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from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import os.path as osp
from glob import glob
from collections import defaultdict
import cv2
import torch
import pickle
import joblib
import argparse
import numpy as np
from loguru import logger
from progress.bar import Bar
from configs import constants as _C
from lib.models.smpl import SMPL
from lib.models.preproc.extractor import FeatureExtractor
from lib.models.preproc.backbone.utils import process_image
from lib.utils import transforms
from lib.utils.imutils import (
flip_kp, flip_bbox
)
dataset = defaultdict(list)
detection_results_dir = 'dataset/detection_results/3DPW'
tcmr_annot_pth = 'dataset/parsed_data/TCMR_preproc/3dpw_dset_db.pt'
@torch.no_grad()
def preprocess(dset, batch_size):
if dset == 'val': _dset = 'validation'
else: _dset = dset
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
save_pth = osp.join(_C.PATHS.PARSED_DATA, f'3pdw_{dset}_vit.pth') # Use ViT feature extractor
extractor = FeatureExtractor(device, flip_eval=True, max_batch_size=batch_size)
tcmr_data = joblib.load(tcmr_annot_pth.replace('dset', dset))
smpl_neutral = SMPL(model_path=_C.BMODEL.FLDR)
annot_file_list, idxs = np.unique(tcmr_data['vid_name'], return_index=True)
idxs = idxs.tolist()
annot_file_list = [annot_file_list[idxs.index(idx)] for idx in sorted(idxs)]
annot_file_list = [osp.join(_C.PATHS.THREEDPW_PTH, 'sequenceFiles', _dset, annot_file[:-2] + '.pkl') for annot_file in annot_file_list]
annot_file_list = list(dict.fromkeys(annot_file_list))
for annot_file in annot_file_list:
seq = annot_file.split('/')[-1].split('.')[0]
data = pickle.load(open(annot_file, 'rb'), encoding='latin1')
num_people = len(data['poses'])
num_frames = len(data['img_frame_ids'])
assert (data['poses2d'][0].shape[0] == num_frames)
K = torch.from_numpy(data['cam_intrinsics']).unsqueeze(0).float()
for p_id in range(num_people):
logger.info(f'==> {seq} {p_id}')
gender = {'m': 'male', 'f': 'female'}[data['genders'][p_id]]
# ======== Add TCMR data ======== #
vid_name = f'{seq}_{p_id}'
tcmr_ids = [i for i, v in enumerate(tcmr_data['vid_name']) if vid_name in v]
frame_ids = tcmr_data['frame_id'][tcmr_ids]
pose = torch.from_numpy(data['poses'][p_id]).float()[frame_ids]
shape = torch.from_numpy(data['betas'][p_id][:10]).float().repeat(pose.size(0), 1)
pose = torch.from_numpy(tcmr_data['pose'][tcmr_ids]).float() # Camera coordinate
cam_poses = torch.from_numpy(data['cam_poses'][frame_ids]).float()
# ======== Get detection results ======== #
fname = f'{seq}_{p_id}.npy'
pred_kp2d = torch.from_numpy(
np.load(osp.join(detection_results_dir, fname))
).float()[frame_ids]
# ======== Get detection results ======== #
img_paths = sorted(glob(osp.join(_C.PATHS.THREEDPW_PTH, 'imageFiles', seq, '*.jpg')))
img_paths = [img_path for i, img_path in enumerate(img_paths) if i in frame_ids]
img = cv2.imread(img_paths[0]); res_h, res_w = img.shape[:2]
vid_idxs = fname.split('.')[0]
# ======== Append data ======== #
dataset['gender'].append(gender)
dataset['vid'].append(vid_idxs)
dataset['pose'].append(pose)
dataset['betas'].append(shape)
dataset['cam_poses'].append(cam_poses)
dataset['frame_id'].append(torch.from_numpy(frame_ids))
dataset['res'].append(torch.tensor([[res_w, res_h]]).repeat(len(frame_ids), 1).float())
