AugmentLabeler

A tool for making a training dataset for image classification, in a situation where one has just a few images of the object to recognize. (targeted mainly for Darknet/YoloV3)

Requirements (tested to work with)

Python 3.7.5

numpy 1.17.3 scipy 1.4.1 Pillow 6.2.1 opencv-python 4.1.0 argparse 1.4.0

pip3 install -U -r requirements.txt

Usage

Install the requirements mentioned above.

Create a directory for every class in the "foregrounds"-directory (the name of the created directory will be used as the name of this class). Fill the foreground-folders with images of the classes.

Fill the "backgrounds"-folder with images that do not contain ANY of the classes being recognized.

Run python Make_A_Set.py (Note arguments explained below).

If all goes well, one should see a new folder: DATASET, in which resides all of the labels and images.

images -folder contains all of the augmented images themselves. and labels -folder contains label txt-files for those images. These are in Darknet format and one can find more of that here: https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data .names -file has all of the classnames written into it.

and for Darknet/Yolo -users the script will also generate the darknet-format train and validation .txt -files

Also a Darknet/Yolo -type .data -file will be generated next to the DATASET-folder itself. Since all of the path's are written in absolute path, training for YoloV3 should be possible just by passing the YoloV3's train.py with --data {{pathto/DATASET.data}}

Make_A_Set.py arguments with default values

There is a set of settings (no pun intended) to tailor the dataset for the user's needs Example usage: python Make_A_Set.py -tsize=20 -vsize=10 -rotate=False -alphacrop=False

-setname

-setname=DATASET The name of the folder and the dataset generated

-huemin -huemax

-huemin=1.0 -huemax=1.0 Minimum and maximum ranges of the hue randomization Not on by default since most of the DNN-frameworks are already equipped with such functionality.

-huemin=0.6 -huemax=1.5

-tsize -vsize

-tsize=1000 -vsize=100 Sizes of the training and validation sets respectively

-rotate

-rotate=True Randomly rotates the foregrounds

-fgminfrac -fgmaxfrac

-fgminfrac=0.4 -fgmaxfrac=1.2 Size multiplier range for foregrounds

-alphacrop

-alphacrop=True Should the foregrounds be cropped by their alpha channel's bounding box? Note: this is done in preprocessing and therefor replaces the old images with cropped ones. To be safe, make a copies of the original ones.

-alphacrop=False

-includenegative

-includenegative=False For every image/label of a foreground, add one "negative"-image. Basically just copies a background image to the training/validation sets without augmenting a foreground onto it. (NOTE: This will double the size of training/validation data)

Acknowledgments

• Inspiration: https://memememememememe.me/post/training-haar-cascades/

• More on YoloV3 usage and custom training: https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data