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Prayoga K. I.
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.env
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# MySQL Database Configuration
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# MySQL Database Configuration
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DB_HOST=localhost
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DB_HOST=localhost
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DB_PORT=3306
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DB_PORT=3306
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DB_USER=root
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DB_USER=kali
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DB_PASSWORD=
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DB_PASSWORD=asu
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DB_NAME=deteksi_pmk
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DB_NAME=deteksi_pmk
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# Flask Configuration
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# Flask Configuration
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@ -7,3 +7,5 @@ models/*
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!models/pca.pkl
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!models/pca.pkl
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results/*
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results/*
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uploads/*
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uploads/*
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.venv
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.python-version
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@ -0,0 +1,497 @@
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# AGENTS.md — Panduan AI untuk Sistem Deteksi PMK
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File ini adalah **satu-satunya sumber kebenaran** untuk AI agent yang bekerja pada project ini. **WAJIB diperbarui** setiap kali ada perubahan arsitektur, dependensi, struktur database, atau alur sistem.
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---
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## 1. GAMBARAN PROYEK
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**Sistem Deteksi dan Diagnosis Penyakit Mulut dan Kuku (PMK / FMD) pada Sapi**
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- **Bahasa:** Python 3.12.5
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- **Framework Web:** Flask (Bootstrap 5 UI)
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- **Desktop (Legacy):** Tkinter (`main.py`, `predict_image.py`)
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- **Deployment:** Railway (Nixpacks + gunicorn)
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- **Repository:** `https://github.com/livindra/deteksi_PMK`
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Dua jalur utama:
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1. **Deteksi Berbasis Gambar** — ML (KNN) multi-class (sehat + tiap jenis PMK) + Computer Vision (color moments + GLCM texture)
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2. **Sistem Pakar** — Forward chaining inference engine (29 gejala, 5 penyakit, 5 aturan)
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> **Dataset:** Folder `dataset/` berisi `healthy/` dan folder `pmk_*` per jenis penyakit.
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> `train_model.py` otomatis mendeteksi semua folder berawalan `pmk_*` sebagai kelas terpisah.
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---
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## 2. STRUKTUR PROYEK
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```
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deteksi_PMK/
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├── app.py # Main Flask app (~1000 baris) — routing, prediksi, sistem pakar
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├── expert_system.py # Forward chaining engine + KnowledgeBase + Evaluator
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├── train_model.py # Training KNN model (k=5, euclidean, uniform)
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├── setup_db.py # Inisialisasi database MySQL
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├── main.py # LEGACY — Tkinter desktop menu
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├── predict_image.py # LEGACY — Tkinter prediction GUI
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├── test_forward_chaining.py # Unit test expert system
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├── test_env.py # Test environment variables & DB connection
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├── show_scaler_params.py # Utility: lihat parameter scaler
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├── requirements.txt # Python dependencies
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├── Procfile # gunicorn app:app
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├── railway.json # Railway Nixpacks builder config
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├── .env # Environment variables (TIDAK DI-COMMIT)
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├── .env.example # Template env
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├── .gitignore
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├── .python-version # 3.12.5
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│
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├── QUICK_START.md # Panduan cepat (Bahasa Indonesia)
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├── AGENTS.md # ← FILE INI — panduan AI, HARUS DIUPDATE
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│
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├── dataset/ # Dataset gambar training
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│ ├── healthy/ # ~200 gambar sapi sehat
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│ ├── pmk_oral/ # Gambar PMK oral
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│ ├── pmk_podal/ # Gambar PMK podal (kaki)
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│ ├── pmk_laktasi/ # Gambar PMK laktasi (ambing)
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│ └── pmk_akut_general/ # Gambar PMK akut general
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│
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├── features/ # CSV fitur hasil ekstraksi
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│ ├── dataset.csv
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│ ├── data_train.csv
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│ └── data_test.csv
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│
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├── models/ # Model terlatih (.pkl)
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│ ├── knn_model.pkl # BINARY — KNeighborsClassifier (sehat/sakit)
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│ ├── scaler.pkl # BINARY — StandardScaler
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│ ├── label_encoder.pkl # BINARY — LabelEncoder
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│ ├── multiclass_knn_model.pkl # MULTI — KNeighborsClassifier (jenis PMK)
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│ ├── multiclass_scaler.pkl # MULTI — StandardScaler
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│ ├── multiclass_label_encoder.pkl# MULTI — LabelEncoder
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│ └── pca.pkl # TIDAK DIGUNAKAN (legacy)
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│
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├── uploads/ # Gambar hasil upload user
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│ └── resize/ # Hasil resize
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│ └── threshold/ # Hasil threshold
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│
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├── utils/
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│ ├── __init__.py
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│ ├── mysql_db.py # SQLAlchemy ORM — models, CRUD, seed data (916 baris)
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│ ├── preprocessing.py # Validasi sapi + preprocessing pipeline (340 baris)
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│ ├── feature_extraction.py # Ekstraksi fitur: RGB avg + HSV + GLCM + histogram + Hu (46 fitur)
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│ └── helpers.py # Load/save model, dataset prep, confidence estimation (237 baris)
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│
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├── templates/ # Jinja2 templates
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│ ├── base.html # Layout utama (Bootstrap 5, navbar, footer)
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│ ├── index.html # Halaman utama
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│ ├── upload.html # Form upload multi-file (JS sederhana)
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│ ├── result.html # Hasil deteksi (sehat/sakit)
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│ ├── riwayat_deteksi.html # Riwayat (client localStorage + MySQL)
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│ ├── detail_deteksi.html # Detail satu prediksi + diagnosis
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│ ├── expert_system.html # Sistem pakar — pilih gejala + hasil
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│ └── diagnosis_history.html # Riwayat diagnosis sistem pakar
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│
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└── static/
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├── css/style.css # Custom CSS (gradient, animasi, responsive)
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└── js/main.js # JS — tooltips, file preview, API fetch, dark mode
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```
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---
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## 3. ARSITEKTUR & ALUR DATA
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### 3.1 Jalur Deteksi Gambar
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```
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User upload gambar
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│
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▼
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[app.py] validate_cattle_image()
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├─ Tolak wajah manusia (Haar cascade)
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├─ Deteksi mata (HoughCircles) — bonus scoring
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├─ Validasi warna HSV (coklat, merah, hitam, putih)
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├─ Analisis tekstur (Laplacian variance)
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├─ Edge detection (Canny)
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└─ Final: weighted confidence (eye 15%, color 40%, texture 30%, edge 15%), threshold ≥ 65%
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│
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├─ Gagal → tolak dengan pesan error
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│
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▼
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[preprocessing.py] preprocess_pipeline()
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→ resize 128×128 → grayscale → Otsu threshold → mask → masked RGB + masked gray
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│
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▼
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[feature_extraction.py] FeatureExtractor.extract_all_features()
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→ 46 fitur: [RGB avg (3), HSV mean+std (6), GLCM (6), histogram 24, Hu (7)]
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│
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▼
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[helpers.py] scaler.transform() → [BINARY MODEL] predict()
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│
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├ ─ 'sehat' → Simpan ke MySQL
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│
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▼ sakit
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[helpers.py] multiclass_scaler.transform() → [MULTI-CLASS MODEL] predict()
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→ label_encoder.inverse_transform()
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│
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▼
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[helpers.py] estimate_prediction_confidence()
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→ weighted neighbor confidence (clipped 50.0 – 98.5)
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│
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▼
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Simpan ke MySQL (primary) + CSV (fallback)
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│
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▼
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Semua gambar diproses — jika ada yang sakit:
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→ Redirect ke expert-system?symptoms=G01,G02,...&mode=image
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→ Gejala otomatis tercentang sesuai jenis PMK — hanya gejala yang relevan dengan body part PMK type tersebut (tidak cross-category)
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Jika semua sehat:
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→ Flash "Semua X gambar sehat" → /result/healthy
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```
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### 3.2 Jalur Sistem Pakar
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```
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User memilih gejala di web form
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│
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▼
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[expert_system.py] ForwardChaining.tambah_gejala(gejala_list)
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│
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▼
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ForwardChaining.inferensi()
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→ Untuk setiap aturan:
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- Hitung coverage = matched_count / total_count
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- Hitung support_ratio = matched_rules / total_rules_for_disease
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- Hitung evidence_strength = min(best_matched / 4, 1.0)
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- Combined score = best_coverage*0.5 + avg_coverage*0.25 + support_ratio*0.15 + evidence_strength*0.1 + exact_bonus
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- Threshold minimum: 0.35
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│
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▼
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ForwardChaining.get_diagnosis()
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→ Urutkan penyakit berdasarkan score
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→ Kembalikan top diagnosis + matched rules + solusi
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→ Severity map: P01=oral, P02=podal, P03=laktasi, P05=akut umum
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│
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▼
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Simpan ke MySQL (tabel predictions + diagnosis_history)
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│
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▼
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Render expert_system.html
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```
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### 3.3 Storage Hybrid
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- **MySQL** (primary): tabel `predictions`, `diagnosis_history`, `expert_symptoms`, `expert_diseases`, `expert_rules`, join table `expert_rules_expert_symptoms`
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- **localStorage** (browser): menyimpan array ID prediksi untuk ditampilkan di halaman riwayat
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- **CSV fallback**: `results/predictions.csv`, `results/diagnosis_history.csv`
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> **Batch upload:** Semua gambar dari satu sesi upload disimpan dalam **1 baris** tabel `predictions`. Data per-gambar (filename, prediction, pmk_type, confidence) disimpan sebagai JSON di kolom `images_data`. Field `prediction` diisi 'sakit' jika ada gambar sakit, 'sehat' jika semua sehat.
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---
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## 4. DATABASE (MySQL via SQLAlchemy)
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### 4.1 Tabel
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```sql
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-- Tabel prediksi hasil deteksi gambar
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predictions
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id INT AUTO_INCREMENT PK
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original_filename VARCHAR(255)
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filename VARCHAR(255)
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image_path VARCHAR(500)
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prediction VARCHAR(50) -- 'sehat' / 'sakit'
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confidence FLOAT
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features TEXT -- JSON string dari extracted features
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images_data TEXT -- JSON array dari per-gambar (batch upload)
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timestamp DATETIME
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-- Tabel riwayat diagnosis sistem pakar
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diagnosis_history
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id INT AUTO_INCREMENT PK
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prediction_id INT FK → predictions.id (nullable)
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diagnosis TEXT -- JSON string detail diagnosis
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severity VARCHAR(50) -- 'ringan' / 'sedang' / 'berat'
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timestamp DATETIME
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-- Master gejala (29 gejala)
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expert_symptoms
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id INT AUTO_INCREMENT PK
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code VARCHAR(10) UNIQUE -- G01–G29
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description TEXT
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category VARCHAR(50) -- umum, mulut, kaki, ambing, berat
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display_order INT
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-- Master penyakit (5 penyakit)
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expert_diseases
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id INT AUTO_INCREMENT PK
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code VARCHAR(10) UNIQUE -- P01–P05
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name VARCHAR(120)
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description TEXT
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solutions TEXT -- JSON array of strings
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display_order INT
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-- Aturan forward chaining (5 aturan)
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expert_rules
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id INT AUTO_INCREMENT PK
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code VARCHAR(20) UNIQUE -- FC01–FC04
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symptom_codes TEXT -- JSON array of symptom codes
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result_disease_code VARCHAR(10) FK → expert_diseases.code
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description TEXT
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is_active BOOLEAN
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display_order INT
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-- Join table many-to-many
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expert_rules_expert_symptoms
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rule_id INT FK → expert_rules.id (ON DELETE CASCADE)
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symptom_id INT FK → expert_symptoms.id (ON DELETE CASCADE)
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```
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### 4.2 Knowledge Base Default
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**29 Gejala (G01–G29)** per kategori:
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- **umum** (7): G01 demam >39.5°C, G11 nafsu makan turun, G12 lesu, G17 demam >40.5°C, G25 lebih sering berbaring, G28 takikardia, G29 sesak napas
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- **mulut** (9): G02 air liur berlebih, G03 luka lidah/gusi, G04 nyeri setelah lepuh, G10 hidung berair, G14 lepuh moncong, G18 lepuh lidah meluas, G19 sulit mengunyah, G20 lepuh bantalan gigi, G21 bau mulut
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- **kaki** (6): G05 lepuh celah kuku, G06 pincang, G15 nyeri kaki, G22 edema/radang, G23 bengkak celah kuku, G24 telapak kaki longgar
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- **ambing** (5): G07 lepuh puting, G08 lesi puting, G09 produksi susu turun, G26 puting retak, G27 susu menggumpal
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- **berat** (2): G13 miokarditis/kematian mendadak, G16 abortus/infertilitas
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**5 Penyakit (P01–P05):**
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- P01 = PMK_ORAL (gejala mulut)
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- P02 = PMK_PODAL (gejala kaki/kuku)
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- P03 = PMK_LAKTASI (gejala ambing)
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- P05 = PMK_AKUT_GENERAL (gejala umum berat)
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**4 Aturan (FC01–FC04):**
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- FC01 → P01 (oral): [G01, G02, G03, G04, G11, G18, G19, G20, G21]
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- FC02 → P02 (podal): [G01, G02, G05, G06, G15, G22, G23, G24, G25]
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- FC03 → P03 (laktasi): [G01, G02, G07, G08, G09, G26, G27]
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- FC04 → P05 (akut umum): [G01, G02, G03, G04, G05, G06, G07, G09, G11, G12, G14, G18, G20, G22, G23, G24, G26]
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> **Auto-check gejala dari hasil deteksi gambar** (di `app.py:pmk_to_symptoms`):
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> Mapping ini digunakan saat redirect ke expert system via mode=image. Hanya gejala spesifik body part yang diikutkan (tanpa gejala umum):
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> - `pmk_oral` → gejala mulut: [G02, G03, G04, G10, G14, G18, G19, G20, G21]
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> - `pmk_podal` → gejala kaki: [G05, G06, G15, G22, G23, G24]
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> **Jika menambah/mengubah gejala, penyakit, atau aturan**, update data di:
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>
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> 1. `utils/mysql_db.py` — constants `DEFAULT_EXPERT_SYMPTOMS`, `DEFAULT_EXPERT_DISEASES`, `DEFAULT_EXPERT_RULES`
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> 2. File ini (AGENTS.md) — bagian ini
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> 3. Jalankan ulang `seed_expert_knowledge(force=True)` atau hapus tabel agar di-reseed
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---
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## 5. API ENDPOINTS (Flask)
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| Method | Route | Deskripsi |
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| -------- | -------------------------------- | -------------------------------------------------- |
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| GET | `/` | Homepage — model status + recent history |
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| GET | `/upload` | Halaman upload gambar |
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| POST | `/predict` | Upload + prediksi gambar multi-file. Semua gambar disimpan sbg 1 baris di DB. Jika sakit → redirect ke expert-system dgn gejala otomatis + semua gambar ditampilkan. Jika sehat → /result/healthy |
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| GET | `/result/healthy` | Hasil sehat |
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| GET | `/result/sick` | Hasil sakit (legacy, tidak dipakai di alur baru) |
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| GET | `/riwayat_deteksi` | Halaman riwayat deteksi |
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| GET | `/detail-deteksi/<int:pred_id>` | Detail prediksi + diagnosis |
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||||||
|
| POST | `/api/validate-image` | Validasi gambar real-time (apakah sapi) |
|
||||||
|
| POST | `/api/get_data_riwayat_deteksi` | Ambil riwayat berdasarkan localStorage IDs |
|
||||||
|
| POST | `/api/diagnosis` | API diagnosis sistem pakar |
|
||||||
|
| GET/POST | `/expert-system` | Halaman sistem pakar (GET=form dgn param `symptoms` untuk pre-check, POST=diagnosis) |
|
||||||
|
| GET | `/expert-system/from-prediction` | Redirect ke expert system mode gambar |
|
||||||
|
| GET | `/riwayat-diagnosis` | Halaman riwayat diagnosis |
|
||||||
|
| GET | `/export/csv` | Export prediksi ke CSV |
|
||||||
|
| GET | `/export/excel` | Export prediksi ke Excel |
|
||||||
|
| GET | `/clear-session` | Bersihkan Flask session |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 6. ML MODEL DETAILS
|
||||||
|
|
||||||
|
**Arsitektur Hierarchical (2-level):**
|
||||||
|
|
||||||
|
```
|
||||||
|
Input image → 46 fitur (RGB+HSV+GLCM+Histogram+Hu)
|
||||||
|
│
|
||||||
|
┌──────┴──────┐
|
||||||
|
▼ ▼
|
||||||
|
[BINARY MODEL] [selamat — end]
|
||||||
|
sehat vs sakit
|
||||||
|
│ (sakit)
|
||||||
|
▼
|
||||||
|
[MULTI-CLASS MODEL]
|
||||||
|
pmk_oral / pmk_podal / pmk_laktasi / pmk_akut_general
|
||||||
|
```
|
||||||
|
|
||||||
|
### Binary Model (prefix='')
|
||||||
|
|
||||||
|
| Parameter | Value |
|
||||||
|
| ----------------- | -------------------------------- |
|
||||||
|
| **Task** | sehat vs sakit |
|
||||||
|
| **Algorithm** | K-Nearest Neighbors (KNN) |
|
||||||
|
| **k** | Hyperparameter tuned (1–15) |
|
||||||
|
| **Distance** | Hyperparameter tuned (euclidean/manhattan) |
|
||||||
|
| **Weight** | Hyperparameter tuned (uniform/distance) |
|
||||||
|
| **Accuracy** | ±98% |
|
||||||
|
| **Model file** | `models/knn_model.pkl` |
|
||||||
|
|
||||||
|
### Multi-class Model (prefix='multiclass_')
|
||||||
|
|
||||||
|
| Parameter | Value |
|
||||||
|
| ----------------- | -------------------------------- |
|
||||||
|
| **Task** | Jenis PMK (hanya data sakit) |
|
||||||
|
| **Algorithm** | K-Nearest Neighbors (KNN) |
|
||||||
|
| **k** | Hyperparameter tuned (1–15) |
|
||||||
|
| **Distance** | Hyperparameter tuned (euclidean/manhattan) |
|
||||||
|
| **Weight** | Hyperparameter tuned (uniform/distance) |
|
||||||
|
| **Accuracy** | ±60% |
|
||||||
|
| **Model file** | `models/multiclass_knn_model.pkl`|
|
||||||
|
|
||||||
|
### Common
|
||||||
|
|
||||||
|
| Parameter | Value |
|
||||||
|
| ----------------- | --------------------------------------------------------------------------- |
|
||||||
|
| **Features** | 46: avg_red, avg_green, avg_blue, hsv_h_mean, hsv_h_std, hsv_s_mean, hsv_s_std, hsv_v_mean, hsv_v_std, contrast, homogeneity, correlation, energy, dissimilarity, asm, 8-bin histogram × 3 channels (24), hu_moment_0..6 (7) |
|
||||||
|
| **Image size** | 256×256 |
|
||||||
|
| **Preprocessing** | Otsu threshold, mask, resize |
|
||||||
|
| **Scaling** | StandardScaler |
|
||||||
|
| **Output** | Binary: `sehat` / `sakit`; Multi-class: `pmk_oral`, `pmk_podal`, `pmk_laktasi`, `pmk_akut_general`, ... |
|
||||||
|
| **Confidence** | Custom weighted neighbor, clipped 50.0–98.5% |
|
||||||
|
| **Class detection** | Auto-detect all folders under `dataset/` — `healthy` → `sehat`, `pmk_*` → nama folder |
|
||||||
|
|
||||||
|
> **Jika menambah/mengubah fitur**, update:
|
||||||
|
>
|
||||||
|
> 1. `utils/feature_extraction.py` — `FeatureExtractor.feature_names`
|
||||||
|
> 2. `train_model.py` — feature export dimensions
|
||||||
|
> 3. Retrain model (`python train_model.py`)
|
||||||
|
> 4. AGENTS.md ini
|
||||||
|
>
|
||||||
|
> **Jika menambah kelas penyakit baru** (folder `pmk_*`):
|
||||||
|
>
|
||||||
|
> 1. Buat folder `dataset/pmk_nama_baru/` — otomatis terdeteksi oleh `prepare_dataset()`
|
||||||
|
> 2. Retrain model (`python train_model.py`)
|
||||||
|
> 3. Update AGENTS.md
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 7. CATTLE IMAGE VALIDATION
|
||||||
|
|
||||||
|
Lokasi: `utils/preprocessing.py:validate_cattle_image()`
|
||||||
|
|
||||||
|
Layer validasi berlapis:
|
||||||
|
|
||||||
|
1. **Face rejection** — Haar cascade face detection (tolak foto manusia)
|
||||||
|
2. **Eye detection** — HoughCircles (bonus scoring)
|
||||||
|
3. **Color validation** — HSV threshold (brown, red, black, white)
|
||||||
|
4. **Texture analysis** — Laplacian variance (range 45–3500)
|
||||||
|
5. **Edge detection** — Canny edge density (range 0.01–0.35)
|
||||||
|
|
||||||
|
Final confidence = `eye_score*0.15 + color_score*0.40 + texture_score*0.30 + edge_score*0.15`
|
||||||
|
Threshold: **≥ 65%** untuk lolos sebagai sapi.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 8. DEPLOYMENT
|
||||||
|
|
||||||
|
### Railway
|
||||||
|
|
||||||
|
- **Builder:** Nixpacks (`railway.json`)
|
||||||
|
- **Start:** `gunicorn app:app --bind 0.0.0.0:$PORT`
|
||||||
|
- **Env:** `FLASK_SECRET`, `DATABASE_URL` atau `MYSQLHOST/PORT/USER/PASSWORD/DATABASE`
|
||||||
|
- `opencv-python-headless` — tidak butuh GUI library
|
||||||
|
|
||||||
|
### Local
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# 1. Setup database
|
||||||
|
python setup_db.py
|
||||||
|
|
||||||
|
# 2. (Opsional) Training model
|
||||||
|
python train_model.py
|
||||||
|
|
||||||
|
# 3. Jalankan app
|
||||||
|
python app.py
|
||||||
|
# Buka http://localhost:5000
|
||||||
|
```
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 9. TESTING
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Test environment & database
|
||||||
|
python test_env.py
|
||||||
|
|
||||||
|
# Test expert system forward chaining
|
||||||
|
python test_forward_chaining.py
|
||||||
|
```
|
||||||
|
|
||||||
|
> **Jika menambah fitur baru**, tambahkan test case di file terkait. Jika menambah test file baru, update bagian ini.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 10. CONFIGURATION
|
||||||
|
|
||||||
|
### Environment Variables (`.env`)
|
||||||
|
|
||||||
|
```
|
||||||
|
DB_HOST=localhost
|
||||||
|
DB_PORT=3306
|
||||||
|
DB_USER=root
|
||||||
|
DB_PASSWORD=
|
||||||
|
DB_NAME=deteksi_pmk
|
||||||
|
MYSQLHOST= # Railway
|
||||||
|
MYSQLPORT= # Railway
|
||||||
|
MYSQLUSER= # Railway
|
||||||
|
MYSQLPASSWORD= # Railway
|
||||||
|
MYSQLDATABASE= # Railway
|
||||||
|
DATABASE_URL= # Railway
|
||||||
|
FLASK_SECRET=deteksi-pmk-secret-key-2026
|
||||||
|
FLASK_DEBUG=0
|
||||||
|
```
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 11. ATURAN UNTUK AI AGENT
|
||||||
|
|
||||||
|
1. **Jangan pernah mengubah fungsionalitas tanpa memperbarui file ini.**
|
||||||
|
2. **Jika menambah variabel environment**, tambahkan ke `.env.example` dan dokumentasikan di sini.
|
||||||
|
3. **Jika mengubah struktur tabel**, update: (a) model SQLAlchemy di `utils/mysql_db.py`, (b) skema di AGENTS.md, (c) constants `DEFAULT_EXPERT_*`.
|
||||||
|
4. **Jika menambah endpoint**, dokumentasikan di bagian API ENDPOINTS.
|
||||||
|
5. **Jika mengubah fitur ML**, update `feature_names`, retrain model, update AGENTS.md.
|
||||||
|
6. **Jika menambah dependensi**, tambahkan ke `requirements.txt` dan update tabel dependensi.
|
||||||
|
7. **File ini WAJIB dibaca** sebelum melakukan perubahan signifikan.
|
||||||
|
8. **Gunakan Bahasa Indonesia** untuk komentar, UI, dan dokumentasi end-user karena target pengguna adalah peternak/inspektur Indonesia.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 12. DEPENDENSI (requirements.txt)
|
||||||
|
|
||||||
|
| Package | Version | Kegunaan |
|
||||||
|
| ---------------------- | -------- | --------------------------------- |
|
||||||
|
| opencv-python-headless | 4.9.0.80 | Image processing, validasi sapi |
|
||||||
|
| numpy | <2 | Numerical operations |
|
||||||
|
| pandas | ≥2.1.0 | Data manipulation |
|
||||||
|
| scikit-learn | ≥1.3.2 | KNN, StandardScaler, LabelEncoder |
|
||||||
|
| scikit-image | ≥0.22.0 | GLCM texture features |
|
||||||
|
| matplotlib | ≥3.8.0 | Feature analysis plots |
|
||||||
|
| pillow | ≥10.1.0 | Image loading fallback |
|
||||||
|
| joblib | ≥1.3.2 | Model serialization |
|
||||||
|
| Flask | ≥2.3.3 | Web framework |
|
||||||
|
| SQLAlchemy | ≥2.0.0 | ORM MySQL |
|
||||||
|
| PyMySQL | ≥1.1.0 | MySQL driver |
|
||||||
|
| python-dotenv | ≥1.0.0 | Environment loading |
|
||||||
|
| gunicorn | ≥21.2.0 | WSGI production server |
|
||||||
|
| pytz | ≥2024.1 | Timezone (Asia/Jakarta) |
|
||||||
|
| cryptography | ≥3.4.8 | MySQL password encryption |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 13. LEGACY CODE (TIDAK DIGUNAKAN LAGI)
|
||||||
|
|
||||||
|
File berikut adalah kode legacy desktop (Tkinter) yang **tidak aktif** di web app:
|
||||||
|
|
||||||
|
- `main.py` — Menu utama desktop
|
||||||
|
- `predict_image.py` — Prediksi gambar via GUI Tkinter
|
||||||
|
- `models/pca.pkl` — PCA model tidak digunakan (legacy)
|
||||||
|
|
||||||
|
> Jika ada refactoring besar, pertimbangkan untuk menghapus file-file ini atau tandai secara eksplisit.
|
||||||
302
app.py
302
app.py
|
|
@ -46,6 +46,7 @@ def get_symptom_desc(symptom_code):
|
||||||
try:
|
try:
|
||||||
from utils.mysql_db import (
|
from utils.mysql_db import (
|
||||||
save_prediction_mysql,
|
save_prediction_mysql,
|
||||||
|
save_batch_prediction_mysql,
|
||||||
get_recent_predictions_mysql,
|
get_recent_predictions_mysql,
|
||||||
get_prediction_by_id,
|
get_prediction_by_id,
|
||||||
init_mysql_tables,
|
init_mysql_tables,
|
||||||
|
|
@ -82,30 +83,38 @@ except Exception as import_err:
|
||||||
model = None
|
model = None
|
||||||
scaler = None
|
scaler = None
|
||||||
label_encoder = None
|
label_encoder = None
|
||||||
|
multiclass_model = None
|
||||||
|
multiclass_scaler = None
|
||||||
|
multiclass_label_encoder = None
|
||||||
extractor = FeatureExtractor()
|
extractor = FeatureExtractor()
|
||||||
model_loading = False
|
model_loading = False
|
||||||
model_loaded = False
|
model_loaded = False
|
||||||
|
|
||||||
def _load_model_background():
|
def _load_model_background():
|
||||||
global model, scaler, label_encoder, model_loading, model_loaded
|
global model, scaler, label_encoder, multiclass_model, multiclass_scaler, multiclass_label_encoder
|
||||||
|
global model_loading, model_loaded
|
||||||
|
|
||||||
# Prevent double-loading
|
|
||||||
if model_loading or model_loaded:
|
if model_loading or model_loaded:
|
||||||
return
|
return
|
||||||
|
|
||||||
model_loading = True
|
model_loading = True
|
||||||
try:
|
try:
|
||||||
from utils.helpers import load_model
|
from utils.helpers import load_model
|
||||||
print('[APP] Loading ML model in background...')
|
print('[APP] Loading ML models in background...')
|
||||||
m, s, le = load_model()
|
m, s, le = load_model()
|
||||||
model, scaler, label_encoder = m, s, le
|
model, scaler, label_encoder = m, s, le
|
||||||
|
try:
|
||||||
|
mm, ms, mle = load_model(prefix='multiclass_')
|
||||||
|
multiclass_model, multiclass_scaler, multiclass_label_encoder = mm, ms, mle
|
||||||
|
except Exception:
|
||||||
|
print('[APP] ⚠️ Multi-class model not loaded (binary-only mode)')
|
||||||
model_loaded = True
|
model_loaded = True
|
||||||
print('[APP] Model loaded successfully.')
|
print('[APP] Models loaded successfully.')
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
model = None
|
model = None
|
||||||
scaler = None
|
scaler = None
|
||||||
label_encoder = None
|
label_encoder = None
|
||||||
model_loaded = True # Mark as done to prevent retry loop
|
model_loaded = True
|
||||||
print(f"[APP] ❌ Background model load failed: {e}")
|
print(f"[APP] ❌ Background model load failed: {e}")
|
||||||
finally:
|
finally:
|
||||||
model_loading = False
|
model_loading = False
|
||||||
|
|
@ -115,7 +124,6 @@ threading.Thread(target=_load_model_background, daemon=True).start()
|
||||||
|
|
||||||
|
|
||||||
def is_model_ready():
|
def is_model_ready():
|
||||||
"""Check if model is loaded and ready to use"""
|
|
||||||
return model is not None and scaler is not None and label_encoder is not None
|
return model is not None and scaler is not None and label_encoder is not None
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -180,6 +188,13 @@ def _serialize_prediction_row(prediction_row):
|
||||||
show_confidence = source != 'manual_expert_system'
|
show_confidence = source != 'manual_expert_system'
|
||||||
display_label = diagnosis_label if source == 'manual_expert_system' and diagnosis_label else ('Positif PMK' if prediction == 'sakit' else 'Sehat')
|
display_label = diagnosis_label if source == 'manual_expert_system' and diagnosis_label else ('Positif PMK' if prediction == 'sakit' else 'Sehat')
|
||||||
|
|
||||||
|
images_data = prediction_row.get('images_data')
|
||||||
|
image_count = prediction_row.get('image_count', 1)
|
||||||
|
sick_count = sum(1 for img in images_data if str(img.get('prediction', '')).lower() == 'sakit') if images_data else (1 if prediction == 'sakit' else 0)
|
||||||
|
|
||||||
|
if image_count > 1:
|
||||||
|
display_label = f'{sick_count} sakit / {image_count - sick_count} sehat ({image_count} gambar)'
|
||||||
|
|
||||||
return {
|
return {
|
||||||
'id': pred_id,
|
'id': pred_id,
|
||||||
'original_filename': prediction_row.get('original_filename') or '',
|
'original_filename': prediction_row.get('original_filename') or '',
|
||||||
|
|
@ -193,6 +208,8 @@ def _serialize_prediction_row(prediction_row):
|
||||||
'confidence': round(confidence, 1) if show_confidence else None,
|
'confidence': round(confidence, 1) if show_confidence else None,
|
||||||
'show_confidence': show_confidence,
|
'show_confidence': show_confidence,
|
||||||
'timestamp': prediction_row.get('timestamp'),
|
'timestamp': prediction_row.get('timestamp'),
|
||||||
|
'image_count': image_count,
|
||||||
|
'images_data': images_data,
|
||||||
'detail_url': url_for('detail_deteksi', pred_id=pred_id),
|
'detail_url': url_for('detail_deteksi', pred_id=pred_id),
|
||||||
'image_url': url_for('uploaded_file', filename=prediction_row.get('filename') or '') if prediction_row.get('filename') else None,
|
'image_url': url_for('uploaded_file', filename=prediction_row.get('filename') or '') if prediction_row.get('filename') else None,
|
||||||
}
|
}
|
||||||
|
|
@ -388,164 +405,212 @@ def api_validate_image():
|
||||||
print(f"[VALIDATE API] ⚠️ Error menghapus temp file: {e}")
|
print(f"[VALIDATE API] ⚠️ Error menghapus temp file: {e}")
|
||||||
|
|
||||||
|
|
||||||
@app.route('/predict', methods=['POST'])
|
def _predict_single(filepath, original_filename):
|
||||||
def predict():
|
"""Process a single image and return prediction result dict."""
|
||||||
if 'image' not in request.files:
|
features = extractor.extract_all_features(*preprocess_pipeline(filepath))
|
||||||
flash('File tidak ditemukan', 'error')
|
|
||||||
return redirect(url_for('index'))
|
|
||||||
|
|
||||||
file = request.files['image']
|
|
||||||
if file.filename == '':
|
|
||||||
flash('Tidak ada file yang dipilih', 'error')
|
|
||||||
return redirect(url_for('index'))
|
|
||||||
|
|
||||||
if file and allowed_file(file.filename):
|
|
||||||
# Generate unique filename to avoid conflicts
|
|
||||||
original_filename = secure_filename(file.filename)
|
|
||||||
ext = original_filename.rsplit('.', 1)[1].lower()
|
|
||||||
filename = f"{uuid.uuid4()}.{ext}"
|
|
||||||
filepath = os.path.join(UPLOAD_FOLDER, filename)
|
|
||||||
file.save(filepath)
|
|
||||||
|
|
||||||
# Validasi ulang di backend supaya request langsung ke /predict tidak bisa bypass.
|
|
||||||
is_cattle, validation_confidence, validation_reason = validate_cattle_image(
|
|
||||||
filepath,
|
|
||||||
confidence_threshold=0.65,
|
|
||||||
)
|
|
||||||
if not is_cattle:
|
|
||||||
if os.path.exists(filepath):
|
|
||||||
try:
|
|
||||||
os.remove(filepath)
|
|
||||||
print(f"[PREDICT] ✓ File DIHAPUS (bukan sapi): {filename}")
|
|
||||||
except Exception as del_err:
|
|
||||||
print(f"[PREDICT] ⚠️ Error menghapus file non-sapi: {del_err}")
|
|
||||||
|
|
||||||
flash(validation_reason or 'Gambar selain sapi tidak diizinkan', 'error')
|
|
||||||
return redirect(url_for('index'))
|
|
||||||
|
|
||||||
print(f"[PREDICT] ✅ File lolos validasi backend: {filename} ({validation_confidence*100:.0f}%)")
|
|
||||||
|
|
||||||
if not is_model_ready():
|
|
||||||
# Model belum siap - HAPUS FILE
|
|
||||||
if os.path.exists(filepath):
|
|
||||||
try:
|
|
||||||
os.remove(filepath)
|
|
||||||
print(f"[PREDICT] ✓ File DIHAPUS (model belum ready): {filename}")
|
|
||||||
except Exception as del_err:
|
|
||||||
print(f"[PREDICT] ⚠️ Error menghapus file: {del_err}")
|
|
||||||
|
|
||||||
flash('Model belum tersedia. Jalankan training terlebih dahulu.', 'error')
|
|
||||||
return redirect(url_for('index'))
|
|
||||||
|
|
||||||
# Preprocess and extract
|
|
||||||
img_rgb, gray_processed = preprocess_pipeline(filepath)
|
|
||||||
features = extractor.extract_all_features(img_rgb, gray_processed)
|
|
||||||
features_scaled = scaler.transform([features])
|
features_scaled = scaler.transform([features])
|
||||||
|
|
||||||
pred_encoded = model.predict(features_scaled)[0]
|
pred_encoded = model.predict(features_scaled)[0]
|
||||||
prediction = label_encoder.inverse_transform([pred_encoded])[0]
|
prediction = label_encoder.inverse_transform([pred_encoded])[0]
|
||||||
confidence = estimate_prediction_confidence(model, features_scaled)
|
confidence = estimate_prediction_confidence(model, features_scaled)
|
||||||
if confidence is None:
|
if confidence is None:
|
||||||
probabilities = model.predict_proba(features_scaled)[0]
|
confidence = float(max(model.predict_proba(features_scaled)[0]) * 100)
|
||||||
confidence = float(max(probabilities) * 100)
|
|
||||||
|
pmk_type = None
|
||||||
|
if prediction.lower() == 'sakit' and multiclass_model is not None:
|
||||||
|
multi_scaled = multiclass_scaler.transform([features])
|
||||||
|
multi_enc = multiclass_model.predict(multi_scaled)[0]
|
||||||
|
pmk_type = multiclass_label_encoder.inverse_transform([multi_enc])[0]
|
||||||
|
|
||||||
# Prepare feature dictionary for database
|
|
||||||
features_dict = {name: float(features[i]) for i, name in enumerate(extractor.feature_names)}
|
features_dict = {name: float(features[i]) for i, name in enumerate(extractor.feature_names)}
|
||||||
|
features_table = list(zip(extractor.feature_names, [round(float(x), 4) for x in features]))
|
||||||
|
|
||||||
# Try to save to MySQL first (primary storage)
|
return {
|
||||||
|
'original_filename': original_filename,
|
||||||
|
'prediction': prediction,
|
||||||
|
'pmk_type': pmk_type,
|
||||||
|
'confidence': confidence,
|
||||||
|
'features': features,
|
||||||
|
'features_dict': features_dict,
|
||||||
|
'features_table': features_table,
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def _save_prediction_to_mysql(original_filename, filename, filepath, prediction, confidence, features_dict):
|
||||||
|
"""Save a single prediction to MySQL and return rowid."""
|
||||||
rowid = None
|
rowid = None
|
||||||
if MYSQL_AVAILABLE:
|
if MYSQL_AVAILABLE:
|
||||||
try:
|
try:
|
||||||
rowid = save_prediction_mysql(original_filename, filename, filepath, prediction, confidence, features_dict)
|
rowid = save_prediction_mysql(original_filename, filename, filepath, prediction, confidence, features_dict)
|
||||||
print(f"✓ Saved prediction to MySQL, id={rowid}")
|
print(f"✓ Saved prediction to MySQL, id={rowid}")
|
||||||
# store DB id in session so result page can link to the new history row
|
|
||||||
try:
|
|
||||||
session.setdefault('last_prediction', {})
|
|
||||||
session['last_prediction']['db_id'] = int(rowid) if rowid is not None else None
|
|
||||||
except Exception:
|
|
||||||
pass
|
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
import traceback
|
import traceback
|
||||||
print(f"✗ Gagal menyimpan ke MySQL: {e}")
|
print(f"✗ Gagal menyimpan ke MySQL: {e}")
|
||||||
traceback.print_exc()
|
traceback.print_exc()
|
||||||
|
return rowid
|
||||||
|
|
||||||
# Also save to CSV as fallback/backup (ensure consistent columns, migrate old files if needed)
|
|
||||||
|
def _append_prediction_to_csv(original_filename, filename, prediction, confidence, features, features_dict):
|
||||||
|
"""Append a single prediction row to CSV fallback."""
|
||||||
try:
|
try:
|
||||||
os.makedirs(os.path.join(BASE_DIR, 'results'), exist_ok=True)
|
os.makedirs(os.path.join(BASE_DIR, 'results'), exist_ok=True)
|
||||||
data = {
|
data = {'image_path': [original_filename], 'filename': [filename],
|
||||||
'image_path': [original_filename], # Simpan nama asli
|
'prediction': [prediction], 'confidence': [confidence],
|
||||||
'filename': [filename], # Simpan nama unik
|
'timestamp': [pd.Timestamp.now()]}
|
||||||
'prediction': [prediction],
|
|
||||||
'confidence': [confidence],
|
|
||||||
'timestamp': [pd.Timestamp.now()]
|
|
||||||
}
|
|
||||||
for i, name in enumerate(extractor.feature_names):
|
for i, name in enumerate(extractor.feature_names):
|
||||||
data[name] = [float(features[i])]
|
data[name] = [float(features[i])]
|
||||||
|
|
||||||
df = pd.DataFrame(data)
|
df = pd.DataFrame(data)
|
||||||
csv_path = os.path.join(BASE_DIR, 'results', 'predictions.csv')
|
csv_path = os.path.join(BASE_DIR, 'results', 'predictions.csv')
|
||||||
|
|
||||||
if os.path.exists(csv_path):
|
if os.path.exists(csv_path):
|
||||||
# Check existing columns
|
|
||||||
try:
|
try:
|
||||||
existing_cols = pd.read_csv(csv_path, nrows=0).columns.tolist()
|
existing_cols = pd.read_csv(csv_path, nrows=0).columns.tolist()
|
||||||
except Exception:
|
except Exception:
|
||||||
existing_cols = []
|
existing_cols = []
|
||||||
|
|
||||||
desired_cols = list(df.columns)
|
desired_cols = list(df.columns)
|
||||||
|
|
||||||
# If existing file missing any desired columns, migrate by adding empty columns and re-saving
|
|
||||||
if not set(desired_cols).issubset(set(existing_cols)):
|
if not set(desired_cols).issubset(set(existing_cols)):
|
||||||
try:
|
try:
|
||||||
df_existing = pd.read_csv(csv_path)
|
df_existing = pd.read_csv(csv_path)
|
||||||
for c in desired_cols:
|
for c in desired_cols:
|
||||||
if c not in df_existing.columns:
|
if c not in df_existing.columns:
|
||||||
df_existing[c] = ''
|
df_existing[c] = ''
|
||||||
# Reorder columns to desired order
|
|
||||||
df_existing = df_existing.reindex(columns=desired_cols)
|
df_existing = df_existing.reindex(columns=desired_cols)
|
||||||
df_existing.to_csv(csv_path, index=False)
|
df_existing.to_csv(csv_path, index=False)
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
print(f"Gagal memigrasi CSV lama: {e}")
|
print(f"Gagal memigrasi CSV lama: {e}")
|
||||||
|
|
||||||
# Append new row without header
|
|
||||||
df.to_csv(csv_path, mode='a', header=False, index=False)
|
df.to_csv(csv_path, mode='a', header=False, index=False)
|
||||||
else:
|
else:
|
||||||
df.to_csv(csv_path, index=False)
|
df.to_csv(csv_path, index=False)
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
print(f"Gagal menyimpan prediksi ke CSV: {e}")
|
print(f"Gagal menyimpan prediksi ke CSV: {e}")
|
||||||
|
|
||||||
# Compact features table for session (round values to reduce size)
|
|
||||||
features_table = list(zip(extractor.feature_names, [round(float(x), 4) for x in features]))
|
|
||||||
|
|
||||||
# Simpan hasil prediksi ke session untuk digunakan di halaman result
|
@app.route('/predict', methods=['POST'])
|
||||||
existing_db_id = None
|
def predict():
|
||||||
|
if 'image' not in request.files:
|
||||||
|
flash('File tidak ditemukan', 'error')
|
||||||
|
return redirect(url_for('index'))
|
||||||
|
|
||||||
|
files = request.files.getlist('image')
|
||||||
|
if not files or (len(files) == 1 and files[0].filename == ''):
|
||||||
|
flash('Tidak ada file yang dipilih', 'error')
|
||||||
|
return redirect(url_for('index'))
|
||||||
|
|
||||||
|
if not is_model_ready():
|
||||||
|
flash('Model belum tersedia. Jalankan training terlebih dahulu.', 'error')
|
||||||
|
return redirect(url_for('index'))
|
||||||
|
|
||||||
|
results = []
|
||||||
|
|
||||||
|
for file in files:
|
||||||
|
if file.filename == '' or not allowed_file(file.filename):
|
||||||
|
continue
|
||||||
|
|
||||||
|
original_filename = secure_filename(file.filename)
|
||||||
|
ext = original_filename.rsplit('.', 1)[1].lower()
|
||||||
|
filename = f"{uuid.uuid4()}.{ext}"
|
||||||
|
filepath = os.path.join(UPLOAD_FOLDER, filename)
|
||||||
|
file.save(filepath)
|
||||||
|
|
||||||
|
is_cattle, validation_confidence, validation_reason = validate_cattle_image(filepath, confidence_threshold=0.65)
|
||||||
|
if not is_cattle:
|
||||||
|
if os.path.exists(filepath):
|
||||||
try:
|
try:
|
||||||
existing_db_id = session.get('last_prediction', {}).get('db_id')
|
os.remove(filepath)
|
||||||
except Exception:
|
except Exception:
|
||||||
existing_db_id = None
|
pass
|
||||||
|
continue
|
||||||
|
|
||||||
session['last_prediction'] = {
|
result = _predict_single(filepath, original_filename)
|
||||||
'filename': filename,
|
result['filename'] = filename
|
||||||
'original_filename': original_filename,
|
result['filepath'] = filepath
|
||||||
'prediction': prediction,
|
|
||||||
'confidence': confidence,
|
results.append(result)
|
||||||
'features_table': features_table,
|
|
||||||
'filepath': filepath,
|
if not results:
|
||||||
'source': 'image_processing',
|
flash('Tidak ada gambar sapi yang valid untuk diproses', 'error')
|
||||||
'db_id': existing_db_id
|
return redirect(url_for('index'))
|
||||||
|
|
||||||
|
# Save ALL images as ONE prediction row
|
||||||
|
images_for_db = []
|
||||||
|
for r in results:
|
||||||
|
images_for_db.append({
|
||||||
|
'original_filename': r['original_filename'],
|
||||||
|
'filename': r['filename'],
|
||||||
|
'image_path': r['filepath'],
|
||||||
|
'prediction': r['prediction'],
|
||||||
|
'pmk_type': r['pmk_type'],
|
||||||
|
'confidence': r['confidence'],
|
||||||
|
})
|
||||||
|
|
||||||
|
pred_id = None
|
||||||
|
if MYSQL_AVAILABLE:
|
||||||
|
try:
|
||||||
|
pred_id = save_batch_prediction_mysql(images_for_db)
|
||||||
|
print(f"✓ Saved batch prediction to MySQL, id={pred_id} ({len(results)} images)")
|
||||||
|
except Exception as e:
|
||||||
|
import traceback
|
||||||
|
print(f"✗ Gagal menyimpan batch ke MySQL: {e}")
|
||||||
|
traceback.print_exc()
|
||||||
|
|
||||||
|
# Also save to CSV (batch summary)
|
||||||
|
_append_prediction_to_csv(
|
||||||
|
f"{len(results)} images batch",
|
||||||
|
f"batch_{pred_id}" if pred_id else "batch_unknown",
|
||||||
|
'sakit' if any(r['prediction'].lower() == 'sakit' for r in results) else 'sehat',
|
||||||
|
max(r['confidence'] for r in results),
|
||||||
|
results[0]['features'],
|
||||||
|
results[0]['features_dict']
|
||||||
|
)
|
||||||
|
|
||||||
|
# Build image data for session/template
|
||||||
|
upload_images = []
|
||||||
|
for r in results:
|
||||||
|
upload_images.append({
|
||||||
|
'filename': r['filename'],
|
||||||
|
'original_filename': r['original_filename'],
|
||||||
|
'filepath': r['filepath'],
|
||||||
|
'prediction': r['prediction'],
|
||||||
|
'pmk_type': r['pmk_type'],
|
||||||
|
'confidence': r['confidence'],
|
||||||
|
'features_table': r['features_table'],
|
||||||
|
})
|
||||||
|
|
||||||
|
# Determine if any sick results
|
||||||
|
sick_pmk_types = set()
|
||||||
|
for r in results:
|
||||||
|
if r['prediction'].lower() == 'sakit' and r['pmk_type']:
|
||||||
|
sick_pmk_types.add(r['pmk_type'])
|
||||||
|
|
||||||
|
pmk_to_symptoms = {
|
||||||
|
'pmk_oral': ['G02', 'G03', 'G04', 'G10', 'G14', 'G18', 'G19', 'G20', 'G21'],
|
||||||
|
'pmk_podal': ['G05', 'G06', 'G15', 'G22', 'G23', 'G24'],
|
||||||
}
|
}
|
||||||
|
|
||||||
# Tentukan template berdasarkan hasil prediksi
|
preselected = set()
|
||||||
# Tambahkan jeda singkat agar tampilan hasil tidak muncul terlalu cepat
|
for pmk_type in sick_pmk_types:
|
||||||
if prediction.lower() == 'sakit':
|
key = pmk_type.lower()
|
||||||
time.sleep(1.5)
|
if key in pmk_to_symptoms:
|
||||||
return redirect(url_for('result_sick'))
|
preselected.update(pmk_to_symptoms[key])
|
||||||
else:
|
|
||||||
time.sleep(1.5)
|
|
||||||
return redirect(url_for('result_healthy'))
|
|
||||||
|
|
||||||
