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from __future__ import annotations
import os
import numpy as np
import torch
import torch.nn as nn
from mmdet.apis import inference_detector, init_detector
from mmpose.apis import inference_top_down_pose_model, init_pose_model, process_mmdet_results
#os.environ["PYOPENGL_PLATFORM"] = "egl"
# project root directory
ROOT_DIR = "./"
VIT_DIR = os.path.join(ROOT_DIR, "vendor/ViTPose")
class ViTPoseModel(object):
MODEL_DICT = {
'ViTPose+-G (multi-task train, COCO)': {
'config': f'{VIT_DIR}/configs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/ViTPose_huge_wholebody_256x192.py',
'model': f'{ROOT_DIR}/_DATA/vitpose_ckpts/vitpose+_huge/wholebody.pth',
},
}
def __init__(self, device: str | torch.device):
self.device = torch.device(device)
self.model_name = 'ViTPose+-G (multi-task train, COCO)'
self.model = self._load_model(self.model_name)
def _load_all_models_once(self) -> None:
for name in self.MODEL_DICT:
self._load_model(name)
def _load_model(self, name: str) -> nn.Module:
dic = self.MODEL_DICT[name]
ckpt_path = dic['model']
model = init_pose_model(dic['config'], ckpt_path, device=self.device)
return model
def set_model(self, name: str) -> None:
if name == self.model_name:
return
self.model_name = name
self.model = self._load_model(name)
def predict_pose(
self,
image: np.ndarray,
det_results: list[np.ndarray],
box_score_threshold: float = 0.5) -> list[dict[str, np.ndarray]]:
image = image[:, :, ::-1] # RGB -> BGR
person_results = process_mmdet_results(det_results, 1)
out, _ = inference_top_down_pose_model(self.model,
image,
person_results=person_results,
bbox_thr=box_score_threshold,
format='xyxy')
return out |