labelme_to_coco.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
from shapely.geometry import Polygon

class labelme2coco(object):
    def __init__(self,labelme_json=[],save_json_path='./new.json'):
        '''
        :param labelme_json: all the labelme json files
        :param save_json_path: the dir of saved json files
        '''
        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)  # load the json files
                self.images.append(self.image(data,num))
                for shapes in data['shapes']:
                    full_label=shapes['label']
                    if full_label.find("spike"):
                        label = "spike"
                    if full_label.find("barley"):
                        label = "barley"
                    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 (num, json_file)

    def image(self,data,num):
        image={}
        img = utils.img_b64_to_arr(data['imageData']) 

        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'] = "wheat"
        categorie['id']=len(self.label)+1 # 0 is default as background
        categorie['name'] = label
        return categorie

    def annotation(self,points,label,num):
        annotation={}
        annotation['segmentation']=[np.asarray(points).flatten().tolist()]
        annotation['iscrowd'] = 0
        annotation['image_id'] = num+1

        annotation['bbox'] = list(map(float,self.getbbox(points)))

        annotation['category_id'] = self.getcatid(label)
        annotation['id'] = self.annID

        # Get the area value
        poly = Polygon(points)
        annotation['area']=round(poly.area, 6)
        return annotation

    def getcatid(self,label):
        for categorie in self.categories:
            if label==categorie['name']:
                return categorie['id']
        return -1

    def getbbox(self,points):

        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()

        json.dump(self.data_coco, open(self.save_json_path, 'w'), indent=4)  # indent=4

# convert train data set json files
labelme_json=glob.glob(r'D:\code\agriculture\wheat\dataset\mask rcnn model\2 Labeled data\train_data\json\*.json')
labelme2coco(labelme_json,'./wheat_spike_train.json')

# Convert test data set json files
labelme_json=glob.glob(r'D:\code\agriculture\wheat\dataset\mask rcnn model\2 Labeled data\val_data\json\*.json')
labelme2coco(labelme_json,'./wheat_spike_val.json')