File size: 13,382 Bytes
25986db | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 | import numpy as np
from lib.test.evaluation.environment import env_settings
from lib.train.data.image_loader import imread_indexed
from collections import OrderedDict
class BaseDataset:
"""Base class for all datasets."""
def __init__(self):
self.env_settings = env_settings()
def __len__(self):
"""Overload this function in your dataset. This should return number of sequences in the dataset."""
raise NotImplementedError
def get_sequence_list(self):
"""Overload this in your dataset. Should return the list of sequences in the dataset."""
raise NotImplementedError
class VideoCude_Sequence:
"""Class for the sequence in an evaluation."""
def __init__(self, name, frames, dataset, ground_truth_rect, ground_truth_seg=None, init_data=None,
object_class=None, target_visible=None, object_ids=None, multiobj_mode=False,
language_query = None):
self.name = name
self.frames = frames
self.dataset = dataset
self.ground_truth_rect = ground_truth_rect
self.ground_truth_seg = ground_truth_seg
self.object_class = object_class
self.target_visible = target_visible
self.object_ids = object_ids
self.multiobj_mode = multiobj_mode
if language_query is not None:
self.language_query = language_query
else:
self.language_query = None
self.init_data = self._construct_init_data(init_data)
self.init_data[0]['nlp'] = self.language_query
self._ensure_start_frame()
def _ensure_start_frame(self):
# Ensure start frame is 0
start_frame = min(list(self.init_data.keys()))
if start_frame > 0:
self.frames = self.frames[start_frame:]
if self.ground_truth_rect is not None:
if isinstance(self.ground_truth_rect, (dict, OrderedDict)):
for obj_id, gt in self.ground_truth_rect.items():
self.ground_truth_rect[obj_id] = gt[start_frame:,:]
else:
self.ground_truth_rect = self.ground_truth_rect[start_frame:,:]
if self.ground_truth_seg is not None:
self.ground_truth_seg = self.ground_truth_seg[start_frame:]
assert len(self.frames) == len(self.ground_truth_seg)
if self.target_visible is not None:
self.target_visible = self.target_visible[start_frame:]
self.init_data = {frame-start_frame: val for frame, val in self.init_data.items()}
def _construct_init_data(self, init_data):
if init_data is not None:
if not self.multiobj_mode:
assert self.object_ids is None or len(self.object_ids) == 1
for frame, init_val in init_data.items():
if 'bbox' in init_val and isinstance(init_val['bbox'], (dict, OrderedDict)):
init_val['bbox'] = init_val['bbox'][self.object_ids[0]]
# convert to list
for frame, init_val in init_data.items():
if 'bbox' in init_val:
if isinstance(init_val['bbox'], (dict, OrderedDict)):
init_val['bbox'] = OrderedDict({obj_id: list(init) for obj_id, init in init_val['bbox'].items()})
else:
init_val['bbox'] = list(init_val['bbox'])
else:
init_data = {0: dict()} # Assume start from frame 0
if self.object_ids is not None:
init_data[0]['object_ids'] = self.object_ids
if self.ground_truth_rect is not None:
if self.multiobj_mode:
assert isinstance(self.ground_truth_rect, (dict, OrderedDict))
init_data[0]['bbox'] = OrderedDict({obj_id: list(gt[0,:]) for obj_id, gt in self.ground_truth_rect.items()})
else:
assert self.object_ids is None or len(self.object_ids) == 1
if isinstance(self.ground_truth_rect, (dict, OrderedDict)):
init_data[0]['bbox'] = list(self.ground_truth_rect[self.object_ids[0]][0, :])
else:
init_data[0]['bbox'] = list(self.ground_truth_rect[0,:])
if self.ground_truth_seg is not None:
init_data[0]['mask'] = self.ground_truth_seg[0]
return init_data
def init_info(self):
info = self.frame_info(frame_num=0)
return info
def frame_info(self, frame_num):
info = self.object_init_data(frame_num=frame_num)
return info
def init_bbox(self, frame_num=0):
return self.object_init_data(frame_num=frame_num).get('init_bbox')
def init_mask(self, frame_num=0):
return self.object_init_data(frame_num=frame_num).get('init_mask')
def init_nlp(self, frame_num=0):
return self.object_init_data(frame_num=frame_num).get('init_nlp')
def get_info(self, keys, frame_num=None):
info = dict()
for k in keys:
val = self.get(k, frame_num=frame_num)
if val is not None:
info[k] = val
return info
def object_init_data(self, frame_num=None) -> dict:
if frame_num is None:
frame_num = 0
if frame_num not in self.init_data:
return dict()
init_data = dict()
for key, val in self.init_data[frame_num].items():
if val is None:
continue
init_data['init_'+key] = val
if 'init_mask' in init_data and init_data['init_mask'] is not None:
anno = imread_indexed(init_data['init_mask'])
if not self.multiobj_mode and self.object_ids is not None:
assert len(self.object_ids) == 1
anno = (anno == int(self.object_ids[0])).astype(np.uint8)
init_data['init_mask'] = anno
if self.object_ids is not None:
init_data['object_ids'] = self.object_ids
init_data['sequence_object_ids'] = self.object_ids
return init_data
def target_class(self, frame_num=None):
return self.object_class
def get(self, name, frame_num=None):
return getattr(self, name)(frame_num)
def __repr__(self):
return "{self.__class__.__name__} {self.name}, length={len} frames".format(self=self, len=len(self.frames))
class Sequence:
"""Class for the sequence in an evaluation."""
