Target detection label format conversion - YOLO format to JSON format

illustrate

In the processing of target detection data sets, we often encounter the conversion of different formats between labels. The following introduces the conversion of labels in YOLO format to JSON format.

Format

The labels marked by labelimg are saved as the following txt file:

txt format YOLO tag
0 0.4296875 0.21597222222222223 0.1609375 0.4319444444444446 0 0.58203125 0.3138888888888889 0.1390625 0.5666666 0.17500
000000000002 0.4152777777777778

The converted json file saved with the same labelme annotation:

code

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)

Visual script attached with txt tag

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"])