faceListId = "suiqiao78"
1
url_01 = "https://api-hjq.cognitiveservices.azure.com/face/v1.0/facelists/" + faceListId # string 拼接
2
url_02 = "https://api-hjq.cognitiveservices.azure.com/face/v1.0/facelists/%s" %(faceListId)
3 
url_03 = "https://api-hjq.cognitiveservices.azure.com/face/v1.0/facelists/{}".format(faceListId)
print(url_01,'\n',url_02,'\n',url_03)

import requests
# 1、create  list列表
# faceListId
faceListId = "suiqiao78"
create_facelists_url =  "https://api2.cognitiveservices.azure.com/face/v1.0/facelists/{}"
subscription_key =  "eaa7f2d3c7e4456fbcc2884e463e77c0" # 输入密钥
assert subscription_key

headers = {
    # Request headers
    'Content-Type': 'application/json',
    'Ocp-Apim-Subscription-Key': subscription_key,
}
data = {
    "name": "qqc" ,
    "userData": "bbq",
   "recognitionModel":"recognition_03",
    
}

r_create = requests.put(create_facelists_url.format(faceListId),headers=headers,json=data)

r_create

r_create.content

# 检查你的facelist的信息
get_facelist_url = "https://api2.cognitiveservices.azure.com/face/v1.0/facelists/{}"
r_get_facelist = requests.get(get_facelist_url.format(faceListId),headers=headers)

r_get_facelist.json()

#先加一张脸试试
# 2、Add face
add_face_url ='https://api2.cognitiveservices.azure.com/face/v1.0/facelists/suiqiao78/persistedfaces'

assert subscription_key
headers = {
    # Request headers
    'Content-Type': 'application/json',
    'Ocp-Apim-Subscription-Key': subscription_key,
}
img_url = "http://huangjieqi.gitee.io/picture_storage/EdisonQXF.jpg"

params_add_face={
  "faceListId": "suiqiao78",
  "userData": "wcq" 
}
r_add_face = requests.post(add_face_url,headers=headers,params=params_add_face,json={"url":img_url})

r_add_face.status_code

r_add_face.json()

def AddFace(img_url=str,userData=str):
    add_face_url ="https://api2.cognitiveservices.azure.com/face/v1.0/facelists/{}/persistedFaces"
    assert subscription_key
    headers = {
        # Request headers
        'Content-Type': 'application/json',
        'Ocp-Apim-Subscription-Key': subscription_key,
    }
    img_url = img_url
    params_add_face={
        "userData":userData
    }
    r_add_face = requests.post(add_face_url.format(faceListId),headers=headers,params=params_add_face,json={"url":img_url})
    return r_add_face.status_code #返回出状态码

AddFace("http://huangjieqi.gitee.io/picture_storage/Autumnhui.jpg","丘天惠")
AddFace("http://huangjieqi.gitee.io/picture_storage/L-Tony-info.jpg","林嘉茵")
AddFace("http://huangjieqi.gitee.io/picture_storage/TLINGP.jpg","汤玲萍")
AddFace("http://huangjieqi.gitee.io/picture_storage/WenYanZeng.jpg","曾雯燕")
AddFace("http://huangjieqi.gitee.io/picture_storage/XIEIC.jpg","谢依希")
AddFace("http://huangjieqi.gitee.io/picture_storage/YuecongYang.png","杨悦聪")
AddFace("http://huangjieqi.gitee.io/picture_storage/Zoezhouyu.jpg","周雨")
AddFace("http://huangjieqi.gitee.io/picture_storage/crayon-heimi.jpg","刘瑜鹏")
AddFace("http://huangjieqi.gitee.io/picture_storage/jiayichen.jpg","陈嘉仪")
AddFace("http://huangjieqi.gitee.io/picture_storage/kg2000.jpg","徐旖芊")
AddFace("http://huangjieqi.gitee.io/picture_storage/liuxinrujiayou.jpg","刘心如")
AddFace("http://huangjieqi.gitee.io/picture_storage/liuyu19.png","刘宇")
AddFace("http://huangjieqi.gitee.io/picture_storage/ltco.jpg","李婷")
AddFace("http://huangjieqi.gitee.io/picture_storage/lucaszy.jpg","黄智毅")
AddFace("http://huangjieqi.gitee.io/picture_storage/pingzi0211.jpg","黄慧文")
AddFace("http://huangjieqi.gitee.io/picture_storage/shmimy-cn.jpg","张铭睿")
AddFace("http://huangjieqi.gitee.io/picture_storage/yichenting.jpg","陈婷")
AddFace("http://huangjieqi.gitee.io/picture_storage/coco022.jpg","洪可凡")
AddFace("http://huangjieqi.gitee.io/picture_storage/lujizhi.png","卢继志")
AddFace("http://huangjieqi.gitee.io/picture_storage/zzlhyy.jpg","张梓乐")