def __init__(self, name, frames, dataset, ground_truth_rect, ground_truth_seg=None, init_data=None,
object_class=None, target_visible=None, object_ids=None, multiobj_mode=False,
language_query = None):
self.name = name
self.frames = frames
self.dataset = dataset
self.ground_truth_rect = ground_truth_rect
self.ground_truth_seg = ground_truth_seg
self.object_class = object_class
self.target_visible = target_visible
self.object_ids = object_ids
self.multiobj_mode = multiobj_mode
self.init_data = self._construct_init_data(init_data)
if language_query is not None:
self.language_query = language_query
self.init_data[0]['nlp'] = self.language_query
self._ensure_start_frame()
def _ensure_start_frame(self):
# Ensure start frame is 0
start_frame = min(list(self.init_data.keys()))
if start_frame > 0:
self.frames = self.frames[start_frame:]
if self.ground_truth_rect is not None:
if isinstance(self.ground_truth_rect, (dict, OrderedDict)):
for obj_id, gt in self.ground_truth_rect.items():
self.ground_truth_rect[obj_id] = gt[start_frame:,:]
else:
self.ground_truth_rect = self.ground_truth_rect[start_frame:,:]
if self.ground_truth_seg is not None:
self.ground_truth_seg = self.ground_truth_seg[start_frame:]
assert len(self.frames) == len(self.ground_truth_seg)
if self.target_visible is not None:
self.target_visible = self.target_visible[start_frame:]
self.init_data = {frame-start_frame: val for frame, val in self.init_data.items()}
def _construct_init_data(self, init_data):
if init_data is not None:
if not self.multiobj_mode:
assert self.object_ids is None or len(self.object_ids) == 1
for frame, init_val in init_data.items():
if 'bbox' in init_val and isinstance(init_val['bbox'], (dict, OrderedDict)):
init_val['bbox'] = init_val['bbox'][self.object_ids[0]]
# convert to list
for frame, init_val in init_data.items():
if 'bbox' in init_val:
if isinstance(init_val['bbox'], (dict, OrderedDict)):
init_val['bbox'] = OrderedDict({obj_id: list(init) for obj_id, init in init_val['bbox'].items()})
else:
init_val['bbox'] = list(init_val['bbox'])
else:
init_data = {0: dict()} # Assume start from frame 0
if self.object_ids is not None:
init_data[0]['object_ids'] = self.object_ids
if self.ground_truth_rect is not None:
if self.multiobj_mode:
assert isinstance(self.ground_truth_rect, (dict, OrderedDict))
init_data[0]['bbox'] = OrderedDict({obj_id: list(gt[0,:]) for obj_id, gt in self.ground_truth_rect.items()})
else:
assert self.object_ids is None or len(self.object_ids) == 1
if isinstance(self.ground_truth_rect, (dict, OrderedDict)):
init_data[0]['bbox'] = list(self.ground_truth_rect[self.object_ids[0]][0, :])
else:
init_data[0]['bbox'] = list(self.ground_truth_rect[0,:])
if self.ground_truth_seg is not None:
init_data[0]['mask'] = self.ground_truth_seg[0]
return init_data
def init_info(self):
info = self.frame_info(frame_num=0)
return info
def frame_info(self, frame_num):
info = self.object_init_data(frame_num=frame_num)
return info
def init_bbox(self, frame_num=0):
return self.object_init_data(frame_num=frame_num).get('init_bbox')
def init_mask(self, frame_num=0):
return self.object_init_data(frame_num=frame_num).get('init_mask')
def init_nlp(self, frame_num=0):
return self.object_init_data(frame_num=frame_num).get('init_nlp')
def get_info(self, keys, frame_num=None):
info = dict()
for k in keys:
val = self.get(k, frame_num=frame_num)
if val is not None:
info[k] = val
return info
def object_init_data(self, frame_num=None) -> dict:
if frame_num is None:
frame_num = 0
if frame_num not in self.init_data:
return dict()
init_data = dict()
for key, val in self.init_data[frame_num].items():
if val is None:
continue
init_data['init_'+key] = val
if 'init_mask' in init_data and init_data['init_mask'] is not None:
anno = imread_indexed(init_data['init_mask'])
if not self.multiobj_mode and self.object_ids is not None:
assert len(self.object_ids) == 1
anno = (anno == int(self.object_ids[0])).astype(np.uint8)
init_data['init_mask'] = anno
if self.object_ids is not None:
init_data['object_ids'] = self.object_ids
init_data['sequence_object_ids'] = self.object_ids
return init_data
def target_class(self, frame_num=None):
return self.object_class
def get(self, name, frame_num=None):
return getattr(self, name)(frame_num)
def __repr__(self):
return "{self.__class__.__name__} {self.name}, length={len} frames".format(self=self, len=len(self.frames))
class SequenceList(list):
"""List of sequences. Supports the addition operator to concatenate sequence lists."""
def __getitem__(self, item):
if isinstance(item, str):
for seq in self:
if seq.name == item:
return seq
raise IndexError('Sequence name not in the dataset.')
elif isinstance(item, int):
return super(SequenceList, self).__getitem__(item)
elif isinstance(item, (tuple, list)):
return SequenceList([super(SequenceList, self).__getitem__(i) for i in item])
else:
return SequenceList(super(SequenceList, self).__getitem__(item))
def __add__(self, other):
return SequenceList(super(SequenceList, self).__add__(other))
def copy(self):
return SequenceList(super(SequenceList, self).copy()) |