Labelme转Voc、Coco

Q：在github找的cv代码基本都是根据现有且流行的公共数据集格式组织的训练数据集，这导致我使用labelme标注好之后需要我们重新组织数据集

labelme2coco

#!/usr/bin/env pythonimport argparse
import collections
import datetime
import glob
import json
import os
import os.path as osp
import sys
import uuidimport imgviz
import numpy as npimport labelmetry:import pycocotools.mask
except ImportError:print("Please install pycocotools:\n\n    pip install pycocotools\n")sys.exit(1)def main():parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)parser.add_argument("input_dir",default=r"data_annotated", help="input annotated directory")parser.add_argument("output_dir",default=r"data_coco", help="output dataset directory")parser.add_argument("--labels",default=r"labels.txt", 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"))if not args.noviz:os.makedirs(osp.join(args.output_dir, "Visualization"))print("Creating dataset:", args.output_dir)now = datetime.datetime.now()data = dict(info=dict(description=None,url=None,version=None,year=now.year,contributor=None,date_created=now.strftime("%Y-%m-%d %H:%M:%S.%f"),),licenses=[dict(url=None, id=0, name=None,)],images=[# license, url, file_name, height, width, date_captured, id],type="instances",annotations=[# segmentation, area, iscrowd, image_id, bbox, category_id, id],categories=[# supercategory, id, name],)class_name_to_id = {}for i, line in enumerate(open(args.labels).readlines()):class_id = i - 1  # starts with -1class_name = line.strip()if class_id == -1:assert class_name == "__ignore__"continueclass_name_to_id[class_name] = class_iddata["categories"].append(dict(supercategory=None, id=class_id, name=class_name,))out_ann_file = osp.join(args.output_dir, "annotations.json")label_files = glob.glob(osp.join(args.input_dir, "*.json"))for image_id, filename in enumerate(label_files):print("Generating dataset from:", filename)label_file = labelme.LabelFile(filename=filename)base = osp.splitext(osp.basename(filename))[0]out_img_file = osp.join(args.output_dir, "JPEGImages", base + ".jpg")img = labelme.utils.img_data_to_arr(label_file.imageData)imgviz.io.imsave(out_img_file, img)data["images"].append(dict(license=0,url=None,file_name=osp.relpath(out_img_file, osp.dirname(out_ann_file)),height=img.shape[0],width=img.shape[1],date_captured=None,id=image_id,))masks = {}  # for areasegmentations = collections.defaultdict(list)  # for segmentationfor shape in label_file.shapes:points = shape["points"]label = shape["label"]group_id = shape.get("group_id")shape_type = shape.get("shape_type", "polygon")mask = labelme.utils.shape_to_mask(img.shape[:2], points, shape_type)if group_id is None:group_id = uuid.uuid1()instance = (label, group_id)if instance in masks:masks[instance] = masks[instance] | maskelse:masks[instance] = maskif shape_type == "rectangle":(x1, y1), (x2, y2) = pointsx1, x2 = sorted([x1, x2])y1, y2 = sorted([y1, y2])points = [x1, y1, x2, y1, x2, y2, x1, y2]if shape_type == "circle":(x1, y1), (x2, y2) = pointsr = np.linalg.norm([x2 - x1, y2 - y1])# r(1-cos(a/2))<x, a=2*pi/N => N>pi/arccos(1-x/r)# x: tolerance of the gap between the arc and the line segmentn_points_circle = max(int(np.pi / np.arccos(1 - 1 / r)), 12)i = np.arange(n_points_circle)x = x1 + r * np.sin(2 * np.pi / n_points_circle * i)y = y1 + r * np.cos(2 * np.pi / n_points_circle * i)points = np.stack((x, y), axis=1).flatten().tolist()else:points = np.asarray(points).flatten().tolist()segmentations[instance].append(points)segmentations = dict(segmentations)for instance, mask in masks.items():cls_name, group_id = instanceif cls_name not in class_name_to_id:continuecls_id = class_name_to_id[cls_name]mask = np.asfortranarray(mask.astype(np.uint8))mask = pycocotools.mask.encode(mask)area = float(pycocotools.mask.area(mask))bbox = pycocotools.mask.toBbox(mask).flatten().tolist()data["annotations"].append(dict(id=len(data["annotations"]),image_id=image_id,category_id=cls_id,segmentation=segmentations[instance],area=area,bbox=bbox,iscrowd=0,))if not args.noviz:viz = imgif masks:labels, captions, masks = zip(*[(class_name_to_id[cnm], cnm, msk)for (cnm, gid), msk in masks.items()if cnm in class_name_to_id])viz = imgviz.instances2rgb(image=img,labels=labels,masks=masks,captions=captions,font_size=15,line_width=2,)out_viz_file = osp.join(args.output_dir, "Visualization", base + ".jpg")imgviz.io.imsave(out_viz_file, viz)with open(out_ann_file, "w") as f:json.dump(data, f)if __name__ == "__main__":main()

