This label_image.py script shows how you can load a pre-trained and converted
TensorFlow Lite model and use it to recognize objects in images. The Python
script accepts arguments specifying the model to use, the corresponding labels
file, and the image to process.
Tip: If you're using a Raspberry Pi, instead try the classify_picamera.py example.
Before you begin, make sure you have TensorFlow installed.
You can use any compatible model, but the following MobileNet v1 model offers a good demonstration of a model trained to recognize 1,000 different objects.
# Get photo
curl https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/lite/examples/label_image/testdata/grace_hopper.bmp > /tmp/grace_hopper.bmp
# Get model
curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz | tar xzv -C /tmp
# Get labels
curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_1.0_224_frozen.tgz | tar xzv -C /tmp mobilenet_v1_1.0_224/labels.txt
mv /tmp/mobilenet_v1_1.0_224/labels.txt /tmp/python3 label_image.py \
--model_file /tmp/mobilenet_v1_1.0_224.tflite \
--label_file /tmp/labels.txt \
--image /tmp/grace_hopper.bmpYou should see results like this:
0.728693: military uniform
0.116163: Windsor tie
0.035517: bow tie
0.014874: mortarboard
0.011758: bolo tie