Yolov8 将名称分类到 txt

Question

我正在使用yolov8和ROS进行物体检测，到目前为止一切顺利。我目前正在尝试实现的是获取已识别且位于已处理图像框中的类的名称，并将其与检测日期/时间和类名称一起放入 txt 文件中，但是这不起作用。

基本上我尝试过的已经在代码中了，我不知道如何进一步进行，我目前正在研究 Ultralytics 文档

#!/usr/bin/env python3

from ultralytics import YOLO    
import rospy
from sensor_msgs.msg import Image
from cv_bridge import CvBridge
import cv2
import os
import datetime

model = YOLO('yolov8n.pt')

# Crie um arquivo para armazenar os rótulos identificados
label_file = open("labels.txt", "w")

def start_node():
    rospy.init_node('detect_pump')
    rospy.loginfo('detect_pump node started')
    rospy.Subscriber("/image", Image, process_image)
    rospy.spin()

def process_image(msg):
    bridge = CvBridge()
    orig = bridge.imgmsg_to_cv2(msg, "bgr8")
    image = orig
    results = model(orig, verbose=True, imgsz=640)
    resultado= model.predict(source="0")
    cv2.imshow('IMAGEM RECEBIDA', orig)
    cv2.waitKey(1)
    res_plotted = results[0].plot()
    cv2.imshow("result", res_plotted)
    cv2.waitKey(1)

    # Obtenha a data e hora atual
    current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")

    # Concatene os rótulos identificados em uma única string
    labels_str = "\n".join(results.names)

    # Escreva os rótulos identificados como uma única string e a data/hora no arquivo
    label_file.write(f"Data/Hora: {current_time}\n")
    label_file.write(f"Labels: \n{labels_str}\n\n")

if __name__ == '__main__':
    try:
        start_node()
    except rospy.ROSInterruptException:
        pass

# Feche o arquivo após terminar de usar
label_file.close()`

txt 中出现的内容不是我需要的，基本上我只需要类，但出现的内容如下：

Data/Hora: 2023-10-14 17:08:30
Resultados: [ultralytics.yolo.engine.results.Results object with attributes:

boxes: ultralytics.yolo.engine.results.Boxes object
keypoints: None
keys: ['boxes']
masks: None
names: {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'}
orig_img: array([[[253, 238, 255],
        [253, 238, 255],
        [253, 238, 255],
        ...,
        [191, 213, 146],
        [191, 222, 129],
        [191, 222, 129]],

       [[253, 238, 255],
        [253, 238, 255],
        [253, 238, 255],
        ...,
        [191, 213, 146],
        [191, 222, 129],
        [191, 222, 129]],

       [[253, 241, 255],
        [253, 241, 255],
        [253, 241, 255],
        ...,
        [192, 209, 156],
        [192, 216, 142],
        [192, 216, 142]],

       ...,

       [[131, 118, 154],
        [131, 118, 154],
        [130, 117, 153],
        ...,
        [ 22,  26,  32],
        [ 20,  25,  28],
        [ 19,  24,  27]],

       [[131, 118, 156],
        [130, 117, 155],
        [129, 116, 154],
        ...,
        [ 24,  28,  34],
        [ 22,  27,  30],
        [ 21,  26,  29]],

       [[131, 118, 156],
        [130, 117, 155],
        [128, 115, 153],
        ...,
        [ 25,  29,  35],
        [ 23,  28,  31],
        [ 22,  27,  30]]], dtype=uint8)
orig_shape: (640, 480)
path: 'image0.jpg'
probs: None
save_dir: None
speed: {'preprocess': 3.4890174865722656, 'inference': 41.97049140930176, 'postprocess': 2.0608901977539062}]

Answer 1

用于从 YOLO 结果获取课程，

使用

results = model('imagepath')
class_names = results[0].names
boxes = results[0].boxes

参考文档