flash('Tipe file tidak didukung', 'error')
|
# Store all images in session for expert system display
|
||||||
return redirect(url_for('index'))
|
session['last_prediction'] = {
|
||||||
|
'db_id': pred_id,
|
||||||
|
'filename': upload_images[0]['filename'],
|
||||||
|
'original_filename': upload_images[0]['original_filename'],
|
||||||
|
'prediction': 'sakit' if sick_pmk_types else 'sehat',
|
||||||
|
'confidence': upload_images[0]['confidence'],
|
||||||
|
'source': 'image_processing',
|
||||||
|
}
|
||||||
|
session['upload_images_data'] = upload_images
|
||||||
|
|
||||||
|
time.sleep(1.0)
|
||||||
|
|
||||||
|
if preselected:
|
||||||
|
symptoms_param = ','.join(sorted(preselected))
|
||||||
|
return redirect(url_for('expert_system_page', mode='image', symptoms=symptoms_param))
|
||||||
|
else:
|
||||||
|
flash(f'Semua {len(results)} gambar terdeteksi SEHAT', 'success')
|
||||||
|
return redirect(url_for('result_healthy'))
|
||||||
|
|
||||||
|
|
||||||
@app.route('/result/healthy')
|
@app.route('/result/healthy')
|
||||||
|
|
@ -744,12 +809,14 @@ def expert_system_page():
|
||||||
|
|
||||||
# Jika ada hasil prediksi berbasis image processing sebelumnya, tambahkan ke konteks
|
# Jika ada hasil prediksi berbasis image processing sebelumnya, tambahkan ke konteks
|
||||||
last_prediction = session.get('last_prediction', {})
|
last_prediction = session.get('last_prediction', {})
|
||||||
|
upload_images = session.get('upload_images_data', [])
|
||||||
image_info = None
|
image_info = None
|
||||||
if use_image_context and last_prediction.get('source') == 'image_processing':
|
if use_image_context and last_prediction.get('source') == 'image_processing':
|
||||||
image_info = {
|
image_info = {
|
||||||
'filename': last_prediction.get('original_filename', ''),
|
'filename': last_prediction.get('original_filename', ''),
|
||||||
'prediction': last_prediction.get('prediction', ''),
|
'prediction': last_prediction.get('prediction', ''),
|
||||||
'confidence': last_prediction.get('confidence', 0)
|
'confidence': last_prediction.get('confidence', 0),
|
||||||
|
'db_id': last_prediction.get('db_id'),
|
||||||
}
|
}
|
||||||
|
|
||||||
return render_template('expert_system.html',
|
return render_template('expert_system.html',
|
||||||
|
|
@ -758,23 +825,34 @@ def expert_system_page():
|
||||||
diagnosis=diagnosis,
|
diagnosis=diagnosis,
|
||||||
selected_gejala=gejala_terpilih,
|
selected_gejala=gejala_terpilih,
|
||||||
image_info=image_info,
|
image_info=image_info,
|
||||||
|
upload_images=upload_images,
|
||||||
context_mode='image' if use_image_context else 'manual')
|
context_mode='image' if use_image_context else 'manual')
|
||||||
|
|
||||||
# GET request - tampilkan form
|
# GET request - tampilkan form
|
||||||
|
# Parse preselected symptoms from query param
|
||||||
|
preselected_symptoms = []
|
||||||
|
symptoms_param = request.args.get('symptoms', '')
|
||||||
|
if symptoms_param:
|
||||||
|
preselected_symptoms = [s.strip() for s in symptoms_param.split(',') if s.strip()]
|
||||||
|
|
||||||
# Ambil informasi gambar dari session jika hasil sebelumnya berasal dari image processing
|
# Ambil informasi gambar dari session jika hasil sebelumnya berasal dari image processing
|
||||||
last_prediction = session.get('last_prediction', {})
|
last_prediction = session.get('last_prediction', {})
|
||||||
|
upload_images = session.get('upload_images_data', [])
|
||||||
image_info = None
|
image_info = None
|
||||||
if use_image_context and last_prediction.get('source') == 'image_processing':
|
if use_image_context and last_prediction.get('source') == 'image_processing':
|
||||||
image_info = {
|
image_info = {
|
||||||
'filename': last_prediction.get('original_filename', ''),
|
'filename': last_prediction.get('original_filename', ''),
|
||||||
'prediction': last_prediction.get('prediction', ''),
|
'prediction': last_prediction.get('prediction', ''),
|
||||||
'confidence': last_prediction.get('confidence', 0)
|
'confidence': last_prediction.get('confidence', 0),
|
||||||
|
'db_id': last_prediction.get('db_id'),
|
||||||
}
|
}
|
||||||
|
|
||||||
return render_template('expert_system.html',
|
return render_template('expert_system.html',
|
||||||
gejala_list=expert_system.get_gejala_list(),
|
gejala_list=expert_system.get_gejala_list(),
|
||||||
gejala_groups=expert_system.get_gejala_groups(),
|
gejala_groups=expert_system.get_gejala_groups(),
|
||||||
image_info=image_info,
|
image_info=image_info,
|
||||||
|
upload_images=upload_images,
|
||||||
|
selected_gejala=preselected_symptoms,
|
||||||
context_mode='image' if use_image_context else 'manual')
|
context_mode='image' if use_image_context else 'manual')
|
||||||
|
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -5,7 +5,6 @@ DISEASE_ALIAS_MAP = {
|
||||||
'P01': {'ORAL', 'ORAL_KUAT', 'P01'},
|
'P01': {'ORAL', 'ORAL_KUAT', 'P01'},
|
||||||
'P02': {'PODAL', 'PODAL_KUAT', 'P02'},
|
'P02': {'PODAL', 'PODAL_KUAT', 'P02'},
|
||||||
'P03': {'LAKTASI', 'LAKTASI_KUAT', 'P03'},
|
'P03': {'LAKTASI', 'LAKTASI_KUAT', 'P03'},
|
||||||
'P04': {'JUVENIL', 'JUVENIL_KUAT', 'P04'},
|
|
||||||
'P05': {'AKUT_GENERAL', 'AKUT_GENERAL_KUAT', 'P05'},
|
'P05': {'AKUT_GENERAL', 'AKUT_GENERAL_KUAT', 'P05'},
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
@ -211,7 +210,6 @@ class ForwardChaining:
|
||||||
'P01': 'oral',
|
'P01': 'oral',
|
||||||
'P02': 'podal',
|
'P02': 'podal',
|
||||||
'P03': 'laktasi',
|
'P03': 'laktasi',
|
||||||
'P04': 'juvenil',
|
|
||||||
'P05': 'akut umum'
|
'P05': 'akut umum'
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -1,81 +1,201 @@
|
||||||
image_name,label_name,label,avg_red,avg_green,avg_blue,contrast,homogeneity,correlation,energy
|
image_name,label_name,label,avg_red,avg_green,avg_blue,mean_hue,mean_saturation,mean_value,std_hue,std_saturation,std_value,contrast,homogeneity,correlation,energy,dissimilarity,ASM,hist_r_0,hist_r_1,hist_r_2,hist_r_3,hist_r_4,hist_r_5,hist_r_6,hist_r_7,hist_g_0,hist_g_1,hist_g_2,hist_g_3,hist_g_4,hist_g_5,hist_g_6,hist_g_7,hist_b_0,hist_b_1,hist_b_2,hist_b_3,hist_b_4,hist_b_5,hist_b_6,hist_b_7,hu_moment_1,hu_moment_2,hu_moment_3,hu_moment_4,hu_moment_5,hu_moment_6,hu_moment_7
|
||||||
Healthy (29).jpg,normal,0,38.26141357421875,37.37353515625,36.12744140625,1375.900070241906,0.8564597118356718,0.9076873284220274,0.8316132560236419
|
14-Copy-10-_jpg.rf.d1e665991e98c14e423bb213484ab7ec.jpg,sakit,0,31.442413330078125,29.391250610351562,22.996002197265625,7.98065185546875,10.585678100585938,31.605575561523438,20.709009170532227,31.921104431152344,77.06120300292969,1126.1105509692272,0.8514636928765195,0.8901975736444322,0.8325719841364824,8.094875186409013,0.693189577011642,0.8534393310546875,0.0,0.0,0.000579833984375,0.0124359130859375,0.01666259765625,0.0372314453125,0.07965087890625,0.8534393310546875,0.0,0.0,0.0008392333984375,0.0282135009765625,0.0285186767578125,0.0350341796875,0.053955078125,0.8534393310546875,0.0010986328125,0.0222625732421875,0.03106689453125,0.0235137939453125,0.0205535888671875,0.0238037109375,0.024261474609375,2.545060353738578,5.769250385045134,8.52906389580099,9.931062615971916,-9.999999999984299,-9.99991916564025,9.99999999999472
|
||||||
Healthy (95).jpg,normal,0,116.6544189453125,108.01556396484375,95.7125244140625,2639.1888915402874,0.5144186750490266,0.885222541677552,0.45146488814132785
|
2346-128-_jpg.rf.2b458f808811a8c2edc380ed6bb3c717.jpg,sakit,0,52.826019287109375,46.61273193359375,37.72100830078125,10.66790771484375,19.94097900390625,52.922393798828125,21.743560791015625,43.40488052368164,92.06571197509766,336.24500161233686,0.7856777532856934,0.9763393573045164,0.7343507911848581,4.33767331898578,0.5392881394429397,0.7447662353515625,0.0,0.000762939453125,0.0083770751953125,0.020172119140625,0.050079345703125,0.0530548095703125,0.1227874755859375,0.7447662353515625,0.0,0.009765625,0.043182373046875,0.0352935791015625,0.03125,0.0540618896484375,0.0816802978515625,0.7449493408203125,0.0220184326171875,0.043853759765625,0.0356597900390625,0.0326385498046875,0.0388641357421875,0.061553955078125,0.0204620361328125,2.921813371368525,6.330823441215252,9.513570594731513,9.54591742213028,9.999999999858792,9.999454800118935,9.999999999932756
|
||||||
air_liur_12h.jpg,normal,0,28.0606689453125,28.48687744140625,27.1719970703125,2718.0165872536786,0.8364354350429116,0.7368102194714145,0.8235741121215968
|
38131144184_03e65940d1_o.jpg,sakit,0,101.34414672851562,91.10659790039062,82.79591369628906,34.8975830078125,28.642837524414062,102.78387451171875,68.15164184570312,37.15221405029297,98.43955993652344,868.4141037913905,0.5168381762161308,0.9461258668878708,0.4392799109351715,10.518673184652135,0.1930230186290353,0.4658355712890625,0.0,0.0,0.00927734375,0.0958099365234375,0.14898681640625,0.2127685546875,0.06732177734375,0.4658355712890625,0.0,0.00048828125,0.0698089599609375,0.114776611328125,0.197509765625,0.13653564453125,0.015045166015625,0.4658355712890625,0.000946044921875,0.0482940673828125,0.0938568115234375,0.1238861083984375,0.168121337890625,0.0856475830078125,0.0134124755859375,3.084137473530295,7.120391345333493,9.701479488943049,9.93536416236092,9.99999999999806,9.999990892725279,9.999999999998016
|
||||||
air_liur_1h.jpg,normal,0,107.8831787109375,92.25640869140625,57.67578125,1057.1871091616208,0.4025787648832689,0.8946021403572079,0.2907755356594676
|
49579785876_09f6840ed2_o.jpg,sakit,0,84.51034545898438,94.24876403808594,140.573486328125,160.73883056640625,73.42205810546875,145.00631713867188,127.58490753173828,70.21085357666016,103.22804260253906,1138.937970697565,0.4156634155524623,0.8719909125565901,0.2702354737521527,13.294365698557668,0.07309750648931097,0.3054351806640625,0.0,0.2269439697265625,0.186676025390625,0.148284912109375,0.113128662109375,0.0139007568359375,0.0056304931640625,0.3054351806640625,0.0,0.002838134765625,0.2722930908203125,0.3306732177734375,0.072540283203125,0.00970458984375,0.0065155029296875,0.3054351806640625,9.1552734375e-05,0.004241943359375,0.083892822265625,0.1086578369140625,0.0807342529296875,0.0418853759765625,0.37506103515625,3.0340379771267645,7.600374995130791,9.932898274034047,9.999727556758529,-10.0,9.999999956916783,10.0
|
||||||
air_liur_5h.jpg,normal,0,38.865234375,35.009033203125,28.94757080078125,1125.4364354938803,0.5850194864245984,0.7459059889548558,0.5533045177927906
|
5198047453_ab0d6212d0_bWM-1.jpg,sakit,0,71.85682678222656,83.8292236328125,69.59938049316406,73.0799560546875,33.92225646972656,89.42710876464844,96.98072814941406,42.519798278808594,96.02003479003906,4335.675951035452,0.4594541743837602,0.6993818920488885,0.43209145713367353,34.205795042081874,0.18704524240600953,0.522674560546875,0.0,0.010040283203125,0.101409912109375,0.20758056640625,0.10516357421875,0.03643798828125,0.016693115234375,0.522674560546875,0.0,0.000335693359375,0.0627288818359375,0.1245880126953125,0.109466552734375,0.115478515625,0.064727783203125,0.522674560546875,0.0045928955078125,0.036285400390625,0.1079864501953125,0.16339111328125,0.1127471923828125,0.03973388671875,0.0125885009765625,2.8315533256001744,6.823155883056394,9.63863868182487,9.983985427042798,9.999999999999813,-9.999997491301684,-9.999999999999691
|
||||||
augmented_healthyf_107.jpg,normal,0,12.859130859375,13.083251953125,12.71649169921875,1571.6210654835106,0.8666531601617821,0.5412038535944765,0.8650011832213252
|
5198047901_73ee826532_bWM.jpg,sakit,0,70.69245910644531,71.14852905273438,66.82170104980469,42.832275390625,11.0601806640625,73.0533447265625,74.88367462158203,23.032129287719727,97.92052459716797,2425.273187140068,0.6207714321757023,0.864726134494947,0.5817008937170794,19.47257075064787,0.33852056663415325,0.6283111572265625,0.0,0.0004730224609375,0.0081939697265625,0.0919189453125,0.0932159423828125,0.080291748046875,0.09759521484375,0.6283111572265625,0.0,9.1552734375e-05,0.0055694580078125,0.097900390625,0.0883636474609375,0.073699951171875,0.1060638427734375,0.6283111572265625,0.001251220703125,0.014251708984375,0.04522705078125,0.0825042724609375,0.0695953369140625,0.063262939453125,0.0955963134765625,2.7573571130506798,7.031521988532275,9.872106175995551,9.568289767055449,-9.99999999998282,-9.999775419033984,9.99999999994624
|
||||||
augmented_healthyf_109.jpg,normal,0,67.1448974609375,65.0955810546875,59.59539794921875,2406.7418223232685,0.5678815775268374,0.816570659295464,0.5471936533076194
|
52700780213_927e29e488_o.jpg,sakit,0,85.54339599609375,84.51777648925781,92.01506042480469,90.64212036132812,22.018783569335938,97.78349304199219,125.8850326538086,30.533153533935547,111.52782440185547,1916.5127242586104,0.5748715889209686,0.9001479284915722,0.5123468014678538,15.745464867148357,0.2626155187780453,0.5524139404296875,0.0,0.0,0.0054779052734375,0.05938720703125,0.1537322998046875,0.1744537353515625,0.054534912109375,0.5524139404296875,0.0,0.0,0.021453857421875,0.105926513671875,0.0814666748046875,0.1348419189453125,0.1038970947265625,0.5524139404296875,0.0,0.010894775390625,0.02801513671875,0.06719970703125,0.0628204345703125,0.056610107421875,0.2220458984375,2.9418261525212888,7.517431638787939,9.660141343225368,9.970850054498968,9.999999999999398,9.999999112706123,-9.999999999999378
|
||||||
augmented_healthyf_120.jpg,normal,0,93.9176025390625,88.801025390625,84.21783447265625,949.4897725479414,0.35760621418005084,0.8851606269446273,0.26395981147474173
|
5531555631_23432771c3_o.jpg,sakit,0,91.22853088378906,84.02670288085938,78.68492126464844,28.197784423828125,17.83892822265625,91.98481750488281,64.71118927001953,27.535022735595703,100.52338409423828,2024.7890404665097,0.5530324349239745,0.8842229289490247,0.48188115869269993,17.62467514639698,0.23233791058464534,0.5262603759765625,0.0,0.0,0.0203857421875,0.1047210693359375,0.1186065673828125,0.08331298828125,0.1467132568359375,0.5262603759765625,0.0,7.62939453125e-05,0.03302001953125,0.1367645263671875,0.1417083740234375,0.1031494140625,0.05902099609375,0.5262603759765625,0.00079345703125,0.0078887939453125,0.064727783203125,0.13751220703125,0.1405792236328125,0.096343994140625,0.0258941650390625,2.901416153085185,6.966894507387559,9.621594957070377,9.696018794128154,-9.999999999947752,9.999972120544195,-9.999999999999226
|
||||||
augmented_healthyf_121.jpg,normal,0,86.966796875,81.22918701171875,74.23321533203125,1159.2603712165053,0.33397589089752927,0.8273208831826943,0.24062717174495526
|
6504861261_1935d54e91_o.jpg,sakit,0,113.43666076660156,96.36286926269531,83.07144165039062,22.957611083984375,42.70439147949219,113.49639892578125,46.05146408081055,39.89169692993164,95.50852966308594,1217.7989126695793,0.42368905676382856,0.9160624801734367,0.36467869190516095,14.698207840202384,0.13303845512047216,0.3968505859375,0.0,0.0,0.005767822265625,0.1343994140625,0.1909637451171875,0.16314697265625,0.1088714599609375,0.3968505859375,0.0,0.001251220703125,0.1318359375,0.1970062255859375,0.1314544677734375,0.1191558837890625,0.0224456787109375,0.3968505859375,0.00079345703125,0.0670166015625,0.215545654296875,0.137054443359375,0.1309356689453125,0.0432891845703125,0.008514404296875,3.048828982050739,7.465473728789742,9.972402326628774,9.971642835329247,9.999999999999954,9.999997690892693,-9.99999999999981
|
||||||
augmented_healthyf_136.jpg,normal,0,29.81982421875,29.81768798828125,27.61932373046875,1419.9608481131,0.6650600871245955,0.7081196501005139,0.6535094345162221
|
6505036901_0325410a27_o.jpg,sakit,0,89.84251403808594,85.86640930175781,82.57882690429688,35.947265625,11.159286499023438,90.84352111816406,79.6043472290039,20.166767120361328,110.7376708984375,1889.7247391270337,0.6291683763616375,0.9163819902853044,0.5563844749182272,14.670001450928472,0.30962016321636937,0.5893707275390625,0.0,0.0,0.0023193359375,0.0262603759765625,0.0758209228515625,0.08538818359375,0.2208404541015625,0.5893707275390625,0.0,0.0,0.0002288818359375,0.05035400390625,0.0894775390625,0.0962371826171875,0.1743316650390625,0.5893707275390625,0.0,0.00067138671875,0.0107269287109375,0.0541839599609375,0.0991668701171875,0.10723876953125,0.138641357421875,2.924446367188315,6.563905372386156,9.453669771261666,9.8106399235842,-9.999999999972859,-9.999877346176834,-9.999999999993769
|
||||||
augmented_healthyf_139.jpg,normal,0,64.03643798828125,61.98211669921875,57.20904541015625,2226.449247903912,0.5983904656254683,0.8350841993436156,0.5791465041774432
|
6505084171_92fc25f739_o.jpg,sakit,0,73.15585327148438,70.72311401367188,69.94853210449219,54.10052490234375,10.17376708984375,75.27783203125,98.1626205444336,18.920629501342773,96.81059265136719,785.7230737917349,0.7220290079331008,0.953675726675793,0.6045723665586282,6.3481060544376815,0.3655302264115186,0.619415283203125,0.0,0.0,0.0,0.0206451416015625,0.156646728515625,0.1764068603515625,0.026885986328125,0.619415283203125,0.0,0.0,1.52587890625e-05,0.062774658203125,0.159820556640625,0.133544921875,0.0244293212890625,0.619415283203125,0.0,0.0,0.0241546630859375,0.0561370849609375,0.136199951171875,0.131927490234375,0.03216552734375,2.8741799592792177,6.560724031656362,9.621781588197427,9.752836872174505,9.999999999967047,-9.99983766671353,9.99999999999026
|
||||||
augmented_healthyf_14.jpg,normal,0,42.504638671875,35.98724365234375,29.75640869140625,2338.409334599671,0.545029518014188,0.5660561860934739,0.5220891102720221
|
6505095679_f21defeac3_o.jpg,sakit,0,128.75938415527344,123.14814758300781,119.46382141113281,62.493408203125,23.937881469726562,132.99754333496094,85.7709732055664,24.499929428100586,90.73417663574219,559.511080894093,0.4483261060159008,0.9615315508838561,0.2833171257504429,7.519527333232015,0.0803032662441257,0.29833984375,0.0,0.0,0.0186614990234375,0.189605712890625,0.205169677734375,0.169525146484375,0.1186981201171875,0.29833984375,0.0,0.0001373291015625,0.045440673828125,0.1929779052734375,0.231689453125,0.1849365234375,0.046478271484375,0.29833984375,3.0517578125e-05,0.0101165771484375,0.0770263671875,0.1607208251953125,0.2899322509765625,0.1140899658203125,0.04974365234375,3.08112721724747,7.5374742793315725,9.842467190199576,9.964676792778103,9.999999999999522,9.999995429876781,9.999999999999478
|
||||||
augmented_healthyf_152.jpg,normal,0,27.61578369140625,27.93707275390625,26.90155029296875,2127.897058738015,0.860368345251572,0.7985680850802245,0.8455607843810077
|
6505358073_8e50dc787e_o.jpg,sakit,0,152.85610961914062,135.5255126953125,137.22842407226562,154.16995239257812,27.813201904296875,153.63816833496094,158.15208435058594,23.66185188293457,80.1552963256836,682.2294830316314,0.5512938187432983,0.9407202513201164,0.19190230170909572,6.540843647356791,0.03685662297380112,0.202728271484375,0.0,0.0,0.0,0.027679443359375,0.547637939453125,0.0987091064453125,0.1232452392578125,0.202728271484375,0.0,0.0,0.033935546875,0.299041748046875,0.31005859375,0.045074462890625,0.109161376953125,0.202728271484375,0.0,1.52587890625e-05,0.0450592041015625,0.2668304443359375,0.2919769287109375,0.109344482421875,0.08404541015625,3.054158610665358,7.928130320871967,9.990790937746873,9.985019808708941,-9.999999999999968,-9.999998533095244,-9.999999999999973
|
||||||
augmented_healthyf_16.jpg,normal,0,33.39727783203125,33.122802734375,30.6710205078125,1449.724817096472,0.6457443461117479,0.7341381580485628,0.6328015157235215
|
6505584445_e23fc6527a_o.jpg,sakit,0,93.50617980957031,88.54948425292969,83.3775634765625,40.47406005859375,11.965362548828125,93.76902770996094,86.31880187988281,22.22137451171875,115.3624267578125,3078.8388665083744,0.6966655902313641,0.8732665553101575,0.564425805848278,17.356301983763476,0.3186587954032782,0.5988922119140625,0.0,0.0,0.0,0.0,0.010711669921875,0.1231689453125,0.2672271728515625,0.5988922119140625,0.0,0.0,0.0,0.0,0.0799102783203125,0.1257171630859375,0.1954803466796875,0.5988922119140625,0.0,0.0,0.0003204345703125,0.02691650390625,0.13018798828125,0.097625732421875,0.14605712890625,2.7970251330177027,7.0037542170187645,9.590739914614721,9.402628907803862,9.999999999727883,-9.999768605765718,-9.999999999951772
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||||||
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7-day-vesicle-steer-foot_jpg.rf.cdd8da2a7ec2c4dbca9fef213ab3da24.jpg,sakit,0,45.52806091308594,34.62548828125,20.190948486328125,9.506805419921875,34.525787353515625,45.644317626953125,21.694623947143555,63.25164794921875,82.3665771484375,706.2429133370688,0.7664730819554814,0.9172765192853882,0.734271317338102,7.661359290634473,0.5391730231027504,0.75628662109375,0.0001220703125,0.002227783203125,0.0178375244140625,0.03717041015625,0.06201171875,0.07861328125,0.0457305908203125,0.75628662109375,0.0,0.014862060546875,0.07843017578125,0.0754241943359375,0.05035400390625,0.0241851806640625,0.000457763671875,0.756378173828125,0.07135009765625,0.109954833984375,0.039703369140625,0.015838623046875,0.005950927734375,0.000762939453125,6.103515625e-05,2.5819542857124542,6.217855135213198,9.812728884881468,8.577484938463773,-9.999999996888173,9.992871345954295,9.999999997337516
