mbari-aidata 1.64.0

Command line tool to do extract, transform, load and download operations on AI data for a number of projects at MBARI that require detection, clustering or classification workflows.

mbari-aidata

A command line tool to do extract, transform, load (ETL) and download operations on AI data for a number of projects at MBARI that require detection, clustering or classification workflows. This tool is designed to work with Tator, a web based platform for video and image annotation and data management and Redis queues for ingesting data from real-time workflows.

More documentation and examples are available at https://docs.mbari.org/internal/ai/data.

🚀 Features

• 🧠 Object Detection/Clustering Integration: Loads detection/classification/clustering output from SDCAT formatted results.
• Flexible Data Export: Downloads from Tator into machine learning formats like COCO, CIFAR, or PASCAL VOC.
• Crop localizaions into optimized datasets for training classification models.
• Real-Time Uploads: Pushes localizations to Tator via Redis queues for real-time workflows.
• Metadata Extraction: Parses images metadata such as GPS/time/date through a plugin-based system (extractors).
• Duplicate Detection & flexible media references: Supports duplicate media load checks with the --check-duplicates flag.
• Images or video can be loaded through a web server without needing to upload or move them from your internal NFS project mounts (e.g. Thalassa)
• Video can be uploaded without needing to figure out how to do the video transcoding required for web viewing.
• Video tracks can be uploaded into Tator for training and evaluation.
• Multiple data versions can be downloaded into a single dataset for training or evaluation using the --version flag with comma separated values. Data is combined through Non-Maximum Suppression (NMS) to remove duplicate boxes.
• Augmentation Support: Augment VOC datasets with Albumentations to boost your object detection model performance.

Requirements

• Python 3.10 or higher
• A Tator API token and (optional) Redis password for the .env file. Contact the MBARI AI team for access.
• 🐳Docker for development and testing only, but it can also be used instead of a local Python installation.
• For video loads, you will need to install the required Python packages listed in the requirements.txt file, ffmpeg, and the mp4dump tool from https://www.bento4.com/

📦 Installation

Install as a Python package:

pip install mbari-aidata

Create the .env file with the following contents in the root directory of the project:

TATOR_TOKEN=your_api_token
REDIS_PASSWORD=your_redis_password
ENVIRONMENT=testing or production

Create a configuration file in the root directory of the project:

touch config_cfe.yaml

Or, use the project specific configuration from our docs server at https://docs.mbari.org/internal/ai/projects/

This file will be used to configure the project data, such as mounts, plugins, and database connections.

aidata download --version Baseline --labels "Diatoms, Copepods" --config https://docs.mbari.org/internal/ai/projects/uav-901902/config_uav.yml

⚙️Example configuration file:

# config_cfe.yml
# Config file for CFE project production
mounts:
  - name: "image"
    path: "/mnt/CFElab"
    host: "https://mantis.shore.mbari.org"
    nginx_root: "/CFElab"

  - name: "video"
    path: "/mnt/CFElab"
    host: "https://mantis.shore.mbari.org"
    nginx_root: "/CFElab"


plugins:
  - name: "extractor"
    module: "mbari_aidata.plugins.extractors.tap_cfe_media"
    function: "extract_media"

redis:
  host: "doris.shore.mbari.org"
  port: 6382

vss:
  project: "902111-CFE"
  model: "google/vit-base-patch16-224"

tator:
  project: "902111-CFE"
  host: "https://mantis.shore.mbari.org"
  image:
    attributes:
      iso_datetime: #<-------Required for images
        type: datetime
      depth:
        type: float
  video:
    attributes:
      iso_start_datetime:  #<-------Required for videos
        type: datetime
  box:
    attributes:
      Label:
        type: string
      score:
        type: float
      cluster:
        type: string
      saliency:
        type: float
      area:
        type: int
      exemplar:
        type: bool
  tdwa_box:
    attributes:
      Label:
        type: string
      score:
        type: float
      verified:
        type: bool
      similarity_score:
        type: float
  track_state:
    attributes:
      Label:
        type: string
      max_score:
        type: float
      num_frames:
        type: int
      verified:
        type: bool
    

Tracks Format

Track data is stored in a compressed .tar.gz file with the -tracks.tar.gz, e.g.

aidata load tracks --input video-tracks/tracks.tar.gz --dry-run --config config_cfe.yml

video-tracks/tracks.tar.gz. This compressed file contains a structure like:

The detections.csv file contains the detections for each frame, e.g.

frame	tracker_id	label	score	x	y	xx	xy
3	2	Copepod	0.6826763153076172	0.7003568708896637	0.4995344939055266	0.7221783697605133	0.5368460761176215
3	1	Copepod	0.7094097137451172	0.2693319320678711	0.6148265485410337	0.29686012864112854	0.6434915330674913
3	3	Detritus	0.2776843011379242	0.2693319320678711	0.6148265485410337	0.29686012864112854	0.6434915330674913
4	1	Copepod	0.49819645285606384	0.2683655321598053	0.6125818323206018	0.2965434789657593	0.6455737643771702

Metadata about video is in the metadata.json file, e.g.

{ "video_name": "video.mp4", 
  "video_path": "/data/input/video.mp4", 
  "processed_at": "2025-11-15T13:37:35.997007Z", 
  "total_frames": 12000, 
  "video_width": 1920, 
  "video_height": 1080, 
  "video_fps": 10, 
  "total_detections": 3000, 
  "unique_tracks": 148, 
  "detection_threshold": 0.15, 
  "min_track_frames": 5, 
  "slice_size": 800, 
  "rfdetr_model": "/mnt/models/best/checkpoint_best_total.pth" }

The tracks.csv file contains the tracks for each frame, e.g.

tracker_id	label	first_frame	last_frame	frame_count	avg_score
2	Detritus	3	37	35	0.3780171153800829
1	Copepod	3	36	31	0.5898609180604258
3	Copepod	3	37	34	0.5619616565458914

🐳 Docker usage

A docker version is also available at mbari/aidata:latest or mbari/aidata:latest:cuda-124. For example, to download data from version Baseline using the docker image:

docker run -it --rm -v $(pwd):/mnt mbari/aidata:latest aidata download --version Baseline --labels "Diatoms, Copepods" --config config_cfe.yml

to download multiple versions

docker run -it --rm -v $(pwd):/mnt mbari/aidata:latest aidata download --version Baseline,ver0 --labels "Diatoms, Copepods" --config config_cfe.yml`

Commands

• aidata download --help - Download data, such as images, boxes, into various formats for machine learning e.g. COCO, CIFAR, or PASCAL VOC format. Augmentation supported for VOC exported data using Albumentations.
• aidata load --help - Load data, such as images, boxes, or clusters into either a Postgres or REDIS database
• aidata db --help - Commands related to database management
• aidata transform --help - Commands related to transforming downloaded data
• aidata -h - Print help message and exit.

Source code is available at github.com/mbari-org/aidata.

Development

See the Development Guide for more information on how to set up the development environment or the justfile

🗓️ Last updated: 2025-11-17