[2 intances of LineCounter]

[michelmadeira]

Hi Skal, I tried to create more instances of LineCounter and I pass the detections as arguments as expected.

The counter works but updates for both lines every time a object cross just 1 of the lines, for exemple.

How can I solve?

Thank you and congratulations for your work

[SkalskiP]

Hi, @michelmadeira 👋🏻! That would be unexpected behavior. Could you sand over your code?

[michelmadeira]

sure. Its just a quick test on your code.

settings

LINE_START = Point(50, 1500)
LINE_END = Point(1900, 1500)

LINE_START2 =  Point(2000,1000)
LINE_END2 = Point(3500,1000)

TARGET_VIDEO_PATH = f"{HOME}/vehicle-counting-result.mp4"

from tqdm.notebook import tqdm


# create BYTETracker instance
byte_tracker = BYTETracker(BYTETrackerArgs())
# create VideoInfo instance
video_info = VideoInfo.from_video_path(SOURCE_VIDEO_PATH)
# create frame generator
generator = get_video_frames_generator(SOURCE_VIDEO_PATH)
# create LineCounter instance

line_counter = LineCounter(start=LINE_START, end=LINE_END)
line_counter2 = LineCounter(LINE_START2, LINE_END2)

# create instance of BoxAnnotator and LineCounterAnnotator
box_annotator = BoxAnnotator(color=ColorPalette(), thickness=4, text_thickness=4, text_scale=2)
line_annotator = LineCounterAnnotator(thickness=4, text_thickness=4, text_scale=2)

# open target video file
with VideoSink(TARGET_VIDEO_PATH, video_info) as sink:
    # loop over video frames
    for frame in tqdm(generator, total=video_info.total_frames):
        # model prediction on single frame and conversion to supervision Detections
        results = model(frame)
        detections = Detections(
            xyxy=results[0].boxes.xyxy.cpu().numpy(),
            confidence=results[0].boxes.conf.cpu().numpy(),
            class_id=results[0].boxes.cls.cpu().numpy().astype(int)
        )
        # filtering out detections with unwanted classes
        mask = np.array([class_id in CLASS_ID for class_id in detections.class_id], dtype=bool)
        detections.filter(mask=mask, inplace=True)
        # tracking detections
        tracks = byte_tracker.update(
            output_results=detections2boxes(detections=detections),
            img_info=frame.shape,
            img_size=frame.shape
        )
        tracker_id = match_detections_with_tracks(detections=detections, tracks=tracks)
        detections.tracker_id = np.array(tracker_id)
        # filtering out detections without trackers
        mask = np.array([tracker_id is not None for tracker_id in detections.tracker_id], dtype=bool)
        detections.filter(mask=mask, inplace=True)
        # format custom labels
        labels = [
            f"#{tracker_id} {CLASS_NAMES_DICT[class_id]} {confidence:0.2f}"
            for _, confidence, class_id, tracker_id
            in detections
        ]

        # updating line counter
        line_counter.update(detections=detections)
        line_counter2.update(detections=detections)

        # annotate and display frame        
        frame = box_annotator.annotate(frame=frame, detections=detections, labels=labels)
        line_annotator.annotate(frame=frame, line_counter=line_counter)
        line_annotator.annotate(frame=frame, line_counter=line_counter2)           
        sink.write_frame(frame)

[michelmadeira]

Hi Skal, any updates on this? thank you

[SkalskiP]

The code looks legit. But I know we already had a similar situation. And the only thing that the user did was migrate to the latest version of Supervision, and it helped. We had several bugs that got fixed along the way. So this is my suggestion.