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 argparseimport jsonimport matplotlib.pyplot as pltimport skimage.io as ioimport cv2from labelme import utilsimport numpy as npimport globimport 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。