# -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is authorized to grant you that right.
# Any use of the computer program without a valid license is prohibited and
# liable to prosecution.
#
# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems. All rights reserved.
#
# Contact: ps-license@tuebingen.mpg.de
from lib.hybrik.models.simple3dpose import HybrIKBaseSMPLCam
from lib.pixielib.utils.config import cfg as pixie_cfg
from lib.pixielib.pixie import PIXIE
import lib.smplx as smplx
# from lib.pare.pare.core.tester import PARETester
from lib.pymaf.utils.geometry import rot6d_to_rotmat, batch_rodrigues, rotation_matrix_to_angle_axis
from lib.pymaf.utils.imutils import process_image
from lib.common.imutils import econ_process_image
from lib.pymaf.core import path_config
from lib.pymaf.models import pymaf_net
from lib.common.config import cfg
from lib.common.render import Render
from lib.dataset.body_model import TetraSMPLModel
from lib.dataset.mesh_util import get_visibility
from utils.smpl_util import SMPLX
import os.path as osp
import os
import torch
import numpy as np
import random
from termcolor import colored
from PIL import ImageFile
from torchvision.models import detection
ImageFile.LOAD_TRUNCATED_IMAGES = True
class SMPLDataset():
def __init__(self, cfg, device):
random.seed(1993)
self.image_dir = cfg['image_dir']
self.seg_dir = cfg['seg_dir']
self.hps_type = cfg['hps_type']
self.smpl_type = 'smpl' if cfg['hps_type'] != 'pixie' else 'smplx'
self.smpl_gender = 'neutral'
self.colab = cfg['colab']
self.device = device
keep_lst = [f"{self.image_dir}/{i}" for i in sorted(os.listdir(self.image_dir))]
img_fmts = ['jpg', 'png', 'jpeg', "JPG", 'bmp']
keep_lst = [item for item in keep_lst if item.split(".")[-1] in img_fmts]
self.subject_list = [item for item in keep_lst if item.split(".")[-1] in img_fmts]
if self.colab:
self.subject_list = [self.subject_list[0]]
# smpl related
self.smpl_data = SMPLX()
# smpl-smplx correspondence
self.smpl_joint_ids_24 = np.arange(22).tolist() + [68, 73]
self.smpl_joint_ids_24_pixie = np.arange(22).tolist() + [68 + 61, 72 + 68]
self.get_smpl_model = lambda smpl_type, smpl_gender: smplx.create(model_path=self.smpl_data.
model_dir,
gender=smpl_gender,
model_type=smpl_type,
ext='npz')
# Load SMPL model
self.smpl_model = self.get_smpl_model(self.smpl_type, self.smpl_gender).to(self.device)
self.faces = self.smpl_model.faces
if self.hps_type == 'pymaf':
self.hps = pymaf_net(path_config.SMPL_MEAN_PARAMS, pretrained=True).to(self.device)
self.hps.load_state_dict(torch.load(path_config.CHECKPOINT_FILE)['model'], strict=True)
self.hps.eval()
elif self.hps_type == 'pare':
self.hps = PARETester(path_config.CFG, path_config.CKPT).model
elif self.hps_type == 'pixie':
self.hps = PIXIE(config=pixie_cfg, device=self.device)
self.smpl_model = self.hps.smplx
elif self.hps_type == 'hybrik':
smpl_path = osp.join(self.smpl_data.model_dir, "smpl/SMPL_NEUTRAL.pkl")
self.hps = HybrIKBaseSMPLCam(cfg_file=path_config.HYBRIK_CFG,
smpl_path=smpl_path,
data_path=path_config.hybrik_data_dir)
self.hps.load_state_dict(torch.load(path_config.HYBRIK_CKPT, map_location='cpu'),
strict=False)
self.hps.to(self.device)
elif self.hps_type == 'bev':
try:
import bev
except:
print('Could not find bev, installing via pip install --upgrade simple-romp')
os.system('pip install simple-romp==1.0.3')
