278 lines
14 KiB
Python
278 lines
14 KiB
Python
import os
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import os.path as osp
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import numpy as np
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import torch
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import cv2
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import json
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import copy
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from pycocotools.coco import COCO
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from config import cfg
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from utils.human_models import smpl_x
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from utils.preprocessing import load_img, process_bbox, augmentation, process_db_coord, process_human_model_output, get_fitting_error_3D
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from utils.transforms import world2cam, cam2pixel, rigid_align
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import random
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from humandata import Cache
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class Human36M(torch.utils.data.Dataset):
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def __init__(self, transform, data_split):
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self.transform = transform
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self.data_split = data_split
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self.img_dir = osp.join(cfg.data_dir, 'Human36M', 'images')
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self.annot_path = osp.join(cfg.data_dir, 'Human36M', 'annotations')
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self.action_name = ['Directions', 'Discussion', 'Eating', 'Greeting', 'Phoning', 'Posing', 'Purchases', 'Sitting', 'SittingDown', 'Smoking', 'Photo', 'Waiting', 'Walking', 'WalkDog', 'WalkTogether']
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# H36M joint set
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self.joint_set = {'joint_num': 17,
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'joints_name': ('Pelvis', 'R_Hip', 'R_Knee', 'R_Ankle', 'L_Hip', 'L_Knee', 'L_Ankle', 'Torso', 'Neck', 'Head', 'Head_top', 'L_Shoulder', 'L_Elbow', 'L_Wrist', 'R_Shoulder', 'R_Elbow', 'R_Wrist'),
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'flip_pairs': ( (1, 4), (2, 5), (3, 6), (14, 11), (15, 12), (16, 13) ),
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'eval_joint': (1, 2, 3, 4, 5, 6, 8, 10, 11, 12, 13, 14, 15, 16),
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'regressor': np.load(osp.join(cfg.data_dir, 'Human36M', 'J_regressor_h36m_smplx.npy'))
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}
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self.joint_set['root_joint_idx'] = self.joint_set['joints_name'].index('Pelvis')
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# self.datalist = self.load_data()
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# load data or cache
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self.use_cache = getattr(cfg, 'use_cache', False)
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self.annot_path_cache = osp.join(cfg.data_dir, 'cache', f'Human36M_{data_split}.npz')
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if self.use_cache and osp.isfile(self.annot_path_cache):
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print(f'[{self.__class__.__name__}] loading cache from {self.annot_path_cache}')
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datalist = Cache(self.annot_path_cache)
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assert datalist.data_strategy == getattr(cfg, 'data_strategy', None), \
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f'Cache data strategy {datalist.data_strategy} does not match current data strategy ' \
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f'{getattr(cfg, "data_strategy", None)}'
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self.datalist = datalist
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else:
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if self.use_cache:
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print(f'[{self.__class__.__name__}] Cache not found, generating cache...')
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self.datalist = self.load_data()
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if self.use_cache:
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print(f'[{self.__class__.__name__}] Caching datalist to {self.annot_path_cache}...')
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Cache.save(
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self.annot_path_cache,
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self.datalist,
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data_strategy=getattr(cfg, 'data_strategy', None)
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)
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def get_subsampling_ratio(self):
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if self.data_split == 'train':
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return 5
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elif self.data_split == 'test':
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return 64
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else:
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assert 0, print('Unknown subset')
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def get_subject(self):
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if self.data_split == 'train':
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subject = [1,5,6,7,8]
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elif self.data_split == 'test':
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subject = [9,11]
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else:
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assert 0, print("Unknown subset")
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return subject
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def load_data(self):
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subject_list = self.get_subject()
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sampling_ratio = self.get_subsampling_ratio()
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# aggregate annotations from each subject
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db = COCO()
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cameras = {}
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joints = {}
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smplx_params = {}
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for subject in subject_list:
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# data load
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with open(osp.join(self.annot_path, 'Human36M_subject' + str(subject) + '_data.json'),'r') as f:
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annot = json.load(f)
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if len(db.dataset) == 0:
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for k,v in annot.items():
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db.dataset[k] = v
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else:
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for k,v in annot.items():
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db.dataset[k] += v
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# camera load
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with open(osp.join(self.annot_path, 'Human36M_subject' + str(subject) + '_camera.json'),'r') as f:
