YOLOv8 Tracking and Counting

[Elsaraf1]

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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)
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 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!