File size: 10,769 Bytes
e8f2571 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 |
# Copyright (c) OpenMMLab. All rights reserved.
# written by lzx
import copy
import os.path as osp
from typing import Any, Callable, List, Optional, Sequence, Tuple, Union
from mmengine.fileio import get_local_path
from mmdet.registry import DATASETS
from mmdet.datasets.api_wrappers import COCO
from mmdet.datasets.base_det_dataset import BaseDetDataset
from mmdet.datasets.coco import CocoDataset
from mmengine.utils import is_abs
@DATASETS.register_module()
class HSIDataset(CocoDataset):
"""Dataset for COCO."""
METAINFO = {
'classes':
('CB', 'MP', 'VO', 'ZO', 'TO', 'FG', 'GS', 'IP', 'IS', 'NP', 'LO', 'NO', 'NC', 'NF', 'K_N', 'K_O', 'P_P', 'P_O', 'V_Y_W', 'C_Y_W','BlueTrap','BrownTrap',
'Airport', 'Brown', 'DarkGreen', 'PeaGreen', 'FauxVineyardGreen'),
# palette is a list of color tuples, which is used for visualization.
'palette':
[(220, 20, 60), (119, 11, 32), (0, 0, 230), (106, 0, 228),
(0, 60, 100), (0, 0, 70), (250, 170, 30),
(100, 170, 30), (220, 220, 0), (175, 116, 175), (250, 0, 30),
(165, 42, 42), (255, 77, 255),(0, 226, 252), (182, 182, 255),
(0, 82, 0), (120, 166, 157),(110, 76, 0), (174, 57, 255),
(199, 100, 0),[0, 0, 255],(199, 100, 0),
]
}
COCOAPI = COCO
# ann_id is unique in coco dataset.
ANN_ID_UNIQUE = True
def __init__(self,
*args,
seg_prefix: Optional[str] = None,
abu_prefix: Optional[str] = None,
**kwargs) -> None:
self.seg_prefix = seg_prefix
self.abu_prefix = abu_prefix
super().__init__(*args, **kwargs)
def _join_prefix(self):
"""Join ``self.data_root`` with ``self.data_prefix`` and
``self.ann_file``.
Examples:
>>> # self.data_prefix contains relative paths
>>> self.data_root = 'a/b/c'
>>> self.data_prefix = dict(img='d/e/')
>>> self.ann_file = 'f'
>>> self._join_prefix()
>>> self.data_prefix
dict(img='a/b/c/d/e')
>>> self.ann_file
'a/b/c/f'
>>> # self.data_prefix contains absolute paths
>>> self.data_root = 'a/b/c'
>>> self.data_prefix = dict(img='/d/e/')
>>> self.ann_file = 'f'
>>> self._join_prefix()
>>> self.data_prefix
dict(img='/d/e')
>>> self.ann_file
'a/b/c/f'
"""
# Automatically join annotation file path with `self.root` if
# `self.ann_file` is not an absolute path.
if not is_abs(self.ann_file) and self.ann_file:
self.ann_file = osp.join(self.data_root, self.ann_file)
# Automatically join data directory with `self.root` if path value in
# `self.data_prefix` is not an absolute path.
for data_key, prefix in self.data_prefix.items():
if isinstance(prefix, str):
if not is_abs(prefix):
self.data_prefix[data_key] = osp.join(
self.data_root, prefix)
else:
self.data_prefix[data_key] = prefix
else:
raise TypeError('prefix should be a string, but got '
f'{type(prefix)}')
if self.seg_prefix is not None:
for data_key, prefix in self.seg_prefix.items():
if isinstance(prefix, str):
if not is_abs(prefix):
self.seg_prefix[data_key] = osp.join(
self.data_root, prefix)
else:
self.seg_prefix[data_key] = prefix
else:
raise TypeError('prefix should be a string, but got '
f'{type(prefix)}')
if self.abu_prefix is not None:
for data_key, prefix in self.abu_prefix.items():
if isinstance(prefix, str):
if not is_abs(prefix):
self.abu_prefix[data_key] = osp.join(
self.data_root, prefix)
else:
self.abu_prefix[data_key] = prefix
else:
raise TypeError('prefix should be a string, but got '
f'{type(prefix)}')
def load_data_list(self) -> List[dict]:
"""Load annotations from an annotation file named as ``self.ann_file``
Returns:
List[dict]: A list of annotation.
