Hands-On AI: Image Processing with Python

Course details

In this course, Eduardo Corpeño—an electrical engineer, computer programmer, and teacher—helps you build a solid foundation in image processing using Python. Begin by creating and manipulating raster images in different resolutions and formats. Learn to apply various filters and delve into edge detection with Sobel filters to extract meaningful image features. Master image transformations like resizing, and grasp the techniques of object removal and seam carving for effective image stitching. Learn how to refine image details using morphological transformations such as erosion, dilation, opening, and closing. Plus, complete practical challenges like removing noise, adjusting resolution, and enhancing image quality. Whether you're looking to enhance a project or embark on a new career in AI, this course equips you with practical knowledge and techniques to handle images efficiently.

Learning objectives
Understand and implement core image processing techniques, including color encoding, grayscale conversion, and filtering.
Apply image transformation methods such as rotations, scaling, and morphological modifications to manipulate visual data.
Develop hands-on experience with convolution filters, edge detection, and adaptive thresholding for enhanced image analysis.
Learn how to stitch images, perform object modifications, and prepare image data for advanced computer vision applications.

Concepts

0. Introduction

01 - Computer vision under the hood
02 - What you should know
03 - Using the exercise files

1. Setting Up Our Development Environment

04 - Testing your environment
05 - Maintaining a clean notebook workspace

2. The Basics of Image Processing

06 - Image representation
07 - Color encoding
08 - Image file management
09 - Resolution
10 - Rotations and flips
11 - Challenge - Manipulate pictures
12 - Solution - Manipulate pictures

3. From Color to Black and White

13 - Average grayscale
14 - Weighted grayscale
15 - Converting grayscale to black and white
16 - Adaptive thresholding
17 - Challenge - Removing color
18 - Solution - Removing color

4. Filters

19 - Convolution filters
20 - Average filters
21 - Median filters
22 - Gaussian filters
23 - Edge-detection filters
24 - Challenge - Convolution filters
25 - Solution - Convolution filters

5. Image Scaling

26 - Image downscaling methods
27 - Downscaling example
28 - Image upscaling methods
29 - Upscaling example
30 - Challenge - Resize a picture
31 - Solution - Resize a picture

6. Fun with Cuts

32 - Image cuts
33 - Stitching two images together
34 - Cuts in panoramic photography
35 - Challenge - Stitch two pictures together
36 - Solution - Stitch two pictures together

7. Morphological Modifications

37 - Why modify objects
38 - Erosion and dilation
39 - Open and close
40 - Challenge - Help a robot
41 - Solution - Help a robot

Conclusion

42 - Next steps