OpenCV Tutorial in Python
OpenCV, short for Open Source Computer Vision Library, is an open-source computer vision and machine learning software library. Originally developed by Intel, it is now maintained by a community of developers under the OpenCV Foundation.
OpenCV
is a huge open-source library for computer vision, machine learning, and image processing. OpenCV supports a wide variety of programming languages like Python, C++, Java, etc. It can process images and videos to identify objects, faces, or even the handwriting of a human. When it is integrated with various libraries, such as
which is a highly optimized library for numerical operations, then the number of weapons increases in your Arsenal i.e. whatever operations one can do in Numpy can be combined with OpenCV. This OpenCV tutorial will help you learn Image-processing from Basics to advanced, like operations on Images and videos using a huge set of Programs and projects.
OpenCV Tutorial in Python
Table of Content
1. Getting Started
Learn how to set up and get started with OpenCV:
- OpenCV – Overview
- Introduction to OpenCV
- Install OpenCV for Python on Windows
- Install OpenCV for Python on Linux
- Set up Opencv with anaconda environment
2. Working with Images
>> 2.1 Getting Started
- Reading an image in OpenCV using Python
- Display an image in OpenCV using Python
- Writing an image in OpenCV using Python
- OpenCV | Saving an Image
- Color Spaces
- Arithmetic operations on Images
- Bitwise Operations on Binary Images
>> 2.2 Image Processing
- Image Resizing
- Eroding an Image
- Blurring an Image
- Create Border around Images
- Grayscaling of Images
- Scaling, Rotating, Shifting and Edge Detection
- Erosion and Dilation of images
- Analyze an image using Histogram
- Histograms Equalization
- Simple Thresholding
- Adaptive Thresholding
- Otsu Thresholding
- Segmentation using Thresholding
- Convert an image from one color space to another
- Filter Color with OpenCV
- Denoising of colored images
- Visualizing image in different color spaces
- Find Co-ordinates of Contours
- Bilateral Filtering
- Image Inpainting using OpenCV
- Intensity Transformation Operations on Images
- Image Registration
- Background subtraction
- Background Subtraction in an Image using Concept of Running Average
- Foreground Extraction in an Image using Grabcut Algorithm
- Morphological Operations in Image Processing (Opening)
- Morphological Operations in Image Processing (Closing)
- Morphological Operations in Image Processing (Gradient)
- Image segmentation using Morphological operations
- Image Translation
- Image Pyramid
>> 2.3 Feature Detection and Description
- 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
>> 2.4 Drawing Functions
- Draw a line
- Draw arrow segment
- Draw an ellipse
- Draw a circle
- Draw a rectangle
- Draw a text string
- Find and Draw Contours
- Draw a triangle with centroid
3. Working with Videos
>> 3.1 Getting Started
>> 3.2 Video Processing
4. Applications and Projects
- 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
- Invisible Cloak using OpenCV
- Unsupervised Face Clustering Pipeline
- Saving Operated Video from a webcam
- Face Detection using Python and OpenCV with webcam
- Opening multiple color windows
- Play a video in reverse mode
- Template matching using OpenCV in Python
- Cartooning an Image using OpenCV – Python
- 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
Further Learning
If you’re interested in more recent articles and updates on OpenCV, explore the latest content here:
