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import argparse
import datetime
import json
import os
from PIL import Image
import numpy as np
import pycococreatortools
def get_arguments():
parser = argparse.ArgumentParser(description="transform mask annotation to coco annotation")
parser.add_argument("--dataset", type=str, default='CIHP', help="name of dataset (CIHP, MHPv2 or VIP)")
parser.add_argument("--json_save_dir", type=str, default='../data/msrcnn_finetune_annotations',
help="path to save coco-style annotation json file")
parser.add_argument("--use_val", type=bool, default=False,
help="use train+val set for finetuning or not")
parser.add_argument("--train_img_dir", type=str, default='../data/instance-level_human_parsing/Training/Images',
help="train image path")
parser.add_argument("--train_anno_dir", type=str,
default='../data/instance-level_human_parsing/Training/Human_ids',
help="train human mask path")
parser.add_argument("--val_img_dir", type=str, default='../data/instance-level_human_parsing/Validation/Images',
help="val image path")
parser.add_argument("--val_anno_dir", type=str,
default='../data/instance-level_human_parsing/Validation/Human_ids',
help="val human mask path")
return parser.parse_args()
def main(args):
INFO = {
"description": args.split_name + " Dataset",
"url": "",
"version": "",
"year": 2019,
"contributor": "xyq",
"date_created": datetime.datetime.utcnow().isoformat(' ')
}
LICENSES = [
{
"id": 1,
"name": "",
"url": ""
}
]
CATEGORIES = [
{
'id': 1,
'name': 'person',
'supercategory': 'person',
},
]
coco_output = {
"info": INFO,
"licenses": LICENSES,
"categories": CATEGORIES,
"images": [],
"annotations": []
}
image_id = 1
segmentation_id = 1
for image_name in os.listdir(args.train_img_dir):
image = Image.open(os.path.join(args.train_img_dir, image_name))
image_info = pycococreatortools.create_image_info(
image_id, image_name, image.size
)
coco_output["images"].append(image_info)
human_mask_name = os.path.splitext(image_name)[0] + '.png'
human_mask = np.asarray(Image.open(os.path.join(args.train_anno_dir, human_mask_name)))
human_gt_labels = np.unique(human_mask)
for i in range(1, len(human_gt_labels)):
category_info = {'id': 1, 'is_crowd': 0}
binary_mask = np.uint8(human_mask == i)
annotation_info = pycococreatortools.create_annotation_info(
segmentation_id, image_id, category_info, binary_mask,
image.size, tolerance=10
)
if annotation_info is not None:
coco_output["annotations"].append(annotation_info)
segmentation_id += 1
image_id += 1
if not os.path.exists(args.json_save_dir):
os.makedirs(args.json_save_dir)
if not args.use_val:
with open('{}/{}_train.json'.format(args.json_save_dir, args.split_name), 'w') as output_json_file:
json.dump(coco_output, output_json_file)
else:
for image_name in os.listdir(args.val_img_dir):
image = Image.open(os.path.join(args.val_img_dir, image_name))
image_info = pycococreatortools.create_image_info(
image_id, image_name, image.size
)
coco_output["images"].append(image_info)
human_mask_name = os.path.splitext(image_name)[0] + '.png'
human_mask = np.asarray(Image.open(os.path.join(args.val_anno_dir, human_mask_name)))
human_gt_labels = np.unique(human_mask)
for i in range(1, len(human_gt_labels)):
category_info = {'id': 1, 'is_crowd': 0}
binary_mask = np.uint8(human_mask == i)
annotation_info = pycococreatortools.create_annotation_info(
segmentation_id, image_id, category_info, binary_mask,
image.size, tolerance=10
)
if annotation_info is not None:
coco_output["annotations"].append(annotation_info)
segmentation_id += 1
image_id += 1
with open('{}/{}_trainval.json'.format(args.json_save_dir, args.split_name), 'w') as output_json_file:
json.dump(coco_output, output_json_file)
coco_output_val = {
"info": INFO,
"licenses": LICENSES,
"categories": CATEGORIES,
"images": [],
"annotations": []
}
image_id_val = 1
segmentation_id_val = 1
for image_name in os.listdir(args.val_img_dir):
image = Image.open(os.path.join(args.val_img_dir, image_name))
image_info = pycococreatortools.create_image_info(
image_id_val, image_name, image.size
)
coco_output_val["images"].append(image_info)
human_mask_name = os.path.splitext(image_name)[0] + '.png'
human_mask = np.asarray(Image.open(os.path.join(args.val_anno_dir, human_mask_name)))
human_gt_labels = np.unique(human_mask)
for i in range(1, len(human_gt_labels)):
category_info = {'id': 1, 'is_crowd': 0}
binary_mask = np.uint8(human_mask == i)
annotation_info = pycococreatortools.create_annotation_info(
segmentation_id_val, image_id_val, category_info, binary_mask,
image.size, tolerance=10
)
if annotation_info is not None:
coco_output_val["annotations"].append(annotation_info)
segmentation_id_val += 1
image_id_val += 1
with open('{}/{}_val.json'.format(args.json_save_dir, args.split_name), 'w') as output_json_file_val:
json.dump(coco_output_val, output_json_file_val)
if __name__ == "__main__":
args = get_arguments()
main(args)
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