Chilkat Examples

ChilkatHOME.NET Core C#Android™AutoItCC#C++Chilkat2-PythonCkPythonClassic ASPDataFlexDelphi ActiveXDelphi DLLGoJavaLianjaMono C#Node.jsObjective-CPHP ActiveXPHP ExtensionPerlPowerBuilderPowerShellPureBasicRubySQL ServerSwift 2Swift 3,4,5...TclUnicode CUnicode C++VB.NETVBScriptVisual Basic 6.0Visual FoxProXojo Plugin

CkPython Web API Examples

Primary Categories

ABN AMRO
AWS Secrets Manager
AWS Security Token Service
AWS Translate
Activix CRM
Adyen
Alibaba Cloud OSS
Amazon Cognito
Amazon DynamoDB
Amazon MWS
Amazon Pay
Amazon Rekognition
Amazon SP-API
Amazon Voice ID
Aruba Fatturazione
Azure Maps
Azure Monitor
Azure OAuth2
Azure Storage Accounts
Backblaze S3
Banco Inter
Belgian eHealth Platform
Bitfinex v2 REST
Bluzone
BrickLink
Bunny CDN
CallRail
CardConnect
Cerved
ClickBank
Clickatell
Cloudfare
Constant Contact
DocuSign
Duo Auth MFA
ETrade
Ecwid
Egypt ITIDA
Egypt eReceipt
Etsy
Facebook
Faire
Frame.io
GeoOp
GetHarvest
Global Payments
Google People
Google Search Console
Google Translate
Google Vision
Hungary NAV Invoicing
IBM Text to Speech
Ibanity
IntakeQ
Jira
Lightspeed
MYOB
Magento
Mailgun
Malaysia MyInvois
Mastercard

MedTunnel
MercadoLibre
MessageMedia
Microsoft Calendar
Microsoft Group
Microsoft Tasks and Plans
Microsoft Teams
Moody's
Okta OAuth/OIDC
OneLogin OIDC
OneNote
OpenAI ChatGPT
PRODA
PayPal
Paynow.pl
Peoplevox
Populi
QuickBooks
Rabobank
Refinitiv
Royal Mail OBA
SCiS Schools Catalogue
SII Chile
SMSAPI
SOAP finkok.com
Salesforce
SendGrid
Shippo
Shopify
Shopware
Shopware 6
SimpleTexting
Square
Stripe
SugarCRM
TicketBAI
TikTok Shop
Trello
Twilio
Twitter API v2
Twitter v1
UPS
UniPin
VoiceBase
Vonage
WaTrend
Walmart v3
Wasabi
WhatsApp
WiX
WooCommerce
WordPress
Xero
Yahoo Mail
Yapily
Yousign
ZATCA
Zendesk
Zoom
_Miscellaneous_
eBay
effectconnect
hacienda.go.cr

 

 

 

(CkPython) Amazon Rekognition - Detect Faces in an Image

See more Amazon Rekognition Examples

Detects faces within an image that is provided as input. This example passes theimage as base64-encoded image bytes.

For more information, see https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectFaces.html

Chilkat Python Downloads

install with pip

pip3 install chilkat

or download... Python Module for Windows, MacOS, Linux, Alpine Linux, Solaris

import sys
import chilkat

rest = chilkat.CkRest()

authAws = chilkat.CkAuthAws()
authAws.put_AccessKey("AWS_ACCESS_KEY")
authAws.put_SecretKey("AWS_SECRET_KEY")
# Don't forget to change the region to your particular region. (Also make the same change in the call to Connect below.)
authAws.put_Region("us-west-2")
authAws.put_ServiceName("rekognition")
# SetAuthAws causes Chilkat to automatically add the following headers: Authorization, X-Amz-Date
rest.SetAuthAws(authAws)

# URL: https://rekognition.us-west-2.amazonaws.com/
bTls = True
port = 443
bAutoReconnect = True
# Don't forget to change the region domain (us-west-2.amazonaws.com) to your particular region.
success = rest.Connect("rekognition.us-west-2.amazonaws.com",port,bTls,bAutoReconnect)
if (success != True):
    print("ConnectFailReason: " + str(rest.get_ConnectFailReason()))
    print(rest.lastErrorText())
    sys.exit()

# Note: The above code does not need to be repeatedly called for each REST request.
# The rest object can be setup once, and then many requests can be sent.  Chilkat will automatically
# reconnect within a FullRequest* method as needed.  It is only the very first connection that is explicitly
# made via the Connect method.

