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)

ShawnHymel

"""
OpenMV Live Image Inference

Continuously captures images and performs image using provided TFLite model
file. Outputs probabilities in console. Displays preview on LCD backpack along
with predicted label.

Author: EdgeImpulse, Inc.
Modified: Shawn Hymel
Date: September 26, 2021

ShawnHymel