import bev
settings = bev.main.default_settings
# change the argparse settings of bev here if you prefer other settings.
settings.mode = 'image'
settings.GPU = int(str(self.device).split(':')[1])
settings.show_largest = True
# settings.show = True # uncommit this to show the original BEV predictions
self.hps = bev.BEV(settings)
self.detector=detection.maskrcnn_resnet50_fpn(pretrained=True)
self.detector.eval()
print(colored(f"Using {self.hps_type} as HPS Estimator\n", "green"))
self.render = Render(size=512, device=device)
def __len__(self):
return len(self.subject_list)
def compute_vis_cmap(self, smpl_verts, smpl_faces):
(xy, z) = torch.as_tensor(smpl_verts).split([2, 1], dim=1)
smpl_vis = get_visibility(xy, -z, torch.as_tensor(smpl_faces).long())
smpl_cmap = self.smpl_data.cmap_smpl_vids(self.smpl_type)
return {
'smpl_vis': smpl_vis.unsqueeze(0).to(self.device),
'smpl_cmap': smpl_cmap.unsqueeze(0).to(self.device),
'smpl_verts': smpl_verts.unsqueeze(0)
}
def compute_voxel_verts(self, body_pose, global_orient, betas, trans, scale):
smpl_path = osp.join(self.smpl_data.model_dir, "smpl/SMPL_NEUTRAL.pkl")
tetra_path = osp.join(self.smpl_data.tedra_dir, 'tetra_neutral_adult_smpl.npz')
smpl_model = TetraSMPLModel(smpl_path, tetra_path, 'adult')
pose = torch.cat([global_orient[0], body_pose[0]], dim=0)
smpl_model.set_params(rotation_matrix_to_angle_axis(rot6d_to_rotmat(pose)), beta=betas[0])
verts = np.concatenate([smpl_model.verts, smpl_model.verts_added],
axis=0) * scale.item() + trans.detach().cpu().numpy()
faces = np.loadtxt(osp.join(self.smpl_data.tedra_dir, 'tetrahedrons_neutral_adult.txt'),
dtype=np.int32) - 1
pad_v_num = int(8000 - verts.shape[0])
pad_f_num = int(25100 - faces.shape[0])
verts = np.pad(verts,
((0, pad_v_num),
(0, 0)), mode='constant', constant_values=0.0).astype(np.float32) * 0.5
faces = np.pad(faces, ((0, pad_f_num), (0, 0)), mode='constant',
constant_values=0.0).astype(np.int32)
verts[:, 2] *= -1.0
voxel_dict = {
'voxel_verts': torch.from_numpy(verts).to(self.device).unsqueeze(0).float(),
'voxel_faces': torch.from_numpy(faces).to(self.device).unsqueeze(0).long(),
'pad_v_num': torch.tensor(pad_v_num).to(self.device).unsqueeze(0).long(),
'pad_f_num': torch.tensor(pad_f_num).to(self.device).unsqueeze(0).long()
}
return voxel_dict
def __getitem__(self, index):
img_path = self.subject_list[index]
img_name = img_path.split("/")[-1].rsplit(".", 1)[0]
print(img_name)
# smplx_param_path=f'./data/thuman2/smplx/{img_name[:-2]}.pkl'
# smplx_param = np.load(smplx_param_path, allow_pickle=True)
if self.seg_dir is None:
img_icon, img_hps, img_ori, img_mask, uncrop_param = process_image(
img_path, self.hps_type, 512, self.device)
data_dict = {
'name': img_name,
'image': img_icon.to(self.device).unsqueeze(0),
'ori_image': img_ori,
'mask': img_mask,
'uncrop_param': uncrop_param
}
else:
img_icon, img_hps, img_ori, img_mask, uncrop_param, segmentations = process_image(
img_path,
self.hps_type,
512,
self.device,
seg_path=os.path.join(self.seg_dir, f'{img_name}.json'))
data_dict = {
'name': img_name,
'image': img_icon.to(self.device).unsqueeze(0),
'ori_image': img_ori,
'mask': img_mask,
'uncrop_param': uncrop_param,
'segmentations': segmentations
}
arr_dict=econ_process_image(img_path,self.hps_type,True,512,self.detector)
data_dict['hands_visibility']=arr_dict['hands_visibility']
with torch.no_grad():
# import ipdb; ipdb.set_trace()
preds_dict = self.hps.forward(img_hps)
data_dict['smpl_faces'] = torch.Tensor(self.faces.astype(np.int64)).long().unsqueeze(0).to(
self.device)
if self.hps_type == 'pymaf':
output = preds_dict['smpl_out'][-1]
scale, tranX, tranY = output['theta'][0, :3]
data_dict['betas'] = output['pred_shape']
data_dict['body_pose'] = output['rotmat'][:, 1:]
data_dict['global_orient'] = output['rotmat'][:, 0:1]
data_dict['smpl_verts'] = output['verts'] # 不确定尺度是否一样
data_dict["type"] = "smpl"
elif self.hps_type == 'pare':
data_dict['body_pose'] = preds_dict['pred_pose'][:, 1:]
data_dict['global_orient'] = preds_dict['pred_pose'][:, 0:1]
data_dict['betas'] = preds_dict['pred_shape']
data_dict['smpl_verts'] = preds_dict['smpl_vertices']
scale, tranX, tranY = preds_dict['pred_cam'][0, :3]
data_dict["type"] = "smpl"
elif self.hps_type == 'pixie':
data_dict.update(preds_dict)
data_dict['body_pose'] = preds_dict['body_pose']
