Update services/detection_service.py

services/detection_service.py

@@ -1,8 +1,7 @@
 import cv2
 import numpy as np
-from ultralytics import YOLO
-import os
 import logging
+from ultralytics import YOLO
 
 # Setup logging
 logging.basicConfig(
@@ -11,63 +10,42 @@ logging.basicConfig(
     format="%(asctime)s - %(levelname)s - %(message)s"
 )
 
-# Load YOLOv8m model for generic detection
-BASE_DIR = os.path.dirname(os.path.abspath(__file__))
-MODEL_PATH = os.path.abspath(os.path.join(BASE_DIR, "../models/yolov8m.pt"))
-try:
-    model = YOLO(MODEL_PATH)
-    logging.info("Loaded YOLOv8m model for generic detection.")
-except Exception as e:
-    logging.error(f"Failed to load YOLOv8m model: {str(e)}")
-    model = None
-
-def process_frame(frame):
+def process_frame(frame: np.ndarray) -> tuple[list[dict[str, any]], np.ndarray]:
     """
-    Process a frame for generic object detection using YOLOv8m.
+    Process a frame using YOLO model to detect cracks and holes.
     Args:
         frame: Input frame (numpy array)
     Returns:
-        list: List of detected objects
-        numpy array: Annotated frame with numbered labels
+        tuple: List of detected items and annotated frame
     """
-    if model is None:
-        logging.error("YOLO model not loaded. Skipping generic detection.")
-        return [], frame
-
     try:
+        model = YOLO("models/yolov8m.pt")
         results = model(frame)
-    except Exception as e:
-        logging.error(f"Error during YOLO inference: {str(e)}")
-        return [], frame
 
-    detections = []
-    line_counter = 1  # Initialize counter for numbered labels
-
-    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]
-            xyxy = box.xyxy[0].cpu().numpy()
-            x_min, y_min, x_max, y_max = map(int, xyxy)
-
-            # Add numbered label
-            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]
-            })
-
-            # Draw bounding box and label
-            color = (0, 255, 255)  # Yellow for generic detections
-            cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), color, 2)
-            cv2.putText(frame, detection_label, (x_min, y_min - 10), 
-                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
-            
-            line_counter += 1
-
-    return detections, frame
+        detections = []
+        line_counter = 1
+
+        for result in results:
+            boxes = result.boxes.xyxy.cpu().numpy()
+            confidences = result.boxes.conf.cpu().numpy()
+            classes = result.boxes.cls.cpu().numpy()
+
+            for box, conf, cls in zip(boxes, confidences, classes):
+                x_min, y_min, x_max, y_max = map(int, box)
+                type_ = "crack" if cls == 0 else "hole"  # Assume class 0=crack, 1=hole
+                severity = "Severe" if conf > 0.8 else "Moderate" if conf > 0.5 else "Minor"
+                label = f"Line {line_counter} - {type_.capitalize()} ({severity})"
+                detections.append({
+                    "type": type_,
+                    "label": label,
+                    "box": [x_min, y_min, x_max, y_max],
+                    "severity": severity,
+                    "confidence": float(conf)
+                })
+                line_counter += 1
+
+        logging.info(f"Detected {len(detections)} objects in frame.")
+        return detections, frame
+    except Exception as e:
+        logging.error(f"Error processing frame: {str(e)}")
+        return [], frame