video-representations-extractor 1.0.9

Video Representations Extractor (VRE) for computing algorithmic or neural representations of each frame.

Video Representation Extractor

1. Description

The purpose of this repository is to export various representations starting from RGB videos only. Representations are defined as ways of 'looking at the world'. One can watch at various levels of information:

• low level: colors, edges
• mid level: depth, orientation of planes (normals)
• high level: semantics and actions

For GitHub users: this is a mirror of the gitlab repository.

Supported representations

• See here for a comprehensive list, since it updates faster than this README.

Weights are stored in this directory using git-lfs: weights dir. If you just want to download the code, without the weights resources, use GIT_LFS_SKIP_SMUDGE=1 git clone ....

2. Usage

Installation is as easy as:

pip install video-representations-extractor

You can however, clone this repository and add it to your paths:

git clone https://gitlab.com/meehai/video-representations-extractor [/some/dir]
# in .bashrc
export PYTHONPATH="$PYTHONPATH:/some/dir"
export PATH="$PATH:/some/dir/bin"

After either option, you should be able to run:

vre <path/to/video.mp4> --cfg_path <path/to/cfg> -o <path/to/export_dir>

The magic happens inside the config file, where we define what representations to extract and what parameters are used to instantiate said representations.

2.1 Single image usage

You can get the representations for a single image (or a directory of images) by placing your image in a standalone directory.

vre <path/to/dir_of_images> --cfg_path <path/to/cfg> -o <path/to/export_dir>

Note: use --cfg_path resources/cfgs/testCfg_ootb.yaml for 'out of the box' working representations.

Note2: Use VRE_DEVICE=cuda vre... to use cuda. For some representations, this speeds up the process by a lot.

3. CFG file

The config file will have the hyperparameters required to instantiate each supported method as well as global hyperparameters for the output. These parameters are sent to the constructor of each representation, so one can pass additional semantics to each representation, such as classes of a semantic segmentation or the maximum global depth value in meters.

High level format:

name of representation:
  type: some high level type (such as depth, semantic, edges etc.)
  name: the implemented method's name (i.e. dexined, dpt etc.)
  dependencies: [a list of dependencies given by their names]
  parameters: # as defined in the constructor of the implementation
    param1: value1
    param2: value2
  device: "cuda" # for representations that have in their vre_setup() method a model.to(device) call

name of representation 2:
  type: some other type
  name: some other method
  dependencies: [name of representation] # since this representation depends on the prev one, it'll be computed after
  parameters: []

Example cfg file: See out of the box supported representations and the CFG defined in the CI process for an actual export that is done at every commit on a real video.

Note: If the topological sort fails (because of cycle dependencies), an error will be thrown.

4. Output format

All the outputs are going to be stored as [0-1] float32 npz files, one for each frame in a directory specified by --output_dir/-o. A subdirectory will be created for each representation.

For the above CFG file, 2 subdirectories will be created:

/path/to/output_dir/
  name of representation/
    npy/ # if export_npy is set
      1.npz, ..., N.npz
    png/ # if export_png is set
      1.png, ..., N.png
  name of representation 2/
    npy/
      1.npz, ..., N.npz

The cfg.yaml file for each representation is created so that we know what parameters were used for that representation.

4.1 Collages

In bin/ we provide a secondary tool, vre_collage that takes all the png files from an output_dir as above and stacks them together in a single image. This is useful if we want to create a single image of all representations which can later be turned into a video as well.

Usage:

vre_collage /path/to/output_dir -o /path/to/collage_dir [--overwrite] [--video] [--fps] [--output_resolution H W]

Note: you can also get video from a collage dir like this (in case you forgot to set --video or want more control):

cd /path/to/collage_dir
ffmpeg -start_number 1 -framerate 30 -i %d.png -c:v libx264 -pix_fmt yuv420p /path/to/collage.mp4;

5. Run in docker

We offer a pre-pushed VRE image in dockerhub.

mkdir example/
chmod 777 -R example/ # optional ?
curl "https://gitlab.com/meehai/video-representations-extractor/-/raw/master/resources/test_video.mp4" \
  -o example/video.mp4 # you can of course use any video, not just our test one
curl https://gitlab.com/meehai/video-representations-extractor/-/raw/master/test/end_to_end/imgur/cfg.yaml -o example/cfg.yaml
docker run -v `pwd`/example:/app/example -v `pwd`/resources/weights:/app/weights \
  --gpus all -e VRE_DEVICE='cuda' -e VRE_WEIGHTS_DIR=/app/weights \
  meehai/vre:latest /app/example/video.mp4 \
  --cfg_path /app/example/cfg.yaml -o /app/example/output_dir --start_frame 100 --end_frame 101

Note: For the --gpus all -e VRE_DEVICE='cuda' part to work, you need to install nvidia-container-toolkit as well. Check NVIDIA's documentation for this. If you are only on a CPU machine, then remove them from the docker run command.