bpycv

bpycv: Computer Vision and Deep Learning Utilities for Blender

Features

bpycv is a versatile tool designed for enhancing computer vision and deep learning tasks within Blender, a popular open-source 3D creation suite. Its wide array of features makes it an invaluable asset for developers and researchers working with synthetic datasets. Here are some of the main features:

• Annotation Rendering: bpycv can render annotations for various segmentation tasks, including semantic, instance, and panoptic segmentation. This allows for precise labeling of different parts of a 3D scene.

• Pose Generation: The tool can generate 6 Degrees of Freedom (6DoF) pose ground truth, which is essential for many robotics and augmented reality applications.

• Depth Rendering: It can render accurate depth maps, providing vital information about the distance of objects from the camera, which is useful in many depth-sensing applications.

• Domain Randomization: Pre-defined randomization options for light, background, and even added objects (like distractors from ShapeNet) help in creating diverse data sets. This aspect is crucial for training robust machine learning models.

• Easy Installation and Usage: With straightforward installation instructions and demo scripts, users can get started quickly with minimal setup.

• Docker Support: For users preferring containerized environments, bpycv provides Docker support, making it easier to integrate into larger automated workflows.

• Synthetic Dataset Creation: A flexible Python codebase allows users to create synthetic datasets tailored to their specific requirements, including support for the Cityscapes annotation format.

• Developer-Friendly: The project is built on native Blender APIs, ensuring easier development and debugging without the headache of complex packaging.

Installation

To use bpycv, you first need Blender 2.9 or later. Once Blender is installed, you can run a series of commands in the terminal to install bpycv. These steps include ensuring you have pip, the Python package installer, updating the toolchain, and finally, installing bpycv. The setup is designed to be as simple as possible, allowing users to start working with minimal hassle.

Demo

bpycv offers several demos to showcase its capabilities:

1. Instance Segmentation and Depth Demo: This script demonstrates how to render images with instance annotations and depth information. By creating a scene with random objects and rendering the setup, users can see firsthand how bpycv handles segmentation and depth tasks.

2. YCB Demo: Integrating bpycv with the YCB dataset, this demo provides a full pipeline for generating synthetic datasets. The focus here is on domain randomization techniques and the generation of varied training data.

3. 6DoF Pose Demo: For applications requiring precise 3D positioning, this demo generates and visualizes 6DoF pose ground truth, an essential feature for many robotics and computer vision applications.

Tips

For users dealing with file formats, bpycv provides valuable insights ensuring compatibility with Blender. It is suggested to use tools like meshlabserver for converting files from YCB and ShapeNet datasets to formats Blender can handle efficiently.

Overall, bpycv is a powerful toolset for anyone looking to leverage Blender's capabilities in computer vision and machine learning applications. Its ease of installation, comprehensive feature set, and robust support make it a go-to choice for synthetic dataset generation and beyond. Whether you're a researcher, developer, or enthusiast, bpycv opens up a world of possibilities for your projects. bpycv welcomes community feedback and contributions, encouraging a collaborative approach to continuous improvement and innovation.