Update app.py

app.py

@@ -6,6 +6,7 @@ import os
 import json
 import logging
 import matplotlib.pyplot as plt
+import csv  # Added for flight logs
 from datetime import datetime
 from collections import Counter
 from typing import List, Dict, Any, Optional
@@ -44,7 +45,7 @@ last_metrics: Dict[str, Any] = {}
 frame_count: int = 0
 SAVE_IMAGE_INTERVAL = 1  # Save every frame with detections
 
-# Detection classes (aligned with model classes, excluding 'Crocodile' and 'Crack')
+# Detection classes (aligned with model classes, excluding 'Crocodile')
 DETECTION_CLASSES = ["Longitudinal", "Pothole", "Transverse"]
 
 # Debug: Check environment
@@ -172,7 +173,7 @@ def process_video(video, resize_width=4000, resize_height=3000, frame_skip=5):
 
     out_width, out_height = resize_width, resize_height
     output_path = os.path.join(OUTPUT_DIR, "processed_output.mp4")
-    codecs = [('mp4v', '.mp4'), ('MJPG', '.avi'), ('XVID', '.avi')]
+    codecs = [('mp4v', '.mp4'), ('XVID', '.avi'), ('MJPG', '.avi')]  # Prioritize mp4v
     out = None
     for codec, ext in codecs:
         fourcc = cv2.VideoWriter_fourcc(*codec)
@@ -287,12 +288,15 @@ def process_video(video, resize_width=4000, resize_height=3000, frame_skip=5):
         detected_counts.append(len(frame_detections))
         all_detections.extend(frame_detections)
 
+        frame_time = (time.time() - frame_start) * 1000
+        frame_times.append(frame_time)  # Fixed frame time tracking
+
         detection_summary = {
             "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
             "video_timestamp": timestamp_str,
             "frame": frame_count,
             "gps": gps_coord,
-            "processing_time_ms": (time.time() - frame_start) * 1000,
+            "processing_time_ms": frame_time,
             "detections": {label: sum(1 for det in frame_detections if det["label"] == label) for label in DETECTION_CLASSES}
         }
         data_lake_submission["analytics"].append(detection_summary)
@@ -330,11 +334,11 @@ def process_video(video, resize_width=4000, resize_height=3000, frame_skip=5):
 
     total_time = time.time() - start_time
     avg_frame_time = sum(frame_times) / len(frame_times) if frame_times else 0
-    log_entries.append(f"Output video: {output_frames} frames, {output_fps} FPS, {output_duration:.2f} seconds")
-    logging.info(f"Output video: {output_frames} frames, {output_fps} FPS, {output_duration:.2f} seconds")
+    log_entries.append(f"Output video: {output_frames} frames, {output_fps:.2f} FPS, {output_duration:.2f} seconds")
+    logging.info(f"Output video: {output_frames} frames, {output_fps:.2f} FPS, {output_duration:.2f} seconds")
     log_entries.append(f"Total processing time: {total_time:.2f} seconds, Avg frame time: {avg_frame_time:.2f} ms, Detection frames: {detection_frame_count}, Output frames: {output_frame_count}")
     logging.info(f"Total processing time: {total_time:.2f} seconds, Avg frame time: {avg_frame_time:.2f} ms, Detection frames: {detection_frame_count}, Output frames: {output_frame_count}")
-    print(f"Output video: {output_frames} frames, {output_fps} FPS, {output_duration:.2f} seconds")
+    print(f"Output video: {output_frames} frames, {output_fps:.2f} FPS, {output_duration:.2f} seconds")
     print(f"Total processing time: {total_time:.2f} seconds, Avg frame time: {avg_frame_time:.2f} ms, Detection frames: {detection_frame_count}, Output frames: {output_frame_count}")
 
     chart_path = generate_line_chart()