语义分割任务中VOC数据集的制作，任务中只有一种标签：gas

1、由黑白图像识别为txt标签

由于使用CycleGAN网络进行风格迁移学习，生成了大量伪标签图像，因此需要自动提取出标签，标签格式为YOLOv8训练所需要的语义分割的标签格式
例如：提取出下图中白色的区域的标签信息

txt标签：

import os
import cv2
import numpy as np
'''
该段代码读取文件夹中的二值图像，输出白色区域的标注信息
格式为YOLOv8图像分割的txt格式
'''
def process_images(input_folder, output_folder):
    # 如果输出文件夹不存在，则创建它
    if not os.path.exists(output_folder):
        os.makedirs(output_folder)
    # 列出输入文件夹中的所有图像文件
    image_files = [f for f in os.listdir(input_folder) if f.endswith(('.png', '.jpg', '.jpeg', '.bmp'))]
    total_images = len(image_files)
    print(f"在文件夹'{input_folder}'中找到了 {total_images} 张图像。")
    # 处理每张图像
    for idx, filename in enumerate(image_files, 1):
        print(f"正在处理图像 {idx} / {total_images} : {filename}")
        # 加载图像
        image_path = os.path.join(input_folder, filename)
        image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
        # 检查图像是否正确加载
        if image is None:
            print(f"图像 {filename} 未能正确加载。")
            continue
        # 对图像进行阈值处理以获得二值图像
        _, binary_image = cv2.threshold(image, 128, 255, cv2.THRESH_BINARY)
        # 寻找轮廓
        contours, _ = cv2.findContours(binary_image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
        # 初始化一个列表来存储轮廓坐标
        contour_coordinates = []
        # 获取图像的尺寸以进行归一化
        height, width = image.shape
        # 遍历轮廓以提取归一化坐标
        for contour in contours:
            # 初始化一个列表来存储单个轮廓的坐标
            coords = ['0']  # 开头的数字0（没有小数点）
            for point in contour:
                # 归一化x和y坐标并添加到列表中
                x_normalized = point[0][0] / width
                y_normalized = point[0][1] / height
                coords.extend([f"{x_normalized:.6f}", f"{y_normalized:.6f}"])
            # 将单个轮廓的坐标添加到主列表中
            contour_coordinates.append(' '.join(coords))
        # 定义输出文本文件的路径
        output_file_path = os.path.join(output_folder, os.path.splitext(filename)[0] + '.txt')
        # 将坐标写入文本文件
        with open(output_file_path, 'w') as file:
            file.write('\n'.join(contour_coordinates))
# 输入和输出文件夹路径（请替换为实际路径）
input_folder_path = 'D:\Desktop\\test0326'  # 替换为实际的输入图片文件夹
output_folder_path = 'D:\Desktop\\test0326'  # 替换为实际的输出txt标签文件夹
# 调用函数以处理图像
process_images(input_folder_path, output_folder_path)

2、txt转json

由txt文件转换为labelme标注的json格式，本人的labelme版本为5.3.1，json文件内涵图像数据，具体格式为：

json_data = {
            "version": "5.3.1",
            "flags": {},
            "shapes": [],
            "imagePath": img_filename,
            "imageData": encoded_string,
            "imageHeight": height,
            "imageWidth": width
        }

代码如下：

import os
import json
import base64
from PIL import Image
import io

# 文件夹路径
label_folder = 'D:\\Desktop\\Unet\\dataset\\500txt'   # txt标签文件夹
image_folder = 'D:\\Desktop\\Unet\\dataset\\500images'  # 图像文件夹
output_folder = 'D:\\Desktop\\Unet\\dataset\\500jsons'  # json标签输出的文件夹

# 确保输出文件夹存在
os.makedirs(output_folder, exist_ok=True)
# 遍历文件夹中的所有 txt 文件
for filename in os.listdir(label_folder):
    if filename.endswith('.txt'):
        # 构造完整的文件路径
        txt_path = os.path.join(label_folder, filename)
        # 构造对应的图像文件路径
        img_filename = filename.replace('.txt', '.jpg')
        img_path = os.path.join(image_folder, img_filename)
        # 读取图像尺寸并转换为 base64
        with Image.open(img_path) as img:
            width, height = img.size
            buffered = io.BytesIO()
            img.save(buffered, format="JPEG")
            encoded_string = base64.b64encode(buffered.getvalue()).decode('utf-8')

