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# -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is authorized to grant you that right.
# Any use of the computer program without a valid license is prohibited and
# liable to prosecution.
#
# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems. All rights reserved.
#
# Contact: ps-license@tuebingen.mpg.de
import joblib
import argparse
from tqdm import tqdm
import json
import os.path as osp
import os
import sys
sys.path.append('.')
import torch
from human_body_prior.tools.omni_tools import copy2cpu as c2c
from human_body_prior.body_model.body_model import BodyModel
from src.datasets import smpl_utils
from src import config
import numpy as np
from PIL import Image
comp_device = torch.device("cpu")
dict_keys = ['betas', 'dmpls', 'gender', 'mocap_framerate', 'poses', 'trans']
action2motion_joints = [8, 1, 2, 3, 4, 5, 6, 7, 0, 9, 10, 11, 12, 13, 14, 21, 24, 38] # [18,]
def get_joints_to_use(args):
joints_to_use = np.array([
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 37
]) # 23 joints + global_orient # 21 base joints + left_index1(22) + right_index1 (37)
return np.arange(0, len(smpl_utils.SMPLH_JOINT_NAMES) * 3).reshape((-1, 3))[joints_to_use].reshape(-1)
framerate_hist = []
all_sequences = [
'ACCAD',
'BioMotionLab_NTroje',
'CMU',
'EKUT',
'Eyes_Japan_Dataset',
'HumanEva',
'KIT',
'MPI_HDM05',
'MPI_Limits',
'MPI_mosh',
'SFU',
'SSM_synced',
'TCD_handMocap',
'TotalCapture',
'Transitions_mocap',
]
amass_test_split = ['Transitions_mocap', 'SSM_synced']
amass_vald_split = ['HumanEva', 'MPI_HDM05', 'SFU', 'MPI_mosh']
amass_train_split = ['BioMotionLab_NTroje', 'Eyes_Japan_Dataset', 'TotalCapture', 'KIT', 'ACCAD', 'CMU', 'MPI_Limits',
'TCD_handMocap', 'EKUT']
# Source - https://github.com/nghorbani/amass/blob/08ca36ce9b37969f72d7251eb61564a7fd421e15/src/amass/data/prepare_data.py#L235
amass_splits = {
'test': amass_test_split,
'vald': amass_vald_split,
'train': amass_train_split
}
assert len(amass_splits['train'] + amass_splits['test'] + amass_splits['vald']) == len(all_sequences) == 15
def read_data(folder, split_name,dataset_name, target_fps, max_fps_dist, joints_to_use, quick_run,babel_labels, clip_images_dir=None):
# sequences = [osp.join(folder, x) for x in sorted(os.listdir(folder)) if osp.isdir(osp.join(folder, x))]
# target_fps = None -> will not resample (for backward compatibility)
if dataset_name == "amass":
sequences = amass_splits[split_name]
else:
sequences = all_sequences
db = {
'vid_names': [],
'thetas': [],
'joints3d': [],
'clip_images': [],
'clip_pathes': [],
'text_raw_labels': [],
'text_proc_labels': [],
'action_cat': []
}
# instance SMPL model
print('Loading Body Models')
body_models = {
'neutral': BodyModel(config.SMPLH_AMASS_MODEL_PATH, num_betas=config.NUM_BETAS).to(comp_device),
}
print('DONE! - Loading Body Models')
clip_images_path = clip_images_dir
assert os.path.isdir(clip_images_path)
for seq_name in sequences:
print(f'Reading {seq_name} sequence...')
