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

It'll cause computational strain and make our program really slow.


<p>It’ll cause computational strain and make our program really slow.</p>


What do you think will happen if we try to read multiple images of a large size ?


<p>What do you think will happen if we try to read multiple images of a large size ?</p>


So, what's the solution?

To solve this issue, we resize the large images into smaller ones.









# Find the dimensions of the image

To find the dimensions of the image, we use the `img.shape` method of the OpenCV library. Here, `img` is the object we’ve stored the image in. Let’s run the program to find the dimensions of the image:


# Import library
import cv2
# Loading an image
img = cv2.imread(r"/usercode/image.jpg")
print(img.shape)

In the output, the first parameters are the height and width, respectively. The third parameter is the number of colors in the color channel—3 for RGB colors.

# Resize the image
To resize the image, it’s a better idea to keep the ratio of the height and the width constant. Therefore, we’ll scale our image up or down with a constant scale.

First, let’s set the scale. We want to reduce the size of the image, so we set the scale to `0.8`.
```
scale = 0.8
```
Next, we reduce the height and width as per the scale. We know that `img.shape` gives us a tuple, and we can access each component of that tuple.


```
width = int(img.shape[1] * scale)
height = int(img.shape[0] * scale)
```

Finally, we use the `cv2.resize` method to resize the image. We use the following command for this:


```
resizedImage = cv2.resize(img, (width, height))
```
# Crop the image



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

Crop and Resize

Find the dimensions of the image