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

Let’s look at an example. We want to add the names of a company to all our images. We can use the OpenCV library to add text to all those images. Below are the steps we’ll follow in the lesson.


## Add text

To add text, we use the `cv2.putText()` function of the OpenCV library. This function requires seven parameters, which are listed below:

- `img` is the image to which we’ll add the text. 
text is the text we want to display in the image.
- `startingPoint` is the starting pixel point of the texts.
- `cv2.FONT_HERSHEY_COMPLEX` is the font type that we’ll display the text in. We can select other font types from the options in the OpenCV library.
- `fontSize` is the font size.
- `blueColor` adds blue color to the text. It can be changed to another color with a different color code. We’ll learn about this in detail in the next chapter.
- `thickness` is the thickness of the text.




# Add text

To add text, we use the `cv2.putText()` function of the OpenCV library. This function requires seven parameters, which are listed below:

- `img` is the image to which we’ll add the text. 
text is the text we want to display in the image.
- `startingPoint` is the starting pixel point of the texts.
- `cv2.FONT_HERSHEY_COMPLEX` is the font type that we’ll display the text in. We can select other font types from the options in the OpenCV library.
- `fontSize` is the font size.
- `blueColor` adds blue color to the text. It can be changed to another color with a different color code. We’ll learn about this in detail in the next chapter.
- `thickness` is the thickness of the text.




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

Add Texts

Add text