def face_rank(page):
cover_id_list, name_list = get_cover_id_list(page)
cover_content_list = get_cover_content_list(cover_id_list)
for cover_content, name, ids in zip(cover_content_list, name_list,
cover_id_list):
ai_obj = apiutil.AiPlat(AppID, AppKey)
rsp = ai_obj.face_detectface(cover_content, 0)
if rsp['ret'] == 0:
for face in rsp['data']['face_list']:
if face['beauty'] > face_min_rank and face[
'gender'] < 50 and face['age'] < max_age:
print('{0}颜值通过 分值{1} 年龄{2}'.format(
name, face['beauty'], face['age']))
saveimage(cover_content, name)
elif rsp['ret'] == -2147483636:
print('{0}识别繁忙，重试'.format(name))
ai_obj = apiutil.AiPlat('1106858595', 'bNUNgOpY6AeeJjFu')
rsp = ai_obj.face_detectface(cover_content, 0)
if rsp['ret'] == 0:
for face in rsp['data']['face_list']:
if face['beauty'] > face_min_rank and face[
'gender'] < 50 and face['age'] < max_age:
print('{0}颜值通过 分值{1} 年龄{2}'.format(
name, face['beauty'], face['age']))
saveimage(cover_content, name)
elif rsp['ret'] == -2147483636:
print('{0}第二次识别失败，放弃识别'.format(name))
saveerrorimage(cover_content, name)
else:
print('{2}识别出错，错误码{0}，错信信息{1}'.format(rsp['ret'], rsp['msg'],
name))
else:
print('{2}识别出错，错误码{0}，错信信息{1}'.format(rsp['ret'], rsp['msg'],
name))
def test_aai_ailab(text='今天天气怎么样'):
speaker = 1
for_mat = {"PCM": 1, "WAV": 2, "MP3": 3}
volume = 0
speed = 100
#text = '今天天气怎么样'
aht = 0
apc = 58
ai_obj = apiutil.AiPlat(app_id, app_key)
print('----------------------SEND REQ----------------------')
rsp = ai_obj.getAaiAiLab(speaker, for_mat['WAV'], volume, speed, text, aht,
apc)
if rsp['ret'] == 0:
print(json.dumps(rsp, ensure_ascii=False, sort_keys=False, indent=4))
speech = rsp['data']['speech']
speech = base64.b64decode(speech)
md5sum = rsp['data']['md5sum']
has_md5 = hashlib.md5(speech)
speech_md5 = has_md5.hexdigest().upper()
if md5sum == speech_md5:
with open('./data/ailab.wav', 'wb') as f:
f.write(speech)
print('----------------------API SUCC----------------------')
else:
print(json.dumps(rsp, ensure_ascii=False, sort_keys=False, indent=4))
print('----------------------API FAIL----------------------')
def Get_Nlp_TextTrans_api(str_text, type=0):
ai_obj = apiutil.AiPlat(app_id, app_key)
rsp = ai_obj.getNlpTextTrans(str_text, type)
data_string = ''
if rsp['ret'] == 0:
data_string = rsp['data']['trans_text']
return data_string
def Get_vision_ImgageToText_api(image_name, mediaId='1535646454'):
with open(image_name, 'rb') as bin_data:
image_data = bin_data.read()
ai_obj = apiutil.AiPlat(app_id, app_key)
rsp = ai_obj.gevision_imgtotext(image_data, mediaId)
text_string = ''
if rsp['ret'] == 0:
text_string = rsp['data']['text']
return text_string
def getWord(self,st):
api_obj=apiutil.AiPlat(self.app_id,self.app_key)
type=10000
rsp = api_obj.getNlpTextChat(type,st)
if rsp['ret'] == 0:
print(rsp['data']['answer'])
else:
print("failed")
print(rsp['ret'])
print(json.dumps(rsp, ensure_ascii=False, sort_keys=False, indent=4))
def Get_Nlp_WordCom_api(str_text):
ai_obj = apiutil.AiPlat(app_id, app_key)
rsp = ai_obj.getNlpWordCom(str_text)
data_list = []
if rsp['ret'] == 0:
intent_dict = {'intent': rsp['data']['intent']}
