Helemaal op z’n kop

Als grap heeft iemand een foto uit een album van een trip naar Sevilla ondersteboven én in spiegelbeeld gezet! We moeten de afbeelding weer rechtzetten door te flippen.

Stad Sevilla ondersteboven

Afbeelding geladen als flipped_seville. Gebruik de NumPy-methoden die je in de cursus hebt geleerd om de afbeelding horizontaal en verticaal te flippen. Toon daarna de gecorrigeerde afbeelding met de functie show_image().

NumPy is al geïmporteerd als np.

Deze oefening maakt deel uit van de cursus

Beeldverwerking in Python

Cursus bekijken

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Flip the image vertically
seville_vertical_flip = ____.____(flipped_seville)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Beeldverwerking in Python

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

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!

Exercise 1: Breng beelden tot leven met scikit-image Exercise 2: Is dit grijs of vol kleur?Exercise 3: RGB naar grijswaarden Exercise 4: NumPy voor afbeeldingen Exercise 5: Helemaal op z’n kop

Huidige oefening

Exercise 6: Histogrammen Exercise 7: Aan de slag met thresholding Exercise 8: Pas globale drempeling toe Exercise 9: Als de achtergrond niet zo duidelijk is Exercise 10: Andere methoden uitproberen Exercise 11: Thresholding toepassen

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

Exercise 1: Jump into filtering Exercise 2: Edge detection Exercise 3: Blurring to reduce noise Exercise 4: Contrast enhancement Exercise 5: What's the contrast of this image?Exercise 6: Medical images Exercise 7: Aerial image Exercise 8: Let's add some impact and contrast Exercise 9: Transformations Exercise 10: Aliasing, rotating and rescaling Exercise 11: Enlarging images Exercise 12: Proportionally resizing Exercise 13: Morphology Exercise 14: Handwritten letters Exercise 15: Improving thresholded image

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

Exercise 1: Image restoration Exercise 2: Let's restore a damaged image Exercise 3: Removing logos Exercise 4: Noise Exercise 5: Let's make some noise!Exercise 6: Reducing noise Exercise 7: Reducing noise while preserving edges Exercise 8: Superpixels & segmentation Exercise 9: Number of pixels Exercise 10: Superpixel segmentation Exercise 11: Finding contours Exercise 12: Contouring shapes Exercise 13: Find contours of an image that is not binary Exercise 14: Count the dots in a dice's image

After completing this chapter, you will have a deeper knowledge of image processing as you will be able to <strong>detect edges, corners, and even faces! </strong> 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 <strong>real-world applications.</strong> Learn to master several widely used image processing techniques with very few lines of code!

Exercise 1: Finding the edges with Canny Exercise 2: Edges Exercise 3: Less edgy Exercise 4: Right around the corner Exercise 5: Perspective Exercise 6: Less corners Exercise 7: Face detection Exercise 8: Is someone there?Exercise 9: Multiple faces Exercise 10: Segmentation and face detection Exercise 11: Real-world applications Exercise 12: Privacy protection Exercise 13: Help Sally restore her graduation photo Exercise 14: Amazing work!