Start a Milvus Server. See tutorial Here.
Start MySQL.
docker run -p 3306:3306 -e MYSQL_ROOT_PASSWORD=123456 -d mysql:5.7
Install the dependencies
pip install -r requirements.txt
Download & decompress the ModelNet40 dataset and weights of the deep learning model.
chmod +x download_data.sh
./download_data.sh
Create two directories to store the pre-processed data for load and search respectively.
mkdir search_features
mkdir load_features
Batch pre-process the data. Takes ~1.5 hrs with ModelNet40 (You can skip this by downloading the pre-processed data directly, see next step). This operation will first compress the 3d models to 1024 faces and then do certain pre-processing steps. load_features directory will be populated.
chmod +x preprocess.sh
./preprocess.sh true
Only do this if you skipped the last step!! Download the pre-processed data from Google Drive.
gdown "https://drive.google.com/uc?id=1XFonx5ubCSTzEQGvGkpX5LXgdAK3yHQX"
tar -xvf load_feature.tar.gz
Start the FASTAPI server
python3 main.py
If you see this in your terminal, Milvus3D has been started successfully.
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$$$$\ $$$$ |$$\ $$ |$$\ $$\ $$\ $$\ $$$$$$$\ \_/ $$ |$$ | $$ |
$$\$$\$$ $$ |$$ |$$ |\$$\ $$ |$$ | $$ |$$ _____| $$$$$ / $$ | $$ |
$$ \$$$ $$ |$$ |$$ | \$$\$$ / $$ | $$ |\$$$$$$\ \___$$\ $$ | $$ |
$$ |\$ /$$ |$$ |$$ | \$$$ / $$ | $$ | \____$$\ $$\ $$ |$$ | $$ |
$$ | \_/ $$ |$$ |$$ | \$ / \$$$$$$ |$$$$$$$ | \$$$$$$ |$$$$$$$ |
\__| \__|\__|\__| \_/ \______/ \_______/ \______/ \_______/
Welcome to Milvus 3D! :)
Author: Sida Shen