lisa_data/grefcoco1.py

import contextlib
import io
import logging
import numpy as np
import os
import random
import copy
import pycocotools.mask as mask_util
from PIL import Image
from tqdm import tqdm
import json

# from detectron2.structures import Boxes, BoxMode, PolygonMasks, RotatedBoxes
# from detectron2.utils.file_io import PathManager

"""
This file contains functions to parse RefCOCO-format annotations into dicts in "Detectron2 format".
"""


logger = logging.getLogger(__name__)

__all__ = ["load_refcoco_json"]


def load_grefcoco_json(refer_root, dataset_name, splitby, split, image_root, extra_annotation_keys=None, extra_refer_keys=None):

    if dataset_name == 'refcocop':
        dataset_name = 'refcoco+'
    if dataset_name == 'refcoco' or dataset_name == 'refcoco+':
        splitby == 'unc'
    if dataset_name == 'refcocog':
        assert splitby == 'umd' or splitby == 'google'

    dataset_id = '_'.join([dataset_name, splitby, split])

    from grefer import G_REFER

    # refer_root = PathManager.get_local_path(refer_root)
    refer_api = G_REFER(data_root=refer_root,
                    dataset=dataset_name,
                    splitBy=splitby)
  
    ref_ids = refer_api.getRefIds(split=split)
    img_ids = refer_api.getImgIds(ref_ids)
    refs = refer_api.loadRefs(ref_ids)
    imgs = [refer_api.loadImgs(ref['image_id'])[0] for ref in refs]
    anns = [refer_api.loadAnns(ref['ann_id']) for ref in refs]
    imgs_refs_anns = list(zip(imgs, refs, anns))

    dataset_dicts = []

    ann_keys = ["iscrowd", "bbox", "category_id"] + (extra_annotation_keys or [])
    ref_keys = ["raw", "sent_id"] + (extra_refer_keys or [])

    ann_lib = {}

    NT_count = 0
    MT_count = 0

    for (img_dict, ref_dict, anno_dicts) in imgs_refs_anns:
        record = {}
        record["source"] = 'grefcoco'
        record["file_name"] = os.path.join(image_root, img_dict["file_name"])
        record["height"] = img_dict["height"]
        record["width"] = img_dict["width"]
        image_id = record["image_id"] = img_dict["id"]

        # Check that information of image, ann and ref match each other
        # This fails only when the data parsing logic or the annotation file is buggy.
        assert ref_dict['image_id'] == image_id
        assert ref_dict['split'] == split
        if not isinstance(ref_dict['ann_id'], list):
            ref_dict['ann_id'] = [ref_dict['ann_id']]

        # No target samples
        if None in anno_dicts:
            assert anno_dicts == [None]
            assert ref_dict['ann_id'] == [-1]
            record['empty'] = True
            obj = {key: None for key in ann_keys if key in ann_keys}
            # obj["bbox_mode"] = BoxMode.XYWH_ABS
            obj["empty"] = True
            obj = [obj]

        # Multi target samples
        else:
            record['empty'] = False
            obj = []
            for anno_dict in anno_dicts:
                ann_id = anno_dict['id']
                if anno_dict['iscrowd']:
                    continue
                assert anno_dict["image_id"] == image_id
                assert ann_id in ref_dict['ann_id']

                if ann_id in ann_lib:
                    ann = ann_lib[ann_id]
                else:
                    ann = {key: anno_dict[key] for key in ann_keys if key in anno_dict}
                    # ann["bbox_mode"] = BoxMode.XYWH_ABS
                    ann["empty"] = False
 
                    segm = anno_dict.get("segmentation", None)
                    assert segm  # either list[list[float]] or dict(RLE)
                    if isinstance(segm, dict):
                        if isinstance(segm["counts"], list):
                            # convert to compressed RLE
                            segm = mask_util.frPyObjects(segm, *segm["size"])
                    else:
                        # filter out invalid polygons (< 3 points)
                        segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6]
                        if len(segm) == 0:
                            num_instances_without_valid_segmentation += 1
                            continue  # ignore this instance
                    ann["segmentation"] = segm
                    ann_lib[ann_id] = ann

                obj.append(ann)

        record["annotations"] = obj

        # Process referring expressions
        sents = ref_dict['sentences']
        for sent in sents:
            ref_record = record.copy()
            ref = {key: sent[key] for key in ref_keys if key in sent}
            ref["ref_id"] = ref_dict["ref_id"]
            ref_record["sentence"] = ref
            dataset_dicts.append(ref_record)
 
    return dataset_dicts

if __name__ == "__main__":
    """
    Test the COCO json dataset loader.

    Usage:
        python -m detectron2.data.datasets.coco \
            path/to/json path/to/image_root dataset_name

        "dataset_name" can be "coco_2014_minival_100", or other
        pre-registered ones
    """
    import sys

    REFCOCO_PATH = '/mnt/workspace/workgroup/yuanyq/code/LISA/dataset/refer_seg'
    COCO_TRAIN_2014_IMAGE_ROOT = '/mnt/lustre/hhding/code/ReLA/datasets/images'
    REFCOCO_DATASET = 'grefs'
    REFCOCO_SPLITBY = 'unc'
    REFCOCO_SPLIT = 'train'

    dicts = load_grefcoco_json(REFCOCO_PATH, REFCOCO_DATASET, REFCOCO_SPLITBY, REFCOCO_SPLIT, COCO_TRAIN_2014_IMAGE_ROOT)
    
    final_data = []
    dic = {}
    for d in tqdm(dicts):
        image = 'refer_seg/images/mscoco/images/train2014/' + d['file_name'].split('/')[-1]
        if 'image' not in dic or image!= dic['image']:
            if 'image' in dic:
                final_data.append(dic.copy())
            dic = {}
            dic['image'] = image
            dic['height'] = d['height']
            dic['width'] = d['width']
            dic['cat'] = []
            dic['masks'] = []
        if d['sentence']['raw'] not in dic['cat']:
            dic['cat'].append(d['sentence']['raw'])
            if d['empty']==True:
                dic['masks'].append(None)
            else:
                masks = []
                for ann in d['annotations']:
                    masks.append(ann['segmentation'])
                dic['masks'].append(masks)
    final_data.append(dic)   
    
    with open('grefcoco.json', 'w') as f:
        f.write(json.dumps(final_data))