fixing camera n1235

src/streamlit_app.py

@@ -300,6 +300,30 @@ def process_video(video_path, model, conf_threshold, frame_skip=1):
     container = None
     stream = None
     codec_name = None
+    error_message = None
+
+    def _process_frame(frame_bgr):
+        nonlocal processed_frames
+        img_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)
+        model.conf = conf_threshold
+        results = model(img_rgb)
+
+        detections = results.pandas().xyxy[0]
+        relevant = detections[detections['name'].isin(WHEELCHAIR_CLASSES.values())]
+
+        for name in relevant['name'].tolist():
+            detection_counts[name] = detection_counts.get(name, 0) + 1
+
+        rendered_frame = results.render()[0]
+        if rendered_frame.shape[1] != width or rendered_frame.shape[0] != height:
+            rendered_frame = cv2.resize(rendered_frame, (width, height))
+
+        processed_frames += 1
+        if total_frames > 0:
+            progress.progress(min(processed_frames / total_frames, 1.0))
+
+        return rendered_frame
+
     try:
         container = av.open(output_path, mode="w")
         try:
@@ -322,43 +346,54 @@ def process_video(video_path, model, conf_threshold, frame_skip=1):
             if frame_skip > 1 and frame_index % frame_skip != 0:
                 continue
 
-            img_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-            model.conf = conf_threshold
-            results = model(img_rgb)
-
-            detections = results.pandas().xyxy[0]
-            relevant = detections[detections['name'].isin(WHEELCHAIR_CLASSES.values())]
-
-            for name in relevant['name'].tolist():
-                detection_counts[name] = detection_counts.get(name, 0) + 1
-
-            rendered_frame = results.render()[0]
-            if rendered_frame.shape[1] != width or rendered_frame.shape[0] != height:
-                rendered_frame = cv2.resize(rendered_frame, (width, height))
+            rendered_frame = _process_frame(frame)
 
             video_frame = av.VideoFrame.from_ndarray(rendered_frame, format="bgr24")
             for packet in stream.encode(video_frame):
                 container.mux(packet)
 
-            processed_frames += 1
-            if total_frames > 0:
-                progress.progress(min(processed_frames / total_frames, 1.0))
-
         for packet in stream.encode():
             container.mux(packet)
-    except Exception:
-        output_path = None
+    except Exception as e:
+        error_message = f"PyAV encode failed: {e}"
     finally:
-        progress.empty()
-        cap.release()
         if container is not None:
             container.close()
 
+    if error_message:
+        try:
+            fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+            writer = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+
+            cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
+            frame_index = 0
+            while True:
+                ret, frame = cap.read()
+                if not ret:
+                    break
+
+                frame_index += 1
+                if frame_skip > 1 and frame_index % frame_skip != 0:
+                    continue
+
+                rendered_frame = _process_frame(frame)
+                writer.write(rendered_frame)
+
+            writer.release()
+            error_message = None
+            codec_name = "mp4v"
+        except Exception as e:
+            error_message = f"OpenCV encode failed: {e}"
+            output_path = None
+    progress.empty()
+    cap.release()
+
     stats = {
         "total_frames": total_frames,
         "processed_frames": processed_frames,
         "detection_counts": detection_counts,
-        "codec": codec_name
+        "codec": codec_name,
+        "error": error_message
     }
 
     return output_path, stats
@@ -606,6 +641,10 @@ def main():
                 stats = st.session_state.video_stats
                 st.metric("🎞️ Total Frames", stats.get("total_frames", 0))
                 st.metric("✅ Processed Frames", stats.get("processed_frames", 0))
+                if stats.get("codec"):
+                    st.caption(f"Codec: {stats.get('codec')}")
+                if stats.get("error"):
+                    st.error(stats.get("error"))
 
                 if stats.get("detection_counts"):
                     counts_df = pd.DataFrame(