Object Detection Data Synthesis

Data Synthesis pipeline to generate object detection data

How to run

Run with pip

pip install mdetsyn

And run in python file

from mdetsyn import run_synthesis, create_args

args = create_args()
run_synthesis(args)

Run with command line

python synthesis.py --backgrounds ./backgrounds \
                    --objects ./objects \
                    --savename ./synthesis \
                    --number 1000 \
                    --class_mapping ./class_mapping.json \
                    --class_txt ./classes.txt

Sample

• Backgrounds folder contain background images (in any folmat)

├── backgrounds/
    ├── background-0.jpg
    ├── background-1.jpg
    └── ...

• Objects folder contain object images in subfolders (the best is .png format with A channel but any format is still runnable)

├── objects/
    ├── class_1/
    │   ├── image-0.png
    │   ├── image-1.png
    │   └── ...
    ├── class_2/
    └── ...

• Each image in objects folder will be synthesis by n times with n is user input

• Output is a synthesis folder contain images and labels dir same as YOLO format

• Sample visualization:

Background	Object	Synthesis

Support synthesis methods

• Random Resize
• Random Rotate
• Random Transparency
• Random Perspective Transform
• Seamless Clone
• Grayscale

Error and TODO

• Sometimes seamless clone does not work
• Input parameter for each augment
• Add default arguments to argparse help