# Load the JPG to be passed as base64 in the JSON.
bdJpg = chilkat.CkBinData()
success = bdJpg.LoadFile("qa_data/jpg/kid_blue_coat.jpg")
if (success != True):
    print("Failed to load the input JPG file.")
    sys.exit()

# We wish to send the following JSON in the body of our HTTP request:

# {
#     "Image": {
#         "Bytes": "base64_image_bytes"
#     }
#     "Attributes": [
#         "ALL"
#     ]
# }

# Here is the image we used for testing:


# Convert binary image bytes to base64.
# Note: We are explicitly keeping the data inside Chilkat to avoid having to pass large strings
# as arguments to function calls.  This is important for some programming languages.
sbJpg = chilkat.CkStringBuilder()
bdJpg.GetEncodedSb("base64",sbJpg)

json = chilkat.CkJsonObject()
json.UpdateSb("Image.Bytes",sbJpg)
json.UpdateString("Attributes[0]","ALL")

rest.AddHeader("Content-Type","application/x-amz-json-1.1")
rest.AddHeader("X-Amz-Target","RekognitionService.DetectFaces")

sbRequestBody = chilkat.CkStringBuilder()
json.EmitSb(sbRequestBody)
sbResponseBody = chilkat.CkStringBuilder()
success = rest.FullRequestSb("POST","/",sbRequestBody,sbResponseBody)
if (success != True):
    print(rest.lastErrorText())
    sys.exit()

respStatusCode = rest.get_ResponseStatusCode()
print("response status code = " + str(respStatusCode))

if (respStatusCode >= 400):
    print("Response Status Code = " + str(respStatusCode))
    print("Response Header:")
    print(rest.responseHeader())
    print("Response Body:")
    print(sbResponseBody.getAsString())
    sys.exit()

jResp = chilkat.CkJsonObject()
jResp.LoadSb(sbResponseBody)

jResp.put_EmitCompact(False)
print(jResp.emit())