seq_folder = osp.join(folder, seq_name)
results_dict = read_single_sequence(split_name, dataset_name, seq_folder, seq_name, body_models, target_fps,
max_fps_dist, joints_to_use, quick_run, clip_images_path, babel_labels)
for k in db.keys(): db[k].extend(results_dict[k])
return db
def read_single_sequence(split_name, dataset_name, folder, seq_name, body_models, target_fps, max_fps_dist,
joints_to_use, quick_run, clip_images_path, fname_to_babel):
# target_fps = None -> will not resample (for backward compatibility)
subjects = os.listdir(folder)
thetas = []
vid_names = []
joints3d = []
clip_images = []
clip_pathes = []
text_raw_labels = []
text_proc_labels = []
action_cat = []
for subject in tqdm(subjects):
actions = [x for x in os.listdir(osp.join(folder, subject)) if x.endswith('.npz')]
for action in actions:
fname = osp.join(folder, subject, action)
if fname.endswith('shape.npz'):
continue
# Remove folder path from fname
folder_path, sequence_name = os.path.split(folder)
seq_subj_action = osp.join(sequence_name, subject, action)
if seq_subj_action in fname_to_babel:
babel_dict = fname_to_babel[seq_subj_action]
else:
print(f"Not in BABEL: {seq_subj_action}")
continue
if dataset_name == "babel":
# # Check if pose belongs to split
babel_split = babel_dict['split'].replace("val", "vald") # Fix diff in split name
if babel_split != split_name:
continue
data = np.load(fname)
duration_t = babel_dict['dur']
fps = data['poses'].shape[0] / duration_t
# Seq. labels
seq_raw_labels, seq_proc_label, seq_act_cat = [], [], []
frame_raw_text_labels = np.full(data['poses'].shape[0], "", dtype=np.object)
frame_proc_text_labels = np.full(data['poses'].shape[0], "", dtype=np.object)
frame_action_cat = np.full(data['poses'].shape[0], "", dtype=np.object)
for label_dict in babel_dict['seq_ann']['labels']:
seq_raw_labels.extend([label_dict['raw_label']])
seq_proc_label.extend([label_dict['proc_label']])
if label_dict['act_cat'] is not None:
seq_act_cat.extend(label_dict['act_cat'])
# Frames labels
if babel_dict['frame_ann'] is None:
frame_raw_labels = "and ".join(seq_raw_labels)
frame_proc_labels = "and ".join(seq_proc_label)
start_frame = 0
end_frame = data['poses'].shape[0]
frame_raw_text_labels[start_frame:end_frame] = frame_raw_labels
frame_proc_text_labels[start_frame:end_frame] = frame_proc_labels
frame_action_cat[start_frame:end_frame] = ",".join(seq_act_cat)
else:
for label_dict in babel_dict['frame_ann']['labels']:
start_frame = round(label_dict['start_t'] * fps)
end_frame = round(label_dict['end_t'] * fps)
frame_raw_text_labels[start_frame:end_frame] = label_dict['raw_label']
frame_proc_text_labels[start_frame:end_frame] = label_dict['proc_label']
if label_dict['act_cat'] is not None:
frame_action_cat[start_frame:end_frame] = str(",".join(label_dict['act_cat']))
if target_fps is not None:
mocap_framerate = float(data['mocap_framerate'])
sampling_freq = round(mocap_framerate / target_fps)
if abs(mocap_framerate / float(sampling_freq) - target_fps) > max_fps_dist:
print('Will not sample [{}]fps seq with sampling_freq [{}], since target_fps=[{}], max_fps_dist=[{}]'
.format(mocap_framerate, sampling_freq, target_fps, max_fps_dist))
continue
# pose = data['poses'][:, joints_to_use]
pose = data['poses'][0::sampling_freq, joints_to_use]
pose_all = data['poses'][0::sampling_freq, :]
frame_raw_text_labels = frame_raw_text_labels[0::sampling_freq]
frame_proc_text_labels = frame_proc_text_labels[0::sampling_freq]
else:
# don't sample
pose = data['poses'][:, joints_to_use]
pose_all = data['poses'][:, :]
if pose.shape[0] < 60:
continue
theta = pose
vid_name = np.array([f'{seq_name}_{subject}_{action[:-4]}']*pose.shape[0])
if quick_run:
joints = None
images = None
else:
root_orient = torch.Tensor(pose_all[:, :(smpl_utils.JOINTS_SART_INDEX * 3)]).to(comp_device)