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cameras[str(subject)] = json.load(f)
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# joint coordinate load
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with open(osp.join(self.annot_path, 'Human36M_subject' + str(subject) + '_joint_3d.json'),'r') as f:
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joints[str(subject)] = json.load(f)
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# smplx parameter load
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with open(osp.join(self.annot_path, 'Human36M_subject' + str(subject) + '_SMPLX_NeuralAnnot.json'),'r') as f:
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smplx_params[str(subject)] = json.load(f)
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db.createIndex()
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datalist = []
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i = 0
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for aid in db.anns.keys():
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i += 1
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if self.data_split == 'train' and i % getattr(cfg, 'Human36M_train_sample_interval', 1) != 0:
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continue
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ann = db.anns[aid]
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image_id = ann['image_id']
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img = db.loadImgs(image_id)[0]
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img_path = osp.join(self.img_dir, img['file_name'])
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img_shape = (img['height'], img['width'])
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# check subject and frame_idx
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frame_idx = img['frame_idx'];
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if frame_idx % sampling_ratio != 0:
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continue
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# smplx parameter
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subject = img['subject']; action_idx = img['action_idx']; subaction_idx = img['subaction_idx']; frame_idx = img['frame_idx']; cam_idx = img['cam_idx'];
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smplx_param = smplx_params[str(subject)][str(action_idx)][str(subaction_idx)][str(frame_idx)]
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# camera parameter
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cam_param = cameras[str(subject)][str(cam_idx)]
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R,t,f,c = np.array(cam_param['R'], dtype=np.float32), np.array(cam_param['t'], dtype=np.float32), np.array(cam_param['f'], dtype=np.float32), np.array(cam_param['c'], dtype=np.float32)
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cam_param = {'R': R, 't': t, 'focal': f, 'princpt': c}
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# only use frontal camera following previous works (HMR and SPIN)
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if self.data_split == 'test' and str(cam_idx) != '4':
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continue
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# project world coordinate to cam, image coordinate space
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joint_world = np.array(joints[str(subject)][str(action_idx)][str(subaction_idx)][str(frame_idx)], dtype=np.float32)
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joint_cam = world2cam(joint_world, R, t)
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joint_img = cam2pixel(joint_cam, f, c)[:,:2]
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joint_valid = np.ones((self.joint_set['joint_num'],1))
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bbox = process_bbox(np.array(ann['bbox']), img['width'], img['height'], ratio=getattr(cfg, 'bbox_ratio', 1.25))
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if bbox is None: continue
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datalist.append({
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'img_path': img_path,
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'img_shape': img_shape,
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'bbox': bbox,
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'joint_img': joint_img,
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'joint_cam': joint_cam,
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'joint_valid': joint_valid,
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'smplx_param': smplx_param,
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'cam_param': cam_param})
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if self.data_split == 'train':
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print('[Human36M train] original size:', len(db.anns.keys()),
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'. Sample interval:', getattr(cfg, 'Human36M_train_sample_interval', 1),
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'. Sampled size:', len(datalist))
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if getattr(cfg, 'data_strategy', None) == 'balance' and self.data_split == 'train':
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print(f'[Human36M] Using [balance] strategy with datalist shuffled...')
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random.shuffle(datalist)
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return datalist
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def __len__(self):
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return len(self.datalist)
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def __getitem__(self, idx):
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data = copy.deepcopy(self.datalist[idx])
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img_path, img_shape, bbox, cam_param = data['img_path'], data['img_shape'], data['bbox'], data['cam_param']
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# img
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img = load_img(img_path)
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img, img2bb_trans, bb2img_trans, rot, do_flip = augmentation(img, bbox, self.data_split)
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img = self.transform(img.astype(np.float32))/255.
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if self.data_split == 'train':
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# h36m gt
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joint_cam = data['joint_cam']
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joint_cam = (joint_cam - joint_cam[self.joint_set['root_joint_idx'],None,:]) / 1000 # root-relative. milimeter to meter.
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joint_img = data['joint_img']
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joint_img = np.concatenate((joint_img[:,:2], joint_cam[:,2:]),1) # x, y, depth
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joint_img[:,2] = (joint_img[:,2] / (cfg.body_3d_size / 2) + 1)/2. * cfg.output_hm_shape[0] # discretize depth
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joint_img, joint_cam, joint_cam_ra, joint_valid, joint_trunc = process_db_coord(joint_img, joint_cam, data['joint_valid'], do_flip, img_shape, self.joint_set['flip_pairs'], img2bb_trans, rot, self.joint_set['joints_name'], smpl_x.joints_name)
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# smplx coordinates and parameters
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smplx_param = data['smplx_param']
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cam_param['t'] /= 1000 # milimeter to meter
