1. 二值图

2. python库

https://github.com/waspinator/pycococreator

3. 二值图转单个物体的二值图

from scipy import ndimage as ndi
import gdalTools
import os
import numpy as np
from skimage import ioif __name__ == '__main__':outRoot = "annotations"fileName = r"F:\paper\fishpond\instance\instanceEval\fishpond\labels\poly.tif"gdalTools.mkdir(outRoot)im_proj, im_geotrans, im_width, im_height, im_data = gdalTools.read_img(fileName)labeled_array, num_features = ndi.label(im_data)for i in range(1, num_features):subLabel = np.where(labeled_array == i, 255, 0)outName = f"1000_fishpond_{i-1}.png"outPath = os.path.join(outRoot, outName)io.imsave(outPath, subLabel)

4. 单个物体的二值图写入json文件

import datetime
import json
import os
import re
import fnmatch
from PIL import Image
import numpy as np
from pycococreatortools import pycococreatortoolsROOT_DIR = './fishpond'
IMAGE_DIR = os.path.join(ROOT_DIR, "shapes_train2018") # 放入影像
ANNOTATION_DIR = os.path.join(ROOT_DIR, "annotations") # 放入单个物体的二值图INFO = {"description": "Leaf Dataset","url": "https://github.com/waspinator/pycococreator","version": "0.1.0","year": 2017,"contributor": "Francis_Liu","date_created": datetime.datetime.utcnow().isoformat(' ')
}LICENSES = [{"id": 1,"name": "Attribution-NonCommercial-ShareAlike License","url": "http://creativecommons.org/licenses/by-nc-sa/2.0/"}
]# 根据自己的需要添加种类
CATEGORIES = [{'id': 1,'name': 'fishpond','supercategory': 'shape',}
]def filter_for_jpeg(root, files):file_types = ['*.jpeg', '*.jpg', '*.png', '*.tif']file_types = r'|'.join([fnmatch.translate(x) for x in file_types])files = [os.path.join(root, f) for f in files]files = [f for f in files if re.match(file_types, f)]return filesdef filter_for_annotations(root, files, image_filename):file_types = ['*.png']file_types = r'|'.join([fnmatch.translate(x) for x in file_types])basename_no_extension = os.path.splitext(os.path.basename(image_filename))[0]file_name_prefix = basename_no_extension + '.*'files = [os.path.join(root, f) for f in files]files = [f for f in files if re.match(file_types, f)]files = [f for f in files if re.match(file_name_prefix, os.path.splitext(os.path.basename(f))[0])]return filesdef main():coco_output = {"info": INFO,"licenses": LICENSES,"categories": CATEGORIES,"images": [],"annotations": []}image_id = 1segmentation_id = 1# filter for jpeg imagesfor root, _, files in os.walk(IMAGE_DIR):image_files = filter_for_jpeg(root, files)# go through each imagefor image_filename in image_files:image = Image.open(image_filename)image_info = pycococreatortools.create_image_info(image_id, os.path.basename(image_filename), image.size)coco_output["images"].append(image_info)# filter for associated png annotationsfor root, _, files in os.walk(ANNOTATION_DIR):annotation_files = filter_for_annotations(root, files, image_filename)# go through each associated annotationfor annotation_filename in annotation_files:print(annotation_filename)class_id = [x['id'] for x in CATEGORIES if x['name'] in annotation_filename][0]category_info = {'id': class_id, 'is_crowd': 'crowd' in image_filename}binary_mask = np.asarray(Image.open(annotation_filename).convert('1')).astype(np.uint8)annotation_info = pycococreatortools.create_annotation_info(segmentation_id, image_id, category_info, binary_mask,image.size, tolerance=2)if annotation_info is not None:coco_output["annotations"].append(annotation_info)segmentation_id = segmentation_id + 1image_id = image_id + 1with open('{}/instances_leaf_train2017.json'.format(ROOT_DIR), 'w') as output_json_file:json.dump(coco_output, output_json_file)if __name__ == "__main__":main()