import cv2
import numpy as np
from ultralytics import YOLO
import os
import random

# Load YOLOv8m-seg model for crack detection
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
MODEL_PATH = os.path.join(BASE_DIR, "../models/yolov8m-seg.pt")
model = YOLO(MODEL_PATH)

def detect_cracks_and_objects(frame):
    """
    Detect cracks and other objects in a frame using YOLOv8m-seg.
    Args:
        frame: Input frame (numpy array)
    Returns:
        list: List of detected items with type, label, coordinates, confidence, and severity
    """
    # Run YOLOv8 inference
    results = model(frame)

    detected_items = []
    line_counter = 1  # Initialize counter for numbered labels

    # Process detections
    for r in results:
        for box in r.boxes:
            conf = float(box.conf[0])
            if conf < 0.5:
                continue
            cls = int(box.cls[0])
            label = model.names[cls]
            if label not in ["crack", "pothole", "object"]:  # Assuming these classes exist
                continue
            xyxy = box.xyxy[0].cpu().numpy()
            x_min, y_min, x_max, y_max = map(int, xyxy)

            # Simulate severity for cracks
            severity = None
            if label == "crack":
                severity = random.choice(["low", "medium", "high"])
            
            # Add numbered label
            detection_label = f"Line {line_counter} - {label.capitalize()} (Conf: {conf:.2f})"
            item = {
                "type": label,
                "label": detection_label,
                "confidence": conf,
                "coordinates": [x_min, y_min, x_max, y_max]
            }
            if severity:
                item["severity"] = severity

            detected_items.append(item)

            line_counter += 1

    return detected_items