yolosplitter 0.2.0

Tool that makes it easy to split YOLOs images and their associated labels into separate sets for training and testing.

yolo-splitter

Tool that makes it easy to split YOLOs images and their associated labels into separate sets for training and testing.

Installation

pip install yolosplitter

Uses

from yolosplitter import YoloSplitter

ys = YoloSplitter(imgFormat=['.jpg', '.jpeg', '.png'], labelFormat=['.txt'] )

# create dataframe
df = ys.from_mixed_dir(main_dir="mydataset/")

# saves the Images and labels in "new_dataset" dir. with data.yaml file.
ys.split_and_save(DF=df,output_dir="new_dataset",train_size=0.70)

df = ys.from_mixed_dir(main_dir="mydataset/")
df

# When Image and Labels are in diffrent directory (Default yolo train and val directories)
df = ys.from_yolo_dir(image_dir="mydataset-splitted/train/images/",label_dir="mydataset-splitted/train/labels/")
df

# Dataframe contains Image names, Label names, annoations and class names.
# In the dataframe, we can observe the number of classes present in each image. 

Input Directory

MyDataset/
├── 02.png
├── 02.txt
├── 03.png
├── 03.txt
├── 04.png
├── 04.txt
├── 05.png
├── 05.txt
├── 06.png
├── 06.txt
├── 07.png
├── 07.txt
├── 08.png
├── 08.txt
├── 09.png
├── 09.txt
├── 10.png
├── 10.txt
├── 11.png
└── 11.txt

Output Directory

MyDataset-splitted/
├── data.yaml
├── train
│   ├── images
│   │   ├── 03.png
│   │   ├── 04.png
│   │   ├── 05.png
│   │   ├── 07.png
│   │   ├── 08.png
│   │   ├── 09.png
│   │   └── 10.png
│   └── labels
│       ├── 03.txt
│       ├── 04.txt
│       ├── 05.txt
│       ├── 07.txt
│       ├── 08.txt
│       ├── 09.txt
│       └── 10.txt
└── val
    ├── images
    │   ├── 02.png
    │   ├── 06.png
    │   └── 11.png
    └── labels
        ├── 02.txt
        ├── 06.txt
        └── 11.txt