Synthetic Image Generator

This code allows you to create a synthetic data-set, for Instance Segmentation or Object Detection. The app.py script outputs data in LabelMe format, which can also be converted to other formats like the COCO JSON format.

Overview

The Synthetic Image Generator script creates synthetic datasets by overlaying foreground images on background images. It also generates JSON annotations for object detection tasks, making it suitable for training AI models.

Features

• Overlay foreground objects onto background images.
• Scale and position foreground objects randomly.
• Support for Albumentations augmentations.
• Generate annotations in JSON format (e.g., bounding boxes).
• Parallelized image generation for faster performance.
• Customizable parameters via command-line arguments.

Requirements

• Python: Version 3.8 or later.
• Dependencies: Install via requirements.txt:

  pip install -r requirements.txt

Directory Structure

Input Directory

The input directory must contain the following subdirectories:

input/
├── backgrounds/
│   ├── background1.jpg
│   ├── background2.png
│   ...
├── foregrounds/
    ├── category1/
    │   ├── object1.png
    │   ├── object2.png
    ├── category2/
        ├── object1.png
        ├── object2.png

Output Directory

The output directory will be created automatically and will store:

• Generated Images: .jpg format.
• Annotations: .json files for each image.

Usage

Command-Line Arguments

python app.py --input_dir INPUT_DIR --output_dir OUTPUT_DIR --image_number N [OPTIONS]

Required Arguments

Argument	Description
--input_dir	Path to the directory containing backgrounds and foregrounds.
--output_dir	Path to the directory where images and annotations will be saved.
--image_number	Number of synthetic images to generate.

Optional Arguments

Argument	Default Value	Description
--max_objects_per_image	3	Maximum number of foreground objects per image.
--image_width	640	Width of the output images.
--image_height	480	Height of the output images.
--augmentation_path	transform.yml	Path to an Albumentations YAML file for augmentations.
--scaling_factors	[0.2, 0.5]	Range for resizing foreground images (proportionally).
--parallelize	False	Enable multi-core processing for faster generation.

Examples

Basic Usage

Generate 10 synthetic images with default parameters:

python app.py --input_dir ./input --output_dir ./output --image_number 10

Add Scaling and Parallel Processing

Generate 20 images, scaling foregrounds to 30%–70% of their original size, and using parallel processing:

python app.py --input_dir ./input --output_dir ./output --image_number 20 --scaling_factors 0.3 0.7 --parallelize

Limit to One Foreground Object per Background

Generate 5 images with only one foreground object per image:

python app.py --input_dir ./input --output_dir ./output --image_number 5 --max_objects_per_image 1

Annotation Format

The JSON annotations for each image include:

{
  "imagePath": "00000001.jpg",
  "imageWidth": 640,
  "imageHeight": 480,
  "shapes": [
    {
      "label": "category1",
      "points": [[x1, y1], [x2, y2]],
      "shape_type": "rectangle"
    }
  ]
}

• shapes: List of objects in the image.
• label: Category of the foreground object.
• points: Bounding box coordinates for the object.
• shape_type: Fixed as "rectangle".

Customizing Foreground Sizes

Modify --scaling_factors to control foreground size:

Smaller Foregrounds

--scaling_factors 0.1 0.3

Larger Foregrounds

--scaling_factors 0.7 1.0

Troubleshooting

Common Errors

1. No valid foreground images found:
   • Ensure the foregrounds/ folder has subdirectories containing .png images.
2. Cannot write mode RGBA as JPEG:
   • Ensure the script converts RGBA to RGB before saving (already fixed in this version).

Logs

The script logs its progress and errors. Check the console for detailed messages.

License

Feel free to use and modify this script for your projects. Attribution is appreciated but not required.