tflite-runtime example

tflite-runtime-example.md

To perform object detection inference using a TensorFlow Lite model (.tflite) on a JPG image with tflite-runtime, you need to follow several steps including installation of the necessary packages, loading the model, preprocessing the input image, running inference, and handling the output. Here's a comprehensive guide:

1. Install tflite-runtime and Image Processing Library

You'll need to install tflite-runtime and Pillow for image processing. If you haven't installed these, you can do so using pip:

pip install tflite-runtime pillow

2. Prepare Your Model and Image

Ensure you have a trained TensorFlow Lite model file (.tflite) and a JPG image ready for detection.

3. Load the TensorFlow Lite Model

You'll need to load your TFLite model and prepare the interpreter:

import tflite_runtime.interpreter as tflite
from PIL import Image
import numpy as np

# Load TFLite model and allocate tensors.
interpreter = tflite.Interpreter(model_path='path_to_your_model.tflite')
interpreter.allocate_tensors()

# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

4. Prepare the Image

You need to preprocess your image to match the input requirements (size, scaling) of the model:

# Load and preprocess an image
image = Image.open('path_to_your_image.jpg').resize(
    (input_details[0]['shape'][2], input_details[0]['shape'][1])
)

# Convert the image to numpy array
input_data = np.expand_dims(np.array(image), axis=0).astype(np.float32)

# Normalize the image if required by your model
input_data = input_data / 255.0

5. Perform Inference

Set the processed image as the input to the model and run the interpreter:

# Set the tensor to point to the input data to be inferred
interpreter.set_tensor(input_details[0]['index'], input_data)

# Run the inference
interpreter.invoke()

6. Handle the Output

The output details depend on your model. Typically, for object detection, you'll have boxes, classes, and scores:

# Retrieve detection results
boxes = interpreter.get_tensor(output_details[0]['index'])[0]  # Bounding box coordinates of detected objects
classes = interpreter.get_tensor(output_details[1]['index'])[0]  # Class index of detected objects
scores = interpreter.get_tensor(output_details[2]['index'])[0]  # Confidence of detected objects

# Print detected objects with confidence higher than a threshold
threshold = 0.5
for i in range(len(scores)):
    if scores[i] > threshold:
        print(f"Detected object {int(classes[i])} at {boxes[i]} with confidence {scores[i]}")

Notes:

• Model Input/Output: Make sure to check your model's expected input dimensions, data type, and output format. Adjust the image preprocessing and output processing accordingly.
• Performance: tflite-runtime is optimized for running on various devices, including low-power edge devices.
• Edge Devices: If deploying on an edge device, ensure your device's environment supports the necessary installations.

This guide should enable you to perform object detection on a JPG image using a TensorFlow Lite model with tflite-runtime.