-
Notifications
You must be signed in to change notification settings - Fork 6
/
coco.py
392 lines (351 loc) · 15.9 KB
/
coco.py
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
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
import logging
import os.path as osp
import tempfile
import mmcv
import numpy as np
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
from mmdet.core import eval_recalls
from mmdet.utils import print_log
from .custom import CustomDataset
from .registry import DATASETS
@DATASETS.register_module
class CocoDataset(CustomDataset):
CLASSES = ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
'train', 'truck', 'boat', 'traffic_light', 'fire_hydrant',
'stop_sign', 'parking_meter', 'bench', 'bird', 'cat', 'dog',
'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports_ball', 'kite', 'baseball_bat',
'baseball_glove', 'skateboard', 'surfboard', 'tennis_racket',
'bottle', 'wine_glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
'hot_dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted_plant', 'bed', 'dining_table', 'toilet', 'tv', 'laptop',
'mouse', 'remote', 'keyboard', 'cell_phone', 'microwave',
'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock',
'vase', 'scissors', 'teddy_bear', 'hair_drier', 'toothbrush')
def load_annotations(self, ann_file):
self.coco = COCO(ann_file)
self.cat_ids = self.coco.getCatIds()
self.cat2label = {
cat_id: i + 1
for i, cat_id in enumerate(self.cat_ids)
}
self.img_ids = self.coco.getImgIds()
img_infos = []
for i in self.img_ids:
info = self.coco.loadImgs([i])[0]
info['filename'] = info['file_name']
img_infos.append(info)
return img_infos
def get_ann_info(self, idx):
img_id = self.img_infos[idx]['id']
ann_ids = self.coco.getAnnIds(imgIds=[img_id])
ann_info = self.coco.loadAnns(ann_ids)
return self._parse_ann_info(self.img_infos[idx], ann_info)
def _filter_imgs(self, min_size=32):
"""Filter images too small or without ground truths."""
valid_inds = []
ids_with_ann = set(_['image_id'] for _ in self.coco.anns.values())
for i, img_info in enumerate(self.img_infos):
if self.filter_empty_gt and self.img_ids[i] not in ids_with_ann:
continue
if min(img_info['width'], img_info['height']) >= min_size:
valid_inds.append(i)
return valid_inds
def _parse_ann_info(self, img_info, ann_info):
"""Parse bbox and mask annotation.
Args:
ann_info (list[dict]): Annotation info of an image.
with_mask (bool): Whether to parse mask annotations.
Returns:
dict: A dict containing the following keys: bboxes, bboxes_ignore,
labels, masks, seg_map. "masks" are raw annotations and not
decoded into binary masks.
"""
gt_bboxes = []
gt_labels = []
gt_bboxes_ignore = []
gt_masks_ann = []
for i, ann in enumerate(ann_info):
if ann.get('ignore', False):
continue
x1, y1, w, h = ann['bbox']
if ann['area'] <= 0 or w < 1 or h < 1:
continue
bbox = [x1, y1, x1 + w - 1, y1 + h - 1]
if ann.get('iscrowd', False):
gt_bboxes_ignore.append(bbox)
else:
gt_bboxes.append(bbox)
gt_labels.append(self.cat2label[ann['category_id']])
gt_masks_ann.append(ann['segmentation'])
if gt_bboxes:
gt_bboxes = np.array(gt_bboxes, dtype=np.float32)
gt_labels = np.array(gt_labels, dtype=np.int64)
else:
gt_bboxes = np.zeros((0, 4), dtype=np.float32)
gt_labels = np.array([], dtype=np.int64)
if gt_bboxes_ignore:
gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)
else:
gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)
seg_map = img_info['filename'].replace('jpg', 'png')
ann = dict(
bboxes=gt_bboxes,
labels=gt_labels,
bboxes_ignore=gt_bboxes_ignore,
masks=gt_masks_ann,
seg_map=seg_map)
return ann
def xyxy2xywh(self, bbox):
_bbox = bbox.tolist()
return [
_bbox[0],
_bbox[1],
_bbox[2] - _bbox[0] + 1,
_bbox[3] - _bbox[1] + 1,
]
def _proposal2json(self, results):
json_results = []
for idx in range(len(self)):
img_id = self.img_ids[idx]
bboxes = results[idx]
for i in range(bboxes.shape[0]):
data = dict()
data['image_id'] = img_id
data['bbox'] = self.xyxy2xywh(bboxes[i])
data['score'] = float(bboxes[i][4])
