Source code for mmpose.datasets.datasets.hand.interhand2d_dataset
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
import warnings
from collections import OrderedDict
import json_tricks as json
import numpy as np
from mmcv import Config, deprecated_api_warning
from mmpose.datasets.builder import DATASETS
from ..base import Kpt2dSviewRgbImgTopDownDataset
[docs]@DATASETS.register_module()
class InterHand2DDataset(Kpt2dSviewRgbImgTopDownDataset):
"""InterHand2.6M 2D dataset for top-down hand pose estimation.
"InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose
Estimation from a Single RGB Image", ECCV'2020.
More details can be found in the `paper
<https://arxiv.org/pdf/2008.09309.pdf>`__ .
The dataset loads raw features and apply specified transforms
to return a dict containing the image tensors and other information.
InterHand2.6M keypoint indexes::
0: 'thumb4',
1: 'thumb3',
2: 'thumb2',
3: 'thumb1',
4: 'forefinger4',
5: 'forefinger3',
6: 'forefinger2',
7: 'forefinger1',
8: 'middle_finger4',
9: 'middle_finger3',
10: 'middle_finger2',
11: 'middle_finger1',
12: 'ring_finger4',
13: 'ring_finger3',
14: 'ring_finger2',
15: 'ring_finger1',
16: 'pinky_finger4',
17: 'pinky_finger3',
18: 'pinky_finger2',
19: 'pinky_finger1',
20: 'wrist'
Args:
ann_file (str): Path to the annotation file.
camera_file (str): Path to the camera file.
joint_file (str): Path to the joint 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 (str): Store True when building test or
validation dataset. Default: False.
"""
def __init__(self,
ann_file,
camera_file,
joint_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/interhand2d.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.camera_file = camera_file
self.joint_file = joint_file
self.db = self._get_db()
print(f'=> num_images: {self.num_images}')
print(f'=> load {len(self.db)} samples')
@staticmethod
def _cam2pixel(cam_coord, f, c):
"""Transform the joints from their camera coordinates to their pixel
coordinates.
Note:
- N: number of joints
Args:
cam_coord (ndarray[N, 3]): 3D joints coordinates
in the camera coordinate system
f (ndarray[2]): focal length of x and y axis
c (ndarray[2]): principal point of x and y axis
Returns:
img_coord (ndarray[N, 3]): the coordinates (x, y, 0)
in the image plane.
"""
x = cam_coord[:, 0] / (cam_coord[:, 2] + 1e-8) * f[0] + c[0]
y = cam_coord[:, 1] / (cam_coord[:, 2] + 1e-8) * f[1] + c[1]
z = np.zeros_like(x)
img_coord = np.concatenate((x[:, None], y[:, None], z[:, None]), 1)
return img_coord
@staticmethod
def _world2cam(world_coord, R, T):
"""Transform the joints from their world coordinates to their camera
coordinates.
Note:
- N: number of joints
Args:
world_coord (ndarray[3, N]): 3D joints coordinates
in the world coordinate system
R (ndarray[3, 3]): camera rotation matrix
T (ndarray[3]): camera position (x, y, z)
Returns:
cam_coord (ndarray[3, N]): 3D joints coordinates
in the camera coordinate system
"""
cam_coord = np.dot(R, world_coord - T)
return cam_coord
def _get_db(self):
"""Load dataset.
Adapted from 'https://github.com/facebookresearch/InterHand2.6M/'
'blob/master/data/InterHand2.6M/dataset.py'
Copyright (c) FaceBook Research, under CC-BY-NC 4.0 license.
"""
with open(self.camera_file, 'r') as f:
cameras = json.load(f)
with open(self.joint_file, 'r') as f:
joints = json.load(f)
gt_db = []
bbox_id = 0
for img_id in self.img_ids:
num_joints = self.ann_info['num_joints']
ann_id = self.coco.getAnnIds(imgIds=img_id, iscrowd=False)
ann = self.coco.loadAnns(ann_id)[0]
img = self.coco.loadImgs(img_id)[0]
capture_id = str(img['capture'])
camera_name = img['camera']
frame_idx = str(img['frame_idx'])
image_file = osp.join(self.img_prefix, self.id2name[img_id])
camera_pos, camera_rot = np.array(
cameras[capture_id]['campos'][camera_name],
dtype=np.float32), np.array(
cameras[capture_id]['camrot'][camera_name],
dtype=np.float32)
focal, principal_pt = np.array(
cameras[capture_id]['focal'][camera_name],
dtype=np.float32), np.array(
cameras[capture_id]['princpt'][camera_name],
dtype=np.float32)
joint_world = np.array(
joints[capture_id][frame_idx]['world_coord'], dtype=np.float32)
joint_cam = self._world2cam(
joint_world.transpose(1, 0), camera_rot,
camera_pos.reshape(3, 1)).transpose(1, 0)
joint_img = self._cam2pixel(joint_cam, focal, principal_pt)[:, :2]
joint_img = joint_img.reshape(2, -1, 2)
joint_valid = np.array(
ann['joint_valid'], dtype=np.float32).reshape(2, -1)
# if root is not valid -> root-relative 3D pose is also not valid.
# Therefore, mark all joints as invalid
for hand in range(2):
joint_valid[hand, :] *= joint_valid[hand][-1]
if np.sum(joint_valid[hand, :]) > 11:
joints_3d = np.zeros((num_joints, 3), dtype=np.float32)
joints_3d_visible = np.zeros((num_joints, 3),
dtype=np.float32)
joints_3d[:, :2] = joint_img[hand, :, :]
joints_3d_visible[:, :2] = np.minimum(
1, joint_valid[hand, :].reshape(-1, 1))
# use the tightest bbox enclosing all keypoints as bbox
bbox = [img['width'], img['height'], 0, 0]
for i in range(num_joints):
if joints_3d_visible[i][0]:
bbox[0] = min(bbox[0], joints_3d[i][0])
bbox[1] = min(bbox[1], joints_3d[i][1])
bbox[2] = max(bbox[2], joints_3d[i][0])
bbox[3] = max(bbox[3], joints_3d[i][1])
bbox[2] -= bbox[0]
bbox[3] -= bbox[1]
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 interhand2d 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, ['Capture12/\
0390_dh_touchROM/cam410209/image62434.jpg']
- output_heatmap (np.ndarray[N, K, H, W]): model outputs.
res_folder (str, optional): The folder to save the testing
results. If not specified, a temp folder will be created.
Default: None.
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