Update app.py

app.py

@@ -291,48 +291,46 @@ def apply_blur(image, x, y, w, h):
 
 def draw_label(image, x, y, w, h, text, color):
     """
-    Smart Annotation:
-    - Draws text OUTSIDE the face box.
-    - Tries to draw ABOVE. If no space, draws BELOW.
-    - Background box matches text size.
+    1. Always draw the border (The primary signal).
+    2. Attempt to draw text inside.
+    3. If text doesn't fit geometrically, hide it automatically.
     """
+    # 1. Dynamic Border Thickness
+    # Big face = 2px border, Tiny face = 1px border (so it doesn't look like a blob)
+    thickness = 2 if w > 40 else 1
+    cv2.rectangle(image, (x, y), (x+w, y+h), color, thickness)
+
+    # 2. Geometry Check: Can we fit text?
+    # We define a 'Minimum Readable Font' (Scale 0.4 is the smallest legible size)
+    min_font_scale = 0.4
     font = cv2.FONT_HERSHEY_SIMPLEX
-    scale = 0.6
-    thickness = 2
-    (tw, th), _ = cv2.getTextSize(text, font, scale, thickness)
+    font_thickness = 1
     
-    margin = 10 # Gap between face and text
+    (tw, th), _ = cv2.getTextSize(text, font, min_font_scale, font_thickness)
     
-    # Check if we have space above the face
-    # We need space for Text Height (th) + Padding (~15px)
-    space_above = y - th - 15
+    # PADDING: We need at least 2 pixels on sides
+    required_width = tw + 4
+    
+    # THE RULE: If the text is wider than the face, DO NOT DRAW IT.
+    if required_width > w:
+        return 
+
+    # 3. If we passed the check, draw it centered
+    center_x = x + (w // 2) - (tw // 2)
+    center_y = y + (h // 2) + (th // 2)
+
+    # Background Strip (for readability against blur)
+    bg_tl = (center_x - 2, center_y - th - 2)
+    bg_br = (center_x + tw + 2, center_y + 2)
+    cv2.rectangle(image, bg_tl, bg_br, color, -1)
+
+    # Text
+    cv2.putText(image, text, (center_x, center_y), font, min_font_scale, (255, 255, 255), font_thickness, cv2.LINE_AA)
     
-    if space_above > 0:
-        # === DRAW ABOVE ===
-        text_y = y - margin
-        
-        # Coordinates for the background rectangle
-        box_tl = (x, text_y - th - 5)
-        box_br = (x + tw + 10, text_y + 5)
-        
-        cv2.rectangle(image, box_tl, box_br, color, -1)
-        cv2.putText(image, text, (x + 5, text_y), font, scale, (255, 255, 255), thickness)
-    else:
-        # === DRAW BELOW ===
-        # y + h is the bottom of the face
-        text_y = y + h + th + margin
-        
-        # Coordinates for the background rectangle
-        box_tl = (x, text_y - th - 5)
-        box_br = (x + tw + 10, text_y + 5)
-        
-        cv2.rectangle(image, box_tl, box_br, color, -1)
-        cv2.putText(image, text, (x + 5, text_y), font, scale, (255, 255, 255), thickness)
 
 def process_frame(image, mode):
     """
-    THE MASTER FUNCTION.
-    Returns: (processed_image, log_string)
+    MASTER FUNCTION (Standardized).
     """
     if image is None: return None, "No Image"
     
@@ -340,59 +338,64 @@ def process_frame(image, mode):
     faces = GLOBAL_DETECTOR.detect(image)
     processed_img = image.copy()
     log_entries = []
+    labels_to_draw = []
     
+    # =================================================
+    # PASS 1: BLUR & ANALYZE
+    # =================================================
     for i, face in enumerate(faces):
         x, y, w, h = face['box']
         
-        # 2. Analysis
-        # Default vars
-        full_log_text = "Unknown"   # Goes to text box
-        img_label_text = "Unknown"  # Goes to image overlay
-        color = (0, 255, 0)
+        # Default: Unknown/Red
+        full_log_text = "Unknown"
+        img_label_text = "Unknown"
+        color = (255, 0, 0) 
         
         if mode in ["data", "smart"]:
-            # Crop and Check DB
             face_crop = image[y:y+h, x:x+w]
             if face_crop.size > 0:
                 res = FACE_DB.recognize(face_crop)
                 
                 if res['match']:
-                    # 1. LOG GETS FULL DETAILS
+                    # Match: Green
                     full_log_text = f"MATCH - {res['name']} (ID: {res['id']})"
                     log_entries.append(f"✅ Face #{i+1}: {full_log_text}")
-                    
-                    # 2. IMAGE GETS ID ONLY
                     img_label_text = f"ID: {res['id']}"
+                    color = (0, 255, 0)
                 else:
+                    # No Match: Red
                     full_log_text = "UNKNOWN"
                     log_entries.append(f"⚠️ Face #{i+1}: {full_log_text}")
                     img_label_text = "Unknown"
-                    
-                color = res['color']
+                    color = (255, 0, 0)
         else:
              log_entries.append(f"🔒 Face #{i+1}: Anonymized")
 
-        # 3. Modification
-        if mode == "privacy":
+        # Action: Blur
+        if mode == "privacy" or mode == "smart":
             processed_img = apply_blur(processed_img, x, y, w, h)
             
-        elif mode == "data":
-            # Draw Box + Label (passing 'h' for smart positioning)
-            cv2.rectangle(processed_img, (x, y), (x+w, y+h), color, 2)
-            draw_label(processed_img, x, y, w, h, img_label_text, color)
-            
-        elif mode == "smart":
-            # Blur + Label (passing 'h' for smart positioning)
-            processed_img = apply_blur(processed_img, x, y, w, h)
-            draw_label(processed_img, x, y, w, h, img_label_text, color)
-            
-    # Create Log String
+        # Action: Queue Badge
+        if mode == "data" or mode == "smart":
+            labels_to_draw.append({
+                "x": x, "y": y, "w": w, "h": h, 
+                "text": img_label_text, "color": color
+            })
+
+    # =================================================
+    # PASS 2: DRAW BADGES (Top Layer)
+    # =================================================
+    for item in labels_to_draw:
+        draw_label(processed_img, item['x'], item['y'], item['w'], item['h'], item['text'], item['color'])
+
+    # Create Log
     if not log_entries:
         final_log = "No faces detected."
     else:
         final_log = "--- Detection Report ---\n" + "\n".join(log_entries)
 
     return processed_img, final_log
+    
 
 # ====================================================
 # 5. VIDEO PROCESSING HELPERS