Science Score: 36.0%
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○CITATION.cff file
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✓codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: arxiv.org, joss.theoj.org -
○Academic email domains
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (14.2%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: Ayas-N
- License: gpl-3.0
- Language: Python
- Default Branch: main
- Size: 165 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
BlenderProc2
A procedural Blender pipeline for photorealistic rendering.
Documentation | Tutorials | Examples | ArXiv paper | Workshop paper | JOSS article
Features
- Loading:
*.obj,*.ply,*.blend,*.fbx, BOP, ShapeNet, Haven, 3D-FRONT, etc. - Objects: Set or sample object poses, apply physics and collision checking.
- Materials: Set or sample physically-based materials and textures
- Lighting: Set or sample lights, automatic lighting of 3D-FRONT scenes.
- Cameras: Set, sample or load camera poses from file.
- Rendering: RGB, stereo, depth, normal and segmentation images/sequences.
- Writing: .hdf5 containers, COCO & BOP annotations.
Installation
Via git
I directly cloned the Blenderproc repository, so clone this repository for access to the package.
To still make use of the blenderproc command and therefore use blenderproc anywhere on your system, make a local pip installation:
bash
cd BlenderProc
pip install -e .
Synthetic Data Generation
To run the synthetic data generation first run:
bash
blenderproc run generate.py
This will runs the script that loads up the dune.blend blender scene file. It then randomises the camera location and angle whilst maintaining focus on the cube.
Images that are generated will be placed in the coco_data/images directory.
The bounding boxes from blenderproc are generated in the coco_annotations.json file, but this format isn't suitable for the Ultralytics package which we are using to run the YOLO model.
To generate labels with the correct format, run:
bash
python json_parser.py
Which generates bounding boxes for each corresponding image in .txt form in the coco_data/labels directory.
YOLO Model
Owner
- Name: Jey Son
- Login: Ayas-N
- Kind: user
- Repositories: 1
- Profile: https://github.com/Ayas-N
GitHub Events
Total
- Member event: 1
- Push event: 1
- Pull request event: 2
- Fork event: 1
- Create event: 3
Last Year
- Member event: 1
- Push event: 1
- Pull request event: 2
- Fork event: 1
- Create event: 3
Dependencies
- actions/checkout v2 composite
- actions/upload-artifact v3 composite
- actions/checkout v2 composite
- actions/upload-artifact v1 composite
- openjournals/openjournals-draft-action master composite
- setuptools *