OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning library. It allows us to process images and videos, detect objects, faces and even handwriting. This tutorial will guide us through image and video processing from the basics to advanced topics using Python and OpenCV. We'll learn how to handle image transformations, feature extraction, object detection and more.

• Introduction to OpenCV
• Install OpenCV for Python on Windows
• Install OpenCV for Python on Linux
• Set up Opencv with anaconda environment
• Getting started with OpenCV
• Essential OpenCV Functions

Working with Images

Here we see how to manipulate and process images with OpenCV in Python. This section will introduce basic image operations like loading, saving and displaying images followed by more advanced image processing tasks.

• Color Spaces
• Arithmetic operations on Images
• Bitwise Operations on Binary Images

1. Image Processing and Enhancement

This includes techniques that transform or enhance images for better visual quality and manipulation:

• Blurring an Image
• Grayscaling of Images
• Scaling, Rotating, Shifting and Edge Detection
• Intensity Transformation Operations on Images
• Image Translation
• Image Pyramid
• Histograms Equalization
• Convert an image from one color space to another
• Visualizing image in different color spaces
• Create Border around Images

2. Image Segmentation and Thresholding

Techniques that help in dividing an image into meaningful regions or objects:

• Simple Thresholding
• Adaptive Thresholding
• Otsu Thresholding
• Image segmentation using Morphological operations

3. Morphological Operations & Filtering

Operations focused on structuring objects and reducing noise or unwanted features:

• Erosion and Dilation of images
• Bilateral Filtering
• Denoising of colored images
• Filter Color with OpenCV
• Python OpenCV - Morphological Operations
• Morphological Operations in Image Processing (Opening)
• Morphological Operations in Image Processing (Closing)
• Morphological Operations in Image Processing (Gradient)

4. Advanced Image Manipulation & Background Subtraction

More advanced techniques for handling complex image manipulations and background removal:

• Image Inpainting using OpenCV
• Image Registration
• Background subtraction
• Background Subtraction in an Image using Concept of Running Average
• Foreground Extraction in an Image using Grabcut Algorithm

5. Feature Detection and Description

Techniques to detect key features like lines, corners and shapes in an image:

• Line detection using Houghline method
• Circle Detection
• Detect corner of an image
• Corner Detection with Shi-Tomasi method
• Corner detection with Harris Corner Detection
• Find Circles and Ellipses in an Image
• Document field detection
• Smile detection
• Feature extraction and image classification using OpenCV

6. Feature Detection & Analysis

Techniques to detect and analyze key features or patterns in an image:

• Find Co-ordinates of Contours
• Analyze an image using Histogram

Working with Videos

This section focuses on how to handle videos using OpenCV. We’ll learn everything from loading videos to advanced processing and creating new videos.

1. Basic Video Operations

These are foundational tasks for handling video input and output including loading and saving video files and capturing video from a webcam.

• OpenCV | Loading Video
• Play a video using OpenCV
• Click response on video output using Events in OpenCV
• Display date and time in videos using OpenCV
• Get video duration using Python OpenCV

2. Video Transformation and Effects

These tasks involve modifying the video content such as adjusting speed, blending videos and converting between color spaces.

• Creating a Slow Motion Video Using OpenCV
• Converting Color video to grayscale using OpenCV in Python
• Blending of two videos using Python
• How to change video resolution in OpenCV in Python
• Faster video file FPS with cv2.VideoCapture and OpenCV
• Faces Blur in Videos using OpenCV
• How to draw Filled rectangle to every frame of video by using Python OpenCV?

3. Video Processing and Advanced Tasks

Advanced video processing techniques such as creating videos from images, extracting frames and saving key event clips using OpenCV.

• Create video using multiple images
• Extract images from video
• Saving key event video clips with OpenCV

Important functions in OpenCV

Lets see some important functions in OpenCV that are important for performing various image and video operations such as handling images, applying transformations and detecting features.

1. Image Handling Functions:

These functions allow us to load and display images which helps in basic image operations in OpenCV.

• cv2.imread() method
• cv2.imshow()
• cv2.imwrite()

2. Image Transformations:

These functions help in resizing, rotating and changing the color space of images which are important for image manipulation in OpenCV.

• cv2.resize()
• cv2.rotate()
• cv2.cvtColor()

3. Drawing Functions:

These functions allow us to draw various shapes and text on images which is useful for visualizing data, marking regions of interest and annotation.

• cv2.line()
• cv2.circle()
• cv2.rectangle()
• Draw a triangle with centroid
• Find and Draw Contours
• cv2.putText()
• cv2.ellipse()
• cv2.arrowedLine()
• drawMatchesKnn()
• cv2.polylines()

4. Image Processing:

These functions are used to enhance images, detect edges and apply various transformations to refine visual data and extract meaningful features.

• cv2.GaussianBlur()
• cv2.Canny()
• cv2.threshold()
• cv2.erode()

5. Object Detection:

These functions are important for detecting objects and contours within images.

• cv2.CascadeClassifier()
• cv2.findContours()
• cv2.HoughCircles()
• cv2.ContourArea()
• cv2.approxPolyDP()

6. Feature Detection:

These functions are used to detect and match keypoints, descriptors and patterns in images for tasks like object recognition and tracking.

• cv2.SIFT()
• cv2.ORB()
• cv2.BFMatcher()

7. Tracking and Motion:

These functions helps in tracking of moving objects and background subtraction used in motion detection and real-time video analysis.

• cv2.calcOpticalFlowPyrLK()
• cv2.backgroundSubtractorMOG2()
• cv2.calcOpticalFlowFarneback()
• cv2.createBackgroundSubtractorMOG2()

Applications of OpenCV

OpenCV is used in various real-world applications like object detection, facial recognition and real-time video analysis.

• Extract frames using OpenCV
• Displaying the coordinates of the points clicked on the image using Python-OpenCV
• White and black dot detection
• OpenCV BGR color palette with trackbars
• Draw rectangular shape and extract objects
• Face Detection using Python and OpenCV with webcam
• Opening multiple color windows
• Play a video in reverse mode
• Saving Operated Video from a webcam

For more applications refer toSome Amazing Applications of OpenCV Library

Projects of OpenCV

• Invisible Cloak using OpenCV
• Unsupervised Face Clustering Pipeline
• Vehicle detection in a Video frame using Python – OpenCV
• Count number of Faces using Python – OpenCV
• Live Webcam Drawing using OpenCV
• Detect and Recognize Car License Plate from a video in real time
• Template matching using OpenCV in Python
• Cartooning an Image using OpenCV – Python

For more Projects refer to15 OpenCV Projects Ideas for Beginners to Practice in 2025

With these foundational OpenCV skills, we're ready to get more advanced projects and upgrade our image and video processing capabilities.

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