labelme标注不同物体显示不同颜色以及批量转换

最近在使用labelme标注数据时遇到一些问题：

如上图中，蓝色分别为crack，person，dog三类。正常应该是3种不同颜色

解决方案：

1.labelme版本

2、下载labelme后进行文件修改

由于博主想要的是rgb三通道的彩色图，所以标注出的是三通道的rgb值。
因此本教程适合想要标注的物体是彩色图案的读者，不是灰度图。

2.1 label.py文件的修改

#博主的参考路径为：
C:\ProgramData\Anaconda3\envs\labelme\Lib\site-packages\imgviz

修改内容如下：

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
        
    #我认为添加的一段，自己设置不同类别的rgb值
    cmap[0,:] = [255,255,255] #背景的rgb值
    cmap[1,:] = [255,0,0] #车的rgb
    cmap[2,:] = [0,255,0] #人的rgb

    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


def label2rgb(
    label,
    img=None,
    alpha=0.5,
    label_names=None,
    font_size=30,
    thresh_suppress=0,
    colormap=None,
    loc="centroid",
):
    """Convert label to rgb. Parameters ---------- label: numpy.ndarray, (H, W), int Label image. img: numpy.ndarray, (H, W, 3), numpy.uint8 RGB image. alpha: float Alpha of RGB (default: 0.5). label_names: list of string Label id to label name. font_size: int Font size (default: 30). thresh_suppress: float Threshold of label ratio in the label image. colormap: numpy.ndarray, (M, 3), numpy.uint8 Label id to color. By default, :func:`~imgviz.label_colormap` is used. loc: string Location of legend (default: 'centroid'). 'lt' and 'rb' are supported. Returns ------- res: numpy.ndarray, (H, W, 3), numpy.uint8 Visualized image. """
    if colormap is None:
        colormap = label_colormap()

    res = colormap[label]

    random_state = np.random.RandomState(seed=1234)

    mask_unlabeled = label < 0
    res[mask_unlabeled] = random_state.rand(*(mask_unlabeled.sum(), 3)) * 255

    if img is not None:
        if img.ndim == 2:
            img = color_module.gray2rgb(img)
        res = (1 - alpha) * img.astype(float) + alpha * res.astype(float)
        res = np.clip(res.round(), 0, 255).astype(np.uint8)

    if label_names is None:
        return res

    if loc == "centroid":
        for label_i in np.unique(label):
            if label_i == -1:
                continue  # unlabeled

            mask = label == label_i
            if 1.0 * mask.sum() / mask.size < thresh_suppress:
                continue
            y, x = np.array(_center_of_mass(mask), dtype=int)

            if label[y, x] != label_i:
                Y, X = np.where(mask)
                point_index = np.random.randint(0, len(Y))
                y, x = Y[point_index], X[point_index]

            text = label_names[label_i]
            height, width = draw_module.text_size(text, size=font_size)
            color = color_module.get_fg_color(res[y, x])
            res = draw_module.text(
                res,
                yx=(y - height // 2, x - width // 2),
                text=text,
                color=color,
                size=font_size,
            )
    elif loc in ["rb", "lt"]:
        unique_labels = np.unique(label)
        unique_labels = unique_labels[unique_labels != -1]
        text_sizes = np.array(
            [
                draw_module.text_size(label_names[l], font_size)
                for l in unique_labels
            ]
        )
        text_height, text_width = text_sizes.max(axis=0)
        legend_height = text_height * len(unique_labels) + 5
        legend_width = text_width + 40

        height, width = label.shape[:2]
        legend = np.zeros((height, width, 3), dtype=np.uint8)
        if loc == "rb":
            aabb2 = np.array([height - 5, width - 5], dtype=float)
            aabb1 = aabb2 - (legend_height, legend_width)
        elif loc == "lt":
            aabb1 = np.array([5, 5], dtype=float)
            aabb2 = aabb1 + (legend_height, legend_width)
        else:
            raise ValueError("unexpected loc: {}".format(loc))
        legend = draw_module.rectangle(
            legend, aabb1, aabb2, fill=(255, 255, 255)
        )

