Awesome

Tatortot

Prototype tool for annotating images.

This package includes one CLI tool:

tator:

Usage: tator [OPTIONS] SRC DEST

Options:
  -C, --overlay_color TEXT     Color to use for overlay. Valid options are: 'b
                               lue','orange','green','red','purple','brown','p
                               ink','gray','olive','cyan'.Defualt: 'cyan'.
  -A, --overlay_alpha FLOAT    Transparency to use for overlay provided as
                               alpha value (0-1).Default: 0.3.
  -w, --img_width INTEGER      Width of src images in pixels. Default is 256
  -h, --img_height INTEGER     Height of src images in pixels. Default is 256
  -W, --viewer_width INTEGER   Width of viewer in pixels. Default is 325
  -H, --viewer_height INTEGER  Height of viewer in pixels. Default is 800
  -f, --filetype TEXT          File format for src images (as file extension).
                               Default is '.jpeg'
  --help                       Show this message and exit.



Note: SRC and DEST should both be local directories. SRC should contain images to annotate, DEST will store results.

This utility provides a simple interface for performing image annotation, and specifically defining binary semantic segmentation.

TODOS:

r/w from rasterio instead of skimage
refactor to make code simpler, add docs
add box selector feature?
r/w from S3 directories as well as local dirs

Installation

Development

Requirements:

General requirements listed above
Anaconda or Miniconda

To set up your local development environment:

This will install the s1_preprocessor package from the local repo in editable mode. Any changes to Python files within the local repo should immediately take effect in this environment.

Clone the repo git clone https://github.com/GeoBigData/tatortot.git
Move into the local repo cd tatortot
Create conda virtual environment conda env create -f environment.yml
Activate the environment conda activate tatortot
Install Python package pip install -r requirements_dev.txt