OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library. It provides a wide range of tools and functions for various image and video processing tasks. Whether you’re a beginner or an experienced developer, this introductory guide will help you get started with OpenCV.
Table of Contents
Installation
- Installing OpenCV on Windows
- Installing OpenCV on macOS
- Installing OpenCV on Linux
Basic Image Operations
- Loading and displaying images
- Image properties (size, channels)
- Saving images
Image Processing
- Grayscale conversion
- Image resizing
- Image cropping
- Image rotation
- Image filtering
1. Installation
Installing OpenCV on Windows
- Download and install Python (if not already installed).
- Open a command prompt or terminal.
- Install OpenCV using pip:
pip install opencv-python
Installing OpenCV on macOS
- Install Homebrew (if not already installed).
- Open a terminal.
- Install OpenCV using Homebrew:
brew install opencv
Installing OpenCV on Linux
- Open a terminal.
- Use your package manager to install OpenCV. For example, on Ubuntu:
sudo apt-get install python3-opencv
2. Basic Image Operations
Loading and Displaying Images
import cv2
image = cv2.imread('image.jpg')
cv2.imshow('Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
Image Properties
You can access image properties like size and channels using:
height, width, channels = image.shape
print(f"Image size: {width}x{height}, Channels: {channels}")
Saving Images
You can save an image using:
cv2.imwrite('new_image.jpg', image)
3. Image Processing
Grayscale Conversion
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
Image Resizing
resized_image = cv2.resize(image, (new_width, new_height))
Image Cropping
cropped_image = image[y1:y2, x1:x2]
Image Rotation
rotation_matrix = cv2.getRotationMatrix2D(center, angle, scale)
rotated_image = cv2.warpAffine(image, rotation_matrix, (width, height))
Image Filtering
blur_image = cv2.GaussianBlur(image, (5, 5), 0)
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