vit-captioner 0.1.2

A package for extracting keyframes from videos and generating captions using ViT-GPT2 model

ViT Captioner

A Python package for extracting keyframes from videos and generating captions using the ViT-GPT2 model.

Features

• Extract keyframes from videos using Katna or uniform sampling
• Generate captions for images using the ViT-GPT2 model
• Match keyframes with timestamps in a video
• Convert videos to SRT subtitle files with captions
• Visualize keyframes and timeline data
• Performance optimized with smart resource management
• Thread-safe image processing and visualization

Installation

pip install vit-captioner

Command Line Usage

Extract keyframes from a video:

vit-captioner extract -V /path/to/video.mp4 -N 10 -v

Generate caption for an image:

vit-captioner caption-image -I /path/to/image.jpg

Convert video to captions:

vit-captioner caption-video -V /path/to/video.mp4 -N 10 -v

The -v flag enables verbose output with progress bars.

Find matching timestamps for keyframes:

vit-captioner find-timestamps -V /path/to/video.mp4 -K /path/to/keyframes_folder -v

Python API Usage

from vit_captioner.keyframes.extractor import KeyFrameExtractor
from vit_captioner.captioning.image import ImageCaptioner
from vit_captioner.captioning.video import VideoToCaption

# Extract keyframes
extractor = KeyFrameExtractor("/path/to/video.mp4")
extractor.extract_key_frames("/path/to/video.mp4", 10)

# Generate caption for an image
captioner = ImageCaptioner()
caption = captioner.predict_caption("/path/to/image.jpg")

# Convert video to captions
# Note: verbose flag enables progress bars
converter = VideoToCaption("/path/to/video.mp4", num_frames=10, verbose=True)
converter.convert()

Performance Optimizations

• Smart resource management with proper cleanup
• Single model loading for multiple frames (improved memory usage)
• Thread-safe image processing with error fallbacks
• Progress bars for tracking long-running operations
• Limited number of concurrent workers to prevent memory issues

Requirements

• Python 3.6+
• OpenCV
• PyTorch
• Transformers
• Katna
• Matplotlib
• tqdm

Source Code

Source code is available on GitHub: https://github.com/lachlanchen/VideoCaptionerWithVit

License

MIT