import os import pickle import math import shutil import numpy as np import lmdb as lmdb import textgrid as tg import pandas as pd import torch import glob import json from termcolor import colored from loguru import logger from collections import defaultdict from torch.utils.data import Dataset import torch.distributed as dist #import pyarrow import pickle import librosa import smplx import glob from .build_vocab import Vocab from .utils.audio_features import Wav2Vec2Model from .data_tools import joints_list from .utils import rotation_conversions as rc from .utils import other_tools # ACCAD 120 # BioMotionLab_NTroje 120 # CMU 很复杂 # EKUT 100 # Eyes_Japan_Dataset 很复杂 # HumanEva 很复杂 # KIT 100 # MPI_HDM05 120 # MPI_Limits 120 # MPI_mosh 很复杂 # SFU 120 # SSM_synced 很复杂 # TCD_handMocap 很复杂 # TotalCapture 60 # Transitions_mocap 120 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'] # 上面这些spilt方式是MOTION CLIP的，但是由于motionx中的framerate处理有问题，我先暂且只挑部分数据集进行训练 # 这些都是120fps的 # amass_test_split = ['SFU'] # amass_vald_split = ['MPI_Limits'] # amass_train_split = ['BioMotionLab_NTroje', 'MPI_HDM05', 'ACCAD','Transitions_mocap'] amass_splits = { 'test': amass_test_split, 'val': amass_vald_split, 'train': amass_train_split } # assert len(amass_splits['train'] + amass_splits['test'] + amass_splits['vald']) == len(all_sequences) == 15 class CustomDataset(Dataset): def __init__(self, args, loader_type, augmentation=None, kwargs=None, build_cache=True): self.args = args self.loader_type = loader_type self.rank = 0 self.ori_stride = self.args.stride self.ori_length = self.args.pose_length self.alignment = [0,0] # for trinity self.ori_joint_list = joints_list[self.args.ori_joints] self.tar_joint_list = joints_list[self.args.tar_joints] if 'smplx' in self.args.pose_rep: self.joint_mask = np.zeros(len(list(self.ori_joint_list.keys()))*3) self.joints = len(list(self.tar_joint_list.keys())) for joint_name in self.tar_joint_list: self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 else: self.joints = len(list(self.ori_joint_list.keys()))+1 self.joint_mask = np.zeros(self.joints*3) for joint_name in self.tar_joint_list: if joint_name == "Hips": self.joint_mask[3:6] = 1 else: self.joint_mask[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 # select trainable joints split_rule = pd.read_csv(args.data_path+"train_test_split.csv") self.selected_file = split_rule.loc[(split_rule['type'] == loader_type) & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))] if args.additional_data and loader_type == 'train': split_b = split_rule.loc[(split_rule['type'] == 'additional') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))] #self.selected_file = split_rule.loc[(split_rule['type'] == 'additional') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))] self.selected_file = pd.concat([self.selected_file, split_b]) if self.selected_file.empty: logger.warning(f"{loader_type} is empty for speaker {self.args.training_speakers}, use train set 0-8 instead") self.selected_file = split_rule.loc[(split_rule['type'] == 'train') & (split_rule['id'].str.split("_").str[0].astype(int).isin(self.args.training_speakers))] self.selected_file = self.selected_file.iloc[0:8] self.data_dir = args.data_path self.use_amass = args.use_amass self.beatx_during_time = 0 self.amass_during_time = 0 if loader_type == "test": self.args.multi_length_training = [1.0] self.max_length = int(args.pose_length * self.args.multi_length_training[-1]) self.max_audio_pre_len = math.floor(args.pose_length / args.pose_fps * self.args.audio_sr) if self.max_audio_pre_len > self.args.test_length*self.args.audio_sr: self.max_audio_pre_len = self.args.test_length*self.args.audio_sr preloaded_dir = self.args.root_path + self.args.cache_path + loader_type + f"/{args.pose_rep}_cache" if self.args.beat_align: if not os.path.exists(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy"): self.calculate_mean_velocity(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy") self.avg_vel = np.load(args.data_path+f"weights/mean_vel_{args.pose_rep}.npy") if build_cache and self.rank == 0: self.build_cache(preloaded_dir) self.lmdb_env = lmdb.open(preloaded_dir, readonly=True, lock=False) with self.lmdb_env.begin() as txn: self.n_samples = txn.stat()["entries"] self.norm = True self.mean = np.load('./mean_std/beatx_2_330_mean.npy') self.std = np.load('./mean_std/beatx_2_330_std.npy') self.trans_mean = np.load('./mean_std/beatx_2_trans_mean.npy') self.trans_std = np.load('./mean_std/beatx_2_trans_std.npy') def load_amass(self,data): ## 这个是用来 # 修改amass数据里面的朝向，原本在blender里面是Z轴向上，目标是Y轴向上，当时面向目前没改 data_dict = {key: data[key] for key in data} frames = data_dict['poses'].shape[0] b = data_dict['poses'][...,:3] b = rc.axis_angle_to_matrix(torch.from_numpy(b)) rot_matrix = np.array([[1.0, 0.0, 0.0], [0.0 , 0.0, 1.0], [0.0, -1.0, 0.0]]) c = np.einsum('ij,kjl->kil',rot_matrix,b) c = rc.matrix_to_axis_angle(torch.from_numpy(c)) data_dict['poses'][...,:3] = c trans_matrix1 = np.array([[1.0, 0.0, 0.0], [0.0 , 0.0, -1.0], [0.0, 1.0, 0.0]]) data_dict['trans'] = np.einsum("bi,ij->bj",data_dict['trans'],trans_matrix1) betas300 = np.zeros(300) betas300[:16] = data_dict['betas'] data_dict['betas'] = betas300 data_dict["expressions"] = np.zeros((frames,100)) return data_dict def calculate_mean_velocity(self, save_path): self.smplx = smplx.create( self.args.data_path_1+"smplx_models/", model_type='smplx', gender='NEUTRAL_2020', use_face_contour=False, num_betas=300, num_expression_coeffs=100, ext='npz', use_pca=False, ).cuda().eval() dir_p = self.data_dir + self.args.pose_rep + "/" all_list = [] from tqdm import tqdm for tar in tqdm(os.listdir(dir_p)): if tar.endswith(".npz"): m_data = np.load(dir_p+tar, allow_pickle=True) betas, poses, trans, exps = m_data["betas"], m_data["poses"], m_data["trans"], m_data["expressions"] n, c = poses.shape[0], poses.shape[1] betas = betas.reshape(1, 300) betas = np.tile(betas, (n, 1)) betas = torch.from_numpy(betas).cuda().float() poses = torch.from_numpy(poses.reshape(n, c)).cuda().float() exps = torch.from_numpy(exps.reshape(n, 100)).cuda().float() trans = torch.from_numpy(trans.reshape(n, 3)).cuda().float() max_length = 128 s, r = n//max_length, n%max_length #print(n, s, r) all_tensor = [] for i in range(s): with torch.no_grad(): joints = self.smplx( betas=betas[i*max_length:(i+1)*max_length], transl=trans[i*max_length:(i+1)*max_length], expression=exps[i*max_length:(i+1)*max_length], jaw_pose=poses[i*max_length:(i+1)*max_length, 66:69], global_orient=poses[i*max_length:(i+1)*max_length,:3], body_pose=poses[i*max_length:(i+1)*max_length,3:21*3+3], left_hand_pose=poses[i*max_length:(i+1)*max_length,25*3:40*3], right_hand_pose=poses[i*max_length:(i+1)*max_length,40*3:55*3], return_verts=True, return_joints=True, leye_pose=poses[i*max_length:(i+1)*max_length, 69:72], reye_pose=poses[i*max_length:(i+1)*max_length, 72:75], )['joints'][:, :55, :].reshape(max_length, 55*3) all_tensor.append(joints) if r != 0: with torch.no_grad(): joints = self.smplx( betas=betas[s*max_length:s*max_length+r], transl=trans[s*max_length:s*max_length+r], expression=exps[s*max_length:s*max_length+r], jaw_pose=poses[s*max_length:s*max_length+r, 