Create services/detection_service.py

services/services/detection_service.py

@@ -0,0 +1,59 @@
+# services/detection_service.py
+import cv2
+import numpy as np
+from ultralytics import YOLO
+import os
+
+# Load YOLOv8m model
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+MODEL_PATH = os.path.join(BASE_DIR, "../models/yolov8m.pt")
+model = YOLO(MODEL_PATH)
+
+def detect_objects(frame):
+    """
+    Detect objects in a frame using YOLOv8m.
+    Args:
+        frame: Input frame (numpy array)
+    Returns:
+        dict: Detection results with numbered labels
+        numpy array: Annotated frame
+    """
+    results = model(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({
+                "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
+            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"]