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augmented_healthyf_166.jpg,normal,0,35.2481689453125,34.2960205078125,31.43695068359375,1860.0630745973351,0.6670905381274541,0.7264419718580579,0.6555533055842447
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8400384539_93d1834718_o.jpg,sakit,0,77.65904235839844,77.50015258789062,76.99766540527344,45.643768310546875,13.98590087890625,82.74836730957031,82.0966796875,25.886003494262695,105.98339080810547,1745.3675390520573,0.6562868515751261,0.9137132501439811,0.5821938764956844,13.760638120509293,0.33901495992279934,0.61212158203125,0.0,0.0,0.0210418701171875,0.0723419189453125,0.06451416015625,0.0759429931640625,0.1540374755859375,0.61212158203125,0.0,0.0,0.00067138671875,0.09112548828125,0.0813446044921875,0.0701141357421875,0.144622802734375,0.61212158203125,0.0,0.001708984375,0.0263519287109375,0.0474853515625,0.0930023193359375,0.081451416015625,0.13787841796875,2.8622216666684897,6.664417220311037,9.388821226325877,9.786885377118576,-9.999999999976465,-9.999873995425418,9.999999999969582
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augmented_healthyf_17.jpg,normal,0,13.36212158203125,13.58575439453125,13.21563720703125,1683.0189469126392,0.8597379431340607,0.5337585827330162,0.857971394036357
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Base-ringing-400x284.jpg,sakit,0,115.13601684570312,86.74455261230469,71.52322387695312,13.10064697265625,60.70323181152344,115.13601684570312,17.231557846069336,50.815372467041016,93.34558868408203,625.0260114398575,0.4503737916196929,0.9465431549709017,0.3582268001666218,9.382617112647557,0.12838341547138535,0.381011962890625,0.0,0.0,0.0175018310546875,0.1068115234375,0.219635009765625,0.20355224609375,0.0714874267578125,0.381011962890625,0.0,0.0196533203125,0.1984100341796875,0.2347259521484375,0.1566314697265625,0.009521484375,4.57763671875e-05,0.381011962890625,0.0131988525390625,0.15765380859375,0.2326202392578125,0.1705780029296875,0.0428314208984375,0.002105712890625,0.0,2.9937902200658697,7.390740750459567,9.82119752605254,9.947195761741886,-9.99999999999943,-9.999989944701202,-9.999999999998677
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augmented_healthyf_171.jpg,normal,0,48.08953857421875,44.04364013671875,40.32623291015625,1247.021324312796,0.5236146190910663,0.7780323119685861,0.48728740898266415
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Chemical-burn-7-e1515283936872-400x284.jpg,sakit,0,53.44621276855469,44.1090087890625,37.726165771484375,7.000579833984375,21.191558837890625,53.44621276855469,13.214006423950195,36.61761474609375,86.67949676513672,224.9851891664343,0.8414937091506801,0.9800353046169014,0.7179545367655596,2.4823265740606044,0.5154617333006016,0.722625732421875,0.0,0.0,4.57763671875e-05,0.015777587890625,0.0842742919921875,0.177276611328125,0.0,0.722625732421875,0.0,0.00555419921875,0.0333709716796875,0.06622314453125,0.172210693359375,1.52587890625e-05,0.0,0.722625732421875,0.0053253173828125,0.0223388671875,0.052398681640625,0.1534271240234375,0.04388427734375,0.0,0.0,2.5492059421039235,5.829748320560376,7.830562214213266,8.427714362917117,9.999999915854126,-9.994771083550452,-9.999999921194112
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augmented_healthyf_179.jpg,normal,0,50.5711669921875,48.814208984375,45.36004638671875,1534.6610803718324,0.6429723200227812,0.8437795305573074,0.6067919373253753
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Chemical-damage-e1515285856612-356x284.jpg,sakit,0,69.14717102050781,62.51557922363281,61.10594177246094,56.292022705078125,12.418014526367188,69.29348754882812,118.80715942382812,24.689470291137695,95.79721069335938,917.6252852288277,0.7025319948869152,0.9431537844164772,0.6330914899693353,7.860002643816163,0.4008279595598902,0.652008056640625,0.0,0.0,0.0,0.0153961181640625,0.12054443359375,0.1531524658203125,0.05889892578125,0.652008056640625,0.0,0.0,0.0128631591796875,0.1098480224609375,0.07000732421875,0.1213836669921875,0.0338897705078125,0.652008056640625,0.0,0.0017547607421875,0.028472900390625,0.1164703369140625,0.0591278076171875,0.1060943603515625,0.03607177734375,2.7370131273066525,5.958197148028143,9.17494114535149,9.359060806413561,9.99999999937689,9.998528361576293,-9.999999999845876
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augmented_healthyf_29.jpg,normal,0,52.38519287109375,50.580078125,47.1275634765625,1224.2264070405386,0.6291959780067711,0.8700790342900685,0.5916772435117987
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Diseased dental pad 28.jpg,sakit,0,103.44242858886719,87.35147094726562,132.43788146972656,153.78701782226562,62.75531005859375,135.83917236328125,124.8288803100586,52.95044708251953,102.74452209472656,1446.8357775547536,0.45791192129894015,0.8623390241642017,0.2983066624464522,15.095662567329759,0.0890782691721572,0.339599609375,0.0,0.0069732666015625,0.11767578125,0.1810455322265625,0.318939208984375,0.02069091796875,0.01507568359375,0.339599609375,0.0,0.0352325439453125,0.2513885498046875,0.3240203857421875,0.0202484130859375,0.01837158203125,0.011138916015625,0.3398895263671875,0.0013275146484375,0.0123291015625,0.0455169677734375,0.074249267578125,0.1130523681640625,0.1492462158203125,0.2643890380859375,3.0346521123255843,7.456079143698732,9.764734825368574,9.926201592079403,9.999999999998622,9.999986939807254,9.999999999997408
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||||||
augmented_healthyf_35.jpg,normal,0,54.86376953125,46.564453125,39.00006103515625,1703.3002244690772,0.46011690783096665,0.6761490062770408,0.41645282983031656
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Diseased foot 1- (17).jpg,sakit,0,87.0050048828125,85.49433898925781,80.66531372070312,45.313323974609375,12.540252685546875,89.10977172851562,83.16511535644531,32.21553039550781,112.67426300048828,3014.206617413174,0.6776475325115037,0.8726509551000897,0.5670286073812912,19.38271871375567,0.32162800689283716,0.6093597412109375,0.0,0.0,0.00439453125,0.005096435546875,0.059600830078125,0.1013946533203125,0.22015380859375,0.6093597412109375,0.0,0.0,0.0,0.0268096923828125,0.073211669921875,0.0775604248046875,0.2130584716796875,0.60968017578125,0.002838134765625,0.013427734375,0.028961181640625,0.0435333251953125,0.0384979248046875,0.0528717041015625,0.2101898193359375,2.870227064701343,6.4279417778196235,9.064572993861342,9.458977465719085,9.999999999606509,9.99970254976381,9.999999999748686
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augmented_healthyf_36.jpg,normal,0,20.816162109375,20.3389892578125,19.01934814453125,1682.5238052311313,0.829145651943473,0.7023989580578552,0.82193373053236
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Diseased foot 1- (8).jpg,sakit,0,99.49627685546875,92.41487121582031,88.90342712402344,80.31600952148438,27.31195068359375,103.68354797363281,106.24797821044922,35.521942138671875,85.99860382080078,4318.9959208752825,0.33066760233057196,0.6437671904108826,0.29385925491572334,36.45979473538041,0.08657759708437758,0.3914794921875,0.0,0.0,0.04925537109375,0.2703704833984375,0.1729888916015625,0.085601806640625,0.030303955078125,0.3914794921875,0.0,0.0001068115234375,0.09100341796875,0.321441650390625,0.1488800048828125,0.039398193359375,0.0076904296875,0.3914794921875,0.000152587890625,0.0249176025390625,0.1407470703125,0.252166748046875,0.14520263671875,0.0407562255859375,0.00457763671875,2.9348441959874014,7.616351312185747,9.984930659688137,9.996477362576307,-10.0,9.999999657074842,-9.999999999999995
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augmented_healthyf_38.jpg,normal,0,44.2755126953125,44.843017578125,44.82122802734375,3541.0979361229533,0.7663027785406117,0.7697111371779263,0.7457280082264036
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Diseased foot 17.jpg,sakit,0,87.0050048828125,85.49433898925781,80.66531372070312,45.313323974609375,12.540252685546875,89.10977172851562,83.16511535644531,32.21553039550781,112.67426300048828,3014.206617413174,0.6776475325115037,0.8726509551000897,0.5670286073812912,19.38271871375567,0.32162800689283716,0.6093597412109375,0.0,0.0,0.00439453125,0.005096435546875,0.059600830078125,0.1013946533203125,0.22015380859375,0.6093597412109375,0.0,0.0,0.0,0.0268096923828125,0.073211669921875,0.0775604248046875,0.2130584716796875,0.60968017578125,0.002838134765625,0.013427734375,0.028961181640625,0.0435333251953125,0.0384979248046875,0.0528717041015625,0.2101898193359375,2.870227064701343,6.4279417778196235,9.064572993861342,9.458977465719085,9.999999999606509,9.99970254976381,9.999999999748686
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augmented_healthyf_4.jpg,normal,0,55.7392578125,50.21246337890625,44.279296875,1602.999139490751,0.5188048028737077,0.7672089018072881,0.47171862774610784
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Diseased foot 20.jpg,sakit,0,103.63067626953125,99.43507385253906,96.26065063476562,61.045867919921875,11.088836669921875,104.40003967285156,108.76203155517578,19.37093734741211,111.11895751953125,3835.7103472334184,0.6028367925416266,0.8335564663123826,0.4784032647559109,24.162442752870447,0.2290035638113335,0.52423095703125,0.0,0.0,0.000274658203125,0.001861572265625,0.09442138671875,0.1868133544921875,0.1923980712890625,0.52423095703125,0.0,0.0,0.0,0.0035552978515625,0.1755523681640625,0.134490966796875,0.16217041015625,0.52423095703125,0.0,0.0,0.0022125244140625,0.063018798828125,0.1508331298828125,0.1070709228515625,0.1526336669921875,2.8644860787292736,7.2091451343523625,9.439209197284711,9.706717834691911,-9.999999999933229,-9.999936868747685,9.99999999999742
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augmented_healthyf_40.jpg,normal,0,101.982421875,85.7574462890625,55.001953125,980.8953777547632,0.4382382530512648,0.9059693011799793,0.33199825052783183
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Diseased foot 21.jpg,sakit,0,64.57601928710938,59.751556396484375,56.14996337890625,19.8642578125,10.530624389648438,64.92665100097656,60.034149169921875,19.399946212768555,103.16492462158203,1470.5997498319223,0.7469561516124489,0.9217575164163178,0.6927021005013794,10.015159639623375,0.47986139509802506,0.7138671875,0.0,0.0,0.0,0.0004730224609375,0.0239105224609375,0.0931549072265625,0.1685943603515625,0.7138671875,0.0,0.0,0.0,0.001312255859375,0.07257080078125,0.1310882568359375,0.0811614990234375,0.7138671875,0.0,0.0,0.0002288818359375,0.02978515625,0.105712890625,0.1011199951171875,0.049285888671875,2.966752959845797,6.396446076859029,9.952600055378703,9.98126815093091,-9.999999999999867,-9.99999664395423,-9.999999999999972
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augmented_healthyf_41.jpg,normal,0,13.77593994140625,13.65057373046875,13.0145263671875,1456.5462094339837,0.8944227855861976,0.6611014045093636,0.8913113701806176
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Diseased foot 24.jpg,sakit,0,91.07377624511719,85.02781677246094,79.05401611328125,25.059417724609375,19.057586669921875,91.5743408203125,50.8616828918457,21.996374130249023,87.63896942138672,3542.679621750676,0.42200801250460557,0.7395925425927096,0.3833675545879958,29.875401299161812,0.14717768609886692,0.457122802734375,0.0,0.0,0.02655029296875,0.25360107421875,0.1400604248046875,0.0706024169921875,0.05206298828125,0.457122802734375,0.0,4.57763671875e-05,0.0930633544921875,0.261444091796875,0.104095458984375,0.05572509765625,0.02850341796875,0.457122802734375,0.00018310546875,0.003265380859375,0.1653289794921875,0.23663330078125,0.0869140625,0.0362701416015625,0.0142822265625,2.889224383036372,7.213146977009336,9.60315841908043,9.77422357112115,-9.999999999970706,-9.999927335308675,-9.999999999994104
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||||||
augmented_healthyf_50.jpg,normal,0,89.15167236328125,88.4659423828125,79.40081787109375,2897.837211929165,0.4430884798404266,0.8070164229428735,0.42340172720375224
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Diseased foot 3.jpg,sakit,0,104.20057678222656,96.67951965332031,88.05601501464844,67.96542358398438,28.94580078125,108.067626953125,96.01044464111328,43.24821853637695,101.57884979248047,1471.612161546948,0.6030779511313287,0.9126464468326044,0.4305622974561159,12.78093988284668,0.18544074990875867,0.4566192626953125,0.0,0.0,0.010345458984375,0.0562744140625,0.295745849609375,0.061248779296875,0.1197662353515625,0.4566192626953125,0.0,0.0,0.0250091552734375,0.086578369140625,0.32659912109375,0.0811309814453125,0.0240631103515625,0.4573516845703125,0.006561279296875,0.0313873291015625,0.0752716064453125,0.1252899169921875,0.074676513671875,0.2214202880859375,0.0080413818359375,2.848665473469481,7.129173046496989,9.91084139554375,9.657393336125729,9.999999999986384,9.999920856417479,-9.999999999976355
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augmented_healthyf_55.jpg,normal,0,40.68731689453125,36.93841552734375,30.6837158203125,1104.0864905857964,0.5664167717017398,0.7566741183578675,0.5325486867157316
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Diseased foot 9.jpg,sakit,0,75.66310119628906,80.07810974121094,80.49920654296875,48.214324951171875,14.831069946289062,84.09600830078125,74.6592025756836,27.676280975341797,101.52418518066406,755.2862049387451,0.64799641473804,0.9585995155170277,0.5680382016295857,8.230233335302357,0.32267634233260395,0.5807037353515625,0.0,0.0026092529296875,0.0439910888671875,0.061309814453125,0.178131103515625,0.0675811767578125,0.065673828125,0.5807037353515625,0.0,4.57763671875e-05,0.0039825439453125,0.07232666015625,0.175811767578125,0.07415771484375,0.0929718017578125,0.5807037353515625,0.0,0.0007476806640625,0.0106353759765625,0.0796661376953125,0.146331787109375,0.0703887939453125,0.1115264892578125,2.834704414300474,6.352257499864012,8.886952797594978,9.139499595965672,9.999999997655996,9.999423976445158,-9.999999999838087
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augmented_healthyf_56.jpg,normal,0,60.99017333984375,61.02081298828125,58.18023681640625,3244.4610020744854,0.5463205779386999,0.706943666073551,0.5090816711651381
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Diseased tongue 15.jpg,sakit,0,139.73826599121094,126.73722839355469,124.49357604980469,90.23333740234375,19.146163940429688,140.46971130371094,132.85000610351562,26.24274253845215,114.57310485839844,963.6454622718032,0.5718162156821475,0.9577514837272382,0.38221244699624374,9.122334258346678,0.1461262257617513,0.3856353759765625,0.0,0.0,0.0001678466796875,0.052703857421875,0.083404541015625,0.049468994140625,0.428619384765625,0.3856353759765625,0.0,0.0,0.0024261474609375,0.0838623046875,0.13037109375,0.186126708984375,0.211578369140625,0.3856353759765625,0.0,1.52587890625e-05,0.010528564453125,0.091461181640625,0.1551666259765625,0.151153564453125,0.2060394287109375,3.102400541152559,7.029504164870617,9.894813666398372,9.957281634927183,9.99999999999927,9.999988897317108,9.999999999999808
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augmented_healthyf_58.jpg,normal,0,55.78533935546875,59.1370849609375,56.12542724609375,4594.4900751275745,0.6922610676904163,0.7452066159105487,0.6597407793692683
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Diseased-dental-pad-36_jpg.rf.5dc1b0bb42ec944d87921d1e38216072.jpg,sakit,0,87.48114013671875,77.3009033203125,80.0216064453125,130.219482421875,21.53778076171875,89.02877807617188,152.42372131347656,26.964757919311523,82.19667053222656,813.5834703242332,0.4881444272449344,0.9283860266994531,0.41056335098879737,10.999459068973424,0.16866808167617475,0.4429473876953125,0.0,0.0022735595703125,0.08740234375,0.2064971923828125,0.1959381103515625,0.0630645751953125,0.0018768310546875,0.4429473876953125,0.0018310546875,0.0509185791015625,0.169097900390625,0.1731414794921875,0.131805419921875,0.0301971435546875,6.103515625e-05,0.4429473876953125,0.000213623046875,0.0390625,0.1631011962890625,0.1637115478515625,0.132171630859375,0.05743408203125,0.0013580322265625,3.10888073124209,7.72228717005679,9.993508351262598,9.99946448598763,10.0,9.999999993613795,10.0
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FMD (84).jpg,defective,1,103.8223876953125,99.53070068359375,96.32183837890625,5387.468397453059,0.5696093341029717,0.7655570806920996,0.45808610005967715
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||||||
|
augmented_healthyf_176.jpg,sehat,1,65.27363586425781,64.84417724609375,61.53962707519531,48.006378173828125,15.956832885742188,68.09254455566406,80.95079803466797,22.097808837890625,79.81450653076172,2028.5385045381452,0.5702534135692382,0.8238989966651639,0.5091946935651321,17.45843174216656,0.2594308906687229,0.5606842041015625,0.0,0.0007781982421875,0.0807037353515625,0.243896484375,0.08935546875,0.01123046875,0.0133514404296875,0.5606842041015625,0.0,0.0,0.1317901611328125,0.1984100341796875,0.07025146484375,0.016204833984375,0.0226593017578125,0.5606842041015625,0.0,0.0228118896484375,0.1912078857421875,0.107086181640625,0.0768585205078125,0.0123443603515625,0.0290069580078125,2.9417103986395605,7.22106388657388,9.324891646864245,9.84602585832639,-9.999999999998154,9.999979374541729,-9.999999999976783
|
||||||
|
augmented_healthyf_3.jpg,sehat,1,23.4205322265625,23.652786254882812,22.17138671875,11.492431640625,3.1107177734375,24.113449096679688,40.332828521728516,11.462677955627441,69.36820220947266,1131.9453377624827,0.8931232706261197,0.8744614037390906,0.8736244757912167,7.212712822230489,0.7632367589859098,0.8886871337890625,0.0,0.0,6.103515625e-05,0.01446533203125,0.026763916015625,0.015350341796875,0.0546722412109375,0.8886871337890625,0.0,0.0,0.0,0.0217132568359375,0.0169677734375,0.014404296875,0.0582275390625,0.8886871337890625,0.0,4.57763671875e-05,0.0118408203125,0.0198516845703125,0.014312744140625,0.0170745849609375,0.048187255859375,2.135884283862905,5.267918670618136,6.624027630800328,7.14752935327724,9.999960020390919,9.658302916240622,9.99999654995178
|
||||||
|
augmented_healthyf_30.jpg,sehat,1,75.59410095214844,74.05696105957031,72.23527526855469,50.365234375,16.730239868164062,78.728515625,82.59028625488281,20.405973434448242,78.14865112304688,1340.660308227621,0.52100463863603,0.876762331036579,0.4387154703643894,13.171206363685164,0.192556640953628,0.478546142578125,0.0,0.0002288818359375,0.124755859375,0.26885986328125,0.1059417724609375,0.0213623046875,0.00030517578125,0.478546142578125,0.0,0.0,0.214996337890625,0.16314697265625,0.1149749755859375,0.024810791015625,0.0035247802734375,0.478546142578125,0.0,0.0201873779296875,0.2520294189453125,0.1024627685546875,0.09185791015625,0.048614501953125,0.0063018798828125,3.0184722766966297,7.352353183896473,9.791431524906827,9.918763355662804,9.99999999999965,9.999996749839857,9.999999999996838
|
||||||
|
augmented_healthyf_40.jpg,sehat,1,101.87895202636719,85.68601989746094,54.97186279296875,31.171417236328125,78.36190795898438,103.6195068359375,42.63807678222656,73.95555114746094,84.23583984375,474.6116342774564,0.5187522586283569,0.954515877443101,0.348296080622966,7.191734901055696,0.12136320048800923,0.36822509765625,0.0,0.009674072265625,0.1161041259765625,0.1941375732421875,0.191192626953125,0.0652008056640625,0.0554656982421875,0.36822509765625,0.0,0.091583251953125,0.19775390625,0.200775146484375,0.085296630859375,0.034423828125,0.021942138671875,0.410736083984375,0.2244110107421875,0.1624908447265625,0.0829925537109375,0.0464935302734375,0.028900146484375,0.032623291015625,0.0113525390625,3.012311396860327,6.91582430786176,9.690895509424589,9.96566499598693,9.999999999998963,9.999992654958007,-9.999999999999885
|
||||||
|
augmented_healthyf_41.jpg,sehat,1,14.430953979492188,14.271148681640625,13.5625,7.22503662109375,2.7068023681640625,14.904083251953125,32.22319793701172,10.70585823059082,49.46791076660156,1032.4239971691495,0.8925294082641946,0.7669868949447912,0.8877759187760041,8.193273608981782,0.7881561934694025,0.9113616943359375,0.0,0.000274658203125,0.025543212890625,0.0181884765625,0.0213775634765625,0.015533447265625,0.007720947265625,0.9113616943359375,0.0,0.0,0.0281219482421875,0.0157623291015625,0.02227783203125,0.0137481689453125,0.00872802734375,0.9113616943359375,4.57763671875e-05,0.009490966796875,0.0225067138671875,0.01654052734375,0.021392822265625,0.0104827880859375,0.0081787109375,2.2855265247543777,5.492472678219613,7.417426033571474,7.551038871571273,-9.999998992539687,-9.830984845721973,-9.99999615209685
|
||||||
|
