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