        # 初始化 JSON 数据结构
        json_data = {
            "version": "5.3.1",
            "flags": {},
            "shapes": [],
            "imagePath": img_filename,
            "imageData": encoded_string,
            "imageHeight": height,
            "imageWidth": width
        }
        # 读取 txt 文件并解析多边形坐标
        with open(txt_path, 'r') as file:
            for line in file:
                parts = line.strip().split()
                label_index = parts[0]  # 假设第一部分是类别索引
                if len(parts) < 3 or len(parts) % 2 == 0:
                    print(f"Unexpected format in {filename}")
                    continue
                # 将相对坐标转换为实际坐标（像素值）
                label = 'gas' if label_index == '0' else 'unknown'
                points = [(float(parts[i]) * width, float(parts[i + 1]) * height) for i in range(1, len(parts), 2)]
                shape_data = {
                    "label": label,
                    "points": points,
                    "group_id": None,
                    "description": "",
                    "shape_type": "polygon",
                    "flags": {}
                }
                json_data["shapes"].append(shape_data)
        # 输出 JSON 文件
        output_path = os.path.join(output_folder, filename.replace('.txt', '.json'))
        with open(output_path, 'w') as jsonfile:
            json.dump(json_data, jsonfile, indent=2)
            print(f"文件 {output_path} 已转换完成！")

print("Conversion to JSON completed.")

3、数据集转VOC格式

准备好原图和json格式的标签后，转换为VOC所需的语义分割数据集
还需要准备一个labels.txt文件，里面写上所有的类别，格式如下：

__ignore__
_background_
gas

由于我的任务中只有gas这一类，所以只需要写gas，如果是多类，则依次往下添加即可，“ignore”和“background”这两行不要动，准备好labels.txt文件后还需要将路径填到下方转换代码中注明的位置：

from __future__ import print_function
import argparse
import glob
import os
import os.path as osp
import sys
import imgviz
import numpy as np
import labelme


def main(args):
    if osp.exists(args.output_dir):
        print("Output directory already exists:", args.output_dir)
        sys.exit(1)
    os.makedirs(args.output_dir)
    os.makedirs(osp.join(args.output_dir, "JPEGImages"))
    os.makedirs(osp.join(args.output_dir, "SegmentationClassnpy"))
    os.makedirs(osp.join(args.output_dir, "SegmentationClass"))
    if not args.noviz:
        os.makedirs(
            osp.join(args.output_dir, "SegmentationClassVisualization")
        )
    print("Creating dataset:", args.output_dir)
    class_names = []
    class_name_to_id = {}
    for i, line in enumerate(open(args.labels).readlines()):
        class_id = i - 1  # starts with -1
        class_name = line.strip()
        class_name_to_id[class_name] = class_id
        if class_id == -1:
            assert class_name == "__ignore__"
            continue
        elif class_id == 0:
            assert class_name == "_background_"
        class_names.append(class_name)
    class_names = tuple(class_names)
    print("class_names:", class_names)
    out_class_names_file = osp.join(args.output_dir, "class_names.txt")
    with open(out_class_names_file, "w") as f:
        f.writelines("\n".join(class_names))
    print("Saved class_names:", out_class_names_file)
    for filename in glob.glob(osp.join(args.input_dir, "*.json")):
        print("Generating dataset from:", filename)
        label_file = labelme.LabelFile(filename=filename)
        base = osp.splitext(osp.basename(filename))[0]
        out_img_file = osp.join(args.output_dir, "JPEGImages", base + ".jpg")
        out_lbl_file = osp.join(
            args.output_dir, "SegmentationClassnpy", base + ".npy"
        )
        out_png_file = osp.join(
            args.output_dir, "SegmentationClass", base + ".png"
        )
        if not args.noviz:
            out_viz_file = osp.join(
                args.output_dir,
                "SegmentationClassVisualization",
                base + ".jpg",
            )
        with open(out_img_file, "wb") as f:
            f.write(label_file.imageData)
        img = labelme.utils.img_data_to_arr(label_file.imageData)
        lbl, _ = labelme.utils.shapes_to_label(
            img_shape=img.shape,
            shapes=label_file.shapes,
            label_name_to_value=class_name_to_id,
        )
        labelme.utils.lblsave(out_png_file, lbl)
        np.save(out_lbl_file, lbl)
        if not args.noviz:
            viz = imgviz.label2rgb(
                label=lbl,
                # img=imgviz.rgb2gray(img),
                # img=img,
                font_size=15,
                label_names=class_names,
                loc="rb",
            )
            imgviz.io.imsave(out_viz_file, viz)

def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--input_dir", default="D:\Desktop\\Unet\dataset\\500jsons", type=str,   ## 在这里输入json标签文件夹
                        help="input annotated directory")
    parser.add_argument("--output_dir", default="D:\Desktop\\Unet\dataset\VOC500images\\", type=str,  ## 在这里输入图片文件夹 
                        help="output dataset directory")
    parser.add_argument("--labels", default="D:\Desktop\\Unet\\dataset\\labels.txt", type=str,   ## 输入labels.txt文件所在的路径
    help="labels file")
    parser.add_argument("--noviz", help="no visualization", action="store_true")
    args = parser.parse_args()
    return args
if __name__ == "__main__":
    args = get_args()
    main(args)