Code to run exported Lobe models in Python using the TensorFlow, TensorFlow Lite, or ONNX options.
# 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 git
pip3 install git+https://github.com/lobe/lobe-pythonNote for Raspbian OS (Raspberry Pi): Please install libjpeg62-turbo instead of libjpeg62-dev
Use a virtual environment with Python 3.7
python3 -m venv .venv
# Mac:
source .venv/bin/activate
# Windows:
.venv\Scripts\activateInstall the library
# make sure pip is up to date
python -m pip install --upgrade pip
# install
pip install git+https://github.com/lobe/lobe-pythonfrom 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.
See the Raspberry Pi Trash Classifier example, and its Adafruit Tutorial.