Localization

Server for image-based localization

Prequsites:

• docker (makes sure to run the linux post-installation steps
• python 3.9+
• nvidia-container-toolkit

Running locally

• docker compose up --build to build and run the containers (add the -d flag if you want it to run in detached mode, aka in the background). The app can then be accessed via localhost:5000
• docker compose down to stop the containers
• docker compose exec api bash to exec into the container

Project architecture

The app is composed of a backend (./api) written in Python using the Flask framework.

Previous projects should be stored in $HOME/datasets under project_1, project_2, etc...

Each project has the following architecture

.
├── ...
├── project_1            # Project
│   ├── rgb              # RGB reference images
│      ├── 1.png         
│      |── 2.png                            
│      └── ...                            
│   ├── depth            # depth reference images
│      ├── 1.png         
│      |── 2.png                            
│      └── ...         
│   |── poses.csv        # image pose of reference camera when each image was captured
│   └── intrinsics.json  # camera intrinsics of reference camera
└── ...

Sample Data

Download this file, unzip it, then save the folder as ~/datasets/project_1.

Google drive link: sample dataset

API Examples

Loading project:

curl http://localhost:5000/api/v1/project/1/load

Providing query camera intrinsics (assuming query camera is different from reference camera):

curl -X POST -H "Content-Type: application/json"  -d @<path-to-intrinsics> http://localhost:5000/api/v1/project/1/intrinsics

Localizing query image:

curl -X POST -F image=@<path-to-img> http://localhost:5000/api/v1/project/1/localize

Sending raw data in zip file to localization server

curl -X POST -H "Content-Type: application/octet-stream" -F "data=@<path-to-zip>" http://localhost:5000/api/v1/project/1/upload