def load_annotations(self, ann_file):
"""Load annotation from COCO style annotation file.
Args:
ann_file (str): Path of annotation file.
Returns:
list[dict]: Annotation info from COCO api.
"""
self.coco = COCO(ann_file)
self.cat_ids = self.coco.get_cat_ids(cat_names=self.CLASSES)
self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
self.img_ids = self.coco.get_img_ids()
data_infos = []
count = 0
for i in self.img_ids:
info = self.coco.load_imgs([i])[0]
info['filename'] = info['file_name']
data_infos.append(info)
count = count + 1
if count > self.select_first_k and self.select_first_k > 0:
break
return data_infos
def load_annotations(self, ann_file):
"""Load annotation from COCO style annotation file.
Args:
ann_file (str): Path of annotation file.
Returns:
list[dict]: Annotation info from COCO api.
"""
if self.ann_file_backend == 'disk':
self.coco = COCO(ann_file)
else:
mmcv_version = digit_version(mmcv.__version__)
if mmcv_version < digit_version('1.3.16'):
raise Exception('Please update mmcv to 1.3.16 or higher '
'to enable "get_local_path" of "FileClient".')
file_client = mmcv.FileClient(backend=self.ann_file_backend)
with file_client.get_local_path(ann_file) as local_path:
self.coco = COCO(local_path)
self.cat_ids = self.coco.get_cat_ids(cat_names=self.CLASSES)
self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
self.img_ids = self.coco.get_img_ids()
data_infos = []
count = 0
for i in self.img_ids:
info = self.coco.load_imgs([i])[0]
info['filename'] = info['file_name']
data_infos.append(info)
count = count + 1
if count > self.select_first_k and self.select_first_k > 0:
break
return data_infos
def load_lvis_anns(self, ann_file):
"""Load annotation from COCO style annotation file.
Args:
ann_file (str): Path of annotation file.
Returns:
list[dict]: Annotation info from COCO api.
"""
self.coco = COCO(ann_file)
self.cat_ids = self.coco.get_cat_ids(cat_names=self.CLASSES)
self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
self.img_ids = self.coco.get_img_ids()
data_infos = []
for i in self.img_ids:
info = self.coco.load_imgs([i])[0]
info['filename'] = info['file_name']
if info['file_name'].startswith('COCO'):
# Convert form the COCO 2014 file naming convention of
# COCO_[train/val/test]2014_000000000000.jpg to the 2017
# naming convention of 000000000000.jpg
# (LVIS v1 will fix this naming issue)
info['filename'] = info['file_name'][-16:]
else:
info['filename'] = info['file_name']
data_infos.append(info)
return data_infos
def load_image_anns(self, ann_file):
"""Load annotations from COCO style annotation file.
Args:
ann_file (str): Path of annotation file.
Returns:
list[dict]: Annotation information from COCO api.
"""
self.coco = COCO(ann_file)
self.cat_ids = self.coco.get_cat_ids(cat_names=self.CLASSES)
self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
all_img_ids = self.coco.get_img_ids()
self.img_ids = []
data_infos = []
for img_id in all_img_ids:
info = self.coco.load_imgs([img_id])[0]
info['filename'] = info['file_name']
if info['is_vid_train_frame']:
self.img_ids.append(img_id)
data_infos.append(info)
return data_infos
class IcdarDataset(CocoDataset):
CLASSES = ('text')
def __init__(self,
ann_file,
pipeline,
classes=None,
data_root=None,
img_prefix='',
seg_prefix=None,
proposal_file=None,
test_mode=False,
filter_empty_gt=True,
select_first_k=-1):
# select first k images for fast debugging.
self.select_first_k = select_first_k
super().__init__(ann_file, pipeline, classes, data_root, img_prefix,
seg_prefix, proposal_file, test_mode, filter_empty_gt)
def load_annotations(self, ann_file):
"""Load annotation from COCO style annotation file.
Args:
ann_file (str): Path of annotation file.
Returns:
list[dict]: Annotation info from COCO api.
"""
self.coco = COCO(ann_file)
self.cat_ids = self.coco.get_cat_ids(cat_names=self.CLASSES)
self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
self.img_ids = self.coco.get_img_ids()
data_infos = []
count = 0
for i in self.img_ids:
info = self.coco.load_imgs([i])[0]
info['filename'] = info['file_name']
data_infos.append(info)
count = count + 1
if count > self.select_first_k and self.select_first_k > 0:
break
return data_infos
def _parse_ann_info(self, img_info, ann_info):
"""Parse bbox and mask annotation.
Args:
ann_info (list[dict]): Annotation info of an image.
Returns:
dict: A dict containing the following keys: bboxes, bboxes_ignore,
labels, masks, masks_ignore, seg_map. "masks" and
"masks_ignore" are represented by polygon boundary
point sequences.
"""
gt_bboxes = []
gt_labels = []
gt_bboxes_ignore = []
gt_masks_ignore = []
gt_masks_ann = []
for ann in 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
if ann['category_id'] not in self.cat_ids:
continue
bbox = [x1, y1, x1 + w, y1 + h]
if ann.get('iscrowd', False):
gt_bboxes_ignore.append(bbox)
gt_masks_ignore.append(ann.get(
'segmentation', None)) # to float32 for latter processing
else:
gt_bboxes.append(bbox)
gt_labels.append(self.cat2label[ann['category_id']])
gt_masks_ann.append(ann.get('segmentation', None))
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_ignore=gt_masks_ignore,
masks=gt_masks_ann,
seg_map=seg_map)
return ann
def evaluate(self,
results,
metric='hmean-iou',
logger=None,
score_thr=0.3,
rank_list=None,
**kwargs):
"""Evaluate the hmean metric.
