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github-python

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ground_pene_dist_list[recording_name] = np.concatenate(ground_pene_dist_list[recording_name], axis=0)
print('\n --------------- evaluation metrics -------------')
skating_list['all'] = np.concatenate([skating_list[recording_name] for recording_name in test_recording_name_list], axis=0)
acc_list['all'] = np.concatenate([acc_list[recording_name] for recording_name in test_recording_name_list], axis=0)
if args.dataset == 'egobody':
acc_error_list['all'] = np.concatenate([acc_error_list[recording_name] for recording_name in test_recording_name_list], axis=0)
joint_mask_list['all'] = np.concatenate([joint_mask_list[recording_name] for recording_name in test_recording_name_list], axis=0)
gmpjpe_list['all'] = np.concatenate([gmpjpe_list[recording_name] for recording_name in test_recording_name_list], axis=0)
mpjpe_list['all'] = np.concatenate([mpjpe_list[recording_name] for recording_name in test_recording_name_list], axis=0)
mpjpe_list_vis['all'] = np.concatenate([mpjpe_list_vis[recording_name] for recording_name in test_recording_name_list], axis=0)
mpjpe_list_occ['all'] = np.concatenate([mpjpe_list_occ[recording_name] for recording_name in test_recording_name_list], axis=0)
ground_pene_freq_list['all'] = np.concatenate([ground_pene_freq_list[recording_name] for recording_name in test_recording_name_list], axis=0)
ground_pene_dist_list['all'] = np.concatenate([ground_pene_dist_list[recording_name] for recording_name in test_recording_name_list], axis=0)
print('skating score: {:0.3f}'.format(skating_list['all'].mean()))
print('||acc|| (m/s^2): {:0.2f}'.format(acc_list['all'].mean())) if args.dataset == 'prox' else None
print('acc errors (m/s^2): {:0.2f}'.format(acc_error_list['all'].mean())) if args.dataset == 'egobody' else None
print('ground_pene_freq score (%): {:0.2f}'.format(ground_pene_freq_list['all'].mean()*100))
print('ground_pene_dist score (mm): {:0.2f}'.format(-ground_pene_dist_list['all'].mean()*1000))
if args.dataset == 'egobody':
print('-------------- gmpjpe/mpjpe/mpjpe-vis/mpjpe-occ (mm) --------------')
print('{:0.2f} / {:0.2f} / {:0.2f} / {:0.2f}'.
format(gmpjpe_list['all'].mean() * 1000, mpjpe_list['all'].mean() * 1000,
mpjpe_list_vis['all'].sum() / ((joint_mask_list['all']).sum()) * 1000,
mpjpe_list_occ['all'].sum() / ((1 - joint_mask_list['all']).sum()) * 1000))
# <FILESEP>
import base64
import hashlib
import json
import os
import random
from collections.abc import Iterable
from urllib.parse import quote
import piexif
import requests
from PIL import Image
from bs4 import BeautifulSoup
from config import APIKEY, BAIDUAPPID, BAIDUAPPKEY
proxies = None
verify = True
def getUuid() -> str:
"""
生成一个uuid
:return: uuid
"""
return str(random.randint(1, 999999)).zfill(6)
def getBaiDuAudio(text: str, filePath: str) -> [str, None]:
"""
利用百度语音合成将文字合成语音
:param text: 合成语音的文字
:param filePath: 文件保存路径
:return: 文件路径或者None
"""
# 生成文件名
md5 = hashlib.md5()
md5.update(text.encode('utf-8'))
fileName = md5.hexdigest() + '.mp3'
fileName = os.path.join(filePath, fileName)
# 文件已经存在的话直接返回
if os.path.exists(fileName):
return fileName
url = 'https://cloud.baidu.com/aidemo'
data = 'type=tns&per=4105&spd=8&pit=7&vol=5&aue=6&tex=' + quote(text)
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.81 Safari/537.36',
'Content-Type': 'application/x-www-form-urlencoded',
'Accept': '*/*',
'Accept-Language': 'zh-CN,zh;q=0.9,en-US;q=0.8,en;q=0.7'
}
res = requests.post(url, data=data, headers=headers, verify=False)
if res.status_code != 200:
return None
res = json.loads(res.text)
if res['msg'] != 'success':
return None
data = res['data'].replace('data:audio/x-mpeg;base64,', '')
if ',' in data:
data = data[:data.find(',')]
data = base64.b64decode(data)
with open(fileName, 'wb') as f:
f.write(data)
return fileName
def decodeBaiduImg(objUrl: str) -> str:
"""
百度图片地址解码函数
:param objUrl: 编码的url
:return: 解码的url