111 lines
3.2 KiB
Python
111 lines
3.2 KiB
Python
import cv2
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import os
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import numpy as np
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import xlsxwriter
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from skimage.measure import regionprops, label
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# Path folder gambar
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image_folder = 'D:/Skripsi/ekstraksi fitur/Corynebacterium Diphteriae'
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# Cek apakah folder gambar ada
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if not os.path.isdir(image_folder):
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print(f"Folder {image_folder} tidak ditemukan.")
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exit()
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# Jumlah gambar dalam folder
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image_files = [f for f in os.listdir(image_folder) if f.endswith('.jpg')]
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num_images = len(image_files)
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# Fungsi untuk menghitung eccentricity
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def calculate_eccentricity(region):
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if region.minor_axis_length == 0:
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return 0
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else:
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return np.sqrt(1 - (region.minor_axis_length / region.major_axis_length)**2)
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# Membuat workbook dan worksheet
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output_file = 'D:/Skripsi/ekstraksi fitur/data_fitur.xlsx'
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workbook = xlsxwriter.Workbook(output_file)
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worksheet = workbook.add_worksheet()
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# Menulis header
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headers = ['File Name', 'Jumlah Objek', 'Eccentricity Rata-Rata', 'Metric Rata-Rata', 'Mean Hue Rata-Rata', 'Mean Saturation Rata-Rata', 'Mean Value Rata-Rata']
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for col, header in enumerate(headers):
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worksheet.write(0, col, header)
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row = 1
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# Ekstraksi fitur
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for i in range(num_images):
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file_name = f'{image_folder}/{image_files[i]}'
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# Preprocessing
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img = cv2.imread(file_name, 1)
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if img is None:
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print(f"Gagal membaca file: {file_name}")
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continue
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blue, green, red = cv2.split(img)
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# Thresholding
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ret, img1 = cv2.threshold(green, 115, 255, cv2.THRESH_BINARY_INV)
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img1 = cv2.dilate(img1.copy(), None, iterations=1)
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img1 = cv2.erode(img1.copy(), None, iterations=4)
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# Labeling
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labeled_img = label(img1)
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regions = regionprops(labeled_img)
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num_objects = len(regions)
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if num_objects == 0:
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continue
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# Inisialisasi variabel untuk agregasi
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total_eccentricity = 0
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total_metric = 0
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total_hue = 0
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total_saturation = 0
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total_value = 0
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for region in regions:
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# Fitur bentuk
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eccentricity = calculate_eccentricity(region)
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area = region.area
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perimeter = region.perimeter
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metric = (4 * np.pi * area) / (perimeter ** 2) if perimeter != 0 else 0
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# Fitur warna HSV
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mask = np.zeros(img.shape[:2], dtype="uint8")
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mask[labeled_img == region.label] = 255
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hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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mean_val = cv2.mean(hsv_img, mask=mask)[:3]
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# Agregasi fitur
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total_eccentricity += eccentricity
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total_metric += metric
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total_hue += mean_val[0]
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total_saturation += mean_val[1]
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total_value += mean_val[2]
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# Hitung rata-rata
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mean_eccentricity = total_eccentricity / num_objects
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mean_metric = total_metric / num_objects
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mean_hue = total_hue / num_objects
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mean_saturation = total_saturation / num_objects
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mean_value = total_value / num_objects
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# Menulis hasil ke worksheet
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worksheet.write(row, 0, image_files[i])
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worksheet.write(row, 1, num_objects)
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worksheet.write(row, 2, mean_eccentricity)
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worksheet.write(row, 3, mean_metric)
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worksheet.write(row, 4, mean_hue)
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worksheet.write(row, 5, mean_saturation)
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worksheet.write(row, 6, mean_value)
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row += 1
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# Menutup workbook
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workbook.close()
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print(f"Hasil ekstraksi fitur disimpan di {output_file}")
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