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

# What is a color space?

A **color space** is a specific organization of color. It is a system of representing an array of pixel colors. There are several color spaces, such as BGR, grayscale, HSV, LAB, and many more. Here, we’ll learn to convert our image from one color space to another.

# Convert from BGR to grayscale

>Note: OpenCV reads the images in BGR format. 
 
To convert the image to grayscale, we use the `cv2.cvtColor()` method of the OpenCV library. We need to give two parameters to this function—the image and the color code:
```
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cv2.imshow('Gray', gray)
```

Here, the color code is `cv2.COLOR_BGR2GRAY`, which converts the image from the `BGR` color space to the `GRAY` color space.

# Convert from BGR to HSV

To convert the image to HSV, we again use the `cv2.cvtColor()` method. However, we specify a different color code as the parameter:

```
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
cv2.imshow('HSV', hsv)
```
Here, the color code is `cv2.COLOR_BGR2HSV`, which converts the image from the `BGR` color space to the `HSV` color space.


# Convert from BGR to LAB

To convert the image to `LAB`, we again use the `cv2.cvtColor()` method. In this case, we specify a different color code:
```
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
cv2.imshow('LAB', lab)
```

Here, the color code is `cv2.COLOR_BGR2LAB`, which converts the image from the `BGR` color space to the `LAB` color space.

# Convert from BGR to RGB


Outside of OpenCV, we use the `RGB` format, which is the inverse of the `BGR` format. To convert the image to `RGB`, we again use the `cv2.cvtColor()` method with a different color code:


```
rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
cv2.imshow('RGB', rgb)
```

Here, the color code is `cv2.COLOR_BGR2RGB`, which converts the image from the `BGR` color space to the `RGB` color space. It will inverse the color of the image. For example, the blue will be red, and the red will be blue.







Learn to distinguish between different color spaces in this lesson.

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

Color Spaces

What is a color space?

Convert from BGR to grayscale