face_api_url = 'https://api2.cognitiveservices.azure.com/face/v1.0/detect'
image_url = 'http://huangjieqi.gitee.io/picture_storage/hjq.jpg'
headers = {'Ocp-Apim-Subscription-Key': subscription_key}
params = {
    'returnFaceId': 'true',
    'returnFaceLandmarks': 'false',
    # 选择模型
    'recognitionModel':'recognition_03',#此参数需与facelist参数一致
    'detectionModel':'detection_01',
    # 可选参数,请仔细阅读API文档
    'returnFaceAttributes': 'age,gender,headPose,smile,facialHair,glasses,emotion,hair,makeup,occlusion,accessories,blur,exposure,noise',
}
response = requests.post(face_api_url, params=params,headers=headers, json={"url": image_url})
response.json()

# 4.返回人脸相似置信度
findsimilars_url = "https://api2.cognitiveservices.azure.com/face/v1.0/findsimilars"
# 请求正文 faceID需要先检测一张照片获取
data_findsimilars = {
    "faceId":"1500d248-389b-48b8-ab2b-cc003d5fb969", #取上方的faceID
    "faceListId": "suiqiao78",
    "maxNumOfCandidatesReturned": 10,
    "mode": "matchFace" #matchPerson #一种为验证模式，一种为相似值模式
    }
r_findsimilars = requests.post(findsimilars_url,headers=headers,json=data_findsimilars)
r_findsimilars.json()

# 查看列表
get_facelist_url = "https://api2.cognitiveservices.azure.com/face/v1.0/facelists/{}"
r_get_facelist = requests.get(get_facelist_url.format(faceListId),headers=headers)
r_get_facelist.json()

# 用Pandas简化数据
import pandas as pd
# 返回facelist数据
adf = pd.json_normalize(r_get_facelist.json()["persistedFaces"])
adf

# 返回相似度数据
bdf = pd.json_normalize(r_findsimilars.json())# 升级pandas才能运行
bdf

pd.merge(adf,bdf,how='inner', on='persistedFaceId').sort_values(by="confidence",ascending = False)

# Detect face 删除列表内人脸id
faceListId = "suiqiao78"

r_delete_face

# 删除列表内人脸
delete_face_url = "https://api2.cognitiveservices.azure.com/face/v1.0/facelists/{}/persistedfaces/{}"
assert subscription_key
# 获取上面的persistedFaceId
persistedFaceId = r_add_face.json()["persistedFaceId"] 
headers = {
    'Content-Type': 'application/json',
    'Ocp-Apim-Subscription-Key': subscription_key,
}
# 注意requests请求为delete
r_delete_face = requests.delete(delete_face_url.format(faceListId,persistedFaceId),headers=headers)

# 删除人脸列表
delete_facelist_url = "https://api2.cognitiveservices.azure.com/face/v1.0/facelists/{}"
assert subscription_key
headers = {
    'Content-Type': 'application/json',
    'Ocp-Apim-Subscription-Key': subscription_key,
}
r_delete_facelist = requests.delete(delete_facelist_url.format(faceListId),headers=headers)

r_delete_facelist

api_secret = "ZChjCWQ7eLeIsAx7Rz4YjJXr4xWYDSeU"
api_key = 'Vp8rarJ3g1kz8mCan2bTMJeCEOLiTXsF'

# 1.FaceSet Create
import requests,json
display_name = "加油666" #自定义人脸集合的名字
outer_id = "1008" #自定义标识
user_data = "suiqiao" #自定义用户信息

