Daha az keskin

Şimdi greyfurtların dış hatlarını, yani daireleri yakalamayı deneyelim. Bunu, görüntüyü daha pürüzsüz hale getirmek için daha güçlü bir Gauss filtresi uygulayarak yapabilirsin. Bu da canny fonksiyonunda daha büyük bir sigma belirleyerek sağlanır.

Bu egzersizde, canny() fonksiyonunun sigma değerleriyle denemeler yapacaksın.

Görüntü grapefruit olarak önceden yüklendi.

show_image zaten önceden yüklendi.

Bu egzersiz

Python ile Görüntü İşleme

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

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

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python ile Görüntü İşleme

kursunun bir parçasıdır

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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: 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 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: Canny ile kenarları bulma Exercise 2: Kenarlar Exercise 3: Daha az keskin

Geçerli Egzersiz

Exercise 4: Köşe başında Exercise 5: Perspektif Exercise 6: Daha az köşe Exercise 7: Yüz algılama Exercise 8: Orada biri var mı?Exercise 9: Birden fazla yüz Exercise 10: Bölütleme ve yüz tespiti Exercise 11: Gerçek dünya uygulamaları Exercise 12: Gizlilik koruması Exercise 13: Sally'nin mezuniyet fotoğrafını geri kazanmaya yardım et Exercise 14: Harika iş!