技術標籤：語義分割pythonjsonlinux神經網路

開發環境：python3.7

下面以pascal voc2012為例進行演示：

1.修改 json_to_dataset.py，這個檔案在解析json檔案的時候會用到；
json_to_dataset.py路徑為：/home/xxx/anaconda3/envs/labelme/lib/python3.7/site-packages/labelme/cli
修改：

// json_to_dataset.py
import argparse
import base64
import json
import os
import os.path as osp

import
 
 imgviz
import PIL.Image

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.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, "airplane": 1, "bicycle": 2, "bird": 3, "boat": 4, "bottle": 5, "bus": 6, "car": 7, "cat": 8, "chair": 9, "cow": 10, "dining table": 11, "dog":12, "horse": 13, "motor bike": 14, "person": 15, "potted plant": 16, "sheep": 17, "sofa": 18. "train": 19, "monitor": 20 }   # 新增指定標籤對應的標籤值
    #註釋下面程式碼
    #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") as f:
        for lbl_name in label_names:
            f.write(lbl_name + "\n")

    logger.info("Saved to: {}".format(out_dir))


if __name__ == "__main__":
    main()

2.修改 label.py 檔案
label.py路徑：/home/xxx/anaconda3/envs/labelme/lib/python3.7/site-packages/imgviz
修改：

// label.py
import numpy as np

from . import color as color_module
from . import draw as draw_module


def label_colormap(n_label=256, value=None):
    """Label colormap.

    Parameters
    ----------
    n_labels: int
        Number of labels (default: 256).
    value: float or int
        Value scale or value of label color in HSV space.

    Returns
    -------
    cmap: numpy.ndarray, (N, 3), numpy.uint8
        Label id to colormap.

    """

    def bitget(byteval, idx):
        return (byteval & (1 << idx)) != 0

    cmap = np.zeros((n_label, 3), dtype=np.uint8)
    for i in range(0, n_label):
        id = i
        r, g, b = 0, 0, 0
        for j in range(0, 8):
            r = np.bitwise_or(r, (bitget(id, 0) << 7 - j))
            g = np.bitwise_or(g, (bitget(id, 1) << 7 - j))
            b = np.bitwise_or(b, (bitget(id, 2) << 7 - j))
            id = id >> 3
        cmap[i, 0] = r
        cmap[i, 1] = g
        cmap[i, 2] = b

       #新增自己的colormap
       cmap[1,:]=[128,0,0]      #airplane
       cmap[2,:]=[0,128,0]      #bicycle
       cmap[3,:]=[128,128,0]    #bird
       cmap[4,:]=[0,0,128]      #boat
       cmap[5,:]=[128,0,128]    #bottle
       cmap[6,:]=[0,128,128]    #bus
       cmap[7,:]=[128,128,128]  #car
       cmap[8,:]=[64,0,0]       #cat
       cmap[9,:]=[192,0,0]      #chair
       cmap[10,:]=[64,128,0]    #cow
       cmap[11,:]=[192,128,0]   #dining table
       cmap[12,:]=[64,0,128]    #dog
       cmap[13,:]=[192,128,0]   #horse
       cmap[14,:]=[64,128,128]  #motor bike
       cmap[15,:]=[192,128,128] #person
       cmap[16,:]=[0,64,0]      #potted plant
       cmap[17,:]=[128,64,0]    #sheep
       cmap[18,:]=[0,192,0]     #sofa
       cmap[19,:]=[128,192,0]   #train
       cmap[20,:]=[0,64,128]    #monitor

    if value is not None:
        hsv = color_module.rgb2hsv(cmap.reshape(1, -1, 3))
        if isinstance(value, float):
            hsv[:, 1:, 2] = hsv[:, 1:, 2].astype(float) * value
        else:
            assert isinstance(value, int)
            hsv[:, 1:, 2] = value
        cmap = color_module.hsv2rgb(hsv).reshape(-1, 3)
    return cmap

cmap的值要跟 json_to_dataset.py 中的label_name_to_value對應

3.之後，新建資料夾，在新建資料夾下，將自己的資料(原始圖片和對應的json檔案)放入同一個 data_annotated 資料夾；之後，製作自己的 labels.txt，拷貝 labelme2voc.py 檔案不需改動；準備情況如下：

labels.txt內容如下(類別順序與 json_to_dataset.py 和 label.py 一致)：

labelme2voc.py 檔案目錄 /labelme/examples/semantic_segmentation 下載連結，因為只用到該檔案，大家可以不用去githup下載，這裡直接給出 labelme2voc.py :

// labelme2voc.py
#!/usr/bin/env python

from __future__ import print_function

import argparse
import glob
import json
import os
import os.path as osp
import sys

import imgviz
import numpy as np
import PIL.Image

import labelme


def main():
    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )
    parser.add_argument('input_dir', help='input annotated directory')
    parser.add_argument('output_dir', help='output dataset directory')
    parser.add_argument('--labels', help='labels file', required=True)
    parser.add_argument(
        '--noviz', help='no visualization', action='store_true'
    )
    args = parser.parse_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, 'SegmentationClass'))
    os.makedirs(osp.join(args.output_dir, 'SegmentationClassPNG'))
    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 label_file in glob.glob(osp.join(args.input_dir, '*.json')):
        print('Generating dataset from:', label_file)
        with open(label_file) as f:
            base = osp.splitext(osp.basename(label_file))[0]
            out_img_file = osp.join(
                args.output_dir, 'JPEGImages', base + '.jpg')
            out_lbl_file = osp.join(
                args.output_dir, 'SegmentationClass', base + '.npy')
            out_png_file = osp.join(
                args.output_dir, 'SegmentationClassPNG', base + '.png')
            if not args.noviz:
                out_viz_file = osp.join(
                    args.output_dir,
                    'SegmentationClassVisualization',
                    base + '.jpg',
                )

            data = json.load(f)

            img_file = osp.join(osp.dirname(label_file), data['imagePath'])
            img = np.asarray(PIL.Image.open(img_file))
            PIL.Image.fromarray(img).save(out_img_file)

            lbl = labelme.utils.shapes_to_label(
                img_shape=img.shape,
                shapes=data['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),
                    font_size=15,
                    label_names=class_names,
                    loc='rb',
                )
                imgviz.io.imsave(out_viz_file, viz)


if __name__ == '__main__':
    main()

最後，在新建資料夾下開啟終端，啟用labelme虛擬環境，執行：

python labelme2voc.py data_annotated data_dataset_voc --labels labels.txt

生成 data_dataset_voc 資料夾，裡面包含：

    JPEGImages存放原圖
    SegmentationClass存放ground truth(mask)的二進位制檔案
    SegmentationClassPNG存放原圖對應的ground truth(mask)
    SegmentationClassVisualization存放原圖與ground truth融合後的圖

感謝各位讀者朋友指正