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Deleted old 2D pose wrapper.

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Daniel
2024-12-19 14:22:48 +01:00
parent 5bc7bd9094
commit 7b073249d6
1 changed files with 0 additions and 71 deletions
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scripts/utils_2d_pose.py
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import os
import numpy as np
from mmpose.apis import MMPoseInferencer
# ==================================================================================================
filepath = os.path.dirname(os.path.realpath(__file__)) + "/"
# ==================================================================================================
def load_model():
print("Loading mmpose model ...")
model = MMPoseInferencer(
pose2d="/mmpose/projects/rtmpose/rtmpose/body_2d_keypoint/rtmpose-m_8xb256-420e_coco-384x288.py",
pose2d_weights="https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-body7_pt-body7_420e-384x288-65e718c4_20230504.pth",
det_model="/mmpose/projects/rtmpose/rtmdet/person/rtmdet_nano_320-8xb32_coco-person.py",
det_weights="https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmdet_nano_8xb32-100e_coco-obj365-person-05d8511e.pth",
det_cat_ids=[0],
)
print("Loaded mmpose model")
return model
def load_wb_model():
print("Loading mmpose whole body model ...")
model = MMPoseInferencer(
pose2d="/mmpose/projects/rtmpose/rtmpose/wholebody_2d_keypoint/rtmpose-l_8xb32-270e_coco-wholebody-384x288.py",
pose2d_weights="https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-coco-wholebody_pt-aic-coco_270e-384x288-eaeb96c8_20230125.pth",
det_model="/mmpose/projects/rtmpose/rtmdet/person/rtmdet_nano_320-8xb32_coco-person.py",
det_weights="https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmdet_nano_8xb32-100e_coco-obj365-person-05d8511e.pth",
det_cat_ids=[0],
)
print("Loaded mmpose model")
return model
# ==================================================================================================
def get_2d_pose(model, imgs, num_joints=17):
"""See: https://mmpose.readthedocs.io/en/latest/user_guides/inference.html#basic-usage"""
result_generator = model(imgs, show=False, bbox_thr=0.3, nms_thr=0.3)
new_poses = []
for _ in range(len(imgs)):
result = next(result_generator)
poses = []
for i in range(len(result["predictions"][0])):
kpts = result["predictions"][0][i]["keypoints"]
scores = result["predictions"][0][i]["keypoint_scores"]
kpts = np.asarray(kpts)
scores = np.asarray(scores).reshape(-1, 1)
scores = np.clip(scores, 0, 1)
pose = np.concatenate((kpts, scores), axis=-1)
poses.append(pose)
if len(poses) == 0:
poses.append(np.zeros([num_joints, 3]))
poses = np.array(poses)
new_poses.append(poses)
return new_poses