Piksel sayısı

Hadi, bu görseldeki toplam piksel sayısını hesaplayalım.

Young woman

Görsel face_image olarak önceden yüklendi

Toplam piksel sayısı, çözünürlüktür. \(Height \times Width\) ile verilir.

Genişliği ve yüksekliği kontrol etmek için, önceden np olarak yüklenmiş NumPy'nin .shape özelliğini konsolda kullan.

Bu egzersiz

Python ile Görüntü İşleme

kursunun bir parçasıdır

Kursu Görüntüle

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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: 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: Görüntü onarımı Exercise 2: Hadi hasarlı bir görüntüyü onaralım Exercise 3: Logoları kaldırma Exercise 4: Gürültü Exercise 5: Hadi biraz gürültü katalım!Exercise 6: Gürültüyü azaltma Exercise 7: Kenarları korurken gürültüyü azaltma Exercise 8: Süper pikseller ve segmentasyon Exercise 9: Piksel sayısı

Geçerli Egzersiz

Exercise 10: Süperpiksel segmentasyonu Exercise 11: Konturları bulma Exercise 12: Şekillerde kontur çıkarma Exercise 13: İkili olmayan bir görselin konturlarını bul Exercise 14: Bir zar görselindeki noktaları say

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!