lib/dataset/TestDataset.py

# -*- coding: utf-8 -*-

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# You can only use this computer program if you have closed
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# program from someone who is authorized to grant you that right.
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#
# 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: [email protected]

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
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

        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.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
            }

        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]
        
        # 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])