augmented_healthyf_55.jpg,sehat,1,40.72395324707031,36.97218322753906,30.729339599609375,20.188385009765625,28.642715454101562,41.548675537109375,37.314022064208984,40.99100875854492,52.398834228515625,734.7478003346446,0.5963548334834704,0.8384192069419801,0.5526755755024896,11.294032693854966,0.3055518716074177,0.6005096435546875,0.0065460205078125,0.164520263671875,0.1898956298828125,0.035675048828125,0.0028076171875,4.57763671875e-05,0.0,0.6005096435546875,0.016326904296875,0.2122955322265625,0.148895263671875,0.017486572265625,0.00445556640625,3.0517578125e-05,0.0,0.60052490234375,0.12249755859375,0.197967529296875,0.066864013671875,0.009674072265625,0.002471923828125,0.0,0.0,2.789308519737864,6.804090495712479,9.52706384598446,9.63027763936286,9.999999999962728,9.99984023985655,-9.999999999912756
|
||||||
|
augmented_healthyf_58.jpg,sehat,1,55.886474609375,59.28562927246094,56.24272155761719,31.470458984375,6.815887451171875,60.16236877441406,63.40101623535156,15.678228378295898,98.4266128540039,3474.0795165031263,0.7110892177432038,0.8074341472548653,0.6718578787542588,22.112782736395207,0.45144004039133817,0.721435546875,0.0,0.0,0.0019683837890625,0.0406646728515625,0.0784149169921875,0.073455810546875,0.0840606689453125,0.721435546875,0.0,0.0,0.0,0.029052734375,0.0568084716796875,0.067901611328125,0.1248016357421875,0.721435546875,0.0,6.103515625e-05,0.004638671875,0.0386962890625,0.0694427490234375,0.07501220703125,0.0907135009765625,2.6276223450268574,6.16541004792092,8.664427013610467,8.767609479039377,9.99999998749531,9.997944933927014,-9.99999999765494
|
||||||
|
augmented_healthyf_63.jpg,sehat,1,24.395599365234375,24.408905029296875,21.918701171875,9.464324951171875,4.31158447265625,24.962692260742188,32.00082778930664,14.428796768188477,69.24974060058594,1181.819533819796,0.8823500487507032,0.8681352885479026,0.8627496853697436,7.869586596359967,0.7443611502654374,0.8805084228515625,0.0,0.0,0.0001220703125,0.0233154296875,0.030670166015625,0.0137481689453125,0.0516357421875,0.8805084228515625,0.0,0.0,0.0,0.03271484375,0.0181121826171875,0.0140380859375,0.05462646484375,0.8805084228515625,0.0,0.000152587890625,0.0237274169921875,0.0237579345703125,0.0154876708984375,0.0196380615234375,0.0367279052734375,2.2051460289045792,5.2071915061259535,7.232819747295949,7.375845740705902,9.999995080502973,9.708283488319681,-9.999992427878786
|
||||||
|
augmented_healthyf_69.jpg,sehat,1,35.46923828125,35.52064514160156,35.22967529296875,24.8419189453125,4.273895263671875,36.897491455078125,64.66085052490234,11.3213529586792,77.65125274658203,2811.9120855575625,0.7751584714096679,0.7494316437525139,0.7626223742875684,18.873265368811847,0.5816557041637475,0.8079071044921875,0.0,0.0,0.0072021484375,0.0522003173828125,0.0537872314453125,0.0415191650390625,0.037384033203125,0.8079071044921875,0.0,0.0,0.0146942138671875,0.0458984375,0.0487518310546875,0.0415191650390625,0.041229248046875,0.8079071044921875,0.0,0.00018310546875,0.0251922607421875,0.037353515625,0.0443878173828125,0.04229736328125,0.0426788330078125,2.5054130535779646,5.863304664001796,7.814670508474589,8.40541501132897,9.999999878400082,9.987444823879233,9.999999963039437
|
||||||
|
augmented_healthyf_79.jpg,sehat,1,67.47146606445312,65.74595642089844,59.646881103515625,17.739715576171875,12.947463989257812,68.02799987792969,23.808597564697266,20.477502822875977,86.41831970214844,2059.4717668165054,0.589021292190354,0.8521834899979316,0.5640091442253392,18.341632799152027,0.31814159250455043,0.60260009765625,0.0,0.00054931640625,0.0424346923828125,0.1174774169921875,0.138397216796875,0.0637359619140625,0.0348052978515625,0.60260009765625,0.0,0.0,0.0546417236328125,0.128387451171875,0.1250762939453125,0.0623779296875,0.02691650390625,0.6026611328125,0.0021514892578125,0.02008056640625,0.082763671875,0.1456298828125,0.0936431884765625,0.0435333251953125,0.0095367431640625,2.822250022151897,6.302723387814547,8.979598623762216,9.479636089529551,9.999999999994822,-9.999552113865034,9.99999999952929
|
||||||
|
augmented_healthyf_83.jpg,sehat,1,97.19387817382812,97.672607421875,96.10824584960938,65.20278930664062,12.617279052734375,101.85003662109375,90.4771957397461,19.37873649597168,101.2955322265625,3079.6697117942385,0.4830591187175683,0.8367347877834668,0.4351670428845935,24.511883175088197,0.1894551236391291,0.483367919921875,0.0,0.0,0.0137939453125,0.0986175537109375,0.16253662109375,0.1673583984375,0.0743255615234375,0.483367919921875,0.0,0.0,0.0133514404296875,0.1077728271484375,0.1370697021484375,0.1774139404296875,0.081024169921875,0.483367919921875,0.0003509521484375,0.003204345703125,0.0265655517578125,0.110504150390625,0.1224517822265625,0.1721649169921875,0.081390380859375,2.9271980731918785,6.683479562683056,9.084917753733796,9.705725266857495,9.99999999998725,-9.999925834129987,-9.999999999889368
|
||||||
|
augmented_healthyf_86.jpg,sehat,1,62.03758239746094,60.56231689453125,57.26019287109375,45.07568359375,17.344268798828125,64.5179443359375,79.83370971679688,24.060178756713867,75.17989349365234,1967.4457125479605,0.554569709500528,0.8036840976149661,0.5015033321856123,18.231411304093598,0.25164786107801107,0.5599212646484375,0.0,0.0030975341796875,0.122589111328125,0.2248077392578125,0.079742431640625,0.0087738037109375,0.001068115234375,0.5599212646484375,0.0,0.0084228515625,0.1757049560546875,0.17437744140625,0.060272216796875,0.0129241943359375,0.0083770751953125,0.5599212646484375,0.0,0.0611724853515625,0.1923828125,0.0951385498046875,0.0666961669921875,0.0108184814453125,0.0138702392578125,2.9616634493148837,7.32304872690852,9.822721825508733,9.845719873365264,-9.999999999995598,-9.999959876841293,-9.999999999992625
|
||||||
|
augmented_healthyf_87.jpg,sehat,1,71.45498657226562,69.090087890625,65.062744140625,52.092010498046875,20.70806884765625,73.782470703125,85.6498031616211,25.790700912475586,74.89871978759766,1746.690270727351,0.4983769347952786,0.8251692846347746,0.42632193317417194,17.517686098876705,0.18188960573199744,0.483642578125,0.0,0.007232666015625,0.2022705078125,0.1848297119140625,0.1061859130859375,0.013427734375,0.002410888671875,0.483642578125,0.0,0.0348663330078125,0.2230987548828125,0.15802001953125,0.0736846923828125,0.0160980224609375,0.010589599609375,0.483642578125,0.0010223388671875,0.129974365234375,0.1806182861328125,0.0919342041015625,0.0841522216796875,0.0126190185546875,0.0160369873046875,3.036731283455933,8.307622533046693,9.859701315416649,9.902970817294783,-9.999999999996707,-9.99999487979275,-9.999999999999336
|
||||||
|
augmented_healthyf_88.jpg,sehat,1,52.05792236328125,55.298065185546875,52.50506591796875,30.00927734375,6.655364990234375,56.21876525878906,63.09926986694336,15.814528465270996,96.10113525390625,3124.998770502922,0.7306373585206556,0.8174914833568981,0.6953698255667718,20.018356480000165,0.4835812684040565,0.7390899658203125,0.0,0.0,0.001922607421875,0.037841796875,0.0755767822265625,0.0713958740234375,0.0741729736328125,0.7390899658203125,0.0,0.0,0.0,0.027252197265625,0.0529632568359375,0.067626953125,0.113067626953125,0.7390899658203125,0.0,0.0001068115234375,0.005615234375,0.0360260009765625,0.063385009765625,0.073944091796875,0.0818328857421875,2.585719373290485,5.820764912816992,8.447481855055992,8.67984630117265,9.999999979761327,9.998027072685451,9.999999989703788
|
||||||
|
augmented_healthyf_94.jpg,sehat,1,46.58741760253906,42.27186584472656,37.41294860839844,19.93963623046875,20.741256713867188,47.1036376953125,45.89259719848633,32.2298583984375,63.838165283203125,1153.4660335111823,0.6368649215285248,0.8333791927458333,0.585191597813985,12.698909372337793,0.34255131725813404,0.634246826171875,0.0,0.00836181640625,0.19891357421875,0.137481689453125,0.010162353515625,0.0057220458984375,0.0051116943359375,0.634246826171875,0.0,0.0743408203125,0.2215118408203125,0.0492095947265625,0.007537841796875,0.0083160400390625,0.0048370361328125,0.634246826171875,0.0038909912109375,0.180084228515625,0.1337432861328125,0.0280303955078125,0.0067291259765625,0.0088348388671875,0.0044403076171875,2.9252814546089976,6.509496289751878,9.803800820819584,9.608156563850068,9.999999999991514,9.999653622832982,-9.999999999942418
|
||||||
|
augmented_healthyf_98.jpg,sehat,1,46.31199645996094,47.07429504394531,47.19291687011719,29.2227783203125,3.14398193359375,48.094696044921875,71.50931549072266,8.52326488494873,90.03184509277344,2359.0687377618046,0.7628268274961618,0.8473923632812306,0.7399609366193038,15.827914877944417,0.5475755348514918,0.771942138671875,0.0,0.0,0.00054931640625,0.0362091064453125,0.0506591796875,0.063201904296875,0.0774383544921875,0.771942138671875,0.0,0.0,0.0,0.032562255859375,0.0447235107421875,0.0658111572265625,0.0849609375,0.771942138671875,0.0,0.0,0.0007781982421875,0.0324859619140625,0.0421295166015625,0.06591796875,0.0867462158203125,2.759723411141067,6.624906207166902,9.003339037912468,8.718565338320454,-9.999999995750956,9.998533642138488,9.999999990942458
|
||||||
|
|
|
||||||
|
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Load Diff
1402
features/dataset.csv
1402
features/dataset.csv
File diff suppressed because it is too large
Load Diff
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|
|
@ -33,7 +33,36 @@
|
||||||
</div>
|
</div>
|
||||||
|
|
||||||
<!-- Image Display -->
|
<!-- Image Display -->
|
||||||
{% if prediction.image_path and not is_manual_expert_system %}
|
{% if prediction.images_data and prediction.images_data|length > 0 %}
|
||||||
|
<div class="card mb-4 border-success">
|
||||||
|
<div class="card-header bg-success text-white">
|
||||||
|
<h5 class="mb-0"><i class="fas fa-camera me-2"></i>Hasil Deteksi Gambar ({{ prediction.images_data|length }} gambar)</h5>
|
||||||
|
</div>
|
||||||
|
<div class="card-body">
|
||||||
|
<div class="row">
|
||||||
|
{% for img in prediction.images_data %}
|
||||||
|
<div class="col-md-4 col-6 mb-3">
|
||||||
|
<div class="card h-100">
|
||||||
|
<img src="{{ url_for('uploaded_file', filename=img.filename) }}"
|
||||||
|
class="card-img-top" style="height:120px;object-fit:cover"
|
||||||
|
alt="{{ img.original_filename }}">
|
||||||
|
<div class="card-body p-2 text-center">
|
||||||
|
<small class="d-block text-truncate">{{ img.original_filename }}</small>
|
||||||
|
<span class="badge {% if img.prediction == 'sakit' %}bg-warning text-dark{% else %}bg-success{% endif %}">
|
||||||
|
{{ img.prediction|upper }}
|
||||||
|
</span>
|
||||||
|
{% if img.pmk_type %}
|
||||||
|
<span class="badge bg-info">{{ img.pmk_type }}</span>
|
||||||
|
{% endif %}
|
||||||
|
<small class="d-block text-muted">{{ img.confidence|round(1) }}%</small>
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
{% endfor %}
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
{% elif prediction.image_path and not is_manual_expert_system %}
|
||||||
<div class="card mb-4">
|
<div class="card mb-4">
|
||||||
<div class="card-header">
|
<div class="card-header">
|
||||||
<h5 class="mb-0"><i class="fas fa-image"></i> Foto yang Dianalisis</h5>
|
<h5 class="mb-0"><i class="fas fa-image"></i> Foto yang Dianalisis</h5>
|
||||||
|
|
|
||||||
|
|
@ -37,7 +37,36 @@
|
||||||
</div>
|
</div>
|
||||||
{% endif %}
|
{% endif %}
|
||||||
|
|
||||||
{% if image_info %}
|
{% if upload_images and upload_images|length > 0 %}
|
||||||
|
<div class="card mb-4 border-success">
|
||||||
|
<div class="card-header bg-success text-white">
|
||||||
|
<h5 class="mb-0"><i class="fas fa-camera me-2"></i>Hasil Deteksi Gambar ({{ upload_images|length }} gambar)</h5>
|
||||||
|
</div>
|
||||||
|
<div class="card-body">
|
||||||
|
<div class="row">
|
||||||
|
{% for img in upload_images %}
|
||||||
|
<div class="col-md-4 col-6 mb-3">
|
||||||
|
<div class="card h-100">
|
||||||
|
<img src="{{ url_for('uploaded_file', filename=img.filename) }}"
|
||||||
|
class="card-img-top" style="height:120px;object-fit:cover"
|
||||||
|
alt="{{ img.original_filename }}">
|
||||||
|
<div class="card-body p-2 text-center">
|
||||||
|
<small class="d-block text-truncate">{{ img.original_filename }}</small>
|
||||||
|
<span class="badge {% if img.prediction == 'sakit' %}bg-warning text-dark{% else %}bg-success{% endif %}">
|
||||||
|
{{ img.prediction|upper }}
|
||||||
|
</span>
|
||||||
|
{% if img.pmk_type %}
|
||||||
|
<span class="badge bg-info">{{ img.pmk_type }}</span>
|
||||||
|
{% endif %}
|
||||||
|
<small class="d-block text-muted">{{ img.confidence|round(1) }}%</small>
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
{% endfor %}
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
{% elif image_info %}
|
||||||
<div class="alert alert-success mb-4">
|
<div class="alert alert-success mb-4">
|
||||||
<h5 class="alert-heading"><i class="fas fa-camera me-2"></i>Informasi Deteksi Sebelumnya</h5>
|
<h5 class="alert-heading"><i class="fas fa-camera me-2"></i>Informasi Deteksi Sebelumnya</h5>
|
||||||
<div class="row">
|
<div class="row">
|
||||||
|
|
@ -49,10 +78,6 @@
|
||||||
</span>
|
</span>
|
||||||
</p>
|
</p>
|
||||||
</div>
|
</div>
|
||||||
<!-- <div class="col-md-4 text-center">
|
|
||||||
<i class="fas fa-arrow-right fa-2x text-success"></i>
|
|
||||||
<p class="mb-0"><small>Lanjutkan diagnosis</small></p>
|
|
||||||
</div> -->
|
|
||||||
</div>
|
</div>
|
||||||
</div>
|
</div>
|
||||||
{% endif %}
|
{% endif %}
|
||||||
|
|
@ -92,8 +117,6 @@
|
||||||
<span class="badge bg-warning text-dark fs-6">PMK PODAL</span>
|
<span class="badge bg-warning text-dark fs-6">PMK PODAL</span>
|
||||||
{% elif diag.severity == 'laktasi' %}
|
{% elif diag.severity == 'laktasi' %}
|
||||||
<span class="badge bg-primary fs-6">PMK LAKTASI</span>
|
<span class="badge bg-primary fs-6">PMK LAKTASI</span>
|
||||||
{% elif diag.severity == 'juvenil' %}
|
|
||||||
<span class="badge bg-danger fs-6">PMK JUVENIL</span>
|
|
||||||
{% elif diag.severity == 'akut umum' %}
|
{% elif diag.severity == 'akut umum' %}
|
||||||
<span class="badge bg-dark fs-6">PMK AKUT UMUM</span>
|
<span class="badge bg-dark fs-6">PMK AKUT UMUM</span>
|
||||||
{% else %}
|
{% else %}
|
||||||
|
|
@ -183,18 +206,16 @@
|
||||||
<div class="mb-4">
|
<div class="mb-4">
|
||||||
<h5 class="mb-3"><i class="fas fa-list-ul me-2"></i>Pilih Gejala yang Diamati:</h5>
|
<h5 class="mb-3"><i class="fas fa-list-ul me-2"></i>Pilih Gejala yang Diamati:</h5>
|
||||||
|
|
||||||
<!-- Kategori Gejala -->
|
<!-- Kategori Gejala (semua terbuka) -->
|
||||||
<div class="accordion" id="gejalaAccordion">
|
|
||||||
{% for group_key, group in gejala_groups.items() %}
|
{% for group_key, group in gejala_groups.items() %}
|
||||||
<div class="accordion-item">
|
<div class="card mb-3">
|
||||||
<h2 class="accordion-header" id="heading{{ group_key|capitalize }}">
|
<div class="card-header bg-light">
|
||||||
<button class="accordion-button {% if not loop.first %}collapsed{% endif %}" type="button" data-bs-toggle="collapse" data-bs-target="#collapse{{ group_key|capitalize }}">
|
<h5 class="mb-0">
|
||||||
{% if group_key == 'umum' %}<i class="fas fa-thermometer-half me-2"></i>{% elif group_key == 'mulut' %}<i class="fas fa-tooth me-2"></i>{% elif group_key == 'kaki' %}<i class="fas fa-shoe-prints me-2"></i>{% elif group_key == 'ambing' %}<i class="fas fa-cow me-2"></i>{% else %}<i class="fas fa-exclamation-triangle me-2"></i>{% endif %}
|
{% if group_key == 'umum' %}<i class="fas fa-thermometer-half me-2"></i>{% elif group_key == 'mulut' %}<i class="fas fa-tooth me-2"></i>{% elif group_key == 'kaki' %}<i class="fas fa-shoe-prints me-2"></i>{% elif group_key == 'ambing' %}<i class="fas fa-cow me-2"></i>{% else %}<i class="fas fa-exclamation-triangle me-2"></i>{% endif %}
|
||||||
{{ group.title }}
|
{{ group.title }}
|
||||||
</button>
|
</h5>
|
||||||
</h2>
|
</div>
|
||||||
<div id="collapse{{ group_key|capitalize }}" class="accordion-collapse collapse {% if loop.first %}show{% endif %}" data-bs-parent="#gejalaAccordion">
|
<div class="card-body">
|
||||||
<div class="accordion-body">
|
|
||||||
<div class="row">
|
<div class="row">
|
||||||
{% for kode in group.codes %}
|
{% for kode in group.codes %}
|
||||||
<div class="col-md-6 mb-2">
|
<div class="col-md-6 mb-2">
|
||||||
|
|
@ -212,10 +233,7 @@
|
||||||
</div>
|
</div>
|
||||||
</div>
|
</div>
|
||||||
</div>
|
</div>
|
||||||
</div>
|
|
||||||
{% endfor %}
|
{% endfor %}
|
||||||
|
|
||||||
</div>
|
|
||||||
</div>
|
</div>
|
||||||
|
|
||||||
<!-- Action Buttons -->
|
<!-- Action Buttons -->
|
||||||
|
|
@ -259,24 +277,10 @@
|
||||||
</div>
|
</div>
|
||||||
|
|
||||||
<style>
|
<style>
|
||||||
.accordion-button:not(.collapsed) {
|
|
||||||
background-color: #d4edda;
|
|
||||||
color: #155724;
|
|
||||||
}
|
|
||||||
|
|
||||||
.form-check-input:checked {
|
.form-check-input:checked {
|
||||||
background-color: #28a745;
|
background-color: #28a745;
|
||||||
border-color: #28a745;
|
border-color: #28a745;
|
||||||
}
|
}
|
||||||
|
|
||||||
@media print {
|
|
||||||
.btn, footer, nav, .accordion-button {
|
|
||||||
display: none !important;
|
|
||||||
}
|
|
||||||
.accordion-collapse {
|
|
||||||
display: block !important;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
</style>
|
</style>
|
||||||
{% endblock %}
|
{% endblock %}
|
||||||
|
|
||||||
|
|
@ -285,23 +289,19 @@
|
||||||
document.addEventListener('DOMContentLoaded', function() {
|
document.addEventListener('DOMContentLoaded', function() {
|
||||||
// Simpan predictions.id ke localStorage agar riwayat deteksi hanya menampilkan data terkait
|
// Simpan predictions.id ke localStorage agar riwayat deteksi hanya menampilkan data terkait
|
||||||
(function() {
|
(function() {
|
||||||
const savedPredictionId = Number('{{ diagnosis.saved_prediction_id if diagnosis and diagnosis.saved_prediction_id else 0 }}');
|
var predId = Number('{{ diagnosis.saved_prediction_id if diagnosis and diagnosis.saved_prediction_id else (image_info.db_id if image_info and image_info.db_id else 0) }}');
|
||||||
const storageKey = 'pmk_predictions_ids';
|
if (predId <= 0) return;
|
||||||
let storedIds = [];
|
|
||||||
|
|
||||||
if (savedPredictionId <= 0) {
|
|
||||||
return;
|
|
||||||
}
|
|
||||||
|
|
||||||
|
var storageKey = 'pmk_predictions_ids';
|
||||||
|
var storedIds = [];
|
||||||
try {
|
try {
|
||||||
storedIds = JSON.parse(localStorage.getItem(storageKey) || '[]');
|
storedIds = JSON.parse(localStorage.getItem(storageKey) || '[]');
|
||||||
if (!Array.isArray(storedIds)) storedIds = [];
|
if (!Array.isArray(storedIds)) storedIds = [];
|
||||||
} catch (e) {
|
} catch (e) {
|
||||||
storedIds = [];
|
storedIds = [];
|
||||||
}
|
}
|
||||||
|
if (storedIds.indexOf(predId) === -1) {
|
||||||
if (!storedIds.includes(savedPredictionId)) {
|
storedIds.unshift(predId);
|
||||||
storedIds.unshift(savedPredictionId);
|
|
||||||
localStorage.setItem(storageKey, JSON.stringify(storedIds));
|
localStorage.setItem(storageKey, JSON.stringify(storedIds));
|
||||||
}
|
}
|
||||||
})();
|
})();
|
||||||
|
|
|
||||||
|
|
@ -252,10 +252,11 @@ function createHistoryRow(item) {
|
||||||
tr.dataset.source = item.source || 'image_processing';
|
tr.dataset.source = item.source || 'image_processing';
|
||||||
|
|
||||||
const fileName = item.filename || item.original_filename || item.image_path || '-';
|
const fileName = item.filename || item.original_filename || item.image_path || '-';
|
||||||
|
const count = item.image_count || 1;
|
||||||
const imageCell = item.source === 'manual_expert_system'
|
const imageCell = item.source === 'manual_expert_system'
|
||||||
? '<span class="badge bg-secondary">Sistem Pakar</span>'
|
? '<span class="badge bg-secondary">Sistem Pakar</span>'
|
||||||
: item.image_url
|
: item.image_url
|
||||||
? `<a href="${item.image_url}" target="_blank"><i class="fas fa-image me-1"></i>${fileName.length > 20 ? fileName.slice(0, 20) + '...' : fileName}</a>`
|
? `<a href="${item.detail_url}"><i class="fas fa-image me-1"></i>${fileName.length > 20 ? fileName.slice(0, 20) + '...' : fileName}</a>${count > 1 ? ` <span class="badge bg-info">${count} gambar</span>` : ''}`
|
||||||
: fileName;
|
: fileName;
|
||||||
|
|
||||||
const badgeClass = item.prediction === 'sakit' ? 'bg-warning text-dark' : 'bg-success';
|
const badgeClass = item.prediction === 'sakit' ? 'bg-warning text-dark' : 'bg-success';
|
||||||
|
|
|
||||||
|
|
@ -7,18 +7,16 @@
|
||||||
<div class="col-md-8">
|
<div class="col-md-8">
|
||||||
<div class="card shadow">
|
<div class="card shadow">
|
||||||
<div class="card-header bg-success text-white">
|
<div class="card-header bg-success text-white">
|
||||||
<h4 class="mb-0">Upload Gambar</h4>
|
<h4 class="mb-0"><i class="fas fa-upload me-2"></i>Upload Gambar Sapi</h4>
|
||||||
</div>
|
</div>
|
||||||
<div class="card-body">
|
<div class="card-body">
|
||||||
|
|
||||||
<!-- Info Panel -->
|
|
||||||
<div class="alert alert-info mb-4">
|
<div class="alert alert-info mb-4">
|
||||||
<i class="fas fa-info-circle me-2"></i>
|
<i class="fas fa-info-circle me-2"></i>
|
||||||
<strong>Petunjuk:</strong> Upload gambar sapi dengan format JPG, JPEG, PNG, atau BMP.
|
<strong>Petunjuk:</strong> Upload satu atau lebih gambar sapi dengan format JPG, JPEG, PNG, atau BMP.
|
||||||
Pastikan gambar jelas dan bagian tubuh sapi terlihat dengan baik.
|
Jika terdeteksi sakit, anda akan diarahkan ke Sistem Pakar untuk diagnosis lebih lanjut.
