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文件路徑組成的列表
        :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)# 加載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'])# 解析原圖片數據
        # img=io.imread(data['imagePath']) # 通過圖片路徑打開圖片
        # 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 默認為背景
        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文件時報錯（不知道為什么）
        # list(map(int,a[1:-1].split(','))) a=annotation['bbox'] 使用該方式轉成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)　# 畫邊界線
        # cv2.fillPoly(img, [np.asarray(points)], 1)　# 畫多邊形 內部像素值為1
        polygons = points
        mask = self.polygons_to_mask([self.height,self.width], polygons)
        return self.mask2box(mask)

    def mask2box(self, mask):
        '''從mask反算出其邊框
        mask：[h,w]　0、1組成的圖片
        1對應對象，只需計算1對應的行列號（左上角行列號，右下角行列號，就可以算出其邊框）
        '''
        # np.where(mask==1)
        index = np.argwhere(mask == 1)
        rows = index[:, 0]
        clos = index[:, 1]
        # 解析左上角行列號
        left_top_r = np.min(rows)# y
        left_top_c = np.min(clos)# x
        
        # 解析右下角行列號
        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] 對應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 更加美觀顯示

labelme_json=glob.glob('./*.json')
# labelme_json=['./1.json']

labelme2coco(labelme_json,'./new.json')