Code to run exported Lobe models in Python using the TensorFlow, TensorFlow Lite, or ONNX options.
Works with Python 3.7, 3.8, 3.9, and 3.10 untested for other versions. [Note: 3.10 only works with the TensorFlow backend]
You can install each of the backends on an individual basis, or all together through pip like so:
# For all of the supported backends (TensorFlow, TensorFlow Lite, ONNX)
pip install lobe[all]
# For TensorFlow only
pip install lobe[tf]
# For TensorFlow Lite only (note for Raspberry Pi see our setup script in scripts/lobe-rpi-install.sh)
pip install lobe[tflite]
# For ONNX only
pip install lobe[onnx]Installing lobe-python without any options (pip install lobe) will only install the base requirements, no backends will be installed.
If you try to load a model with a backend that hasn't been installed, an error message will
show you the instructions to install the correct backend.
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
# Swap out the 'all' option here for your desired backend from 'backend options with pip' above.
pip3 install lobe[all]For Raspberry Pi OS (Raspian) run:
cd ~
wget https://raw.githubusercontent.com/lobe/lobe-python/master/scripts/lobe-rpi-install.sh
chmod 755 lobe-rpi-install.sh
sudo ./lobe-rpi-install.shWe recommend using a virtual environment:
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
# Swap out the 'all' option here for your desired backend from 'backend options with pip' above.
pip install lobe[all]from lobe import ImageModel
model = ImageModel.load('path/to/exported/model/folder')
# 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}%")
# Visualize the heatmap of the prediction on the image
# this shows where the model was looking to make its prediction.
heatmap = model.visualize(img)
heatmap.show()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.