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# COCO categories for zero-shot setting
# 65 categories in total, 48 base categories for training, 17 unseen categories are only used in testing
# from http://ankan.umiacs.io/files/mscoco_seen_classes.json, http://ankan.umiacs.io/files/mscoco_unseen_classes.json
# 17 class names in order, obtained from load_coco_json() function
COCO_UNSEEN_CLS = ['airplane', 'bus', 'cat', 'dog', 'cow', 'elephant', 'umbrella', \
'tie', 'snowboard', 'skateboard', 'cup', 'knife', 'cake', 'couch', 'keyboard', \
'sink', 'scissors']
# 48 class names in order, obtained from load_coco_json() function
COCO_SEEN_CLS = ['person', 'bicycle', 'car', 'motorcycle', 'train', 'truck', \
'boat', 'bench', 'bird', 'horse', 'sheep', 'bear', 'zebra', 'giraffe', \
'backpack', 'handbag', 'suitcase', 'frisbee', 'skis', 'kite', 'surfboard', \
'bottle', 'fork', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', \
'broccoli', 'carrot', 'pizza', 'donut', 'chair', 'bed', 'toilet', 'tv', \
'laptop', 'mouse', 'remote', 'microwave', 'oven', 'toaster', \
'refrigerator', 'book', 'clock', 'vase', 'toothbrush']
# 65 class names in order, obtained from load_coco_json() function
COCO_OVD_ALL_CLS = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', \
'bus', 'train', 'truck', 'boat', 'bench', 'bird', 'cat', 'dog', 'horse', \
'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', \
'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'kite', 'skateboard', \
'surfboard', 'bottle', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', \
'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'pizza', 'donut', 'cake', \
'chair', 'couch', 'bed', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', \
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', \
'scissors', 'toothbrush']
# 80 class names
COCO_80_ALL_CLS = {1: 'person',
2: 'bicycle',
3: 'car',
4: 'motorcycle',
5: 'airplane',
6: 'bus',
7: 'train',
8: 'truck',
9: 'boat',
10: 'traffic light',
11: 'fire hydrant',
12: 'stop sign',
13: 'parking meter',
14: 'bench',
15: 'bird',
16: 'cat',
17: 'dog',
18: 'horse',
19: 'sheep',
20: 'cow',
21: 'elephant',
22: 'bear',
23: 'zebra',
24: 'giraffe',
25: 'backpack',
26: 'umbrella',
27: 'handbag',
28: 'tie',
29: 'suitcase',
30: 'frisbee',
31: 'skis',
32: 'snowboard',
33: 'sports ball',
34: 'kite',
35: 'baseball bat',
36: 'baseball glove',
37: 'skateboard',
38: 'surfboard',
39: 'tennis racket',
40: 'bottle',
41: 'wine glass',
42: 'cup',
43: 'fork',
44: 'knife',
45: 'spoon',
46: 'bowl',
47: 'banana',
48: 'apple',
49: 'sandwich',
50: 'orange',
51: 'broccoli',
52: 'carrot',
53: 'hot dog',
54: 'pizza',
55: 'donut',
56: 'cake',
57: 'chair',
58: 'couch',
59: 'potted plant',
60: 'bed',
61: 'dining table',
62: 'toilet',
63: 'tv',
64: 'laptop',
65: 'mouse',
66: 'remote',
67: 'keyboard',
68: 'cell phone',
69: 'microwave',
70: 'oven',
71: 'toaster',
72: 'sink',
73: 'refrigerator',
74: 'book',
75: 'clock',
76: 'vase',
77: 'scissors',
78: 'teddy bear',
79: 'hair drier',
80: 'toothbrush'}
if __name__ == "__main__":
# from https://github.com/alirezazareian/ovr-cnn/blob/master/ipynb/001.ipynb
# Create zero-shot setting data split in COCO
import json
import ipdb
with open('./datasets/coco/annotations/instances_train2017.json', 'r') as fin:
coco_train_anno_all = json.load(fin)
with open('./datasets/coco/annotations/instances_train2017.json', 'r') as fin:
coco_train_anno_seen = json.load(fin)
with open('./datasets/coco/annotations/instances_train2017.json', 'r') as fin:
coco_train_anno_unseen = json.load(fin)
with open('./datasets/coco/annotations/instances_val2017.json', 'r') as fin:
coco_val_anno_all = json.load(fin)
with open('./datasets/coco/annotations/instances_val2017.json', 'r') as fin:
coco_val_anno_seen = json.load(fin)
with open('./datasets/coco/annotations/instances_val2017.json', 'r') as fin:
coco_val_anno_unseen = json.load(fin)
labels_seen = COCO_SEEN_CLS
labels_unseen = COCO_UNSEEN_CLS
labels_all = [item['name'] for item in coco_val_anno_all['categories']] # 80 class names
# len(labels_seen), len(labels_unseen)
# set(labels_seen) - set(labels_all)
# set(labels_unseen) - set(labels_all)
class_id_to_split = {} # {1: 'seen', 2: 'seen', 3: 'seen', 4: 'seen', 5: 'unseen',...}
class_name_to_split = {} # {'person': 'seen', 'bicycle': 'seen', 'car': 'seen', 'motorcycle': 'seen', 'airplane': 'unseen',...}
for item in coco_val_anno_all['categories']:
if item['name'] in labels_seen:
class_id_to_split[item['id']] = 'seen'
class_name_to_split[item['name']] = 'seen'
elif item['name'] in labels_unseen:
class_id_to_split[item['id']] = 'unseen'
class_name_to_split[item['name']] = 'unseen'
# class_name_to_emb = {}
# with open('../datasets/coco/zero-shot/glove.6B.300d.txt', 'r') as fin:
# for row in fin:
# row_tk = row.split()
# if row_tk[0] in class_name_to_split:
# class_name_to_emb[row_tk[0]] = [float(num) for num in row_tk[1:]]
# len(class_name_to_emb), len(class_name_to_split)
def filter_annotation(anno_dict, split_name_list):
"""
COCO annotations have fields: dict_keys(['info', 'licenses', 'images', 'annotations', 'categories'])
This function (1) filters the category metadata (list) in 'categories';
(2) filter instance annotation in 'annotations'; (3) filter image metadata (list) in 'images
"""
filtered_categories = []
for item in anno_dict['categories']:
if class_id_to_split.get(item['id']) in split_name_list:
#item['embedding'] = class_name_to_emb[item['name']]
item['split'] = class_id_to_split.get(item['id'])
filtered_categories.append(item)
anno_dict['categories'] = filtered_categories
filtered_images = []
filtered_annotations = []
useful_image_ids = set()
for item in anno_dict['annotations']:
if class_id_to_split.get(item['category_id']) in split_name_list:
filtered_annotations.append(item)
useful_image_ids.add(item['image_id'])
for item in anno_dict['images']:
if item['id'] in useful_image_ids:
filtered_images.append(item)
anno_dict['annotations'] = filtered_annotations
anno_dict['images'] = filtered_images
filter_annotation(coco_train_anno_seen, ['seen'])
filter_annotation(coco_train_anno_unseen, ['unseen'])
filter_annotation(coco_train_anno_all, ['seen', 'unseen'])
filter_annotation(coco_val_anno_seen, ['seen'])
filter_annotation(coco_val_anno_unseen, ['unseen'])
filter_annotation(coco_val_anno_all, ['seen', 'unseen'])
with open('./datasets/coco/annotations/ovd_ins_train2017_b.json', 'w') as fout:
json.dump(coco_train_anno_seen, fout)
with open('./datasets/coco/annotations/ovd_ins_train2017_t.json', 'w') as fout:
json.dump(coco_train_anno_unseen, fout)
with open('./datasets/coco/annotations/ovd_ins_train2017_all.json', 'w') as fout:
json.dump(coco_train_anno_all, fout)
with open('./datasets/coco/annotations/ovd_ins_val2017_b.json', 'w') as fout:
json.dump(coco_val_anno_seen, fout)
with open('./datasets/coco/annotations/ovd_ins_val2017_t.json', 'w') as fout:
json.dump(coco_val_anno_unseen, fout)
with open('./datasets/coco/annotations/ovd_ins_val2017_all.json', 'w') as fout:
json.dump(coco_val_anno_all, fout) |