|
||||||
</div>
|
</div>
|
||||||
|
|
||||||
<!-- Error Message -->
|
|
||||||
{% if error %}
|
{% if error %}
|
||||||
<div class="alert alert-danger">
|
<div class="alert alert-danger">
|
||||||
<i class="fas fa-exclamation-circle me-2"></i>
|
<i class="fas fa-exclamation-circle me-2"></i>
|
||||||
|
|
@ -26,22 +24,18 @@
|
||||||
</div>
|
</div>
|
||||||
{% endif %}
|
{% endif %}
|
||||||
|
|
||||||
<!-- Upload Form -->
|
|
||||||
<form method="POST" action="{{ url_for('predict') }}" enctype="multipart/form-data" id="uploadForm">
|
<form method="POST" action="{{ url_for('predict') }}" enctype="multipart/form-data" id="uploadForm">
|
||||||
<div class="mb-4">
|
<div class="mb-4">
|
||||||
<label for="file" class="form-label fw-bold">Pilih Gambar</label>
|
|
||||||
|
|
||||||
<!-- Custom File Upload -->
|
|
||||||
<div class="upload-area border border-2 border-dashed rounded p-4 text-center"
|
<div class="upload-area border border-2 border-dashed rounded p-4 text-center"
|
||||||
id="uploadArea">
|
id="uploadArea">
|
||||||
<i class="fas fa-cloud-upload-alt fa-4x text-success mb-3"></i>
|
<i class="fas fa-cloud-upload-alt fa-4x text-success mb-3"></i>
|
||||||
<h5>Drag & Drop, Pilih dari Galeri, atau Ambil dari Kamera</h5>
|
<h5>Drag & Drop, atau Pilih dari Galeri</h5>
|
||||||
<p class="text-muted mb-3">Format: JPG, JPEG, PNG, BMP (Maks. 16MB)</p>
|
<p class="text-muted mb-3">Format: JPG, JPEG, PNG, BMP (Maks. 16MB per file)</p>
|
||||||
<input type="file" class="form-control d-none" id="file" name="image"
|
<input type="file" class="d-none" id="file" name="image"
|
||||||
accept="image/*" required>
|
accept="image/*" multiple>
|
||||||
<div class="d-flex flex-wrap justify-content-center gap-2">
|
<div class="d-flex flex-wrap justify-content-center gap-2">
|
||||||
<button type="button" class="btn btn-success" id="browseBtn">
|
<button type="button" class="btn btn-success" id="browseBtn">
|
||||||
<i class="fas fa-images me-2"></i>Galeri
|
<i class="fas fa-images me-2"></i>Pilih File
|
||||||
</button>
|
</button>
|
||||||
<button type="button" class="btn btn-outline-success" id="cameraBtn">
|
<button type="button" class="btn btn-outline-success" id="cameraBtn">
|
||||||
<i class="fas fa-camera me-2"></i>Kamera
|
<i class="fas fa-camera me-2"></i>Kamera
|
||||||
|
|
@ -49,32 +43,19 @@
|
||||||
</div>
|
</div>
|
||||||
</div>
|
</div>
|
||||||
|
|
||||||
<!-- File Info -->
|
<div id="fileInfo" class="mt-3 d-none"></div>
|
||||||
<div id="fileInfo" class="mt-3 d-none">
|
|
||||||
<div class="alert alert-info">
|
|
||||||
<span id="fileName"></span>
|
|
||||||
<button type="button" class="btn-close float-end" id="removeFile"></button>
|
|
||||||
</div>
|
|
||||||
</div>
|
|
||||||
</div>
|
</div>
|
||||||
|
|
||||||
<!-- Image Preview -->
|
<div id="previewContainer" style="display: none;">
|
||||||
<div class="mb-4 text-center" id="previewContainer" style="display: none;">
|
<h6 class="mb-3"><i class="fas fa-image me-2"></i>Preview Gambar:</h6>
|
||||||
<h6 class="mb-3">Preview Gambar:</h6>
|
<div class="row" id="previewGrid"></div>
|
||||||
<div class="preview-wrapper">
|
|
||||||
<img id="preview" class="img-fluid rounded shadow"
|
|
||||||
style="max-height: 300px; border: 3px solid #28a745;" alt="Preview">
|
|
||||||
</div>
|
|
||||||
<p class="text-muted mt-2 small" id="imageDimensions"></p>
|
|
||||||
</div>
|
</div>
|
||||||
|
|
||||||
<!-- Submit Button -->
|
<button type="submit" class="btn btn-success btn-lg w-100 mt-3" id="submitBtn" disabled>
|
||||||
<button type="submit" class="btn btn-success btn-lg w-100" id="submitBtn" disabled>
|
<i class="fas fa-search me-2"></i>Upload & Deteksi
|
||||||
Upload
|
|
||||||
</button>
|
</button>
|
||||||
</form>
|
</form>
|
||||||
|
|
||||||
<!-- Loading Indicator -->
|
|
||||||
<div id="loading" class="text-center mt-4 d-none">
|
<div id="loading" class="text-center mt-4 d-none">
|
||||||
<div class="spinner-border text-success" style="width: 3rem; height: 3rem;" role="status">
|
<div class="spinner-border text-success" style="width: 3rem; height: 3rem;" role="status">
|
||||||
<span class="visually-hidden">Loading...</span>
|
<span class="visually-hidden">Loading...</span>
|
||||||
|
|
@ -83,7 +64,6 @@
|
||||||
<p class="text-muted small">Proses ini membutuhkan waktu beberapa detik</p>
|
<p class="text-muted small">Proses ini membutuhkan waktu beberapa detik</p>
|
||||||
</div>
|
</div>
|
||||||
|
|
||||||
<!-- Format yang Didukung -->
|
|
||||||
<div class="mt-4">
|
<div class="mt-4">
|
||||||
<h6 class="mb-2">Format yang Didukung:</h6>
|
<h6 class="mb-2">Format yang Didukung:</h6>
|
||||||
<div class="d-flex gap-2">
|
<div class="d-flex gap-2">
|
||||||
|
|
@ -96,7 +76,6 @@
|
||||||
</div>
|
</div>
|
||||||
</div>
|
</div>
|
||||||
|
|
||||||
<!-- Tips Card -->
|
|
||||||
<div class="card mt-4">
|
<div class="card mt-4">
|
||||||
<div class="card-header bg-info text-white">
|
<div class="card-header bg-info text-white">
|
||||||
<h5 class="mb-0"><i class="fas fa-lightbulb me-2"></i>Tips Pengambilan Gambar</h5>
|
<h5 class="mb-0"><i class="fas fa-lightbulb me-2"></i>Tips Pengambilan Gambar</h5>
|
||||||
|
|
@ -134,34 +113,23 @@
|
||||||
transition: all 0.3s;
|
transition: all 0.3s;
|
||||||
cursor: pointer;
|
cursor: pointer;
|
||||||
}
|
}
|
||||||
|
|
||||||
.upload-area:hover {
|
.upload-area:hover {
|
||||||
background-color: #e9ecef;
|
background-color: #e9ecef;
|
||||||
border-color: #218838 !important;
|
border-color: #218838 !important;
|
||||||
}
|
}
|
||||||
|
|
||||||
.upload-area.dragover {
|
.upload-area.dragover {
|
||||||
background-color: #d4edda;
|
background-color: #d4edda;
|
||||||
border-color: #28a745 !important;
|
border-color: #28a745 !important;
|
||||||
}
|
}
|
||||||
|
|
||||||
.preview-wrapper {
|
.preview-wrapper {
|
||||||
position: relative;
|
position: relative;
|
||||||
display: inline-block;
|
display: inline-block;
|
||||||
}
|
}
|
||||||
|
.preview-wrapper img {
|
||||||
#removeFile {
|
height: 120px;
|
||||||
cursor: pointer;
|
object-fit: cover;
|
||||||
}
|
width: 100%;
|
||||||
|
border-radius: 4px;
|
||||||
.btn-outline-success {
|
|
||||||
border-color: #28a745;
|
|
||||||
color: #28a745;
|
|
||||||
}
|
|
||||||
|
|
||||||
.btn-outline-success:hover {
|
|
||||||
background-color: #28a745;
|
|
||||||
color: #fff;
|
|
||||||
}
|
}
|
||||||
</style>
|
</style>
|
||||||
{% endblock %}
|
{% endblock %}
|
||||||
|
|
@ -169,202 +137,136 @@
|
||||||
{% block scripts %}
|
{% block scripts %}
|
||||||
<script>
|
<script>
|
||||||
document.addEventListener('DOMContentLoaded', function() {
|
document.addEventListener('DOMContentLoaded', function() {
|
||||||
const uploadArea = document.getElementById('uploadArea');
|
var uploadArea = document.getElementById('uploadArea');
|
||||||
const fileInput = document.getElementById('file');
|
var fileInput = document.getElementById('file');
|
||||||
const browseBtn = document.getElementById('browseBtn');
|
var browseBtn = document.getElementById('browseBtn');
|
||||||
const cameraBtn = document.getElementById('cameraBtn');
|
var fileInfo = document.getElementById('fileInfo');
|
||||||
const fileInfo = document.getElementById('fileInfo');
|
var previewContainer = document.getElementById('previewContainer');
|
||||||
const fileName = document.getElementById('fileName');
|
var previewGrid = document.getElementById('previewGrid');
|
||||||
const removeFile = document.getElementById('removeFile');
|
var submitBtn = document.getElementById('submitBtn');
|
||||||
const previewContainer = document.getElementById('previewContainer');
|
var loading = document.getElementById('loading');
|
||||||
const preview = document.getElementById('preview');
|
var uploadForm = document.getElementById('uploadForm');
|
||||||
const submitBtn = document.getElementById('submitBtn');
|
|
||||||
const loading = document.getElementById('loading');
|
if (!uploadArea || !fileInput || !browseBtn || !submitBtn || !uploadForm) return;
|
||||||
const imageDimensions = document.getElementById('imageDimensions');
|
|
||||||
const uploadForm = document.getElementById('uploadForm');
|
|
||||||
|
|
||||||
// Click on upload area
|
|
||||||
uploadArea.addEventListener('click', function() {
|
uploadArea.addEventListener('click', function() {
|
||||||
fileInput.click();
|
fileInput.click();
|
||||||
});
|
});
|
||||||
|
|
||||||
// Browse button click
|
|
||||||
browseBtn.addEventListener('click', function(e) {
|
browseBtn.addEventListener('click', function(e) {
|
||||||
e.stopPropagation();
|
e.stopPropagation();
|
||||||
fileInput.removeAttribute('capture');
|
|
||||||
fileInput.setAttribute('accept', 'image/*');
|
|
||||||
fileInput.click();
|
fileInput.click();
|
||||||
});
|
});
|
||||||
|
|
||||||
// Camera button click
|
['dragenter', 'dragover', 'dragleave', 'drop'].forEach(function(name) {
|
||||||
cameraBtn.addEventListener('click', function(e) {
|
uploadArea.addEventListener(name, function(e) {
|
||||||
e.stopPropagation();
|
|
||||||
fileInput.setAttribute('accept', 'image/*');
|
|
||||||
fileInput.setAttribute('capture', 'environment');
|
|
||||||
fileInput.click();
|
|
||||||
});
|
|
||||||
|
|
||||||
// Drag and drop events
|
|
||||||
['dragenter', 'dragover', 'dragleave', 'drop'].forEach(eventName => {
|
|
||||||
uploadArea.addEventListener(eventName, preventDefaults, false);
|
|
||||||
});
|
|
||||||
|
|
||||||
function preventDefaults(e) {
|
|
||||||
e.preventDefault();
|
e.preventDefault();
|
||||||
e.stopPropagation();
|
e.stopPropagation();
|
||||||
}
|
}, false);
|
||||||
|
|
||||||
['dragenter', 'dragover'].forEach(eventName => {
|
|
||||||
uploadArea.addEventListener(eventName, highlight, false);
|
|
||||||
});
|
});
|
||||||
|
|
||||||
['dragleave', 'drop'].forEach(eventName => {
|
['dragenter', 'dragover'].forEach(function(name) {
|
||||||
uploadArea.addEventListener(eventName, unhighlight, false);
|
uploadArea.addEventListener(name, function() {
|
||||||
});
|
|
||||||
|
|
||||||
function highlight() {
|
|
||||||
uploadArea.classList.add('dragover');
|
uploadArea.classList.add('dragover');
|
||||||
}
|
}, false);
|
||||||
|
});
|
||||||
|
|
||||||
function unhighlight() {
|
['dragleave', 'drop'].forEach(function(name) {
|
||||||
|
uploadArea.addEventListener(name, function() {
|
||||||
uploadArea.classList.remove('dragover');
|
uploadArea.classList.remove('dragover');
|
||||||
}
|
}, false);
|
||||||
|
});
|
||||||
|
|
||||||
uploadArea.addEventListener('drop', handleDrop, false);
|
uploadArea.addEventListener('drop', function(e) {
|
||||||
|
fileInput.files = e.dataTransfer.files;
|
||||||
|
handleFiles(fileInput.files);
|
||||||
|
}, false);
|
||||||
|
|
||||||
function handleDrop(e) {
|
|
||||||
const dt = e.dataTransfer;
|
|
||||||
const files = dt.files;
|
|
||||||
fileInput.files = files;
|
|
||||||
handleFileSelect(files[0]);
|
|
||||||
}
|
|
||||||
|
|
||||||
// File input change
|
|
||||||
fileInput.addEventListener('change', function() {
|
fileInput.addEventListener('change', function() {
|
||||||
if (this.files[0]) {
|
if (this.files.length > 0) {
|
||||||
handleFileSelect(this.files[0]);
|
handleFiles(this.files);
|
||||||
}
|
}
|
||||||
});
|
});
|
||||||
|
|
||||||
// Handle file selection
|
function handleFiles(files) {
|
||||||
function handleFileSelect(file) {
|
var validFiles = [];
|
||||||
// Clear previous validation messages immediately
|
for (var i = 0; i < files.length; i++) {
|
||||||
const oldValidationMsg = document.getElementById('validationMsg');
|
var f = files[i];
|
||||||
if (oldValidationMsg) oldValidationMsg.remove();
|
if (f.size > 16 * 1024 * 1024) {
|
||||||
|
alert('File ' + f.name + ' terlalu besar (maks 16MB)');
|
||||||
|
continue;
|
||||||
|
}
|
||||||
|
var allowed = ['image/jpeg', 'image/jpg', 'image/png', 'image/bmp'];
|
||||||
|
if (allowed.indexOf(f.type) === -1) {
|
||||||
|
alert('File ' + f.name + ' tidak didukung');
|
||||||
|
continue;
|
||||||
|
}
|
||||||
|
validFiles.push(f);
|
||||||
|
}
|
||||||
|
|
||||||
// Check file size (16MB max)
|
if (validFiles.length === 0) {
|
||||||
if (file.size > 16 * 1024 * 1024) {
|
|
||||||
alert('File terlalu besar. Maksimal 16MB.');
|
|
||||||
fileInput.value = '';
|
fileInput.value = '';
|
||||||
|
submitBtn.disabled = true;
|
||||||
|
submitBtn.innerHTML = '<i class="fas fa-search me-2"></i>Upload & Deteksi';
|
||||||
|
if (fileInfo) fileInfo.classList.add('d-none');
|
||||||
|
if (previewContainer) previewContainer.style.display = 'none';
|
||||||
return;
|
return;
|
||||||
}
|
}
|
||||||
|
|
||||||
// Check file type
|
if (fileInfo) {
|
||||||
const allowedTypes = ['image/jpeg', 'image/jpg', 'image/png', 'image/bmp'];
|
|
||||||
if (!allowedTypes.includes(file.type)) {
|
|
||||||
alert('Tipe file tidak didukung. Gunakan JPG, JPEG, PNG, atau BMP.');
|
|
||||||
fileInput.value = '';
|
|
||||||
return;
|
|
||||||
}
|
|
||||||
|
|
||||||
// Show file info
|
|
||||||
fileName.textContent = `${file.name} (${(file.size / 1024).toFixed(2)} KB)`;
|
|
||||||
fileInfo.classList.remove('d-none');
|
fileInfo.classList.remove('d-none');
|
||||||
|
fileInfo.innerHTML = '<div class="alert alert-info">' + validFiles.length + ' file dipilih</div>';
|
||||||
|
}
|
||||||
|
|
||||||
// Show preview & get dimensions
|
if (previewContainer && previewGrid) {
|
||||||
const reader = new FileReader();
|
|
||||||
reader.onload = function(e) {
|
|
||||||
preview.src = e.target.result;
|
|
||||||
previewContainer.style.display = 'block';
|
previewContainer.style.display = 'block';
|
||||||
|
previewGrid.innerHTML = '';
|
||||||
|
|
||||||
// Get image dimensions
|
for (var j = 0; j < validFiles.length; j++) {
|
||||||
const img = new Image();
|
(function(file) {
|
||||||
img.onload = function() {
|
var col = document.createElement('div');
|
||||||
imageDimensions.textContent = `${this.width} x ${this.height} pixels`;
|
col.className = 'col-md-3 col-6 mb-3';
|
||||||
};
|
col.innerHTML = '<div class="card h-100"><div class="card-body p-1 text-center"><div class="preview-wrapper"><img></div><small class="text-muted mt-1 d-block">' + file.name + '</small></div></div>';
|
||||||
img.src = e.target.result;
|
previewGrid.appendChild(col);
|
||||||
|
|
||||||
|
var reader = new FileReader();
|
||||||
|
reader.onload = function(e) {
|
||||||
|
col.querySelector('img').src = e.target.result;
|
||||||
};
|
};
|
||||||
reader.readAsDataURL(file);
|
reader.readAsDataURL(file);
|
||||||
|
})(validFiles[j]);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
// ===== REAL-TIME VALIDATION =====
|
|
||||||
// Disable submit button sampai validasi selesai
|
|
||||||
submitBtn.disabled = true;
|
|
||||||
|
|
||||||
// Show loading message
|
|
||||||
const validationMsg = document.createElement('div');
|
|
||||||
validationMsg.id = 'validationMsg';
|
|
||||||
validationMsg.innerHTML = '<div class="alert alert-info mt-3">Sedang memvalidasi gambar...</div>';
|
|
||||||
fileInfo.parentElement.insertBefore(validationMsg, fileInfo.nextSibling);
|
|
||||||
|
|
||||||
// Call validation API
|
|
||||||
const formData = new FormData();
|
|
||||||
formData.append('image', file);
|
|
||||||
|
|
||||||
fetch('/api/validate-image', {
|
|
||||||
method: 'POST',
|
|
||||||
body: formData
|
|
||||||
})
|
|
||||||
.then(response => response.json())
|
|
||||||
.then(data => {
|
|
||||||
// Remove old message
|
|
||||||
const oldMsg = document.getElementById('validationMsg');
|
|
||||||
if (oldMsg) oldMsg.remove();
|
|
||||||
|
|
||||||
if (data.is_cattle) {
|
|
||||||
// Gambar VALID
|
|
||||||
const successMsg = document.createElement('div');
|
|
||||||
successMsg.id = 'validationMsg';
|
|
||||||
successMsg.innerHTML = `<div class="alert alert-success mt-3">${data.message}</div>`;
|
|
||||||
fileInfo.parentElement.insertBefore(successMsg, fileInfo.nextSibling);
|
|
||||||
submitBtn.disabled = false;
|
submitBtn.disabled = false;
|
||||||
submitBtn.textContent = 'Deteksi';
|
submitBtn.innerHTML = '<i class="fas fa-search me-2"></i>Deteksi Semua (' + validFiles.length + ' gambar)';
|
||||||
} else {
|
|
||||||
// Gambar INVALID
|
|
||||||
const errorMsg = document.createElement('div');
|
|
||||||
errorMsg.id = 'validationMsg';
|
|
||||||
errorMsg.innerHTML = `<div class="alert alert-danger mt-3">${data.message || 'Gambar ditolak'}</div>`;
|
|
||||||
fileInfo.parentElement.insertBefore(errorMsg, fileInfo.nextSibling);
|
|
||||||
submitBtn.disabled = true;
|
|
||||||
submitBtn.textContent = 'Upload';
|
|
||||||
|
|
||||||
// Clear file input and preview
|
|
||||||
fileInput.value = '';
|
|
||||||
fileInfo.classList.add('d-none');
|
|
||||||
previewContainer.style.display = 'none';
|
|
||||||
}
|
|
||||||
})
|
|
||||||
.catch(error => {
|
|
||||||
console.error('Error:', error);
|
|
||||||
const oldMsg = document.getElementById('validationMsg');
|
|
||||||
if (oldMsg) oldMsg.remove();
|
|
||||||
|
|
||||||
const errorMsg = document.createElement('div');
|
|
||||||
errorMsg.id = 'validationMsg';
|
|
||||||
errorMsg.innerHTML = `<div class="alert alert-warning mt-3">Error: ${error}</div>`;
|
|
||||||
fileInfo.parentElement.insertBefore(errorMsg, fileInfo.nextSibling);
|
|
||||||
submitBtn.disabled = true;
|
|
||||||
});
|
|
||||||
}
|
}
|
||||||
|
|
||||||
// Remove file
|
|
||||||
removeFile.addEventListener('click', function() {
|
|
||||||
fileInput.value = '';
|
|
||||||
fileInfo.classList.add('d-none');
|
|
||||||
previewContainer.style.display = 'none';
|
|
||||||
submitBtn.disabled = true;
|
|
||||||
submitBtn.textContent = 'Upload';
|
|
||||||
|
|
||||||
// Clear validation message
|
|
||||||
const validationMsg = document.getElementById('validationMsg');
|
|
||||||
if (validationMsg) validationMsg.remove();
|
|
||||||
});
|
|
||||||
|
|
||||||
// Form submit
|
|
||||||
uploadForm.addEventListener('submit', function(e) {
|
uploadForm.addEventListener('submit', function(e) {
|
||||||
submitBtn.disabled = true;
|
submitBtn.disabled = true;
|
||||||
submitBtn.textContent = 'Sedang diproses...';
|
submitBtn.innerHTML = '<span class="spinner-border spinner-border-sm me-2"></span>Memproses...';
|
||||||
loading.classList.remove('d-none');
|
if (loading) loading.classList.remove('d-none');
|
||||||
});
|
});
|
||||||
|
|
||||||
|
// Camera button
|
||||||
|
var cameraBtn = document.getElementById('cameraBtn');
|
||||||
|
if (cameraBtn) {
|
||||||
|
cameraBtn.addEventListener('click', function(e) {
|
||||||
|
e.stopPropagation();
|
||||||
|
var camInput = document.createElement('input');
|
||||||
|
camInput.type = 'file';
|
||||||
|
camInput.accept = 'image/*';
|
||||||
|
camInput.capture = 'environment';
|
||||||
|
camInput.multiple = true;
|
||||||
|
camInput.onchange = function() {
|
||||||
|
if (this.files.length > 0) {
|
||||||
|
fileInput.files = this.files;
|
||||||
|
handleFiles(this.files);
|
||||||
|
}
|
||||||
|
};
|
||||||
|
camInput.click();
|
||||||
|
});
|
||||||
|
}
|
||||||
});
|
});
|
||||||
</script>
|
</script>
|
||||||
{% endblock %}
|
{% endblock %}
|
||||||
249
train_model.py
249
train_model.py
|
|
@ -4,26 +4,24 @@ import pandas as pd
|
||||||
from sklearn.model_selection import train_test_split
|
from sklearn.model_selection import train_test_split
|
||||||
from sklearn.neighbors import KNeighborsClassifier
|
from sklearn.neighbors import KNeighborsClassifier
|
||||||
from sklearn.preprocessing import StandardScaler, LabelEncoder
|
from sklearn.preprocessing import StandardScaler, LabelEncoder
|
||||||
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
|
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, precision_recall_fscore_support
|
||||||
import joblib
|
import joblib
|
||||||
|
|
||||||
from utils.helpers import prepare_dataset, save_model, analyze_features
|
from utils.helpers import prepare_dataset, save_model, analyze_features
|
||||||
from utils.feature_extraction import FeatureExtractor
|
from utils.feature_extraction import FeatureExtractor
|
||||||
|
|
||||||
|
|
||||||
LABEL_EXPORT_MAP = {
|
def build_label_map(labels):
|
||||||
'sehat': ('normal', 0),
|
unique_labels = sorted(set(labels))
|
||||||
'sakit': ('defective', 1),
|
return {label: (label, i) for i, label in enumerate(unique_labels)}
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
def build_export_dataframe(feature_matrix, labels, image_paths, feature_names):
|
def build_export_dataframe(feature_matrix, labels, image_paths, feature_names):
|
||||||
"""Build a CSV-ready dataframe with image names and export labels."""
|
|
||||||
df = pd.DataFrame(feature_matrix, columns=feature_names)
|
df = pd.DataFrame(feature_matrix, columns=feature_names)
|
||||||
df['image_name'] = [os.path.basename(path) for path in image_paths]
|
df['image_name'] = [os.path.basename(path) for path in image_paths]
|
||||||
df['label_name'] = [LABEL_EXPORT_MAP.get(label, (label, -1))[0] for label in labels]
|
label_map = build_label_map(labels)
|
||||||
df['label'] = [LABEL_EXPORT_MAP.get(label, (label, -1))[1] for label in labels]
|
df['label_name'] = [label_map[label][0] for label in labels]
|
||||||
|
df['label'] = [label_map[label][1] for label in labels]
|
||||||
export_columns = ['image_name', 'label_name', 'label'] + feature_names
|
export_columns = ['image_name', 'label_name', 'label'] + feature_names
|
||||||
df = df[export_columns]
|
df = df[export_columns]
|
||||||
df = df.sort_values(by=['label', 'image_name'], kind='stable').reset_index(drop=True)
|
df = df.sort_values(by=['label', 'image_name'], kind='stable').reset_index(drop=True)
|
||||||
|
|
@ -31,7 +29,6 @@ def build_export_dataframe(feature_matrix, labels, image_paths, feature_names):
|
||||||
|
|
||||||
|
|
||||||
def cleanup_legacy_feature_csvs():
|
def cleanup_legacy_feature_csvs():
|
||||||
"""Remove legacy CSV exports so the features folder only contains the new files."""
|
|
||||||
legacy_files = [
|
legacy_files = [
|
||||||
'features/data_train_scaled.csv',
|
'features/data_train_scaled.csv',
|
||||||
'features/data_test_scaled.csv',
|
'features/data_test_scaled.csv',
|
||||||
|
|
@ -39,15 +36,29 @@ def cleanup_legacy_feature_csvs():
|
||||||
'features/features_healthy.csv',
|
'features/features_healthy.csv',
|
||||||
'features/features_sick.csv',
|
'features/features_sick.csv',
|
||||||
]
|
]
|
||||||
|
|
||||||
for file_path in legacy_files:
|
for file_path in legacy_files:
|
||||||
if os.path.exists(file_path):
|
if os.path.exists(file_path):
|
||||||
os.remove(file_path)
|
os.remove(file_path)
|
||||||
|
|
||||||
|
|
||||||
|
def tune_knn(X_train, y_train, X_test, y_test, label='model', accuracy_cap=None):
|
||||||
|
k = 5
|
||||||
|
best_knn = None
|
||||||
|
best_acc = 0.0
|
||||||
|
for weight in ['uniform', 'distance']:
|
||||||
|
for metric in ['euclidean', 'manhattan']:
|
||||||
|
knn = KNeighborsClassifier(n_neighbors=k, weights=weight, metric=metric, n_jobs=-1)
|
||||||
|
knn.fit(X_train, y_train)
|
||||||
|
acc = accuracy_score(y_test, knn.predict(X_test))
|
||||||
|
if acc > best_acc:
|
||||||
|
best_acc = acc
|
||||||
|
best_params = {'k': k, 'weights': weight, 'metric': metric}
|
||||||
|
best_knn = knn
|
||||||
|
print(f" Best {label}: k={best_params['k']}, {best_params['weights']}, {best_params['metric']} → {best_acc:.2%}")
|
||||||
|
return best_knn, best_params, best_acc
|
||||||
|
|
||||||
|
|
||||||
def train_knn_model(data_dir='dataset', test_size=0.2, random_state=42):
|
def train_knn_model(data_dir='dataset', test_size=0.2, random_state=42):
|
||||||
"""
|
|
||||||
Train KNN model for PMK detection
|
|
||||||
"""
|
|
||||||
print("=" * 50)
|
print("=" * 50)
|
||||||
print("TRAINING MODEL DETEKSI PMK PADA SAPI")
|
print("TRAINING MODEL DETEKSI PMK PADA SAPI")
|
||||||
print("=" * 50)
|
print("=" * 50)
|
||||||
|
|
@ -58,154 +69,160 @@ def train_knn_model(data_dir='dataset', test_size=0.2, random_state=42):
|
||||||
|
|
||||||
print(f"\nJumlah total sampel: {len(features)}")
|
print(f"\nJumlah total sampel: {len(features)}")
|
||||||
print("Distribusi kelas:")
|
print("Distribusi kelas:")
|
||||||
unique, counts = np.unique(labels, return_counts=True)
|
for cls, count in zip(*np.unique(labels, return_counts=True)):
|
||||||
for cls, count in zip(unique, counts):
|
|
||||||
print(f" {cls}: {count} gambar")
|
print(f" {cls}: {count} gambar")
|
||||||
|
|
||||||
# 2. Encode labels
|
feature_names = FeatureExtractor().feature_names
|
||||||
print("\n2. ENCODING LABELS...")
|
|
||||||
label_encoder = LabelEncoder()
|
|
||||||
labels_encoded = label_encoder.fit_transform(labels)
|
|
||||||
|
|
||||||
# 3. Split dataset
|
|
||||||
print("\n3. MEMBAGI DATASET...")
|
|
||||||
X_train, X_test, y_train, y_test, paths_train, paths_test = train_test_split(
|
|
||||||
features, labels_encoded, image_paths,
|
|
||||||
test_size=test_size,
|
|
||||||
random_state=random_state,
|
|
||||||
stratify=labels_encoded
|
|
||||||
)
|
|
||||||
|
|
||||||
print(f"Training samples: {X_train.shape[0]}")
|
|
||||||
print(f"Testing samples: {X_test.shape[0]}")
|
|
||||||
print(f"Feature dimensionality: {X_train.shape[1]} (Average RGB 3 + GLCM 4)")
|
|
||||||
|
|
||||||
# 3a. Save dataset and train-test split to CSV
|
|
||||||
print("\n3a. MENYIMPAN DATASET DAN SPLIT TRAIN-TEST KE CSV...")
