Data Generation

Scripts for generating synthetic datasets for deep learning. Includes random augmentation and supports dynamic overlays, automatic class mapping, and even distribution of target images across all backgrounds.

Directory Setup

This code expects:

DATA_ROOT/
├── backgrounds/        # Folder containing background images (.jpg/.png)
├── targets_original/   # Folder containing real target images (.jpg/.png)
├── targets_fake/       # Folder containing fake target images (.jpg/.png)

Usage

Run from the project root:

without fake images (generates 10):

python generate.py \
  --backgrounds_dir backgrounds \
  --real_targets_dir targets_original \
  --output_img_dir output_images \
  --output_yolo_dir output_yolo \
  --max_attempts 20 \
  --num_backgrounds 10

with fake images (adds a single fake image (randomly chosen from a set of fake images) along with real target images)

python generate.py \
  --backgrounds_dir backgrounds \
  --real_targets_dir targets_original \
  --fake_targets_dir targets_fake \
  --output_img_dir output_images \
  --output_yolo_dir output_yolo \
  --max_attempts 20 \
  --num_backgrounds 10

torpedo_overlay.py run command (must already have output folders WITHOUT fake images)

python -m compositor.overlay.overlay_torpedo \
  --images_dir output_images \
  --yolo_dir output_yolo \
  --output_dir torpedo_images \
  --output_yolo_dir torpedo_yolo