> ## Documentation Index > Fetch the complete documentation index at: https://mintlify.com/opencv/opencv/llms.txt > Use this file to discover all available pages before exploring further. # OpenCV Documentation

Open Source Computer Vision Library

Build powerful computer vision applications with the world's most popular open-source CV library. Process images, detect objects, and deploy AI models across every platform.

Quick start

Get up and running with OpenCV in minutes

Choose your preferred installation method based on your language and platform. ```bash Python (pip) theme={null} pip install opencv-python ``` ```bash C++ (Ubuntu/Debian) theme={null} sudo apt-get install libopencv-dev ``` ```bash macOS (Homebrew) theme={null} brew install opencv ``` ```bash Windows (vcpkg) theme={null} vcpkg install opencv ``` Start with the most basic operation: reading and displaying an image. ```python theme={null} import cv2 # Read an image image = cv2.imread('image.jpg') # Display the image cv2.imshow('My Image', image) cv2.waitKey(0) cv2.destroyAllWindows() ``` ```cpp theme={null} #include int main() { // Read an image cv::Mat image = cv::imread("image.jpg"); // Display the image cv::imshow("My Image", image); cv::waitKey(0); return 0; } ``` Apply image processing operations like converting to grayscale and edge detection. ```python theme={null} import cv2 # Read image image = cv2.imread('image.jpg') # Convert to grayscale gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Apply Canny edge detection edges = cv2.Canny(gray, 100, 200) # Save the result cv2.imwrite('edges.jpg', edges) ``` ```cpp theme={null} #include int main() { cv::Mat image = cv::imread("image.jpg"); cv::Mat gray, edges; // Convert to grayscale cv::cvtColor(image, gray, cv::COLOR_BGR2GRAY); // Apply Canny edge detection cv::Canny(gray, edges, 100, 200); // Save the result cv::imwrite("edges.jpg", edges); return 0; } ``` OpenCV uses BGR color format by default, not RGB. Keep this in mind when working with color images. Now you're ready to explore object detection, face recognition, deep learning, and more. Check out the [tutorials](/tutorials/image-operations) or dive into the [API reference](/api/core/mat) to learn about specific modules.

Explore by topic

Discover what you can build with OpenCV

Image Processing

Filtering, transformations, color space conversions, and geometric operations

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Object Detection

Detect faces, people, vehicles, and custom objects using cascade classifiers and deep learning

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Deep Learning

Run neural networks from TensorFlow, PyTorch, ONNX, and other frameworks

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Video Analysis

Object tracking, motion detection, optical flow, and background subtraction

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3D Reconstruction

Camera calibration, pose estimation, stereo vision, and 3D reconstruction

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Feature Detection

SIFT, SURF, ORB, and other keypoint detectors and descriptors

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Resources

Everything you need to succeed with OpenCV

Step-by-step guides for common computer vision tasks Complete documentation of all OpenCV modules and functions Real-world code examples in Python and C++ View source code, report issues, and contribute

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