Update services/detection_service.py

services/detection_service.py

@@ -1,24 +1,44 @@
-# services/detection_service.py
 import cv2
 import numpy as np
 from ultralytics import YOLO
 import os
+import logging
 
-# Load YOLOv8m model
+# Setup logging
+logging.basicConfig(
+    filename="app.log",
+    level=logging.INFO,
+    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.join(BASE_DIR, "../models/yolov8m.pt")
-model = YOLO(MODEL_PATH)
+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 detect_objects(frame):
+def process_frame(frame):
     """
-    Detect objects in a frame using YOLOv8m.
+    Process a frame for generic object detection using YOLOv8m.
     Args:
         frame: Input frame (numpy array)
     Returns:
-        dict: Detection results with numbered labels
-        numpy array: Annotated frame
+        list: List of detected objects
+        numpy array: Annotated frame with numbered labels
     """
-    results = model(frame)
+    if model is None:
+        logging.error("YOLO model not loaded. Skipping generic detection.")
+        return [], frame
+
+    try:
+        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
@@ -36,24 +56,18 @@ def detect_objects(frame):
             # 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, 0)  # Green for generic objects
+            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": detections, "frame": frame}
-
-def process_frame(frame):
-    """
-    Wrapper function for integration with app.py.
-    """
-    result = detect_objects(frame)
-    return result["detections"], result["frame"]
+    return detections, frame