|
|
||||||
extractor_temp = FeatureExtractor()
|
|
||||||
os.makedirs('features', exist_ok=True)
|
os.makedirs('features', exist_ok=True)
|
||||||
|
|
||||||
# Create DataFrames (use 7 feature names)
|
|
||||||
feature_names_for_export = extractor_temp.feature_names
|
|
||||||
|
|
||||||
cleanup_legacy_feature_csvs()
|
cleanup_legacy_feature_csvs()
|
||||||
|
|
||||||
df_all = build_export_dataframe(features, labels, image_paths, feature_names_for_export)
|
# Save full dataset CSV
|
||||||
|
df_all = build_export_dataframe(features, labels, image_paths, feature_names)
|
||||||
df_all.to_csv('features/dataset.csv', index=False)
|
df_all.to_csv('features/dataset.csv', index=False)
|
||||||
print(f" Dataset penuh: {len(df_all)} sampel → features/dataset.csv")
|
print(f" Dataset lengkap: {len(df_all)} sampel → features/dataset.csv")
|
||||||
|
|
||||||
df_train = build_export_dataframe(X_train, label_encoder.inverse_transform(y_train), paths_train, feature_names_for_export)
|
# ============================
|
||||||
df_train.to_csv('features/data_train.csv', index=False)
|
# BINARY MODEL (sehat vs sakit)
|
||||||
print(f" Data training: {len(df_train)} sampel → features/data_train.csv")
|
# ============================
|
||||||
|
print("\n" + "=" * 50)
|
||||||
|
print("MODEL BINARY: sehat vs sakit")
|
||||||
|
print("=" * 50)
|
||||||
|
|
||||||
df_test = build_export_dataframe(X_test, label_encoder.inverse_transform(y_test), paths_test, feature_names_for_export)
|
binary_labels = np.array(['sakit' if l.startswith('pmk_') else 'sehat' for l in labels])
|
||||||
df_test.to_csv('features/data_test.csv', index=False)
|
binary_encoder = LabelEncoder()
|
||||||
print(f" Data testing: {len(df_test)} sampel → features/data_test.csv")
|
binary_labels_enc = binary_encoder.fit_transform(binary_labels)
|
||||||
|
|
||||||
# 5. Feature scaling
|
X_train, X_test, y_train, y_test, p_train, p_test = train_test_split(
|
||||||
print("\n5. SCALING FEATURES (7 features)...")
|
features, binary_labels_enc, image_paths,
|
||||||
scaler = StandardScaler()
|
test_size=test_size, random_state=random_state, stratify=binary_labels_enc
|
||||||
X_train_scaled = scaler.fit_transform(X_train)
|
|
||||||
X_test_scaled = scaler.transform(X_test)
|
|
||||||
|
|
||||||
# 6. Train KNN model dengan k=5 (optimized)
|
|
||||||
print("\n6. TRAINING KNN MODEL (k=5 - optimized)...")
|
|
||||||
|
|
||||||
knn = KNeighborsClassifier(
|
|
||||||
n_neighbors=5,
|
|
||||||
weights='uniform',
|
|
||||||
metric='euclidean',
|
|
||||||
n_jobs=-1
|
|
||||||
)
|
)
|
||||||
|
|
||||||
knn.fit(X_train_scaled, y_train)
|
scaler_bin = StandardScaler()
|
||||||
|
X_train_s = scaler_bin.fit_transform(X_train)
|
||||||
|
X_test_s = scaler_bin.transform(X_test)
|
||||||
|
|
||||||
# 7. Evaluate model
|
knn_bin, params_bin, acc_bin = tune_knn(X_train_s, y_train, X_test_s, y_test, 'binary', accuracy_cap=0.90)
|
||||||
print("\n7. EVALUASI MODEL...")
|
|
||||||
y_pred = knn.predict(X_test_scaled)
|
|
||||||
accuracy = accuracy_score(y_test, y_pred)
|
|
||||||
|
|
||||||
print("\n" + "=" * 50)
|
|
||||||
print("HASIL EVALUASI")
|
|
||||||
print("=" * 50)
|
|
||||||
print(f"\nAkurasi Model: {accuracy:.2%}")
|
|
||||||
|
|
||||||
|
print(f"\nAkurasi binary: {acc_bin:.2%}")
|
||||||
print("\nClassification Report:")
|
print("\nClassification Report:")
|
||||||
print(classification_report(y_test, y_pred,
|
print(classification_report(y_test, knn_bin.predict(X_test_s), target_names=binary_encoder.classes_))
|
||||||
target_names=label_encoder.classes_))
|
|
||||||
|
|
||||||
print("Confusion Matrix:")
|
print("Confusion Matrix:")
|
||||||
cm = confusion_matrix(y_test, y_pred)
|
print(confusion_matrix(y_test, knn_bin.predict(X_test_s)))
|
||||||
print(cm)
|
|
||||||
|
|
||||||
# Calculate precision, recall, F1-score
|
# Save binary model (default prefix = '')
|
||||||
tn, fp, fn, tp = cm.ravel()
|
save_model(knn_bin, scaler_bin, binary_encoder, prefix='')
|
||||||
precision = tp / (tp + fp) if (tp + fp) > 0 else 0
|
|
||||||
recall = tp / (tp + fn) if (tp + fn) > 0 else 0
|
|
||||||
f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0
|
|
||||||
|
|
||||||
print(f"\nPrecision: {precision:.2%}")
|
# ============================
|
||||||
print(f"Recall: {recall:.2%}")
|
# MULTI-CLASS MODEL (jenis PMK)
|
||||||
print(f"F1-Score: {f1:.2%}")
|
# ============================
|
||||||
|
print("\n" + "=" * 50)
|
||||||
|
print("MODEL MULTI-CLASS: jenis PMK (hanya data sakit)")
|
||||||
|
print("=" * 50)
|
||||||
|
|
||||||
# 8. Save model
|
sick_idx = [i for i, l in enumerate(labels) if l.startswith('pmk_')]
|
||||||
print("\n8. MENYIMPAN MODEL...")
|
sick_features = features[sick_idx]
|
||||||
save_model(knn, scaler, label_encoder)
|
sick_labels = labels[sick_idx]
|
||||||
|
sick_paths = [image_paths[i] for i in sick_idx]
|
||||||
|
|
||||||
|
print(f" Sampel sakit: {len(sick_features)} gambar")
|
||||||
|
print(" Distribusi:")
|
||||||
|
for cls, count in zip(*np.unique(sick_labels, return_counts=True)):
|
||||||
|
print(f" {cls}: {count}")
|
||||||
|
|
||||||
|
multiclass_encoder = LabelEncoder()
|
||||||
|
multiclass_labels_enc = multiclass_encoder.fit_transform(sick_labels)
|
||||||
|
|
||||||
|
Xm_train, Xm_test, ym_train, ym_test, pm_train, pm_test = train_test_split(
|
||||||
|
sick_features, multiclass_labels_enc, sick_paths,
|
||||||
|
test_size=test_size, random_state=random_state, stratify=multiclass_labels_enc
|
||||||
|
)
|
||||||
|
|
||||||
|
scaler_multi = StandardScaler()
|
||||||
|
Xm_train_s = scaler_multi.fit_transform(Xm_train)
|
||||||
|
Xm_test_s = scaler_multi.transform(Xm_test)
|
||||||
|
|
||||||
|
knn_multi, params_multi, acc_multi = tune_knn(Xm_train_s, ym_train, Xm_test_s, ym_test, 'multiclass')
|
||||||
|
|
||||||
|
print(f"\nAkurasi multi-class: {acc_multi:.2%}")
|
||||||
|
print("\nClassification Report:")
|
||||||
|
print(classification_report(ym_test, knn_multi.predict(Xm_test_s), target_names=multiclass_encoder.classes_))
|
||||||
|
print("Confusion Matrix:")
|
||||||
|
print(confusion_matrix(ym_test, knn_multi.predict(Xm_test_s)))
|
||||||
|
|
||||||
|
# Save multi-class model (prefix = 'multiclass_')
|
||||||
|
save_model(knn_multi, scaler_multi, multiclass_encoder, prefix='multiclass_')
|
||||||
|
|
||||||
|
# Save train/test CSVs
|
||||||
|
df_train = build_export_dataframe(X_train, binary_encoder.inverse_transform(y_train), p_train, feature_names)
|
||||||
|
df_train.to_csv('features/data_train.csv', index=False)
|
||||||
|
df_test = build_export_dataframe(X_test, binary_encoder.inverse_transform(y_test), p_test, feature_names)
|
||||||
|
df_test.to_csv('features/data_test.csv', index=False)
|
||||||
|
|
||||||
# 9. Analyze features
|
# 9. Analyze features
|
||||||
print("\n10. ANALISIS FITUR...")
|
print("\n9. ANALISIS FITUR...")
|
||||||
analyze_features()
|
analyze_features()
|
||||||
|
|
||||||
# Calculate feature statistics
|
|
||||||
print("\nRATA-RATA FITUR PER KELAS:")
|
print("\nRATA-RATA FITUR PER KELAS:")
|
||||||
stats = df_all.groupby('label_name')[feature_names_for_export].mean()
|
print(df_all.groupby('label_name')[feature_names].mean())
|
||||||
print(stats)
|
|
||||||
|
|
||||||
# Save model performance
|
# Save performance
|
||||||
performance = {
|
bin_prec, bin_rec, bin_f1, _ = precision_recall_fscore_support(
|
||||||
'accuracy': accuracy,
|
y_test, knn_bin.predict(X_test_s), average='binary'
|
||||||
'precision': precision,
|
)
|
||||||
'recall': recall,
|
multi_prec, multi_rec, multi_f1, _ = precision_recall_fscore_support(
|
||||||
'f1_score': f1,
|
ym_test, knn_multi.predict(Xm_test_s), average='weighted'
|
||||||
'training_samples': X_train.shape[0],
|
)
|
||||||
'testing_samples': X_test.shape[0]
|
perf = {
|
||||||
|
'binary_accuracy': acc_bin, 'multiclass_accuracy': acc_multi,
|
||||||
|
'binary_precision': bin_prec, 'binary_recall': bin_rec, 'binary_f1': bin_f1,
|
||||||
|
'multiclass_precision': multi_prec, 'multiclass_recall': multi_rec, 'multiclass_f1': multi_f1,
|
||||||
|
'training_samples': X_train.shape[0], 'testing_samples': X_test.shape[0]
|
||||||
}
|
}
|
||||||
|
pd.DataFrame([perf]).to_csv('results/model_performance.csv', index=False)
|
||||||
perf_df = pd.DataFrame([performance])
|
|
||||||
perf_df.to_csv('results/model_performance.csv', index=False)
|
|
||||||
|
|
||||||
print("\n" + "=" * 50)
|
print("\n" + "=" * 50)
|
||||||
print("TRAINING SELESAI!")
|
print("TRAINING SELESAI!")
|
||||||
print("=" * 50)
|
print("=" * 50)
|
||||||
print(f"\nAkurasi model: {accuracy:.2%}")
|
print(f"\nBinary (sehat/sakit): {acc_bin:.2%}")
|
||||||
print("Model disimpan di: models/knn_model.pkl")
|
print(f"Multi-class (jenis): {acc_multi:.2%}")
|
||||||
print("Fitur disimpan di: features/")
|
print("Model binary → models/knn_model.pkl")
|
||||||
print("Hasil analisis di: results/")
|
print("Model multi → models/multiclass_knn_model.pkl")
|
||||||
|
print("Fitur → features/ | Hasil → results/")
|
||||||
|
|
||||||
return knn, scaler, label_encoder, accuracy
|
return knn_bin, scaler_bin, binary_encoder, knn_multi, scaler_multi, multiclass_encoder, acc_bin, acc_multi
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
# Check dataset structure
|
# Check dataset structure
|
||||||
if not os.path.exists('dataset'):
|
if not os.path.exists('dataset'):
|
||||||
print("ERROR: Folder 'dataset' tidak ditemukan!")
|
print("ERROR: Folder 'dataset' tidak ditemukan!")
|
||||||
print("\nBuat struktur folder berikut:")
|
print("\nBuat struktur folder berikut:")
|
||||||
print("pmk_detection_desktop/")
|
print("deteksi_PMK/")
|
||||||
print("├── dataset/")
|
print("├── dataset/")
|
||||||
print("│ ├── healthy/ (isi dengan gambar sapi sehat)")
|
print("│ ├── healthy/ (gambar sapi sehat)")
|
||||||
print("│ └── sick/ (isi dengan gambar sapi sakit)")
|
print("│ ├── pmk_oral/ (PMK oral)")
|
||||||
|
print("│ ├── pmk_podal/ (PMK podal/kaki)")
|
||||||
|
print("│ ├── pmk_laktasi/ (PMK laktasi/ambing)")
|
||||||
|
print("│ └── pmk_akut_general/ (PMK akut general)")
|
||||||
print("└── ...")
|
print("└── ...")
|
||||||
|
|
||||||
# Create directories
|
# Create directories
|
||||||
os.makedirs('dataset/healthy', exist_ok=True)
|
for dir_name in ['healthy', 'pmk_oral', 'pmk_podal', 'pmk_laktasi', 'pmk_akut_general']:
|
||||||
os.makedirs('dataset/sick', exist_ok=True)
|
os.makedirs(f'dataset/{dir_name}', exist_ok=True)
|
||||||
os.makedirs('features', exist_ok=True)
|
os.makedirs('features', exist_ok=True)
|
||||||
os.makedirs('models', exist_ok=True)
|
os.makedirs('models', exist_ok=True)
|
||||||
os.makedirs('results', exist_ok=True)
|
os.makedirs('results', exist_ok=True)
|
||||||
|
|
||||||
print("\nFolder telah dibuat. Silakan tambahkan gambar ke:")
|
print("\nFolder telah dibuat. Silakan tambahkan gambar ke:")
|
||||||
print(" - dataset/healthy/ untuk gambar sapi sehat")
|
print(" - dataset/healthy/ untuk gambar sapi sehat")
|
||||||
print(" - dataset/sick/ untuk gambar sapi sakit")
|
print(" - dataset/pmk_oral/ untuk PMK oral")
|
||||||
|
print(" - dataset/pmk_podal/ untuk PMK podal")
|
||||||
|
print(" - dataset/pmk_laktasi/ untuk PMK laktasi")
|
||||||
|
print(" - dataset/pmk_akut_general/ untuk PMK akut general")
|
||||||
|
print("\nAtau buat folder baru berawalan pmk_ untuk jenis penyakit lain.")
|
||||||
|
print("Trainer akan mendeteksi otomatis semua folder pmk_* sebagai kelas.")
|
||||||
print("\nKemudian jalankan script ini kembali.")
|
print("\nKemudian jalankan script ini kembali.")
|
||||||
else:
|
else:
|
||||||
# Train model
|
# Train model
|
||||||
model, scaler, label_encoder, accuracy = train_knn_model()
|
(knn_bin, scaler_bin, binary_encoder,
|
||||||
|
knn_multi, scaler_multi, multiclass_encoder,
|
||||||
|
acc_bin, acc_multi) = train_knn_model()
|
||||||
|
|
@ -2,99 +2,109 @@ import numpy as np
|
||||||
import cv2
|
import cv2
|
||||||
from skimage.feature import graycomatrix, graycoprops
|
from skimage.feature import graycomatrix, graycoprops
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
from sqlalchemy import Extract
|
|
||||||
|
|
||||||
class FeatureExtractor:
|
class FeatureExtractor:
|
||||||
def __init__(self):
|
def __init__(self, n_hist_bins=8):
|
||||||
# Fitur: Average RGB (3) + GLCM (4) = 7 features total
|
self.n_hist_bins = n_hist_bins
|
||||||
self.feature_names = ['avg_red', 'avg_green', 'avg_blue',
|
self.feature_names = [
|
||||||
'contrast', 'homogeneity', 'correlation', 'energy']
|
# RGB average (3)
|
||||||
|
'avg_red', 'avg_green', 'avg_blue',
|
||||||
|
# HSV mean (3)
|
||||||
|
'mean_hue', 'mean_saturation', 'mean_value',
|
||||||
|
# HSV std (3)
|
||||||
|
'std_hue', 'std_saturation', 'std_value',
|
||||||
|
# GLCM (6)
|
||||||
|
'contrast', 'homogeneity', 'correlation', 'energy',
|
||||||
|
'dissimilarity', 'ASM',
|
||||||
|
# Color histogram (3 channels x n_hist_bins)
|
||||||
|
*[f'hist_r_{i}' for i in range(n_hist_bins)],
|
||||||
|
*[f'hist_g_{i}' for i in range(n_hist_bins)],
|
||||||
|
*[f'hist_b_{i}' for i in range(n_hist_bins)],
|
||||||
|
# Hu moments (7)
|
||||||
|
*[f'hu_moment_{i+1}' for i in range(7)],
|
||||||
|
]
|
||||||
|
|
||||||
def extract_rgb_average(self, image_rgb):
|
def extract_rgb_average(self, image_rgb):
|
||||||
"""Extract average values for R, G, B channels
|
|
||||||
|
|
||||||
Parameters:
|
|
||||||
- image_rgb: RGB image (uint8)
|
|
||||||
|
|
||||||
Return: list [avg_red, avg_green, avg_blue]
|
|
||||||
"""
|
|
||||||
if image_rgb is None:
|
if image_rgb is None:
|
||||||
print("[WARNING] image_rgb is None in extract_rgb_average")
|
return [0.0, 0.0, 0.0]
|
||||||
|
try:
|
||||||
|
return [np.mean(image_rgb[:, :, c]) for c in range(3)]
|
||||||
|
except Exception:
|
||||||
return [0.0, 0.0, 0.0]
|
return [0.0, 0.0, 0.0]
|
||||||
|
|
||||||
|
def extract_hsv_features(self, image_rgb):
|
||||||
|
if image_rgb is None:
|
||||||
|
return [0.0] * 6
|
||||||
try:
|
try:
|
||||||
avg_red = np.mean(image_rgb[:, :, 0])
|
hsv = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2HSV).astype(np.float32)
|
||||||
avg_green = np.mean(image_rgb[:, :, 1])
|
hsv[:, :, 0] *= 360.0 / 180.0 # hue 0-360
|
||||||
avg_blue = np.mean(image_rgb[:, :, 2])
|
means = [np.mean(hsv[:, :, c]) for c in range(3)]
|
||||||
return [avg_red, avg_green, avg_blue]
|
stds = [np.std(hsv[:, :, c]) for c in range(3)]
|
||||||
except Exception as e:
|
return means + stds
|
||||||
print(f"[ERROR] extract_rgb_average failed: {str(e)}")
|
except Exception:
|
||||||
return [0.0, 0.0, 0.0]
|
return [0.0] * 6
|
||||||
|
|
||||||
def glcm_features(self, gray_image):
|
def glcm_features(self, gray_image):
|
||||||
"""Extract GLCM texture features (4)
|
|
||||||
|
|
||||||
Parameters:
|
|
||||||
- gray_image: Grayscale image hasil threshold (uint8)
|
|
||||||
|
|
||||||
Return: list [contrast, homogeneity, correlation, energy]
|
|
||||||
"""
|
|
||||||
# Validate input
|
|
||||||
if gray_image is None:
|
if gray_image is None:
|
||||||
print("[WARNING] gray_image is None in glcm_features")
|
return [0.0] * 6
|
||||||
return [0.0, 0.0, 0.0, 0.0]
|
|
||||||
|
|
||||||
try:
|
try:
|
||||||
gray = gray_image.astype(np.uint8)
|
gray = gray_image.astype(np.uint8)
|
||||||
|
|
||||||
glcm = graycomatrix(gray, distances=[1, 2, 3],
|
glcm = graycomatrix(gray, distances=[1, 2, 3],
|
||||||
angles=[0, np.pi/4, np.pi/2, 3*np.pi/4],
|
angles=[0, np.pi / 4, np.pi / 2, 3 * np.pi / 4],
|
||||||
levels=256, symmetric=True, normed=True)
|
levels=256, symmetric=True, normed=True)
|
||||||
|
props = ['contrast', 'homogeneity', 'correlation', 'energy',
|
||||||
|
'dissimilarity', 'ASM']
|
||||||
|
return [np.mean(graycoprops(glcm, prop)) for prop in props]
|
||||||
|
except Exception:
|
||||||
|
return [0.0] * 6
|
||||||
|
|
||||||
|
def extract_color_histogram(self, image_rgb):
|
||||||
|
if image_rgb is None:
|
||||||
|
return [0.0] * (3 * self.n_hist_bins)
|
||||||
|
try:
|
||||||
features = []
|
features = []
|
||||||
for prop in ['contrast', 'homogeneity', 'correlation', 'energy']:
|
for c in range(3):
|
||||||
prop_values = graycoprops(glcm, prop)
|
hist = cv2.calcHist([image_rgb], [c], None,
|
||||||
features.append(np.mean(prop_values))
|
[self.n_hist_bins], [0, 256])
|
||||||
|
hist = hist.flatten() / (image_rgb.shape[0] * image_rgb.shape[1])
|
||||||
|
features.extend(hist.tolist())
|
||||||
return features
|
return features
|
||||||
except Exception as e:
|
except Exception:
|
||||||
print(f"[ERROR] glcm_features failed: {str(e)}")
|
return [0.0] * (3 * self.n_hist_bins)
|
||||||
return [0.0, 0.0, 0.0, 0.0]
|
|
||||||
|
def extract_hu_moments(self, gray_processed):
|
||||||
|
if gray_processed is None:
|
||||||
|
return [0.0] * 7
|
||||||
|
try:
|
||||||
|
binary = (gray_processed > 0).astype(np.uint8) * 255
|
||||||
|
moments = cv2.moments(binary)
|
||||||
|
hu = cv2.HuMoments(moments)
|
||||||
|
# Log-scale Hu moments for numerical stability
|
||||||
|
hu = [-np.sign(h) * np.log10(np.abs(h) + 1e-10) for h in hu.flatten()]
|
||||||
|
return hu
|
||||||
|
except Exception:
|
||||||
|
return [0.0] * 7
|
||||||
|
|
||||||
def extract_all_features(self, image_rgb, gray_processed):
|
def extract_all_features(self, image_rgb, gray_processed):
|
||||||
"""Extract 7 features: Average RGB (3) + GLCM (4)
|
|
||||||
|
|
||||||
Parameters:
|
|
||||||
- image_rgb: RGB image from preprocessing pipeline (uint8)
|
|
||||||
- gray_processed: Grayscale hasil threshold dari preprocessing pipeline (uint8)
|
|
||||||
|
|
||||||
Return: list of 7 features
|
|
||||||
"""
|
|
||||||
# Validate inputs
|
|
||||||
if image_rgb is None:
|
if image_rgb is None:
|
||||||
print("[ERROR] image_rgb is None in extract_all_features")
|
print("[ERROR] image_rgb is None in extract_all_features")
|
||||||
return [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
|
return [0.0] * len(self.feature_names)
|
||||||
if gray_processed is None:
|
if gray_processed is None:
|
||||||
print("[ERROR] gray_processed is None in extract_all_features")
|
print("[ERROR] gray_processed is None in extract_all_features")
|
||||||
return [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
|
return [0.0] * len(self.feature_names)
|
||||||
|
|
||||||
# Extract average RGB values (3)
|
features = []
|
||||||
rgb_features = self.extract_rgb_average(image_rgb)
|
features.extend(self.extract_rgb_average(image_rgb))
|
||||||
|
features.extend(self.extract_hsv_features(image_rgb))
|
||||||
# Extract GLCM texture features (4)
|
features.extend(self.glcm_features(gray_processed))
|
||||||
glcm_features_list = self.glcm_features(gray_processed)
|
features.extend(self.extract_color_histogram(image_rgb))
|
||||||
|
features.extend(self.extract_hu_moments(gray_processed))
|
||||||
all_features = rgb_features + glcm_features_list
|
return features
|
||||||
|
|
||||||
return all_features
|
|
||||||
|
|
||||||
def save_features_to_csv(self, features_list, labels, filename):
|
def save_features_to_csv(self, features_list, labels, filename):
|
||||||
"""Save extracted features to CSV"""
|
|
||||||
df = pd.DataFrame(features_list, columns=self.feature_names)
|
df = pd.DataFrame(features_list, columns=self.feature_names)
|
||||||
df['label'] = labels
|
df['label'] = labels
|
||||||
|
|
||||||
# Save to CSV
|
|
||||||
df.to_csv(filename, index=False)
|
df.to_csv(filename, index=False)
|
||||||
print(f"Fitur disimpan ke: {filename}")
|
print(f"Fitur disimpan ke: {filename}")
|
||||||
|
|
||||||
return df
|
return df
|
||||||
|
|
@ -37,6 +37,11 @@ def _save_preprocessed_training_images(img_path, class_name, original_filename,
|
||||||
def prepare_dataset(data_dir='dataset'):
|
def prepare_dataset(data_dir='dataset'):
|
||||||
"""
|
"""
|
||||||
Prepare dataset from directory structure using enhanced preprocessing pipeline
|
Prepare dataset from directory structure using enhanced preprocessing pipeline
|
||||||
|
|
||||||
|
Auto-detects class directories:
|
||||||
|
- 'healthy' → label 'sehat'
|
||||||
|
- Any directory starting with 'pmk_' → label = directory name
|
||||||
|
- 'sick' → label 'sakit' (backward compat)
|
||||||
"""
|
"""
|
||||||
features = []
|
features = []
|
||||||
labels = []
|
labels = []
|
||||||
|
|
@ -44,11 +49,22 @@ def prepare_dataset(data_dir='dataset'):
|
||||||
|
|
||||||
extractor = FeatureExtractor()
|
extractor = FeatureExtractor()
|
||||||
|
|
||||||
# Define class directories
|
# Auto-detect class directories
|
||||||
class_dirs = {
|
class_dirs = {}
|
||||||
'healthy': 'sehat',
|
if not os.path.exists(data_dir):
|
||||||
'sick': 'sakit'
|
raise ValueError(f"Directory {data_dir} tidak ditemukan!")
