#coding:utf-8
#! C:\Developer\python36\python3.exe
import aip
from aip import AipNlp
from aip import AipFace
""" 你的 APPID AK SK """
APP_ID = '10253494'
API_KEY = 'NAh5KkgrKTPyxzUBewCAakvm'
SECRET_KEY = 'nTrHzPhh0CQP9H9Idz3jftsji9NgVdvF'
aipFace = AipFace(APP_ID, API_KEY, SECRET_KEY)
# 读取图片
def get_file_content(filePath):
with open(filePath, 'rb') as fp:
return fp.read()
options = {
'max_face_num': 1,
'face_fields': "age,beauty",
}
# 调用人脸属性检测接口
result = aipFace.detect(get_file_content('1.jpg'), options)
print('年龄：' + str(int(result['result'][0]['location']['age'])))
print('颜值：' + str(result['result'][0]['location']['beauty']))
print(result)
import cv2
from aip import AipFace
import utils
client = AipFace(utils.BAIDU_AI_APP_ID, utils.BAIDU_AI_API_KEY,
utils.BAIDU_AI_SECRET_KEY)
image_type = "BASE64"
group_id = "ahojcn"
user_id = "fancheng"
options = {"user_info": "范程"}
image = cv2.imread("./img/fancheng.jpg")
image_base64 = utils.image_to_base64(image)
# print(client.addUser(image_base64, image_type, group_id, user_id, options))
import requests, json
# host = f'https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id={utils.BAIDU_AI_API_KEY}&client_secret={utils.BAIDU_AI_SECRET_KEY}'
# response = requests.get(host)
# if response:
# print(response.json())
access_token = '24.e4265a7b77a8cadbdb194334fa9b5cbf.2592000.1611411456.282335-18145377'
request_url = 'https://aip.baidubce.com/rest/2.0/face/v3/faceset/user/add'
data = {
"image": image_base64,
"image_type": "BASE64",
"group_id": "ahojcn",
"user_id": "fancheng",
from aip import AipFace
from requests.api import options
import base64
APP_ID = '16850595'
API_KEY = '6uhScb0HHcPHLGXFMYUOglEp'
SECRET_KEY = 'TiTuvl5mUupxxvsBTq9HH6R6tCZZcMf8'
client = AipFace(APP_ID, API_KEY, SECRET_KEY)
def get_file_content(image):
with open(image, 'rb') as fp:
content = base64.b64encode(fp.read())
return content.decode('utf-8')
for i in range(1, 50):
image = (str(i) + ".jpg")
imageType = "BASE64"
options = {}
options['face_field'] = 'age,gender,beauty'
# options = {}
# options["face_field"] = "age"
# options["max_face_num"] = 2
# options["face_type"] = "LIVE"
# options["liveness_control"] = "LOW"
""" 调用人脸检测 """
# 人脸识别评分，python3.6.6(代码比较乱，不喜欢迎来喷，谢谢)
# 需要安装pip install baidu-aip
from aip import AipFace
import base64
# 百度开发者官网获取key、id注册后就可以获取。
APP_ID = ''
API_KEY = ''
SECRET_KEY = ' '
# 初始化AipFace对象
aipFace = AipFace(APP_ID, API_KEY, SECRET_KEY)
imageType = "BASE64"
options = {}
options["face_field"] = "age,gender,beauty"
def get_file_content(filePath):
with open(filePath, 'rb') as fp:
content = base64.b64encode(fp.read())
return content.decode('utf-8')
def run(self):
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
time.sleep(3)
global frame_of_stream, mutex
global g_face_locations, g_face_names, mutex1
APP_ID = '11155090'
API_KEY = "rG3P2789bywKHfGN0OnEwAgg"
SECRET_KEY = "yTjcZQ92dwGBl3zDY1yWzEz9fb4FeYMc"
client = AipFace(APP_ID, API_KEY, SECRET_KEY)
options = {
"ext_fields": "faceliveness",
"detect_top_num": 10, # 检测多少个人脸进行比对，默认值1（最对返回10个）
"user_top_num": 1 # 返回识别结果top人数”当同一个人有多张图片时，只返回比对最高的1个分数
}
Group_Id = "test"
while True:
if mutex.acquire():
loc_frame = frame_of_stream
mutex.release()
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(loc_frame, (0, 0), fx=0.25, fy=0.25)
