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 io


if __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+1):
        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 pycococreatortools

ROOT_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 files


def 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 files


def main():
    coco_output = {
        "info": INFO,
        "licenses": LICENSES,
        "categories": CATEGORIES,
        "images": [],
        "annotations": []
    }

    image_id = 1
    segmentation_id = 1

    # filter for jpeg images
    for root, _, files in os.walk(IMAGE_DIR):
        image_files = filter_for_jpeg(root, files)

        # go through each image
        for 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 annotations
            for root, _, files in os.walk(ANNOTATION_DIR):
                annotation_files = filter_for_annotations(root, files, image_filename)

                # go through each associated annotation
                for 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 + 1

            image_id = image_id + 1

    with open('{}/instances_leaf_train2017.json'.format(ROOT_DIR), 'w') as output_json_file:
        json.dump(coco_output, output_json_file)


if __name__ == "__main__":
    main()