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| import pandas as pd | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| import streamlit as st | |
| import plotly.express as px | |
| import os | |
| import tensorflow as tf | |
| from PIL import Image | |
| from tensorflow.keras.preprocessing.image import ImageDataGenerator | |
| import warnings | |
| warnings.filterwarnings('ignore') | |
| st.set_page_config( | |
| page_title= 'Data Klasifikasi Beras', | |
| layout= 'wide', | |
| initial_sidebar_state= 'expanded' | |
| ) | |
| def run(): | |
| # membuat title | |
| st.title ('Klasifikasi Data Beras') | |
| # buat sub header | |
| st.subheader('Exploratory Data Analysis') | |
| # membuat gambar | |
| st.image('data:image/jpeg;base64,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') | |
| # deskripsi | |
| st.write('# Latar Belakang ') | |
| st.write(''' | |
| Beras adalah salah satu makanan pokok di dunia, Namun menjadi salah satu paling penting khususnya Benua Asia, dikarenakan tempat beras menjadi makanan pokok untuk mayoritas penduduk(terutama di kalangan menengah kebawah masyarakat). Benua Asia sendiri menjadi tempat tinggal petani, dapat diperkirakan 90% produksi beras oleh petani beras. | |
| Namun beras tidak memiliki satu tipe beras, melainkan berbagai macam tipe beras. Klasifikasi beras ternyata memiliki pengaruh pada setiap iklimnya masing - masing negara. Maka dari itu dibutuh model, atau deep learning computer vision untuk mempelajari jenis - jenis beras. | |
| Pada Kasus ini akan menggunakan data beras yang terdiri dari lima kelas. Kelas masing - masing beras terdiri dari karacadag,ipsala,jasmine,arborio,dan basmati. Harapannya dengan pembentukan model, Hasil deployment dapat melakukan identifikasi beras tentunya menentukan kelima beras tersebut.''') | |
| st.write('# Problem Statement') | |
| st.write(''' | |
| Melakukan klasifikasi beras dan prediksi gambar dengan Computer Vision.''') | |
| st.divider() | |
| # mencoba menampilkan gambar | |
| st.write('# Data Beras') | |
| # fungsi untuk memanggil gambar | |
| def load_images_from_folder(folder): | |
| images = [] | |
| for filename in os.listdir(folder): | |
| if filename.endswith(".jpg") or filename.endswith(".png") or filename.endswith(".jpeg"): | |
| img = Image.open(os.path.join(folder, filename)) | |
| if img is not None: | |
| images.append((filename, img)) | |
| return images | |
| # fungsi memplot gambar untuk streamlit | |
| def plot_images(base_folder_path): | |
| # Path menuju "Training" subfolder | |
| training_folder_path = os.path.join(base_folder_path, "training") | |
| # mengambil subfolder dari training | |
| subfolders = os.listdir(training_folder_path) | |
| for subfolder in subfolders: | |
| subfolder_path = os.path.join(training_folder_path, subfolder) | |
| if not os.path.isdir(subfolder_path): | |
| continue # lalui apabila bukan folder | |
| st.write(f'Class: {subfolder}') | |
| # Load gambar dari subfolder | |
| images = load_images_from_folder(subfolder_path) | |
| # memastikan kita hanya mengambil 5 gambar | |
| images_to_display = images[:5] | |
| fig = plt.figure(figsize=(20, 4)) # penyesuaian gambar | |
| columns = 5 | |
| rows = 1 | |
| for index in range(1, columns * rows + 1): | |
| if index > len(images_to_display): | |
| break | |
| filename, image = images_to_display[index - 1] | |
| fig.add_subplot(rows, columns, index) | |
| plt.imshow(image) | |
| plt.axis("off") | |
| plt.title(filename, fontsize=8) | |
| st.pyplot(fig) | |
| plt.close(fig) # menutupkan gambar untuk memori | |
| # judul | |
| st.write("### menunjukan kelas beras berbeda") | |
| # lebih spesifik untuk folder | |
| folder_path = r"Rice_Image_Dataset" # Main folder path memiliki "training" | |
| # Plot images dari setiap subfolder didalam "training" | |
| plot_images(folder_path) | |
| st.write(''' | |
| **Informasi bentuk Gambar** | |
| - ketiga gambar yang dipisahkan sementar dan dibentuk, masing2 memiliki bentuk yang sama yaitu panjang , dan lebar sebesar **224**. Hal ini akan menjadi acuan sebagai parameter untuk modeling. | |
| **Informasi Dataset,Sampel,Kelas,dan tipe Beras** | |
| - informasi yang diberikan menunjukan bahwwa akan melakukan tiga pembagian dataset yaitu : `Training`,`Validation`, dan `Test`. | |
| - jumlah sample yang dibagi sebesar **3000**,**2000**, dan **1250**. | |
| - memiliki lima kelas, terdiri dari : `arborio:0`,`basmati:1`,`ipsala:2`,`jasmine:3`,`karacadag:4` | |
| ''') | |
| if __name__ == '__main__' : | |
| run() | |