""" # noqa: E501
with get_local_path(
self.ann_file, backend_args=self.backend_args) as local_path:
self.coco = self.COCOAPI(local_path)
# The order of returned `cat_ids` will not
# change with the order of the `classes`
self.cat_ids = self.coco.get_cat_ids(
cat_names=self.metainfo['classes'])
self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
self.cat_img_map = copy.deepcopy(self.coco.cat_img_map)
img_ids = self.coco.get_img_ids()
data_list = []
total_ann_ids = []
for img_id in img_ids:
raw_img_info = self.coco.load_imgs([img_id])[0]
raw_img_info['img_id'] = img_id
ann_ids = self.coco.get_ann_ids(img_ids=[img_id])
raw_ann_info = self.coco.load_anns(ann_ids)
total_ann_ids.extend(ann_ids)
parsed_data_info = self.parse_data_info({
'raw_ann_info':
raw_ann_info,
'raw_img_info':
raw_img_info
})
data_list.append(parsed_data_info)
if self.ANN_ID_UNIQUE:
assert len(set(total_ann_ids)) == len(
total_ann_ids
), f"Annotation ids in '{self.ann_file}' are not unique!"
del self.coco
return data_list
def parse_data_info(self, raw_data_info: dict) -> Union[dict, List[dict]]:
"""Parse raw annotation to target format.
Args:
raw_data_info (dict): Raw data information load from ``ann_file``
Returns:
Union[dict, List[dict]]: Parsed annotation.
"""
img_info = raw_data_info['raw_img_info']
ann_info = raw_data_info['raw_ann_info']
data_info = {}
# TODO: need to change data_prefix['img'] to data_prefix['img_path']
img_path = osp.join(self.data_prefix['img'], img_info['file_name'])
if self.data_prefix.get('seg', None):
seg_map_path = osp.join(
self.data_prefix['seg'],
img_info['file_name'].rsplit('.', 1)[0] + self.seg_map_suffix)
else:
seg_map_path = None
# if self.seg_prefix is not None:
if self.seg_prefix is not None:
seg_path = osp.join(self.seg_prefix['img'], img_info['file_name']).replace('.npy', '.png')
else:
seg_path = None
if self.abu_prefix is not None:
abu_path = osp.join(self.abu_prefix['img'], img_info['file_name']).replace('.npy', '.mat')
else:
abu_path = None
data_info['img_path'] = img_path
data_info['img_id'] = img_info['img_id']
data_info['seg_map_path'] = seg_map_path
data_info['seg_path'] = seg_path
data_info['abu_path'] = abu_path
data_info['height'] = img_info['height']
data_info['width'] = img_info['width']
instances = []
for i, ann in enumerate(ann_info):
instance = {}
if ann.get('ignore', False):
continue
x1, y1, w, h = ann['bbox']
inter_w = max(0, min(x1 + w, img_info['width']) - max(x1, 0))
inter_h = max(0, min(y1 + h, img_info['height']) - max(y1, 0))
if inter_w * inter_h == 0:
continue
if ann['area'] <= 0 or w < 1 or h < 1:
continue
if ann['category_id'] not in self.cat_ids:
continue
bbox = [x1, y1, x1 + w, y1 + h]
if ann.get('iscrowd', False):
instance['ignore_flag'] = 1
else:
instance['ignore_flag'] = 0
instance['bbox'] = bbox
instance['bbox_label'] = self.cat2label[ann['category_id']]
if ann.get('segmentation', None):
instance['mask'] = ann['segmentation']
instances.append(instance)
data_info['instances'] = instances
return data_info
def filter_data(self) -> List[dict]:
"""Filter annotations according to filter_cfg.
Returns:
List[dict]: Filtered results.
"""
if self.test_mode:
return self.data_list
if self.filter_cfg is None:
return self.data_list
filter_empty_gt = self.filter_cfg.get('filter_empty_gt', False)
min_size = self.filter_cfg.get('min_size', 0)
# obtain images that contain annotation
ids_with_ann = set(data_info['img_id'] for data_info in self.data_list)
# obtain images that contain annotations of the required categories
ids_in_cat = set()
for i, class_id in enumerate(self.cat_ids):
ids_in_cat |= set(self.cat_img_map[class_id])
# merge the image id sets of the two conditions and use the merged set
# to filter out images if self.filter_empty_gt=True
ids_in_cat &= ids_with_ann
valid_data_infos = []
for i, data_info in enumerate(self.data_list):
img_id = data_info['img_id']
width = data_info['width']
height = data_info['height']
if filter_empty_gt and img_id not in ids_in_cat:
continue
if min(width, height) >= min_size:
valid_data_infos.append(data_info)
return valid_data_infos
# @DATASETS.register_module()
# class HSIDataset16(HSIDataset):
# """Dataset for COCO."""
#
# METAINFO = {
# 'classes':
# ( 'TO', 'FG', 'GS', 'IP', 'IS', 'NP', 'LO', 'NO', 'NC', 'NF', 'K_N', 'K_O', 'P_P', 'P_O', 'V_Y_W', 'C_Y_W'),
# # palette is a list of color tuples, which is used for visualization.
# 'palette':
# [
# (0, 60, 100), (0, 0, 70), (250, 170, 30),
# (100, 170, 30), (220, 220, 0), (175, 116, 175), (250, 0, 30),
# (165, 42, 42), (255, 77, 255),(0, 226, 252), (182, 182, 255),
# (0, 82, 0), (120, 166, 157),(110, 76, 0), (174, 57, 255),
# (199, 100, 0),]
# }
# COCOAPI = COCO
# # ann_id is unique in coco dataset.
# ANN_ID_UNIQUE = True
|