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import json |
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from refer import REFER |
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import random |
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from pycocotools import mask as maskUtils |
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from tqdm import tqdm |
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def annToMask(mask_ann, h=None, w=None): |
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if isinstance(mask_ann, list): |
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rles = maskUtils.frPyObjects(mask_ann, h, w) |
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rle = maskUtils.merge(rles) |
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elif isinstance(mask_ann['counts'], list): |
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rle = maskUtils.frPyObjects(mask_ann, h, w) |
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else: |
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rle = mask_ann |
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mask = maskUtils.decode(rle) |
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return mask |
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refer_api = REFER('/mnt/workspace/workgroup/yuanyq/code/LISA/dataset/refer_seg', 'refcocog', 'umd') |
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ref_ids_train = refer_api.getRefIds(split="val") |
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images_ids_train = refer_api.getImgIds(ref_ids=ref_ids_train) |
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refs_train = refer_api.loadRefs(ref_ids=ref_ids_train) |
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annotation = refer_api.Anns |
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img2refs = {} |
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for ref in refs_train: |
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image_id = ref["image_id"] |
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img2refs[image_id] = img2refs.get(image_id, []) + [ |
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ref, |
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] |
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final_data = [] |
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for idx in tqdm(img2refs): |
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dic = {} |
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data = img2refs[idx] |
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img_idx = data[0]['image_id'] |
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image = refer_api.loadImgs(image_ids=img_idx)[0] |
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dic['image'] = 'refer_seg/images/mscoco/images/train2014/'+image['file_name'] |
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dic['height']= image['height'] |
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dic['width'] = image['width'] |
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cats = [] |
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masks = [] |
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for ann in data: |
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ann_idx = ann['ann_id'] |
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annotation_ = annotation[ann_idx] |
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cat_list = set() |
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for cat in ann['sentences']: |
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cat_list.add(cat['sent']) |
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cat_list = list(cat_list) |
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cats+=cat_list |
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for i in range(len(cat_list)): |
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masks.append(annotation_['segmentation']) |
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dic['cat'] = cats |
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dic['masks'] = masks |
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final_data.append(dic) |
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print(len(final_data)) |
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with open('/mnt/workspace/workgroup/yuanyq/code/seg-llava/val/refcocog_val.json', 'w') as f: |
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f.write(json.dumps(final_data)) |
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