Introduction to OpenCV OpenCV (Open Source Computer Vision Library) is an open-source library used for computer vision, image processing, and machine learning. It provides various tools and functionalities to process real-time images and videos, making it widely used in AI, robotics, and automation. Developed By: Intel Supports: Windows, Linux, macOS, Android, iOS Programming Languages: Python, C++, Java OpenCV allows developers to perform tasks like object detection, face recognition, motion tracking, and image transformations. Why Use OpenCV? Fast and Efficient: Written in C++ for high-speed image processing. Extensive Functionality: Supports image processing, video analysis, and feature detection. Wide Community Support: Open-source library with active contributors. Integration with AI/ML: Works well with TensorFlow, PyTorch, and OpenAI models. Real-Time Processing: Optimized for real-time applications in surveillance, robotics, and automation. Installing OpenCV in Python Installing OpenCV in Python To install OpenCV, use the following pip command: pip install opencv-python For full OpenCV functionality, install opencv-contrib-python: pip install opencv-contrib-python To verify the installation: import cv2 print(cv2.__version__) Key Features of OpenCV Image Processing Reading and Writing Images (cv2.imread(), cv2.imwrite()) Resizing and Cropping Color Space Conversion (RGB ↔ Grayscale, HSV, YCrCb) Blurring and Smoothing Edge Detection (Canny, Sobel, Laplacian) Video Processing Reading and Writing Videos (cv2.VideoCapture(), cv2.VideoWriter()) Frame Manipulation Background Subtraction Motion Detection Object Detection & Recognition Face Detection using Haar Cascades YOLO and SSD for Object Detection Template Matching Contour Detection and Shape Analysis Feature Detection & Tracking SIFT (Scale-Invariant Feature Transform) SURF (Speeded-Up Robust Features) ORB (Oriented FAST and Rotated BRIEF) Machine Learning with OpenCV Handwritten Digit Recognition using SVM/KNN Facial Recognition using Eigenfaces/Fisherfaces Pose Estimation and Gesture Recognition Augmented Reality (AR) & Deep Learning AR Applications (Adding Virtual Objects to Real Scenes) Integration with TensorFlow and PyTorch for Deep Learning Basic Operations in OpenCV 1. Reading and Displaying an Image import cv2 # Load an image image = cv2.imread('image.jpg') # Display the image cv2.imshow('Sample Image', image) cv2.waitKey(0) cv2.destroyAllWindows() 2. Converting an Image to Grayscale gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) cv2.imshow('Grayscale Image', gray) cv2.waitKey(0) cv2.destroyAllWindows() 3. Edge Detection using Canny Algorithm edges = cv2.Canny(image, 100, 200) cv2.imshow('Edge Detection', edges) cv2.waitKey(0) cv2.destroyAllWindows() Use Cases of OpenCV IndustryUse CaseHealthcareMedical image analysis, MRI and X-ray interpretationSecurity & SurveillanceFace recognition, motion detection, number plate recognitionAutomotiveSelf-driving cars, lane detection, pedestrian detectionRetail & E-commerceVirtual try-on, product recognition, barcode scanningEntertainmentGesture recognition, AR filters (like Snapchat, Instagram)AgricultureCrop monitoring, disease detection using image analysis Django & Flask Integration with OpenCV OpenCV can be integrated with web frameworks like Django and Flask to develop real-time image processing web applications. Example: Flask + OpenCV: Build a face recognition web app. Django + OpenCV: Automate image enhancement for e-commerce platforms. Comparison of OpenCV with Other Libraries FeatureOpenCVPIL (Pillow)TensorFlow/KerasImage Processing✅ Excellent⚠️ Basic✅ Advanced (Deep Learning)Video Processing✅ Yes❌ No✅ YesReal-Time Processing✅ Yes❌ No⚠️ SlowMachine Learning Integration✅ Yes❌ No✅ Yes Interview Questions: 1. What are the advantages of OpenCV over other image processing libraries?(Google) Answer: Open-source and highly optimized for real-time applications. Supports multiple programming languages. Has built-in ML and deep learning modules. Works efficiently on edge devices like Raspberry Pi. 2. How does OpenCV handle real-time video processing?(NVIDIA) Answer: Uses cv2.VideoCapture() for real-time streaming. Supports background subtraction for motion tracking. Leverages CUDA acceleration for high-performance processing. 3. What are Haar Cascades in OpenCV?(Meta) Answer: Haar Cascades are pre-trained classifiers for object detection. Used for face, eye, and hand gesture recognition. Based on edge and texture detection using AdaBoost.