data['category_id'] = 1
json_results.append(data)
return json_results
def _det2json(self, results):
json_results = []
for idx in range(len(self)):
img_id = self.img_ids[idx]
result = results[idx]
for label in range(len(result)):
bboxes = result[label]
for i in range(bboxes.shape[0]):
data = dict()
data['image_id'] = img_id
data['bbox'] = self.xyxy2xywh(bboxes[i])
data['score'] = float(bboxes[i][4])
data['category_id'] = self.cat_ids[label]
json_results.append(data)
return json_results
def _segm2json(self, results):
bbox_json_results = []
segm_json_results = []
for idx in range(len(self)):
img_id = self.img_ids[idx]
det, seg = results[idx]
for label in range(len(det)):
# bbox results
bboxes = det[label]
for i in range(bboxes.shape[0]):
data = dict()
data['image_id'] = img_id
data['bbox'] = self.xyxy2xywh(bboxes[i])
data['score'] = float(bboxes[i][4])
data['category_id'] = self.cat_ids[label]
bbox_json_results.append(data)
# segm results
# some detectors use different scores for bbox and mask
if isinstance(seg, tuple):
segms = seg[0][label]
mask_score = seg[1][label]
else:
segms = seg[label]
mask_score = [bbox[4] for bbox in bboxes]
for i in range(bboxes.shape[0]):
data = dict()
data['image_id'] = img_id
data['bbox'] = self.xyxy2xywh(bboxes[i])
data['score'] = float(mask_score[i])
data['category_id'] = self.cat_ids[label]
if isinstance(segms[i]['counts'], bytes):
segms[i]['counts'] = segms[i]['counts'].decode()
data['segmentation'] = segms[i]
segm_json_results.append(data)
return bbox_json_results, segm_json_results
def results2json(self, results, outfile_prefix):
"""Dump the detection results to a json file.
There are 3 types of results: proposals, bbox predictions, mask
predictions, and they have different data types. This method will
automatically recognize the type, and dump them to json files.
Args:
results (list[list | tuple | ndarray]): Testing results of the
dataset.
outfile_prefix (str): The filename prefix of the json files. If the
prefix is "somepath/xxx", the json files will be named
"somepath/xxx.bbox.json", "somepath/xxx.segm.json",
"somepath/xxx.proposal.json".
Returns:
dict[str: str]: Possible keys are "bbox", "segm", "proposal", and
values are corresponding filenames.
"""
result_files = dict()
if isinstance(results[0], list):
json_results = self._det2json(results)
result_files['bbox'] = '{}.{}.json'.format(outfile_prefix, 'bbox')
result_files['proposal'] = '{}.{}.json'.format(
outfile_prefix, 'bbox')
mmcv.dump(json_results, result_files['bbox'])
elif isinstance(results[0], tuple):
json_results = self._segm2json(results)
result_files['bbox'] = '{}.{}.json'.format(outfile_prefix, 'bbox')
result_files['proposal'] = '{}.{}.json'.format(
outfile_prefix, 'bbox')
result_files['segm'] = '{}.{}.json'.format(outfile_prefix, 'segm')
mmcv.dump(json_results[0], result_files['bbox'])
mmcv.dump(json_results[1], result_files['segm'])
elif isinstance(results[0], np.ndarray):
json_results = self._proposal2json(results)
result_files['proposal'] = '{}.{}.json'.format(
outfile_prefix, 'proposal')
mmcv.dump(json_results, result_files['proposal'])
else:
raise TypeError('invalid type of results')
return result_files
def fast_eval_recall(self, results, proposal_nums, iou_thrs, logger=None):
gt_bboxes = []
for i in range(len(self.img_ids)):
ann_ids = self.coco.getAnnIds(imgIds=self.img_ids[i])
ann_info = self.coco.loadAnns(ann_ids)
if len(ann_info) == 0:
gt_bboxes.append(np.zeros((0, 4)))
continue
bboxes = []
for ann in ann_info:
if ann.get('ignore', False) or ann['iscrowd']:
continue
x1, y1, w, h = ann['bbox']
bboxes.append([x1, y1, x1 + w - 1, y1 + h - 1])
bboxes = np.array(bboxes, dtype=np.float32)
if bboxes.shape[0] == 0:
bboxes = np.zeros((0, 4))
gt_bboxes.append(bboxes)
recalls = eval_recalls(
gt_bboxes, results, proposal_nums, iou_thrs, logger=logger)
ar = recalls.mean(axis=1)
return ar
def format_results(self, results, jsonfile_prefix=None, **kwargs):
"""Format the results to json (standard format for COCO evaluation).
