Fix: Zähllinie wird nicht mehr als Objekt erkannt

Problem: YOLO hat die gelbe Zähllinie als "Zahnbürste" erkannt
Lösung: Zähllinie wird jetzt NACH der YOLO-Erkennung gezeichnet

Änderungen:
- YOLO-Erkennung läuft auf dem Original-Frame
- Zähllinie wird erst danach als Overlay gezeichnet
- Linie ist jetzt etwas dicker (3px statt 2px) für bessere Sichtbarkeit

Resultat: Keine falschen Objekterkennungen mehr durch die Zähllinie

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

app.py

@@ -109,21 +109,7 @@ def detect_objects_from_webcam(line_data):
         # Resize the frame to (1020, 600)
         frame = cv2.resize(frame, (1020, 600))
 
-        # Draw counting line (dashed yellow line with black gaps)
+        # Run YOLOv8 tracking on the frame (BEFORE drawing the counting line!)
-        cv2.line(frame, line_start, line_end, (0, 255, 255), 2, cv2.LINE_AA)
-        # Draw dashed effect
-        line_length = int(np.sqrt((line_end[0] - line_start[0])**2 + (line_end[1] - line_start[1])**2))
-        dash_length = 20
-        for i in range(0, line_length, dash_length * 2):
-            t1 = i / line_length
-            t2 = min((i + dash_length) / line_length, 1.0)
-            x1 = int(line_start[0] + t1 * (line_end[0] - line_start[0]))
-            y1 = int(line_start[1] + t1 * (line_end[1] - line_start[1]))
-            x2 = int(line_start[0] + t2 * (line_end[0] - line_start[0]))
-            y2 = int(line_start[1] + t2 * (line_end[1] - line_start[1]))
-            cv2.line(frame, (x1, y1), (x2, y2), (0, 0, 0), 2)
-
-        # Run YOLOv8 tracking on the frame
         results = model.track(frame, persist=True)
 
         if results[0].boxes is not None and results[0].boxes.id is not None:
@@ -171,6 +157,20 @@ def detect_objects_from_webcam(line_data):
                 # Draw center point (yellow)
                 cv2.circle(frame, (center_x, center_y), 3, (0, 255, 255), -1)
 
+        # Draw counting line AFTER YOLO detection (dashed yellow line)
+        cv2.line(frame, line_start, line_end, (0, 255, 255), 3, cv2.LINE_AA)
+        # Draw dashed effect
+        line_length = int(np.sqrt((line_end[0] - line_start[0])**2 + (line_end[1] - line_start[1])**2))
+        dash_length = 20
+        for i in range(0, line_length, dash_length * 2):
+            t1 = i / line_length
+            t2 = min((i + dash_length) / line_length, 1.0)
+            x1 = int(line_start[0] + t1 * (line_end[0] - line_start[0]))
+            y1 = int(line_start[1] + t1 * (line_end[1] - line_start[1]))
+            x2 = int(line_start[0] + t2 * (line_end[0] - line_start[0]))
+            y2 = int(line_start[1] + t2 * (line_end[1] - line_start[1]))
+            cv2.line(frame, (x1, y1), (x2, y2), (0, 0, 0), 3)
+
         # Display vehicle count with type breakdown
         cv2.rectangle(frame, (10, 10), (350, 140), (0, 0, 0), -1)
         cv2.putText(frame, f'Gesamt: {vehicle_count}', (20, 35), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
@@ -247,21 +247,7 @@ def detect_objects_from_video(video_path, line_data):
         # Resize the frame to (1020, 600)
         frame = cv2.resize(frame, (1020, 600))
 
-        # Draw counting line (dashed yellow line with black gaps)
+        # Run YOLOv8 tracking on the frame (BEFORE drawing the counting line!)
-        cv2.line(frame, line_start, line_end, (0, 255, 255), 2, cv2.LINE_AA)
-        # Draw dashed effect
-        line_length = int(np.sqrt((line_end[0] - line_start[0])**2 + (line_end[1] - line_start[1])**2))
-        dash_length = 20
-        for i in range(0, line_length, dash_length * 2):
-            t1 = i / line_length
-            t2 = min((i + dash_length) / line_length, 1.0)
-            x1 = int(line_start[0] + t1 * (line_end[0] - line_start[0]))
-            y1 = int(line_start[1] + t1 * (line_end[1] - line_start[1]))
-            x2 = int(line_start[0] + t2 * (line_end[0] - line_start[0]))
-            y2 = int(line_start[1] + t2 * (line_end[1] - line_start[1]))
-            cv2.line(frame, (x1, y1), (x2, y2), (0, 0, 0), 2)
-
-        # Run YOLOv8 tracking on the frame
         results = model.track(frame, persist=True)
 
         if results[0].boxes is not None and results[0].boxes.id is not None:
@@ -309,6 +295,20 @@ def detect_objects_from_video(video_path, line_data):
                 # Draw center point (yellow)
                 cv2.circle(frame, (center_x, center_y), 3, (0, 255, 255), -1)
 
+        # Draw counting line AFTER YOLO detection (dashed yellow line)
+        cv2.line(frame, line_start, line_end, (0, 255, 255), 3, cv2.LINE_AA)
+        # Draw dashed effect
+        line_length = int(np.sqrt((line_end[0] - line_start[0])**2 + (line_end[1] - line_start[1])**2))
+        dash_length = 20
+        for i in range(0, line_length, dash_length * 2):
+            t1 = i / line_length
+            t2 = min((i + dash_length) / line_length, 1.0)
+            x1 = int(line_start[0] + t1 * (line_end[0] - line_start[0]))
+            y1 = int(line_start[1] + t1 * (line_end[1] - line_start[1]))
+            x2 = int(line_start[0] + t2 * (line_end[0] - line_start[0]))
+            y2 = int(line_start[1] + t2 * (line_end[1] - line_start[1]))
+            cv2.line(frame, (x1, y1), (x2, y2), (0, 0, 0), 3)
+
         # Display vehicle count with type breakdown
         cv2.rectangle(frame, (10, 10), (350, 140), (0, 0, 0), -1)
         cv2.putText(frame, f'Gesamt: {vehicle_count}', (20, 35), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)