import cv2
import os
import numpy as np
import xlsxwriter
from skimage.measure import regionprops, label

# Path folder gambar
folders = {
    'Corynebacterium Diphteriae': 'D:/Skripsi/ekstraksi fitur/Corynebacterium Diphteriae',
    'Mycobacterium Tuberculosis': 'D:/Skripsi/ekstraksi fitur/Mycobacterium Tuberculosis',
    'Neiseria Gonorroea': 'D:/Skripsi/ekstraksi fitur/Neiseria Gonorroea',
    'Staphylococcus Aureus': 'D:/Skripsi/ekstraksi fitur/Staphylococcus Aureus',
    'Streptococcus Pneumoniae': 'D:/Skripsi/ekstraksi fitur/Streptococcus Pneumoniae'
}

# Fungsi untuk menghitung eccentricity
def calculate_eccentricity(region):
    if region.minor_axis_length == 0:
        return 0
    else:
        return np.sqrt(1 - (region.minor_axis_length / region.major_axis_length)**2)

# Membuat workbook dan worksheet
output_file = 'D:/Skripsi/ekstraksi fitur/data_fitur.xlsx'
workbook = xlsxwriter.Workbook(output_file)
worksheet = workbook.add_worksheet()

# Menulis header
headers = ['File Name', 'Jumlah Objek', 'Eccentricity Rata-Rata', 'Metric Rata-Rata', 'Area Total', 'Perimeter Total', 'Label']
for col, header in enumerate(headers):
    worksheet.write(0, col, header)

row = 1

for label_name, image_folder in folders.items():
    # Cek apakah folder gambar ada
    if not os.path.isdir(image_folder):
        print(f"Folder {image_folder} tidak ditemukan.")
        continue

    # Jumlah gambar dalam folder
    image_files = [f for f in os.listdir(image_folder) if f.endswith('.jpg')]
    num_images = len(image_files)

    # Ekstraksi fitur
    for i in range(num_images):
        file_name = f'{image_folder}/{image_files[i]}'

        # Preprocessing
        img = cv2.imread(file_name, 1)
        if img is None:
            print(f"Gagal membaca file: {file_name}")
            continue

        blue, green, red = cv2.split(img)

        # Thresholding
        if label_name == 'Corynebacterium Diphteriae':
            ret, img_bin = cv2.threshold(green, 115, 255, cv2.THRESH_BINARY_INV)
            img_bin = cv2.erode(cv2.dilate(img_bin, None, iterations=1), None, iterations=4)
        elif label_name == 'Mycobacterium Tuberculosis':
            ret, img_bin = cv2.threshold(green, 135, 255, cv2.THRESH_BINARY_INV)
            img_bin = cv2.dilate(img_bin, None, iterations=1)

            # Labeling objek dan filtering berdasarkan ukuran
            num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(img_bin, connectivity=8)
            mask = np.zeros_like(img_bin)
            min_size = 50
            max_size = 400

            for j in range(1, num_labels):  # Mulai dari 1 untuk melewatkan background
                if min_size <= stats[j, cv2.CC_STAT_AREA] <= max_size:
                    mask[labels == j] = 255

            img_bin = mask
        elif label_name == 'Neiseria Gonorroea':
            ret, img_bin = cv2.threshold(green, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
            img_bin = cv2.dilate(img_bin, None, iterations=1)

            # Labeling objek dan filtering berdasarkan ukuran
            num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(img_bin, connectivity=8)
            mask = np.zeros_like(img_bin)
            min_size = 200
            max_size = 2500

            for j in range(1, num_labels):  # Mulai dari 1 untuk melewatkan background
                if min_size <= stats[j, cv2.CC_STAT_AREA] <= max_size:
                    mask[labels == j] = 255

            img_bin = mask
        elif label_name == 'Staphylococcus Aureus':
            ret, img1 = cv2.threshold(green, 110, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
            img_bin = cv2.erode(cv2.dilate(img1.copy(), None, iterations=1), None, iterations=1)
            img_bin = cv2.morphologyEx(img_bin.copy(), cv2.MORPH_OPEN, None)
        elif label_name == 'Streptococcus Pneumoniae':
            ret, img1 = cv2.threshold(green, 110, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
            img_bin = cv2.erode(img1.copy(), None, iterations=1)
            img_bin = cv2.dilate(img1.copy(), None, iterations=1)

        # Labeling
        labeled_img = label(img_bin)
        regions = regionprops(labeled_img)
        num_objects = len(regions)

        if num_objects == 0:
            continue

        # Inisialisasi variabel untuk agregasi
        total_eccentricity = 0
        total_metric = 0
        total_area = 0
        total_perimeter = 0

        for region in regions:
            # Fitur bentuk
            eccentricity = calculate_eccentricity(region)
            area = region.area
            perimeter = region.perimeter
            metric = (4 * np.pi * area) / (perimeter ** 2) if perimeter != 0 else 0

            # Agregasi fitur
            total_eccentricity += eccentricity
            total_metric += metric
            total_area += area
            total_perimeter += perimeter

        # Hitung rata-rata
        mean_eccentricity = total_eccentricity / num_objects
        mean_metric = total_metric / num_objects

        # Menulis hasil ke worksheet
        worksheet.write(row, 0, image_files[i])
        worksheet.write(row, 1, num_objects)
        worksheet.write(row, 2, mean_eccentricity)
        worksheet.write(row, 3, mean_metric)
        worksheet.write(row, 4, total_area)
        worksheet.write(row, 5, total_perimeter)
        worksheet.write(row, 6, label_name)  # Menambahkan label kelas

        row += 1

# Menutup workbook
workbook.close()
print(f"Hasil ekstraksi fitur disimpan di {output_file}")