Image Tools CLI 🖼️

Command line interface for pre-processing images for model training.

Features

create a virtual environment and imgtools-cli through pip:

python3 -m venv venv

source venv/bin/activate

pip install imgtools-cli

python -m imgtools_cli -h

-D {url}, {output directory}

python -m imgtools_cli -D https://www.gutenberg.org/cache/epub/67098/pg67098-images.html /Users/ootie/images

-I {input directory}

python -m imgtools_cli -I /Users/ootie/image_files

-r {input directory}, {width}, {height}, {crop_focal_point}, {dnn_model_path}

Using crop focal point:

python -m imgtools_cli -r /Users/ootie/images 512 512 True None

Passing in a haar xml to focal crop faces:

python -m imgtools_cli -r /Users/ootie/images 512 512 True /Users/ootie/models/haarcascade_frontalface_default.xml

Easily take large images and split them into smaller squares for training.

Example: You may want to train on this image but need to split it into smaller squares for training.

Put it through the chunker and you get this:

chunk 1	chunk 2	chunk 3

-C {dimensions}, {input_directory}, {output_directory}

python -m imgtools_cli -C 512 /Users/ootie/input /Users/ootie/output

Uses face detection to remove images with more than one person. Helpful for cleaning source images to be used for Stable Diffusion training.

Example: If your input images have more than one person, the image will be deleted:

-R {input_directory}, {path_to_cascade_xml}

python -m imgtools_cli -R /Users/ootie/image-tools/images/ /Users/ootie/image-tools/examples/haarcascade_frontalface_default.xml