Class none person, how to remove it ?

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Question

I'm running this code on my raspberry pi 4 with picamera to detect and count people, using yolov8n, but sometimes it detects a class called none person, and then shows several boxes, when crossing the line it ends up counting these boxes with the class none person, and then, it ends up uncalibrating the count... I didn't find it in the documentation about this class called none person... how to disable it?

`import cv2 import json import numpy as np from picamera2 import Picamera2 from ultralytics import YOLO import supervision as sv import os

class PiLineCounter: def init(self, lines_json_path, model_path): with open(lines_json_path, 'r') as f: self.lines_data = json.load(f) self.model = YOLO(model_path)

    # Inicialização dos anotadores
    self.line_annotator = sv.LineZoneAnnotator(
        thickness=1,
        text_thickness=1,
        text_scale=1,
        custom_in_text="entrando",
        custom_out_text="saindo"
    )

    self.box_annotator = sv.BoxAnnotator(
        thickness=2,
        text_thickness=1,
        text_scale=0.5
    )

    # Inicialização da PiCamera2
    self.picam2 = Picamera2()
    preview_config = self.picam2.create_preview_configuration()
    self.picam2.configure(preview_config)
    self.picam2.start()

    # Inicialização dos contadores de linha
    self.line_counters = {}
    self.initialize_counters()

def initialize_counters(self):
    for line_key, line_value in self.lines_data.items():
        # Usar as coordenadas das linhas diretamente do JSON
        start_point_x, start_point_y = line_value['points'][0]
        end_point_x, end_point_y = line_value['points'][1]
        start_point = sv.Point(start_point_x, start_point_y)
        end_point = sv.Point(end_point_x, end_point_y)
        self.line_counters[line_key] = sv.LineZone(start=start_point, end=end_point)

def run(self):
    while True:
            frame = self.picam2.capture_array()
            frame = cv2.cvtColor(np.array(frame), cv2.COLOR_RGB2BGR)

            results = self.model.track(frame, show=False, stream=False, agnostic_nms=True, imgsz=320)
            print(f"Número de resultados de detecção: {len(results)}")
            for result in results:
                detections = sv.Detections.from_ultralytics(result)

                if detections is None or len(detections.xyxy) == 0:
                    print("Nenhuma detecção neste frame. Pulando...")
                    continue

                # Imprimir todas as detecções e seus respectivos class_id, labels e confianças
                for d in detections:
                    class_id = d[3]
                    label = self.model.model.names[class_id]
                    confidence = d[2]
                    print(f"Detecção: class_id={class_id}, label={label}, confiança={confidence:.2f}")

                detections_filtered = [d for d in detections if d[3] == 0]
                print(f"Número de detecções de pessoas: {len(detections_filtered)}")

                labels = [f"{d[4]} {self.model.model.names[d[3]]} {d[2]:0.2f}" for d in detections_filtered]

                for line_key in self.line_counters.keys():
                    in_count, out_count = self.line_counters[line_key].trigger(detections=detections_filtered)
                    print(f"Linha {line_key}: Entrando - {in_count}, Saindo - {out_count}")

                # Criar um objeto Detections que possa ser usado pelo BoxAnnotator
                if len(detections_filtered) > 0:
                    xyxy = np.array([d[0] for d in detections_filtered])
                    confidences = np.array([d[2] for d in detections_filtered])
                    class_ids = np.array([d[3] for d in detections_filtered])
                    tracker_ids = np.array([d[4] for d in detections_filtered])

                    detections_for_annotation = sv.Detections(xyxy=xyxy, confidence=confidences, class_id=class_ids, tracker_id=tracker_ids)

                    frame = self.box_annotator.annotate(
                        scene=frame,
                        detections=detections_for_annotation,
                        labels=labels
                    )
                else:
                    print("Nenhuma detecção de pessoas neste frame.")

                for line_key in self.line_counters.keys():
                    self.line_annotator.annotate(frame=frame, line_counter=self.line_counters[line_key])

            # Exibir o frame original sem redimensionamento
            cv2.imshow("PiCamera Line Counter", frame)
            #cv2.imwrite('/dev/shm/frame.jpg', frame)

            if cv2.waitKey(1) & 0xFF == ord('q'):
                break
    cv2.destroyAllWindows()

if name == 'main': lines_json_path = "lines_with_doubled_data.json"

model_path = "yolov8n.pt"

pi_line_counter = PiLineCounter(
    lines_json_path=lines_json_path,
    model_path=model_path
)
pi_line_counter.run()

Additional

lines_with_doubled_data.json

roboflow / supervision