data_dict['global_orient'] = preds_dict['global_pose']
data_dict['betas'] = preds_dict['shape']
data_dict['smpl_verts'] = preds_dict['vertices']
scale, tranX, tranY = preds_dict['cam'][0, :3]
data_dict["type"] = "smplx"
elif self.hps_type == 'hybrik':
data_dict['body_pose'] = preds_dict['pred_theta_mats'][:, 1:]
data_dict['global_orient'] = preds_dict['pred_theta_mats'][:, [0]]
data_dict['betas'] = preds_dict['pred_shape']
data_dict['smpl_verts'] = preds_dict['pred_vertices']
scale, tranX, tranY = preds_dict['pred_camera'][0, :3]
scale = scale * 2
data_dict["type"] = "smpl"
elif self.hps_type == 'bev':
data_dict['betas'] = torch.from_numpy(preds_dict['smpl_betas'])[[0], :10].to(
self.device).float()
pred_thetas = batch_rodrigues(
torch.from_numpy(preds_dict['smpl_thetas'][0]).reshape(-1, 3)).float()
data_dict['body_pose'] = pred_thetas[1:][None].to(self.device)
data_dict['global_orient'] = pred_thetas[[0]][None].to(self.device)
data_dict['smpl_verts'] = torch.from_numpy(preds_dict['verts'][[0]]).to(
self.device).float()
tranX = preds_dict['cam_trans'][0, 0]
tranY = preds_dict['cam'][0, 1] + 0.28
scale = preds_dict['cam'][0, 0] * 1.1
data_dict["type"] = "smpl"
data_dict['scale'] = scale
data_dict['trans'] = torch.tensor([tranX, tranY, 0.0]).unsqueeze(0).to(self.device).float()
# data_dict info (key-shape):
# scale, tranX, tranY - tensor.float
# betas - [1,10] / [1, 200]
# body_pose - [1, 23, 3, 3] / [1, 21, 3, 3]
# global_orient - [1, 1, 3, 3]
# smpl_verts - [1, 6890, 3] / [1, 10475, 3]
# from rot_mat to rot_6d for better optimization
N_body = data_dict["body_pose"].shape[1]
data_dict["body_pose"] = data_dict["body_pose"][:, :, :, :2].reshape(1, N_body, -1)
data_dict["global_orient"] = data_dict["global_orient"][:, :, :, :2].reshape(1, 1, -1)
return data_dict
def render_normal(self, verts, faces):
# render optimized mesh (normal, T_normal, image [-1,1])
self.render.load_meshes(verts, faces)
return self.render.get_rgb_image()
def render_depth(self, verts, faces):
# render optimized mesh (normal, T_normal, image [-1,1])
self.render.load_meshes(verts, faces)
return self.render.get_depth_map(cam_ids=[0, 2])
def visualize_alignment(self, data):
import vedo
import trimesh
if self.hps_type != 'pixie':
smpl_out = self.smpl_model(betas=data['betas'],
body_pose=data['body_pose'],
global_orient=data['global_orient'],
pose2rot=False)
smpl_verts = ((smpl_out.vertices + data['trans']) *
data['scale']).detach().cpu().numpy()[0]
else:
smpl_verts, _, _ = self.smpl_model(shape_params=data['betas'],
expression_params=data['exp'],
body_pose=data['body_pose'],
global_pose=data['global_orient'],
jaw_pose=data['jaw_pose'],
left_hand_pose=data['left_hand_pose'],
right_hand_pose=data['right_hand_pose'])
smpl_verts = ((smpl_verts + data['trans']) * data['scale']).detach().cpu().numpy()[0]
smpl_verts *= np.array([1.0, -1.0, -1.0])
faces = data['smpl_faces'][0].detach().cpu().numpy()
image_P = data['image']
image_F, image_B = self.render_normal(smpl_verts, faces)
# create plot
vp = vedo.Plotter(title="", size=(1500, 1500))
vis_list = []
image_F = (0.5 * (1.0 + image_F[0].permute(1, 2, 0).detach().cpu().numpy()) * 255.0)
image_B = (0.5 * (1.0 + image_B[0].permute(1, 2, 0).detach().cpu().numpy()) * 255.0)
image_P = (0.5 * (1.0 + image_P[0].permute(1, 2, 0).detach().cpu().numpy()) * 255.0)
vis_list.append(
vedo.Picture(image_P * 0.5 + image_F * 0.5).scale(2.0 / image_P.shape[0]).pos(
-1.0, -1.0, 1.0))
vis_list.append(vedo.Picture(image_F).scale(2.0 / image_F.shape[0]).pos(-1.0, -1.0, -0.5))
vis_list.append(vedo.Picture(image_B).scale(2.0 / image_B.shape[0]).pos(-1.0, -1.0, -1.0))
# create a mesh
mesh = trimesh.Trimesh(smpl_verts, faces, process=False)
mesh.visual.vertex_colors = [200, 200, 0]
vis_list.append(mesh)
vp.show(*vis_list, bg="white", axes=1, interactive=True)
if __name__ == '__main__':
cfg.merge_from_file("./configs/icon-filter.yaml")
cfg.merge_from_file('./lib/pymaf/configs/pymaf_config.yaml')
cfg_show_list = ['test_gpus', ['0'], 'mcube_res', 512, 'clean_mesh', False]
cfg.merge_from_list(cfg_show_list)
cfg.freeze()
device = torch.device('cuda:0')
dataset = SMPLDataset(
{
'image_dir': "./examples",
'has_det': True, # w/ or w/o detection
'hps_type': 'bev' # pymaf/pare/pixie/hybrik/bev
},
device)
for i in range(len(dataset)):
dataset.visualize_alignment(dataset[i])