OpenCV Documentation

OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library. It is designed for computational photography and real-time computer vision. OpenCV is used for various applications such as facial recognition, object detection, and augmented reality.

Features

• Cross-platform: Supports Windows, Linux, macOS, Android, and iOS.
• Written in C++, Python, and Java.
• Large Community: A vast community of developers contributes to its development.
• High Performance: Optimized for speed and efficiency.
• Extensive Documentation: Detailed guides and tutorials are available.

Quick Start

To get started with OpenCV, you can download it from the official website and install it on your system. Download OpenCV.

Once installed, you can use the Python bindings to get started with basic operations. Here's an example:

import cv2

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

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

Further Reading

For more detailed information, you can refer to the official OpenCV documentation. It covers a wide range of topics, including tutorials, guides, and API references.

• Official OpenCV Documentation

Tutorials

If you're looking for step-by-step tutorials, here are some recommended resources:

• OpenCV Python Tutorials
• OpenCV C++ Tutorials

Sample Image

Here's an example of a simple image processing operation using OpenCV:

# Convert an image to grayscale
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

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

By following these resources, you'll be well on your way to mastering OpenCV!