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


# Capture the webcam's feed 
To capture a webcam’s feed, we use the `cv2.VideoCapture(0)` method. Here, `0` represents the default camera being used to capture the video. 
 
```
vid = cv2.VideoCapture(0)
```
Note: We need to import the `cv2` library before using the function.

# Set the display frame
We need to create the frame to display the live webcam. For the webcam, we use the `.set()` method. Inside the parenthesis, we need to include the ID number of the required property and its value. For example, the ID number of width is `3`. Can you guess what the ID number of height is?


It’s `4`. Below is what the code should look like:

 
```
frameWidth = 640
frameHeight = 480
vid.set(3, frameWidth)
vid.set(4, frameHeight)
```

# Read the webcam's feed
To read the webcam’s feed, we use the `read()` function for the captured webcam’s feed. Look at the following code:

```
while True:
  success, img = vid.read()
````
Here, we store our image in the `img` object. The `success` keyword stores a boolean value representing whether or not it was done successfully.


# Display the webcam's feed

After reading the feed, we display it frame by frame. One frame is simply an image. Therefore, we again use the `cv2.imshow()` and `waitKey()` functions to pause each frame in the live feed and display the frames continuously to display video.


```
img = cv2.imshow("Webcam",img)
```     

The Following is what our in the local IDE should look like:

Learn to read the webcam's feed and display it.

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

Read Webcam

Capture the webcam’s feed

Set the display