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(
rgb_small_frame)
face_encodings = face_recognition.face_encodings(
rgb_small_frame, face_locations)
face_names = []
face_baidu_loc = []
if len(face_locations) == 0:
if mutex1.acquire():
g_face_locations = []
g_face_names = []
mutex1.release()
continue
else:
# Use the KNN model to find the best matches for the test face
closest_distances = self.knn_clf.kneighbors(face_encodings,
n_neighbors=1)
matches = [
closest_distances[0][i][0] <= self.distance_threshold
for i in range(len(face_locations))
]
print(g_face_locations)
# Use Baidu API to find the best matches for the test face
cv2.imwrite('face.jpg', loc_frame)
with open("face.jpg", 'rb') as fp:
image = fp.read()
Res = client.multiIdentify(Group_Id, image, options)
try:
Res['result']
except KeyError:
continue
else:
for i in range(len(Res['result'])):
res = Res['result'][i]['uid']
print('res:', res)
pos = Res['result'][i]['position']['left']
print('pos:', pos)
face_names.append(res)
face_baidu_loc = self.judge_pos(
face_locations=face_locations,
baidu_Res=Res,
_range=range(len(Res['result'])))
if mutex1.acquire():
g_face_locations = face_baidu_loc
g_face_names = face_names
mutex1.release()
from flask import Flask, render_template, request
from aip import AipFace
import random, time, sys, datetime
# 百度API配置
APP_ID = '24096837'
API_KEY = '4EkfjzDIVIdC47wrdkgcQwK8'
SECRET_KEY = 'e3mgFT9uROQ24UYOCUygGQmx1YRhfydK'
# 初始化client
client = AipFace(APP_ID, API_KEY, SECRET_KEY)
app = Flask(__name__)
# 单注模式
def create_lucky(seed):
LUCKY_SEED = int(seed)
redball = list(range(1, 36))
blueball = list(range(1, 13))
redball_left = 35
blueball_left = 12
lucky_ball = 0
redball_pool = []
blueball_pool = []
random.shuffle(redball)
random.shuffle(blueball)
for i in range(5):
time.sleep(random.randint(1, LUCKY_SEED) / LUCKY_SEED)
for j in range(random.randint(1, LUCKY_SEED) % redball_left):
random.shuffle(redball)
from aip import AipFace
import base64
APP_ID = '15926606'
API_KEY = 'IDpHxZLsVhUtv6XErETilME5'
SECRET_KEY = 'kPf8gtTfmkzLDArHmkRs48R4bacAURbY'
client = AipFace(APP_ID, API_KEY, SECRET_KEY)
for i in range(1,10): #range表示每次循环i从1到10，这里代表上传10个用户
filePath ="user"+ "%04d" % i +".jpg"
with open(filePath,"rb") as f:
base64_data = base64.b64encode(f.read())
image = str(base64_data,'utf-8')
imageType = "BASE64"
groupId = "student"
userId = "user"+ "%04d" % i
print(userId)
client.addUser(image, imageType, groupId, userId);
pass
if __name__ == '__main__':
"""
完成利用多个百度云帐号实现图片批量分析
"""
# 扫描本地人脸相册
util.print_a('正在扫描本地人脸相册')
file_list = os.listdir(config.ALBUM)
util.print_t('相册中包含{}张照片'.format(len(file_list)))
# 批量创建AIP连接对象
util.print_a('正在批量创建AIP连接对象')
aip_list = []
for aip_key in config.AIPFace:
aip_list.append(AipFace(**aip_key))
if len(aip_list) == 0:
util.print_w('没有AIP帐号怎么识别鸭')
exit()
util.print_a('正在进行识别前的初始化')
# 建立线程池，当连接对象过多时需要改写为线程池！！
all_work = [] # 线程管理数组
executor = ThreadPoolExecutor(max_workers=len(aip_list))
# 建立MongoDB数据库连接
mongo_conn = dao.mongo_conn()
mongo_set = mongo_conn[config.MONGO_SET]
exclude_list = [] # 检索需要排除的任务编号
for face in mongo_set.find({}, {'_id': False, 'face_plus': False}):
if 'aip_face' in face and len(face['aip_face'].keys()) != 0:
#!/usr/bin/env python # -*- coding: utf-8 -*- """ 百度图像识别基础服务 """ from aip import AipFace, AipOcr, AipImageClassify # 文字识别 char_APP_ID = "10683930" char_API_KEY = "KWupRWsdl3zgmy01HBSgVqup" char_SECRET_KEY = "hNVMDcsLnPenwyx4XGOa7v3ne8cdOvUy" ocr = AipOcr(char_APP_ID, char_API_KEY, char_SECRET_KEY) # 图像识别 image_APP_ID = "15066160" image_API_KEY = "eIftSwlyrBcECm0T4hDlB3R5" image_SECRET_KEY = "1Xq85wRk7YRl8M1nnm6xKqP7rzPf6PN4" image = AipImageClassify(image_APP_ID, image_API_KEY, image_SECRET_KEY) # 人脸识别 face_APP_ID = "15066160" face_API_KEY = "4h5hph54DK2kYWwN2mfVe6cZ" face_SECRET_KEY = "x9LbeBxKrPGqg7b4vzek56sB7tyK6aw1" face = AipFace(face_APP_ID, face_API_KEY, face_SECRET_KEY)
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from aip import AipFace
Ak = 'TBPtvVXmPoAnSX4ljiTkrpji'
Sk = '2wRiOMvA1yEvY09GRm4v6fSvW9H0Kzcj'
Ai = '9647392'
aipFace = AipFace(Ai, Ak, Sk)
aipFace.setConnectionTimeoutInMillis(2000)
aipFace.setSocketTimeoutInMillis(60000)
options = {
'max_face_num': 5,
'face_fields': "age,beauty,expression,faceshape,gender,glasses,landmark,race,qualities"
}
# 读取图片
def get_file_content(filePath):
with open(filePath, 'rb') as fp:
return fp.read()
path = 'F:\\KwDownload\\3.jpg'
response = aipFace.detect(get_file_content(path), options)
print(response)
@author: Administrator
"""
import base64
import cv2
from aip import AipFace
""" 你的 APPID AK SK """
APP_ID = '11406086'
API_KEY = 'ayMYdnBIf6Q5BwsDw2aTs6va'
SECRET_KEY = 'EkVpE7ao8ssPBAMGjI6ZgPxtmhdxZCeR'
imageType = "BASE64"
""" 如果有可选参数 """
options = {}
options["max_face_num"] = 10
client = AipFace(APP_ID, API_KEY, SECRET_KEY)
cap = cv2.cv2.VideoCapture(0)
#fourcc=cv2.cv2.VideoWriter_fourcc(*'XVID')
#out=cv2.cv2.VideoWriter('output.avi',fourcc,20.0,(640,480))
while True:
ret, frame = cap.read()
if ret:
cv2.cv2.imwrite('D:/12/l66l.JPEG', frame)
with open("D:/12/l66l.JPEG", "rb") as f:
# b64encode是编码，b64decode是解码
base64_data = base64.b64encode(f.read())
# -*- coding: utf-8 -*-
from aip import AipFace
""" 你的 APPID AK SK """
APP_ID = '14844983'
API_KEY = 'XV5SI5ejk8UGWP8igRkvE3Wa'
SECRET_KEY = '2L4VoMk3sUfj8Fs0p2mliT1gsGe1htAI'
client = AipFace(APP_ID, API_KEY, SECRET_KEY)
""" 读取图片 """
def get_file_content(filePath):
with open(filePath, 'rb') as fp:
return fp.read()
image = get_file_content(
'C:\Users\Administrator\Desktop\WebCrawler\day17\\6.jpg')
""" 如果有可选参数 """
options = {}
options["max_face_num"] = 2
options["face_fields"] = "age,beauty"
""" 带参数调用人脸检测 """
final = client.detect(image, options)
print(final)
def run(self):
# Initialize some variables
save_pic_count = 0
db = pymysql.connect("139.199.124.196",
"hawuser",
"1504030521Lqt?",
"hawDB",
charset='utf8')
cursor = db.cursor()
face_locations = []
face_encodings = []
face_names = []
time.sleep(1)
global frame_of_stream, mutex
global g_face_locations, g_face_names, mutex1
global g_stu_uid, mutex2
APP_ID = '11155090'
API_KEY = "rG3P2789bywKHfGN0OnEwAgg"
SECRET_KEY = "yTjcZQ92dwGBl3zDY1yWzEz9fb4FeYMc"
client = AipFace(APP_ID, API_KEY, SECRET_KEY)
options = {
"ext_fields": "faceliveness",
"detect_top_num": 10, # 检测多少个人脸进行比对，默认值1（最对返回10个）
"user_top_num": 1 # 返回识别结果top人数”当同一个人有多张图片时，只返回比对最高的1个分数
}
Group_Id = "test"
while True:
if mutex.acquire():
loc_frame = frame_of_stream
mutex.release()
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(loc_frame, (0, 0), fx=0.25, fy=0.25)
