274 lines
10 KiB
Python
274 lines
10 KiB
Python
from django.shortcuts import render
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import cv2
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import numpy as np
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from matplotlib import pyplot as plt
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from django.http import HttpResponse
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from scipy import stats
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from django.http import JsonResponse
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from django.views.decorators.csrf import csrf_exempt
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from PIL import Image
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import os
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def crop_image(image, x, y, width, height):
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cropped_image = image[y:y+height, x:x+width]
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return cropped_image
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def calculate_normalized_lbp_histogram(img_gray):
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height, width = img_gray.shape
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lbp_histogram = np.zeros(256, dtype=int)
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for i in range(1, height - 1):
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for j in range(1, width - 1):
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lbp_val = lbp_calculated_pixel(img_gray, i, j)
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lbp_histogram[lbp_val] += 1
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# Normalize the histogram
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lbp_histogram = lbp_histogram / sum(lbp_histogram)
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return lbp_histogram
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def get_pixel(img, center, x, y):
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new_value = 0
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try:
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if img[x][y] >= center:
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new_value = 1
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except:
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pass
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return new_value
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def lbp_calculated_pixel(img, x, y):
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center = img[x][y]
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val_ar = []
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val_ar.append(get_pixel(img, center, x-1, y-1))
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val_ar.append(get_pixel(img, center, x-1, y))
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val_ar.append(get_pixel(img, center, x-1, y + 1))
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val_ar.append(get_pixel(img, center, x, y + 1))
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val_ar.append(get_pixel(img, center, x + 1, y + 1))
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val_ar.append(get_pixel(img, center, x + 1, y))
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val_ar.append(get_pixel(img, center, x + 1, y-1))
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val_ar.append(get_pixel(img, center, x, y-1))
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power_val = [1, 2, 4, 8, 16, 32, 64, 128]
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val = 0
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for i in range(len(val_ar)):
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val += val_ar[i] * power_val[i]
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return val
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def find_mode_pixel_value(img):
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img_flat = img.ravel()
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mode_value = int(np.median(img_flat))
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return mode_value
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def calculate_lbp_image(img_gray):
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height, width = img_gray.shape
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lbp_image = np.zeros((height-2, width-2), dtype=np.uint8)
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for i in range(1, height - 1):
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for j in range(1, width - 1):
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lbp_val = lbp_calculated_pixel(img_gray, i, j)
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lbp_image[i-1, j-1] = lbp_val
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return lbp_image
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def save_lbp_to_txt(lbp_image, filename):
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with open(filename, 'w') as f:
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for row in lbp_image:
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row_str = ' '.join(map(str, row))
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f.write(row_str + '\n')
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def save_normalized_lbp_histogram_to_txt(lbp_histogram, filename):
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with open(filename, 'w') as f:
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for bin_val, freq in enumerate(lbp_histogram):
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f.write(f"LBP Code: {bin_val}, Normalized Frequency: {freq}\n")
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def find_mode_lbp_histogram(lbp_histogram):
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mode_value = np.argmax(lbp_histogram)
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mode_frequency = lbp_histogram[mode_value]
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return mode_value, mode_frequency
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# Definisi kriteria penilaian kesuburan tanah dalam bentuk dictionary
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kriteria_kesuburan_tanah = {
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"KT001": "Sangat Rendah",
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"KT002": "Rendah",
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"KT003": "Sedang",
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"KT004": "Tinggi",
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"KT005": "Sangat Tinggi"
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}
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# Definisi sifat tanah berdasarkan rentang kadar NPK
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sifat_tanah_npk = {
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"NKT001": ("Nitrogen Sangat Rendah", 0.00, 0.10),
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"NKT002": ("Nitrogen Rendah", 0.10, 0.20),
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"NKT003": ("Nitrogen Sedang", 0.21, 0.50),
