Source code for mmpose.apis.inferencers.mmpose_inferencer
# Copyright (c) OpenMMLab. All rights reserved.
import warnings
from typing import Dict, List, Optional, Sequence, Union
import numpy as np
import torch
from mmengine.config import Config, ConfigDict
from mmengine.infer.infer import ModelType
from mmengine.structures import InstanceData
from rich.progress import track
from .base_mmpose_inferencer import BaseMMPoseInferencer
from .hand3d_inferencer import Hand3DInferencer
from .pose2d_inferencer import Pose2DInferencer
from .pose3d_inferencer import Pose3DInferencer
InstanceList = List[InstanceData]
InputType = Union[str, np.ndarray]
InputsType = Union[InputType, Sequence[InputType]]
PredType = Union[InstanceData, InstanceList]
ImgType = Union[np.ndarray, Sequence[np.ndarray]]
ConfigType = Union[Config, ConfigDict]
ResType = Union[Dict, List[Dict], InstanceData, List[InstanceData]]
[docs]class MMPoseInferencer(BaseMMPoseInferencer):
"""MMPose Inferencer. It's a unified inferencer interface for pose
estimation task, currently including: Pose2D. and it can be used to perform
2D keypoint detection.
Args:
pose2d (str, optional): Pretrained 2D pose estimation algorithm.
It's the path to the config file or the model name defined in
metafile. For example, it could be:
- model alias, e.g. ``'body'``,
- config name, e.g. ``'simcc_res50_8xb64-210e_coco-256x192'``,
- config path
Defaults to ``None``.
pose2d_weights (str, optional): Path to the custom checkpoint file of
the selected pose2d model. If it is not specified and "pose2d" is
a model name of metafile, the weights will be loaded from
metafile. Defaults to None.
device (str, optional): Device to run inference. If None, the
available device will be automatically used. Defaults to None.
scope (str, optional): The scope of the model. Defaults to "mmpose".
det_model(str, optional): Config path or alias of detection model.
Defaults to None.
det_weights(str, optional): Path to the checkpoints of detection
model. Defaults to None.
det_cat_ids(int or list[int], optional): Category id for
detection model. Defaults to None.
output_heatmaps (bool, optional): Flag to visualize predicted
heatmaps. If set to None, the default setting from the model
config will be used. Default is None.
"""
preprocess_kwargs: set = {
'bbox_thr', 'nms_thr', 'bboxes', 'use_oks_tracking', 'tracking_thr',
'disable_norm_pose_2d'
}
forward_kwargs: set = {
'merge_results', 'disable_rebase_keypoint', 'pose_based_nms'
}
visualize_kwargs: set = {
'return_vis', 'show', 'wait_time', 'draw_bbox', 'radius', 'thickness',
'kpt_thr', 'vis_out_dir', 'skeleton_style', 'draw_heatmap',
'black_background', 'num_instances'
}
postprocess_kwargs: set = {'pred_out_dir', 'return_datasample'}
def __init__(self,
pose2d: Optional[str] = None,
pose2d_weights: Optional[str] = None,
pose3d: Optional[str] = None,
pose3d_weights: Optional[str] = None,
device: Optional[str] = None,
scope: str = 'mmpose',
det_model: Optional[Union[ModelType, str]] = None,
det_weights: Optional[str] = None,
det_cat_ids: Optional[Union[int, List]] = None,
show_progress: bool = False) -> None:
self.visualizer = None
self.show_progress = show_progress
if pose3d is not None:
if 'hand3d' in pose3d:
self.inferencer = Hand3DInferencer(pose3d, pose3d_weights,
device, scope, det_model,
det_weights, det_cat_ids,
show_progress)
else:
self.inferencer = Pose3DInferencer(pose3d, pose3d_weights,
pose2d, pose2d_weights,
device, scope, det_model,
det_weights, det_cat_ids,
show_progress)
elif pose2d is not None:
self.inferencer = Pose2DInferencer(pose2d, pose2d_weights, device,
scope, det_model, det_weights,
det_cat_ids, show_progress)
else:
raise ValueError('Either 2d or 3d pose estimation algorithm '
'should be provided.')
[docs] def preprocess(self, inputs: InputsType, batch_size: int = 1, **kwargs):
"""Process the inputs into a model-feedable format.
Args:
inputs (InputsType): Inputs given by user.
batch_size (int): batch size. Defaults to 1.
Yields:
Any: Data processed by the ``pipeline`` and ``collate_fn``.
