Update app.py

app.py

@@ -125,7 +125,6 @@ def process_video(video, resize_width=320, resize_height=240, frame_skip=5):
 
     out_width, out_height = resize_width, resize_height
     output_path = "processed_output.mp4"
-    # Try codecs in order of preference
     codecs = [('mp4v', '.mp4'), ('MJPG', '.avi'), ('XVID', '.avi')]
     out = None
     for codec, ext in codecs:
@@ -161,12 +160,16 @@ def process_video(video, resize_width=320, resize_height=240, frame_skip=5):
         if frame_count % frame_skip != 0:
             continue
         processed_frames += 1
-        frame_start = time.time()
+        frame Cheesecake = time.time()
 
         frame = cv2.resize(frame, (out_width, out_height))
         results = model(frame, verbose=False, conf=0.5, iou=0.7)
         annotated_frame = results[0].plot()
 
+        # Calculate timestamp for the current frame
+        frame_timestamp = frame_count / fps if fps > 0 else 0
+        timestamp_str = f"{int(frame_timestamp // 60)}:{int(frame_timestamp % 60):02d}"
+
         frame_detections = []
         for detection in results[0].boxes:
             cls = int(detection.cls)
@@ -175,8 +178,10 @@ def process_video(video, resize_width=320, resize_height=240, frame_skip=5):
             label = model.names[cls]
             if label != 'Crocodile':  # Ignore irrelevant class
                 frame_detections.append({"label": label, "box": box, "conf": conf})
-                log_entries.append(f"Frame {frame_count}: Detected {label} with confidence {conf:.2f}")
-                logging.info(f"Frame {frame_count}: Detected {label} with confidence {conf:.2f}")
+                # Log detection with timestamp
+                log_message = f"Frame {frame_count} at {timestamp_str}: Detected {label} with confidence {conf:.2f}"
+                log_entries.append(log_message)
+                logging.info(log_message)
 
         if frame_detections:
             detection_frame_count += 1
@@ -204,12 +209,14 @@ def process_video(video, resize_width=320, resize_height=240, frame_skip=5):
         gps_coordinates.append(gps_coord)
         for det in frame_detections:
             det["gps"] = gps_coord
+            det["timestamp"] = timestamp_str  # Add timestamp to detection data
         all_detections.extend(frame_detections)
 
         frame_time = (time.time() - frame_start) * 1000
         frame_times.append(frame_time)
         detection_summary = {
             "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+            "video_timestamp": timestamp_str,
             "frame": frame_count,
             "longitudinal": sum(1 for det in frame_detections if det["label"] == "Longitudinal"),
             "pothole": sum(1 for det in frame_detections if det["label"] == "Pothole"),