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(
rgb_small_frame)
face_encodings = face_recognition.face_encodings(
rgb_small_frame, face_locations)
face_names = []
if len(face_locations) == 0:
if mutex1.acquire():
g_face_locations = []
g_face_names = []
mutex1.release()
continue
else:
# Use the KNN model to find the best matches for the test face
closest_distances = self.knn_clf.kneighbors(face_encodings,
n_neighbors=1)
matches = [
closest_distances[0][i][0] <= self.distance_threshold
for i in range(len(face_locations))
]
print(g_face_locations)
# Use Baidu API to find the best matches for the test face
cv2.imwrite('face.jpg', loc_frame)
with open("face.jpg", 'rb') as fp:
image = fp.read()
Res = client.multiIdentify(Group_Id, image, options)
# pandeku de daima
if 'error_msg' in Res:
print(Res['error_msg'])
else:
for i in range(Res['result_num']):
uid = Res['result'][i]['uid']
info = str(
Res['result'][i]['user_info']
) + "," + str(Res['result'][i]['uid']) + "," + str(
Res['result'][i]['group_id']) + "," + str(
Res['result'][i]['scores'])
with open("right_result_time_ues.txt", 'a') as f:
f.write(info + "\n")
# pandeku de daima
try:
Res['result']
except KeyError:
continue
else:
for i in range(len(Res['result'])):
if Res['result'][i]['scores'][0] > 70:
res = Res['result'][i]['uid']
face_names.append(res)
else:
face_names.append('0')
save_path = '/Users/liuqingtong/Desktop/PycharmProjects/Haw/Haw/DB/Dataset/%s_%d.jpg' % (
res, save_pic_count)
save_pic_count += 1
print('save_path', save_path)
cv2.imwrite(save_path, small_frame)
print('res:', res)
if mutex1.acquire():
g_face_locations = face_locations
g_face_names = face_names
mutex1.release()
for uid in face_names:
times = datetime.datetime.now().strftime(
'%Y-%m-%d %H:%M:%S')
sql = "UPDATE stu_info SET Is_Attend='%s' WHERE User_Id = '%s'" % (
"已签到!", uid)
# sql1 = "UPDATE login_register_attend_user SET Attend_Time=CURRENT_TIMESTAMP WHERE User_Id = '%s'" % (uid)
sql1 = "UPDATE stu_info SET Attend_Time='%s' WHERE User_Id = '%s'" % (
times, uid)
try:
cursor.execute(sql) # 执行更新
cursor.execute(sql1) # 执行更新
db.commit() # 提交
except:
db.rollback() # 发生错误,回滚
print("Error: unable to update data")
print('DB Operation Done!')
print(
'_______________________________________________'
)
from aip import AipFace
import cv2
import base64
""" 你的 APPID AK SK """
APP_ID = '10454819'
API_KEY = 'q2bA1wTuhPtoGRoYp0ROXUwQ'
SECRET_KEY = '0T37uW8DXMCftWSPboo5ZjGd3QgYYpqo'
image = 'D:/pproject/ppop/image/xcc.jpg'
imageType = "BASE64"
groupIdList = "kaifa"
""" 调用人脸搜索 """
client = AipFace(APP_ID, API_KEY, SECRET_KEY)
with open(image, mode='rb') as f:
image_base64 = bytes.decode(base64.b64encode(f.read()))
print(client.search(image_base64, imageType, groupIdList))
# face_token人脸标志
# user_list 匹配的用户信息列表
# +group_id用户所属的group_id
# +user_id 用户的user_id
# +user_info 注册用户时携带的user_info
# score 用户的匹配得分
""" 如果有可选参数 """
options = {}
options[
"quality_control"] = "NORMAL" #图片质量控制 NONE: 不进行控制 LOW:较低的质量要求 NORMAL: 一般的质量要求 HIGH: 较高的质量要求 默认 NONE
options[
"liveness_control"] = "LOW" #活体检测控制 NONE: 不进行控制 LOW:较低的活体要求(高通过率 低攻击拒绝率) NORMAL: 一般的活体要求(平衡的攻击拒绝率, 通过率) HIGH: 较高的活体要求(高攻击拒绝率 低通过率) 默认NONE
options["user_id"] = "002" #当需要对特定用户进行比对时，指定user_id进行比对。即人脸认证功能。
options["max_user_num"] = 3 #查找后返回的用户数量。返回相似度最高的几个用户，默认为1，最多返回20个。
