最近在爬一些数据，有些网址的验证码比较难搞，于是使用yolov8来解决。

一、数据打标签并转为txt

使用的软件为X-AnyLabeling。内置各种模型，方便打标。

打标完成后由于是json格式，所以我们使用python转换即可

import json
import os

#矩形框时

def labelme_to_yolo(label_me_json_file, cls2id_dict):
    label_me_json = json.load(open(label_me_json_file, mode='r', encoding='UTF-8'))
    shapes = label_me_json['shapes']
    img_width, img_height = label_me_json['imageWidth'], label_me_json['imageHeight']
    img_path = label_me_json['imagePath']
    img_data = label_me_json['imageData'] if 'imageData' in label_me_json else ''

    labels = []
    for s in shapes:
        s_type = s['shape_type']
        s_type = s_type.lower()
        if s_type == 'rectangle':
            pts = s['points']
            x1, y1 = pts[0]  # left corner
            x2, y2 = pts[1]  # right corner
            x = (x1 + x2) / 2 / img_width
            y = (y1 + y2) / 2 / img_height
            w = abs(x2 - x1) / img_width
            h = abs(y2 - y1) / img_height
            cid = cls2id_dict[s['label']]
            labels.append(f'{cid} {x} {y} {w} {h}')

    return labels


def write_label2txt(save_txt_path, label_list):
    f = open(save_txt_path, "w", encoding="UTF-8")

    for label in label_list:
        temp_list = label.split(" ")
        f.write(temp_list[0])
        f.write(" ")
        f.write(temp_list[1])
        f.write(" ")
        f.write(temp_list[2])
        f.write(" ")
        f.write(temp_list[3])
        f.write(" ")
        f.write(temp_list[4])
        f.write("\n")


if __name__ == '__main__':
    # 原始图片文件夹路径
    img_dir = r"D:\pic\pic"
    # 原始JSON标签文件夹路径
    json_dir = r"D:\pic\label_json"
    # 生成保存TXT文件夹路径
    save_dir = r"D:\pic\label_txt"
    # 类别和序号的映射字典
    cls2id_dict = {"building1": "0"}

    if not os.path.exists(save_dir):
        os.makedirs(save_dir)

    for json_name in os.listdir(json_dir):
        json_path = os.path.join(json_dir, json_name)
        txt_name = json_name.split(".")[0] + ".txt"
        save_txt_path = os.path.join(save_dir, txt_name)
        labels = labelme_to_yolo(json_path, cls2id_dict)
        write_label2txt(save_txt_path, labels)

# 处理 X-Anylabeling 多边形矩阵的标注 json 转化 txt，提取点
import json
import os

name2id = { '球体'  : 0,
            '立方体': 1,
            '圆锥体': 2,
            '圆柱体': 3,
            '多面体': 4
           }  # 修改你的类别并且赋与 index


def decode_json(json_floder_path, txt_outer_path, json_name):
    txt_name = os.path.join(txt_outer_path,json_name[:-5]) + '.txt'
    with open(txt_name, 'a') as f:
        json_path = os.path.join(json_floder_path, json_name)
        data = json.load(open(json_path, 'r', encoding='utf8', errors='ignore'))
        img_w = data['imageWidth']
        img_h = data['imageHeight']
        isshape_type = data['shapes'][0]['shape_type']
        print(isshape_type)
        dw = 1. / (img_w)
        dh = 1. / (img_h)
        for i in data['shapes']:
            label_name = i['label']
            if (i['shape_type'] == 'polygon'):
                point = []
                for lk in range(len(i['points'])):
                    x = float(i['points'][lk][0])
                    y = float(i['points'][lk][1])
                    point_x = x * dw
                    point_y = y * dh
                    point.append(point_x)
                    point.append(point_y)
                try:
                    formatted_line = f"{name2id[label_name]} {' '.join(str(a) for a in point)}\n"
                    f.write(formatted_line)
                except KeyError:
                    print(f"Warning: Label name '{label_name}' not found in name2id mapping.")
        f.close()


if __name__ == "__main__":
    json_floder_path = r'D:\pic\label_json'  # 存放 json 的文件夹的绝对路径
    txt_outer_path = r'D:\pic\label_txt'  # 存放 txt 的文件夹绝对路径
    json_names = os.listdir(json_floder_path)
    flagcount = 0
    for json_name in json_names:
        decode_json(json_floder_path, txt_outer_path, json_name)
        flagcount += 1

    print('-----------转化完毕------------')

二、使用yolov8训练

2.1 将图片和标签分别放在datasets目录下

2.2创建yaml文件

trian为训练的图片

val为预测的图片

train: D:\\software\\PyCharm\\workspace\\ultralytics\\datasest\\mypic\\images 
val: D:\\software\\PyCharm\\workspace\\SomeTry\\yanzhengma\\val 

names:
  0: '球体'
  1: '立方体'
  2: '圆锥体'
  3: '圆柱体'
  4: '多面体'

2.3 创建训练代码

YOLOv8文档

from ultralytics import YOLO


model = YOLO('yolov8n.pt')
model.train(data='mypic.yaml', epochs=100, imgsz=640, batch=8)





#用训练后的模型进行预测
yolo predict model=runs/detect/train/weights/best.pt source=D:\\software\\PyCharm\\workspace\\SomeTry\\yanzhengma\\val\\1719060455810geetest_image.jpg

训练结果