import os
from collections import OrderedDict
import json_tricks as json
import numpy as np
from mmpose.core.evaluation.top_down_eval import (keypoint_nme,
keypoint_pck_accuracy)
from ...builder import DATASETS
from .animal_base_dataset import AnimalBaseDataset
[文档]@DATASETS.register_module()
class AnimalHorse10Dataset(AnimalBaseDataset):
"""AnimalHorse10Dataset for animal pose estimation.
`Pretraining boosts out-of-domain robustness for pose estimation'
WACV'2021. More details can be found in the `paper
<https://arxiv.org/pdf/1909.11229.pdf>`__ .
The dataset loads raw features and apply specified transforms
to return a dict containing the image tensors and other information.
Horse-10 keypoint indexes::
0: 'Nose',
1: 'Eye',
2: 'Nearknee',
3: 'Nearfrontfetlock',
4: 'Nearfrontfoot',
5: 'Offknee',
6: 'Offfrontfetlock',
7: 'Offfrontfoot',
8: 'Shoulder',
9: 'Midshoulder',
10: 'Elbow',
11: 'Girth',
12: 'Wither',
13: 'Nearhindhock',
14: 'Nearhindfetlock',
15: 'Nearhindfoot',
16: 'Hip',
17: 'Stifle',
18: 'Offhindhock',
19: 'Offhindfetlock',
20: 'Offhindfoot',
21: 'Ischium'
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().__init__(
ann_file, img_prefix, data_cfg, pipeline, test_mode=test_mode)
self.ann_info['use_different_joint_weights'] = False
assert self.ann_info['num_joints'] == 22
self.ann_info['joint_weights'] = \
np.ones((self.ann_info['num_joints'], 1), dtype=np.float32)
self.dataset_name = 'horse10'
self.db = self._get_db()
print(f'=> num_images: {self.num_images}')
print(f'=> load {len(self.db)} samples')
def _get_db(self):
"""Load dataset."""
gt_db = []
bbox_id = 0
num_joints = self.ann_info['num_joints']
for img_id in self.img_ids:
ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=False)
objs = self.coco.loadAnns(ann_ids)
for obj in objs:
if max(obj['keypoints']) == 0:
continue
joints_3d = np.zeros((num_joints, 3), dtype=np.float32)
joints_3d_visible = np.zeros((num_joints, 3), dtype=np.float32)
keypoints = np.array(obj['keypoints']).reshape(-1, 3)
joints_3d[:, :2] = keypoints[:, :2]
joints_3d_visible[:, :2] = np.minimum(1, keypoints[:, 2:3])
# use 1.25 padded bbox as input
center, scale = self._xywh2cs(*obj['bbox'][:4], 1.25)
image_file = os.path.join(self.img_prefix,
self.id2name[img_id])
gt_db.append({
'image_file': image_file,
'center': center,
'scale': scale,
'rotation': 0,
'joints_3d': joints_3d,
'joints_3d_visible': joints_3d_visible,
'dataset': self.dataset_name,
'bbox': obj['bbox'],
'bbox_score': 1,
'bbox_id': bbox_id
})
bbox_id = bbox_id + 1
gt_db = sorted(gt_db, key=lambda x: x['bbox_id'])
return gt_db
def _get_normalize_factor(self, gts):
"""Get inter-ocular distance as the normalize factor, measured as the
Euclidean distance between the outer corners of the eyes.
Args:
gts (np.ndarray[N, K, 2]): Groundtruth keypoint location.
Return:
np.ndarray[N, 2]: normalized factor
"""
interocular = np.linalg.norm(
gts[:, 0, :] - gts[:, 1, :], axis=1, keepdims=True)
return np.tile(interocular, [1, 2])
def _report_metric(self, res_file, metrics, pck_thr=0.3):
"""Keypoint evaluation.
Args:
res_file (str): Json file stored prediction results.
metrics (str | list[str]): Metric to be performed.
Options: 'PCK', 'NME'.
pck_thr (float): PCK threshold, default: 0.3.
Returns:
dict: Evaluation results for evaluation metric.
"""
info_str = []
with open(res_file, 'r') as fin:
preds = json.load(fin)
assert len(preds) == len(self.db)
outputs = []
gts = []
masks = []
for pred, item in zip(preds, self.db):
outputs.append(np.array(pred['keypoints'])[:, :-1])
gts.append(np.array(item['joints_3d'])[:, :-1])
masks.append((np.array(item['joints_3d_visible'])[:, 0]) > 0)
outputs = np.array(outputs)
gts = np.array(gts)
masks = np.array(masks)
normalize_factor = self._get_normalize_factor(gts)
if 'PCK' in metrics:
_, pck, _ = keypoint_pck_accuracy(outputs, gts, masks, pck_thr,
normalize_factor)
info_str.append(('PCK', pck))
if 'NME' in metrics:
info_str.append(
('NME', keypoint_nme(outputs, gts, masks, normalize_factor)))
return info_str
[文档] def evaluate(self, outputs, res_folder, metric='PCK', **kwargs):
"""Evaluate horse-10 keypoint results. The pose prediction results will
be saved in `${res_folder}/result_keypoints.json`.
Note:
batch_size: N
num_keypoints: K
heatmap height: H
heatmap width: W
Args:
outputs (list(preds, boxes, image_path, output_heatmap))
:preds (np.ndarray[N,K,3]): The first two dimensions are
coordinates, score is the third dimension of the array.
:boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
, scale[1],area, score]
:image_paths (list[str]): For example, ['Test/source/0.jpg']
:output_heatmap (np.ndarray[N, K, H, W]): model outpus.
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed.
Options: 'PCK', 'NME'.
Returns:
dict: Evaluation results for evaluation metric.
"""
metrics = metric if isinstance(metric, list) else [metric]
allowed_metrics = ['PCK', 'NME']
for metric in metrics:
if metric not in allowed_metrics:
raise KeyError(f'metric {metric} is not supported')
res_file = os.path.join(res_folder, 'result_keypoints.json')
kpts = []
for output in outputs:
preds = output['preds']
boxes = output['boxes']
image_paths = output['image_paths']
bbox_ids = output['bbox_ids']
batch_size = len(image_paths)
for i in range(batch_size):
image_id = self.name2id[image_paths[i][len(self.img_prefix):]]
kpts.append({
'keypoints': preds[i].tolist(),
'center': boxes[i][0:2].tolist(),
'scale': boxes[i][2:4].tolist(),
'area': float(boxes[i][4]),
'score': float(boxes[i][5]),
'image_id': image_id,
'bbox_id': bbox_ids[i]
})
kpts = self._sort_and_unique_bboxes(kpts)
self._write_keypoint_results(kpts, res_file)
info_str = self._report_metric(res_file, metrics)
name_value = OrderedDict(info_str)
return name_value