Latest: Imagine if your Raspberry Pi could see and react You could imagine it like a smart camera that records only when it sees a person. Or perhaps a plant watering system that waters plants when they need it. And that is the importance of computer vision. It allows computers to perceive and understand images. Not limited to a mini-computer, the Raspberry Pi By means of the computer vision.
There is potential in computer vision. That includes automating tasks, enhancing security, and making interactive experiences possible. Because it’s cheap, the Raspberry Pi is well-suited to this task. It’s also flexible and supported by a large community. So, are you ready to dive in?
Where To Use Raspberry Pi For Computer Vision?
Why use Raspberry Pi for your computer vision projects? This tiny board packs quite the punch! It provides great advantages. Here is what makes it remarkable.
Cost-Effectiveness
Raspberry Pi’s cost very little. The Pi is more affordable than other computer vision solutions. You could get an entire setup for less than $100. This low-cost alternative allows you to experiment without spending a fortune. That’s a major benefit to hobbyists and students.
Accessibility and Ease of Use
Raspberry Pi is beginner-friendly It is a very supportive ecosystem. There’s plenty of online tutorials, and a whole host of people willing to help. Most projects already have code snippets and instructions for you to follow. This makes it much easier to learn computer vision.
Portable and Low Power Consumption
Raspberry Pi’s are small, don’t pull much power. Which make them ideal for mobile and embedded projects. Consider a wildlife camera powered by batteries. Or a robot that uses computer vision to navigate. It doesn’t take much to imagine the possibilities, given the size and low energy consumption of the Pi.
Building a Computer Vision System With Raspberry Pi
Preparing your Raspberry Pi for computer vision A beginners guide to setting up the program. You will find out what hardware you need and how to install the software. Pi in action Get your Pi in action!
Hardware Requirements
There are several things you will require to start. You require a Raspberry Pi model (such as a Pi 4 or Pi 5). You will also need a camera module (official or USB). You will also need an SD card, and a power supply. Now, you are all set to kick things off!
Step 2: Download and Install OS and Required Libraries
Step 1: Install Raspberry Pi OS on Your SD Card Go to settings and turn on the camera interface. OpenCV and TensorFlow Lite would be downloaded next. These libraries guide you on how to handle images. Run these commands in the terminal:
sudo apt update
You are common paths to install but command will depend on different OS and flavour.
pip3 install –upgrade tensorflow tflite-runtime
Configuring the Camera
Time to test your camera! Set the resolution as per your requirements. The corresponding way of taking a test image for us with Python code:
import cv2
camera = cv2.VideoCapture(0)
return_value, image = camera. read()
cv2. imwrite(“test. jpg”, image)
del(camera)
The codetakes a shot and saves it as ”test. jpg”. Your camera, if it works, is all set.
Raspberry Pi: Core Computer Vision Concepts
Now for computer vision, we need a few basic concepts. Now let us understand the images and the object detection and the image classification. This should set this up for success.
Image Processing Fundamentals
Images are made of pixels. Each pixel has its own color channels where RGB values are stored. Image filtering is used to blurred or sharpen the images. Edge detection identifies edges between objects. These are what form the foundation of computer vision.
Detection and Recognition of Objects
🔍 Object Detection – Find objects in an image. A naive approach to approach this is simple Visual Object Detection techniques such as Haar cascades and HOG. Object Detection — Pre-trained models also perform the object detection. These models have already been trained to identify common objects. They simplify object detection.
Image Classification
Image classification is the process of categorizing an image into a class. There you can use a classification problem for example, classify your images as “cat” OR “dog”. Pre trained models are useful such as MobileNet. On Raspberry Pi, you’ll find optimized TensorFlow Lite models.
Computer vision projects using Raspberry Pi
Ready for some fun? Flare up some awesome computer vision projects. You will be inspired by these examples. They’ll also provide you with hands-on experience.
Real-Time Object Detection
Well, let’s create an object detection project that works in real-time. We’ll use a camera and OpenCV. This code detects faces:
import cv2
face_cascade = cv2. CascadeClassifier(‘haarcascade_frontalface_default. xml’)
video = cv2.VideoCapture(0)
while True:
check, frame = video. read()
gray = cv2. cvtColor(frame, cv2. COLOR_BGR2GRAY)
faces = face_cascade. detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)
for (x, y, w, h) in faces:
cv2. rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv2. imshow(‘Video’, frame)
key = cv2.waitKey(1)
if key == ord(‘q’):
break
video.release()
cv2.destroyAllWindows()
This code then opens your camera and draws boxes around any face it sees. Cool, right?
License Plate Recognition
It is very neat to build a license plate recognition (LPR) system. Then, you would need to find the license plate in the image. Then, extract the characters. Lastly, read the text via OCR (Optical Character Recognition)
Home Security System
Develop a security system using motion detection and object recognition. If motion detected, see if it’s a person. If the answer is yes, trigger an alert via email or push notification. This gives your home a more intelligent personal and secure touch.
But today, let us optimize CV performance on raspberry pi.
Raspberry Pi’s have limits. Now, let us learn how to speed up computer vision. These optimization tips will prove useful.
Implementing TensorFlow Lite and Alternative Libraries
Optimizes models for both size and speed. It suits the resource constraints of the Raspberry Pi to a T. Run large models efficiently using TensorFlow Lite.
Hardware Acceleration
The GPU in a Raspberry Pi can accelerate image processing. This enables using the GPU to accelerate tasks. This can lead to a huge performance increase.
Code Optimization
Write efficient Python code. NumPy: NumPy is used to perform array operations. Minimize loops. These are a few tips that can speed up your code on the Pi.
Conclusion
There are many possibilities with computer vision on Raspberry Pi. Whether on smart cameras or automated systems, the applications are myriad. The Raspberry Pi is ideal due to its low cost, small size, and the fact that it’s easier to find and carry around than many other computers. “Run your own computer vision project. Explore the possibilities. PCV: Computer Vision with Raspberry Pi
