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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=['paper_model'], type=str)
args = parser.parse_args()
speaker = args.speaker
test_audios = sorted(glob('pose_dataset/videos/test_audios/%s/*.wav'%(speaker)))
precision_list=[]
recall_list=[]
accuracy_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)
if gt_poses.shape[0] < 50:
continue
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 = valid_points(gt_poses)
pred_valid_points = valid_points(pred_poses)
# print(gt_valid_points.shape, pred_valid_points.shape)
gt_mode_transition_seq = mode_transition_seq(gt_valid_points, speaker)#(B, N)
pred_mode_transition_seq = mode_transition_seq(pred_valid_points, speaker)#(B, N)
# baseline = np.random.randint(0, 2, size=pred_mode_transition_seq.shape)
# pred_mode_transition_seq = baseline
precision, recall, accuracy = mode_transition_consistency(pred_mode_transition_seq, gt_mode_transition_seq)
precision_list.append(precision)
recall_list.append(recall)
accuracy_list.append(accuracy)
print(len(precision_list), len(recall_list), len(accuracy_list))
precision_list = np.mean(precision_list)
recall_list = np.mean(recall_list)
accuracy_list = np.mean(accuracy_list)
print('precision, recall, accu:', precision_list, recall_list, accuracy_list)
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