RGB ke skala keabuan

Pada latihan ini Anda akan memuat gambar dari modul data milik scikit-image dan mengubahnya menjadi skala keabuan, lalu membandingkan keduanya pada keluaran.

Kami telah memuat sebelumnya fungsi show_image(image, title='Image') yang menampilkan gambar menggunakan Matplotlib. Anda dapat memeriksa parameter-parameter lainnya dengan ?show_image() atau help(show_image) di konsol.

Rocket

Latihan ini adalah bagian dari kursus

Pemrosesan Citra dengan Python

Petunjuk latihan

Impor modul data dan color dari Scikit-image. Modul pertama menyediakan contoh gambar, dan yang kedua menyediakan fungsi transformasi warna.
Muat gambar rocket.
Konversi gambar roket RGB-3 ke skala keabuan.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Import the modules from skimage
from skimage import ____, ____

# Load the rocket image
rocket = data.____()

# Convert the image to grayscale
gray_scaled_rocket = color.____(____)

# Show the original image
show_image(rocket, 'Original RGB image')

# Show the grayscale image
show_image(gray_scaled_rocket, 'Grayscale image')

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Pemrosesan Citra dengan Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

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: Hidupkan citra dengan scikit-image Exercise 2: Ini abu-abu atau penuh warna?Exercise 3: RGB ke skala keabuan

Latihan Saat Ini

Exercise 4: NumPy untuk citra Exercise 5: Membalik gambar Exercise 6: Histogram Exercise 7: Mulai menggunakan thresholding Exercise 8: Terapkan thresholding global Exercise 9: Saat latar belakang tidak begitu jelas Exercise 10: Mencoba metode lain Exercise 11: Terapkan 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: 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!