inferencer_demo.py

# Copyright (c) OpenMMLab. All rights reserved.
from argparse import ArgumentParser
from typing import Dict

import sys
from mmpose.apis.inferencers import MMPoseInferencer, get_model_aliases


filter_args = dict(bbox_thr=0.3, nms_thr=0.3, pose_based_nms=False)
POSE2D_SPECIFIC_ARGS = dict(
   yoloxpose=dict(bbox_thr=0.01, nms_thr=0.65, pose_based_nms=True),
   rtmo=dict(bbox_thr=0.1, nms_thr=0.65, pose_based_nms=True),
)




def parse_args():
   parser = ArgumentParser()
   parser.add_argument(
       'inputs',
       type=str,
       nargs='?',
       help='Input image/video path or folder path.')


   # init args
   parser.add_argument(
       '--pose2d',
       type=str,
       default="wholebody",
       help='Pretrained 2D pose estimation algorithm. It\'s the path to the '
       'config file or the model name defined in metafile.')
   parser.add_argument(
       '--pose2d-weights',
       type=str,
       default=None,
       help='Path to the custom checkpoint file of the selected pose model. '
       'If it is not specified and "pose2d" is a model name of metafile, '
       'the weights will be loaded from metafile.')
   parser.add_argument(
       '--pose3d',
       type=str,
       default=None,
       help='Pretrained 3D pose estimation algorithm. It\'s the path to the '
       'config file or the model name defined in metafile.')
   parser.add_argument(
       '--pose3d-weights',
       type=str,
       default=None,
       help='Path to the custom checkpoint file of the selected pose model. '
       'If it is not specified and "pose3d" is a model name of metafile, '
       'the weights will be loaded from metafile.')
   parser.add_argument(
       '--det-model',
       type=str,
       default=None,
       help='Config path or alias of detection model.')
   parser.add_argument(
       '--det-weights',
       type=str,
       default=None,
       help='Path to the checkpoints of detection model.')
   parser.add_argument(
       '--det-cat-ids',
       type=int,
       nargs='+',
       default=0,
       help='Category id for detection model.')
   parser.add_argument(
       '--scope',
       type=str,
       default='mmpose',
       help='Scope where modules are defined.')
   parser.add_argument(
       '--device',
       type=str,
       default=None,
       help='Device used for inference. '
       'If not specified, the available device will be automatically used.')
   parser.add_argument(
       '--show-progress',
       action='store_true',
       help='Display the progress bar during inference.')


   # The default arguments for prediction filtering differ for top-down
   # and bottom-up models. We assign the default arguments according to the
   # selected pose2d model
   args, _ = parser.parse_known_args()
   for model in POSE2D_SPECIFIC_ARGS:
       if model in args.pose2d:
           filter_args.update(POSE2D_SPECIFIC_ARGS[model])
           break


   # call args
   parser.add_argument(
       '--show',
       action='store_true',
       help='Display the image/video in a popup window.')
   parser.add_argument(
       '--draw-bbox',
       action='store_true',
       help='Whether to draw the bounding boxes.')
   parser.add_argument(
       '--draw-heatmap',
       action='store_true',
       default=False,
       help='Whether to draw the predicted heatmaps.')
   parser.add_argument(
       '--bbox-thr',
       type=float,
       default=filter_args['bbox_thr'],
       help='Bounding box score threshold')
   parser.add_argument(
       '--nms-thr',
       type=float,
       default=filter_args['nms_thr'],
       help='IoU threshold for bounding box NMS')
   parser.add_argument(
       '--pose-based-nms',
       type=lambda arg: arg.lower() in ('true', 'yes', 't', 'y', '1'),
       default=filter_args['pose_based_nms'],
       help='Whether to use pose-based NMS')
   parser.add_argument(
       '--kpt-thr', type=float, default=0.3, help='Keypoint score threshold')
   parser.add_argument(
       '--tracking-thr', type=float, default=0.3, help='Tracking threshold')
   parser.add_argument(
       '--use-oks-tracking',
       action='store_true',
       help='Whether to use OKS as similarity in tracking')
   parser.add_argument(
       '--disable-norm-pose-2d',
       action='store_true',
       help='Whether to scale the bbox (along with the 2D pose) to the '
       'average bbox scale of the dataset, and move the bbox (along with the '
       '2D pose) to the average bbox center of the dataset. This is useful '
       'when bbox is small, especially in multi-person scenarios.')
   parser.add_argument(
       '--disable-rebase-keypoint',
       action='store_true',
       default=False,
       help='Whether to disable rebasing the predicted 3D pose so its '
       'lowest keypoint has a height of 0 (landing on the ground). Rebase '
       'is useful for visualization when the model do not predict the '
       'global position of the 3D pose.')
   parser.add_argument(
       '--num-instances',
       type=int,
       default=1,
       help='The number of 3D poses to be visualized in every frame. If '
       'less than 0, it will be set to the number of pose results in the '
       'first frame.')
   parser.add_argument(
       '--radius',
       type=int,
       default=3,
       help='Keypoint radius for visualization.')
   parser.add_argument(
       '--thickness',
       type=int,
       default=1,
       help='Link thickness for visualization.')
   parser.add_argument(
       '--skeleton-style',
       default='mmpose',
       type=str,
       choices=['mmpose', 'openpose'],
       help='Skeleton style selection')
   parser.add_argument(
       '--black-background',
       action='store_true',
       help='Plot predictions on a black image')
   parser.add_argument(
       '--vis-out-dir',
       type=str,
       default='', #'tmp/nouse/',
       help='Directory for saving visualized results.')
   parser.add_argument(
       '--pred-out-dir',
       type=str,
       default='tmp/',
       help='Directory for saving inference results.')
   parser.add_argument(
       '--show-alias',
       action='store_true',
       help='Display all the available model aliases.')


   call_args = vars(parser.parse_args())


   init_kws = [
       'pose2d', 'pose2d_weights', 'scope', 'device', 'det_model',
       'det_weights', 'det_cat_ids', 'pose3d', 'pose3d_weights',
       'show_progress'
   ]
   init_args = {}
   for init_kw in init_kws:
       init_args[init_kw] = call_args.pop(init_kw)


   display_alias = call_args.pop('show_alias')


   return init_args, call_args, display_alias




def display_model_aliases(model_aliases: Dict[str, str]) -> None:
   """Display the available model aliases and their corresponding model
   names."""
   aliases = list(model_aliases.keys())
   max_alias_length = max(map(len, aliases))
   print(f'{"ALIAS".ljust(max_alias_length+2)}MODEL_NAME')
   for alias in sorted(aliases):
       print(f'{alias.ljust(max_alias_length+2)}{model_aliases[alias]}')




def main():
   init_args, call_args, display_alias = parse_args()
   if display_alias:
       model_alises = get_model_aliases(init_args['scope'])
       display_model_aliases(model_alises)
   else:
       inferencer = MMPoseInferencer(**init_args)
       for _ in inferencer(**call_args):
           pass




if __name__ == '__main__':
   main()