List[str or np.ndarray]: List of original inputs in the batch
"""
for data in self.inferencer.preprocess(inputs, batch_size, **kwargs):
yield data
[docs] @torch.no_grad()
def forward(self, inputs: InputType, **forward_kwargs) -> PredType:
"""Forward the inputs to the model.
Args:
inputs (InputsType): The inputs to be forwarded.
Returns:
Dict: The prediction results. Possibly with keys "pose2d".
"""
return self.inferencer.forward(inputs, **forward_kwargs)
def __call__(
self,
inputs: InputsType,
return_datasamples: bool = False,
batch_size: int = 1,
out_dir: Optional[str] = None,
**kwargs,
) -> dict:
"""Call the inferencer.
Args:
inputs (InputsType): Inputs for the inferencer.
return_datasamples (bool): Whether to return results as
:obj:`BaseDataElement`. Defaults to False.
batch_size (int): Batch size. Defaults to 1.
out_dir (str, optional): directory to save visualization
results and predictions. Will be overoden if vis_out_dir or
pred_out_dir are given. Defaults to None
**kwargs: Key words arguments passed to :meth:`preprocess`,
:meth:`forward`, :meth:`visualize` and :meth:`postprocess`.
Each key in kwargs should be in the corresponding set of
``preprocess_kwargs``, ``forward_kwargs``,
``visualize_kwargs`` and ``postprocess_kwargs``.
Returns:
dict: Inference and visualization results.
"""
if out_dir is not None:
if 'vis_out_dir' not in kwargs:
kwargs['vis_out_dir'] = f'{out_dir}/visualizations'
if 'pred_out_dir' not in kwargs:
kwargs['pred_out_dir'] = f'{out_dir}/predictions'
kwargs = {
key: value
for key, value in kwargs.items()
if key in set.union(self.inferencer.preprocess_kwargs,
self.inferencer.forward_kwargs,
self.inferencer.visualize_kwargs,
self.inferencer.postprocess_kwargs)
}
(
preprocess_kwargs,
forward_kwargs,
visualize_kwargs,
postprocess_kwargs,
) = self._dispatch_kwargs(**kwargs)
self.inferencer.update_model_visualizer_settings(**kwargs)
# preprocessing
if isinstance(inputs, str) and inputs.startswith('webcam'):
inputs = self.inferencer._get_webcam_inputs(inputs)
batch_size = 1
if not visualize_kwargs.get('show', False):
warnings.warn('The display mode is closed when using webcam '
'input. It will be turned on automatically.')
visualize_kwargs['show'] = True
else:
inputs = self.inferencer._inputs_to_list(inputs)
self._video_input = self.inferencer._video_input
if self._video_input:
self.video_info = self.inferencer.video_info
inputs = self.preprocess(
inputs, batch_size=batch_size, **preprocess_kwargs)
# forward
if 'bbox_thr' in self.inferencer.forward_kwargs:
forward_kwargs['bbox_thr'] = preprocess_kwargs.get('bbox_thr', -1)
preds = []
for proc_inputs, ori_inputs in (track(inputs, description='Inference')
if self.show_progress else inputs):
preds = self.forward(proc_inputs, **forward_kwargs)
visualization = self.visualize(ori_inputs, preds,
**visualize_kwargs)
results = self.postprocess(
preds,
visualization,
return_datasamples=return_datasamples,
**postprocess_kwargs)
yield results
if self._video_input:
self._finalize_video_processing(
postprocess_kwargs.get('pred_out_dir', ''))
[docs] def visualize(self, inputs: InputsType, preds: PredType,
**kwargs) -> List[np.ndarray]:
"""Visualize predictions.
Args:
inputs (list): Inputs preprocessed by :meth:`_inputs_to_list`.
preds (Any): Predictions of the model.
return_vis (bool): Whether to return images with predicted results.
show (bool): Whether to display the image in a popup window.
Defaults to False.
show_interval (int): The interval of show (s). Defaults to 0
radius (int): Keypoint radius for visualization. Defaults to 3
thickness (int): Link thickness for visualization. Defaults to 1
kpt_thr (float): The threshold to visualize the keypoints.
Defaults to 0.3
vis_out_dir (str, optional): directory to save visualization
results w/o predictions. If left as empty, no file will
be saved. Defaults to ''.
Returns:
List[np.ndarray]: Visualization results.
"""
window_name = ''
if self.inferencer._video_input:
window_name = self.inferencer.video_info['name']
return self.inferencer.visualize(
inputs, preds, window_name=window_name, **kwargs)