Spaces:
Sleeping
Sleeping
File size: 1,958 Bytes
99afdfe |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 |
'''
LVD: different initial pose
diversity: same initial pose
'''
import os
import sys
sys.path.append(os.getcwd())
from glob import glob
from argparse import ArgumentParser
import json
from evaluation.util import *
from evaluation.metrics import *
from tqdm import tqdm
parser = ArgumentParser()
parser.add_argument('--speaker', required=True, type=str)
parser.add_argument('--post_fix', nargs='+', default=['base'], type=str)
args = parser.parse_args()
speaker = args.speaker
test_audios = sorted(glob('pose_dataset/videos/test_audios/%s/*.wav'%(speaker)))
LVD_list = []
diversity_list = []
for aud in tqdm(test_audios):
base_name = os.path.splitext(aud)[0]
gt_path = get_full_path(aud, speaker, 'val')
_, gt_poses, _ = get_gts(gt_path)
gt_poses = gt_poses[np.newaxis,...]
# print(gt_poses.shape)#(seq_len, 135*2)pose, lhand, rhand, face
for post_fix in args.post_fix:
pred_path = base_name + '_'+post_fix+'.json'
pred_poses = np.array(json.load(open(pred_path)))
# print(pred_poses.shape)#(B, seq_len, 108)
pred_poses = cvt25(pred_poses, gt_poses)
# print(pred_poses.shape)#(B, seq, pose_dim)
gt_valid_points = hand_points(gt_poses)
pred_valid_points = hand_points(pred_poses)
lvd = LVD(gt_valid_points, pred_valid_points)
# div = diversity(pred_valid_points)
LVD_list.append(lvd)
# diversity_list.append(div)
# gt_velocity = peak_velocity(gt_valid_points, order=2)
# pred_velocity = peak_velocity(pred_valid_points, order=2)
# gt_consistency = velocity_consistency(gt_velocity, pred_velocity)
# pred_consistency = velocity_consistency(pred_velocity, gt_velocity)
# gt_consistency_list.append(gt_consistency)
# pred_consistency_list.append(pred_consistency)
lvd = np.mean(LVD_list)
# diversity_list = np.mean(diversity_list)
print('LVD:', lvd)
# print("diversity:", diversity_list) |