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| from flask import Flask, render_template, request, jsonify | |
| import os | |
| import base64 | |
| import numpy as np | |
| import cv2 | |
| from ultralytics import YOLO | |
| app = Flask(__name__) | |
| # ======================================================= | |
| # 🔥 LAZY LOAD — Model hanya diload saat pertama dipakai | |
| # ======================================================= | |
| YOLO_MODEL = None | |
| def load_yolo(): | |
| global YOLO_MODEL | |
| if YOLO_MODEL is not None: | |
| return YOLO_MODEL | |
| try: | |
| # Prefer load dari file lokal (HuggingFace tidak download ulang) | |
| if os.path.exists("yolov8n.pt"): | |
| print("🚀 Loading YOLO locally...") | |
| YOLO_MODEL = YOLO("yolov8n.pt") | |
| else: | |
| print("⬇️ Downloading YOLO from Ultralytics...") | |
| YOLO_MODEL = YOLO("yolov8n.pt") # auto-download 6MB | |
| print("✅ YOLO Ready!") | |
| except Exception as e: | |
| print("❌ YOLO gagal load:", e) | |
| YOLO_MODEL = None | |
| return YOLO_MODEL | |
| # ======================================================= | |
| # 🔥 HOME PAGE | |
| # ======================================================= | |
| def home(): | |
| return render_template("object_detection.html") | |
| # ======================================================= | |
| # 🔥 YOLO DETECTION API | |
| # ======================================================= | |
| def object_detect_api(): | |
| model = load_yolo() | |
| if model is None: | |
| return jsonify({"ok": False, "msg": "Model YOLO gagal load!"}), 500 | |
| if "image" not in request.files: | |
| return jsonify({"ok": False, "msg": "Tidak ada file gambar"}), 400 | |
| file = request.files["image"] | |
| img_bytes = file.read() | |
| # Convert image | |
| nparr = np.frombuffer(img_bytes, np.uint8) | |
| img = cv2.imdecode(nparr, cv2.IMREAD_COLOR) | |
| if img is None: | |
| return jsonify({"ok": False, "msg": "Gambar tidak bisa dibaca"}), 400 | |
| # YOLO inference | |
| results = model(img) | |
| objects = [] | |
| found = False | |
| for result in results: | |
| for box in result.boxes: | |
| found = True | |
| x1, y1, x2, y2 = [int(v) for v in box.xyxy[0]] | |
| conf = float(box.conf[0]) | |
| cls = int(box.cls.item()) | |
| label = model.names.get(cls, str(cls)) | |
| objects.append({ | |
| "label": label, | |
| "confidence": round(conf * 100, 2) | |
| }) | |
| # Draw box | |
| cv2.rectangle(img, (x1, y1), (x2, y2), (30, 200, 255), 3) | |
| cv2.putText( | |
| img, | |
| f"{label} {conf:.2f}", | |
| (x1, y1 - 5), | |
| cv2.FONT_HERSHEY_SIMPLEX, | |
| 0.6, | |
| (30, 200, 255), | |
| 2 | |
| ) | |
| _, buffer = cv2.imencode(".jpg", img) | |
| img_base64 = base64.b64encode(buffer).decode() | |
| return jsonify({ | |
| "ok": True, | |
| "found": found, | |
| "objects": objects, | |
| "image": img_base64 | |
| }) | |
| # ======================================================= | |
| # LOCAL RUN | |
| # ======================================================= | |
| if __name__ == "__main__": | |
| app.run(host="0.0.0.0", port=8080) | |