Video to Slides Converter: Transform Video Lectures into Slide Presentations

This is a simple video-to-slide converter application that aims to obtain slide images (or pdf) given slide or lecture videos.

This is highly useful when one wishes to have a video lecture(with or without animations) in the form of slides – either a ppt or pdf. However, more often than not, slides are not provided when such video lectures are hosted on platforms like YouTube. This project aims to build a robust application that can convert video lectures into corresponding slides using techniques such as basic frame differencing and statistical background subtraction models such as KNN or GMG.

Notes:

• It will take some time to complete (about half of the original video length), so stay tuned!
• If the YouTube video link doesn't work, you can try again later or download the video and process it locally
• For slide videos with full background animation similar to this video, the algorithm cannot extract the right frames after tthe animation ends. In that case, using Frame Differencing (--type Frame_Diff) will yield better results, but the results are still decent.
• You may want to trim the video before processing it for better results.

Dependencies

1. Install OpenCV If you already have opencv installed, skip this step

   pip install opencv-contrib-python

2. Install other packages

   pip install -r requirements.txt

3. Install gradio (optional) If you don't use the GUI version, skip this step

    pip install gradio>=4.0

Command-Line Options

usage: video_2_slides.py [-h] [-v VIDEO_FILE_PATH] [-o OUT_DIR] [--type {Frame_Diff,GMG,KNN}] [-hf {dhash,phash,ahash}] [-hs {8,12,16}]
                         [--threshold {90,91,92,93,94,95,96,97,98,99,100}] [-q QUEUE_LEN] [--no_post_process] [--convert_to_pdf]

This script is used to convert video frames into slide PDF.

optional arguments:
  -h, --help            show this help message and exit
  -v VIDEO_PATH, --video_path VIDEO_FILE_PATH
                        Path to the video file, video url, or YouTube video link
  -o OUT_DIR, --out_dir OUT_DIR
                        Path to the output directory
  --type {Frame_Diff,GMG,KNN}
                        type of background subtraction to be used
  -hf {dhash,phash,ahash}, --hash-func {dhash,phash,ahash}
                        Hash function to use for image hashing. Only effective if post-processing is enabled
  -hs {8,12,16}, --hash-size {8,12,16}
                        Hash size to use for image hashing. Only effective if post-processing is enabled
  --threshold {90,91,92,93,94,95,96,97,98,99,100}
                        Minimum similarity threshold (in percent) to consider 2 images to be similar. Only effective if post-processing is enabled
  -q QUEUE_LEN, --queue-len QUEUE_LEN
                        Number of history images used to find out duplicate image. Only effective if post-processing is enabled
  --no_post_process     flag to apply post processing or not
  --convert_to_pdf      flag to convert the entire image set to pdf or not

If you want to manually remove some images before generating final PDF file, you can use the convert_to_pdf.py script later to convert the entire image set to pdf

usage: convert_to_pdf.py [-h] [-f FOLDER] [-o OUT_PATH]

This script is used to convert video frames into slide PDFs.

optional arguments:
  -h, --help            show this help message and exit
  -f FOLDER, --folder FOLDER
                        Path to the image folder
  -o OUT_PATH, --out_path OUT_PATH
                        Path to the output PDF file. If None, the image
                        directory will be used to store the output file.

Gradio App

gradio app.py

The application will be available at http://localhost:7680

Sample outputs

Video file	Output
https://www.youtube.com/watch?v=bfmFfD2RIcg	output_results/Neural Network In 5 Minutes.pdf
https://www.youtube.com/watch?v=rQijrDj1wCQ	output_results/What is Docker Container Docker explained in 5 minutes.pdf
https://www.youtube.com/watch?v=YxlDoz_P4kc	output_results/Introduction to Stable Diffusion.pdf
sample_vids/react-in-5-minutes.mp4	output_results/react-in-5-minutes.pdf

References

1. OpenCV blog
2. Simple Background Estimation in Videos using OpenCV
3. Image Hashing