        alpha = 0.5
        y1, x1 = aabb1.round().astype(int)
        y2, x2 = aabb2.round().astype(int)
        res[y1:y2, x1:x2] = (
            alpha * res[y1:y2, x1:x2] + alpha * legend[y1:y2, x1:x2]
        )

        for i, l in enumerate(unique_labels):
            box_aabb1 = aabb1 + (i * text_height + 5, 5)
            box_aabb2 = box_aabb1 + (text_height - 10, 20)
            res = draw_module.rectangle(
                res, aabb1=box_aabb1, aabb2=box_aabb2, fill=colormap[l]
            )
            res = draw_module.text(
                res,
                yx=aabb1 + (i * text_height, 30),
                text=label_names[l],
                size=font_size,
            )
    else:
        raise ValueError("unsupported loc: {}".format(loc))

    return res


def _center_of_mass(mask):
    assert mask.ndim == 2 and mask.dtype == bool
    mask = 1.0 * mask / mask.sum()
    dx = np.sum(mask, 0)
    dy = np.sum(mask, 1)
    cx = np.sum(dx * np.arange(mask.shape[1]))
    cy = np.sum(dy * np.arange(mask.shape[0]))
    return cy, cx

需要修改的地方博主已经做了注释和说明。

2.2 json_to_dataset.py文件的修改

#博主的参考路径：
C:\ProgramData\Anaconda3\envs\labelme\Lib\site-packages\labelme\cli

实现json数据的批量转换，输出到指定目录。
修改如下内容：

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(
        "批量处理json文件。"
        "注意！文件夹中不能有其他文件存在。"
    )
    parser = argparse.ArgumentParser()
    parser.add_argument('json_file')
    parser.add_argument('-o', '--out', default=None)
    args = parser.parse_args()
 
    json_file = args.json_file
 
    # 输出路径
    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.makedirs(out_dir)
 
    # 处理多个文件
    json_files_names = os.listdir(json_file)
    for i in range(0, len(json_files_names)):
        path = os.path.join(json_file, json_files_names[i])
        logger.info("正在处理: {}".format(path))
        if os.path.isfile(path):
            try:
                data = json.load(open(path, encoding='UTF-8'))
            except:
                data = json.load(open(path))
 
            if data['imageData']:
                imageData = data['imageData']
            else:
                imagePath = os.path.join(os.path.dirname(path), data['imagePath'])
                try:
                    with open(imagePath, 'rb', encoding='UTF-8') as f:
                        imageData = f.read(encoding='UTF-8')
                        imageData = base64.b64encode(imageData).decode('utf-8')
                except:
                    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,'car':1,'person':2}  #在这一行更改，改成自己的类别数
            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"
            )
            # 新建路径
            save_dir = osp.join(out_dir,  json_files_names[i].split('.')[0])
            if not os.path.exists(save_dir):
                os.makedirs(save_dir)
 
            PIL.Image.fromarray(img).save(osp.join(save_dir, 'img.png'))
            utils.lblsave(osp.join(save_dir, 'label.png'), lbl)
            PIL.Image.fromarray(lbl_viz).save(osp.join(save_dir, 'label_viz.png'))
 
            with open(osp.join(save_dir, 'label_names.txt'), 'w') as f:
                for lbl_name in label_names:
                    f.write(lbl_name + '\n')
 
            logger.info("Saved to: {}".format(save_dir))

3、运行转换程序

1、激活安装的labelme环境。

查看环境
conda info --envs
激活环境
activate labelme

2.进入 labelme_json_to_dataset.exe 文件所在路径，也就是第2步你复制的路径，进入命令如下

cd D:\Anaconda3\envs\labelme\Scripts

3.输入 labelme_json_to_dataset.exe+空格+【你待转化的 json 文件所在路径】

labelme_json_to_dataset.exe 【你待转化的 json 文件所在路径】

4、 查看转化结果

上述两个图中黄色的标注为dog，同一颜色表示

Tips：

针对以上代码，因为labelme的版本问题，可能会存在的一些小问题，如：
imgviz 版本问题，1.5.0有问题，1.2.0测试没问题
会报错：TypeError: label2rgb() got an unexpected keyword argument ‘img’

解决办法：

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple imgviz==1.2.0