Source code for mmpose.datasets.datasets.animal.animal_fly_dataset
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
import warnings
from collections import OrderedDict
import numpy as np
from mmcv import Config, deprecated_api_warning
from ...builder import DATASETS
from ..base import Kpt2dSviewRgbImgTopDownDataset
[docs]@DATASETS.register_module()
class AnimalFlyDataset(Kpt2dSviewRgbImgTopDownDataset):
"""AnimalFlyDataset for animal pose estimation.
"Fast animal pose estimation using deep neural networks"
Nature methods'2019. More details can be found in the `paper
<https://www.biorxiv.org/content/biorxiv/\
early/2018/05/25/331181.full.pdf>`__ .
The dataset loads raw features and apply specified transforms
to return a dict containing the image tensors and other information.
Vinegar Fly keypoint indexes::
0: "head",
1: "eyeL",
2: "eyeR",
3: "neck",
4: "thorax",
5: "abdomen",
6: "forelegR1",
7: "forelegR2",
8: "forelegR3",
9: "forelegR4",
10: "midlegR1",
11: "midlegR2",
12: "midlegR3",
13: "midlegR4",
14: "hindlegR1",
15: "hindlegR2",
16: "hindlegR3",
17: "hindlegR4",
18: "forelegL1",
19: "forelegL2",
20: "forelegL3",
21: "forelegL4",
22: "midlegL1",
23: "midlegL2",
24: "midlegL3",
25: "midlegL4",
26: "hindlegL1",
27: "hindlegL2",
28: "hindlegL3",
29: "hindlegL4",
30: "wingL",
31: "wingR"
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.
dataset_info (DatasetInfo): A class containing all dataset info.
test_mode (bool): Store True when building test or
validation dataset. Default: False.
"""
def __init__(self,
ann_file,
img_prefix,
data_cfg,
pipeline,
dataset_info=None,
test_mode=False):
if dataset_info is None:
warnings.warn(
'dataset_info is missing. '
'Check https://github.com/open-mmlab/mmpose/pull/663 '
'for details.', DeprecationWarning)
cfg = Config.fromfile('configs/_base_/datasets/fly.py')
dataset_info = cfg._cfg_dict['dataset_info']
super().__init__(
ann_file,
img_prefix,
data_cfg,
pipeline,
dataset_info=dataset_info,
test_mode=test_mode)
self.ann_info['use_different_joint_weights'] = False
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 the entire image which is 192x192
bbox = np.array([0, 0, 192, 192], dtype=np.float32)
image_file = osp.join(self.img_prefix, self.id2name[img_id])
gt_db.append({
'image_file': image_file,
'rotation': 0,
'joints_3d': joints_3d,
'joints_3d_visible': joints_3d_visible,
'dataset': self.dataset_name,
'bbox': 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
[docs] @deprecated_api_warning(name_dict=dict(outputs='results'))
def evaluate(self, results, res_folder=None, metric='PCK', **kwargs):
"""Evaluate Fly 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:
results (list[dict]): Testing results containing the following
items:
- 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 outputs.
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed.
Options: 'PCK', 'AUC', 'EPE'.
Returns:
dict: Evaluation results for evaluation metric.
"""
metrics = metric if isinstance(metric, list) else [metric]
allowed_metrics = ['PCK', 'AUC', 'EPE']
for metric in metrics:
if metric not in allowed_metrics:
raise KeyError(f'metric {metric} is not supported')
if res_folder is not None:
tmp_folder = None
res_file = osp.join(res_folder, 'result_keypoints.json')
else:
tmp_folder = tempfile.TemporaryDirectory()
res_file = osp.join(tmp_folder.name, 'result_keypoints.json')
kpts = []
for result in results:
preds = result['preds']
boxes = result['boxes']
image_paths = result['image_paths']
bbox_ids = result['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)
if tmp_folder is not None:
tmp_folder.cleanup()
return name_value