Gain insights into OpenCV with C++. Delve into image editing, machine learning functions, and practical applications like object detection. Discover real-world projects to elevate your programming skills.

open_cv_cpp.tar.gz

OpenCV

OpenCV-GUI

OpenCV is extensively used in the business world for various applications, including facial recognition, object detection, and image and video manipulation. 

In this course, you'll learn the basics of OpenCV—an open-source machine learning library. You'll begin with an overview of the OpenCV library before moving on to practical applications like viewing images, videos, and webcam input. Next, you'll learn how to edit images with tools like decolorizing, cropping, resizing, and captioning. You'll dive into machine learning functions like edge detection, contour detection, corner detection, and face detection. Finally, you'll work on practical projects like document scanners and object trackers. 

By the end of this course, you’ll have a solid understanding of OpenCV and machine learning with C++. You'll be able to create functional applications using computer vision principles, preparing you for real-world projects. Taking this course will allow you to take your programming skills to the next level.

Learning OpenCV from Scratch with C++

Let’s learn some advanced techniques to edit images in OpenCV. 

Color channels are the main component of a color image. Learning to separate these color channels will help us understand different color intensities in the image and allows us to create monochrome images.

## What are color channels?

A color image consists of three color channels—blue, green, and red. We merge these three **color channels** to create color images. OpenCV allows us to split an image into its respective color channels. Additionally, it allows us to merge the color channels to form a color image.


## Split color channels
To split color channels, we use the `split()` method of the OpenCV library.







Let’s learn some advanced techniques to edit images in OpenCV. 

Color channels are the main component of a color image. Learning to separate these color channels will help us understand different color intensities in the image and allows us to create monochrome images.

# What are color channels?

A color image consists of three color channels—blue, green, and red. We merge these three **color channels** to create color images. OpenCV allows us to split an image into its respective color channels. Additionally, it allows us to merge the color channels to form a color image.


# Split color channels
To split color channels, we use the `split()` method of the OpenCV library.







Learn about color channels and splitting and merging them.

Introduction to C++ OpenCV

Reading Media

Editing Images

Reading and Editing Images with OpenCV

Advanced Editing

Detection

Assessment on Advanced Editing and Detection

Real-Life Projects

Conclusion

Appendix

Split and Merge Color Channels

What are color channels?

Split color channels