import os import os.path as osp import numpy as np from config import cfg import copy import json import cv2 import torch from pycocotools.coco import COCO from utils.human_models import smpl_x from utils.preprocessing import load_img, process_bbox, augmentation, process_db_coord, \ process_human_model_output import random from humandata import Cache # from utils.vis import vis_keypoints, vis_mesh, save_obj class MPII(torch.utils.data.Dataset): def __init__(self, transform, data_split): self.transform = transform self.data_split = data_split self.img_path = osp.join(cfg.data_dir, 'MPII', 'data') self.annot_path = osp.join(cfg.data_dir, 'MPII', 'data', 'annotations') # mpii skeleton self.joint_set = { 'joint_num': 16, 'joints_name': ('R_Ankle', 'R_Knee', 'R_Hip', 'L_Hip', 'L_Knee', 'L_Ankle', 'Pelvis', 'Thorax', 'Neck', 'Head_top', 'R_Wrist', 'R_Elbow', 'R_Shoulder', 'L_Shoulder', 'L_Elbow', 'L_Wrist'), 'flip_pairs': ( (0,5), (1,4), (2,3), (10,15), (11,14), (12,13) ), } # self.datalist = self.load_data() # load data or cache self.use_cache = getattr(cfg, 'use_cache', False) self.annot_path_cache = osp.join(cfg.data_dir, 'cache', f'MPII_{data_split}.npz') if self.use_cache and osp.isfile(self.annot_path_cache): print(f'[{self.__class__.__name__}] loading cache from {self.annot_path_cache}') datalist = Cache(self.annot_path_cache) assert datalist.data_strategy == getattr(cfg, 'data_strategy', None), \ f'Cache data strategy {datalist.data_strategy} does not match current data strategy ' \ f'{getattr(cfg, "data_strategy", None)}' self.datalist = datalist else: if self.use_cache: print(f'[{self.__class__.__name__}] Cache not found, generating cache...') self.datalist = self.load_data() if self.use_cache: print(f'[{self.__class__.__name__}] Caching datalist to {self.annot_path_cache}...') Cache.save( self.annot_path_cache, self.datalist, data_strategy=getattr(cfg, 'data_strategy', None) ) def load_data(self): db = COCO(osp.join(self.annot_path, 'train.json')) with open(osp.join(self.annot_path, 'MPII_train_SMPLX_NeuralAnnot.json')) as f: smplx_params = json.load(f) datalist = [] i = 0 for aid in db.anns.keys(): i += 1 if self.data_split == 'train' and i % getattr(cfg, 'MPII_train_sample_interval', 1) != 0: continue ann = db.anns[aid] img = db.loadImgs(ann['image_id'])[0] imgname = img['file_name'] img_path = osp.join(self.img_path, imgname) # bbox bbox = process_bbox(ann['bbox'], img['width'], img['height'], ratio=getattr(cfg, 'bbox_ratio', 1.25)) if bbox is None: continue # joint coordinates joint_img = np.array(ann['keypoints'], dtype=np.float32).reshape(-1,3) joint_valid = joint_img[:,2:].copy() joint_img[:,2] = 0 # smplx parameter if str(aid) in smplx_params: smplx_param = smplx_params[str(aid)] else: smplx_param = None datalist.append({ 'img_path': img_path, 'img_shape': (img['height'], img['width']), 'bbox': bbox, 'joint_img': joint_img, 'joint_valid': joint_valid, 'smplx_param': smplx_param }) if self.data_split == 'train': print('[MPII train] original size:', len(db.anns.keys()), '. Sample interval:', getattr(cfg, 'MPII_train_sample_interval', 1), '. Sampled size:', len(datalist)) if getattr(cfg, 'data_strategy', None) == 'balance' and self.data_split == 'train': print(f'[MPII] Using [balance] strategy with datalist shuffled...') random.shuffle(datalist) return datalist def __len__(self): return len(self.datalist) def __getitem__(self, idx): data = copy.deepcopy(self.datalist[idx]) img_path, img_shape, bbox = data['img_path'], data['img_shape'], data['bbox'] # image load and affine transform img = load_img(img_path) img, img2bb_trans, bb2img_trans, rot, do_flip = augmentation(img, bbox, self.data_split) img = self.transform(img.astype(np.float32))/255. # mpii gt dummy_coord = np.zeros((self.joint_set['joint_num'],3), dtype=np.float32) joint_img = data['joint_img'] joint_img = np.concatenate((joint_img[:,:2], np.zeros_like(joint_img[:,:1])),1) # x, y, dummy depth joint_img, joint_cam, joint_cam_ra, joint_valid, joint_trunc = process_db_coord(joint_img, dummy_coord, data['joint_valid'], do_flip, img_shape, self.joint_set['flip_pairs'], img2bb_trans, rot, self.joint_set['joints_name'], smpl_x.joints_name) # smplx coordinates and parameters smplx_param = data['smplx_param'] if smplx_param is not None: smplx_joint_img, smplx_joint_cam, smplx_joint_trunc, smplx_pose, smplx_shape, smplx_expr, smplx_pose_valid, smplx_joint_valid, smplx_expr_valid, smplx_mesh_cam_orig = process_human_model_output(smplx_param['smplx_param'], smplx_param['cam_param'], do_flip, img_shape, img2bb_trans, rot, 'smplx') is_valid_fit = True else: # dummy values smplx_joint_img = np.zeros((smpl_x.joint_num,3), dtype=np.float32) smplx_joint_cam = np.zeros((smpl_x.joint_num,3), dtype=np.float32) smplx_joint_trunc = np.zeros((smpl_x.joint_num,1), dtype=np.float32) smplx_joint_valid = np.zeros((smpl_x.joint_num), dtype=np.float32) smplx_pose = np.zeros((smpl_x.orig_joint_num*3), dtype=np.float32) smplx_shape = np.zeros((smpl_x.shape_param_dim), dtype=np.float32) smplx_expr = np.zeros((smpl_x.expr_code_dim), dtype=np.float32) smplx_pose_valid = np.zeros((smpl_x.orig_joint_num), dtype=np.float32) smplx_expr_valid = False is_valid_fit = False # SMPLX pose parameter validity for name in ('L_Ankle', 'R_Ankle', 'L_Wrist', 'R_Wrist'): smplx_pose_valid[smpl_x.orig_joints_name.index(name)] = 0 smplx_pose_valid = np.tile(smplx_pose_valid[:,None], (1,3)).reshape(-1) # SMPLX joint coordinate validity for name in ('L_Big_toe', 'L_Small_toe', 'L_Heel', 'R_Big_toe', 'R_Small_toe', 'R_Heel'): smplx_joint_valid[smpl_x.joints_name.index(name)] = 0 smplx_joint_valid = smplx_joint_valid[:,None] smplx_joint_trunc = smplx_joint_valid * smplx_joint_trunc # make zero mask for invalid fit if not is_valid_fit: smplx_pose_valid[:] = 0 smplx_joint_valid[:] = 0 smplx_joint_trunc[:] = 0 smplx_shape_valid = False else: smplx_shape_valid = True # dummy hand/face bbox dummy_center = np.zeros((2), dtype=np.float32) dummy_size = np.zeros((2), dtype=np.float32) inputs = {'img': img} targets = {'joint_img': joint_img, 'smplx_joint_img': smplx_joint_img, 'joint_cam': joint_cam, 'smplx_joint_cam': smplx_joint_cam, 'smplx_pose': smplx_pose, 'smplx_shape': smplx_shape, 'smplx_expr': smplx_expr, 'lhand_bbox_center': dummy_center, 'lhand_bbox_size': dummy_size, 'rhand_bbox_center': dummy_center, 'rhand_bbox_size': dummy_size, 'face_bbox_center': dummy_center, 'face_bbox_size': dummy_size} meta_info = {'joint_valid': joint_valid, 'joint_trunc': joint_trunc, 'smplx_joint_valid': smplx_joint_valid, 'smplx_joint_trunc': smplx_joint_trunc, 'smplx_pose_valid': smplx_pose_valid, 'smplx_shape_valid': float(smplx_shape_valid), 'smplx_expr_valid': float(smplx_expr_valid), 'is_3D': float(False), 'lhand_bbox_valid': float(False), 'rhand_bbox_valid': float(False), 'face_bbox_valid': float(False)} return inputs, targets, meta_info