首先是labelme的安装
pip intalll labelme
没什么说的
然后是json文件转换
重点来了，设置.labelmerc文件的时候，需要保存图片数据
auto_save: true 自动保存
store_data: true 是否保存图片数据
这个是默认的先不要改
接着是转换代码
import os
import argparse
import json
from labelme import utils
import numpy as np
import glob
import PIL.Image
i=0
j=0
class labelme2coco(object):
def __init__(self, labelme_json=[], save_json_path="./coco.json"):
"""
:param labelme_json: the list of all labelme json file paths
:param save_json_path: the path to save new json
"""
self.labelme_json = labelme_json
self.save_json_path = save_json_path
self.images = []
self.categories = []
self.annotations = []
self.label = []
self.annID = 1
self.height = 0
self.width = 0
self.save_json()
def data_transfer(self):
for num, json_file in enumerate(self.labelme_json):
with open(json_file, "r",encoding='utf-8') as fp:
try:
data = json.load(fp)
global i
i+=1
except(UnicodeDecodeError):
print(f'第{i}张图片解码失败，检查中文问题')
global j
j += 1
self.images.append(self.image(data, num))
for shapes in data["shapes"]:
label = shapes["label"].split("_")
if label not in self.label:
self.label.append(label)
points = shapes["points"]
self.annotations.append(self.annotation(points, label, num))
self.annID += 1
# Sort all text labels so they are in the same order across data splits.
self.label.sort()
for label in self.label:
self.categories.append(self.category(label))
for annotation in self.annotations:
annotation["category_id"] = self.getcatid(annotation["category_id"])
def image(self, data, num):
image = {}
img = utils.img_b64_to_arr(data["imageData"])
height, width = img.shape[:2]
img = None
image["height"] = height
image["width"] = width
image["id"] = num
print(f'正在操作第{i}张图片')
image["file_name"] = data["imagePath"].split('/')[-1]
self.height = height
self.width = width
return image
def category(self, label):
category = {}
category["supercategory"] = label[0]
category["id"] = len(self.categories)
category["name"] = label[0]
return category
def annotation(self, points, label, num):
annotation = {}
contour = np.array(points)
x = contour[:, 0]
y = contour[:, 1]
area = 0.5 * np.abs(np.dot(x, np.roll(y, 1)) - np.dot(y, np.roll(x, 1)))
annotation["segmentation"] = [list(np.asarray(points).flatten())]
annotation["iscrowd"] = 0
annotation["area"] = area
annotation["image_id"] = num
annotation["bbox"] = list(map(float, self.getbbox(points)))
annotation["category_id"] = label[0] # self.getcatid(label)
annotation["id"] = self.annID
return annotation
def getcatid(self, label):
for category in self.categories:
if label == category["name"]:
return category["id"]
print("label: {} not in categories: {}.".format(label, self.categories))
exit()
return -1
def getbbox(self, points):
polygons = points
mask = self.polygons_to_mask([self.height, self.width], polygons)
return self.mask2box(mask)
def mask2box(self, mask):
index = np.argwhere(mask == 1)
rows = index[:, 0]
clos = index[:, 1]
left_top_r = np.min(rows) # y
left_top_c = np.min(clos) # x
right_bottom_r = np.max(rows)
right_bottom_c = np.max(clos)
return [
left_top_c,
left_top_r,
right_bottom_c - left_top_c,
right_bottom_r - left_top_r,
]
def polygons_to_mask(self, img_shape, polygons):
mask = np.zeros(img_shape, dtype=np.uint8)
mask = PIL.Image.fromarray(mask)
xy = list(map(tuple, polygons))
PIL.ImageDraw.Draw(mask).polygon(xy=xy, outline=1, fill=1)
mask = np.array(mask, dtype=bool)
return mask
def data2coco(self):
data_coco = {}
data_coco["images"] = self.images
data_coco["categories"] = self.categories
data_coco["annotations"] = self.annotations
return data_coco
def save_json(self):
print("save coco json")
self.data_transfer()
self.data_coco = self.data2coco()
print(self.save_json_path)
os.makedirs(
os.path.dirname(os.path.abspath(self.save_json_path)), exist_ok=True
)
json.dump(self.data_coco, open(self.save_json_path, "w"), indent=4)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(
description="labelme annotation to coco data json file."
)
'''parser.add_argument(
"labelme_images",
help="Directory to labelme images and annotation json files.",
type=str,
)'''
parser.add_argument(
"--output", help="Output json file path.", default="train.json"
)
args = parser.parse_args()
labelme_json = glob.glob(os.path.join('ObjectDetect', "*.json")) #将当前aaaa.py文件和ObjectDetect文件夹放在同一个路径，其中ObjectDetect里存放被标注图片和标注文件
labelme2coco(labelme_json, args.output)
print(f'有{i}个转换成功')
print(f'有{j}个转换失败')
"""
在Anaconda Prompt里找 conda activate py39
跳转路径 到aaaa.py所在地运行 ，在当前路径下生成.json文件即可视为成功
"""
随便建个文件把图片和标注放在一起
然后调用
path:图片和标注所在文件夹
输出json文件保存路径(有默认的可以不填)
python labelme2coco.py path --output "trainval.json"
标注不保存imgdata这块就会报错
因为json文件里面 imgdata是null