Computer Vision with Python

Duration: 8 min

This module delves into the fascinating world of computer vision using Python, a crucial skill for developing intelligent systems. We will explore how to manipulate and analyze visual data, enabling machines to interpret and understand the visual world. Understanding computer vision is essential for applications ranging from autonomous vehicles to medical image analysis.

Image Loading and Display

To begin with, we need to learn how to load and display images using Python. This is a fundamental step in any computer vision task. We'll use libraries like OpenCV and Matplotlib to handle image data effectively. These tools provide the necessary functions to read, manipulate, and visualize images, which are essential for further processing.

example1.py

import cv2
import matplotlib.pyplot as plt

# Load an image from file
image = cv2.imread('path/to/image.jpg')

# Convert color from BGR to RGB (OpenCV uses BGR by default)
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Display the image using Matplotlib
plt.imshow(image_rgb)
plt.title('Sample Image')
plt.axis('off')
plt.show()

Try it in Google Colab:

Image titled 'Sample Image' is displayed with no axes.

Edge Detection

Edge detection is a fundamental task in computer vision that involves identifying points in a digital image where the image brightness changes sharply. This is crucial for object detection and image segmentation. We'll use the Canny edge detection algorithm, which is widely used for its effectiveness and efficiency.

example2.py

import cv2
import matplotlib.pyplot as plt

# Load an image from file
image = cv2.imread('path/to/image.jpg', cv2.IMREAD_GRAYSCALE)

# Apply GaussianBlur to reduce noise and improve edge detection
image_blur = cv2.GaussianBlur(image, (5, 5), 1.4)

# Apply Canny edge detection
edges = cv2.Canny(image_blur, 100, 200)

# Display the original and edge-detected images
plt.subplot(1, 2, 1)
plt.imshow(image, cmap='gray')
plt.title('Original Image')
plt.axis('off')

plt.subplot(1, 2, 2)
plt.imshow(edges, cmap='gray')
plt.title('Edge Detection')
plt.axis('off')

plt.show()

💡 Tip: When using Canny edge detection, adjusting the threshold values is crucial. Too low values can miss edges, while too high values can produce too many false edges.