dataset['bbox'].append(torch.from_numpy(tcmr_data['bbox'][tcmr_ids].copy()).float())
dataset['kp2d'].append(pred_kp2d)
# Flipped data
dataset['flipped_bbox'].append(
torch.from_numpy(flip_bbox(dataset['bbox'][-1].clone().numpy(), res_w, res_h)).float()
)
dataset['flipped_kp2d'].append(
torch.from_numpy(flip_kp(dataset['kp2d'][-1].clone().numpy(), res_w)).float()
)
# ======== Append data ======== #
# ======== Extract features ======== #
patch_list = []
bboxes = dataset['bbox'][-1].clone().numpy()
bar = Bar(f'Load images', fill='#', max=len(img_paths))
for img_path, bbox in zip(img_paths, bboxes):
img_rgb = cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2RGB)
norm_img, crop_img = process_image(img_rgb, bbox[:2], bbox[2] / 200, 256, 256)
patch_list.append(torch.from_numpy(norm_img).unsqueeze(0).float())
bar.next()
patch_list = torch.split(torch.cat(patch_list), batch_size)
features, flipped_features = [], []
for i, patch in enumerate(patch_list):
feature = extractor.model(patch.cuda(), encode=True)
features.append(feature.cpu())
flipped_feature = extractor.model(torch.flip(patch, (3, )).cuda(), encode=True)
flipped_features.append(flipped_feature.cpu())
if i == 0:
init_patch = patch[[0]].clone()
features = torch.cat(features)
flipped_features = torch.cat(flipped_features)
dataset['features'].append(features)
dataset['flipped_features'].append(flipped_features)
# ======== Extract features ======== #
# Pad 1 frame
for key, val in dataset.items():
if isinstance(val[-1], torch.Tensor):
dataset[key][-1] = torch.cat((val[-1][:1].clone(), val[-1][:]), dim=0)
# Initial predictions
bbox = torch.from_numpy(bboxes[:1].copy()).float().cuda()
bbox_center = bbox[:, :2].clone()
bbox_scale = bbox[:, 2].clone() / 200
kwargs = {'img_w': torch.tensor(res_w).repeat(1).float().cuda(),
'img_h': torch.tensor(res_h).repeat(1).float().cuda(),
'bbox_center': bbox_center, 'bbox_scale': bbox_scale}
pred_global_orient, pred_pose, pred_shape, _ = extractor.model(init_patch.cuda(), **kwargs)
pred_output = smpl_neutral.get_output(global_orient=pred_global_orient.cpu(),
body_pose=pred_pose.cpu(),
betas=pred_shape.cpu(),
pose2rot=False)
init_kp3d = pred_output.joints
init_pose = transforms.matrix_to_axis_angle(torch.cat((pred_global_orient, pred_pose), dim=1))
dataset['init_kp3d'].append(init_kp3d)
dataset['init_pose'].append(init_pose.cpu())
# Flipped initial predictions
bbox_center[:, 0] = res_w - bbox_center[:, 0]
pred_global_orient, pred_pose, pred_shape, _ = extractor.model(torch.flip(init_patch, (3, )).cuda(), **kwargs)
pred_output = smpl_neutral.get_output(global_orient=pred_global_orient.cpu(),
body_pose=pred_pose.cpu(),
betas=pred_shape.cpu(),
pose2rot=False)
init_kp3d = pred_output.joints
init_pose = transforms.matrix_to_axis_angle(torch.cat((pred_global_orient, pred_pose), dim=1))
dataset['flipped_init_kp3d'].append(init_kp3d)
dataset['flipped_init_pose'].append(init_pose.cpu())
joblib.dump(dataset, save_pth)
logger.info(f'\n ==> Done !')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-s', '--split', type=str, choices=['val', 'test'], help='Data split')
parser.add_argument('-b', '--batch_size', type=int, default=128, help='Data split')
args = parser.parse_args()
preprocess(args.split, args.batch_size) |