for this benchmark:

SamplerConfig(min_frame_interval_sec=1.0, keyframes_only=True, buffer_size=30, hash_size=X, queue_wait=0.1, debug=True)

Video	Total frames	Hash size	Decoded	Saved
SmolCat	2936	8	118	106
SmolCat	-	4	-	61
Fat Cat	4462	8	179	163
Fat Cat	-	4	-	101
HighLemurs	4020	8	161	154
HighLemurs	-	4	-	126

---

SamplerConfig(
    min_frame_interval_sec=1.0,
    keyframes_only=True,
    queue_wait=0.1,
    debug=False,
    print_stats=True,
    buffer_config={'type': 'entropy'/'gzip', 'size': 30, 'debug': False, 'hash_size': 8, 'expiry': 50}
)

Video	Total frames	Type	Decoded	Saved
SmolCat	2936	entropy	118	39
SmolCat	-	gzip	-	39
Fat Cat	4462	entropy	179	64
Fat Cat	-	gzip	-	73
HighLemurs	4020	entropy	161	59
HighLemurs	-	gzip	-	63

Benchmark videos

• SmolCat
• Fat Cat
• HighLemurs
• MadLad

Flit commands

Build

flit build

Install

flit install

Publish

Remember to bump the version in pyproject.toml before publishing.

flit publish

🛡 License

This project is licensed under the terms of the MIT license. See LICENSE for more details.

📃 Citation

@misc{video-sampler,
  author = {video-sampler},
  title = {Video sampler allows you to efficiently sample video frames},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/LemurPwned/video-sampler}}
}