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