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VFRAME Synthetic Image Training Data
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Synthetic Image Training Data with Blender

Code for using Blender to generate synthetic image training data. This codebase is used by the VFRAME project for generating images to train object detection and image classification computer vision models for OSINT computer vision projects.

This is a prototype release. Included is a basic demo using a static scene that can be used to generate synthetic data for a danger sign. There are still many parts of the project under development so check back in a month or two for updates.

For more information on how VFRAME trains with synthetic data visit

Mask Real Composite


Running Blender Scripts

The basic functions are:

  demo      Runs Blender demos and test
  generate  Runs Blender synthetic data generator
  images_to_gif       Converts still image to GIF
  images_to_overlays  Composites real and mask images, writes optional video
  images_to_video     Converts still image sequence to video
  mask_to_bbox        Converts image, masks, and metadata to CSV annotations


Demo shows how to generate synthetic data for a simple scene with 3 objects (sign, tree, and rock). Demo is not intended to generate actual training data, but only to show basic functionality. More demos will be published later.

1. Download Blender demo file

vframe_danger_sign_static_public.blend to data_store/blenders

2. Edit config .yml

  • open configs/demo/static_sign.yml and edit the render:output:filepath

3. Run Blender with Python Script

cd cli/
python demo --blend ../data_store/blenders/vframe_danger_sign_static_public.blend --python app/blender/tests/ --config configs/demo/static_sign.yml

This creates a metadata.csv file. See example metadata.csv

color_b color_g color_r description label label_index mat_id object_idx
255 255 0 "UXO Danger Sign Arabic" danger_sign 0 0

4. Generate Annotations from Metadata

python mask_to_bbox -i ../data_store/renders/demo_danger_sign

This creates an annotations.csv file. See example annotations.csv

x1 y1 x2 y2 label label_index filename color
0.4375 0.35555555555555557 0.528125 0.43333333333333335 danger_sign 0 cam_0000_frame_0001.png 0x00ffff
0.45 0.35555555555555557 0.58125 0.6111111111111112 danger_sign 0 cam_0000_frame_0009.png 0x00ffff

5. Train

Not covered here. But your next step would be to convert the bounding box data to the object training framework of your choice (Darknet, PyTorch, TensorFlow, etc...)

Visualize Results

Composite images:

export d=../data_store/renders/demo_danger_sign
python images_to_overlays -i $d
python images_to_overlays -i $d --video  # with video
python images_to_overlays -i $d --video --fps 2 # at 2 FPS

Generate vidoes from directory of images:

export d=../data_store/renders/demo_danger_sign
python images_to_video -i $d/comp --fps 2  # video comp
python images_to_video -i $d/real --fps 2  # video real
python images_to_video -i $d/mask --fps 2  # video mask

Animated GIF:

export d=../data_store/renders/demo_danger_sign
python images_to_gif -i $d/comp --fps 4  # gif comp
python images_to_gif -i $d/real --fps 4  # gif real
python images_to_gif -i $d/mask --fps 4  # gif mask

Custom Scripts

  • add your custom scripts in cli/commands/custom to avoid conflict with future project updates
  • to run your custom scripts use python example for example


  • Add demo for camera jittering with static system
  • Add demo for world background cycling
  • Add demo for ground material cycling
  • Add demo for object color node cycling
  • Add demo for emitter system
  • Dockerize for production

Help Wanted

  • Determine how to use rigid body objects with emitter system to avoid object overlaps in hair emitter
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