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Computer Vision with OpenCV in
Python – Complete Study Notes
Introduction
Computer Vision is a field of artificial intelligence that enables machines to interpret and understand visual information from the world, such as images and videos. It is widely used in applications like face recognition, object detection, medical imaging, and autonomous vehicles. In Python, OpenCV is one of the most popular libraries used for computer vision tasks.
Definition
Computer Vision refers to techniques that allow computers to analyze and extract meaningful information from images and videos. It combines image processing, machine learning, and deep learning methods to understand visual data.
OpenCV Overview
OpenCV (Open Source Computer Vision Library) is an open-source library used for real- time computer vision applications. It supports image processing, video capture, object detection, and more. It can be installed using pip: pip install opencv-python.
Reading and Displaying Images
Images can be loaded using cv2.imread() and displayed using cv2.imshow(). Example Code: import cv img = cv2.imread('image.jpg') cv2.imshow('Image', img) cv2.waitKey(0) cv2.destroyAllWindows()
Image Properties
Images are represented as arrays in OpenCV. Shape gives dimensions (height, width, channels). Example: img.shape
Image Manipulation
Images can be resized, rotated, and cropped. Example Code: resized = cv2.resize(img, (200,200)) cropped = img[50:200, 50:200]
Color Spaces
OpenCV uses BGR format by default. Images can be converted to grayscale or other color spaces. Example Code: gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
Drawing on Images
Shapes and text can be drawn on images. Example Code: cv2.rectangle(img, (50,50), (200,200), (255,0,0), 2) cv2.putText(img, 'Hello', (50,50), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2)
Image Thresholding
Thresholding converts images to binary format. It is used in segmentation. Example Code: _, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
Edge Detection
Edge detection identifies boundaries in images.
Example Code: blur = cv2.GaussianBlur(img, (5,5), 0)
Applications
Used in facial recognition systems. Used in medical image analysis. Used in security and surveillance. Used in self-driving cars.
Mini Project: Face Detection System
A simple project is detecting faces from webcam feed. Steps: Capture video, convert to grayscale, detect faces, draw rectangles. This demonstrates real-world application of computer vision.
Advantages
Automates visual data processing. Improves accuracy in detection tasks. Used in real-time systems.
Disadvantages
Requires high computational power. Sensitive to lighting conditions. Complex for beginners.
Summary
Computer vision enables machines to understand images and videos. OpenCV provides tools to implement vision applications. It is widely used in AI and automation.