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"NKT004": ("Nitrogen Tinggi", 0.51, 0.75),
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"NKT005": ("Nitrogen Sangat Tinggi", 0.76, float("inf")),
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"FKT001": ("Fosfor Sangat Rendah", 0, 15),
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"FKT002": ("Fosfor Rendah", 15, 20),
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"FKT003": ("Fosfor Sedang", 21, 40),
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"FKT004": ("Fosfor Tinggi", 41, 60),
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"FKT005": ("Fosfor Sangat Tinggi", 61, float("inf")),
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"KKT001": ("Kalium Sangat Rendah", 0, 10),
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"KKT002": ("Kalium Rendah", 10, 20),
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"KKT003": ("Kalium Sedang", 21, 40),
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"KKT004": ("Kalium Tinggi", 41, 60),
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"KKT005": ("Kalium Sangat Tinggi", 61, float("inf"))
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}
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# Definisi rekomendasi tanaman berdasarkan tingkat kesesuaian tanah
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rekomendasi_tanaman = {
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"R001": "Kategori S1 Sangat Sesuai Untuk Ditanami Padi Sawah Irigasi, Wortel, Bawang Merah, Bawang Putih",
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"R002": "Kategori S2 Cukup Sesuai (Sedang) Untuk Ditanami Padi Sawah Irigasi, Wortel, Bawang Merah, Bawang Putih",
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"R003": "Kategori S3 Sesuai Marginal (Rendah) Untuk Ditanami Sawah Irigasi, Wortel, Bawang Merah, Bawang Putih",
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"R004": "Kategori S1 Sangat Sesuai Untuk Ditanami Jagung, Sorgum, Gandum, Kedelai, Kacang Tanah, Tebu",
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"R005": "Kategori S2 Cukup Sesuai (Sedang) Untuk Ditanami Jagung, Sorgum, Gandum, Kedelai, Kacang Tanah, Tebu",
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"R006": "Kategori S3 Sesuai Marginal (Rendah) Untuk Ditanami Jagung, Sorgum, Gandum, Kedelai, Kacang Tanah, Tebu",
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"R007": "Kategori S1 Sangat Sesuai Untuk ditanami Ubi Kayu, Ubi Jalar",
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"R008": "Kategori S2 Cukup Sesuai (Sedang) Untuk ditanami Ubi Kayu, Ubi Jalar",
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"R009": "Kategori S3 Sesuai Marginal (Rendah) Untuk ditanami Ubi Kayu, Ubi Jalar",
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"R010": "Kategori S1 Sangat Sesuai Untuk Ditanami Kakao, Kapas, Kayu Manis, Vanili",
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"R011": "Kategori S2 Cukup Sesuai (Sedang) Untuk Ditanami Kakao, Kapas, Kayu Manis, Vanili",
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"R012": "Kategori S3 Sesuai Marginal (Rendah) Untuk Ditanami Kakao, Kapas, Kayu Manis, Vanili"
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}
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# Definisi aturan rekomendasi tanaman
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rule_rekomendasi = {
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("NKT003", "FKT004", "KKT003"): "R001",
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("NKT002", "FKT003", "KKT002"): "R002",
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("NKT001", "FKT002", "KKT003"): "R003",
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("NKT001", "FKT001", "KKT003"): "R003",
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("NKT003", "FKT004", "KKT004"): "R004",
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("NKT002", "FKT003", "KKT002"): "R005",
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("NKT001", "FKT002", "KKT001"): "R006",
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("NKT001", "FKT001", "KKT002"): "R006",
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("NKT001", "FKT002", "KKT002"): "R006",
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("NKT001", "FKT001", "KKT001"): "R006",
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("NKT003", "FKT003", "KKT003"): "R007",
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("NKT002", "FKT002", "KKT002"): "R008",
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("NKT001", "FKT001", "KKT001"): "R009",
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("NKT003", "FKT003", "KKT004"): "R010",
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("NKT002", "FKT002", "KKT003"): "R011",
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("NKT002", "FKT001", "KKT003"): "R011",
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("NKT001", "FKT001", "KKT002"): "R012",
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("NKT001", "FKT001", "KKT001"): "R012"
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}
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# Fungsi untuk mendapatkan sifat tanah berdasarkan kadar
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def get_sifat_tanah(kadar, jenis):
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for kode, (sifat, min_val, max_val) in sifat_tanah_npk.items():
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if jenis in kode and min_val <= kadar <= max_val:
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return kode
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return None
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# Fungsi untuk mendapatkan rekomendasi tanaman
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def get_rekomendasi(nitrogen, fosfor, kalium):
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kode_n = get_sifat_tanah(nitrogen, "NKT")
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kode_f = get_sifat_tanah(fosfor, "FKT")
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kode_k = get_sifat_tanah(kalium, "KKT")
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rekomendasi = rule_rekomendasi.get((kode_n, kode_f, kode_k), "Tidak ada rekomendasi yang sesuai")
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return rekomendasi_tanaman.get(rekomendasi, "Tidak ada rekomendasi")
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def lbp(request):
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# Proses Memasukkan Image
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path = 'media/lahan 5 50cm.jpeg'
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img_bgr = cv2.imread(path, 1)
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plt.imshow(cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB))
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plt.title('Original Image')
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plt.axis('off') # Tidak menampilkan sumbu x dan y
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plt.show()
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# Melakukan Cropping Image
