File size: 9,923 Bytes
2d5f249
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257

# -*- 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

import os

import lib.smplx as smplx
from lib.pymaf.utils.geometry import rotation_matrix_to_angle_axis, batch_rodrigues
from lib.pymaf.utils.imutils import 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, SMPLX
import os.path as osp
import torch
import numpy as np
import random
import human_det
from termcolor import colored
from PIL import ImageFile
from huggingface_hub import cached_download

ImageFile.LOAD_TRUNCATED_IMAGES = True


class TestDataset():
    def __init__(self, cfg, device):

        random.seed(1993)

        self.image_path = cfg['image_path']
        self.seg_dir = cfg['seg_dir']
        self.has_det = cfg['has_det']
        self.hps_type = cfg['hps_type']
        self.smpl_type = 'smpl' if cfg['hps_type'] != 'pixie' else 'smplx'
        self.smpl_gender = 'neutral'

        self.device = device

        if self.has_det:
            self.det = human_det.Detection()
        else:
            self.det = None


        self.subject_list = [self.image_path]

        # smpl related
        self.smpl_data = SMPLX()

        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

        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()

        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())
        if self.smpl_type == 'smpl':
            smplx_ind = self.smpl_data.smpl2smplx(np.arange(smpl_vis.shape[0]))
        else:
            smplx_ind = np.arange(smpl_vis.shape[0])
        smpl_cmap = self.smpl_data.get_smpl_mat(smplx_ind)

        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 = cached_download(osp.join(self.smpl_data.model_dir, "smpl/SMPL_NEUTRAL.pkl"), use_auth_token=os.environ['ICON'])
        tetra_path = cached_download(osp.join(self.smpl_data.tedra_dir,
                              'tetra_neutral_adult_smpl.npz'), use_auth_token=os.environ['ICON'])
        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(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(cached_download(osp.join(self.smpl_data.tedra_dir,
                                    'tetrahedrons_neutral_adult.txt'), use_auth_token=os.environ['ICON']),
                           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]

        if self.seg_dir is None:
            img_icon, img_hps, img_ori, img_mask, uncrop_param = process_image(
                img_path, self.det, 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.det, 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
            }

        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.int16)).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']

        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]

        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]

        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

        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['scale'] = scale
        data_dict['trans'] = torch.tensor(
            [tranX, tranY, 0.0]).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]

        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])