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

While editing images, we also might want to join the images to display more images at once and make them more creative and visually appealing. We will be doing these tasks during the whole lesson.

# What is NumPy?
NumPy (Numerical Python) is a library of multidimensional arrays and routines for processing arrays. It is mainly used for mathematical and logical operations. We’ll use NumPy to join images in this lesson.

# Join the images horizontally
To join images horizontally, we use the `np.hstack()` method of the OpenCV library. This method requires a parameter, `(img1, img2),` which represents a tuple of images we want to stack.

> **Note**: All the images to be stacked should be the same size.

```
imgHor = np.hstack((img1,img2))
cv2.imshow("Horizontal",imgHor)
```
# Join the images vertically
To join the images vertically, we use the `np.vstack()` method of the OpenCV library. This method requires a parameter, `(img1, img2),` which represents a tuple of images we want to stack.
```
imgVer = np.vstack((img1,img2))
cv2.imshow("Vertical",imgVer)
```
Here is what our code in the IDE should look like:






Follow the step-by-step instructions to join images horizontally and vertically.

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

Join Images

What is NumPy?