# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
import mmcv
import numpy as np
import pytest
import torch
from mmpose.apis import (extract_pose_sequence, inference_interhand_3d_model,
inference_mesh_model, inference_pose_lifter_model,
init_pose_model, vis_3d_mesh_result,
vis_3d_pose_result)
from mmpose.datasets.dataset_info import DatasetInfo
from tests.utils.mesh_utils import generate_smpl_weight_file
def test_pose_lifter_demo():
# H36M demo
pose_model = init_pose_model(
'configs/body/3d_kpt_sview_rgb_img/pose_lift/'
'h36m/simplebaseline3d_h36m.py',
None,
device='cpu')
pose_det_result = {
'keypoints': np.zeros((17, 3)),
'bbox': [50, 50, 50, 50],
'track_id': 0,
'image_name': 'tests/data/h36m/S1_Directions_1.54138969_000001.jpg',
}
pose_results_2d = [[pose_det_result]]
dataset_info = DatasetInfo(pose_model.cfg.data['test']['dataset_info'])
pose_results_2d = extract_pose_sequence(
pose_results_2d, frame_idx=0, causal=False, seq_len=1, step=1)
_ = inference_pose_lifter_model(
pose_model,
pose_results_2d,
dataset_info=dataset_info,
with_track_id=False)
pose_lift_results = inference_pose_lifter_model(
pose_model,
pose_results_2d,
dataset_info=dataset_info,
with_track_id=True)
for res in pose_lift_results:
res['title'] = 'title'
vis_3d_pose_result(
pose_model,
pose_lift_results,
img=pose_results_2d[0][0]['image_name'],
dataset_info=dataset_info)
# test special cases
# Empty 2D results
_ = inference_pose_lifter_model(
pose_model, [[]], dataset_info=dataset_info, with_track_id=False)
if torch.cuda.is_available():
_ = inference_pose_lifter_model(
pose_model.cuda(),
pose_results_2d,
dataset_info=dataset_info,
with_track_id=False)
# test videopose3d
pose_model = init_pose_model(
'configs/body/3d_kpt_sview_rgb_vid/video_pose_lift/h36m/'
'videopose3d_h36m_243frames_fullconv_supervised_cpn_ft.py',
None,
device='cpu')
pose_det_result_0 = {
'keypoints': np.ones((17, 3)),
'bbox': [50, 50, 100, 100],
'track_id': 0,
'image_name': 'tests/data/h36m/S1_Directions_1.54138969_000001.jpg',
}
pose_det_result_1 = {
'keypoints': np.ones((17, 3)),
'bbox': [50, 50, 100, 100],
'track_id': 1,
'image_name': 'tests/data/h36m/S5_SittingDown.54138969_002061.jpg',
}
pose_det_result_2 = {
'keypoints': np.ones((17, 3)),
'bbox': [50, 50, 100, 100],
'track_id': 2,
'image_name': 'tests/data/h36m/S7_Greeting.55011271_000396.jpg',
}
pose_results_2d = [[pose_det_result_0], [pose_det_result_1],
[pose_det_result_2]]
dataset_info = DatasetInfo(pose_model.cfg.data['test']['dataset_info'])
seq_len = pose_model.cfg.test_data_cfg.seq_len
pose_results_2d_seq = extract_pose_sequence(
pose_results_2d, 1, causal=False, seq_len=seq_len, step=1)
pose_lift_results = inference_pose_lifter_model(
pose_model,
pose_results_2d_seq,
dataset_info=dataset_info,
with_track_id=True,
image_size=[1000, 1000],
norm_pose_2d=True)
for res in pose_lift_results:
res['title'] = 'title'
vis_3d_pose_result(
pose_model,
pose_lift_results,
img=pose_results_2d[0][0]['image_name'],
dataset_info=dataset_info,
)
def test_interhand3d_demo():
# H36M demo
pose_model = init_pose_model(
'configs/hand/3d_kpt_sview_rgb_img/internet/interhand3d/'
'res50_interhand3d_all_256x256.py',
None,
device='cpu')
image_name = 'tests/data/interhand2.6m/image2017.jpg'
det_result = {
'image_name': image_name,
'bbox': [50, 50, 50, 50], # bbox format is 'xywh'
'camera_param': None,
'keypoints_3d_gt': None
}
det_results = [det_result]
dataset = pose_model.cfg.data['test']['type']
dataset_info = DatasetInfo(pose_model.cfg.data['test']['dataset_info'])
pose_results = inference_interhand_3d_model(
pose_model, image_name, det_results, dataset=dataset)
for res in pose_results:
res['title'] = 'title'
vis_3d_pose_result(
pose_model,
result=pose_results,
img=det_results[0]['image_name'],
dataset_info=dataset_info,
)
# test special cases
# Empty det results
_ = inference_interhand_3d_model(
pose_model, image_name, [], dataset=dataset)
if torch.cuda.is_available():
_ = inference_interhand_3d_model(
pose_model.cuda(), image_name, det_results, dataset=dataset)
with pytest.raises(NotImplementedError):
_ = inference_interhand_3d_model(
pose_model, image_name, det_results, dataset='test')
def test_body_mesh_demo():
# H36M demo
config = 'configs/body/3d_mesh_sview_rgb_img/hmr' \
'/mixed/res50_mixed_224x224.py'
config = mmcv.Config.fromfile(config)
config.model.mesh_head.smpl_mean_params = \
'tests/data/smpl/smpl_mean_params.npz'
pose_model = None
with tempfile.TemporaryDirectory() as tmpdir:
config.model.smpl.smpl_path = tmpdir
config.model.smpl.joints_regressor = osp.join(
tmpdir, 'test_joint_regressor.npy')
# generate weight file for SMPL model.
generate_smpl_weight_file(tmpdir)
pose_model = init_pose_model(config, device='cpu')
assert pose_model is not None, 'Fail to build pose model'
image_name = 'tests/data/h36m/S1_Directions_1.54138969_000001.jpg'
det_result = {
'keypoints': np.zeros((17, 3)),
'bbox': [50, 50, 50, 50],
'image_name': image_name,
}
# make person bounding boxes
person_results = [det_result]
dataset = pose_model.cfg.data['test']['type']
# test a single image, with a list of bboxes
pose_results = inference_mesh_model(
pose_model,
image_name,
person_results,
bbox_thr=None,
format='xywh',
dataset=dataset)
vis_3d_mesh_result(pose_model, pose_results, image_name)
