README.md

Synthetic Data creation with Blender

• Intro
  • Why Blender
  • Open Source Image Data Sets
  • Synthetic Data Sets
  • A Combination
• How
  • Create Realistic scenes
  • Randomise the scene with Python scripts
    • Complex Scenes
  • Perfectly Annotated Rendered Images
  • Out of the Box
    • Depth Data
    • Object Segmentation
  • Script the Augmentation/Render/Annotate workflow
  • (Demonstration) Build an annotated synthetic data set
  • Scaling - Run blender headless on a server with GPU

Intro

Computer Vision A.I. requires many thousands of anottated images for training. 3D rendered images can provide many variations with flawless automatic annotations. Blender is a great tool for creating computer vision training datasets.

Why Blender

Blender is free to use, cross-platform & provides the tools required setting up our environment & creating the dataset

• Modelling
• Shading
• Rendering
• Compositing
• Custom Annotation
• Python API
• Scaling

Open Source Image Data Sets

• Pros

  • Freely available
  • Many objects available with annotations

• Cons

  • Many mistakes
  • May not be specific enough
  • Adding your own objects & annotations is an expensive time consuming process
  • Missing Annotation

Synthetic Data Sets

• Pros

  • Flawless Annotations
  • Automatic Annotations
  • Impossible Annotations
    • Hidden Key Points on Objects
    • Perfect Depth Maps for every image
  • Perfect Segmentation Maps for every object in image
  • Custom annotations
    • Python API lets us know everything about the scene
    • Create annotations in any format you need using Python
  • Infinite augmentations
  • Re-create rare cases with many augmentations

• Cons

  • May not be representative of real world
  • Effort required for photorealism
  • Requires GPU access for speed/scalability

A Combination

Combining Real & Synthetic Data may give even better results

How

Create Realistic scenes

• Add meshes for objects you need in your images
• Create textures etc from photos, add them to mesh objects
• Match camera perspective using tools like f-spy
• Use CYCLES rendering engine for realistic ray traced lighting

Randomise the scene with Python scripts

Use the blender python api bpy

Create as many iterations & augmentations as required.
Changing things such as:

• Object Scale, Position, Orientation, Texture, Count
• Camera Orientation, Focal Length, FOV
• Lighting
• Swap textures
• Swap background

Complex Scenes

Collision detection required for more complex scenes

• Avoid Floating Objects
• Avoid Intersecting Objects

Perfectly Annotated Rendered Images

We can know everything about the scene, making it possible to generate an image of the scene & corresponding

• object segmentation maps/masks
• depth-map
• key points
• labels
• bounding boxes
• pose co-ordinates

Out of the Box

Depth Data

Depth Data is available out of the box in blender.
Here a composition shows it visually.

Object Segmentation

Individual Object Segmentation Maps can be made by marking each object with a unique pass index & enabling Object Index Pass as a render Option
Here a composition shows it visually.

Script the Augmentation/Render/Annotate workflow

Using blender's Python API we can script the changes and render workflow.

The following example scripts will generate 10 sets of

• Segmentation Map
• RGB Image
• Depth Map

Each set with a new position for the objects in the scene with name Box & Pallet

import cv2
import bpy
import bpycv
import random

for n in range(10):
    print(n) 

    # move box/pallet obj randomly in scene
    for obj in bpy.data.objects:
        print(obj.name)
        if "Box" in obj.name:
            obj.location[0]=random.uniform(0, 1.7)-0.5
            obj.location[1]=random.uniform(0, 2.0)-1.0
        if "Pallet" in obj.name:
            obj.location[0]=random.uniform(0, 1.7)-0.5
            obj.location[1]=random.uniform(0, 2.0)-1.0
    

    # give each mesh uniq inst_id for segmentation
    counter=1
    for obj in bpy.data.objects:
        if obj.type == "MESH":
            obj = bpy.context.active_object
            obj["inst_id"] = counter
            counter=counter+1

    # render image, instance annotation and depth in one line code
    result = bpycv.render_data()

    # write visualization inst|rgb|depth 
    cv2.imwrite(
        "(inst|rgb|depth)." + str(n) + ".jpg", cv2.cvtColor(result.vis(), cv2.COLOR_RGB2BGR)
    )

(Demonstration) Build an annotated synthetic data set

• Images show some generated(segmentation-map, rgb, depth-map) sets for visualization

Scaling - Run blender headless on a server with GPU

• Render faster with a GPU
• Blender can be run without GUI, supplying --background option

blender --background warehouse_scene.blend --python generate_synthetic_data.py