Computer Vision with Python Tutorial

This tutorial will guide you through the basics of computer vision using Python. We'll cover key concepts and practical examples to help you get started.

Prerequisites

Before diving into computer vision with Python, make sure you have the following prerequisites:

Basic understanding of Python programming
Familiarity with libraries such as NumPy, OpenCV, and scikit-image

Key Concepts

Computer vision is a field of study that enables computers to interpret and understand visual information from the world. Here are some key concepts:

Image Processing: Manipulating and analyzing images to extract useful information.
Object Detection: Identifying and locating objects within images.
Recognition: Recognizing objects, faces, or scenes within images.

Getting Started

To get started with computer vision in Python, you can use the following libraries:

OpenCV: A powerful library for computer vision tasks.
scikit-image: A collection of algorithms for image processing.

You can install these libraries using pip:

pip install opencv-python scikit-image

Practical Examples

Basic Image Processing

Here's a simple example of loading and displaying an image using OpenCV:

import cv2

# Load an image
image = cv2.imread('path_to_image.jpg')

# Display the image
cv2.imshow('Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Object Detection

Object detection is a common task in computer vision. Here's an example using OpenCV's Haar cascades:

import cv2

# Load an image
image = cv2.imread('path_to_image.jpg')

# Load a pre-trained Haar cascade for face detection
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Detect faces in the image
faces = face_cascade.detectMultiScale(image, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

# Draw rectangles around 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
cv2.imshow('Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Recognition

Recognition tasks can be more complex, but scikit-image provides a range of algorithms for image recognition.

For example, to recognize edges in an image:

from skimage import feature

# Load an image
image = cv2.imread('path_to_image.jpg', cv2.IMREAD_GRAYSCALE)

# Detect edges using Canny edge detection
edges = feature.canny(image)

# Display the image
plt.imshow(edges, cmap='gray')
plt.show()