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# Tentukan koordinat titik awal dan dimensi untuk cropping
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x = 0 # Koordinat x titik awal
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y = 0 # Koordinat y titik awal
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width = 640 # Lebar area yang akan dipotong
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height = 480 # Tinggi area yang akan dipotong
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# Lakukan cropping pada gambar
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cropped_image = crop_image(img_bgr, x, y, width, height) # Ganti nilai x, y, width, dan height sesuai kebutuhan
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plt.imshow(cv2.cvtColor(cropped_image, cv2.COLOR_BGR2RGB))
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plt.title('Preview Gambar Crop 640 * 480 Pixel')
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plt.axis('off') # Tidak menampilkan sumbu x dan y
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plt.show()
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# Konversi Image Ke Grayscale
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img_gray = cv2.cvtColor(cropped_image, cv2.COLOR_BGR2GRAY)
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plt.imshow(img_gray, cmap='gray')
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plt.title('Grayscale Image')
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plt.axis('off')
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plt.show()
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# Equalisasi histogram Gaussian Blur
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equalized_image = cv2.equalizeHist(img_gray)
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# Filter tapis (Gaussian Blur)
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blurred_image = cv2.GaussianBlur(equalized_image, (5, 5), 0)
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# Menampilkan gambar hasil Gaussian Blur
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plt.imshow(blurred_image, cmap='gray')
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plt.title('Blurred Image (Gaussian Blur)')
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plt.axis('off')
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plt.show()
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# Hitung dan tampilkan gambar LBP
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lbp_image = calculate_lbp_image(blurred_image)
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plt.imshow(lbp_image, cmap='gray')
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plt.title('LBP Image')
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plt.axis('off')
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plt.show()
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# Simpan hasil LBP ke file teks
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save_lbp_to_txt(lbp_image, 'media/lbp_result.txt')
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print("Hasil LBP telah disimpan dalam file 'lbp_result.txt'")
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# Hitung histogram normalisasi LBP
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lbp_histogram = calculate_normalized_lbp_histogram(blurred_image)
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plt.figure(figsize=(10, 6))
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plt.bar(range(len(lbp_histogram)), lbp_histogram, color='b', width=1)
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plt.title('Normalized LBP Histogram')
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plt.xlabel('LBP Code')
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plt.ylabel('Normalized Frequency')
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plt.grid(True)
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plt.show()
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# Simpan histogram normalisasi LBP ke file teks
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save_normalized_lbp_histogram_to_txt(lbp_histogram, 'media/lbp_normalized_histogram.txt')
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print("Histogram normalisasi LBP telah disimpan dalam file 'lbp_normalized_histogram.txt'")
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# Temukan nilai yang paling sering muncul dalam histogram
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mode_lbp_value, mode_lbp_frequency = find_mode_lbp_histogram(lbp_histogram)
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print(f"Nilai histogram normalisasi yang paling sering muncul: {mode_lbp_value}")
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print(f"Frekuensi ternormalisasi dari nilai yang paling sering muncul: {mode_lbp_frequency}")
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# Hitung kadar NPK berdasarkan mode LBP frequency
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hasil_operasi_Natrium = mode_lbp_frequency * 0.1928 + 0.021
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nitrogen = round(hasil_operasi_Natrium, 2)
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print(f"Hasil Nilai N (Nitrogen): {nitrogen}")
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hasil_operasi_fosfor = (mode_lbp_frequency * -10.725) + 16.533
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fosfor = round(hasil_operasi_fosfor, 2)
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print(f"Hasil Nilai P (Fosfor): {fosfor}")
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hasil_operasi_Kalium = mode_lbp_frequency * -0.1864 + 0.2471
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kalium = round(hasil_operasi_Kalium, 2)
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print(f"Hasil Nilai K (Kalium): {kalium}")
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# Analisis sifat tanah berdasarkan kadar NPK
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sifat_nitrogen = get_sifat_tanah(nitrogen, "NKT")
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sifat_fosfor = get_sifat_tanah(fosfor, "FKT")
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sifat_kalium = get_sifat_tanah(kalium, "KKT")
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print(f"Sifat tanah berdasarkan kadar Nitrogen: {sifat_tanah_npk.get(sifat_nitrogen, ('Tidak ditemukan',))[0]}")
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print(f"Sifat tanah berdasarkan kadar Fosfor: {sifat_tanah_npk.get(sifat_fosfor, ('Tidak ditemukan',))[0]}")
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print(f"Sifat tanah berdasarkan kadar Kalium: {sifat_tanah_npk.get(sifat_kalium, ('Tidak ditemukan',))[0]}")
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# Menampilkan rekomendasi tanaman
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rekomendasi = get_rekomendasi(nitrogen, fosfor, kalium)
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print(f"Rekomendasi tanaman: {rekomendasi}")
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lbp(None)
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