Update app.py

app.py

@@ -14,10 +14,10 @@ model = YOLO(MODEL_PATH)
 TRUCK_CLASS_ID = 7  # "truck"
 
 # Initialize SORT tracker
-tracker = Sort(max_age=20, min_hits=3, iou_threshold=0.3)  # Improved tracking stability
+tracker = Sort()
 
 # Minimum confidence threshold for detection
-CONFIDENCE_THRESHOLD = 0.4  # Adjusted to capture more trucks
+CONFIDENCE_THRESHOLD = 0.4  # Lowered for better detection
 
 # Distance threshold to avoid duplicate counts
 DISTANCE_THRESHOLD = 50
@@ -30,9 +30,12 @@ TIME_INTERVALS = {
 
 def determine_time_interval(video_filename):
     """ Determines frame skip interval based on keywords in the filename. """
+    print(f"Checking filename: {video_filename}")  # Debugging
     for keyword, interval in TIME_INTERVALS.items():
         if keyword in video_filename:
+            print(f"Matched keyword: {keyword} -> Interval: {interval}")  # Debugging
             return interval
+    print("No keyword match, using default interval: 5")  # Debugging
     return 5  # Default interval
 
 def count_unique_trucks(video_path):
@@ -56,8 +59,8 @@ def count_unique_trucks(video_path):
     # Get total frames in the video
     total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
 
-    # Dynamically adjust frame skipping based on FPS and movement density
-    frame_skip = max(1, min(fps * time_interval // 2, total_frames // 10))
+    # Ensure frame_skip does not exceed total frames
+    frame_skip = min(fps * time_interval, total_frames // 2)  # Reduced skipping
 
     frame_count = 0
 
@@ -84,36 +87,38 @@ def count_unique_trucks(video_path):
                     x1, y1, x2, y2 = map(int, box.xyxy[0])  # Get bounding box
                     detections.append([x1, y1, x2, y2, confidence])
 
-        # Convert detections to numpy array for SORT
-        detections = np.array(detections) if len(detections) > 0 else np.empty((0, 5))
+        # Debugging: Check detections
+        print(f"Frame {frame_count}: Detections -> {detections}")
 
-        # Update SORT tracker
-        tracked_objects = tracker.update(detections)
+        if len(detections) > 0:
+            detections = np.array(detections)
+            tracked_objects = tracker.update(detections)
+        else:
+            tracked_objects = []  # Prevent tracker from resetting
+
+        # Debugging: Check tracked objects
+        print(f"Frame {frame_count}: Tracked Objects -> {tracked_objects}")
 
-        # Track movement history to avoid duplicate counts
         for obj in tracked_objects:
             truck_id = int(obj[4])  # Unique ID assigned by SORT
-            x1, y1, x2, y2 = obj[:4]  # Get bounding box coordinates
+            x1, y1, x2, y2 = obj[:4]  # Get the bounding box coordinates
 
             truck_center = (x1 + x2) / 2, (y1 + y2) / 2  # Calculate truck center
 
-            # Entry-exit zone logic (e.g., bottom 20% of the frame)
-            frame_height, frame_width = frame.shape[:2]
-            entry_line = frame_height * 0.8  # Bottom 20% of the frame
-            exit_line = frame_height * 0.2  # Top 20% of the frame
+            # If truck is already in history, check movement distance
+            if truck_id in truck_history:
+                last_position = truck_history[truck_id]["position"]
+                distance = np.linalg.norm(np.array(truck_center) - np.array(last_position))
+
+                if distance > DISTANCE_THRESHOLD:
+                    unique_truck_ids.add(truck_id)  # Add only if moved significantly
 
-            if truck_id not in truck_history:
-                # New truck detected
+            else:
+                # If truck is not in history, add it
                 truck_history[truck_id] = {
-                    "position": truck_center,
-                    "crossed_entry": truck_center[1] > entry_line,
-                    "crossed_exit": False
+                    "frame_count": frame_count,
+                    "position": truck_center
                 }
-                continue
-
-            # If the truck crosses from entry to exit, count it
-            if truck_history[truck_id]["crossed_entry"] and truck_center[1] < exit_line:
-                truck_history[truck_id]["crossed_exit"] = True
                 unique_truck_ids.add(truck_id)
 
     cap.release()