CreateFace_Url = "https://api-cn.faceplusplus.com/facepp/v3/faceset/create" #调用URL
payload = {
    # 请求参数
    'api_key': api_key,
    'api_secret': api_secret,
    'display_name':display_name,
    'outer_id':outer_id,
    'user_data':user_data
}
r = requests.post(CreateFace_Url, params=payload)
r.json()

# 2.FaceSet GetDetail（获取人脸集合信息）
GetDetail_Url = "https://api-cn.faceplusplus.com/facepp/v3/faceset/getdetail"
payload = {
    'api_key': api_key,
    'api_secret': api_secret,
    'outer_id':outer_id,
}
r = requests.post(GetDetail_Url,params=payload)
r.json()

# 3.FaceSet AddFace（增加人脸信息）
AddFace_url = " https://api-cn.faceplusplus.com/facepp/v3/faceset/addface"

payload = {
    'api_key': api_key,
    'api_secret': api_secret,
    'faceset_token':'5eb076ca8c1e7e438101eadc212e9afc',
    'face_tokens':'1aff0090208b37e2f8de321a8e2e96c7', # qianmiande
}
r = requests.post(AddFace_url,params=payload)
r.json()

# 4.FaceSet RemoveFace（移除人脸信息）
RemoveFace_url = " https://api-cn.faceplusplus.com/facepp/v3/faceset/removeface"

payload = {
    'api_key': api_key,
    'api_secret': api_secret,
    'faceset_token':'5eb076ca8c1e7e438101eadc212e9afc',
    'face_tokens':'1aff0090208b37e2f8de321a8e2e96c7',
}
r = requests.post(RemoveFace_url,params=payload)
r.json()

Update_url = "https://api-cn.faceplusplus.com/facepp/v3/faceset/update"
payload = {
    'api_key': api_key,
    'api_secret': api_secret,
    'faceset_token':'5eb076ca8c1e7e438101eadc212e9afc',
    'user_data':"Test.",
}
r = requests.post(Update_url,params=payload)
r.json()

# 6.Compare Face（对比人脸相似度）
yyqx =  "https://i.loli.net/2020/10/24/mq6CidMSutr4Plx.jpg"
yyqx2 = "https://i.loli.net/2020/10/24/M4Y9qz6KVQ2XRyi.jpg"
xiaozhai = "https://i.loli.net/2020/10/24/HWAMEkcYur5NwyU.jpg"

Compare_url = "https://api-cn.faceplusplus.com/facepp/v3/compare"
payload ={
    'api_key': api_key,
    'api_secret': api_secret,
    'image_url1':liudehua01,
    'image_url2':wangzulan
}
r = requests.post(Compare_url,params=payload)
r.json()

Secret_key = 'DH3uIYICbg34zpPXdnNsTHGnoAgKVhjr'
API_key = 'BRogF97Qkfqtzuh5Q8Ekhiq4'
import requests
import base64
import matplotlib.pyplot as plt # plt 用于显示图片
import matplotlib.image as mpimg # mpimg 用于读取图片
#获取access_token
def gettoken():
    host = 'https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id=BRogF97Qkfqtzuh5Q8Ekhiq4&client_secret=DH3uIYICbg34zpPXdnNsTHGnoAgKVhjr'
    response = requests.get(host)
    if response:
        return response.json()['access_token']

def getscore(url):
    f=open(url,'rb')#二进制读写，转换base64
    base64_data = base64.b64encode(f.read())
    request_url = "https://aip.baidubce.com/rest/2.0/face/v3/detect"
    # 参数 
    params = {
        "image":base64_data,
        "image_type":"BASE64",
        "face_field":"beauty"
    }
    #请求
    request_url = request_url + "?access_token=" + gettoken()
    headers = {'content-type': 'application/json'}
    response = requests.post(request_url, data=params, headers=headers)
    if response:
        print ("经过百度智能云的AI大数据评分，您的颜值分数为：",response.json()['result']['face_list'][0]['beauty'])
        lena = mpimg.imread(url) 
        plt.imshow(lena) 
        plt.axis('off')
        plt.show()

getscore('C:/Users/ycqq/Desktop/翟.jpg')

