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import numpy as np
import json
import os
from pycocotools import mask as maskUtils
from PIL import Image
from tqdm import tqdm
from pycocotools.coco import COCO
import random
def singleMask2rle(mask):
rle = maskUtils.encode(np.array(mask[:, :, None], order='F', dtype="uint8"))[0]
rle["counts"] = rle["counts"].decode("utf-8")
return rle
def init_pascal_part(base_image_dir):
coco_api_pascal_part = COCO(
os.path.join(base_image_dir, "vlpart", "pascal_part", "train.json")
)
all_classes = coco_api_pascal_part.loadCats(coco_api_pascal_part.getCatIds())
class_map_pascal_part = {}
for cat in all_classes:
cat_main, cat_part = cat["name"].strip().split(":")
name = (cat_main, cat_part)
class_map_pascal_part[cat["id"]] = name
img_ids = coco_api_pascal_part.getImgIds()
print("pascal_part: ", len(img_ids))
return class_map_pascal_part, img_ids, coco_api_pascal_part
base_image_dir = '/mnt/workspace/workgroup/yuanyq/code/LISA/dataset'
class_map, img_ids, coco_api = init_pascal_part(base_image_dir)
final_data = []
for idx in tqdm(range(len(img_ids))):
dic = {}
img_id = img_ids[idx]
image_info = coco_api.loadImgs([img_id])[0]
file_name = image_info["file_name"]
file_name = os.path.join(
"VOCdevkit", "VOC2010", "JPEGImages", file_name
)
annIds = coco_api.getAnnIds(imgIds=image_info["id"])
anns = coco_api.loadAnns(annIds)
cats = []
for ann in anns:
sampled_cls = class_map[ann["category_id"]]
if isinstance(sampled_cls, tuple):
obj, part = sampled_cls
if random.random() < 0.5:
name = obj + " " + part
else:
name = "the {} of the {}".format(part, obj)
else:
name = sampled_cls
cats.append(name)
masks = []
for ann in anns:
masks.append(singleMask2rle(coco_api.annToMask(ann)))
dic['image'] = 'vlpart/pascal_part/'+file_name
dic['cat'] = cats
dic['masks'] = masks
final_data.append(dic)
print(len(final_data))
with open('pascal_part.json', 'w') as f:
f.write(json.dumps(final_data)) |