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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 |