Deep debug: log environment, versions, and every major step in process_video_movenet

app.py

@@ -174,8 +174,15 @@ def after_request(response):
 def process_video_movenet(video_path):
     try:
         print("[DEBUG] Starting MoveNet video processing")
+        print(f"[DEBUG] Python version: {sys.version}")
+        print(f"[DEBUG] OpenCV version: {cv2.__version__}")
+        print(f"[DEBUG] TensorFlow version: {tf.__version__}")
+        print(f"[DEBUG] Upload dir contents: {os.listdir(os.path.dirname(video_path))}")
+        print(f"[DEBUG] Current working dir: {os.getcwd()}")
+        print(f"[DEBUG] Model dir contents: {os.listdir(os.path.join(BASE_DIR, 'external', 'BodybuildingPoseClassifier'))}")
         cap = cv2.VideoCapture(video_path)
         if not cap.isOpened():
+            print(f"[ERROR] Could not open video file: {video_path}")
             raise ValueError("Could not open video file")
         
         # Get video properties
@@ -187,9 +194,16 @@ def process_video_movenet(video_path):
         
         # Force MoveNet to CPU to avoid GPU JIT error
         print("[DEBUG] Forcing CPU for MoveNet (due to GPU JIT error)")
-        with tf.device('/CPU:0'):
-            movenet_model = hub.load("https://tfhub.dev/google/movenet/singlepose/lightning/4")
-            movenet = movenet_model.signatures['serving_default']
+        try:
+            with tf.device('/CPU:0'):
+                print("[DEBUG] Loading MoveNet model...")
+                movenet_model = hub.load("https://tfhub.dev/google/movenet/singlepose/lightning/4")
+                movenet = movenet_model.signatures['serving_default']
+                print("[DEBUG] MoveNet model loaded.")
+        except Exception as e:
+            print(f"[ERROR] Exception during MoveNet model load: {e}")
+            import traceback; traceback.print_exc()
+            raise
         
         # Create output video writer
         output_filename = f'output_movenet_lightning.mp4'
@@ -199,23 +213,25 @@ def process_video_movenet(video_path):
         fourcc = cv2.VideoWriter_fourcc(*'mp4v')
         out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
         if not out.isOpened():
+            print(f"[ERROR] Failed to create output video writer at {output_path}")
             raise ValueError(f"Failed to create output video writer at {output_path}")
         
         frame_count = 0
         processed_frames = 0
         first_frame_shape = None
+        print("[DEBUG] Entering frame loop...")
         
         while cap.isOpened():
-            ret, frame = cap.read()
-            print(f"[DEBUG] Frame {frame_count+1}: ret={ret}, frame is None: {frame is None}")
-            if not ret or frame is None:
-                print(f"[DEBUG] Stopping at frame {frame_count+1}: ret={ret}, frame is None: {frame is None}")
-                break
-            if first_frame_shape is None:
-                first_frame_shape = frame.shape
-                print(f"[DEBUG] First frame shape: {first_frame_shape}")
-            frame_count += 1
             try:
+                ret, frame = cap.read()
+                print(f"[DEBUG] Frame {frame_count+1}: ret={ret}, frame is None: {frame is None}")
+                if not ret or frame is None:
+                    print(f"[DEBUG] Stopping at frame {frame_count+1}: ret={ret}, frame is None: {frame is None}")
+                    break
+                if first_frame_shape is None:
+                    first_frame_shape = frame.shape
+                    print(f"[DEBUG] First frame shape: {first_frame_shape}")
+                frame_count += 1
                 # Ensure frame size matches VideoWriter
                 if frame.shape[1] != width or frame.shape[0] != height:
                     print(f"[WARNING] Frame size {frame.shape[1]}x{frame.shape[0]} does not match VideoWriter size {width}x{height}. Resizing.")
@@ -225,9 +241,14 @@ def process_video_movenet(video_path):
                 img = tf.image.resize_with_pad(tf.expand_dims(img, axis=0), 192, 192)
                 img = tf.cast(img, dtype=tf.int32)
                 # Always run inference on CPU
-                with tf.device('/CPU:0'):
-                    results = movenet(img)
-                    keypoints = results['output_0'].numpy()
+                try:
+                    with tf.device('/CPU:0'):
+                        results = movenet(img)
+                        keypoints = results['output_0'].numpy()
+                except Exception as e:
+                    print(f"[ERROR] Exception during MoveNet inference on frame {frame_count}: {e}")
+                    import traceback; traceback.print_exc()
+                    continue
                 # Process keypoints and draw on frame
                 y, x, c = frame.shape
                 shaped = np.squeeze(keypoints)
@@ -240,24 +261,27 @@ def process_video_movenet(video_path):
                 processed_frames += 1
                 print(f"[DEBUG] Wrote frame {frame_count} to output video.")
             except Exception as e:
-                print(f"[ERROR] Error processing frame {frame_count}: {str(e)}")
+                print(f"[ERROR] Exception in frame loop at frame {frame_count+1}: {e}")
+                import traceback; traceback.print_exc()
                 continue
         cap.release()
         out.release()
         print(f"[DEBUG] Processed {processed_frames} frames out of {total_frames} total frames")
         # Check output file size
         if not os.path.exists(output_path):
+            print(f"[ERROR] Output video file was not created: {output_path}")
             raise ValueError(f"Output video file was not created: {output_path}")
         file_size = os.path.getsize(output_path)
         print(f"[DEBUG] Output video file size: {file_size} bytes")
         if processed_frames == 0 or file_size < 1000:
+            print(f"[ERROR] Output video file is empty or too small: {output_path}")
             raise ValueError(f"Output video file is empty or too small: {output_path}")
         video_url = url_for('serve_video', filename=output_filename, _external=False)
         print(f"[DEBUG] Returning video URL: {video_url}")
         return video_url
     except Exception as e:
-        print(f"[ERROR] Error in process_video_movenet: {str(e)}")
-        traceback.print_exc()
+        print(f"[FATAL ERROR] Uncaught exception in process_video_movenet: {e}")
+        import traceback; traceback.print_exc()
         raise
 
 def process_video_mediapipe(video_path):