目标检测标签格式转化——YOLO格式转JSON格式

说明

在目标检测数据集处理中，我们经常会遇到标签之间不同格式的转化，以下介绍YOLO格式的标签转JSON格式。

格式

通过labelimg标注的标签保存为如下txt文件：

txt格式YOLO标签
0 0.4296875 0.21597222222222223 0.1609375 0.43194444444444446
0 0.58203125 0.3138888888888889 0.1390625 0.5666666666666667
0 0.08554687500000001 0.19791666666666669 0.15703125 0.37083333333333335
0 0.90234375 0.23402777777777778 0.17500000000000002 0.4152777777777778

转化后的同labelme标注保存的json文件：

代码

import os
import json
import base64
import cv2

def read_txt_file(txt_file):
    with open(txt_file, 'r') as f:
        lines = f.readlines()
    data = []
    for line in lines:
        line = line.strip().split()
        class_name = line[0]
        bbox = [coord for coord in line[1:]]
        data.append({'class_name': class_name, 'bbox': bbox})
    return data

def convert_to_labelme(data, image_path, image_size):
    labelme_data = {
        'version': '4.5.6',
        'flags': {},
        'shapes': [],
        'imagePath': json_image_path,
        'imageData': None,
        'imageHeight': image_size[0],
        'imageWidth': image_size[1]
    }
    for obj in data:
        dx = obj['bbox'][0]
        dy = obj['bbox'][1]
        dw = obj['bbox'][2]
        dh = obj['bbox'][3]

        w = eval(dw) * image_size[1]
        h = eval(dh) * image_size[0]
        center_x = eval(dx) * image_size[1]
        center_y = eval(dy) * image_size[0]
        x1 = center_x - w/2
        y1 = center_y - h/2
        x2 = center_x + w/2
        y2 = center_y + h/2

        if obj['class_name'] == '0': #判断对应的标签名称，写入json文件中
            label = str('grape')
        else:
            label = obj['class_name']
        shape_data = {
            'label': label,
            'points': [[x1, y1], [x2, y2]],
            'group_id': None,
            'shape_type': 'rectangle',
            'flags': {}
        }
        labelme_data['shapes'].append(shape_data)
    return labelme_data

def save_labelme_json(labelme_data, image_path, output_file):
    with open(image_path, 'rb') as f:
        image_data = f.read()
    labelme_data['imageData'] = base64.b64encode(image_data).decode('utf-8')

    with open(output_file, 'w') as f:
        json.dump(labelme_data, f, indent=4)

# 设置文件夹路径和输出文件夹路径
txt_folder = r"E:\lwf_files\wanfu\01-yolo\yoloair\grape_dataset\labels\train"  # 存放LabelImg标注的txt文件的文件夹路径
output_folder = r"E:\lwf_files\wanfu\01-yolo\yoloair\grape_dataset\labels\train"  # 输出LabelMe标注的json文件的文件夹路径
img_folder = r"E:\lwf_files\wanfu\01-yolo\yoloair\grape_dataset\images\train" #存放对应标签的图片文件夹路径

# 创建输出文件夹
if not os.path.exists(output_folder):
    os.makedirs(output_folder)

# 遍历txt文件夹中的所有文件
for filename in os.listdir(txt_folder):
    if filename.endswith('.txt'):
        # 生成对应的输出文件名
        output_filename = os.path.splitext(filename)[0] + '.json'

        # 读取txt文件
        txt_file = os.path.join(txt_folder, filename)
        data = read_txt_file(txt_file)

        # 设置图片路径和尺寸
        image_filename = os.path.splitext(filename)[0] + '.png'  # 图片文件名与txt文件名相同，后缀为.jpg
        image_path = os.path.join(img_folder, image_filename)
        # image_size = (1280, 720)  # 根据实际情况修改
        json_image_path = image_path.split('\\')[-1]
        image_size = cv2.imread(image_path).shape

        # 转化为LabelMe格式
        labelme_data = convert_to_labelme(data, image_path, image_size)

        # 保存为LabelMe JSON文件
        output_file = os.path.join(output_folder, output_filename)
        save_labelme_json(labelme_data, image_path, output_file)

附 txt标签可视化脚本

import cv2
import numpy as np

# 定义可视化函数
def visualize(image_path, label_path, class_names):
    # 读取图片
    image = cv2.imread(image_path)

    # 获取图片的大小
    height, width, _ = image.shape

    # 读取标签文件
    with open(label_path, "r") as f:
        lines = f.readlines()

    # 遍历每个标签
    for line in lines:
        # 解析标签
        class_id, x, y, w, h = map(float, line.split())
        class_name = class_names[int(class_id)]

        # 计算 bounding box 的坐标
        left = int((x - w / 2) * width)
        top = int((y - h / 2) * height)
        right = int((x + w / 2) * width)
        bottom = int((y + h / 2) * height)

        # 绘制 bounding box
        cv2.rectangle(image, (left, top), (right, bottom), (0, 255, 0), 2)

        # 绘制类别名称
        text_size, _ = cv2.getTextSize(class_name, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 2)
        cv2.putText(image, class_name, (left, top - text_size[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

    # 显示图片
    cv2.imshow("visualization", image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

# 调用函数，可视化 YOLO 标签（请替换为你的图片路径、标签路径和类别名称列表）
visualize(r"F:\文件B_数集文件\B_葡萄数据\07-230612葡萄\0620_2_1.png", r"F:\文件B_数集文件\B_葡萄数据\07-230612葡萄\0620_2_1.txt", ["类别1", "类别2", "类别3"])