Penggunaan Singular Value Decopotition (SVD) Pada Citra | Pengolahan Citra | Python

Ini scriptnya: Import necessary modules import requests import cv2 import numpy as np import matplotlib.pyplot as plt Assign and open imagem url = 'https://cdn.pixabay.com/photo/2016/07... response = requests.get(url, stream=True) with open('image.png', 'wb') as f: f.write(response.content) img = cv2.imread('image.png') Converting the image into gray scale for faster computation gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) Calculating the SVD u, s, v = np.linalg.svd(gray_image, full_matrices=False) Inspect shapes of the matrices print(f'u.shape: {u.shape}, s.shape: {s.shape}, v.shape: {v.shape}') import seaborn as sns Variance explained var_explained = np.round(s**2 / np.sum(s**2), decimals=6) Variance explained by top singular vectors print(f'Variance Explained by Top 20 singular values:\n{var_explained[0:20]}') sns.barplot(x=list(range(1, 21)), y=var_explained[0:20], color="dodgerblue") plt.title('Variance Explained Graph') plt.xlabel('Singular Vector', fontsize=16) plt.ylabel('Variance Explained', fontsize=16) plt.tight_layout() plt.show() Plot images with different number of components comps = [3648, 1, 5, 10, 15, 20] plt.figure(figsize=(12, 6)) for i in range(len(comps)): low_rank = u[:, :comps[i]] @ np.diag(s[:comps[i]]) @ v[:comps[i], :] plt.subplot(2, 3, i + 1) plt.imshow(low_rank, cmap='gray') if i == 0: plt.title(f'Actual Image with n_components = {comps[i]}') else: plt.title(f'n_components = {comps[i]}') plt.tight_layout() plt.show()