Source code for mmrotate.datasets.hrsc
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import xml.etree.ElementTree as ET
from collections import OrderedDict
import mmcv
import numpy as np
from mmcv import print_log
from mmdet.datasets import CustomDataset
from PIL import Image
from mmrotate.core import eval_rbbox_map, obb2poly_np, poly2obb_np
from .builder import ROTATED_DATASETS
[docs]@ROTATED_DATASETS.register_module()
class HRSCDataset(CustomDataset):
"""HRSC dataset for detection.
Args:
ann_file (str): Annotation file path.
pipeline (list[dict]): Processing pipeline.
img_subdir (str): Subdir where images are stored. Default: JPEGImages.
ann_subdir (str): Subdir where annotations are. Default: Annotations.
classwise (bool): Whether to use all classes or only ship.
version (str, optional): Angle representations. Defaults to 'oc'.
"""
CLASSES = None
HRSC_CLASS = ('ship', )
HRSC_CLASSES = ('ship', 'aircraft carrier', 'warcraft', 'merchant ship',
'Nimitz', 'Enterprise', 'Arleigh Burke', 'WhidbeyIsland',
'Perry', 'Sanantonio', 'Ticonderoga', 'Kitty Hawk',
'Kuznetsov', 'Abukuma', 'Austen', 'Tarawa', 'Blue Ridge',
'Container', 'OXo|--)', 'Car carrier([]==[])',
'Hovercraft', 'yacht', 'CntShip(_|.--.--|_]=', 'Cruise',
'submarine', 'lute', 'Medical', 'Car carrier(======|',
'Ford-class', 'Midway-class', 'Invincible-class')
HRSC_CLASSES_ID = ('01', '02', '03', '04', '05', '06', '07', '08', '09',
'10', '11', '12', '13', '14', '15', '16', '17', '18',
'19', '20', '22', '24', '25', '26', '27', '28', '29',
'30', '31', '32', '33')
PALETTE = [
(0, 255, 0),
]
CLASSWISE_PALETTE = [(220, 20, 60), (119, 11, 32),
(0, 0, 142), (0, 0, 230), (106, 0, 228), (0, 60, 100),
(0, 80, 100), (0, 0, 70), (0, 0, 192), (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), (72, 0, 118), (255, 179, 240),
(0, 125, 92), (209, 0, 151), (188, 208, 182),
(0, 220, 176), (255, 99, 164), (92, 0, 73)]
def __init__(self,
ann_file,
pipeline,
img_subdir='JPEGImages',
ann_subdir='Annotations',
classwise=False,
version='oc',
**kwargs):
self.img_subdir = img_subdir
self.ann_subdir = ann_subdir
self.classwise = classwise
self.version = version
if self.classwise:
HRSCDataset.PALETTE = HRSCDataset.CLASSWISE_PALETTE
HRSCDataset.CLASSES = self.HRSC_CLASSES
self.catid2label = {
('1' + '0' * 6 + cls_id): i
for i, cls_id in enumerate(self.HRSC_CLASSES_ID)
}
else:
HRSCDataset.CLASSES = self.HRSC_CLASS
# self.cat2label = {cat: i for i, cat in enumerate(self.CLASSES)}
super(HRSCDataset, self).__init__(ann_file, pipeline, **kwargs)
[docs] def load_annotations(self, ann_file):
"""Load annotation from XML style ann_file.
Args:
ann_file (str): Path of Imageset file.
Returns:
list[dict]: Annotation info from XML file.