66:69], global_orient=poses[s*max_length:s*max_length+r,:3], body_pose=poses[s*max_length:s*max_length+r,3:21*3+3], left_hand_pose=poses[s*max_length:s*max_length+r,25*3:40*3], right_hand_pose=poses[s*max_length:s*max_length+r,40*3:55*3], return_verts=True, return_joints=True, leye_pose=poses[s*max_length:s*max_length+r, 69:72], reye_pose=poses[s*max_length:s*max_length+r, 72:75], )['joints'][:, :55, :].reshape(r, 55*3) all_tensor.append(joints) joints = torch.cat(all_tensor, axis=0) joints = joints.permute(1, 0) dt = 1/30 # first steps is forward diff (t+1 - t) / dt init_vel = (joints[:, 1:2] - joints[:, :1]) / dt # middle steps are second order (t+1 - t-1) / 2dt middle_vel = (joints[:, 2:] - joints[:, 0:-2]) / (2 * dt) # last step is backward diff (t - t-1) / dt final_vel = (joints[:, -1:] - joints[:, -2:-1]) / dt #print(joints.shape, init_vel.shape, middle_vel.shape, final_vel.shape) vel_seq = torch.cat([init_vel, middle_vel, final_vel], dim=1).permute(1, 0).reshape(n, 55, 3) #print(vel_seq.shape) #.permute(1, 0).reshape(n, 55, 3) vel_seq_np = vel_seq.cpu().numpy() vel_joints_np = np.linalg.norm(vel_seq_np, axis=2) # n * 55 all_list.append(vel_joints_np) avg_vel = np.mean(np.concatenate(all_list, axis=0),axis=0) # 55 np.save(save_path, avg_vel) def build_cache(self, preloaded_dir): logger.info(f"Audio bit rate: {self.args.audio_fps}") logger.info("Reading data '{}'...".format(self.data_dir)) logger.info("Creating the dataset cache...") if self.args.new_cache: if os.path.exists(preloaded_dir): shutil.rmtree(preloaded_dir) if os.path.exists(preloaded_dir): logger.info("Found the cache {}".format(preloaded_dir)) elif self.loader_type == "test": self.cache_generation( preloaded_dir, True, 0, 0, is_test=True) else: self.cache_generation( preloaded_dir, self.args.disable_filtering, self.args.clean_first_seconds, self.args.clean_final_seconds, is_test=False) logger.info(f"BEATX during time is {self.beatx_during_time}s !") logger.info(f"AMASS during time is {self.amass_during_time}s !") ## 对于BEATX train ,val ,test: 69800s ,7695s, 18672s ,总计 26.7h ## def __len__(self): return self.n_samples def cache_generation(self, out_lmdb_dir, disable_filtering, clean_first_seconds, clean_final_seconds, is_test=False): # if "wav2vec2" in self.args.audio_rep: # self.wav2vec_model = Wav2Vec2Model.from_pretrained(f"{self.args.data_path_1}/hub/transformer/wav2vec2-base-960h") # self.wav2vec_model.feature_extractor._freeze_parameters() # self.wav2vec_model = self.wav2vec_model.cuda() # self.wav2vec_model.eval() self.n_out_samples = 0 # create db for samples if not os.path.exists(out_lmdb_dir): os.makedirs(out_lmdb_dir) dst_lmdb_env = lmdb.open(out_lmdb_dir, map_size= int(1024 ** 3 * 50))# 50G n_filtered_out = defaultdict(int) for index, file_name in self.selected_file.iterrows(): f_name = file_name["id"] ext = ".npz" if "smplx" in self.args.pose_rep else ".bvh" pose_file = self.data_dir + self.args.pose_rep + "/" + f_name + ext pose_each_file = [] trans_each_file = [] trans_v_each_file = [] shape_each_file = [] audio_each_file = [] facial_each_file = [] word_each_file = [] emo_each_file = [] sem_each_file = [] vid_each_file = [] id_pose = f_name #1_wayne_0_1_1 logger.info(colored(f"# ---- Building cache for Pose {id_pose} ---- #", "blue")) if "smplx" in self.args.pose_rep: pose_data = np.load(pose_file, allow_pickle=True) assert 30%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 30' stride = int(30/self.args.pose_fps) pose_each_file = pose_data["poses"][::stride] * self.joint_mask pose_each_file = pose_each_file[:, self.joint_mask.astype(bool)] # print(pose_each_file.shape) self.beatx_during_time += pose_each_file.shape[0]/30 trans_each_file = pose_data["trans"][::stride] trans_each_file[:,0] = trans_each_file[:,0] - trans_each_file[0,0] trans_each_file[:,2] = trans_each_file[:,2] - trans_each_file[0,2] trans_v_each_file = np.zeros_like(trans_each_file) trans_v_each_file[1:,0] = trans_each_file[1:,0] - trans_each_file[:-1,0] trans_v_each_file[0,0] = trans_v_each_file[1,0] trans_v_each_file[1:,2] = trans_each_file[1:,2] - trans_each_file[:-1,2] trans_v_each_file[0,2] = trans_v_each_file[1,2] trans_v_each_file[:,1] = trans_each_file[:,1] shape_each_file = np.repeat(pose_data["betas"].reshape(1, 300), pose_each_file.shape[0], axis=0) if self.args.facial_rep is not None: logger.info(f"# ---- Building cache for Facial {id_pose} and Pose {id_pose} ---- #") facial_each_file = pose_data["expressions"][::stride] if self.args.facial_norm: facial_each_file = (facial_each_file - self.mean_facial) / self.std_facial if self.args.id_rep is not None: vid_each_file = np.repeat(np.array(int(f_name.split("_")[0])-1).reshape(1, 1), pose_each_file.shape[0], axis=0) filtered_result = self._sample_from_clip( dst_lmdb_env, pose_each_file, trans_each_file,trans_v_each_file, shape_each_file, vid_each_file, disable_filtering, clean_first_seconds, clean_final_seconds, is_test, ) for type in filtered_result.keys(): n_filtered_out[type] += filtered_result[type] if self.args.use_amass: amass_dir = '/mnt/fu09a/chenbohong/PantoMatrix/scripts/EMAGE_2024/datasets/AMASS_SMPLX' for dataset in amass_splits[self.loader_type]: search_path = os.path.join(amass_dir,dataset, '**', '*.npz') npz_files = glob.glob(search_path, recursive=True) for index, file_name in enumerate(npz_files): f_name = file_name.split('/')[-1] ext = ".npz" if "smplx" in self.args.pose_rep else ".bvh" pose_file = file_name pose_each_file = [] trans_each_file = [] trans_v_each_file = [] shape_each_file = [] audio_each_file = [] facial_each_file = [] word_each_file = [] emo_each_file = [] sem_each_file = [] vid_each_file = [] id_pose = f_name #1_wayne_0_1_1 logger.info(colored(f"# ---- Building cache for Pose {id_pose} ---- #", "blue")) if "smplx" in self.args.pose_rep: pose_data = np.load(pose_file, allow_pickle=True) if len(pose_data.files)==6: logger.info(colored(f"# ---- state file ---- #", "red")) continue assert 30%self.args.pose_fps == 0, 'pose_fps should be an aliquot part of 30' pose_each_file = self.load_amass(pose_data) fps = pose_data['mocap_frame_rate'] stride =round(fps/30) pose_each_file = pose_data["poses"][::stride] * self.joint_mask pose_each_file = pose_each_file[:, self.joint_mask.astype(bool)] trans_each_file = pose_data["trans"][::stride] trans_each_file[:,0] = trans_each_file[:,0] - trans_each_file[0,0] trans_each_file[:,2] = trans_each_file[:,2] - trans_each_file[0,2] trans_v_each_file = np.zeros_like(trans_each_file) trans_v_each_file[1:,0] = trans_each_file[1:,0] - trans_each_file[:-1,0] trans_v_each_file[0,0] = trans_v_each_file[1,0] trans_v_each_file[1:,2] = trans_each_file[1:,2] - trans_each_file[:-1,2] trans_v_each_file[0,2] = trans_v_each_file[1,2] trans_v_each_file[:,1] = trans_each_file[:,1] shape_each_file = np.repeat(pose_data["betas"].reshape(1, -1), pose_each_file.shape[0], axis=0) if self.args.id_rep is not None: vid_each_file = np.repeat(np.array(int(100)-1).reshape(1, 1), pose_each_file.shape[0], axis=0) filtered_result = self._sample_from_clip( dst_lmdb_env, pose_each_file, trans_each_file,trans_v_each_file, shape_each_file, vid_each_file, disable_filtering, clean_first_seconds, clean_final_seconds, is_test, ) for type in filtered_result.keys(): n_filtered_out[type] += filtered_result[type] with dst_lmdb_env.begin() as txn: logger.info(colored(f"no. of samples: {txn.stat()['entries']}", "cyan")) n_total_filtered = 0 for type, n_filtered in n_filtered_out.items(): logger.info("{}: {}".format(type, n_filtered)) n_total_filtered += n_filtered logger.info(colored("no. of excluded samples: {} ({:.1f}%)".format( n_total_filtered, 100 * n_total_filtered / (txn.stat()["entries"] + n_total_filtered)), "cyan")) dst_lmdb_env.sync() dst_lmdb_env.close() def _sample_from_clip( self, dst_lmdb_env, pose_each_file, trans_each_file, trans_v_each_file,shape_each_file, vid_each_file, disable_filtering, clean_first_seconds, clean_final_seconds, is_test, ): """ for data cleaning, we ignore the data for first and final n s for test, we return all data """ # audio_start = int(self.alignment[0] * self.args.audio_fps) # pose_start = int(self.alignment[1] * self.args.pose_fps) #logger.info(f"before: {audio_each_file.shape} {pose_each_file.shape}") # audio_each_file = audio_each_file[audio_start:] # pose_each_file = pose_each_file[pose_start:] # trans_each_file = #logger.info(f"after alignment: {audio_each_file.shape} {pose_each_file.shape}") #print(pose_each_file.shape) round_seconds_skeleton = pose_each_file.shape[0] // self.args.pose_fps # assume 1500 frames / 15 fps = 100 s #print(round_seconds_skeleton) clip_s_t, clip_e_t = clean_first_seconds, round_seconds_skeleton - clean_final_seconds # assume [10, 90]s clip_s_f_audio, clip_e_f_audio = self.args.audio_fps * clip_s_t, clip_e_t * self.args.audio_fps # [160,000,90*160,000] clip_s_f_pose, clip_e_f_pose = clip_s_t * self.args.pose_fps, clip_e_t * self.args.pose_fps # [150,90*15] for ratio in self.args.multi_length_training: if is_test:# stride = length for test cut_length = clip_e_f_pose - clip_s_f_pose self.args.stride = cut_length self.max_length = cut_length else: self.args.stride = int(ratio*self.ori_stride) cut_length = int(self.ori_length*ratio) num_subdivision = math.floor((clip_e_f_pose - clip_s_f_pose - cut_length) / self.args.stride) + 1 logger.info(f"pose from frame {clip_s_f_pose} to {clip_e_f_pose}, length {cut_length}") logger.info(f"{num_subdivision} clips is expected with stride {self.args.stride}") n_filtered_out = defaultdict(int) sample_pose_list = [] sample_audio_list = [] sample_shape_list = [] sample_vid_list = [] sample_trans_list = [] sample_trans_v_list = [] for i in range(num_subdivision): # cut into around 2s chip, (self npose) start_idx = clip_s_f_pose + i * self.args.stride fin_idx = start_idx + cut_length sample_pose = pose_each_file[start_idx:fin_idx] sample_trans = trans_each_file[start_idx:fin_idx] sample_trans_v = trans_v_each_file[start_idx:fin_idx] sample_shape = shape_each_file[start_idx:fin_idx] # print(sample_pose.shape) sample_vid = vid_each_file[start_idx:fin_idx] if self.args.id_rep is not None else np.array([-1]) if sample_pose.any() != None: # filtering motion skeleton data sample_pose, filtering_message = MotionPreprocessor(sample_pose).get() is_correct_motion = (sample_pose is not None) if is_correct_motion or disable_filtering: sample_pose_list.append(sample_pose) sample_shape_list.append(sample_shape) sample_vid_list.append(sample_vid) sample_trans_list.append(sample_trans) sample_trans_v_list.append(sample_trans_v) else: n_filtered_out[filtering_message] += 1 if len(sample_pose_list) > 0: with dst_lmdb_env.begin(write=True) as txn: for pose, shape, vid, trans,trans_v in zip( sample_pose_list, sample_shape_list, sample_vid_list, sample_trans_list, sample_trans_v_list, ): k = "{:005}".format(self.n_out_samples).encode("ascii") v = [pose , shape, vid, trans,trans_v] v = pickle.dumps(v,5) txn.put(k, v) self.n_out_samples += 1 return n_filtered_out def __getitem__(self, idx): with