data_list.append(intent_dict)
for obj in rsp['data']['com_tokens']:
if obj['com_type'] < 50:
data_list.append(obj)
return data_list
def anso(questionS):
str_question = questionS
session = 10000
ai_obj = apiutil.AiPlat(app_id, app_key)
rsp = ai_obj.getNlpTextChat(session, str_question)
if rsp['ret'] == 0:
ask = (rsp['data'])['answer']
return (ask)
else:
print(json.dumps(rsp, ensure_ascii=False, sort_keys=False, indent=4))
def get_Aai_ToTts_api(str_text, mediaId='451454543', Speech_ID=6, speed=0):
ai_obj = apiutil.AiPlat(app_id, app_key)
rsp = ai_obj.getAaiToTts(str_text, Speech_ID,
speed) # 普通话男声 1 静琪女声 5 欢馨女声 6 欢馨女声 6
if rsp['ret'] == 0:
str_data = rsp['data']['voice']
speech_chunk = base64.b64decode(str_data)
file_name = './data/%s.mp3' % mediaId
file_data = open(file_name, 'wb')
file_data.write(speech_chunk)
file_data.close()
return file_name
def Get_vision_objectr_api(image_name, topk_number=1):
with open(image_name, 'rb') as bin_data:
image_data = bin_data.read()
ai_obj = apiutil.AiPlat(app_id, app_key)
rsp = ai_obj.getvision_objectr(image_data, topk_number) # 返回结果个数（已按置信度倒排）
data_lists = []
if rsp['ret'] == 0:
for obj in rsp['data']['object_list']:
data_list = [str(obj['label_id']), str(obj['label_confd'])]
data_lists.append(data_list)
return data_lists
def Get_Ocr_GeneralOcr_api(image_name):
with open(image_name, 'rb') as bin_data:
image_data = bin_data.read()
ai_obj = apiutil.AiPlat(app_id, app_key)
rsp = ai_obj.getOcrGeneralocr(image_data)
data_list = []
if rsp['ret'] == 0:
for i in rsp['data']['item_list']:
data_list.append(i['itemstring'])
return data_list
def test_nlp_texttrans():
str_text = '今天天气怎么样'
type = 0
ai_obj = apiutil.AiPlat(app_id, app_key)
print('----------------------SEND REQ----------------------')
rsp = ai_obj.getNlpTextTrans(str_text, type)
if rsp['ret'] == 0:
print(json.dumps(rsp, ensure_ascii=False, sort_keys=False, indent=4))
print('----------------------API SUCC----------------------')
else:
print(json.dumps(rsp, ensure_ascii=False, sort_keys=False, indent=4))
print('----------------------API FAIL----------------------')
def getWordTEST(self,st):
print(time.asctime())
print("NOW START TO TEST THE AI CHAT")
api_obj=apiutil.AiPlat(self.app_id,self.app_key)
type=10000
rsp = api_obj.getNlpTextChat(type,st)
if rsp['ret'] == 0:
print("success")
print(rsp['data']['answer'])
else:
print("failed")
print(rsp['ret'])
print(json.dumps(rsp, ensure_ascii=False, sort_keys=False, indent=4))
def Get_face_sticker_api(image_name, mediaId, model=random.randint(1, 31)):
with open(image_name, 'rb') as bin_data:
image_data = bin_data.read() # 原始图片的base64编码数据（原图大小上限500KB)
ai_obj = apiutil.AiPlat(app_id, app_key)
rsp = ai_obj.getfacesticker(image_data, model)
image_file_name = "./image/%s_s.jpg" % mediaId
if rsp['ret'] == 0:
strs = rsp['data']['image']
imgdata = base64.b64decode(strs)
file_image = open(image_file_name, 'wb')
file_image.write(imgdata)
file_image.close()
return image_file_name
def test_aai_wxasrs():
seq = 0
for_mat = 8
rate = 16000
bits = 16
cont_res = 1
once_size = 6400
file_path = './data/wxasrs.mp3'
f = open(file_path, 'rb')
md5obj = hashlib.md5()