"""
data_infos = []
img_ids = mmcv.list_from_file(ann_file)
for img_id in img_ids:
data_info = {}
filename = osp.join(self.img_subdir, f'{img_id}.bmp')
data_info['filename'] = f'{img_id}.bmp'
xml_path = osp.join(self.img_prefix, self.ann_subdir,
f'{img_id}.xml')
tree = ET.parse(xml_path)
root = tree.getroot()
width = int(root.find('Img_SizeWidth').text)
height = int(root.find('Img_SizeHeight').text)
if width is None or height is None:
img_path = osp.join(self.img_prefix, filename)
img = Image.open(img_path)
width, height = img.size
data_info['width'] = width
data_info['height'] = height
data_info['ann'] = {}
gt_bboxes = []
gt_labels = []
gt_polygons = []
gt_headers = []
gt_bboxes_ignore = []
gt_labels_ignore = []
gt_polygons_ignore = []
gt_headers_ignore = []
for obj in root.findall('HRSC_Objects/HRSC_Object'):
if self.classwise:
class_id = obj.find('Class_ID').text
label = self.catid2label.get(class_id)
if label is None:
continue
else:
label = 0
# Add an extra score to use obb2poly_np
bbox = np.array([[
float(obj.find('mbox_cx').text),
float(obj.find('mbox_cy').text),
float(obj.find('mbox_w').text),
float(obj.find('mbox_h').text),
float(obj.find('mbox_ang').text), 0
]],
dtype=np.float32)
polygon = obb2poly_np(bbox, 'le90')[0, :-1].astype(np.float32)
if self.version != 'le90':
bbox = np.array(
poly2obb_np(polygon, self.version), dtype=np.float32)
else:
bbox = bbox[0, :-1]
head = np.array([
int(obj.find('header_x').text),
int(obj.find('header_y').text)
],
dtype=np.int64)
gt_bboxes.append(bbox)
gt_labels.append(label)
gt_polygons.append(polygon)
gt_headers.append(head)
if gt_bboxes:
data_info['ann']['bboxes'] = np.array(
gt_bboxes, dtype=np.float32)
data_info['ann']['labels'] = np.array(
gt_labels, dtype=np.int64)
data_info['ann']['polygons'] = np.array(
gt_polygons, dtype=np.float32)
data_info['ann']['headers'] = np.array(
gt_headers, dtype=np.int64)
else:
data_info['ann']['bboxes'] = np.zeros((0, 5), dtype=np.float32)
data_info['ann']['labels'] = np.array([], dtype=np.int64)
data_info['ann']['polygons'] = np.zeros((0, 8),
dtype=np.float32)
data_info['ann']['headers'] = np.zeros((0, 2),
dtype=np.float32)
if gt_polygons_ignore:
data_info['ann']['bboxes_ignore'] = np.array(
gt_bboxes_ignore, dtype=np.float32)
data_info['ann']['labels_ignore'] = np.array(
gt_labels_ignore, dtype=np.int64)
data_info['ann']['polygons_ignore'] = np.array(
gt_polygons_ignore, dtype=np.float32)
data_info['ann']['headers_ignore'] = np.array(
gt_headers_ignore, dtype=np.float32)
else:
data_info['ann']['bboxes_ignore'] = np.zeros((0, 5),
dtype=np.float32)
data_info['ann']['labels_ignore'] = np.array([],
dtype=np.int64)
data_info['ann']['polygons_ignore'] = np.zeros(
(0, 8), dtype=np.float32)
data_info['ann']['headers_ignore'] = np.zeros((0, 2),
dtype=np.float32)
data_infos.append(data_info)
return data_infos
def _filter_imgs(self):
"""Filter images without ground truths."""
valid_inds = []
for i, data_info in enumerate(self.data_infos):
if (not self.filter_empty_gt
or data_info['ann']['labels'].size > 0):
valid_inds.append(i)
return valid_inds
[docs] def evaluate(
self,
results,
metric='mAP',
logger=None,
proposal_nums=(100, 300, 1000),
iou_thr=[0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95],
scale_ranges=None,
use_07_metric=True,
nproc=4):
"""Evaluate the dataset.
Args:
results (list): Testing results of the dataset.
metric (str | list[str]): Metrics to be evaluated.
logger (logging.Logger | None | str): Logger used for printing
related information during evaluation. Default: None.
proposal_nums (Sequence[int]): Proposal number used for evaluating
recalls, such as recall@100, recall@1000.
Default: (100, 300, 1000).
iou_thr (float | list[float]): IoU threshold. It must be a float
when evaluating mAP, and can be a list when evaluating recall.
Default: 0.5.
scale_ranges (list[tuple] | None): Scale ranges for evaluating mAP.
Default: None.
use_07_metric (bool): Whether to use the voc07 metric.
nproc (int): Processes used for computing TP and FP.
Default: 4.
"""
if not isinstance(metric, str):
assert len(metric) == 1
metric = metric[0]
allowed_metrics = ['mAP', 'recall']
if metric not in allowed_metrics:
raise KeyError(f'metric {metric} is not supported')
annotations = [self.get_ann_info(i) for i in range(len(self))]
eval_results = OrderedDict()
iou_thrs = [iou_thr] if isinstance(iou_thr, float) else iou_thr
if metric == 'mAP':
assert isinstance(iou_thrs, list)
mean_aps = []
for iou_thr in iou_thrs:
print_log(f'\n{"-" * 15}iou_thr: {iou_thr}{"-" * 15}')
mean_ap, _ = eval_rbbox_map(
results,
annotations,
scale_ranges=scale_ranges,
iou_thr=iou_thr,
use_07_metric=use_07_metric,
dataset=self.CLASSES,
logger=logger,
nproc=nproc)
mean_aps.append(mean_ap)
eval_results[f'AP{int(iou_thr * 100):02d}'] = round(mean_ap, 3)
eval_results['mAP'] = sum(mean_aps) / len(mean_aps)
eval_results.move_to_end('mAP', last=False)
elif metric == 'recall':
raise NotImplementedError
return eval_results