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  • 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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  • 原文地址:https://www.cnblogs.com/herd/p/10437790.html
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