Bu gri mi yoksa rengârenk mi?

Aşağıda gösterilen görüntüler arasındaki temel fark nedir?

Bu görüntüler scikit-image data modülünden önceden coffee_image ve coins_image olarak yüklendi:

coffee_image = data.coffee()
coins_image = data.coins()

Renk ve boyutsal yapıyla ilgili ana farkı en iyi açıklayan doğru yanıtı seç.

Konsolda, görüntü şekillerini (Yükseklik, Genişlik, Boyutlar) elde etmek için NumPy içindeki shape() fonksiyonunu kullan ve keşfet. NumPy zaten np olarak içe aktarılmış durumda.

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Python ile Görüntü İşleme

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Python ile Görüntü İşleme

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Kursa Ücretsiz Başlayın

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: scikit-image ile görüntülere hayat ver Exercise 2: Bu gri mi yoksa rengârenk mi?

Geçerli Egzersiz

Exercise 3: RGB'den gri tonlamaya Exercise 4: Görüntüler için NumPy Exercise 5: Ters düz çevirme Exercise 6: Histogramlar Exercise 7: Eşikleme ile başlarken Exercise 8: Küresel eşikleme uygula Exercise 9: Arka plan bu kadar bariz olmadığında Exercise 10: Diğer yöntemleri denemek Exercise 11: Eşikleme uygula

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.

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 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.

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 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!

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!