pose_hand = torch.Tensor(pose_all[:, (smpl_utils.L_HAND_START_INDEX * 3):]).to(comp_device)
pose_body = torch.Tensor(pose_all[:, (smpl_utils.JOINTS_SART_INDEX * 3):(
smpl_utils.L_HAND_START_INDEX * 3)]).to(comp_device)
body_model = body_models['neutral']
body_motion = body_model(pose_body=pose_body, pose_hand=pose_hand, root_orient=root_orient)
joints = c2c(body_motion.Jtr) # [seq_len, 52, 3]
joints = joints[:, action2motion_joints] # [seq_len, 18, 3]
images = None
images_path = None
if clip_images_path is not None:
images_path = [os.path.join(clip_images_path, f) for f in os.listdir(clip_images_path) if f.startswith(vid_name[0]) and f.endswith('.png')]
images_path.sort(key=lambda x: int(x.replace('.png', '').split('frame')[-1]))
images_path = np.array(images_path)
images = [np.asarray(Image.open(im)) for im in images_path]
images = np.concatenate([np.expand_dims(im, 0) for im in images], axis=0)
vid_names.append(vid_name)
thetas.append(theta)
joints3d.append(joints)
clip_images.append(images)
clip_pathes.append(images_path)
text_raw_labels.append(frame_raw_text_labels)
text_proc_labels.append(frame_proc_text_labels)
action_cat.append(frame_action_cat)
# return np.concatenate(thetas, axis=0), np.concatenate(vid_names, axis=0)
return {
# 'betas': betas,
'vid_names': vid_names,
'thetas': thetas,
'joints3d': joints3d,
'clip_images': clip_images,
'clip_pathes': clip_pathes,
'text_raw_labels': text_raw_labels,
'text_proc_labels': text_proc_labels,
'action_cat': action_cat
}
def get_babel_labels(babel_dir_path):
print("Loading babel labels")
l_babel_dense_files = ['train', 'val']
# BABEL Dataset
pose_file_to_babel = {}
for file in l_babel_dense_files:
path = os.path.join(babel_dir_path, file + '.json')
data = json.load(open(path))
for seq_id, seq_dict in data.items():
npz_path = os.path.join(*(seq_dict['feat_p'].split(os.path.sep)[1:]))
seq_dict['split'] = file
pose_file_to_babel[npz_path] = seq_dict
print("DONE! - Loading babel labels")
return pose_file_to_babel
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--input_dir', type=str, help='dataset directory', default='./data/amass')
parser.add_argument('--output_dir', type=str, help='target directory', default='./data/amass_db')
parser.add_argument('--clip_images_dir', type=str, help='dataset directory', default='./data/render')
parser.add_argument('--target_fps', type=int, choices=[10, 30, 60], default=30)
parser.add_argument('--quick_run', action='store_true', help='quick_run wo saving and modeling 3d positions with smpl, just for debug')
parser.add_argument('--dataset_name', required=True, type=str, choices=['amass', 'babel'], default='amass',
help='choose which dataset you want to create')
parser.add_argument('--babel_dir', type=str, help='path to processed BABEL downloaded dir BABEL file',
default='./data/babel_v1.0_release')
args = parser.parse_args()
fname_to_babel = get_babel_labels(args.babel_dir)
joints_to_use = get_joints_to_use(args)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
max_fps_dist = 5 # max distance from target fps that can be tolerated
if args.quick_run:
print('quick_run mode')
for split_name in amass_splits.keys():
db = read_data(args.input_dir,
split_name=split_name,
dataset_name=args.dataset_name,
target_fps=args.target_fps,
max_fps_dist=max_fps_dist,
joints_to_use=joints_to_use,
quick_run=args.quick_run,
babel_labels=fname_to_babel,
clip_images_dir=args.clip_images_dir
)
db_file = osp.join(args.output_dir, '{}_{}fps'.format(args.dataset_name, args.target_fps))
db_file += '_{}.pt'.format(split_name)
if args.quick_run:
print(f'quick_run mode - file should be saved to {db_file}')
else:
print(f'Saving AMASS dataset to {db_file}')
joblib.dump(db, db_file)
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