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  3. Image Processing in Python
  • 1

    Introducing Image Processing and scikit-image

    Gratuito

    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!

  • 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.

  • 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.

  • 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!

Initializing

Ejercicio

Number of pixels

Let's calculate the total number of pixels in this image.

Young woman
Image preloaded as face_image

The total amount of pixel is its resolution. Given by \(Height \times Width\).

Use .shape from NumPy which is preloaded as np, in the console to check the width and height of the image.

Instrucciones

50 XP

Respuestas posibles