yinwanqi
186 lines
8.5 KiB
Python
186 lines
8.5 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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from config import cfg
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import copy
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import json
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import cv2
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import torch
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from pycocotools.coco import COCO
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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, \
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process_human_model_output
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import random
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from humandata import Cache
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# from utils.vis import vis_keypoints, vis_mesh, save_obj
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class MPII(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_path = osp.join(cfg.data_dir, 'MPII', 'data')
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self.annot_path = osp.join(cfg.data_dir, 'MPII', 'data', 'annotations')
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# mpii skeleton
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self.joint_set = {
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'joint_num': 16,
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'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'),
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'flip_pairs': ( (0,5), (1,4), (2,3), (10,15), (11,14), (12,13) ),
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}
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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'MPII_{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 load_data(self):
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db = COCO(osp.join(self.annot_path, 'train.json'))
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with open(osp.join(self.annot_path, 'MPII_train_SMPLX_NeuralAnnot.json')) as f:
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smplx_params = json.load(f)
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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, 'MPII_train_sample_interval', 1) != 0:
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continue
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ann = db.anns[aid]
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img = db.loadImgs(ann['image_id'])[0]
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imgname = img['file_name']
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img_path = osp.join(self.img_path, imgname)
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# bbox
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bbox = process_bbox(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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# joint coordinates
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joint_img = np.array(ann['keypoints'], dtype=np.float32).reshape(-1,3)
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joint_valid = joint_img[:,2:].copy()
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joint_img[:,2] = 0
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# smplx parameter
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if str(aid) in smplx_params:
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smplx_param = smplx_params[str(aid)]
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else:
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smplx_param = None
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datalist.append({
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'img_path': img_path,
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'img_shape': (img['height'], img['width']),
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'bbox': bbox,
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'joint_img': joint_img,
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'joint_valid': joint_valid,
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'smplx_param': smplx_param
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})
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if self.data_split == 'train':
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print('[MPII train] original size:', len(db.anns.keys()),
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'. Sample interval:', getattr(cfg, 'MPII_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'[MPII] 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 = data['img_path'], data['img_shape'], data['bbox']
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# image load and affine transform
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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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# mpii gt
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dummy_coord = np.zeros((self.joint_set['joint_num'],3), dtype=np.float32)
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joint_img = data['joint_img']
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joint_img = np.concatenate((joint_img[:,:2], np.zeros_like(joint_img[:,:1])),1) # x, y, dummy depth
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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)
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# smplx coordinates and parameters
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smplx_param = data['smplx_param']
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if smplx_param is not None:
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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')
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is_valid_fit = True
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else:
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# dummy values
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smplx_joint_img = np.zeros((smpl_x.joint_num,3), dtype=np.float32)
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smplx_joint_cam = np.zeros((smpl_x.joint_num,3), dtype=np.float32)
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smplx_joint_trunc = np.zeros((smpl_x.joint_num,1), dtype=np.float32)
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smplx_joint_valid = np.zeros((smpl_x.joint_num), dtype=np.float32)
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smplx_pose = np.zeros((smpl_x.orig_joint_num*3), dtype=np.float32)
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smplx_shape = np.zeros((smpl_x.shape_param_dim), dtype=np.float32)
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smplx_expr = np.zeros((smpl_x.expr_code_dim), dtype=np.float32)
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smplx_pose_valid = np.zeros((smpl_x.orig_joint_num), dtype=np.float32)
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smplx_expr_valid = False
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is_valid_fit = False
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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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# make zero mask for invalid fit
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if not is_valid_fit:
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smplx_pose_valid[:] = 0
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smplx_joint_valid[:] = 0
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smplx_joint_trunc[:] = 0
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smplx_shape_valid = False
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else:
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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': joint_img, 'smplx_joint_img': smplx_joint_img,
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'joint_cam': joint_cam, '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': joint_valid, 'joint_trunc': joint_trunc,
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'smplx_joint_valid': smplx_joint_valid,
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'smplx_joint_trunc': smplx_joint_trunc, 'smplx_pose_valid': smplx_pose_valid,
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'smplx_shape_valid': float(smplx_shape_valid),
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'smplx_expr_valid': float(smplx_expr_valid), 'is_3D': float(False),
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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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