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| import cv2 | |
| import numpy as np | |
| import os | |
| from skimage.feature import graycomatrix | |
| def calculate_glcm_features(glcm): | |
| # Mendapatkan jumlah level abu-abu | |
| L = glcm.shape[0] | |
| # Inisialisasi variabel fitur | |
| asm = 0 | |
| contrast = 0 | |
| idm = 0 | |
| entropy = 0 | |
| correlation = 0 | |
| # Mean dan standar deviasi untuk perhitungan korelasi | |
| px = np.sum(glcm, axis=1) | |
| py = np.sum(glcm, axis=0) | |
| mean_i = np.sum(np.arange(L) * px) | |
| mean_j = np.sum(np.arange(L) * py) | |
| std_i = np.sqrt(np.sum((np.arange(L) - mean_i)**2 * px)) | |
| std_j = np.sqrt(np.sum((np.arange(L) - mean_j)**2 * py)) | |
| # Menghitung fitur GLCM | |
| for i in range(L): | |
| for j in range(L): | |
| p_ij = glcm[i, j] | |
| if p_ij > 0: | |
| asm += p_ij**2 | |
| contrast += (i - j)**2 * p_ij | |
| idm += p_ij / (1 + (i - j)**2) | |
| entropy -= p_ij * np.log2(p_ij) | |
| correlation += ((i - mean_i) * (j - mean_j) * p_ij) / (std_i * std_j) | |
| return asm, contrast, idm, entropy, correlation | |
| def ekstrakTekstur(image): | |
| # Memuat gambar dalam grayscale | |
| imageGray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
| # Resize gambar ke ukuran yang diinginkan (misalnya 512x512) | |
| image_resized = cv2.resize(imageGray, (512, 512)) | |
| # Mendefinisikan jarak dan sudut untuk perhitungan GLCM | |
| distances = [1] # Menggunakan berbagai jarak | |
| angles = [0, np.pi/4, np.pi/2, 3*np.pi/4] # 0, 45, 90, 135 derajat | |
| # Inisialisasi array untuk menyimpan fitur untuk setiap sudut dan jarak | |
| asm_result = [] | |
| kontras_result = [] | |
| idm_result = [] | |
| entropy_result = [] | |
| korelasi_result = [] | |
| # Menghitung GLCM dan mengekstraksi fitur | |
| for distance in distances: | |
| glcm = graycomatrix(image_resized, [distance], angles, 256, symmetric=True, normed=True) | |
| for angle_idx in range(len(angles)): | |
| asm, contrast, idm, entropy, correlation = calculate_glcm_features(glcm[:, :, 0, angle_idx]) | |
| asm_result.append(asm) | |
| kontras_result.append(contrast) | |
| idm_result.append(idm) | |
| entropy_result.append(entropy) | |
| korelasi_result.append(correlation) | |
| print(entropy_result) | |
| return asm_result, kontras_result, idm_result, entropy_result, korelasi_result | |