Daha az köşe

Bu egzersizde, köşe tepeleri arasındaki minimum mesafeyi daha yüksek bir değere ayarladığında ne olduğunu test edeceksin. Bunu corner_peaks() fonksiyonunun min_distance parametresiyle yaptığını unutma. threshold_rel parametresi ise tepelerin minimum yoğunluğunu belirtir.

Alttan perspektifle bir bina

Görüntü building_image olarak önceden yüklendi.

show_image(), show_image_with_corners() fonksiyonları ve gerekli paketler senin için zaten yüklendi. Ayrıca köşeleri bulmaya yönelik önceki tüm kodlar da hazır. corner_harris() ile elde edilen Harris ölçüm tepki görüntüsü measure_image olarak yüklendi.

Bu egzersiz

Python ile Görüntü İşleme

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Find the peaks with a min distance of 10 pixels
coords_w_min_10 = ____(measure_image, min_distance=____, threshold_rel=0.02)
print("With a min_distance set to 10, we detect a total", len(coords_w_min_10), "corners in the image.")

Kodu Düzenle ve Çalıştır

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: 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: Canny ile kenarları bulma Exercise 2: Kenarlar Exercise 3: Daha az keskin Exercise 4: Köşe başında Exercise 5: Perspektif Exercise 6: Daha az köşe

Geçerli Egzersiz

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!