Numero di pixel

Calcoliamo il numero totale di pixel in questa immagine.

Young woman

Immagine precaricata come face_image

La quantità totale di pixel è la sua risoluzione, data da \(Height \times Width\).

Usa .shape di NumPy, già importato come np, nella console per verificare larghezza e altezza dell'immagine.

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Elaborazione di immagini in Python

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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: Ripristino dell'immagine Exercise 2: Ripristiniamo un'immagine danneggiata Exercise 3: Rimozione di loghi Exercise 4: Rumore Exercise 5: Facciamo un po' di rumore!Exercise 6: Riduzione del rumore Exercise 7: Ridurre il rumore preservando i bordi Exercise 8: Superpixel e segmentazione Exercise 9: Numero di pixel

Esercizio in corso

Exercise 10: Segmentazione in superpixel Exercise 11: Trovare i contorni Exercise 12: Contorni delle forme Exercise 13: Trova i contorni di un'immagine che non è binaria Exercise 14: Conta i puntini nell'immagine di un dado

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!