labelme安装：
pip install labelme
lebelme的json格式：
{
"version": "3.10.1",
"flags": {},
"shapes": [
{
"label": "car",
"line_color": null,
"fill_color": null,
"points": [
[
341,
350
],
[
325,
341
],
[
316,
341
]
],
"shape_type": "polygon"
},
{
"label": "bike",
"line_color": null,
"fill_color": null,
"points": [
[
631,
1302
],
[
644,
1288
],
[
653,
1280
],
[
652,
1268
],
[
649,
1242
]
],
"shape_type": "polygon"
}
],
"lineColor": [
0,
255,
0,
128
],
"fillColor": [
255,
0,
0,
128
],
"imagePath": "0279.jpg",
"imageData": "iVBORw0KGgoAAAANSUhEUVORK5CYII=",
"imageHeight": 1536,
"imageWidth": 2048
}coco instance 格式：
{
"info": {
"description": "COCO 2014 Dataset",
"url": "http://cocodataset.org",
"version": "1.0",
"year": 2014,
"contributor": "COCO Consortium",
"date_created": "2017/09/01"
},
"images": [{
"license": 3,
"file_name": "COCO_val2014_000000391895.jpg",
"coco_url": "http://images.cocodataset.org/val2014/COCO_val2014_000000391895.jpg",
"height": 360,
"width": 640,
"date_captured": "2013-11-14 11:18:45",
"flickr_url": "http://farm9.staticflickr.com/8186/8119368305_4e622c8349_z.jpg",
"id": 391895
}, {
"segmentation": {
"counts": [264.15, 20, 329, 4, 9953],
"size": [426, 640]
},
"area": 220834,
"iscrowd": 1,
"image_id": 250282,
"bbox": [0, 34, 639, 388],
"category_id": 1,
"id": 900100250282
}, {
"segmentation": {
"counts": [69473, 12, 323, 12, 324, 10, 326, 8, 329, 4, 9953],
"size": [335, 640]
},
"area": 3051,
"iscrowd": 1,
"image_id": 46847,
"bbox": [207, 89, 403, 50],
"category_id": 1,
"id": 900100046847
}],
"categories": [{
"supercategory": "person",
"id": 1,
"name": "person"
}]
}转化代码：
# -*- coding:utf-8 -*-
# !/usr/bin/env python
import argparse
import json
import matplotlib.pyplot as plt
import skimage.io as io
import cv2
from labelme import utils
import numpy as np
import glob
import PIL.Image
class MyEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.integer):
return int(obj)
elif isinstance(obj, np.floating):
return float(obj)
elif isinstance(obj, np.ndarray):
return obj.tolist()
else:
return super(MyEncoder, self).default(obj)
class labelme2coco(object):
def __init__(self, labelme_json=[], save_json_path='./tran.json'):
'''
:param labelme_json: 所有labelme的json文件路径组成的列表
:param save_json_path: json保存位置
'''
self.labelme_json = labelme_json
self.save_json_path = save_json_path
self.images = []
self.categories = []
self.annotations = []
# self.data_coco = {}
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') as fp:
data = json.load(fp) # 加载json文件
self.images.append(self.image(data, num))
for shapes in data['shapes']:
label = shapes['label']
if label not in self.label:
self.categories.append(self.categorie(label))
self.label.append(label)
points = shapes['points']#这里的point是用rectangle标注得到的，只有两个点，需要转成四个点
points.append([points[0][0],points[1][1]])
points.append([points[1][0],points[0][1]])
self.annotations.append(self.annotation(points, label, num))
self.annID += 1
def image(self, data, num):
image = {}
img = utils.img_b64_to_arr(data['imageData']) # 解析原图片数据
# img=io.imread(data['imagePath']) # 通过图片路径打开图片
# img = cv2.imread(data['imagePath'], 0)
height, width = img.shape[:2]
img = None
image['height'] = height
image['width'] = width
image['id'] = num + 1
image['file_name'] = data['imagePath'].split('/')[-1]
self.height = height
self.width = width
return image
def categorie(self, label):
categorie = {}
categorie['supercategory'] = 'Cancer'
categorie['id'] = len(self.label) + 1 # 0 默认为背景
categorie['name'] = label
return categorie
def annotation(self, points, label, num):
annotation = {}
annotation['segmentation'] = [list(np.asarray(points).flatten())]
annotation['iscrowd'] = 0
annotation['image_id'] = num + 1
# annotation['bbox'] = str(self.getbbox(points)) # 使用list保存json文件时报错（不知道为什么）
# list(map(int,a[1:-1].split(','))) a=annotation['bbox'] 使用该方式转成list
annotation['bbox'] = list(map(float, self.getbbox(points)))
annotation['area'] = annotation['bbox'][2] * annotation['bbox'][3]
# annotation['category_id'] = self.getcatid(label)
annotation['category_id'] = self.getcatid(label)#注意，源代码默认为1
annotation['id'] = self.annID
return annotation
def getcatid(self, label):
for categorie in self.categories:
if label == categorie['name']:
return categorie['id']
return 1
def getbbox(self, points):
# img = np.zeros([self.height,self.width],np.uint8)
# cv2.polylines(img, [np.asarray(points)], True, 1, lineType=cv2.LINE_AA) # 画边界线
# cv2.fillPoly(img, [np.asarray(points)], 1) # 画多边形 内部像素值为1
polygons = points
mask = self.polygons_to_mask([self.height, self.width], polygons)
return self.mask2box(mask)
def mask2box(self, mask):
'''从mask反算出其边框
mask：[h,w] 0、1组成的图片
1对应对象，只需计算1对应的行列号（左上角行列号，右下角行列号，就可以算出其边框）
'''
# np.where(mask==1)
index = np.argwhere(mask == 1)
rows = index[:, 0]
clos = index[:, 1]
# 解析左上角行列号
left_top_r = np.min(rows) # y
left_top_c = np.min(clos) # x
# 解析右下角行列号
right_bottom_r = np.max(rows)
right_bottom_c = np.max(clos)
# return [(left_top_r,left_top_c),(right_bottom_r,right_bottom_c)]
# return [(left_top_c, left_top_r), (right_bottom_c, right_bottom_r)]
# return [left_top_c, left_top_r, right_bottom_c, right_bottom_r] # [x1,y1,x2,y2]
return [left_top_c, left_top_r, right_bottom_c - left_top_c,
right_bottom_r - left_top_r] # [x1,y1,w,h] 对应COCO的bbox格式
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):
self.data_transfer()
self.data_coco = self.data2coco()
# 保存json文件
json.dump(self.data_coco, open(self.save_json_path, 'w'), indent=4, cls=MyEncoder) # indent=4 更加美观显示
labelme_json = glob.glob('./Annotations/*.json')
# labelme_json=['./Annotations/*.json']
labelme2coco(labelme_json, './json/test.json')