Args:
results (list[dict]): 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.
rank_list (str): json file used to save eval result
of each image after ranking.
Returns:
dict[dict[str: float]]: The evaluation results.
"""
assert utils.is_type_list(results, dict)
metrics = metric if isinstance(metric, list) else [metric]
allowed_metrics = ['hmean-iou', 'hmean-ic13']
metrics = set(metrics) & set(allowed_metrics)
img_infos = []
ann_infos = []
for i in range(len(self)):
img_info = {'filename': self.data_infos[i]['file_name']}
img_infos.append(img_info)
ann_infos.append(self.get_ann_info(i))
eval_results = eval_hmean(results,
img_infos,
ann_infos,
metrics=metrics,
score_thr=score_thr,
logger=logger,
rank_list=rank_list)
return eval_results
class IcdarDataset(CocoDataset):
"""Dataset for text detection while ann_file in coco format.
Args:
ann_file_backend (str): Storage backend for annotation file,
should be one in ['disk', 'petrel', 'http']. Default to 'disk'.
"""
CLASSES = ('text')
def __init__(self,
ann_file,
pipeline,
classes=None,
data_root=None,
img_prefix='',
seg_prefix=None,
proposal_file=None,
test_mode=False,
filter_empty_gt=True,
select_first_k=-1,
ann_file_backend='disk'):
# select first k images for fast debugging.
self.select_first_k = select_first_k
assert ann_file_backend in ['disk', 'petrel', 'http']
self.ann_file_backend = ann_file_backend
super().__init__(ann_file, pipeline, classes, data_root, img_prefix,
seg_prefix, proposal_file, test_mode, filter_empty_gt)
# Set dummy flags just to be compatible with MMDet
self.flag = np.zeros(len(self), dtype=np.uint8)
def load_annotations(self, ann_file):
"""Load annotation from COCO style annotation file.
Args:
ann_file (str): Path of annotation file.
Returns:
list[dict]: Annotation info from COCO api.
"""
if self.ann_file_backend == 'disk':
self.coco = COCO(ann_file)
else:
mmcv_version = digit_version(mmcv.__version__)
if mmcv_version < digit_version('1.3.16'):
raise Exception('Please update mmcv to 1.3.16 or higher '
'to enable "get_local_path" of "FileClient".')
file_client = mmcv.FileClient(backend=self.ann_file_backend)
with file_client.get_local_path(ann_file) as local_path:
self.coco = COCO(local_path)
self.cat_ids = self.coco.get_cat_ids(cat_names=self.CLASSES)
self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
self.img_ids = self.coco.get_img_ids()
data_infos = []
count = 0
for i in self.img_ids:
info = self.coco.load_imgs([i])[0]
info['filename'] = info['file_name']
data_infos.append(info)
count = count + 1
if count > self.select_first_k and self.select_first_k > 0:
break
return data_infos
def _parse_ann_info(self, img_info, ann_info):
"""Parse bbox and mask annotation.
Args:
ann_info (list[dict]): Annotation info of an image.
Returns:
dict: A dict containing the following keys: bboxes, bboxes_ignore,
labels, masks, masks_ignore, seg_map. "masks" and
"masks_ignore" are represented by polygon boundary
point sequences.
"""
gt_bboxes = []
gt_labels = []
gt_bboxes_ignore = []
gt_masks_ignore = []
gt_masks_ann = []
for ann in 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
if ann['category_id'] not in self.cat_ids:
continue
bbox = [x1, y1, x1 + w, y1 + h]
if ann.get('iscrowd', False):
gt_bboxes_ignore.append(bbox)
gt_masks_ignore.append(ann.get(
'segmentation', None)) # to float32 for latter processing
else:
gt_bboxes.append(bbox)
gt_labels.append(self.cat2label[ann['category_id']])
gt_masks_ann.append(ann.get('segmentation', None))
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_ignore=gt_masks_ignore,
masks=gt_masks_ann,
seg_map=seg_map)
return ann
def evaluate(self,
results,
metric='hmean-iou',
logger=None,
score_thr=None,
min_score_thr=0.3,
max_score_thr=0.9,
step=0.1,
rank_list=None,
**kwargs):
"""Evaluate the hmean metric.
Args:
results (list[dict]): 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.
score_thr (float): Deprecated. Please use min_score_thr instead.
min_score_thr (float): Minimum score threshold of prediction map.
max_score_thr (float): Maximum score threshold of prediction map.
step (float): The spacing between score thresholds.
rank_list (str): json file used to save eval result
of each image after ranking.
Returns:
dict[dict[str: float]]: The evaluation results.
"""
assert utils.is_type_list(results, dict)
metrics = metric if isinstance(metric, list) else [metric]
allowed_metrics = ['hmean-iou', 'hmean-ic13']
metrics = set(metrics) & set(allowed_metrics)
img_infos = []
ann_infos = []
for i in range(len(self)):
img_info = {'filename': self.data_infos[i]['file_name']}
img_infos.append(img_info)
ann_infos.append(self.get_ann_info(i))
eval_results = eval_hmean(results,
img_infos,
ann_infos,
metrics=metrics,
score_thr=score_thr,
min_score_thr=min_score_thr,
max_score_thr=max_score_thr,
step=step,
logger=logger,
rank_list=rank_list)
return eval_results