Unet如何制作自己的训练集

最近一直在学unet，所以分享一个如何做一个自己想要的训练集，而不是去从网上找的博客。
首先打开cmd

输入你自己的虚拟环境，我是在base直接把所有环境都安好了，就不想在创建虚拟环境在安装了。
然后

pip install labelme==3.16.7

下载好了之后直接输入labelme

这就是打开后的样子
然后对所选择的图形进行描图，就像这样

最后他会在你的指定文件下生成一个josn文件，你现在还不能直接打开这个josn文件，需要运行代码

import argparse
import base64
import json
import os
import os.path as osp
import warnings

import numpy as np
import PIL.Image
import yaml
from labelme import utils


if __name__ == '__main__':
    jpgs_path = "datasets/JPEGImages"
    pngs_path = "datasets/SegmentationClass"
    classes = ["_background_", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow",
               "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train",
               "tvmonitor"]
    # classes = ["_background_","cat","dog"]

    count = os.listdir("./datasets/before/")
    for i in range(0, len(count)):
        path = os.path.join("./datasets/before", count[i])

        if os.path.isfile(path) and path.endswith('json'):
            data = json.load(open(path))

            if data['imageData']:
                imageData = data['imageData']
            else:
                imagePath = os.path.join(os.path.dirname(path), data['imagePath'])
                with open(imagePath, 'rb') as f:
                    imageData = f.read()
                    imageData = base64.b64encode(imageData).decode('utf-8')

            img = utils.img_b64_to_arr(imageData)
            label_name_to_value = {
     '_background_': 0}
            for shape in data['shapes']:
                label_name = shape['label']
                if label_name in label_name_to_value:
                    label_value = label_name_to_value[label_name]
                else:
                    label_value = len(label_name_to_value)
                    label_name_to_value[label_name] = label_value

            # label_values must be dense
            label_values, label_names = [], []
            for ln, lv in sorted(label_name_to_value.items(), key=lambda x: x[1]):
                label_values.append(lv)
                label_names.append(ln)
            assert label_values == list(range(len(label_values)))

            lbl = utils.shapes_to_label(img.shape, data['shapes'], label_name_to_value)

            PIL.Image.fromarray(img).save(osp.join(jpgs_path, count[i].split(".")[0] + '.jpg'))

            new = np.zeros([np.shape(img)[0], np.shape(img)[1]])
            for name in label_names:
                index_json = label_names.index(name)
                index_all = classes.index(name)
                new = new + index_all * (np.array(lbl) == index_json)

            utils.lblsave(osp.join(pngs_path, count[i].split(".")[0] + '.png'), new)
            print('Saved ' + count[i].split(".")[0] + '.jpg and ' + count[i].split(".")[0] + '.png')

在这里说一下classes这一行代码，这个是需要你根据你所处理图像里面的内容而定的。这里需要稍做修改。

最后，你所需要的unet训练集就出来了。