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Image Processing in Python

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

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4 Hours16 Videos54 Exercises28,569 Learners4450 XPImage Processing TrackMachine Learning Scientist Track

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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!

  1. 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!

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    Make images come alive with scikit-image
    50 xp
    Is this gray or full of color?
    50 xp
    RGB to grayscale
    100 xp
    NumPy for images
    50 xp
    Flipping out
    100 xp
    100 xp
    Getting started with thresholding
    50 xp
    Apply global thresholding
    100 xp
    When the background isn't that obvious
    100 xp
    Trying other methods
    100 xp
    Apply thresholding
    100 xp
  2. 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.

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  3. 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.

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  4. 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!

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In the following tracks

Image ProcessingMachine Learning Scientist




sara-billenSara Billenhillary-green-lermanHillary Green-Lerman
Rebeca Gonzalez Headshot

Rebeca Gonzalez

Data Engineer

Rebeca is a data engineer and an entrepreneurial spirit. She has worked in companies like Ayesa and is now co-founder of and APTIC, a startup that focuses on helping visually impaired people to see through Artificial Vision. Besides this, she loves animals, brainstorming sessions, and meeting new people. You can follow or contact her on Twitter and LinkedIn.
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