Lebih sedikit sudut

Dalam latihan ini, Anda akan menguji apa yang terjadi saat Anda menetapkan jarak minimum antar puncak sudut ke angka yang lebih besar. Ingat, Anda melakukannya dengan parameter atribut min_distance dari fungsi corner_peaks(). Parameter threshold_rel akan menentukan intensitas minimum puncak.

Bangunan dari perspektif bawah

Gambar telah dimuat sebagai building_image.

Fungsi show_image(), show_image_with_corners() dan paket yang diperlukan sudah dimuat untuk Anda. Begitu juga semua kode sebelumnya untuk menemukan sudut. Citra respons ukuran Harris yang diperoleh dengan corner_harris() sudah dimuat sebagai measure_image.

Latihan ini adalah bagian dari kursus

Pemrosesan Citra dengan Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# 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.")

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: 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: Menemukan tepi dengan Canny Exercise 2: Tepi (Edges)Exercise 3: Kurangi Tepi Exercise 4: Tepat di sudut Exercise 5: Perspektif Exercise 6: Lebih sedikit sudut

Latihan Saat Ini

Exercise 7: Deteksi wajah Exercise 8: Apakah ada seseorang di sana?Exercise 9: Beberapa wajah Exercise 10: Segmentasi dan deteksi wajah Exercise 11: Aplikasi dunia nyata Exercise 12: Perlindungan privasi Exercise 13: Bantu Sally memulihkan foto kelulusannya Exercise 14: Kerja yang luar biasa!