How to Use OpenCV on Raspberry Pi

OpenCV (Open Source Computer Vision Library) is an open-source computer vision library that provides tools for image and video processing.

OpenCV is used in a wide variety of applications, from facial recognition and image analysis to robotics and automation.

Some of the most important features of OpenCV are

OpenCV allows performing advanced image processing tasks on the Raspberry Pi, in projects such as implementing motion detection or facial recognition systems, robots that can navigate and recognize objects in their environment, or creating intelligent systems that respond to visual stimuli.

How to Install OpenCV on Raspberry Pi

To use OpenCV with Python on Raspberry Pi, we first need to install the library. There are several ways to do it, but the simplest and recommended option is to use the pip package system. Let’s cover the installation process step by step.

Before installing any software, it’s good practice to update the system. Run the following commands in the terminal:

OpenCV requires several dependencies that we must install first. Run the following command to install the necessary libraries:

Now that we have all the dependencies, we can proceed to install OpenCV. First, we’ll install pip and numpy, which are needed to handle Python packages and matrices:

With pip installed, we can install OpenCV using the following command:

To make sure OpenCV has been installed correctly, we can verify the library version. Open a Python interpreter and run the following code:

import cv2
print(cv2.__version__)

If everything is correct, you should see the OpenCV version printed on the screen.

Using OpenCV with Python

Now that we have OpenCV installed, we can start using it in our projects. Let’s look at some basic examples to illustrate how to use OpenCV with Python.

import cv2

# Read an image
image = cv2.imread('path/to/your/image.jpg')

# Display the image in a window
cv2.imshow('Image', image)

# Wait until a key is pressed
cv2.waitKey(0)

# Close all windows
cv2.destroyAllWindows()

import cv2

# Start capturing video from the camera
capture = cv2.VideoCapture(0)

while True:
    # Capture frame by frame
    ret, frame = capture.read()

    # Display the frame in a window
    cv2.imshow('Live Video', frame)

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

# Release the capture and close the windows
capture.release()
cv2.destroyAllWindows()

import cv2

# Load the cascade classifier for face detection
classifier = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Read the image
image = cv2.imread('path/to/your/image.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Detect faces in the image
faces = classifier.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)

# Draw rectangles around the detected faces
for (x, y, w, h) in faces:
    cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 2)

# Display the image with detected faces
cv2.imshow('Detected Faces', image)
cv2.waitKey(0)
cv2.destroyAllWindows()