将labelme标记的数据转为coco格式

将labelme标记的数据转为coco格式

将labelme标记的数据转为coco格式

这次转化主要是为了使用官方的mask_rcnn源码。
coco数据集主要是images,categories,annotations（数据格式都是list）
coco是把所有图片的信息都整合在一起了，成了一个dict

images:主要有height，width，id，file_name#id是图片id
categories:主要有supercategory,id,name#id是category的id
annotations:主要有segmentation，iscrowd,image_id,bbox,area,category_id,id
要注意在annotations里id之间的关系

coco的格式是
{
images:[{height，width，id，file_name}, {height，width，id，file_name}…]
categories：[{supercategory,id,name},{supercategory,id,name}…]
annotations:[{segmentation，iscrowd,image_id,bbox,area,category_id,id},{segmentation，iscrowd,image_id,bbox,area,category_id,id}]

转为coco格式

import numpy as np
import json
import glob
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 tococo(object):
    def __init__(self,jsonfile,save_path):
        self.images = []
        self.categories = []
        self.annotations = []
        self.jsonfile = jsonfile
        self.save_path = save_path#保存json的路径
        self.class_ids = {
    
    "BG":0}
        self.class_id = 0
        self.coco = {
    
    }
        

    def labelme_to_coco(self):
        annid = 0
        for num,json_file in enumerate(self.jsonfile):
            data = open(json_file,"r")
            data = json.load(data)
            self.images.append(self.get_images(data["imagePath"],data["imageHeight"],data["imageWidth"],num))
            shapes = data["shapes"]
            for shape in shapes:
                
                if shape["label"] in self.class_ids:
                    
                    
                    pass
                if shape["label"] not in self.class_ids and shape["label"] == "car":
                    self.class_id = self.class_id +1
                      
                    self.class_ids[shape["label"]] = self.class_id
                    self.categories.append(self.get_categories(shape["label"],self.class_id))
                  
                self.annotations.append(self.get_annotations(shape["points"],num,annid,shape["label"]))
                
                  
                annid = annid+1


              
 
        
        
        self.coco["images"] = self.images
        self.coco["categories"] = self.categories
        self.coco["annotations"] = self.annotations
        
        
    def get_images(self,filename,height,width,image_id):
        image = {
    
    }
        image["height"] = height
        image['width'] = width
        image["id"] = image_id
        image["file_name"] = filename
        return image
    def get_categories(self,name,class_id):
        category = {
    
    }
        category["supercategory"] = "Cancer"
        category['id'] = class_id
        category['name'] = name

        return category
    def get_annotations(self,points,image_id,ann_id,calss_name):
        annotation = {
    
    }
        mins = np.amin(points,axis = 0)
        maxs = np.amax(points,axis = 0)
        wh = maxs -mins
        x = mins[0]
        y = mins[1]
        w = wh[0]
        h = wh[1]
        area = w*h
        annotation['segmentation'] = [list(np.asarray(points).flatten())]
        annotation['iscrowd'] = 0
        annotation['image_id'] = image_id
        annotation['bbox'] = [x,y,w,h]
        annotation['area'] = area
        annotation['category_id'] = self.class_ids[calss_name]
        annotation['id'] = ann_id
        return annotation
    
    def save_json(self):
        self.labelme_to_coco()
        coco_data = self.coco
        # 保存json文件
        json.dump(coco_data, open(self.save_path, 'w'), indent=4, cls=MyEncoder)  # indent=4 更加美观显示
import glob      


labelme_json = glob.glob('G:/train/*.json')
c = tococo(labelme_json, 'G:/data/parking/cars//annotations/train232.json')      
c.save_json()