labelme coco
list_files.py
from labelme2coco2 import labelme2coco import os import glob # 获取文件名 file_names = os.listdir("./img2/") json_files = [] new_json_files = [] for file_name in file_names: if ".json" in file_name: print(file_name) new_json_file_name = file_name.replace(".json","-1.json") json_files.append(file_name) new_json_files.append(new_json_file_name) # 文件名拼接路径 new_file_list = [os.path.join("F:/TensorflowProject/img2/",file) for file in new_json_files] file_list = [os.path.join("F:/TensorflowProject/img2/",file) for file in json_files] #print(file_list) for i in range(len(file_list)): print(file_list[i]) print(new_file_list[i]) labelme_json = glob.glob(file_list[i]) #labelme_json = file_list[i] new_labelme_json = new_file_list[i] labelme2coco(labelme_json, new_labelme_json)
labelme2coco2.py
# -*- 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=''): ''' :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('F:\\TensorflowProject\\img1\\02.json') # labelme_json=['./Annotations/*.json'] labelme2coco(labelme_json, 'F:\\TensorflowProject\\img1\\02-1.json') '''
参考：https://blog.csdn.net/qq_34713831/article/details/88891529
QQ 3087438119