labelme2voc

#!/usr/bin/env pythonfrom __future__ import print_functionimport argparse
import glob
import os
import os.path as osp
import sysimport imgviz
import numpy as npimport labelmedef 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"))os.makedirs(osp.join(args.output_dir, "SegmentationObject"))os.makedirs(osp.join(args.output_dir, "SegmentationObjectPNG"))if not args.noviz:os.makedirs(osp.join(args.output_dir, "SegmentationObjectVisualization"))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 -1class_name = line.strip()class_name_to_id[class_name] = class_idif class_id == -1:assert class_name == "__ignore__"continueelif 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 filename in glob.glob(osp.join(args.input_dir, "*.json")):print("Generating dataset from:", filename)label_file = labelme.LabelFile(filename=filename)base = osp.splitext(osp.basename(filename))[0]out_img_file = osp.join(args.output_dir, "JPEGImages", base + ".jpg")out_cls_file = osp.join(args.output_dir, "SegmentationClass", base + ".npy")out_clsp_file = osp.join(args.output_dir, "SegmentationClassPNG", base + ".png")if not args.noviz:out_clsv_file = osp.join(args.output_dir,"SegmentationClassVisualization",base + ".jpg",)out_ins_file = osp.join(args.output_dir, "SegmentationObject", base + ".npy")out_insp_file = osp.join(args.output_dir, "SegmentationObjectPNG", base + ".png")if not args.noviz:out_insv_file = osp.join(args.output_dir,"SegmentationObjectVisualization",base + ".jpg",)img = labelme.utils.img_data_to_arr(label_file.imageData)imgviz.io.imsave(out_img_file, img)cls, ins = labelme.utils.shapes_to_label(img_shape=img.shape,shapes=label_file.shapes,label_name_to_value=class_name_to_id,)ins[cls == -1] = 0  # ignore it.# class labellabelme.utils.lblsave(out_clsp_file, cls)np.save(out_cls_file, cls)if not args.noviz:clsv = imgviz.label2rgb(cls,imgviz.rgb2gray(img),label_names=class_names,font_size=15,loc="rb",)imgviz.io.imsave(out_clsv_file, clsv)# instance labellabelme.utils.lblsave(out_insp_file, ins)np.save(out_ins_file, ins)if not args.noviz:instance_ids = np.unique(ins)instance_names = [str(i) for i in range(max(instance_ids) + 1)]insv = imgviz.label2rgb(ins,imgviz.rgb2gray(img),label_names=instance_names,font_size=15,loc="rb",)imgviz.io.imsave(out_insv_file, insv)if __name__ == "__main__":main()

使用说明：

cd进入你的数据库(data_annotated文件夹所在位置)，原始标注好文件(jpg、json)存放在data_annotated文件夹，先建立一个labels.txt文件内容为：

__ignore__
_background_
类别1
类别2
类别3
...

重要说明：

• 第一行必须是 __ignore__

• 第二行必须是 _background_

从第三行开始才是您的实际类别名称

• 每个类别占一行

• 不能有空行

• 注意下划线的数量：

• __ignore__ 是两个下划线

• _background_ 是一个下划线

然后根据你的需求选择下列命令之一

python labelme2voc.py data_annotated data_dataset_voc --labels labels.txt
python labelme2coco.py data_annotated data_dataset_coco --labels labels.txt 

 

运行画面：

 如果运行报错大概率是环境问题

环境参考：

Package                 Version
----------------------- --------------------
absl-py                 2.1.0
asttokens               2.4.1
backcall                0.2.0
beautifulsoup4          4.12.3
cachetools              5.3.3
certifi                 2024.2.2
charset-normalizer      3.3.2
colorama                0.4.6
coloredlogs             15.0.1
contourpy               1.1.1
cycler                  0.12.1
Cython                  3.0.9
decorator               5.1.1
executing               2.0.1
filelock                3.13.1
flatbuffers             24.3.7
fonttools               4.50.0
gdown                   5.1.0
gitdb                   4.0.11
GitPython               3.1.42
google-auth             2.29.0
google-auth-oauthlib    1.0.0
grpcio                  1.62.1
humanfriendly           10.0
idna                    3.6
imageio                 2.34.0
imgviz                  1.7.5
importlib_metadata      7.1.0
importlib_resources     6.4.0
ipython                 8.12.3
jedi                    0.19.1
kiwisolver              1.4.5
labelImg                1.8.6
labelme                 5.4.1
lazy_loader             0.3
lxml                    5.1.0
Markdown                3.6
MarkupSafe              2.1.5
matplotlib              3.7.5
matplotlib-inline       0.1.6
mpmath                  1.3.0
natsort                 8.4.0
networkx                3.1
numpy                   1.24.4
oauthlib                3.2.2
onnxruntime             1.17.1
opencv-python           4.9.0.80
packaging               24.0
pandas                  2.0.3
parso                   0.8.3
pickleshare             0.7.5
pillow                  10.2.0
pip                     23.3.1
prompt-toolkit          3.0.43
protobuf                5.26.0
psutil                  5.9.8
pure-eval               0.2.2
py-cpuinfo              9.0.0
pyasn1                  0.5.1
pyasn1-modules          0.3.0
pycocotools-windows     2.0.0.2
Pygments                2.17.2
pyparsing               3.1.2
PyQt5                   5.15.10
PyQt5-Qt5               5.15.2
PyQt5-sip               12.13.0
pyreadline3             3.4.1
PySocks                 1.7.1
python-dateutil         2.9.0.post0
pytz                    2024.1
PyWavelets              1.4.1
PyYAML                  6.0.1
QtPy                    2.4.1
requests                2.31.0
requests-oauthlib       2.0.0
rsa                     4.9
scikit-image            0.21.0
scipy                   1.10.1
seaborn                 0.13.2
setuptools              68.2.2
six                     1.16.0
smmap                   5.0.1
soupsieve               2.5
stack-data              0.6.3
sympy                   1.12
tensorboard             2.14.0
tensorboard-data-server 0.7.2
termcolor               2.4.0
thop                    0.1.1.post2209072238
tifffile                2023.7.10
torch                   1.8.0+cu111
torchaudio              0.8.0
torchvision             0.9.0+cu111
tqdm                    4.66.2
traitlets               5.14.2
typing_extensions       4.10.0
tzdata                  2024.1
ultralytics             8.1.34
urllib3                 2.2.1
wcwidth                 0.2.13
Werkzeug                3.0.1
wheel                   0.41.2
zipp                    3.18.1