md5obj.update(f.read())
hash = md5obj.hexdigest()
speech_id = str(hash).upper()
f.close()
f = open(file_path, 'rb')
file_size = os.path.getsize(file_path)
try:
while True:
chunk = f.read(once_size)
if not chunk:
break
else:
chunk_size = len(chunk)
if (seq + chunk_size) == file_size:
end = 1
else:
end = 0
ai_obj = apiutil.AiPlat(app_id, app_key)
print('----------------------SEND REQ----------------------')
rsp = ai_obj.getAaiWxAsrs(chunk, speech_id, end, for_mat, rate,
bits, seq, chunk_size, cont_res)
seq += chunk_size
if rsp['ret'] == 0:
print(
json.dumps(rsp,
ensure_ascii=False,
sort_keys=False,
indent=4))
print('----------------------API SUCC----------------------')
else:
print(
json.dumps(rsp,
ensure_ascii=False,
sort_keys=False,
indent=4))
print('----------------------API FAIL----------------------')
finally:
f.close()
def ai_image(image_data):
time.sleep(0.5)
ai_obj = apiutil.AiPlat(App_ID, App_Key)
print('-----------')
rsp = ai_obj.getRenlianFenxi(image_data)
if rsp['ret'] == 0:
for i in rsp['data']['face_list']:
print(i['beauty'])
print('----')
return int(i['beauty'])
else:
# print('无返回')
print(rsp['ret'])
return int(rsp['ret'])
def recognize_image(self):
#https://ai.qq.com/product/ocr.shtml#identify 优图ocr地址，免费申请
app_id = 'appid'
app_key = 'appkey'
with open('./captcha.jpg', 'rb') as bin_data:
image_data = bin_data.read()
ai_obj = apiutil.AiPlat(app_id, app_key)
print('----------------------SEND REQ----------------------')
rsp = ai_obj.getOcrGeneralocr(image_data)
print(rsp)
return rsp
def Get_Nlp_ImageTrans_api(image_name,
mediaId='451454543',
source='auto',
target='auto'):
with open(image_name, 'rb') as bin_data:
image_data = bin_data.read()
ai_obj = apiutil.AiPlat(app_id, app_key)
rsp = ai_obj.getNlpImageTrans(image_data, mediaId, source, target)
data_lists = []
if rsp['ret'] == 0:
for obj in rsp['data']['image_records']:
data_list = [obj['source_text'], obj['target_text']]
data_lists.append(data_list)
return data_lists
def ocr(image):
with open(image, 'rb') as bin_data:
image_data = bin_data.read()
ai_obj = apiutil.AiPlat(app_id, app_key)
# print ('----------------------SEND REQ----------------------')
rsp = ai_obj.getOcrGeneralocr(image_data)
if rsp['ret'] == 0:
for i in rsp['data']['item_list']:
return i['itemstring']
# print('----------------------API SUCC----------------------')
else:
print(json.dumps(rsp, ensure_ascii=False, sort_keys=False, indent=4))
print('----------------------API FAIL----------------------')
def get_response(questionS): #AI回复信息
app_id = 'XXXX' #控制台应用 APP ID
app_key = 'XXXX' #控制台 应用APP Key
str_question = questionS #传参
session = 10000
ai_obj = apiutil.AiPlat(app_id, app_key) #调用SDK AiPlat()方法
rsp = ai_obj.getNlpTextChat(session, str_question) #调用SDK方法
if rsp['ret'] == 0: #判断参数问题,并反馈信息
ask = (rsp['data'])['answer']
print(ask)
else:
print(json.dumps(rsp, ensure_ascii=False, sort_keys=False, indent=4))
def soundTotext():
app_key = '申请的Key'
app_id = '申请的Id'
seq = 0
for_mat = 2
rate = 16000
bits = 16
cont_res = 0
once_size = 41000
file_path = FILE_NAME
#计算音频MD5
with open(file_path, 'rb') as f:
md5obj = hashlib.md5()