Args:
results (list): Testing results of the dataset.
jsonfile_prefix (str | None): The prefix of json files. It includes
the file path and the prefix of filename, e.g., "a/b/prefix".
If not specified, a temp file will be created. Default: None.
Returns:
tuple: (result_files, tmp_dir), result_files is a dict containing
the json filepaths, tmp_dir is the temporal directory created
for saving json files when jsonfile_prefix is not specified.
"""
assert isinstance(results, list), 'results must be a list'
assert len(results) == len(self), (
'The length of results is not equal to the dataset len: {} != {}'.
format(len(results), len(self)))
if jsonfile_prefix is None:
tmp_dir = tempfile.TemporaryDirectory()
jsonfile_prefix = osp.join(tmp_dir.name, 'results')
else:
tmp_dir = None
result_files = self.results2json(results, jsonfile_prefix)
return result_files, tmp_dir
def evaluate(self,
results,
metric='bbox',
logger=None,
jsonfile_prefix=None,
classwise=False,
proposal_nums=(100, 300, 1000),
iou_thrs=np.arange(0.5, 0.96, 0.05)):
"""Evaluation in COCO protocol.
Args:
results (list): Testing results of the dataset.
metric (str | list[str]): Metrics to be evaluated.
logger (logging.Logger | str | None): Logger used for printing
related information during evaluation. Default: None.
jsonfile_prefix (str | None): The prefix of json files. It includes
the file path and the prefix of filename, e.g., "a/b/prefix".
If not specified, a temp file will be created. Default: None.
classwise (bool): Whether to evaluating the AP for each class.
proposal_nums (Sequence[int]): Proposal number used for evaluating
recalls, such as recall@100, recall@1000.
Default: (100, 300, 1000).
iou_thrs (Sequence[float]): IoU threshold used for evaluating
recalls. If set to a list, the average recall of all IoUs will
also be computed. Default: 0.5.
Returns:
dict[str: float]
"""
metrics = metric if isinstance(metric, list) else [metric]
allowed_metrics = ['bbox', 'segm', 'proposal', 'proposal_fast']
for metric in metrics:
if metric not in allowed_metrics:
raise KeyError('metric {} is not supported'.format(metric))
result_files, tmp_dir = self.format_results(results, jsonfile_prefix)
eval_results = {}
cocoGt = self.coco
for metric in metrics:
msg = 'Evaluating {}...'.format(metric)
if logger is None:
msg = '\n' + msg
print_log(msg, logger=logger)
if metric == 'proposal_fast':
ar = self.fast_eval_recall(
results, proposal_nums, iou_thrs, logger='silent')
log_msg = []
for i, num in enumerate(proposal_nums):
eval_results['AR@{}'.format(num)] = ar[i]
log_msg.append('\nAR@{}\t{:.4f}'.format(num, ar[i]))
log_msg = ''.join(log_msg)
print_log(log_msg, logger=logger)
continue
if metric not in result_files:
raise KeyError('{} is not in results'.format(metric))
try:
cocoDt = cocoGt.loadRes(result_files[metric])
except IndexError:
print_log(
'The testing results of the whole dataset is empty.',
logger=logger,
level=logging.ERROR)
break
iou_type = 'bbox' if metric == 'proposal' else metric
cocoEval = COCOeval(cocoGt, cocoDt, iou_type)
cocoEval.params.imgIds = self.img_ids
if metric == 'proposal':
cocoEval.params.useCats = 0
cocoEval.params.maxDets = list(proposal_nums)
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
metric_items = [
'AR@100', 'AR@300', 'AR@1000', 'AR_s@1000', 'AR_m@1000',
'AR_l@1000'
]
for i, item in enumerate(metric_items):
val = float('{:.3f}'.format(cocoEval.stats[i + 6]))
eval_results[item] = val
else:
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
if classwise: # Compute per-category AP
pass # TODO
metric_items = [
'mAP', 'mAP_50', 'mAP_75', 'mAP_s', 'mAP_m', 'mAP_l'
]
for i in range(len(metric_items)):
key = '{}_{}'.format(metric, metric_items[i])
val = float('{:.3f}'.format(cocoEval.stats[i]))
eval_results[key] = val
eval_results['{}_mAP_copypaste'.format(metric)] = (
'{ap[0]:.3f} {ap[1]:.3f} {ap[2]:.3f} {ap[3]:.3f} '
'{ap[4]:.3f} {ap[5]:.3f}').format(ap=cocoEval.stats[:6])
if tmp_dir is not None:
tmp_dir.cleanup()
return eval_results