ONNX Object Localization Network

Python scripts performing class agnostic object localization using the Object Localization Network model in ONNX.

Original image: https://en.wikipedia.org/wiki/File:Interior_design_865875.jpg

Important

• I added a bit of logic to the box color selection to make it look nicer. Since it performs K-Means for each box, it might be slow. If you only care about speed, you can either set all the boxes to the same color or use random colors.

Requirements

• Check the requirements.txt file.
• For ONNX, if you have a NVIDIA GPU, then install the onnxruntime-gpu, otherwise use the onnxruntime library.
• Additionally, pafy and youtube-dl are required for youtube video inference.

Installation

git clone https://github.com/ibaiGorordo/ONNX-Object-Localization-Network.git
cd ONNX-Object-Localization-Network
pip install -r requirements.txt

ONNX Runtime

For Nvidia GPU computers: pip install onnxruntime-gpu

Otherwise: pip install onnxruntime

For youtube video inference

pip install youtube_dl
pip install git+https://github.com/zizo-pro/pafy@b8976f22c19e4ab5515cacbfae0a3970370c102b

ONNX model

The original model was converted to ONNX by PINTO0309, download the models from the download script in his repository and save them into the models folder.

• The License of the models is Apache-2.0 License: https://github.com/mcahny/object_localization_network/blob/main/LICENSE

Pytorch model

The original Pytorch model can be found in this repository: https://github.com/mcahny/object_localization_network

Examples

• Image inference:

python image_object_localization.py

• Webcam inference:

python webcam_object_localization.py

• Video inference: https://youtu.be/n9qhQJXYUWo

python video_object_localization.py

Original video: https://youtu.be/vgJUXvkdS78

References:

• Object-Localization-Network model: https://github.com/mcahny/object_localization_network
• PINTO0309's model zoo: https://github.com/PINTO0309/PINTO_model_zoo
• Original paper: https://arxiv.org/abs/2108.06753