|
|
| import os
|
| import numpy as np
|
| import warnings
|
|
|
| try:
|
| import mmcv
|
| except ImportError:
|
| warnings.warn(
|
| "The module 'mmcv' is not installed. The package will have limited functionality. Please install it using the command: mim install 'mmcv>=2.0.1'"
|
| )
|
|
|
| try:
|
| from mmpose.apis import inference_topdown
|
| from mmpose.apis import init_model as init_pose_estimator
|
| from mmpose.evaluation.functional import nms
|
| from mmpose.utils import adapt_mmdet_pipeline
|
| from mmpose.structures import merge_data_samples
|
| except ImportError:
|
| warnings.warn(
|
| "The module 'mmpose' is not installed. The package will have limited functionality. Please install it using the command: mim install 'mmpose>=1.1.0'"
|
| )
|
|
|
| try:
|
| from mmdet.apis import inference_detector, init_detector
|
| except ImportError:
|
| warnings.warn(
|
| "The module 'mmdet' is not installed. The package will have limited functionality. Please install it using the command: mim install 'mmdet>=3.1.0'"
|
| )
|
|
|
|
|
| class Wholebody:
|
| def __init__(self,
|
| det_config=None, det_ckpt=None,
|
| pose_config=None, pose_ckpt=None,
|
| device="cpu"):
|
|
|
| if det_config is None:
|
| det_config = os.path.join(os.path.dirname(__file__), "yolox_config/yolox_l_8xb8-300e_coco.py")
|
|
|
| if pose_config is None:
|
| pose_config = os.path.join(os.path.dirname(__file__), "dwpose_config/dwpose-l_384x288.py")
|
|
|
| if det_ckpt is None:
|
| det_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_l_8x8_300e_coco/yolox_l_8x8_300e_coco_20211126_140236-d3bd2b23.pth'
|
|
|
| if pose_ckpt is None:
|
| pose_ckpt = "https://huggingface.co/wanghaofan/dw-ll_ucoco_384/resolve/main/dw-ll_ucoco_384.pth"
|
|
|
|
|
| self.detector = init_detector(det_config, det_ckpt, device=device)
|
| self.detector.cfg = adapt_mmdet_pipeline(self.detector.cfg)
|
|
|
|
|
| self.pose_estimator = init_pose_estimator(
|
| pose_config,
|
| pose_ckpt,
|
| device=device)
|
|
|
| def to(self, device):
|
| self.detector.to(device)
|
| self.pose_estimator.to(device)
|
| return self
|
|
|
| def __call__(self, oriImg):
|
|
|
| det_result = inference_detector(self.detector, oriImg)
|
| pred_instance = det_result.pred_instances.cpu().numpy()
|
| bboxes = np.concatenate(
|
| (pred_instance.bboxes, pred_instance.scores[:, None]), axis=1)
|
| bboxes = bboxes[np.logical_and(pred_instance.labels == 0,
|
| pred_instance.scores > 0.5)]
|
|
|
|
|
| bboxes = bboxes[nms(bboxes, 0.7), :4]
|
|
|
|
|
| if len(bboxes) == 0:
|
| pose_results = inference_topdown(self.pose_estimator, oriImg)
|
| else:
|
| pose_results = inference_topdown(self.pose_estimator, oriImg, bboxes)
|
| preds = merge_data_samples(pose_results)
|
| preds = preds.pred_instances
|
|
|
|
|
| keypoints = preds.get('transformed_keypoints',
|
| preds.keypoints)
|
| if 'keypoint_scores' in preds:
|
| scores = preds.keypoint_scores
|
| else:
|
| scores = np.ones(keypoints.shape[:-1])
|
|
|
| if 'keypoints_visible' in preds:
|
| visible = preds.keypoints_visible
|
| else:
|
| visible = np.ones(keypoints.shape[:-1])
|
| keypoints_info = np.concatenate(
|
| (keypoints, scores[..., None], visible[..., None]),
|
| axis=-1)
|
|
|
| neck = np.mean(keypoints_info[:, [5, 6]], axis=1)
|
|
|
| neck[:, 2:4] = np.logical_and(
|
| keypoints_info[:, 5, 2:4] > 0.3,
|
| keypoints_info[:, 6, 2:4] > 0.3).astype(int)
|
| new_keypoints_info = np.insert(
|
| keypoints_info, 17, neck, axis=1)
|
| mmpose_idx = [
|
| 17, 6, 8, 10, 7, 9, 12, 14, 16, 13, 15, 2, 1, 4, 3
|
| ]
|
| openpose_idx = [
|
| 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17
|
| ]
|
| new_keypoints_info[:, openpose_idx] = \
|
| new_keypoints_info[:, mmpose_idx]
|
| keypoints_info = new_keypoints_info
|
|
|
| keypoints, scores, visible = keypoints_info[
|
| ..., :2], keypoints_info[..., 2], keypoints_info[..., 3]
|
|
|
| return keypoints, scores
|
|
|
