Code comments Removed Update

backend/vision.py

@@ -4,15 +4,15 @@ from ultralytics import YOLO
 from scipy.spatial.distance import cdist
 from scipy.optimize import linear_sum_assignment
 
-# Load YOLOv8 nano model (downloads automatically if not found)
-# 'yolov8n.pt' is lightweight and fast for this purpose
+
+
 try:
     model = YOLO("yolov8n.pt")
 except Exception as e:
     print(f"Error loading YOLO model: {e}")
     model = None
 
-# COCO Class IDs
+
 PERSON_CLASS_ID = 0
 CHAIR_CLASS_ID = 56
 
@@ -20,16 +20,16 @@ def analyze_room_image(image_bytes: bytes):
     if not model:
         return {"error": "Vision model is not loaded"}
 
-    # Convert bytes to numpy array then to cv2 image
+
     nparr = np.frombuffer(image_bytes, np.uint8)
     img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
-    
+
     if img is None:
         return {"error": "Invalid image format"}
 
-    # Run inference
+
     results = model(img)
-    
+
     people = []
     chairs = []
 
@@ -38,25 +38,25 @@ def analyze_room_image(image_bytes: bytes):
         for box in boxes:
             cls_id = int(box.cls[0])
             conf = float(box.conf[0])
-            
-            # Extract center of bounding box
+
+
             x1, y1, x2, y2 = box.xyxy[0]
             cx = (x1 + x2) / 2.0
             cy = (y1 + y2) / 2.0
             centroid = [float(cx), float(cy)]
 
-            # Only consider detections with confidence > 0.3
+
             if conf > 0.3:
                 if cls_id == PERSON_CLASS_ID:
                     people.append({
-                        "centroid": centroid, 
-                        "box": [float(x1), float(y1), float(x2), float(y2)], 
+                        "centroid": centroid,
+                        "box": [float(x1), float(y1), float(x2), float(y2)],
                         "conf": conf
                     })
                 elif cls_id == CHAIR_CLASS_ID:
                     chairs.append({
-                        "centroid": centroid, 
-                        "box": [float(x1), float(y1), float(x2), float(y2)], 
+                        "centroid": centroid,
+                        "box": [float(x1), float(y1), float(x2), float(y2)],
                         "conf": conf
                     })
 
@@ -64,18 +64,18 @@ def analyze_room_image(image_bytes: bytes):
     num_chairs = len(chairs)
 
     pairs = []
-    
-    # Bipartite matching if both people and chairs exist
+
+
     if num_people > 0 and num_chairs > 0:
         people_coords = [p["centroid"] for p in people]
         chairs_coords = [c["centroid"] for c in chairs]
-        
-        # Distance matrix (Euclidean distances)
+
+
         dist_matrix = cdist(people_coords, chairs_coords, metric='euclidean')
-        
-        # Hungarian algorithm to minimize total distance for pairings
+
+
         row_ind, col_ind = linear_sum_assignment(dist_matrix)
-        
+
         for person_idx, chair_idx in zip(row_ind, col_ind):
             distance = float(dist_matrix[person_idx, chair_idx])
             pairs.append({
@@ -84,8 +84,8 @@ def analyze_room_image(image_bytes: bytes):
                 "distance": distance
             })
 
-    # Basic logic: room is full if there are at least as many people as chairs. 
-    # Can be adjusted based on specific room definitions
+
+
     is_full = num_people >= num_chairs if num_chairs > 0 else False
 
     return {