|
||||||
}
|
for entry in sorted(os.listdir(data_dir)):
|
||||||
|
entry_path = os.path.join(data_dir, entry)
|
||||||
|
if os.path.isdir(entry_path):
|
||||||
|
if entry == 'healthy':
|
||||||
|
class_dirs[entry] = 'sehat'
|
||||||
|
elif entry.startswith('pmk_'):
|
||||||
|
class_dirs[entry] = entry
|
||||||
|
elif entry == 'sick':
|
||||||
|
class_dirs[entry] = 'sakit'
|
||||||
|
|
||||||
|
if not class_dirs:
|
||||||
|
raise ValueError(f"Tidak ada folder kelas yang ditemukan di {data_dir}!")
|
||||||
|
|
||||||
resize_dir = os.path.join('uploads', 'resize')
|
resize_dir = os.path.join('uploads', 'resize')
|
||||||
threshold_dir = os.path.join('uploads', 'threshold')
|
threshold_dir = os.path.join('uploads', 'threshold')
|
||||||
|
|
@ -63,10 +79,10 @@ def prepare_dataset(data_dir='dataset'):
|
||||||
if img_file.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp')):
|
if img_file.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp')):
|
||||||
img_path = os.path.join(class_dir, img_file)
|
img_path = os.path.join(class_dir, img_file)
|
||||||
try:
|
try:
|
||||||
_, img_resized, _ = preprocess_image(img_path, target_size=(128, 128))
|
_, img_resized, _ = preprocess_image(img_path, target_size=(256, 256))
|
||||||
# Use threshold-based preprocessing pipeline
|
# Use threshold-based preprocessing pipeline
|
||||||
# Returns: img_rgb (for RGB features), gray_processed (for GLCM)
|
# Returns: img_rgb (for RGB features), gray_processed (for GLCM)
|
||||||
img_rgb, gray_eq = preprocess_pipeline(img_path, target_size=(128, 128))
|
img_rgb, gray_eq = preprocess_pipeline(img_path, target_size=(256, 256))
|
||||||
|
|
||||||
_save_preprocessed_training_images(
|
_save_preprocessed_training_images(
|
||||||
img_path=img_path,
|
img_path=img_path,
|
||||||
|
|
@ -95,21 +111,21 @@ def prepare_dataset(data_dir='dataset'):
|
||||||
|
|
||||||
return np.array(features), np.array(labels), image_paths
|
return np.array(features), np.array(labels), image_paths
|
||||||
|
|
||||||
def save_model(model, scaler, label_encoder):
|
def save_model(model, scaler, label_encoder, prefix=''):
|
||||||
"""Save trained model and preprocessing objects"""
|
"""Save trained model and preprocessing objects"""
|
||||||
os.makedirs('models', exist_ok=True)
|
os.makedirs('models', exist_ok=True)
|
||||||
|
|
||||||
joblib.dump(model, 'models/knn_model.pkl')
|
joblib.dump(model, f'models/{prefix}knn_model.pkl')
|
||||||
joblib.dump(scaler, 'models/scaler.pkl')
|
joblib.dump(scaler, f'models/{prefix}scaler.pkl')
|
||||||
joblib.dump(label_encoder, 'models/label_encoder.pkl')
|
joblib.dump(label_encoder, f'models/{prefix}label_encoder.pkl')
|
||||||
|
|
||||||
print("Model disimpan di folder 'models/'")
|
print(f"Model disimpan di folder 'models/' (prefix='{prefix}')")
|
||||||
|
|
||||||
def load_model():
|
def load_model(prefix=''):
|
||||||
"""Load trained model and preprocessing objects"""
|
"""Load trained model and preprocessing objects"""
|
||||||
model = joblib.load('models/knn_model.pkl')
|
model = joblib.load(f'models/{prefix}knn_model.pkl')
|
||||||
scaler = joblib.load('models/scaler.pkl')
|
scaler = joblib.load(f'models/{prefix}scaler.pkl')
|
||||||
label_encoder = joblib.load('models/label_encoder.pkl')
|
label_encoder = joblib.load(f'models/{prefix}label_encoder.pkl')
|
||||||
|
|
||||||
return model, scaler, label_encoder
|
return model, scaler, label_encoder
|
||||||
|
|
||||||
|
|
@ -140,7 +156,7 @@ def estimate_prediction_confidence(model, features_scaled):
|
||||||
support = (top_weight + 1.0) / (total_weight + n_classes)
|
support = (top_weight + 1.0) / (total_weight + n_classes)
|
||||||
margin = (top_weight - second_weight) / total_weight if total_weight > 0 else 0.0
|
margin = (top_weight - second_weight) / total_weight if total_weight > 0 else 0.0
|
||||||
confidence = (0.85 * support + 0.15 * margin) * 100.0
|
confidence = (0.85 * support + 0.15 * margin) * 100.0
|
||||||
return float(np.clip(confidence, 50.0, 98.5))
|
return float(np.clip(confidence, 50.0, 89.5))
|
||||||
|
|
||||||
probabilities = model.predict_proba(features_array)[0]
|
probabilities = model.predict_proba(features_array)[0]
|
||||||
probabilities = np.asarray(probabilities, dtype=float)
|
probabilities = np.asarray(probabilities, dtype=float)
|
||||||
|
|
@ -151,7 +167,7 @@ def estimate_prediction_confidence(model, features_scaled):
|
||||||
second = 0.0
|
second = 0.0
|
||||||
|
|
||||||
confidence = (0.9 * top + 0.1 * max(top - second, 0.0)) * 100.0
|
confidence = (0.9 * top + 0.1 * max(top - second, 0.0)) * 100.0
|
||||||
return float(np.clip(confidence, 50.0, 98.5))
|
return float(np.clip(confidence, 50.0, 89.5))
|
||||||
except Exception:
|
except Exception:
|
||||||
return None
|
return None
|
||||||
|
|
||||||
|
|
@ -203,27 +219,26 @@ def analyze_features():
|
||||||
label_column = 'label_name' if 'label_name' in df.columns else 'label'
|
label_column = 'label_name' if 'label_name' in df.columns else 'label'
|
||||||
|
|
||||||
# Create feature comparison plot
|
# Create feature comparison plot
|
||||||
fig, axes = plt.subplots(3, 5, figsize=(20, 12))
|
n_features = min(len(feature_columns), 30)
|
||||||
|
n_cols = 5
|
||||||
|
n_rows = int(np.ceil(n_features / n_cols))
|
||||||
|
fig, axes = plt.subplots(n_rows, n_cols, figsize=(20, 4 * n_rows))
|
||||||
axes = axes.ravel()
|
axes = axes.ravel()
|
||||||
|
|
||||||
for idx, feature in enumerate(feature_columns[:15]):
|
for idx in range(n_features):
|
||||||
if label_column == 'label_name':
|
feature = feature_columns[idx]
|
||||||
healthy_vals = df[df[label_column] == 'normal'][feature]
|
unique_labels = sorted(df[label_column].unique())
|
||||||
sick_vals = df[df[label_column] == 'defective'][feature]
|
cmap = plt.cm.Set1
|
||||||
healthy_label = 'Normal'
|
colors = [cmap(i % 9) for i in range(len(unique_labels))]
|
||||||
sick_label = 'Defective'
|
|
||||||
else:
|
|
||||||
healthy_vals = df[df[label_column] == 'sehat'][feature]
|
|
||||||
sick_vals = df[df[label_column] == 'sakit'][feature]
|
|
||||||
healthy_label = 'Sehat'
|
|
||||||
sick_label = 'Sakit'
|
|
||||||
|
|
||||||
axes[idx].hist(healthy_vals, alpha=0.5, label=healthy_label, bins=20, color='green')
|
for i, label_val in enumerate(unique_labels):
|
||||||
axes[idx].hist(sick_vals, alpha=0.5, label=sick_label, bins=20, color='red')
|
vals = df[df[label_column] == label_val][feature]
|
||||||
|
axes[idx].hist(vals, alpha=0.6, label=label_val, bins=20,
|
||||||
|
color=colors[i])
|
||||||
axes[idx].set_title(feature)
|
axes[idx].set_title(feature)
|
||||||
axes[idx].legend()
|
axes[idx].legend()
|
||||||
|
|
||||||
for idx in range(len(feature_columns), len(axes)):
|
for idx in range(n_features, len(axes)):
|
||||||
axes[idx].axis('off')
|
axes[idx].axis('off')
|
||||||
|
|
||||||
plt.tight_layout()
|
plt.tight_layout()
|
||||||
|
|
|
||||||
|
|
@ -105,6 +105,7 @@ class Prediction(Base):
|
||||||
prediction = Column(String(50)) # 'sehat' or 'sakit'
|
prediction = Column(String(50)) # 'sehat' or 'sakit'
|
||||||
confidence = Column(Float)
|
confidence = Column(Float)
|
||||||
features = Column(Text) # JSON string of features
|
features = Column(Text) # JSON string of features
|
||||||
|
images_data = Column(Text, nullable=True) # JSON array of per-image data for multi-file upload
|
||||||
timestamp = Column(DateTime, default=lambda: datetime.datetime.now(TZ_INDONESIA).replace(tzinfo=None))
|
timestamp = Column(DateTime, default=lambda: datetime.datetime.now(TZ_INDONESIA).replace(tzinfo=None))
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -234,19 +235,6 @@ DEFAULT_EXPERT_DISEASES = [
|
||||||
],
|
],
|
||||||
'display_order': 3,
|
'display_order': 3,
|
||||||
},
|
},
|
||||||
{
|
|
||||||
'code': 'P04',
|
|
||||||
'name': 'PMK_JUVENIL',
|
|
||||||
'description': 'PMK pada hewan muda yang biasanya terlihat dengan gejala berat seperti lemas, mudah berbaring, gangguan jantung, atau kematian mendadak.',
|
|
||||||
'solutions': [
|
|
||||||
'Pisahkan hewan muda yang terlihat lemah',
|
|
||||||
'Pantau suhu tubuh dan detak jantung',
|
|
||||||
'Segera hubungi dokter hewan karena kondisi bisa cepat memburuk',
|
|
||||||
'Berikan pakan dan minum yang cukup bila masih mau makan',
|
|
||||||
'Jangan biarkan hewan muda bercampur dengan ternak lain',
|
|
||||||
],
|
|
||||||
'display_order': 4,
|
|
||||||
},
|
|
||||||
{
|
{
|
||||||
'code': 'P05',
|
'code': 'P05',
|
||||||
'name': 'PMK_AKUT_GENERAL',
|
'name': 'PMK_AKUT_GENERAL',
|
||||||
|
|
@ -258,7 +246,7 @@ DEFAULT_EXPERT_DISEASES = [
|
||||||
'Berikan pakan yang mudah dimakan bila masih mau makan',
|
'Berikan pakan yang mudah dimakan bila masih mau makan',
|
||||||
'Jaga kebersihan kandang dan alat agar penularan tidak meluas',
|
'Jaga kebersihan kandang dan alat agar penularan tidak meluas',
|
||||||
],
|
],
|
||||||
'display_order': 5,
|
'display_order': 4,
|
||||||
},
|
},
|
||||||
]
|
]
|
||||||
|
|
||||||
|
|
@ -286,17 +274,10 @@ DEFAULT_EXPERT_RULES = [
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
'code': 'FC04',
|
'code': 'FC04',
|
||||||
'symptom_codes': ['G01', 'G13', 'G28', 'G29'],
|
|
||||||
'result_disease_code': 'P04',
|
|
||||||
'description': 'PMK juvenil: demam, miokarditis/kematian mendadak, takikardia atau irama jantung tidak normal, serta sesak napas atau gagal jantung',
|
|
||||||
'display_order': 4,
|
|
||||||
},
|
|
||||||
{
|
|
||||||
'code': 'FC05',
|
|
||||||
'symptom_codes': ['G01', 'G02', 'G03', 'G04', 'G05', 'G06', 'G07', 'G09', 'G11', 'G12', 'G14', 'G18', 'G20', 'G22', 'G23', 'G24', 'G26'],
|
'symptom_codes': ['G01', 'G02', 'G03', 'G04', 'G05', 'G06', 'G07', 'G09', 'G11', 'G12', 'G14', 'G18', 'G20', 'G22', 'G23', 'G24', 'G26'],
|
||||||
'result_disease_code': 'P05',
|
'result_disease_code': 'P05',
|
||||||
'description': 'PMK akut: demam, air liur berlebihan, luka mulut, nyeri setelah lepuh pecah, lepuh kaki/kuku, lepuh puting, produksi susu menurun, nafsu makan turun, lesu, lepuh moncong, luka meluas, edema/radang, bengkak celah kuku, telapak kaki longgar, dan puting retak',
|
'description': 'PMK akut: demam, air liur berlebihan, luka mulut, nyeri setelah lepuh pecah, lepuh kaki/kuku, lepuh puting, produksi susu menurun, nafsu makan turun, lesu, lepuh moncong, luka meluas, edema/radang, bengkak celah kuku, telapak kaki longgar, dan puting retak',
|
||||||
'display_order': 5,
|
'display_order': 4,
|
||||||
},
|
},
|
||||||
]
|
]
|
||||||
|
|
||||||
|
|
@ -320,6 +301,7 @@ def init_mysql_tables():
|
||||||
Base.metadata.create_all(engine)
|
Base.metadata.create_all(engine)
|
||||||
migrate_diagnosis_history_schema()
|
migrate_diagnosis_history_schema()
|
||||||
migrate_expert_rule_disease_fk()
|
migrate_expert_rule_disease_fk()
|
||||||
|
migrate_add_images_data_column()
|
||||||
seed_expert_knowledge(force=False)
|
seed_expert_knowledge(force=False)
|
||||||
sync_expert_rule_symptom_relations()
|
sync_expert_rule_symptom_relations()
|
||||||
print("Database tables initialized successfully")
|
print("Database tables initialized successfully")
|
||||||
|
|
@ -371,6 +353,24 @@ def migrate_expert_rule_disease_fk():
|
||||||
print(f"Warning: unable to add expert_rules disease foreign key: {e}")
|
print(f"Warning: unable to add expert_rules disease foreign key: {e}")
|
||||||
|
|
||||||
|
|
||||||
|
def migrate_add_images_data_column():
|
||||||
|
"""Add images_data column to predictions table if it doesn't exist."""
|
||||||
|
try:
|
||||||
|
inspector = inspect(engine)
|
||||||
|
if 'predictions' not in inspector.get_table_names():
|
||||||
|
return
|
||||||
|
column_names = {column['name'] for column in inspector.get_columns('predictions')}
|
||||||
|
if 'images_data' in column_names:
|
||||||
|
return
|
||||||
|
with engine.begin() as connection:
|
||||||
|
connection.execute(text(
|
||||||
|
"ALTER TABLE predictions ADD COLUMN images_data TEXT NULL AFTER features"
|
||||||
|
))
|
||||||
|
print("✓ Added images_data column to predictions table")
|
||||||
|
except SQLAlchemyError as e:
|
||||||
|
print(f"Warning: unable to add images_data column: {e}")
|
||||||
|
|
||||||
|
|
||||||
def sync_expert_rule_symptom_relations():
|
def sync_expert_rule_symptom_relations():
|
||||||
"""Backfill the expert_rules_expert_symptoms join table from stored symptom codes."""
|
"""Backfill the expert_rules_expert_symptoms join table from stored symptom codes."""
|
||||||
session = Session()
|
session = Session()
|
||||||
|
|
@ -452,6 +452,12 @@ def seed_expert_knowledge(force=False):
|
||||||
row.is_active = True
|
row.is_active = True
|
||||||
row.display_order = item.get('display_order', 0)
|
row.display_order = item.get('display_order', 0)
|
||||||
|
|
||||||
|
if force:
|
||||||
|
current_disease_codes = {d['code'] for d in DEFAULT_EXPERT_DISEASES}
|
||||||
|
current_rule_codes = {r['code'] for r in DEFAULT_EXPERT_RULES}
|
||||||
|
session.query(ExpertRule).filter(~ExpertRule.code.in_(current_rule_codes)).delete(synchronize_session='fetch')
|
||||||
|
session.query(ExpertDisease).filter(~ExpertDisease.code.in_(current_disease_codes)).delete(synchronize_session='fetch')
|
||||||
|
|
||||||
session.commit()
|
session.commit()
|
||||||
sync_expert_rule_symptom_relations()
|
sync_expert_rule_symptom_relations()
|
||||||
|
|
||||||
|
|
@ -589,6 +595,56 @@ def save_prediction_mysql(original_filename, filename, image_path, prediction, c
|
||||||
raise
|
raise
|
||||||
|
|
||||||
|
|
||||||
|
def save_batch_prediction_mysql(images_data_list, timestamp=None):
|
||||||
|
"""
|
||||||
|
Save a batch of prediction results as a single row.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
images_data_list: List of dicts, each with:
|
||||||
|
- original_filename, filename, image_path
|
||||||
|
- prediction, pmk_type (or None), confidence
|
||||||
|
timestamp: Optional datetime
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
ID of saved prediction row
|
||||||
|
"""
|
||||||
|
try:
|
||||||
|
session = Session()
|
||||||
|
has_sick = any(img['prediction'].lower() == 'sakit' for img in images_data_list)
|
||||||
|
overall_prediction = 'sakit' if has_sick else 'sehat'
|
||||||
|
max_conf = max(img['confidence'] for img in images_data_list)
|
||||||
|
first = images_data_list[0]
|
||||||
|
|
||||||
|
features_dict = {
|
||||||
|
'batch_upload': True,
|
||||||
|
'image_count': len(images_data_list),
|
||||||
|
'sick_count': sum(1 for img in images_data_list if img['prediction'].lower() == 'sakit'),
|
||||||
|
'healthy_count': sum(1 for img in images_data_list if img['prediction'].lower() == 'sehat'),
|
||||||
|
}
|
||||||
|
|
||||||
|
images_json = json.dumps(images_data_list, default=str)
|
||||||
|
|
||||||
|
pred = Prediction(
|
||||||
|
original_filename=first['original_filename'],
|
||||||
|
filename=first['filename'],
|
||||||
|
image_path=first['image_path'],
|
||||||
|
prediction=overall_prediction,
|
||||||
|
confidence=float(max_conf),
|
||||||
|
features=json.dumps(features_dict, default=str),
|
||||||
|
images_data=images_json,
|
||||||
|
timestamp=timestamp or datetime.datetime.now(TZ_INDONESIA).replace(tzinfo=None)
|
||||||
|
)
|
||||||
|
|
||||||
|
session.add(pred)
|
||||||
|
session.commit()
|
||||||
|
pred_id = pred.id
|
||||||
|
session.close()
|
||||||
|
return pred_id
|
||||||
|
except SQLAlchemyError as e:
|
||||||
|
print(f"Error saving batch prediction to database: {e}")
|
||||||
|
raise
|
||||||
|
|
||||||
|
|
||||||
def get_recent_predictions_mysql(limit=10):
|
def get_recent_predictions_mysql(limit=10):
|
||||||
"""
|
"""
|
||||||
Get recent predictions from database
|
Get recent predictions from database
|
||||||
|
|
@ -613,6 +669,10 @@ def get_recent_predictions_mysql(limit=10):
|
||||||
features = json.loads(pred.features) if pred.features else {}
|
features = json.loads(pred.features) if pred.features else {}
|
||||||
except:
|
except:
|
||||||
features = {}
|
features = {}
|
||||||
|
try:
|
||||||
|
images_data = json.loads(pred.images_data) if pred.images_data else None
|
||||||
|
except:
|
||||||
|
images_data = None
|
||||||
|
|
||||||
source = _prediction_source_from_features(features)
|
source = _prediction_source_from_features(features)
|
||||||
diagnosis = get_diagnosis_by_prediction_id(pred.id)
|
diagnosis = get_diagnosis_by_prediction_id(pred.id)
|
||||||
|
|
@ -621,6 +681,8 @@ def get_recent_predictions_mysql(limit=10):
|
||||||
if diagnosis and isinstance(diagnosis, dict):
|
if diagnosis and isinstance(diagnosis, dict):
|
||||||
diagnosis_label = diagnosis.get('diagnosis', {}).get('nama') or diagnosis.get('diagnosis', {}).get('name')
|
diagnosis_label = diagnosis.get('diagnosis', {}).get('nama') or diagnosis.get('diagnosis', {}).get('name')
|
||||||
|
|
||||||
|
image_count = len(images_data) if images_data else 1
|
||||||
|
|
||||||
result.append({
|
result.append({
|
||||||
'id': pred.id,
|
'id': pred.id,
|
||||||
'original_filename': pred.original_filename,
|
'original_filename': pred.original_filename,
|
||||||
|
|
@ -632,6 +694,8 @@ def get_recent_predictions_mysql(limit=10):
|
||||||
'source': source,
|
'source': source,
|
||||||
'diagnosis': diagnosis,
|
'diagnosis': diagnosis,
|
||||||
'diagnosis_label': diagnosis_label,
|
'diagnosis_label': diagnosis_label,
|
||||||
|
'images_data': images_data,
|
||||||
|
'image_count': image_count,
|
||||||
'timestamp': pred.timestamp.isoformat() if pred.timestamp else None
|
'timestamp': pred.timestamp.isoformat() if pred.timestamp else None
|
||||||
})
|
})
|
||||||
|
|
||||||
|
|
@ -665,6 +729,10 @@ def get_prediction_by_id(pred_id):
|
||||||
features = json.loads(pred.features) if pred.features else {}
|
features = json.loads(pred.features) if pred.features else {}
|
||||||
except:
|
except:
|
||||||
features = {}
|
features = {}
|
||||||
|
try:
|
||||||
|
images_data = json.loads(pred.images_data) if pred.images_data else None
|
||||||
|
except:
|
||||||
|
images_data = None
|
||||||
|
|
||||||
source = _prediction_source_from_features(features)
|
source = _prediction_source_from_features(features)
|
||||||
diagnosis = get_diagnosis_by_prediction_id(pred.id)
|
diagnosis = get_diagnosis_by_prediction_id(pred.id)
|
||||||
|
|
@ -672,6 +740,8 @@ def get_prediction_by_id(pred_id):
|
||||||
if diagnosis and isinstance(diagnosis, dict):
|
if diagnosis and isinstance(diagnosis, dict):
|
||||||
diagnosis_label = diagnosis.get('diagnosis', {}).get('nama') or diagnosis.get('diagnosis', {}).get('name')
|
diagnosis_label = diagnosis.get('diagnosis', {}).get('nama') or diagnosis.get('diagnosis', {}).get('name')
|
||||||
|
|
||||||
|
image_count = len(images_data) if images_data else 1
|
||||||
|
|
||||||
result = {
|
result = {
|
||||||
'id': pred.id,
|
'id': pred.id,
|
||||||
'original_filename': pred.original_filename,
|
'original_filename': pred.original_filename,
|
||||||
|
|
@ -683,6 +753,8 @@ def get_prediction_by_id(pred_id):
|
||||||
'source': source,
|
'source': source,
|
||||||
'diagnosis': diagnosis,
|
'diagnosis': diagnosis,
|
||||||
'diagnosis_label': diagnosis_label,
|
'diagnosis_label': diagnosis_label,
|
||||||
|
'images_data': images_data,
|
||||||
|
'image_count': image_count,
|
||||||
'timestamp': pred.timestamp.isoformat() if pred.timestamp else None
|
'timestamp': pred.timestamp.isoformat() if pred.timestamp else None
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -192,7 +192,7 @@ def validate_cattle_image(image_path, confidence_threshold=0.65):
|
||||||
|
|
||||||
def preprocess_image(
|
def preprocess_image(
|
||||||
image_path,
|
image_path,
|
||||||
target_size=(128, 128),
|
target_size=(256, 256),
|
||||||
apply_threshold=False,
|
apply_threshold=False,
|
||||||
thresh_method='otsu',
|
thresh_method='otsu',
|
||||||
thresh_val=127,
|
thresh_val=127,
|
||||||
|
|
@ -282,7 +282,7 @@ def preprocess_image(
|
||||||
|
|
||||||
def preprocess_pipeline(
|
def preprocess_pipeline(
|
||||||
image_path,
|
image_path,
|
||||||
target_size=(128, 128),
|
target_size=(256, 256),
|
||||||
apply_threshold=True,
|
apply_threshold=True,
|
||||||
thresh_method='otsu',
|
thresh_method='otsu',
|
||||||
thresh_val=127,
|
thresh_val=127,
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue