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_paco_lvis(base_image_dir):
    coco_api_paco_lvis = COCO(
        os.path.join(
            base_image_dir, "vlpart", "paco", "annotations", "paco_lvis_v1_train.json"
        )
    )
    all_classes = coco_api_paco_lvis.loadCats(coco_api_paco_lvis.getCatIds())
    class_map_paco_lvis = {}
    for cat in all_classes:
        cat_split = cat["name"].strip().split(":")
        if len(cat_split) == 1:
            name = cat_split[0].split("_(")[0]
        else:
            assert len(cat_split) == 2
            obj, part = cat_split
            obj = obj.split("_(")[0]
            part = part.split("_(")[0]
            name = (obj, part)
        class_map_paco_lvis[cat["id"]] = name
    img_ids = coco_api_paco_lvis.getImgIds()
    print("paco_lvis: ", len(img_ids))
    return class_map_paco_lvis, img_ids, coco_api_paco_lvis


base_image_dir = '/mnt/workspace/workgroup/yuanyq/code/LISA/dataset'
class_map, img_ids, coco_api = init_paco_lvis(base_image_dir)
final_data = []
for idx in tqdm(range(len(img_ids))):
    try:
        dic = {}
        
        img_id = img_ids[idx]
        image_info = coco_api.loadImgs([img_id])[0]
        file_name = image_info["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'] = 'coco/'+file_name
        dic['cat'] = cats
        dic['masks'] = masks
        final_data.append(dic)

    except:
        continue

    
print(len(final_data))
with open('paco_lvis.json', 'w') as f:
    f.write(json.dumps(final_data))