Discover OpenCV to enhance AI in computer vision. Learn image/video processing, editing, and basic machine learning like edge, object, and face detection with real-world projects.

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OpenCV is an open-source machine learning library for Python that adds enhanced artificial intelligence capabilities in computer vision and image processing.

This course is a comprehensive introduction to building applications using OpenCV, including viewing, identifying, and editing image and video files using Python. You’ll start with an introduction to the library before immediately applying the framework to view images, video, webcam input, and text. You’ll then take your image captures and apply various edits, including decolorization, cropping, resizing, and captioning with layered text and objects. You’ll continue with basic machine learning functions, including edge, object, and face detection. With the basics in hand, you’ll wrap up with real-world projects to build document scanners and object trackers.

By the end of this course, you’ll have a working knowledge of OpenCV and machine learning. You’ll be prepared to build functional applications using the principles of computer vision in Python.

Mastering Computer Vision in Python with OpenCV

We can use the bitwise operators to mask images. Masking is another essential tool to edit images in OpenCV. In this lesson, we’ll follow the steps below.


First, we’ll create a blank image.

To create a blank image, we use the `np.zeros()` function of the OpenCV library.
```
blank = np.zeros(img.shape[:2], dtype='uint8')
```
## What is masking?
Masking is an image editing technique used to highlight a specific part of an image. This technique is used to combine different images, as well as highlight the required portion in the combined images.

> **Note**: While masking, the size of the mask image should be the same size as the image being masked.

## Create the mask
First, we’ll create a crescent-shaped mask for our image. We create two different blank images with a circle and a rectangle. Next, we use the AND bitwise operator to combine them.









First, we’ll create a blank image.

To create a blank image, we use the `np.zeros()` function of the OpenCV library.
```
blank = np.zeros(img.shape[:2], dtype='uint8')
```
# What is masking?
Masking is an image editing technique used to highlight a specific part of an image. This technique is used to combine different images, as well as highlight the required portion in the combined images.

> **Note**: While masking, the size of the mask image should be the same size as the image being masked.

# Create the mask
First, we’ll create a crescent-shaped mask for our image. We create two different blank images with a circle and a rectangle. Next, we use the AND bitwise operator to combine them.









Introduction to Python OpenCV

Read Images, Videos, and Webcam

Editing Images

Reading and Editing Images with OpenCV

Advanced Editings

Detection

Assessment on Advanced Editing and Detection

Real-life projects

Conclusion

Tracking Moving Objects Using Python

Mask Images

What is masking?