import requests 

# client_id 为官网获取的API Key， client_secret 为官网获取的Secret Key
host = 'https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id={}&client_secret={}'
client_id = "tGtBNMzw74rxzFS3kqzStlkm"
client_secret = "V1cHTUxTeKs8cntxObggHGoEQPh6n80v"
response = requests.get(host.format(client_id, client_secret))
if response:
    print(response.json())

request_url = "https://aip.baidubce.com/rest/2.0/face/v3/detect"

params = "{\"image\":\"https://ss2.bdstatic.com/70cFvnSh_Q1YnxGkpoWK1HF6hhy/it/u=3430692674,459091344&fm=26&gp=0.jpg\",\"image_type\":\"URL\",\"face_field\":\"faceshape,facetype\"}"
# image_type 图片类型：BASE64；URL；FACE_TOKEN。

access_token = '25.119b23100eca9380c8e2140144971a07.315360000.1918882605.282335-22868460' # 这里不太会
request_url = request_url + "?access_token=" + access_token
headers = {'content-type': 'application/json'}
response = requests.post(request_url, data=params, headers=headers)
response.json()

import requests
# If you are using a Jupyter notebook, uncomment the following line.
# %matplotlib inline

import matplotlib.pyplot as plt
import json
from PIL import Image
from io import BytesIO

# Add your Computer Vision subscription key and endpoint to your environment variables.
# if 'COMPUTER_VISION_SUBSCRIPTION_KEY' in os.environ:
#     subscription_key = os.environ['COMPUTER_VISION_SUBSCRIPTION_KEY']
# else:
#     print("\nSet the COMPUTER_VISION_SUBSCRIPTION_KEY environment variable.\n**Restart your shell or IDE for changes to take effect.**")
#     sys.exit()

endpoint = "https://gxl.cognitiveservices.azure.com/"
# if 'COMPUTER_VISION_ENDPOINT' in os.environ:
#     endpoint = os.environ['COMPUTER_VISION_ENDPOINT']
subscription_key = "754e142aa949439aa27e58b26ae6534c"

# base url
analyze_url = endpoint+ "vision/v2.1/analyze"

# Set image_url to the URL of an image that you want to analyze.
image_url = "https://i.loli.net/2020/10/24/DlRLJIBWowp32cU.jpg"

headers = {'Ocp-Apim-Subscription-Key': subscription_key}
# 参数
params = {'visualFeatures': 'Categories,Description,Color'}
# 请求主体body
data = {'url': image_url}
response = requests.post(analyze_url, headers=headers,
                         params=params, json=data)
response.raise_for_status()

# The 'analysis' object contains various fields that describe the image. The most
# relevant caption for the image is obtained from the 'description' property.
analysis = response.json()
print(json.dumps(response.json()))
image_caption = analysis["description"]["captions"][0]["text"].capitalize()

# Display the image and overlay it with the caption.
image = Image.open(BytesIO(requests.get(image_url).content))
plt.imshow(image)
plt.axis("off")
_ = plt.title(image_caption, size="x-large", y=-0.1)
plt.show()
response.json()

import os
import sys
import requests
# If you are using a Jupyter notebook, uncomment the following line.
# %matplotlib inline
import matplotlib.pyplot as plt
from PIL import Image
from io import BytesIO

# Add your Computer Vision subscription key and endpoint to your environment variables.
# if 'COMPUTER_VISION_SUBSCRIPTION_KEY' in os.environ:
#     subscription_key = os.environ['COMPUTER_VISION_SUBSCRIPTION_KEY']
# else:
#     print("\nSet the COMPUTER_VISION_SUBSCRIPTION_KEY environment variable.\n**Restart your shell or IDE for changes to take effect.**")
#     sys.exit()

# if 'COMPUTER_VISION_ENDPOINT' in os.environ:
#     endpoint = os.environ['COMPUTER_VISION_ENDPOINT']

# analyze_url = endpoint + "vision/v2.1/analyze"

# Set image_path to the local path of an image that you want to analyze.
image_path = "C:/Users/ycqq/Desktop/一.jpg"
analyze_url = "https://gxl.cognitiveservices.azure.com/vision/v2.1/analyze"
# Read the image into a byte array
image_data = open(image_path,"rb").read()
headers = {'Ocp-Apim-Subscription-Key': "754e142aa949439aa27e58b26ae6534c",
           'Content-Type': 'application/octet-stream'}
params = {'visualFeatures': 'Categories,Description,Color'}
response = requests.post(
    analyze_url, headers=headers, params=params, data=image_data)
response.raise_for_status()

