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from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import os
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/EMDB'
def is_dset(emdb_pkl_file, dset):
target_dset = 'emdb' + dset
with open(emdb_pkl_file, "rb") as f:
data = pickle.load(f)
return data[target_dset]
@torch.no_grad()
def preprocess(dset, batch_size):
tt = lambda x: torch.from_numpy(x).float()
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
save_pth = osp.join(_C.PATHS.PARSED_DATA, f'emdb_{dset}_vit.pth') # Use ViT feature extractor
extractor = FeatureExtractor(device, flip_eval=True, max_batch_size=batch_size)
all_emdb_pkl_files = sorted(glob(os.path.join(_C.PATHS.EMDB_PTH, "*/*/*_data.pkl")))
emdb_sequence_roots = []
both = []
for emdb_pkl_file in all_emdb_pkl_files:
if is_dset(emdb_pkl_file, dset):
emdb_sequence_roots.append(os.path.dirname(emdb_pkl_file))
smpl = {
'neutral': SMPL(model_path=_C.BMODEL.FLDR),
'male': SMPL(model_path=_C.BMODEL.FLDR, gender='male'),
'female': SMPL(model_path=_C.BMODEL.FLDR, gender='female'),
}
for sequence in emdb_sequence_roots:
subj, seq = sequence.split('/')[-2:]
annot_pth = glob(osp.join(sequence, '*_data.pkl'))[0]
annot = pickle.load(open(annot_pth, 'rb'))
# Get ground truth data
gender = annot['gender']
masks = annot['good_frames_mask']
poses_body = annot["smpl"]["poses_body"]
poses_root = annot["smpl"]["poses_root"]
betas = np.repeat(annot["smpl"]["betas"].reshape((1, -1)), repeats=annot["n_frames"], axis=0)
extrinsics = annot["camera"]["extrinsics"]
width, height = annot['camera']['width'], annot['camera']['height']
xyxys = annot['bboxes']['bboxes']
# Map to camear coordinate
poses_root_cam = transforms.matrix_to_axis_angle(tt(extrinsics[:, :3, :3]) @ transforms.axis_angle_to_matrix(tt(poses_root)))
poses = np.concatenate([poses_root_cam, poses_body], axis=-1)
pred_kp2d = np.load(osp.join(detection_results_dir, f'{subj}_{seq}.npy'))
# ======== Extract features ======== #
imname_list = sorted(glob(osp.join(sequence, 'images/*')))
bboxes, frame_ids, patch_list, features, flipped_features = [], [], [], [], []
bar = Bar(f'Load images', fill='#', max=len(imname_list))
for idx, (imname, xyxy, mask) in enumerate(zip(imname_list, xyxys, masks)):
if not mask: continue
# ========= Load image ========= #
img_rgb = cv2.cvtColor(cv2.imread(imname), cv2.COLOR_BGR2RGB)
# ========= Load bbox ========= #
x1, y1, x2, y2 = xyxy
bbox = np.array([(x1 + x2)/2., (y1 + y2)/2., max(x2 - x1, y2 - y1) / 1.1])
# ========= Process image ========= #
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())
bboxes.append(bbox)
frame_ids.append(idx)
bar.next()
patch_list = torch.split(torch.cat(patch_list), batch_size)
bboxes = torch.from_numpy(np.stack(bboxes)).float()
for i, patch in enumerate(patch_list):
bbox = bboxes[i*batch_size:min((i+1)*batch_size, len(frame_ids))].float().cuda()
bbox_center = bbox[:, :2]
bbox_scale = bbox[:, 2] / 200
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)
res_h, res_w = img_rgb.shape[:2]
# ======== Append data ======== #
dataset['gender'].append(gender)
dataset['bbox'].append(bboxes)
dataset['res'].append(torch.tensor([[width, height]]).repeat(len(frame_ids), 1).float())
dataset['vid'].append(f'{subj}_{seq}')
dataset['pose'].append(tt(poses)[frame_ids])
dataset['betas'].append(tt(betas)[frame_ids])
dataset['kp2d'].append(tt(pred_kp2d)[frame_ids])
dataset['frame_id'].append(torch.from_numpy(np.array(frame_ids)))
dataset['cam_poses'].append(tt(extrinsics)[frame_ids])
dataset['features'].append(features)
dataset['flipped_features'].append(flipped_features)
# 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 ======== #
# 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 = bboxes[:1].clone().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'==> Done !')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-s', '--split', type=str, choices=['1', '2'], 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) |