Learn OpenCV using Python With Practice

OpenCV with Python

What you'll learn:
Learn OpenCV with Python
Learn Basic Operations with OpenCV
Learn Pixel Data
Learn Data Types, Structures, Image Types, Colour Schemas/Channels
Learn Pixel Manipulation and Filtering
Learn Blur, Dilation and Erosion
Learn Scale and Rotate Images and use input videos
Learn Object Detection with OpenCV
Learn Segmentation and Binary Images
Learn Simple and Adaptive Thresholding
Learn Skin Detection
Learn Contours and Object Detection
Learn Area, Perimeter, Centre and Curvatures
Learn Canny Edge Detection
Learn Face Detection with OpenCV
Learn Template Matching
Learn Haar Cascading
Learn Face & Eye Detection

Requirements:
Some programming experience
Passionate to learn the stuff
This course is meant for people with at least some programming experience

Description:
OpenCV is a library of programming functions mainly aimed at real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage then Itseez. The library is cross-platform and free for use under the open-source Apache 2 License.(Wikipedia)

It is one of the most popular tools for facial recognition, used in a wide variety of security, marketing, and photography applications, and it powers a lot of cutting-edge tech, including augmented reality and robotics. This course offers Python developers a detailed introduction to OpenCV 3, starting with installing and configuring your Mac, Windows, or Linux development environment along with Python 3. Learn about the data and image types unique to OpenCV, and find out how to manipulate pixels and images. Then comes the real power of OpenCV: object, facial, and feature detection. Learn how to leverage the image-processing power of OpenCV using methods like template matching and machine learning data to identify and recognise features.


Introduction to OpenCV
Installation & Personalisation
Basic operations
Pixel Data, Data Types, Structures
Pixel manipulation & filtering
Object Detection with OpenCV
Segmentation & Binary Images
Simple & Adaptive Thresholds
Contours , Area perimeters, Center & Curvature 
Object, Canny Edges Detection
Assignments

Areas of Application
2D & 3D Toolkits
Egomotion Estimation
Facial & Gesture recognition
Human computer interaction
Mobile robotics
SFM
Motion Tracking
Augmented Reality
Object Detection
Segmentation & Recognition

Who this course is for:
Have some programming experiences who wants to enter in computer vision and object detection world in data science!

More Info