import json_tricks as json
import numpy as np
from xtcocotools.coco import COCO
from xtcocotools.cocoeval import COCOeval
from mmpose.datasets.builder import DATASETS
from .bottom_up_coco import BottomUpCocoDataset
[文档]@DATASETS.register_module()
class BottomUpCrowdPoseDataset(BottomUpCocoDataset):
"""CrowdPose dataset for bottom-up pose estimation.
The dataset loads raw features and apply specified transforms
to return a dict containing the image tensors and other information.
CrowdPose keypoint indexes::
0: 'left_shoulder',
1: 'right_shoulder',
2: 'left_elbow',
3: 'right_elbow',
4: 'left_wrist',
5: 'right_wrist',
6: 'left_hip',
7: 'right_hip',
8: 'left_knee',
9: 'right_knee',
10: 'left_ankle',
11: 'right_ankle',
12: 'top_head',
13: 'neck'
Args:
ann_file (str): Path to the annotation file.
img_prefix (str): Path to a directory where images are held.
Default: None.
data_cfg (dict): config
pipeline (list[dict | callable]): A sequence of data transforms.
test_mode (bool): Store True when building test or
validation dataset. Default: False.
"""
def __init__(self,
ann_file,
img_prefix,
data_cfg,
pipeline,
test_mode=False):
super(BottomUpCocoDataset, self).__init__(
ann_file, img_prefix, data_cfg, pipeline, test_mode=test_mode)
self.ann_info['flip_index'] = [
1, 0, 3, 2, 5, 4, 7, 6, 9, 8, 11, 10, 12, 13
]
self.ann_info['use_different_joint_weights'] = False
self.ann_info['joint_weights'] = np.array(
[
0.2, 0.2, 0.2, 1.3, 1.5, 0.2, 1.3, 1.5, 0.2, 0.2, 0.5, 0.2,
0.2, 0.5
],
dtype=np.float32).reshape((self.ann_info['num_joints'], 1))
# 'https://github.com/Jeff-sjtu/CrowdPose/blob/master/crowdpose-api/'
# 'PythonAPI/crowdposetools/cocoeval.py#L224'
self.sigmas = np.array([
.79, .79, .72, .72, .62, .62, 1.07, 1.07, .87, .87, .89, .89, .79,
.79
]) / 10.0
self.coco = COCO(ann_file)
cats = [
cat['name'] for cat in self.coco.loadCats(self.coco.getCatIds())
]
self.classes = ['__background__'] + cats
self.num_classes = len(self.classes)
self._class_to_ind = dict(zip(self.classes, range(self.num_classes)))
self._class_to_coco_ind = dict(zip(cats, self.coco.getCatIds()))
self._coco_ind_to_class_ind = dict(
(self._class_to_coco_ind[cls], self._class_to_ind[cls])
for cls in self.classes[1:])
self.img_ids = self.coco.getImgIds()
if not test_mode:
self.img_ids = [
img_id for img_id in self.img_ids
if len(self.coco.getAnnIds(imgIds=img_id, iscrowd=None)) > 0
]
self.num_images = len(self.img_ids)
self.id2name, self.name2id = self._get_mapping_id_name(self.coco.imgs)
self.dataset_name = 'crowdpose'
print(f'=> num_images: {self.num_images}')
def _do_python_keypoint_eval(self, res_file):
"""Keypoint evaluation using COCOAPI."""
stats_names = [
'AP', 'AP .5', 'AP .75', 'AR', 'AR .5', 'AR .75', 'AP(E)', 'AP(M)',
'AP(H)'
]
with open(res_file, 'r') as file:
res_json = json.load(file)
if not res_json:
info_str = list(zip(stats_names, [
0,
] * len(stats_names)))
return info_str
coco_det = self.coco.loadRes(res_file)
coco_eval = COCOeval(
self.coco,
coco_det,
'keypoints_crowd',
self.sigmas,
use_area=False)
coco_eval.params.useSegm = None
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
info_str = list(zip(stats_names, coco_eval.stats))
return info_str