下面代码包括了转化分割，keypoints的多重功能。

支持coco的instances_train2017.json，person_keypoints_train2017.json两种任务格式。

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'] = 'person'
        categorie['id'] = len(self.label) + 1  # 0 默认为背景
        categorie['name'] = label
        
        
        categorie['keypoints']=["head", "left_eye0","left_eye1","right_eye0","right_eye1","nose","left_mouse","right_mouse",
        "left_shoulder0", "left_shoulder1","left_shoulder2",
        "left_elbow0" ,"left_elbow1","left_elbow2","left_elbow3",
        "left_wrist0","left_wrist1",
        "left_hand",
        "left_wrist2","left_wrist3",
        "left_elbow4" ,"left_elbow5","left_elbow6",
        "left_chest0","left_chest1","left_chest2","left_chest3","left_chest4",
        "left_hip0","left_hip1","left_hip2",
        "left_knee0","left_knee1",
        "left_ankle0","left_ankle1",
        "left_knee2","left_knee3",
        "left_hip3","left_hip4","left_hip5",
        "right_hip0","right_hip1",
        "right_knee0","right_knee1",
        "right_ankle0","right_ankle1",
        "right_knee2","right_knee3",
        "right_hip2","right_hip3","right_hip4",
        "right_chest0","right_chest1","right_chest2","right_chest3","right_chest4",
        "right_elbow4" ,"right_elbow5","right_elbow6",
        "right_wrist2","right_wrist3",
        "right_hand",
        "right_wrist0","right_wrist1",
        "right_elbow0" ,"right_elbow1","right_elbow2","right_elbow3",
        "right_shoulder0", "right_shoulder1","right_shoulder2"]
        
        categorie['skeleton']=[[1, 2], [2, 3], [3, 4],
                  [4, 5], [5, 6], [6, 7], [7, 8],
                  [8, 9], [9, 10], [10, 11], [11, 12],
                  [12, 13], [13, 14], [14, 15], [15, 16],
                  [16, 17], [17, 18], [18,19] , [19,20],
                  [20,21], [21,22], [22,23], [23,24],
                  [24,25], [25,26], [26,27], [27,28],
                  [28,29], [29,30], [30,31], [31,32],
                  [32,33],[33,34], [34,35], [35,36],
                  [36,37],[37,38],[38,39],[39,40],
                  [40,41], [41,42], [42,43], [43,44],
                  [44,45], [45,46], [46,47], [47,48],
                  [48,49], [49,50], [50,51], [51,52],
                  [52,53], [53,54], [54,55], [55,56],
                  [56,57], [57,58], [58,59], [59,60],
                  [60,61], [61,62], [62,63], [63,64],
                  [64,65], [65,66], [66,67], [67,68],
                  [68,69], [69,70],[70,71]]
        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
        
        annotation['keypoints']=[]
        for p in points:
            annotation['keypoints'].extend([p[0],p[1],2])
        annotation['num_keypoints']=len(points)
        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('./contour-71/train2020/*.json')
# labelme_json=['./Annotations/*.json']
 
labelme2coco(labelme_json, './contour-71.json')