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smplx_joint_img, smplx_joint_cam, smplx_joint_trunc, smplx_pose, smplx_shape, smplx_expr, \
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smplx_pose_valid, smplx_joint_valid, smplx_expr_valid, smplx_mesh_cam_orig = \
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process_human_model_output(smplx_param, cam_param, do_flip, img_shape, img2bb_trans, rot, 'smplx')
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# reverse ra
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smplx_joint_cam_wo_ra = smplx_joint_cam.copy()
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smplx_joint_cam_wo_ra[smpl_x.joint_part['lhand'], :] = smplx_joint_cam_wo_ra[smpl_x.joint_part['lhand'], :] \
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+ smplx_joint_cam_wo_ra[smpl_x.lwrist_idx, None, :] # left hand root-relative
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smplx_joint_cam_wo_ra[smpl_x.joint_part['rhand'], :] = smplx_joint_cam_wo_ra[smpl_x.joint_part['rhand'], :] \
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+ smplx_joint_cam_wo_ra[smpl_x.rwrist_idx, None, :] # right hand root-relative
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smplx_joint_cam_wo_ra[smpl_x.joint_part['face'], :] = smplx_joint_cam_wo_ra[smpl_x.joint_part['face'], :] \
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+ smplx_joint_cam_wo_ra[smpl_x.neck_idx, None,: ] # face root-relative
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# SMPLX pose parameter validity
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for name in ('L_Ankle', 'R_Ankle', 'L_Wrist', 'R_Wrist'):
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smplx_pose_valid[smpl_x.orig_joints_name.index(name)] = 0
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smplx_pose_valid = np.tile(smplx_pose_valid[:,None], (1,3)).reshape(-1)
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# SMPLX joint coordinate validity
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for name in ('L_Big_toe', 'L_Small_toe', 'L_Heel', 'R_Big_toe', 'R_Small_toe', 'R_Heel'):
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smplx_joint_valid[smpl_x.joints_name.index(name)] = 0
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smplx_joint_valid = smplx_joint_valid[:,None]
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smplx_joint_trunc = smplx_joint_valid * smplx_joint_trunc
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smplx_shape_valid = True
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# dummy hand/face bbox
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dummy_center = np.zeros((2), dtype=np.float32)
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dummy_size = np.zeros((2), dtype=np.float32)
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inputs = {'img': img}
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targets = {'joint_img': smplx_joint_img, 'smplx_joint_img': smplx_joint_img,
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'joint_cam': smplx_joint_cam_wo_ra, 'smplx_joint_cam': smplx_joint_cam,
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'smplx_pose': smplx_pose, 'smplx_shape': smplx_shape, 'smplx_expr': smplx_expr,
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'lhand_bbox_center': dummy_center, 'lhand_bbox_size': dummy_size,
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'rhand_bbox_center': dummy_center, 'rhand_bbox_size': dummy_size,
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'face_bbox_center': dummy_center, 'face_bbox_size': dummy_size}
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meta_info = {'joint_valid': smplx_joint_valid, 'joint_trunc': smplx_joint_trunc,
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'smplx_joint_valid': smplx_joint_valid, 'smplx_joint_trunc': smplx_joint_trunc,
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'smplx_pose_valid': smplx_pose_valid, 'smplx_shape_valid': float(smplx_shape_valid),
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'smplx_expr_valid': float(smplx_expr_valid), 'is_3D': float(True),
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'lhand_bbox_valid': float(False), 'rhand_bbox_valid': float(False),
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'face_bbox_valid': float(False)}
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return inputs, targets, meta_info
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else:
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inputs = {'img': img}
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targets = {}
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meta_info = {}
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return inputs, targets, meta_info
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def evaluate(self, outs, cur_sample_idx):
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annots = self.datalist
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sample_num = len(outs)
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eval_result = {'mpjpe': [], 'pa_mpjpe': []}
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for n in range(sample_num):
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annot = annots[cur_sample_idx + n]
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out = outs[n]
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# h36m joint from gt mesh
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joint_gt = annot['joint_cam']
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joint_gt = joint_gt - joint_gt[self.joint_set['root_joint_idx'],None] # root-relative
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joint_gt = joint_gt[self.joint_set['eval_joint'],:]
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# h36m joint from param mesh
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mesh_out = out['smpl_mesh_cam'] * 1000 # meter to milimeter
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joint_out = np.dot(self.joint_set['regressor'], mesh_out) # meter to milimeter
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joint_out = joint_out - joint_out[self.joint_set['root_joint_idx'],None] # root-relative
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joint_out = joint_out[self.joint_set['eval_joint'],:]
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joint_out_aligned = rigid_align(joint_out, joint_gt)
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eval_result['mpjpe'].append(np.sqrt(np.sum((joint_out - joint_gt)**2,1)).mean())
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eval_result['pa_mpjpe'].append(np.sqrt(np.sum((joint_out_aligned - joint_gt)**2,1)).mean())
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vis = False
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if vis:
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from utils.vis import vis_keypoints, vis_mesh, save_obj
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filename = annot['img_path'].split('/')[-1][:-4]
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img = load_img(annot['img_path'])[:,:,::-1]
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img = vis_mesh(img, mesh_out_img, 0.5)
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cv2.imwrite(filename + '.jpg', img)
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save_obj(mesh_out, smpl_x.face, filename + '.obj')
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return eval_result
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def print_eval_result(self, eval_result):
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print('MPJPE: %.2f mm' % np.mean(eval_result['mpjpe']))
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print('PA MPJPE: %.2f mm' % np.mean(eval_result['pa_mpjpe']))
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