aipipeline is a library for running ai pipelines and monitoring the performance of the pipelines, e.g. accuracy, precision, recall, F1 score. This may include object detection, clustering, classification, and vector search algorithms. It is designed to be used for a number of projects at MBARI that require advanced workflows to process large amounts of images or video.

See the MBARI AI documentation for more information on the tools and services used in the pipelines.

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Example plots from the t-SNE, confusion matrix and accuracy analysis of examplar data.

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Requirements

Three tools are required to run the code in this repository:

Anaconda environment

This is a package manager for python. We recommend using the Miniconda version of Anaconda. Install on Mac OS X with the following command:

brew install miniconda

or on Ubuntu with the following command:

sudo apt install miniconda

Docker

This is a containerization tool that allows you to run code in a container.

just tool.

This is a handy tool for running scripts in the project. This is easier to use than make and more clean than bash scripts. Try it out!

Install on Mac OS X with the following command:

port install just

or on Ubuntu with the following command:

sudo apt install just

Installation

Clone the repository and run the setup command.

git clone http://github.com/mbari-org/aipipeline.git
cd aipipeline
just setup

Sensitive information is stored in a .env file in the root directory of the project, so you need to create a .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

Usage

Recipes are available to run the pipelines. To see the available recipes, run the following command:

just list

Recipe Name	Description
list	List recipes
install	Setup the environment
cp-env	Copy the default .env file to the project
update_trackers	Update the environment. Run this command after checking out any code changes
cp-core	Copy core dev code to the project on doris
cp-dev-uav	Copy uav dev code to the project on doris
cp-dev-bio	Copy bio dev code to the project on doris
cp-dev-i2map	Copy i2map dev code to the project on doris
plot-tsne-vss project='uav'	Generate a tsne plot of the VSS database
optimize-vss project='uav'	Optimize the VSS database
calc-acc-vss project='uav'	Calculate the accuracy of the VSS database; run after download, then optimize
reset-vss-all	Reset the VSS database, removing all data. Proceed with caution!!
reset-vss project='uav'	Reset the VSS database, removing all data. Run before init-vss or when creating the database.
remove-vss project='uav'	Remove an entry from the VSS database
init-vss project='uav'	Initialize the VSS database for a project
load-vss project='uav'	Load already computed exemplars into the VSS database
load-cfe-isiis-videos	Load CFE ISII mission videos
cluster-uav	Cluster mission in UAV data
detect-uav	Detect mission in UAV data
detect-uav-test	Detect mission data in UAV data
load-uav-images	Load UAV mission images
load-uav type="cluster"	Load UAV detections/clusters
fix-uav-metadata	Fix UAV metadata lat/lon/alt
compute-saliency project='uav'	Compute saliency for VOC data
crop project='uav'	Crop detections from VOC formatted downloads
download-crop project='uav'	Download and crop
download project='uav'	Download only
predict-vss project='uav'	Predict images using the VSS database
run-ctenoA-test	Run strided inference on a single video
run-ctenoA-prod	Run strided inference on a collection of videos
run-mega-inference	Run mega strided inference on a single video
run-mega-track-bio-video	Run mega tracking pipeline on a single video for bio project
run-mega-track-bio-dive	Run mega tracking pipeline on an entire dive for bio project
run-mega-track-i2map-video	Run mega tracking pipeline on a single video for i2map project
run-mega-track-test-1min	Run mega tracking pipeline on a single video for testing
run-mega-track-test-fastapiyv5	Test pipeline using FastAPI
gen-cfe-data	Generate training data for the CFE project
gen-i2map-data	Generate training data for the i2map project
gen-i2mapbulk-data	Generate training data for the i2map project from the bulk server
gen-uav-data	Generate training data for the UAV project

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Related projects

• aidata -A tool to extract, transform, load and download operations on AI data.
• sdcat - Sliced Detection and Clustering Analysis Toolkit; a tool to detect and cluster objects in images.
• deepsea-ai - A tool to train and run object detection and tracking on video at scale in the cloud (AWS).
• fastapi-yolov5 - A RESTful API for running YOLOv5 object detection models on images either locally or in the cloud (AWS).
• fastapi-vss - A RESTful API for vector similarity search using foundational models.
• fastapi-tator - A RESTful API server for bulk operations on a Tator annotation database.

updated: 2024-12-12