labelme2COCO
# -*- 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 from shapely.geometry import Polygon class labelme2coco(object): def __init__(self,labelme_json=[],save_json_path='./new.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'].split('_') label=shapes['label'][:-1] print(shapes['label']) print(label) if label not in self.label: self.categories.append(self.categorie(label)) self.label.append(label) points=shapes['points'] self.annotations.append(self.annotation(points,label,num)) self.annID+=1 print(self.label) def image(self,data,num): image={} img = utils.img_b64_to_array(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'] = label 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())] poly = Polygon(points) area_ = round(poly.area,6) annotation['area'] = area_ 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['category_id'] = self.getcatid(label) 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)# indent=4 更加美观显示 labelme_json=glob.glob('./*.json') # labelme_json=['./1.json'] labelme2coco(labelme_json,'./new.json')
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