On retourne tout

Pour faire une blague, quelqu’un a retourné une photo d’un album de voyage à Séville, à la fois à l’envers et en miroir ! Nous devons remettre l’image dans le bon sens en la retournant.

Ville de Séville à l’envers

Image chargée sous le nom flipped_seville. En utilisant les méthodes NumPy vues dans le cours, retournez l’image horizontalement et verticalement. Affichez ensuite l’image corrigée à l’aide de la fonction show_image().

NumPy est déjà importé sous le nom np.

Cet exercice fait partie du cours

<cours>Traitement d’images en Python</cours>

Exercice interactif pratique

Essayez cet exercice en complétant ce code d’exemple.

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

Modifier et exécuter le code

Cet exercice fait partie du cours

<cours>Traitement d’images en Python</cours>

IntermédiaireNiveau de compétence

4.8+

Commencer le cours gratuitement

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: Donnez vie aux images avec scikit-image Exercise 2: Niveaux de gris ou pleine couleur ?Exercise 3: De RGB à niveaux de gris Exercise 4: NumPy pour les images Exercise 5: On retourne tout

Exercice actuel

Exercise 6: Histogrammes Exercise 7: Premiers pas avec le seuillage Exercise 8: Appliquer un seuillage global Exercise 9: Quand l’arrière-plan n’est pas si évident Exercise 10: Essayer d’autres méthodes Exercise 11: Appliquer un seuillage

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!