YOLOv8 Tracking and Counting #355
Description
Search before asking
- 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!