dataset-tools

Tools for quickly normalizing image datasets for machine learning. I maintain a series of video tutorials on normalizing image datasets—many utilizing this set of scripts—on my YouTube page.

Installation

Note: If you’re installing this on a Mac, I highly recommend installing it alongside Anaconda. A video tutorial is available here.

git clone https://github.com/dvschultz/dataset-tools.git
cd dataset-tools
pip install -r requirements.txt

Basic Usage

python dataset-tools.py --input_folder path/to/input/ --output_folder path/to/output/

Documentation

You can view auto generated documentation in docs.md

All Options

dataset_tools.py

--verbose: Print progress to console.
--input_folder: Directory path to the inputs folder. Default: ./input/
--output_folder: Directory path to the outputs folder. Default: ./output/
--process_type: Process to use. Options: resize,square,crop,crop_to_square,canny,canny-pix2pix,scale,crop_square_patch,many_squares Default: resize
--blur_type: Blur process to use. Use with --process_type canny. Options: none, gaussian, median. Default: none
--blur_amount: Amount of blur to apply (use odd integers only). Use with --blur_type. Default: 1
--max_size: Maximum width or height of the output images. Default: 512
--force_max: forces the resize to the max size (by default --max_size only scales down)
--direction: Paired Direction. For use with pix2pix process. Options: AtoB,BtoA. Default: AtoB
--mirror: Adds mirror augmentation.
--rotate: Adds 90 degree rotation augmentation.
--border_type: Border style to use when using the square process type Options: stretch,reflect,solid (solid requires --border-color) Default: stretch
--border_color: border color to use with the solid border type; use BGR values from 0 to 255 Example: 255,0,0 is blue
--height: height of crop in pixels; use with --process_type crop or --process_type resize (when used with resize it will distort the aspect ratio)
--width: width of crop in pixels; use with --process_type crop or --process_type resize (when used with resize it will distort the aspect ratio)
--shift_y: y (Top to bottom) amount to shift in pixels; negative values will move it up, positive will move it down; use with --process_type crop
--shift_x: x (Left to right) amount to shift in pixels; negative values will move it left, positive will move it right; use with --process_type crop
--file_extension: file format to output Options: jpg,png Default: png

dedupe.py

Remove duplicate images from your dataset

--absolute: Use absolute matching. Default
--avg_match: average pixel difference between images (use with --relative) Default: 1.0
--file_extension: file format to output Options: jpg,png Default: png
--input_folder: Directory path to the inputs folder. Default: ./input/
--output_folder: Directory path to the outputs folder. Default: ./output/
--relative: Use relative matching.
--verbose: Print progress to console.

Basic usage (absolute)

python dedupe.py --input_folder path/to/input/ --output_folder path/to/output/

Basic usage (relative)

python dedupe.py --input_folder path/to/input/ --output_folder path/to/output/ --relative

multicrop.py

This tool produces randomized multi-scale crops. A video tutorial is here

--input_folder: Directory path to the inputs folder. Default: ./input/
--output_folder: Directory path to the outputs folder. Default: ./output/
--file_extension: file format to output Options: jpg,png Default: png
--max_size: Maximum width and height of the crop. Default: None
--min_size: Minimum width and height of the crop. Default: 1024
--resize: size to resize crops to (if None it will default to min_size). Default: None
--no_resize: If set the crops will not be resized. Default: False
--verbose: Print progress to console.

sort.py

--file_extension: file format to output Options: jpg,png Default: png
--verbose: Print progress to console.
--input_folder: Directory path to the inputs folder. Default: ./input/
--output_folder: Directory path to the outputs folder. Default: ./output/
--process_type: Process to use. Options: sort,exclude Default: exclude
--max_size: Maximum width or height of the output images. Default: 2048
--min_size: Minimum width or height of the output images. Default: 1024
--min_ratio: Ratio of image (height/width). Default: 1.0
--exact: Match to exact specs. Use --min_size for shorter dimension, --max_size for longer dimension

interactive.py

YouTube Demo

nmvbxcz/dataset-tools