ShawnHymel

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

ShawnHymel

#!/usr/bin/env python

import device_patches       # Device specific patches for Jetson Nano (needs to be before importing cv2)

import cv2
import os
import sys, getopt
import numpy as np
from edge_impulse_linux.image import ImageImpulseRunner

ShawnHymel

ShawnHymel

# This works for gym 0.26.2. It might not work on previous versions.

import gym
import cv2

fps = 30

# Create environment
env = gym.make('CartPole-v1', render_mode='rgb_array')

ShawnHymel

/**
 * Magic Wand Inference Demo
 * 
 * Attach a button to pin 2. Press button to start collection. Raw data will be
 * collected and fed to the impulse for inference. Inference results are printed
 * to the serial terminal.
 * 
 * Author: Shawn Hymel (EdgeImpulse, Inc.)
 * Date: October 10, 2022
 * License: Apache-2.0 (apache.org/licenses/LICENSE-2.0)

ShawnHymel

"""
PyGame Display Test

Run to see if you can draw things in Pygame. Press 'esc` to exit. Run with
root privileges to draw on external monitor (e.g. from SSH).
"""

import pygame

# Initialize pygame

ShawnHymel

#include <stdarg.h>

// Makeshift printf for the Serial terminal
void aprintf(const char *format, ...) {
    static char print_buf[1024] = { 0 };

    va_list args;
    va_start(args, format);
    int r = vsnprintf(print_buf, sizeof(print_buf), format, args);
    va_end(args);

ShawnHymel

ShawnHymel

# TFLite quantized inference example
#
# Based on:
# https://www.tensorflow.org/lite/performance/post_training_integer_quant
# https://www.tensorflow.org/lite/api_docs/java/org/tensorflow/lite/Tensor.QuantizationParams

import numpy as np
import tensorflow as tf

# Location of tflite model file (float32 or int8 quantized)

ShawnHymel

import board
import busio
import time
from random import random

from adafruit_ht16k33 import matrix

# Initialize LED matrix
i2c = busio.I2C(board.SCL, board.SDA)
eye =matrix.Matrix8x8(i2c)