Ini abu-abu atau penuh warna?

Apa perbedaan utama antara gambar-gambar di bawah ini?

Gambar-gambar ini telah dimuat sebelumnya sebagai coffee_image dan coins_image dari modul data milik scikit-image menggunakan:

coffee_image = data.coffee()
coins_image = data.coins()

Pilih jawaban yang paling tepat menggambarkan perbedaan utama terkait warna dan struktur dimensi.

Di konsol, gunakan fungsi shape() dari NumPy untuk memperoleh bentuk gambar (Tinggi, Lebar, Dimensi) dan cari tahu. NumPy sudah diimpor sebagai np.

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Pemrosesan Citra dengan Python

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Latihan ini adalah bagian dari kursus

Pemrosesan Citra dengan Python

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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?

Latihan Saat Ini

Exercise 3: RGB ke skala keabuan 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 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: 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!