# -*- coding:utf-8 -*-

 from __future__ import print_function

 import json

 #json文件的地址 需要手动设置

 json_file='../pycocotools/instances_train2014.json' # # Object Instance 类型的标注

 # person_keypoints_val2017.json  # Object Keypoint 类型的标注格式

 # captions_val2017.json  # Image Caption的标注格式

 data=json.load(open(json_file,'r'))

 #设置需要提取的图片数量 我设置提取82000张

 for i in range(82000):

     data_2 = {}

     data_2['info'] = data['info']

     data_2['licenses'] = data['licenses']

     data_2['images'] = [data['images'][i]]  # 只提取第一张图片

     data_2['categories'] = data['categories']

     annotation = []

     # 通过imgID 找到其所有对象

     imgID = data_2['images'][0]['id']

     for ann in data['annotations']:

         if ann['image_id'] == imgID:

             annotation.append(ann)

     data_2['annotations'] = annotation

     # 保存到新的JSON文件，便于查看数据特点

     #img_file 获取图片名称

     img_file=data_2['images'][0]['file_name']

     img_first=img_file.split(".")[0]

     #将提取出的图片写入data.txt文件中并换行 (optional)

     # with open('./coco_single_object/data.txt',mode='a') as f:

     #         f.write(img_file)

     #         f.write("\n")

     #设置存储目录 我的是存在当前目录下coco_single_object文件夹下 需要手动创建空文件夹

     json.dump(data_2, open('./coco_single_object/'+img_first+'.json', 'w'), indent=4)  # indent=4 更加美观显示

 from __future__ import print_function

 from pycocotools.coco import COCO

 import os, sys, zipfile

 import urllib.request

 import shutil

 import numpy as np

 import skimage.io as io

 import matplotlib.pyplot as plt

 import pylab

 pylab.rcParams['figure.figsize'] = (8.0, 10.0)

 import os

 def get_single_binaryImg(json_path,img_path,color_img_save,binary_img_save):

     # json_path json文件路径  从coco数据集的annotations标注json文件中提取出的单个json文件

     #  img_path 原图目录   下载coco数据集时的原图目录

     # color_img_save 原图存放目录

     # binary_img_save 二值图存放目录

     dir=os.listdir(json_path)

     for jfile in dir:

         annFile =os.path.join(json_path,jfile)

         coco = COCO(annFile)

         imgIds = coco.getImgIds()

         img = coco.loadImgs(imgIds[0])[0]

         dataDir = img_path

         shutil.copy(os.path.join(dataDir, img['file_name']), color_img_save)

         # load and display instance annotations

         # 加载实例掩膜

         catIds = []

         for ann in coco.dataset['annotations']:

             if ann['image_id'] == imgIds[0]:

                 catIds.append(ann['category_id'])

         annIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)

         width = img['width']

         height = img['height']

         anns = coco.loadAnns(annIds)

         mask_pic = np.zeros((height, width))

         for single in anns:

             mask_single = coco.annToMask(single)

             mask_pic += mask_single

         for row in range(height):

             for col in range(width):

                 if (mask_pic[row][col] > 0):

                     mask_pic[row][col] = 255

         imgs = np.zeros(shape=(height, width, 3), dtype=np.float32)

         imgs[:, :, 0] = mask_pic[:, :]

         imgs[:, :, 1] = mask_pic[:, :]

         imgs[:, :, 2] = mask_pic[:, :]

         imgs = imgs.astype(int)

         img_name = img['file_name'].split(".")[0]

         plt.imsave(binary_img_save + "/" + img_name + ".png", imgs)

 if __name__ == '__main__':

     json_path =r"G:\jianfeng\code\dataset\cocoapi-master\PythonAPI\get_json\test"

     img_path=r"G:\jianfeng\code\dataset\coco\train2014"

     color_img_save = r"G:\jianfeng\code\dataset\cocoapi-master\PythonAPI\get_json\color_img"

     binary_img_save = r"G:\jianfeng\code\dataset\cocoapi-master\PythonAPI\get_json\binary_img"

     get_single_binaryImg(json_path,img_path,color_img_save,binary_img_save)