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Course

Image Processing in Python

IntermediateSkill Level

4.8+

Updated 05/2024

Learn to process, transform, and manipulate images at your will.

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PythonMachine Learning4 hr16 videos54 Exercises4,450 XP55,316Statement of Accomplishment

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Course Description

Images are everywhere! We live in a time where images contain lots of information, which is sometimes difficult to obtain. This is why image pre-processing has become a highly valuable skill, applicable in many use cases. In this course, you will learn to process, transform, and manipulate images at your will, even when they come in thousands. You will also learn to restore damaged images, perform noise reduction, smart-resize images, count the number of dots on a dice, apply facial detection, and much more, using scikit-image. After completing this course, you will be able to apply your knowledge to different domains such as machine learning and artificial intelligence, machine and robotic vision, space and medical image analysis, retailing, and many more. Take the step and dive into the wonderful world that is computer vision!

Prerequisites

1

Introducing Image Processing and scikit-image

Jump into digital image structures and learn to process them! Extract data, transform and analyze images using NumPy and Scikit-image. With just a few lines of code, you will convert RGB images to grayscale, get data from them, obtain histograms containing very useful information, and separate objects from the background!

Make images come alive with scikit-image

Is this gray or full of color?

RGB to grayscale

NumPy for images

Flipping out

Getting started with thresholding

Apply global thresholding

When the background isn't that obvious

Trying other methods

Apply thresholding

2

Filters, Contrast, Transformation and Morphology

You will learn to detect object shapes using edge detection filters, improve medical images with contrast enhancement and even enlarge pictures to five times its original size! You will also apply morphology to make thresholding more accurate when segmenting images and go to the next level of processing images with Python.

Jump into filtering

Edge detection

Blurring to reduce noise

Contrast enhancement

What's the contrast of this image?

Medical images

Aerial image

Let's add some impact and contrast

Transformations

Aliasing, rotating and rescaling

Enlarging images

Proportionally resizing

Handwritten letters

Improving thresholded image

3

Image restoration, Noise, Segmentation and Contours

So far, you have done some very cool things with your image processing skills!In this chapter, you will apply image restoration to remove objects, logos, text, or damaged areas in pictures! You will also learn how to apply noise, use segmentation to speed up processing, and find elements in images by their contours.

Image restoration

Let's restore a damaged image

Removing logos

Let's make some noise!

Reducing noise

Reducing noise while preserving edges

Superpixels & segmentation

Number of pixels

Superpixel segmentation

Finding contours

Contouring shapes

Find contours of an image that is not binary

Count the dots in a dice's image

4

Advanced Operations, Detecting Faces and Features

After completing this chapter, you will have a deeper knowledge of image processing as you will be able to detect edges, corners, and even faces! You will learn how to detect not just front faces but also face profiles, cat, or dogs. You will apply your skills to more complex real-world applications. Learn to master several widely used image processing techniques with very few lines of code!

Finding the edges with Canny

Right around the corner

Perspective

Less corners

Face detection

Is someone there?

Multiple faces

Segmentation and face detection

Real-world applications

Privacy protection

Help Sally restore her graduation photo

Amazing work!

Image Processing in Python

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FAQs

What Python library is used for image processing in this course?

You will use scikit-image throughout the course, along with NumPy for working with image arrays and pixel data.

What image processing tasks does this course teach?

You will learn to restore damaged images, reduce noise, smart-resize images, count objects in images, and apply facial detection techniques.

What prior Python knowledge do I need?

This is an intermediate course. You should know Python functions, iterators, and basic Python, as the exercises involve writing custom image processing workflows.

What careers use image processing skills?

Image processing is used in medical image analysis, robotic vision, retail product recognition, space imaging, and as a foundation for machine learning on visual data.

How is the course structured?

It has 4 chapters and 54 exercises covering image fundamentals, filters, object measurement, and advanced techniques like facial detection.

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