I am running streamlit locally(still developing) and after doing the inference on the video stream I have a stream of frames that I am getting and want to display as a video using streamlit
how can this be done?
Here is the sample code
def main(
weight_path: str,
rtsp_url: str,
zone_configuration_path: str,
model_id: str,
confidence: float,
iou: float,
classes: List[int],
) -> None:
sink = CustomSink(weights_path=weight_path ,zone_configuration_path=zone_configuration_path, classes=classes)
pipeline = InferencePipeline.init_with_custom_logic(
video_reference=rtsp_url,
on_video_frame=sink.infer,
on_prediction=sink.on_prediction
)
class CustomSink:
def __init__(self, weights_path: str, zone_configuration_path: str, classes: List[int]):
// initialization etc
def on_prediction(self, result: dict, frame: VideoFrame) -> None:
// doing some frame processing here
annotated_frame = frame.image.copy()
annotated_frame = sv.draw_text(
scene=annotated_frame,
text=f"{fps:.1f}",
text_anchor=sv.Point(40, 30),
)
labels = [
f"#{tracker_id} {int(time // 60):02d}:{int(time % 60):02d}" if class_id != 2 else f"#{tracker_id}"
for tracker_id, time, class_id in zip(detections_in_zone.tracker_id, time_in_zone,detections_in_zone.class_id)
]
annotated_frame = LABEL_ANNOTATOR.annotate(
scene=annotated_frame,
detections=detections_in_zone,
labels=labels,
custom_color_lookup=custom_color_lookup,
)
st.image(annotated_frame)
try:
# replace this with streamlit
# Resize the frame to fit the window size
resized_frame = cv2.resize(annotated_frame, (width, height))
except cv2.error as e:
print(f"Error resizing frame: {e}")
# Display the resized frame in the window
cv2.imshow('Resizable Window', resized_frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
cv2.destroyAllWindows()
raise SystemExit("Program terminated by user")
I am using the inference pipeline to do the inference on the video stream. To display the video I just pass the annoated_frame to the open cv and I get the video. I want to do the same thing but with the streamlit.
I tried the st.image(annotated_frame) but I am getting a series of images on the web page instead of video since it is inside the on_prediction callback
I tried st.video as well but not working
so my question is how to display a stream of processed frames as video on the web suing streamlit?
You can read about the inference pipeline here: Inference Pipeline - Roboflow Inference
