将labelme格式数据转化为标准的coco数据集格式方式

labelme标注图像生成的json格式：

{
 "version": "3.11.2",
 "flags": {},
 "shapes": [# 每个对象的形状
 { # 第一个对象
  "label": "malignant",
  "line_color": null,
  "fill_color": null,
  "points": [# 边缘是由点构成，将这些点连在一起就是对象的边缘多边形
  [
   371, # 第一个点 x 坐标
   257 # 第一个点 y 坐标
  ],
  ...
  [
   412,
   255
  ]
  ],
  "shape_type": "polygon" # 形状类型：多边形
 },
 {
  "label": "malignant", # 第一个对象的标签
  "line_color": null,
  "fill_color": null,
  "points": [# 第二个对象
  [
   522,
   274
  ],
  ...
  [
   561,
   303
  ]
  ],
  "shape_type": "polygon"
 },
 {
  "label": "malignant", # 第二个对象的标签
  "line_color": null,
  "fill_color": null,
 "imagePath": "../../val2017/000001.jpg", # 原始图片的路径
 "imageData":"something too long ",# 原图像数据 通过该字段可以解析出原图像数据
 "imageHeight": 768,
 "imageWidth": 1024
}

coco标准数据集格式：

COCO通过大量使用Amazon Mechanical Turk来收集数据。COCO数据集现在有3种标注类型：object instances（目标实例）, object keypoints（目标上的关键点）, and image captions（看图说话），使用JSON文件存储。

基本的JSON结构体类型

这3种类型共享下面所列的基本类型，包括image、categories、annotation类型。

Images类型：

"images": [
  {
   "height": 768,
   "width": 1024,
   "id": 1, #图片id
   "file_name": "000002.jpg"
  }
]

categories类型：

"categories": [
  {
   "supercategory": "Cancer", #父类
   "id": 1,   #标签类别id，0表示背景
   "name": "benign" #子类
  },
  {
   "supercategory": "Cancer",
   "id": 2,
   "name": "malignant"
  }
 ],

annotations类型：

"annotations": [
  {
   "segmentation": [#坐标点的坐标值
    [
     418,
     256,
     391,
     293,
     406,
     323,
     432,
     340,
     452,
     329,
     458,
     311,
     458,
     286,
     455,
     277,
     439,
     264,
     418,
     293,
     391,
     256
    ]
   ],
   "iscrowd": 0, #单个的对象（iscrowd=0)可能需要多个polygon来表示
   "image_id": 1, #和image的id保持一致
   "bbox": [  #标注的边框值 bbox是将segmentation包起来的水平矩形
    391.0,
    256.0,
    67.0,
    84.0
   ],
   "area": 5628.0, #标注的边框面积
   "category_id": 1, #所属类别id
   "id": 1   #标注边框的id : 1,2,3...,n
  }
]

labelme 转化为coco

# -*- 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]
  image['file_name'] = data['imagePath'][3:14]
  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')

以上这篇将labelme格式数据转化为标准的coco数据集格式方式就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持三水点靠木。