Bu görüntünün kontrastı nedir?

Siyah beyaz, asılı ve sallanan saat Saat görüntüsünün histogramı

Histogram bize bunu söylüyor.

Daha önce gördüğümüz gibi, kontrastı piksel şiddetlerinin aralığını hesaplayarak bulabilirsin; yani histogramdaki en küçük piksel şiddeti değerini en büyük olandan çıkararak.

Görüntünün en büyük piksel şiddetini NumPy'nin np.max() yöntemiyle, en küçüğünü ise np.min() ile konsolda elde edebilirsin.

Görüntü clock_image olarak, NumPy np olarak ve show_image() fonksiyonu ise hazır yüklü.

Bu egzersiz

Python ile Görüntü İşleme

kursunun bir parçasıdır

Kursu Görüntüle

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Egzersizi başlat

Bu egzersiz

Python ile Görüntü İşleme

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

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 <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: Filtrelemeye hızlı bir başlangıç Exercise 2: Kenar algılama Exercise 3: Gürültüyü azaltmak için bulanıklaştırma Exercise 4: Kontrast iyileştirme Exercise 5: Bu görüntünün kontrastı nedir?

Geçerli Egzersiz

Exercise 6: Tıbbi görüntüler Exercise 7: Hava görüntüsü Exercise 8: Hadi biraz etki ve kontrast katalım Exercise 9: Dönüşümler Exercise 10: Aliasing, döndürme ve yeniden ölçekleme Exercise 11: Görüntüleri büyütme Exercise 12: Oranlı yeniden boyutlandırma Exercise 13: Morfoloji Exercise 14: El yazısı harfler Exercise 15: Eşiklenmiş görüntüyü iyileştirme

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!