services/operations_maintenance/crack_detection.py

import cv2
import numpy as np
from ultralytics import YOLO
import os
import random
import logging
from typing import List, Dict, Any

# Configure logging
logging.basicConfig(
    filename="app.log",
    level=logging.INFO,
    format="%(asctime)s - %(levelname)s - %(message)s"
)

# Define base directory and model path
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
MODEL_PATH = os.path.abspath(os.path.join(BASE_DIR, "../../models/yolov8m.pt"))

# Initialize YOLO model
try:
    model = YOLO(MODEL_PATH)
    logging.info("Loaded YOLOv8m model for crack detection.")
    logging.info(f"Model class names: {model.names}")
except Exception as e:
    logging.error(f"Failed to load YOLOv8m model: {str(e)}")
    model = None

def detect_cracks_and_objects(frame: np.ndarray) -> List[Dict[str, Any]]:
    # Validate input frame
    if not isinstance(frame, np.ndarray) or frame.size == 0:
        logging.error("Invalid input frame provided to crack_detection.")
        return []

    # Check if model is loaded
    if model is None:
        logging.error("YOLO model not loaded. Skipping crack detection.")
        return []

    try:
        # Perform YOLO inference
        results = model(frame, verbose=False)
        logging.debug("Completed YOLO inference for crack detection.")
    except Exception as e:
        logging.error(f"Error during YOLO inference: {str(e)}")
        return []

    detections: List[Dict[str, Any]] = []
    line_counter = 1

    for r in results:
        for box in r.boxes:
            conf = float(box.conf[0])
            if conf < 0.3:
                continue
            cls = int(box.cls[0])
            label = model.names[cls]
            if label != "crack":
                continue
            xyxy = box.xyxy[0].cpu().numpy().astype(int)
            x_min, y_min, x_max, y_max = xyxy

            severity = random.choice(["low", "medium", "high"])
            detection_label = f"Line {line_counter} - {label.capitalize()} (Conf: {conf:.2f})"
            detections.append({
                "type": label,
                "label": detection_label,
                "confidence": conf,
                "coordinates": [x_min, y_min, x_max, y_max],
                "severity": severity
            })

            line_counter += 1

    logging.info(f"Detected {len(detections)} cracks in operations_maintenance.")
    return detections