Overview of Rasbery Pi, various applications and concrete implementation examples

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What is Rasbery Pi?

Raspberry Pi is a Single Board Computer (SBC), a small computer developed by the Raspberry Pi Foundation in the UK. Its name is derived from a dessert called “Raspberry Pi,” which is popular in the UK.

The Raspberry Pi will be a low-cost, small yet versatile computer that runs a Linux-based operating system and can be used for a variety of applications. The first model was released in 2012, and since then numerous versions have been developed, with the Raspberry Pi4 in 2023 being the latest model.

The main features are as follows

Small and compact: The Raspberry Pi integrates the main components of a computer (CPU, RAM, I/O ports, etc.) on one small board. This makes it small in size and compact enough to fit in the palm of your hand.
Low cost: Raspberry Pi is offered at a very reasonable price. As a result, it is widely used by educational institutions, individuals, and research and development.
Versatility: Raspberry Pi can be used as a general-purpose computer and has a wide range of applications such as web browsing, media playback, learning programming, and controlling Internet of Things (IoT) devices.
GPIO pins: The Raspberry Pi has General Purpose Input/Output (GPIO) pins. This allows sensors, actuators, etc. to be connected and programmatically controlled.
Customizable: Raspberry Pi is an open platform that can be customized as desired, allowing users to choose and build their own operating system and applications to run on the Raspberry Pi.

Raspberry Pi is a widely used hardware for learning programming and computer science in educational settings, hobby projects, and building practical devices, and also plays an important role in the development of IoT devices and sensor networks. It also plays an important role in the development of IoT devices and sensor networks.

Application examples using Rasbery Pi

The Raspberry Pi can be used to build a variety of projects, including machine learning research, prototype development, and real-time control and prediction. The following are specific examples of applications using the Raspberry Pi.

Image Recognition: Using a camera module connected to the Raspberry Pi, it is possible to build a system that recognizes objects from images and videos, where machine learning algorithms (e.g., TensorFlow, OpenCV) are used to process input data from the camera for The system can process the input data from the camera and perform tasks such as object recognition and face recognition.
Autonomous robots: Raspberry Pi can be embedded in a robot to collect sensor data and use machine learning to control the robot’s movement and navigation. This enables the development of autonomous robots.
Sensor data analysis: Raspberry Pi has GPIO pins and can be connected to various sensors (temperature, humidity, distance, etc.) to build systems that use machine learning algorithms to analyze data from these sensors to extract patterns and make predictions.
IoT devices: Raspberry Pi is also suitable for developing IoT devices. Sensor data can be collected and linked to machine learning models in the cloud for real-time analysis and prediction.
Educational Applications: Raspberry Pi is being used in schools and educational institutions for projects to teach the basics of machine learning; Raspberry Pi has become an educational platform for teaching children the basics of programming and machine learning.

Although Raspberry Pi is limited in its ability to run large-scale machine learning models due to its limited resources, it is nonetheless used in a variety of applications, including education, hobbyist projects, and small-scale applications, making it a useful platform for promoting and learning machine learning.

Specific implementations of those use cases are described below.

Example implementation of building a web server using Rasbery Pi

The Raspberry Pi can be used to build a server suitable for small projects or personal use. An example implementation is shown below.

OS installation: First, install the appropriate operating system (e.g., Raspberry Pi OS, Ubuntu) on the Raspberry Pi.
Updating packages: Open a terminal and update the package list with the following command

sudo apt update
sudo apt upgrade

Web server software installation: Install web server software such as Apache or Nginx. As an example, to install Apache, execute the following command.

sudo apt install apache2

Firewall configuration: Configure a firewall for the installed web server to enhance security. For example, use UFW (Uncomplicated Firewall) to open the necessary ports.

sudo ufw allow 80 # Open HTTP port
sudo ufw enable # Enable UFW

5.Web page placement: Place the web page file you wish to display in the document root of the web server (usually /var/www/html/). For example, to place the file index.html, do the following

sudo cp /path/to/index.html /var/www/html/

Confirmation of operation: To check that the server has been built correctly, obtain the IP address of the Raspberry Pi and access that IP address with a web browser. The deployed web page will then be displayed.

In this way, a simple web server can be built using Raspberry Pi. Further extensions can be made, such as expanding the server’s functionality or enhancing security. Various extensions are also possible, such as adding a database server or an application server, depending on the application.

However, Raspberry Pi is not suitable as a production server that handles large amounts of traffic due to its limited resources. It is typically used mainly for learning and hobby projects and small private applications.

Example implementation of image recognition using Rasbery Pi

When using Raspberry Pi for image recognition, it is common to utilize OpenCV and TensorFlow. The following is an example implementation of object recognition from camera images using Raspberry Pi.

Connecting the camera: Connecting the camera module to the Raspberry Pi. Many Raspberry Pi models have a port to connect a camera through the CSI interface. Make sure the camera is properly identified.
Prepare the OS: Install the Raspberry Pi OS, open a terminal, and run the following command to update the necessary packages.

sudo apt update
sudo apt upgrade

3. Install OpenCV: OpenCV is an image processing and computer vision library that will be widely used for image processing on the Raspberry Pi.

sudo apt install python3-opencv

4.Install TensorFlow: TensorFlow is a machine learning framework and will be used for image recognition.

pip3 install tensorflow

Creating an object recognition script: Create a Python script and write a program to analyze the video acquired from the camera and perform object recognition, using OpenCV to capture the video from the camera and load an object detection model already trained in TensorFlow to perform object recognition.As an example, the following is a Python script for object recognition using TensorFlow’s object detection API.

import cv2
import tensorflow as tf
import numpy as np

# Load TensorFlow's object detection API
model = tf.saved_model.load('path/to/saved_model')

# Start Camera Capture
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()

    # Resize image to model input size
    resized_frame = cv2.resize(frame, (300, 300))

    # Convert image data to Numpy array
    input_data = np.expand_dims(resized_frame, axis=0)

    # Perform object detection
    detections = model(input_data)

    # Processing of detection results
    # Example: Surround the detected object with a rectangle and display its label and confidence level
    for detection in detections['detection_boxes'][0]:
        ymin, xmin, ymax, xmax = detection
        h, w, _ = frame.shape
        left, right, top, bottom = int(xmin * w), int(xmax * w), int(ymin * h), int(ymax * h)
        label = 'object'
        confidence = detections['detection_scores'][0][0]
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)
        cv2.putText(frame, f'{label}: {confidence:.2f}', (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

    # Show Image
    cv2.imshow('Object Detection', frame)

    # Exit with 'q' key
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release Capture
cap.release()
cv2.destroyAllWindows()

This script detects objects from the camera video, encloses the detected objects in a rectangle, and displays the label and confidence level. besides TensorFlow’s object detection API, various machine learning models and approaches can be used to perform object recognition.

When the above script is executed on a Raspberry Pi, object recognition is performed from the camera image.

Example of implementation of a free-standing robot using Rasbery Pi

Self-supporting robots can be implemented using Raspberry Pi. In the following implementation example, a Raspberry Pi is used as the control unit to create a robot that moves autonomously by acquiring information from sensors and controlling motors and actuators.

In this project, a Raspberry Pi is used as the control unit to create a line follower robot that moves autonomously by following a black line using line sensors.

Required parts:

Raspberry Pi (Raspberry Pi 3 or Raspberry Pi 4 recommended)
Camera Module for Raspi
Motor Driver Shield
DC motors (2 or more)
Battery pack
Line sensor module (infrared sensor)
Caster wheel
Robot Chassis
Electronic components such as jumper wires and screws

Procedure:

Install Raspberry Pi OS: Install the Raspberry Pi OS on the Raspberry Pi. Also, enable SSH, VNC, etc. to enable remote control.
Assemble the hardware: Install the motor driver shield, motors, line sensor module, etc. to the chassis. Also, install the battery pack and caster wheels.
Programming: Create a program to control the robot’s movements using Python. Implement a control algorithm to acquire data from the line sensor and follow the line, and use the GPIO library to control the motors.
Run the program: Run the program you created on the Raspberry Pi to verify that the line follower robot follows the line autonomously.
Add feedback control (optional): feedback control can also be implemented to improve control of the robot using sensor data feedback.

With the above implementation, a stand-alone line follower robot can be built using a Raspberry Pi. The line follower robot serves not only as a learning platform, but also as a way to understand the basics of robotics. Based on this example, a variety of freestanding robots can also be created by adding sensors and actuators and implementing other algorithms.

Example implementation of sensor data analysis using Rasbery Pi

An example implementation can be built to analyze sensor data using the Raspberry Pi. The following describes a project that collects data from sensors connected to a Raspberry Pi and analyzes and processes that data to obtain information. The following is an example of using a temperature sensor (DHT11) to collect temperature and humidity data and display it on a graph.

Required parts:

Raspberry Pi (Raspberry Pi 3 or Raspberry Pi 4 recommended)
Temperature/humidity sensor (e.g. DHT11)
Electronic components such as jumper wires

Procedure:

Install Raspberry Pi OS: Install the Raspberry Pi OS on the Raspberry Pi. Also, enable SSH, VNC, etc. for remote control.
Hardware Connection: Connect the temperature/humidity sensor (DHT11) to the Raspberry Pi, connect the DHT11 data pins to the GPIO pins, and connect power and ground.
Install the library: Install the Adafruit_DHT library to get the sensor data in Python.Raspberry Pi

pip3 install adafruit-dht

Create a program: use Python to retrieve temperature and humidity data and save it to a file. Also, use the matplotlib library to plot the data into a graph.

import Adafruit_DHT
import matplotlib.pyplot as plt
import time

# Specify DHT sensor data pins
sensor = Adafruit_DHT.DHT11
pin = 4

# List to store temperature and humidity data
temperature_data = []
humidity_data = []

# Repeat data acquisition and graph drawing
try:
    while True:
        # Acquire sensor data
        humidity, temperature = Adafruit_DHT.read_retry(sensor, pin)

        # Processed only when data is available
        if humidity is not None and temperature is not None:
            print(f"Temperature: {temperature}°C, humidity: {humidity}%")
            temperature_data.append(temperature)
            humidity_data.append(humidity)

        # Standby for 2 seconds
        time.sleep(2)

except KeyboardInterrupt:
    # Exit when Ctrl+C is pressed
    pass

# Plot a graph of temperature and humidity data
plt.plot(temperature_data, label='Temperature (C)')
plt.plot(humidity_data, label='Humidity (%)')
plt.xlabel('Time')
plt.ylabel('Value')
plt.legend()
plt.show()

Run the program: Run the Python script and verify that the temperature and humidity data are acquired in real time and plotted on a graph.

In this example, data from the temperature and humidity sensor (DHT11) is collected, and changes in temperature and humidity are plotted on a graph in real time. It is also possible to collect other sensor data and analyze the data. For example, it is possible to collect environmental data using light sensors and distance sensors and use the data.

Example of machine learning implementation using Rasbery Pi

The Raspberry Pi can be used to implement machine learning, including data collection, data processing, model training, and prediction. The following is an example of their implementation.

In this project, an image classification model is trained on the Raspberry Pi to create a system that classifies objects captured by a camera in real time; the model is built using TensorFlow, and images are acquired using the Raspberry Pi’s camera module.

Required parts:

Raspberry Pi (Raspberry Pi 3 or Raspberry Pi 4 recommended)
Camera Module for Raspi
Image data sets (folders containing images for each category you wish to classify)
Installing TensorFlow and Keras

Procedure:

Install Raspberry Pi OS: Install the Raspberry Pi OS on the Raspberry Pi. Also, SSH and VNC are enabled and configured for remote control.
Install libraries: TensorFlow and Keras for machine learning in Python.

pip3 install tensorflow keras

Dataset Preparation: Prepare an image dataset, create a folder for each category, and place the images.

dataset/
    ├── category1/
    │   ├── image1.jpg
    │   ├── image2.jpg
    │   └── ...
    ├── category2/
    │   ├── image1.jpg
    │   ├── image2.jpg
    │   └── ...
    └── ...

Train the model: use TensorFlow and Keras to train an image classification model using an image dataset. Store the trained model on the Raspberry Pi.
Create a program: Create a Python program that acquires images from a camera on a Raspberry Pi and classifies objects using a trained model.

import cv2
import numpy as np
from keras.models import load_model

# Load trained models
model = load_model('path/to/your/trained_model.h5')

# List of classification categories
categories = ['category1', 'category2', 'category3', ...]

# Start Camera Capture
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()

    # Resize image to model input size
    resized_frame = cv2.resize(frame, (224, 224))

    # Normalize image data
    normalized_frame = resized_frame / 255.0

    # Add dimension of batch size 1 (to match input format to Keras model)
    input_data = np.expand_dims(normalized_frame, axis=0)

    # Input images to the model and obtain predictions
    prediction = model.predict(input_data)

    # Get the most probable class
    predicted_class = np.argmax(prediction)

    # Display classification results on screen
    cv2.putText(frame, categories[predicted_class], (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
    cv2.imshow('Classification', frame)

    # Exit with 'q' key
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release Capture
cap.release()
cv2.destroyAllWindows()

Run the program: Run the Python script and verify that the object classification is performed from the camera video.

In this example, an image classification model is trained on a Raspberry Pi, and the trained model is used to classify objects from camera footage in real time. This is a simple example and can be applied to more complex machine learning projects. For example, many machine learning projects can be considered that utilize the Raspberry Pi, such as speech recognition and sensor data analysis.

reference book

Reference book is “Exploring Raspberry Pi: Interfacing to the Real World with Embedded Linux”

“Beginning Raspberry Pi”

“Yocto for Raspberry Pi”