Membuat kontur bentuk

Dalam latihan ini, kita akan menemukan kontur seekor kuda.

Untuk itu, kita akan menggunakan citra biner yang disediakan oleh scikit-image dalam modul data. Citra biner lebih mudah diproses saat mencari kontur dengan algoritma ini. Ingat bahwa pencarian kontur hanya mendukung larik citra 2D.

Setelah kontur terdeteksi, kita akan menampilkannya bersama citra aslinya. Dengan begitu kita dapat memeriksa apakah analisis kita sudah benar!

show_image_contour(image, contours) adalah fungsi yang sudah dimuat sebelumnya untuk menampilkan citra beserta semua kontur yang ditemukan menggunakan Matplotlib.

Bentuk kuda dalam hitam dan putih

Ingat Anda dapat menggunakan fungsi find_contours() dari modul measure, dengan memberikan citra bertambat ambang dan sebuah nilai konstan.

Latihan ini adalah bagian dari kursus

Pemrosesan Citra dengan Python

Petunjuk latihan

Impor modul data dan modul yang diperlukan untuk deteksi kontur.
Dapatkan citra kuda yang ditampilkan pada area konteks.
Temukan kontur citra kuda menggunakan nilai level konstan 0.8.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Import the modules
from skimage import ____, ____

# Obtain the horse image
horse_image = ____.horse()

# Find the contours with a constant level value of 0.8
contours = measure.____(____, ____)

# Shows the image with contours found
show_image_contour(horse_image, contours)

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: 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: Pemulihan citra Exercise 2: Mari memulihkan gambar yang rusak Exercise 3: Menghapus logo Exercise 4: Derau Exercise 5: Ayo tambahkan noise!Exercise 6: Mengurangi derau Exercise 7: Mengurangi noise sambil mempertahankan tepi Exercise 8: Superpiksel & segmentasi Exercise 9: Jumlah piksel Exercise 10: Segmentasi superpiksel Exercise 11: Menemukan kontur Exercise 12: Membuat kontur bentuk

Latihan Saat Ini

Exercise 13: Temukan kontur dari gambar yang bukan biner Exercise 14: Hitung jumlah titik pada gambar dadu

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!