YOLOv8 Tracking and Counting

[Elsaraf1]

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• I have searched the Roboflow Notebooks issues and found no similar bug report.

Notebook name

YOLOv8 Tracking and Counting

Bug

I didn't change any in the code but i got this error

AttributeError                            Traceback (most recent call last)
[<ipython-input-38-62b2abab0e76>](https://localhost:8080/#) in <cell line: 17>()
     30         # print(detections)
     31         # tracking detections
---> 32         tracks = byte_tracker.update(
     33             output_results=detections2boxes(detections=detections),
     34             img_info=frame.shape,

3 frames
[/usr/local/lib/python3.10/dist-packages/numpy/__init__.py](https://localhost:8080/#) in __getattr__(attr)
    322 
    323         if attr in __former_attrs__:
--> 324             raise AttributeError(__former_attrs__[attr])
    325 
    326         if attr == 'testing':

AttributeError: module 'numpy' has no attribute 'float'.
`np.float` was a deprecated alias for the builtin `float`. To avoid this error in existing code, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
    I believe that is related to byte_racker

Environment

Google colab

Minimal Reproducible Example

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)
# 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().astype(float),
            confidence=results[0].boxes.conf.cpu().numpy(),
            class_id=results[0].boxes.cls.cpu().numpy()
        )
        # 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)
        # print(detections)
        # 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)
        # annotate and display frame
        frame = box_annotator.annotate(frame=frame, detections=detections, labels=labels)
        line_annotator.annotate(frame=frame, line_counter=line_counter)
        sink.write_frame(frame)

Additional

No response

Are you willing to submit a PR?

• Yes I'd like to help by submitting a PR!

[SkalskiP]

Hi @Elsaraf1 👋🏻 It looks like you're using an outdated version of Google Colab. The code we showed in the YouTube tutorial no longer works, but we have prepared a new version of this code. I'm closing this issue but feel free to reopen it if you encounter further issues.