| /** | |
| * Adventures in Science: Fred Bot | |
| * SparkFun Electronics | |
| * Author: M. Hord (October 8, 2013) | |
| * Modified by: B. Huang (October 31, 2014) | |
| * Modified by: Shawn Hymel (July 21, 2017) | |
| * | |
| * Use two magnetic encoders on Fred's motor shafts (48:1 | |
| * gearbox, 60mm wheels) to make him move in a straight line for | |
| * 1m. |
| """ | |
| Ollama Light Assistant | |
| Test the ability for LLMs to call tools (i.e. functions) from within the Ollama | |
| environment on a Raspberry Pi with speech-to-text. Tested with a Raspberry Pi 5 | |
| (8 GB) and this USB microphone: https://www.adafruit.com/product/3367. | |
| Connect an LED to GPIO 17 (physical pin 11 on the board) in an active high | |
| configuration: |
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:
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
| # 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) |
| #include <WiFi.h> | |
| #include <SPIFFS.h> | |
| #include <ESPAsyncWebServer.h> | |
| #include <WebSocketsServer.h> | |
| // Constants | |
| const char *ssid = "ESP32-AP"; | |
| const char *password = "LetMeInPlz"; | |
| const char *msg_toggle_led = "toggleLED"; | |
| const char *msg_get_led = "getLEDState"; |
| /** | |
| * 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) |
| #!/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 |
| /** | |
| * GridEye LED Array Demo | |
| * Author: Shawn Hymel (SparkFun Electronics) | |
| * Date: May 1, 2018 | |
| * | |
| * Displays a heat map of what the GridEye sees (8x8 pixels). | |
| * | |
| * Required Components | |
| * - Arduino: https://www.sparkfun.com/products/13975 | |
| * - Qwiic Shield: https://www.sparkfun.com/products/14352 |