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#!/usr/bin/env python3
import datetime
import json
import os
import re
import fnmatch
from PIL import Image
import numpy as np
from pycococreatortools import pycococreatortools
ROOT_DIR = 'train'
IMAGE_DIR = os.path.join(ROOT_DIR, "shapes_train2018")
ANNOTATION_DIR = os.path.join(ROOT_DIR, "annotations")
INFO = {
"description": "Example Dataset",
"url": "https://github.com/waspinator/pycococreator",
"version": "0.1.0",
"year": 2018,
"contributor": "waspinator",
"date_created": datetime.datetime.utcnow().isoformat(' ')
}
LICENSES = [
{
"id": 1,
"name": "Attribution-NonCommercial-ShareAlike License",
"url": "http://creativecommons.org/licenses/by-nc-sa/2.0/"
}
]
CATEGORIES = [
{
'id': 1,
'name': 'square',
'supercategory': 'shape',
},
{
'id': 2,
'name': 'circle',
'supercategory': 'shape',
},
{
'id': 3,
'name': 'triangle',
'supercategory': 'shape',
},
]
def filter_for_jpeg(root, files):
file_types = ['*.jpeg', '*.jpg']
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_shape_train2018.json'.format(ROOT_DIR), 'w') as output_json_file:
json.dump(coco_output, output_json_file)
if __name__ == "__main__":
main()
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