Official Raspberry Pi AI Kit Documentation
The Official Raspberry Pi AI Kit is a powerful single-board computer designed for machine learning and artificial intelligence applications. This kit combines the Raspberry Pi 4 Model B with the Google Coral Accelerator, a compact, low-power System-on-Module (SoM) that provides high-performance machine learning inference capabilities.
Raspberry Pi 4 Model B
Google Coral Accelerator (SoM)
MicroSD card slot
Gigabit Ethernet port
2x USB 3.0 ports
2x USB 2.0 ports
HDMI port
3.5mm audio jack
Power button
The Official Raspberry Pi AI Kit supports the following operating systems:
Raspberry Pi OS
Ubuntu Core
Google's Mendel Linux
### Example 1: Image Classification using TensorFlow Lite
In this example, we'll use the Official Raspberry Pi AI Kit to classify images using TensorFlow Lite and the Coral Accelerator.
Official Raspberry Pi AI Kit
USB camera (e.g., Raspberry Pi Camera v2)
MicroSD card with Raspberry Pi OS or Ubuntu Core
TensorFlow Lite runtime (pre-installed on Raspberry Pi OS)
Python 3.x
Code:
```python
import os
import numpy as np
from PIL import Image
import tensorflow as tf
# Load the image classification model
model_path = '/usr/share/tflite-python/examples/models/mobilenet_v1_1.0_224.tflite'
interpreter = tf.lite.Interpreter(model_path)
interpreter.allocate_tensors()
# Get the input and output tensors
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Capture an image from the camera
camera = cv2.VideoCapture(0)
ret, frame = camera.read()
camera.release()
cv2.destroyAllWindows()
# Pre-process the image
img = Image.fromarray(frame)
img = img.resize((224, 224), Image.ANTIALIAS)
input_data = np.array(img)
# Run the inference
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
# Get the output
output_data = interpreter.get_tensor(output_details[0]['index'])
print('Image classification result:', np.argmax(output_data))
```
### Example 2: Speech Recognition using Google's TensorFlow Lite Models
In this example, we'll use the Official Raspberry Pi AI Kit to recognize spoken words using Google's TensorFlow Lite models and the Coral Accelerator.
Official Raspberry Pi AI Kit
USB microphone (e.g., USB Microphone UAC-1)
MicroSD card with Raspberry Pi OS or Ubuntu Core
TensorFlow Lite runtime (pre-installed on Raspberry Pi OS)
Python 3.x
Code:
```python
import os
import numpy as np
import tensorflow as tf
import pyaudio
# Load the speech recognition model
model_path = '/usr/share/tflite-python/examples/models/speech_recognition.tflite'
interpreter = tf.lite.Interpreter(model_path)
interpreter.allocate_tensors()
# Get the input and output tensors
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Initialize the PyAudio stream
p = pyaudio.PyAudio()
stream = p.open(format=pyaudio.paInt16, channels=1, rate=16000, input=True, frames_per_buffer=1024)
# Record audio data
audio_data = np.empty((16000, 1), dtype=np.int16)
while True:
frame = stream.read(1024)
audio_data = np.concatenate((audio_data, frame), axis=0)
if len(audio_data) >= 16000:
break
# Pre-process the audio data
audio_data = audio_data[:16000]
input_data = np.array(audio_data, dtype=np.float32)
input_data = input_data / 32768.0
# Run the inference
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
# Get the output
output_data = interpreter.get_tensor(output_details[0]['index'])
print('Recognized speech:', np.argmax(output_data))
stream.stop_stream()
stream.close()
p.terminate()
```
These examples demonstrate the capabilities of the Official Raspberry Pi AI Kit for machine learning and AI applications. By leveraging the Coral Accelerator and TensorFlow Lite, you can build efficient and accurate AI models that run directly on the device.
