fixing camera n1235

src/streamlit_app.py

@@ -291,53 +291,74 @@ def process_video(video_path, model, conf_threshold, frame_skip=1):
     output_fd, output_path = tempfile.mkstemp(suffix=".mp4")
     os.close(output_fd)
 
-    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
-    writer = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
-
     total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) or 0
     processed_frames = 0
     detection_counts = {}
 
     progress = st.progress(0, text="Processing video frames...")
 
-    frame_index = 0
-    while True:
-        ret, frame = cap.read()
-        if not ret:
-            break
+    container = None
+    stream = None
+    codec_name = None
+    try:
+        container = av.open(output_path, mode="w")
+        try:
+            stream = container.add_stream("libx264", rate=fps)
+            codec_name = "libx264"
+        except av.AVError:
+            stream = container.add_stream("mpeg4", rate=fps)
+            codec_name = "mpeg4"
+        stream.width = width
+        stream.height = height
+        stream.pix_fmt = "yuv420p"
+
+        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:
-            writer.write(frame)
-            continue
+            frame_index += 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)
+            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())]
+            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
 
-        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 = results.render()[0]
-        if rendered_frame.shape[1] != width or rendered_frame.shape[0] != height:
-            rendered_frame = cv2.resize(rendered_frame, (width, height))
-        writer.write(rendered_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))
+            processed_frames += 1
+            if total_frames > 0:
+                progress.progress(min(processed_frames / total_frames, 1.0))
 
-    progress.empty()
-    cap.release()
-    writer.release()
+        for packet in stream.encode():
+            container.mux(packet)
+    except Exception:
+        output_path = None
+    finally:
+        progress.empty()
+        cap.release()
+        if container is not None:
+            container.close()
 
     stats = {
         "total_frames": total_frames,
         "processed_frames": processed_frames,
-        "detection_counts": detection_counts
+        "detection_counts": detection_counts,
+        "codec": codec_name
     }
 
     return output_path, stats