# The 'analysis' object contains various fields that describe the image. The most
# relevant caption for the image is obtained from the 'description' property.
analysis = response.json()
print(analysis)
image_caption = analysis["description"]["captions"][0]["text"].capitalize()

# Display the image and overlay it with the caption.
image = Image.open(BytesIO(image_data))
plt.imshow(image)
plt.axis("off")
_ = plt.title(image_caption, size="x-large", y=-0.1)

import os
import sys
import requests
# If you are using a Jupyter notebook, uncomment the following line.
# %matplotlib inline
import matplotlib.pyplot as plt
from PIL import Image
from io import BytesIO

# Add your Computer Vision subscription key and endpoint to your environment variables.
# if 'COMPUTER_VISION_SUBSCRIPTION_KEY' in os.environ:
#     subscription_key = os.environ['COMPUTER_VISION_SUBSCRIPTION_KEY']
# else:
#     print("\nSet the COMPUTER_VISION_SUBSCRIPTION_KEY environment variable.\n**Restart your shell or IDE for changes to take effect.**")
#     sys.exit()

# if 'COMPUTER_VISION_ENDPOINT' in os.environ:
#     endpoint = os.environ['COMPUTER_VISION_ENDPOINT']

thumbnail_url = "https://gxl.cognitiveservices.azure.com/" + "vision/v2.1/generateThumbnail"

# Set image_url to the URL of an image that you want to analyze.
image_url = "https://i.loli.net/2020/10/24/xpe5E4JWiX279qV.jpg "

headers = {'Ocp-Apim-Subscription-Key': "754e142aa949439aa27e58b26ae6534c"}
params = {'width': '100', 'height': '100', 'smartCropping': 'true'}
data = {'url': image_url}
response = requests.post(thumbnail_url, headers=headers,
                         params=params, json=data)
response.raise_for_status()

thumbnail = Image.open(BytesIO(response.content))

# Display the thumbnail.
plt.imshow(thumbnail)
plt.axis("off")

# Verify the thumbnail size.
print("Thumbnail is {0}-by-{1}".format(*thumbnail.size))

import json
import os
import sys
import requests
import time
%matplotlib inline
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
from PIL import Image
from io import BytesIO

text_recognition_url = endpoint + "/vision/v3.0/read/analyze"

# Set image_url to the URL of an image that you want to recognize.
image_url = "https://i.loli.net/2020/10/24/5sHzZ7jaXP6lMc1.jpg"

headers = {'Ocp-Apim-Subscription-Key': "754e142aa949439aa27e58b26ae6534c"}
data = {'url': image_url}
response = requests.post(text_recognition_url, headers=headers, json=data)
response.raise_for_status()

# Extracting text requires two API calls: One call to submit the
# image for processing, the other to retrieve the text found in the image.

# Holds the URI used to retrieve the recognized text.
operation_url = response.headers["Operation-Location"]

# The recognized text isn't immediately available, so poll to wait for completion.
analysis = {}
poll = True
while (poll):
    response_final = requests.get(
        response.headers["Operation-Location"], headers=headers)
    analysis = response_final.json()
    
    print(json.dumps(analysis, indent=4))

    time.sleep(1)
    if ("analyzeResult" in analysis):
        poll = False
    if ("status" in analysis and analysis['status'] == 'failed'):
        poll = False

polygons = []
if ("analyzeResult" in analysis):
    # Extract the recognized text, with bounding boxes.
    polygons = [(line["boundingBox"], line["text"])
                for line in analysis["analyzeResult"]["readResults"][0]["lines"]]

# Display the image and overlay it with the extracted text.
image = Image.open(BytesIO(requests.get(image_url).content))
ax = plt.imshow(image)
for polygon in polygons:
    vertices = [(polygon[0][i], polygon[0][i+1])
                for i in range(0, len(polygon[0]), 2)]
    text = polygon[1]
    patch = Polygon(vertices, closed=True, fill=False, linewidth=2, color='y')
    ax.axes.add_patch(patch)
    plt.text(vertices[0][0], vertices[0][1], text, fontsize=20, va="top")
plt.show()