#490 first working combination for proper shutdown

examples/opencv_webcam/main.py

@@ -2,14 +2,19 @@
 import asyncio
 import base64
 import concurrent.futures
+import signal
 import time
 from typing import Optional
 
 import cv2
 import numpy as np
+import psutil
 from fastapi import Response
+from icecream import ic
 
-from nicegui import app, ui
+from nicegui import app
+from nicegui import globals as nicegui_globals
+from nicegui import ui
 
 # we need two executors to schedule IO and CPU intensive tasks with loop.run_in_executor()
 process_pool_executor = concurrent.futures.ProcessPoolExecutor()
@@ -24,6 +29,8 @@ video_capture = cv2.VideoCapture(0)
 
 
 def convert(frame: np.ndarray) -> Optional[bytes]:
+    if not frame_updater.active:
+        return None
     _, imencode_image = cv2.imencode('.jpg', frame)
     return imencode_image.tobytes()
 
@@ -31,14 +38,16 @@ def convert(frame: np.ndarray) -> Optional[bytes]:
 @app.get('/video/frame')
 # thanks to FastAPI's "app.get" it is easy to create a web route which always provides the latest image from OpenCV
 async def grab_video_frame() -> Response:
+    if not frame_updater.active:
+        return placeholder
     loop = asyncio.get_running_loop()
     if not video_capture.isOpened():
         return placeholder
-    # video_capture.read() is a blocking function, so we run it in a separate thread it to avoid blocking the event loop
+    # the video_capture.read call is a blocking function, so we run it in a separate thread it to avoid blocking the event loop
     _, frame = await loop.run_in_executor(thread_pool_executor, video_capture.read)
     if frame is None:
         return placeholder
-    # convert() is a cpu intensive function, so we run it in a separate process to avoid blocking the event loop and GIL
+    # "convert" is a cpu intensive function, so we run it in a separate process to avoid blocking the event loop and GIL
     jpeg = await loop.run_in_executor(process_pool_executor, convert, frame)
     if not jpeg:
         return placeholder
@@ -47,10 +56,26 @@ async def grab_video_frame() -> Response:
 # For non-flickering image updates an interactive image is much better than ui.image().
 video_image = ui.interactive_image().classes('w-full h-full')
 # A timer constantly updates the source of the image.
-# But because the path is always the same, we must force an update by adding the current timestamp to the source.
-ui.timer(interval=0.01, callback=lambda: video_image.set_source(f'/video/frame?{time.time()}'))
+# Because data from same paths are cached by the browser, we must force an update by adding the current timestamp to the source.
+frame_updater = ui.timer(interval=0.1, callback=lambda: video_image.set_source(f'/video/frame?{time.time()}'))
+
+
+def stop_updates(signum, frame):
+    frame_updater.active = False
+    ui.timer(1, lambda: signal.default_int_handler(signum, frame), once=True)
+
+
+async def cleanup():
+    for client in nicegui_globals.clients.keys():
+        await app.sio.disconnect(client)
+    video_capture.release()
+    thread_pool_executor.shutdown()
+    # the process pool executor must be shutdown when the app is closed, otherwise the process will not exit
+    process_pool_executor.shutdown()
+    await asyncio.sleep(1)
+
+app.on_shutdown(cleanup)
 
-# the process pool executor must be shutdown when the app is closed, otherwise the process will not exit
-app.on_shutdown(lambda: process_pool_executor.shutdown(wait=True, cancel_futures=True))
+signal.signal(signal.SIGINT, stop_updates)
 
 ui.run()