研一 | labelme转yolo格式

关于labelme标注数据：

1、必须是左上开始、右下结束。 2、labelme：开启自动保存；关闭 同时保存图像数据。 3、可更改保存路径。（把标注的json文件都保存到一起）

代码：将labelme标注的json格式转成yolo的txt格式

import os
import json
import shutil
import numpy as np
from tqdm import tqdm

Dataset_root = 'C:/……/pic_labelme'

# 框的类别
bbox_class = {
    'person': 0
}
# 关键点的类别
keypoint_class = ['0', '1', '2','3','4','5','6','7','8','9','10','11','12','13','14','15','16']
# 更改当前工作目录到指定的路径 `Dataset_root`
os.chdir(Dataset_root)
# 创建新文件夹
os.mkdir('labels')
os.mkdir('labels/train')

def process_single_json(labelme_path, save_folder):
    with open(labelme_path, 'r', encoding='utf-8') as f:
        labelme = json.load(f)

    img_width = labelme['imageWidth']  # 图像宽度
    img_height = labelme['imageHeight']  # 图像高度

    # 生成 YOLO 格式的 txt 文件
    suffix = labelme_path.split('.')[-2]
    yolo_txt_path = suffix + '.txt'

    with open(yolo_txt_path, 'w', encoding='utf-8') as f:
        for each_ann in labelme['shapes']:  # 遍历每个标注
            if each_ann['shape_type'] == 'rectangle':  # 每个框，在 txt 里写一行
                yolo_str = ''
                # 框的信息
                # 框的类别 ID
                bbox_class_id = bbox_class[each_ann['label']]
                yolo_str += '{} '.format(bbox_class_id)

                # 左上角和右下角的 XY 像素坐标
                bbox_top_left_x = int(min(each_ann['points'][0][0], each_ann['points'][1][0]))
                bbox_bottom_right_x = int(max(each_ann['points'][0][0], each_ann['points'][1][0]))
                bbox_top_left_y = int(min(each_ann['points'][0][1], each_ann['points'][1][1]))
                bbox_bottom_right_y = int(max(each_ann['points'][0][1], each_ann['points'][1][1]))
                
                # 框中心点的 XY 像素坐标
                bbox_center_x = int((bbox_top_left_x + bbox_bottom_right_x) / 2)
                bbox_center_y = int((bbox_top_left_y + bbox_bottom_right_y) / 2)
                
                # 框宽度
                bbox_width = bbox_bottom_right_x - bbox_top_left_x
                # 框高度
                bbox_height = bbox_bottom_right_y - bbox_top_left_y
               
                # 框中心点归一化坐标
                bbox_center_x_norm = bbox_center_x / img_width
                bbox_center_y_norm = bbox_center_y / img_height
                
                # 框归一化宽度
                bbox_width_norm = bbox_width / img_width
                
                # 框归一化高度
                bbox_height_norm = bbox_height / img_height

                yolo_str += '{:.5f} {:.5f} {:.5f} {:.5f} '.format(bbox_center_x_norm, bbox_center_y_norm,
                                                                  bbox_width_norm, bbox_height_norm)

                # 找到该框中所有关键点，存在字典 bbox_keypoints_dict 中
                bbox_keypoints_dict = {}
                for each_ann in labelme['shapes']:  # 遍历所有标注
                    if each_ann['shape_type'] == 'point':  # 筛选出关键点标注
                        # 关键点XY坐标、类别
                        x = int(each_ann['points'][0][0])
                        y = int(each_ann['points'][0][1])
                        label = each_ann['label']
                        if (x > bbox_top_left_x) & (x < bbox_bottom_right_x) & (y < bbox_bottom_right_y) & (
                                y > bbox_top_left_y):  # 筛选出在该个体框中的关键点
                            bbox_keypoints_dict[label] = [x, y]

                ## 把关键点按顺序排好
                for each_class in keypoint_class:  # 遍历每一类关键点
                    if each_class in bbox_keypoints_dict:
                        keypoint_x_norm = bbox_keypoints_dict[each_class][0] / img_width
                        keypoint_y_norm = bbox_keypoints_dict[each_class][1] / img_height
                        yolo_str += '{:.5f} {:.5f} {} '.format(keypoint_x_norm, keypoint_y_norm, each_class)  # 2-可见不遮挡 1-遮挡 0-没有点
                    else:  # 不存在的点，一律为0
                        yolo_str += '0 0 0 '
                # 写入 txt 文件中
                f.write(yolo_str + '\n')
                
    shutil.move(yolo_txt_path, save_folder)
    print('{} --> {} 转换完成'.format(labelme_path, yolo_txt_path))

save_folder = 'C:/……/pic_labelme/labels/train'
for labelme_path in os.listdir():
    print('正在处理：', labelme_path)
    try:
        process_single_json(labelme_path, save_folder=save_folder)
    except:
        print('******有误******', labelme_path)
# print('YOLO格式的txt标注文件已保存至 ', save_folder)

其中Dataset_root和save_folder均可更改。根据文件夹需要自己更改路径即可