md5obj.update(f.read())
hash = md5obj.hexdigest()
speech_id = str(hash).upper()
#读取音频内容，每次41000字节
f = open(file_path, 'rb')
file_size = os.path.getsize(file_path)
try:
while True:
chunk = f.read(once_size)
if not chunk:
break
else:
chunk_size = len(chunk)
if (seq + chunk_size) == file_size:
end = 1
else:
end = 0
#初始化AIPlat接口
ai_obj = apiutil.AiPlat(app_id, app_key)
#调用语音识别-流式版(WeChat AI),传入参数
rsp = ai_obj.getAaiWxAsrs(chunk, speech_id, end, for_mat, rate,
bits, seq, chunk_size, cont_res)
seq += chunk_size
if rsp['ret'] == 0:
return rsp['data']['speech_text']
else:
print("调用腾讯API失败")
return None
finally:
f.close()
def TransWord(self,st):
print("翻译开始")
api_obj=apiutil.AiPlat(self.app_id,self.app_key)
type=0
print("打开连接")
rsp = api_obj.getNlpTextChat(type,st)
if rsp['ret'] == 0:
print("成功")
print(json.dumps(rsp, ensure_ascii=False, sort_keys=False, indent=4))
print(rsp['data']['answer'])
else:
print("失败")
print(rsp['ret'])
#self.chatLOG(rsp['ret'])
print(json.dumps(rsp, ensure_ascii=False, sort_keys=False, indent=4))
print("exit code 0")
exit(0)
def Start(self, content, sender):
global authorg
authorg = sender
if content.startswith("echo") == False:
return
str_text = content.replace("echo ", "")
ai_obj = apiutil.AiPlat(app_id, app_key)
rsp = ai_obj.getAaiTts(str_text)
print rsp['ret']
if rsp['ret'] == 0:
f = open('test.wav', 'wb')
f.write(base64.b64decode(rsp["data"]["speech"]))
f.close()
res = commands.getoutput("omxplayer test.wav")
else:
res = 'API FAIL'
sender.send(res)
def start(content):
print "ok"
print app_key
print app_id
if content.startswith("echo") == False:
return
str_text = content.replace("echo ","")
ai_obj = apiutil.AiPlat(app_id, app_key)
rsp = ai_obj.getAaiTts(str_text)
print rsp['ret']
if rsp['ret'] == 0:
f = open('test.wav', 'wb')
f.write(base64.b64decode(rsp["data"]["speech"]))
f.close()
res = commands.getoutput("omxplayer test.wav")
else:
res = 'API FAIL'
print res
def test_aai_youtu():
speed = 0
text = '今天天气怎么样'
model_type = 0
ai_obj = apiutil.AiPlat(app_id, app_key)
print('----------------------SEND REQ----------------------')
rsp = ai_obj.getAaiYoutu(speed, text, model_type)
if rsp['ret'] == 0:
print(json.dumps(rsp, ensure_ascii=False, sort_keys=False, indent=4))
speech = rsp['data']['voice']
speech = base64.b64decode(speech)
with open('./data/youtu.mp3', 'wb') as f:
f.write(speech)
print('----------------------API SUCC----------------------')
else:
print(json.dumps(rsp, ensure_ascii=False, sort_keys=False, indent=4))
print('----------------------API FAIL----------------------')
def acquiredata(self, word):
if self.args == 'Tencent':
app_id = '1106881265' # You can replace it with your own app id.
app_key = 'cdjZ2xHc3vRoQrUi' # You can replace it with your own app key.
type = 0 # 0:Automatic identification( https://ai.qq.com/doc/nlptrans.shtml )
ai_obj = apiutil.AiPlat(app_id, app_key)
return ai_obj.getNlpTextTrans(word, type)
if self.args == 'Youdao':
request = _YOUDAO_API + quote(self.word)
elif self.args == 'Jinshan':
request = _CIBA_API + quote(
self.word) + "&type=json&key=0EAE08A016D6688F64AB3EBB2337BFB0"
else:
print("Invalid dictionary!")