self.lmdb_env.begin(write=False) as txn: key = "{:005}".format(idx).encode("ascii") sample = txn.get(key) sample = pickle.loads(sample) tar_pose, in_shape, vid, trans,trans_v = sample tar_pose = torch.from_numpy(tar_pose).float() tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(-1, 55, 3)) tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(-1, 55*6) if self.norm: tar_pose = (tar_pose - self.mean) / self.std trans_v = (trans_v-self.trans_mean)/self.trans_std if self.loader_type == "test": tar_pose = tar_pose.float() trans = torch.from_numpy(trans).float() trans_v = torch.from_numpy(trans_v).float() vid = torch.from_numpy(vid).float() in_shape = torch.from_numpy(in_shape).float() else: in_shape = torch.from_numpy(in_shape).reshape((in_shape.shape[0], -1)).float() trans = torch.from_numpy(trans).reshape((trans.shape[0], -1)).float() trans_v = torch.from_numpy(trans_v).reshape((trans_v.shape[0], -1)).float() vid = torch.from_numpy(vid).reshape((vid.shape[0], -1)).float() tar_pose = tar_pose.reshape((tar_pose.shape[0], -1)).float() tar_pose = torch.cat([tar_pose, trans_v], dim=1) return tar_pose class MotionPreprocessor: def __init__(self, skeletons): self.skeletons = skeletons #self.mean_pose = mean_pose self.filtering_message = "PASS" def get(self): assert (self.skeletons is not None) # filtering if self.skeletons is not None: if self.check_pose_diff(): self.skeletons = [] self.filtering_message = "pose" # elif self.check_spine_angle(): # self.skeletons = [] # self.filtering_message = "spine angle" # elif self.check_static_motion(): # self.skeletons = [] # self.filtering_message = "motion" # if self.skeletons != []: # self.skeletons = self.skeletons.tolist() # for i, frame in enumerate(self.skeletons): # assert not np.isnan(self.skeletons[i]).any() # missing joints return self.skeletons, self.filtering_message def check_static_motion(self, verbose=True): def get_variance(skeleton, joint_idx): wrist_pos = skeleton[:, joint_idx] variance = np.sum(np.var(wrist_pos, axis=0)) return variance left_arm_var = get_variance(self.skeletons, 6) right_arm_var = get_variance(self.skeletons, 9) th = 0.0014 # exclude 13110 # th = 0.002 # exclude 16905 if left_arm_var < th and right_arm_var < th: if verbose: print("skip - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var)) return True else: if verbose: print("pass - check_static_motion left var {}, right var {}".format(left_arm_var, right_arm_var)) return False def check_pose_diff(self, verbose=False): # diff = np.abs(self.skeletons - self.mean_pose) # 186*1 # diff = np.mean(diff) # # th = 0.017 # th = 0.02 #0.02 # exclude 3594 # if diff < th: # if verbose: # print("skip - check_pose_diff {:.5f}".format(diff)) # return True # # th = 3.5 #0.02 # exclude 3594 # # if 3.5 < diff < 5: # # if verbose: # # print("skip - check_pose_diff {:.5f}".format(diff)) # # return True # else: # if verbose: # print("pass - check_pose_diff {:.5f}".format(diff)) return False def check_spine_angle(self, verbose=True): def angle_between(v1, v2): v1_u = v1 / np.linalg.norm(v1) v2_u = v2 / np.linalg.norm(v2) return np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0)) angles = [] for i in range(self.skeletons.shape[0]): spine_vec = self.skeletons[i, 1] - self.skeletons[i, 0] angle = angle_between(spine_vec, [0, -1, 0]) angles.append(angle) if np.rad2deg(max(angles)) > 30 or np.rad2deg(np.mean(angles)) > 20: # exclude 4495 # if np.rad2deg(max(angles)) > 20: # exclude 8270 if verbose: print("skip - check_spine_angle {:.5f}, {:.5f}".format(max(angles), np.mean(angles))) return True else: if verbose: print("pass - check_spine_angle {:.5f}".format(max(angles))) return False