Meno spigoloso

Proviamo ora a individuare solo il contorno esterno dei pompelmi, cioè i cerchi. Puoi farlo applicando un filtro Gaussiano più intenso per rendere l'immagine più uniforme. Questo si ottiene impostando un sigma più grande nella funzione canny.

In questo esercizio, farai esperimenti con i valori di sigma della funzione canny().

Grapefruits

Immagine precaricata come grapefruit.

La funzione show_image è già stata precaricata.

Questo esercizio fa parte del corso

Elaborazione di immagini in Python

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Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Apply canny edge detector with a sigma of 1.8
canny_edges = canny(grapefruit, sigma=____)

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Questo esercizio fa parte del corso

Elaborazione di immagini in Python

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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: Make images come alive with scikit-image Exercise 2: Is this gray or full of color?Exercise 3: RGB to grayscale Exercise 4: NumPy for images Exercise 5: Flipping out Exercise 6: Histograms Exercise 7: Getting started with thresholding Exercise 8: Apply global thresholding Exercise 9: When the background isn't that obvious Exercise 10: Trying other methods Exercise 11: Apply thresholding

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: Trovare i bordi con Canny Exercise 2: Bordi Exercise 3: Meno spigoloso

Esercizio in corso

Exercise 4: Proprio dietro l'angolo Exercise 5: Prospettiva Exercise 6: Meno angoli Exercise 7: Rilevamento del volto Exercise 8: C'è qualcuno là?Exercise 9: Volti multipli Exercise 10: Segmentazione e rilevamento facciale Exercise 11: Applicazioni reali Exercise 12: Protezione della privacy Exercise 13: Aiuta Sally a restaurare la foto di laurea Exercise 14: Ottimo lavoro!