Application keeps crashing and is very laggy: IndexError: index 0 is out of bounds for dimension 0 with size 0

Question

import cv2
import argparse
import pandas as pd
from ultralytics import YOLO
import supervision as sv
import numpy as np
from supervision.tools.detections import Detections, BoxAnnotator
from supervision.tools.line_counter import LineCounter, LineCounterAnnotator
from supervision.draw.color import ColorPalette
from supervision import *


def parse_arguments() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description="YOLOv8 live")
    parser.add_argument(
        "--webcam-resolution", 
        default=[1280, 720], 
        nargs=2, 
        type=int
    )
    args = parser.parse_args()
    return args

def main():
    args = parse_arguments()
    frame_width, frame_height = args.webcam_resolution

    cap = cv2.VideoCapture(0)
    cap.set(cv2.CAP_PROP_FRAME_WIDTH, frame_width)
    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, frame_height)

    model = YOLO("yolov8l.pt")
    # dict maping class_id to class_name
    CLASS_NAMES_DICT = model.model.names
    # class_ids of interest - car, motorcycle, bus and truck
    CLASS_ID = [2, 3, 5, 7, 9]


    box_annotator = BoxAnnotator(
        color= ColorPalette(),
        thickness=2,
        text_thickness=2,
        text_scale=1
    )

    while True:
        ret, frame = cap.read()

        results = model(frame, agnostic_nms=True)[0]
        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)
            )
        labels = [
            f"{CLASS_NAMES_DICT[class_id]} {confidence:0.2f}"
            for _, confidence, class_id, _
            in detections
        ]
        frame = box_annotator.annotate(
            frame =frame, 
            detections=detections, 
            labels=labels
        ) 
        
        cv2.imshow("yolov8", frame)

        if (cv2. waitKey(20)& 0xFF==ord('d')):
            break

if __name__ == "__main__":
    main()

So the above code is me trying to use live camera feed to detect vehicles and to draw a line to detect entrace and exiting of the vehicles. The code runs but it crashes after a few seconds and the time before crash is not consistent. It also returns this error.

return Boxes(self.boxes[idx], self.orig_shape)
IndexError: index 0 is out of bounds for dimension 0 with size 0
(base)

I believe the code is ending when it doesn’t detect anything. Could you help me find out the cause of this issue?

Asked By: Surya

||

Source

Answer 1

You have to check whether you detected something or not before you start extracting bbox data:

results = model.predict(source="0", show=True, stream=True, classes=0, device='cpu')
for i, result in enumerate(results):
    boxes = result.boxes
    if boxes is not None:
        ...

Answered By: Mike B

Answer 2

My guess is the problem is you do:

results = model(frame, agnostic_nms=True)[0]

and

xyxy=results[0].boxes.xyxy.cpu().numpy()

What I mean by this is that you access the first element of an iterable object two times – essentially, you do model(frame, agnostic_nms=True)[0][0].

Just replace corresponding lines with those:

results = model(frame, agnostic_nms=True)[0]
detections = Detections(
    xyxy=results.boxes.xyxy.cpu().numpy(),
    confidence=results.boxes.conf.cpu().numpy(),
    class_id=results.boxes.cls.cpu().numpy().astype(int)
)

Answered By: Piotr Skalski

Application keeps crashing and is very laggy: IndexError: index 0 is out of bounds for dimension 0 with size 0

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