Lobe Python API

Code to run exported Lobe models in Python using the TensorFlow, TensorFlow Lite, or ONNX options.

Install

Linux

Before running these commands, make sure that you have git installed.

# Install Python3
sudo apt update
sudo apt install -y python3-dev python3-pip

# Install Pillow dependencies
sudo apt update
sudo apt install -y libatlas-base-dev libopenjp2-7 libtiff5 libjpeg62-dev

# Install lobe-python
pip3 install setuptools
pip3 install git+https://github.com/lobe/lobe-python --no-cache-dir

For Raspberry Pi OS (Raspian) run:

cd ~
wget https://raw.githubusercontent.com/lobe/lobe-python/master/scripts/lobe-rpi-install.sh
sudo ./lobe-rpi-install.sh

Mac/Windows

Use a virtual environment with Python 3.7 or 3.8

python3 -m venv .venv

# Mac:
source .venv/bin/activate

# Windows:
.venv\Scripts\activate

Install the library

# make sure pip is up to date
python -m pip install --upgrade pip
# install
pip install git+https://github.com/lobe/lobe-python

Usage

from lobe import ImageModel

model = ImageModel.load('path/to/exported/model')

# OPTION 1: Predict from an image file
result = model.predict_from_file('path/to/file.jpg')

# OPTION 2: Predict from an image url
result = model.predict_from_url('http://url/to/file.jpg')

# OPTION 3: Predict from Pillow image
from PIL import Image
img = Image.open('path/to/file.jpg')
result = model.predict(img)

# Print top prediction
print(result.prediction)

# Print all classes
for label, confidence in result.labels:
    print(f"{label}: {confidence*100}%")

Note: model predict functions should be thread-safe. If you find bugs please file an issue.

Resources

See the Raspberry Pi Trash Classifier example, and its Adafruit Tutorial.