ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

[harsh854]

Search before asking

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

Notebook name

YOLOv8 object tracking + ByteTrack + Supervision

Bug

Traceback (most recent call last):
File "C:\Users\khars\PycharmProjects\Bytetrack\main.py", line 33, in
results = model(frame)[0] # Pass the frame as a list
File "C:\Users\khars\PycharmProjects\Bytetrack\venv\lib\site-packages\ultralytics\yolo\engine\model.py", line 58, in call
return self.predict(source, **kwargs)
File "C:\Users\khars\PycharmProjects\Bytetrack\venv\lib\site-packages\torch\utils_contextlib.py", line 116, in decorate_context
return func(*args, **kwargs)
File "C:\Users\khars\PycharmProjects\Bytetrack\venv\lib\site-packages\ultralytics\yolo\engine\model.py", line 130, in predict
predictor.setup(model=self.model, source=source)
File "C:\Users\khars\PycharmProjects\Bytetrack\venv\lib\site-packages\ultralytics\yolo\engine\predictor.py", line 111, in setup
source = str(source or self.args.source)
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

Process finished with exit code 1

Environment

Local OS : Windows 11
python: 3.10.11

Minimal Reproducible Example

from ultralytics import YOLO
import cv2
import torch
from supervision.video.source import get_video_frames_generator
from supervision.notebook.utils import show_frame_in_notebook
from supervision.draw.color import ColorPalette
from supervision.tools.detections import Detections, BoxAnnotator
from supervision.video.sink import VideoSink
from supervision.video.dataclasses import VideoInfo

Load the YOLO model

MODEL = "yolov8x.pt"
model = YOLO(MODEL)
model.fuse()

source = "output5.mp4"

CLASS_NAMES_DICT = model.model.names
CLASS_ID = [0]

Generate video frames from the source

generator = get_video_frames_generator("output5.mp4")
iterator = iter(generator)

Initialize box annotator

box_annotator = BoxAnnotator(color=ColorPalette(), thickness=4, text_thickness=4, text_scale=2)

video_info = VideoInfo.from_video_path(source)

Get the first frame from the video

frame = next(iterator)

results = model(frame)[0] # Pass the frame as a list

detections = Detections(
xyxy=results.boxes.xyxy.cpu().numpy(),
confidence=results.boxes.conf.cpu().numpy(),
class_id=results.boxes.cls.cpu().numpy().astype(int)
)

labels = [
f"{CLASS_NAMES_DICT[class_id]} {confidence:0.2f}"
for _, confidence, class_id, tracker_id
in detections
]

frame = box_annotator.annotate(frame=frame, detections=detections, labels=labels)

show_frame_in_notebook(frame, (16,16))

Additional

I was following the roboflow tutorial from youtube and I faced this issue. I have tried many things search everywhere but not any success. I have also tried the new notebook but still the same error

Are you willing to submit a PR?

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