Mask Rcnn环境搭建并训练自己的数据集（二）准备自己的数据集

安装并运行labelme来标注数据集：

#安装labelme
pip install pyqt5
pip install labelme

#运行labelme
labelme

使用labelme标注之后每张图片都会生成对应的json标注文件。

将数据集转换成mask-rcnn训练数据集：

首先需要修改labelme的一些转换文件，找到labelme的安装位置，并找到json_to_dataset.py这个文件。我的文件在D:\anaconda\envs\labelme\Lib\site-packages\labelme\cli   这里面。

修改为下面代码：（最新版本的labelme删掉了yaml，我们重新添加上去，这样就可以生成yaml标注信息）

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

import imgviz
import PIL.Image
import yaml
from labelme.logger import logger
from labelme import utils


def main():
    logger.warning(
        "This script is aimed to demonstrate how to convert the "
        "JSON file to a single image dataset."
    )
    logger.warning(
        "It won't handle multiple JSON files to generate a "
        "real-use dataset."
    )

    parser = argparse.ArgumentParser()
    parser.add_argument("json_file")
    parser.add_argument("-o", "--out", default=None)
    args = parser.parse_args()

    json_file = args.json_file

    if args.out is None:
        out_dir = osp.basename(json_file).replace(".", "_")
        #out_dir = osp.basename(json_file)
        out_dir = osp.join(osp.dirname(json_file), out_dir)
    else:
        out_dir = args.out
    if not osp.exists(out_dir):
        os.mkdir(out_dir)

    data = json.load(open(json_file))
    imageData = data.get("imageData")

    if not imageData:
        imagePath = os.path.join(os.path.dirname(json_file), 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 sorted(data["shapes"], key=lambda x: x["label"]):
        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
    lbl, _ = utils.shapes_to_label(
        img.shape, data["shapes"], label_name_to_value
    )

    label_names = [None] * (max(label_name_to_value.values()) + 1)
    for name, value in label_name_to_value.items():
        label_names[value] = name

    lbl_viz = imgviz.label2rgb(
        label=lbl, img=imgviz.asgray(img), label_names=label_names, loc="rb"
    )

    PIL.Image.fromarray(img).save(osp.join(out_dir, "img.png"))
    utils.lblsave(osp.join(out_dir, "label.png"), lbl)
    PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, "label_viz.png"))

    with open(osp.join(out_dir, "label_names.txt"), "w",encoding='utf-8') as f:
        for lbl_name in label_names:
            f.write(lbl_name + "\n")
            
            
    logger.warning('info.yaml is being replaced by label_names.txt')
    info = dict(label_names=label_names)
    with open(osp.join(out_dir, 'info.yaml'), 'w',encoding='utf-8') as f:
        yaml.safe_dump(info, f, default_flow_style=False)
    logger.info("Saved to: {}".format(out_dir))


if __name__ == "__main__":
    main()

会对每一个json问价能生成一个文件夹，里面包括 原图，mask图片，yaml，txt和mask的视图。我们只用到原图，mask图片和yaml文件。

 

修改完之后保存，然后运行下面的脚本就可以了。

import os
path = 'C:/Users/86183/Desktop/json1'  # path为json文件存放的路径
json_file = os.listdir(path)
os.system("activate labelme") # 博主labelme所在的环境名就叫labelme，读者应修改成activate [自己labelme所在的环境名]
for file in json_file: 
    os.system("labelme_json_to_dataset.exe %s"%(path + '/' + file))

到目前为止我们已经把数据集都准备好了。如下图所示：