try:
response = urllib.request.urlopen(request)
except urllib.error.URLError:
raise Exception(SNIPPET_ERROR_TIMEOUT)
data = response.read().decode('utf-8')
return (json.loads(data))
def image2json(image_file, json_dir):
'''
通过调用Tencent Common OCR API将图片转为json文件。
:image_file: 待处理图像
:json_dir: 用于保存json文件的文件夹
:return None:
'''
# 获取对应的json文件名，并跳过已转换的图片
json_file = os.path.split(image_file)[1]
json_file = json_file.split('.')[0] + '.json'
json_file = os.path.join(json_dir, json_file)
if os.path.exists(json_file):
return
ai_obj = apiutil.AiPlat(app_id, app_key)
rsp = ai_obj.getOcrGeneralocr(image_file)
if rsp['ret'] == 16447: # 通用OCR识别错误
print('No character in image %s' % image_file)
return
with open(json_file, 'w') as f:
json.dump(rsp, f)
print('Image %s has been converted to json.' % image_file)
def Get_face_detectface_api(image_name):
with open(image_name, 'rb') as bin_data:
image_data = bin_data.read()
ai_obj = apiutil.AiPlat(app_id, app_key)
rsp = ai_obj.getdetectface(image_data)
detect_data_dict = []
if rsp['ret'] == 0:
for obj in rsp['data']['face_list']:
if obj['gender'] > 50:
gender = "男"
else:
gender = "女"
if obj['expression'] < 10:
smile = "黯然伤神"
elif obj['expression'] < 20:
smile = "半嗔半喜"
elif obj['expression'] < 30:
smile = "似笑非笑"
elif obj['expression'] < 40:
smile = "笑逐颜开"
elif obj['expression'] < 60:
smile = "喜上眉梢"
elif obj['expression'] < 80:
smile = "心花怒放"
else:
smile = "一笑倾城"
detect_data_dict.append({
'性别': gender,
'年龄': obj['age'],
'情感': smile,
'魅力': obj['beauty']
})
return detect_data_dict
def get_Aai_WxAsrs_api(file_path,
for_mat=8,
rate=16000,
bits=16,
seq=0,
cont_res=1):
once_size = 6400
f = open(file_path, 'r')
md5obj = hashlib.md5()
md5obj.update(f.read())
hash = md5obj.hexdigest()
speech_id = str(hash).upper()
f.close()
f = open(file_path, 'rb')
file_size = os.path.getsize(file_path)
data_list = []
try:
while True:
chunk = f.read(once_size)
if not chunk:
break
else:
chunk_size = len(chunk)
if (seq + chunk_size) == file_size:
end = 1
else:
end = 0
ai_obj = apiutil.AiPlat(app_id, app_key)
rsp = ai_obj.getAaiWxAsrs(chunk, speech_id, end, for_mat, rate,
bits, seq, chunk_size, cont_res)
seq += chunk_size
if rsp['ret'] == 0:
data_list.append(rsp['data']['speech_text'])
return data_list
finally:
f.close()
f.close()
f = open(file_path, 'rb')
file_size = os.path.getsize(file_path)
try:
while True:
chunk = f.read(once_size)
if not chunk:
break
else:
chunk_size = len(chunk)
if (seq + chunk_size) == file_size:
end = 1
else:
end = 0
ai_obj = apiutil.AiPlat(app_id, app_key)
print '----------------------SEND REQ----------------------'
rsp = ai_obj.getAaiWxAsrs(chunk, speech_id, end, for_mat, rate,
bits, seq, chunk_size, cont_res)
seq += chunk_size
if rsp['ret'] == 0:
print json.dumps(rsp,
encoding="UTF-8",
ensure_ascii=False,
sort_keys=False,
indent=4)
print '----------------------API SUCC----------------------'
else:
print json.dumps(rsp,
encoding="UTF-8",
#!encoding=utf-8
import apiutil
from PIL import Image
import json
AppID = '1106954466'
AppKey = '5u6ehLVCgxhznKhX'
ai_obj = apiutil.AiPlat(AppID, AppKey)
with open('1.png', 'rb') as bin_data:
image_data = bin_data.read()
#rsp = ai_obj.getFaceInfo(image_data, 0)
rsp = ai_obj.getOcrGeneralocr(image_data)
print json.dumps(rsp)
exit()
if rsp['ret'] == 0:
beauty = 0
for face in rsp['data']['face_list']:
#print(face)
face_area = (face['x'], face['y'], face['x'] + face['width'],
face['y'] + face['height'])
#print(face_area)
img = Image.open("1.png")
cropped_img = img.crop(face_area).convert('RGB')
cropped_img.save('data/' + face['face_id'] + '.png')