dane-video-segmentation-worker

Running Scenedetect to detect shots and select keyframes. Including code for extracting keyframes, extracting audio, and generating spectrograms.

Installation

Use Poetry to install this project into a virtualenv.

poetry install

Installing python-opencv in a virtualenv, and thus not as a system package, could cause certain shared-objects to be missing. So far it seems libgl1 might be missing this way. Install it using:

apt-get install libgl1

To make sure the unit-test work as well

apt-get install ffmpeg

Run in local Python virtualenv

For local testing, make sure to put a config.yml in the root of this repo:

cp ./config/config.yml config.yml

Then make sure to activate your virtual environment:

poetry shell

Then run ./scripts/check-project.sh to:

• linting (Using flake8)
• type checking (Using mypy)
• unit testing (Using pytest)

Run test file in local Docker Engine

This form of testing/running avoids connecting to DANE:

• No connection to DANE RabbitMQ is made
• No connection to DANE ElasticSearch is made

This is ideal for testing:

• main_data_processor.py, which uses VISXP_PREP.TEST_INPUT_FILE (see config.yml) to produce this worker's output
• I/O steps taken after the output is generated, i.e. deletion of input/output and transfer of output to S3

docker build -t dane-video-segmentation-worker .

Check out the docker-compose.yml to learn about how the main process is started. As you can see there are two volumes mounted and an environment file is loaded:

version: '3'
services:
  web:
    image: dane-video-segmentation-worker:latest  # your locally built docker image
    volumes:
      - ./data:/data  # put input files in ./data and update VISXP_PREP.TEST_INPUT_FILE in ./config/config.yml
      - ./config:/root/.DANE  # ./config/config.yml is mounted to configure the main process
    container_name: visxp
    command: --run-test-file  # NOTE: comment this line to spin up th worker
    env_file:
      - s3-creds.env  # create this file with AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY to allow boto3 to connect to your AWS S3 bucket (see OUTPUT.S3_* variables in config.yml)
    logging:
      options:
        max-size: 20m
    restart: unless-stopped

There is no need to update the docker-compose.yml, but make sure to:

• adapt ./config/config.yml (see next sub-section for details)
• create s3-creds.env to allow the worker to upload output to your AWS S3 bucket

Config

The following parts are relevant for local testing (without connecting to DANE). All defaults are fine for testing, except:

• VISXP_PREP.TEST_INPUT_FILE: make sure to supply your own mp4 file in ./data
• S3_ENDPOINT_URL: ask your DANE admin for the endpoint URL
• S3_BUCKET: ask your DANE admin for the bucket name

FILE_SYSTEM:
    BASE_MOUNT: /data
    INPUT_DIR: input-files
    OUTPUT_DIR: output-files/visxp_prep
PATHS:
    TEMP_FOLDER: /data/input-files
    OUT_FOLDER: /data/output-files
VISXP_PREP:
    RUN_KEYFRAME_EXTRACTION: true
    RUN_AUDIO_EXTRACTION: false
    SPECTROGRAM_WINDOW_SIZE_MS: 1000
    SPECTROGRAM_SAMPLERATE_HZ:
        - 24000
    TEST_INPUT_FILE: /data/testob-take-2.mp4
INPUT:
    DELETE_ON_COMPLETION: False  # NOTE: set to True in production environment
OUTPUT:
    DELETE_ON_COMPLETION: True
    TRANSFER_ON_COMPLETION: True
    S3_ENDPOINT_URL: https://your-s3-host/
    S3_BUCKET: your-s3-bucket  # bucket reserved for 1 type of output
    S3_FOLDER_IN_BUCKET: assets  # folder within the bucket

Relevant links

Also see: https://stackoverflow.com/questions/64664094/i-cannot-use-opencv2-and-received-importerror-libgl-so-1-cannot-open-shared-obj

https://docs.opencv.org/4.x/d2/de6/tutorial_py_setup_in_ubuntu.html