# Sample JSON response:
# (Sample code for parsing the JSON response is shown below)

# {
#   "FaceDetails": [
#     {
#       "AgeRange": {
#         "High": 18,
#         "Low": 8
#       },
#       "Beard": {
#         "Confidence": 98.06282806396484,
#         "Value": false
#       },
#       "BoundingBox": {
#         "Height": 0.327279269695282,
#         "Left": 0.5339247584342957,
#         "Top": 0.23660442233085632,
#         "Width": 0.35611653327941895
#       },
#       "Confidence": 99.99732971191406,
#       "Emotions": [
#         {
#           "Confidence": 99.5849380493164,
#           "Type": "HAPPY"
#         },
#         {
#           "Confidence": 0.15533843636512756,
#           "Type": "CALM"
#         },
#         {
#           "Confidence": 0.08864031732082367,
#           "Type": "SURPRISED"
#         },
#         {
#           "Confidence": 0.05476664379239082,
#           "Type": "SAD"
#         },
#         {
#           "Confidence": 0.042048510164022446,
#           "Type": "CONFUSED"
#         },
#         {
#           "Confidence": 0.038942769169807434,
#           "Type": "DISGUSTED"
#         },
#         {
#           "Confidence": 0.021463459357619286,
#           "Type": "FEAR"
#         },
#         {
#           "Confidence": 0.013858155347406864,
#           "Type": "ANGRY"
#         }
#       ],
#       "Eyeglasses": {
#         "Confidence": 98.5116195678711,
#         "Value": false
#       },
#       "EyesOpen": {
#         "Confidence": 99.65477752685547,
#         "Value": true
#       },
#       "Gender": {
#         "Confidence": 97.1164321899414,
#         "Value": "Female"
#       },
#       "Landmarks": [
#         {
#           "Type": "eyeLeft",
#           "X": 0.6554790735244751,
#           "Y": 0.35153862833976746
#         },
#         {
#           "Type": "eyeRight",
#           "X": 0.7940073609352112,
#           "Y": 0.38292214274406433
#         },
#         {
#           "Type": "mouthLeft",
#           "X": 0.6188991069793701,
#           "Y": 0.46431097388267517
#         },
#         {
#           "Type": "mouthRight",
#           "X": 0.7352844476699829,
#           "Y": 0.490242063999176
#         },
#         {
#           "Type": "nose",
#           "X": 0.7125006914138794,
#           "Y": 0.44607019424438477
#         },
#         {
#           "Type": "leftEyeBrowLeft",
#           "X": 0.6096581220626831,
#           "Y": 0.3071737587451935
#         },
#         {
#           "Type": "leftEyeBrowRight",
#           "X": 0.6628581285476685,
#           "Y": 0.3133310079574585
#         },
#         {
#           "Type": "leftEyeBrowUp",
#           "X": 0.7027584314346313,
#           "Y": 0.33200803399086
#         },
#         {
#           "Type": "rightEyeBrowLeft",
#           "X": 0.7813941240310669,
#           "Y": 0.35023579001426697
#         },
#         {
#           "Type": "rightEyeBrowRight",
#           "X": 0.8213478922843933,
#           "Y": 0.34993964433670044
#         },
#         {
#           "Type": "rightEyeBrowUp",
#           "X": 0.8495538234710693,
#           "Y": 0.36189284920692444
#         },
#         {
#           "Type": "leftEyeLeft",
#           "X": 0.629088282585144,
#           "Y": 0.34286588430404663
#         },
#         {
#           "Type": "leftEyeRight",
#           "X": 0.6820939183235168,
#           "Y": 0.3586524724960327
#         },
#         {
#           "Type": "leftEyeUp",
#           "X": 0.6580297946929932,
#           "Y": 0.3468707501888275
#         },
#         {
#           "Type": "leftEyeDown",
#           "X": 0.6537532210350037,
#           "Y": 0.35663917660713196
#         },
#         {
#           "Type": "rightEyeLeft",
#           "X": 0.7655976414680481,
#           "Y": 0.3776427209377289
#         },
#         {
#           "Type": "rightEyeRight",
#           "X": 0.8166338801383972,
#           "Y": 0.38544225692749023
#         },
#         {
#           "Type": "rightEyeUp",
#           "X": 0.7969376444816589,
#           "Y": 0.37844377756118774
#         },
#         {
#           "Type": "rightEyeDown",
#           "X": 0.7909533977508545,
#           "Y": 0.3877102732658386
#         },
#         {
#           "Type": "noseLeft",
#           "X": 0.6727234721183777,
#           "Y": 0.44030481576919556
#         },
#         {
#           "Type": "noseRight",
#           "X": 0.7237889170646667,
#           "Y": 0.45200300216674805
#         },
#         {
#           "Type": "mouthUp",
#           "X": 0.6882695555686951,
#           "Y": 0.4740942418575287
#         },
#         {
#           "Type": "mouthDown",
#           "X": 0.6720560789108276,
#           "Y": 0.5046101808547974
#         },
#         {
#           "Type": "leftPupil",
#           "X": 0.6554790735244751,
#           "Y": 0.35153862833976746
#         },
#         {
#           "Type": "rightPupil",
#           "X": 0.7940073609352112,
#           "Y": 0.38292214274406433
#         },
#         {
#           "Type": "upperJawlineLeft",
#           "X": 0.5517005324363708,
#           "Y": 0.30355724692344666
#         },
#         {
#           "Type": "midJawlineLeft",
#           "X": 0.5320234894752502,
#           "Y": 0.43352627754211426
#         },
#         {
#           "Type": "chinBottom",
#           "X": 0.6419994831085205,
#           "Y": 0.5531964302062988
#         },
#         {
#           "Type": "midJawlineRight",
#           "X": 0.7752369046211243,
#           "Y": 0.48957017064094543
#         },
#         {
#           "Type": "upperJawlineRight",
#           "X": 0.8515444397926331,
#           "Y": 0.37258899211883545
#         }
#       ],
#       "MouthOpen": {
#         "Confidence": 68.26280212402344,
#         "Value": false
#       },
#       "Mustache": {
#         "Confidence": 99.73213195800781,
#         "Value": false
#       },
#       "Pose": {
#         "Pitch": -11.299633026123047,
#         "Roll": 17.6924991607666,
#         "Yaw": 13.582314491271973
#       },
#       "Quality": {
#         "Brightness": 83.72581481933594,
#         "Sharpness": 67.22731018066406
#       },
#       "Smile": {
#         "Confidence": 98.4793930053711,
#         "Value": true
#       },
#       "Sunglasses": {
#         "Confidence": 99.3582992553711,
#         "Value": false
#       }
#     }
#   ]
# }

# Sample code for parsing the JSON response...
# Use the following online tool to generate parsing code from sample JSON:
# Generate Parsing Code from JSON

i = 0
count_i = jResp.SizeOfArray("FaceDetails")
while i < count_i :
    jResp.put_I(i)
    AgeRangeHigh = jResp.IntOf("FaceDetails[i].AgeRange.High")
    AgeRangeLow = jResp.IntOf("FaceDetails[i].AgeRange.Low")
    BeardConfidence = jResp.stringOf("FaceDetails[i].Beard.Confidence")
    BeardValue = jResp.BoolOf("FaceDetails[i].Beard.Value")
    BoundingBoxHeight = jResp.stringOf("FaceDetails[i].BoundingBox.Height")
    BoundingBoxLeft = jResp.stringOf("FaceDetails[i].BoundingBox.Left")
    BoundingBoxTop = jResp.stringOf("FaceDetails[i].BoundingBox.Top")
    BoundingBoxWidth = jResp.stringOf("FaceDetails[i].BoundingBox.Width")
    Confidence = jResp.stringOf("FaceDetails[i].Confidence")
    EyeglassesConfidence = jResp.stringOf("FaceDetails[i].Eyeglasses.Confidence")
    EyeglassesValue = jResp.BoolOf("FaceDetails[i].Eyeglasses.Value")
    EyesOpenConfidence = jResp.stringOf("FaceDetails[i].EyesOpen.Confidence")
    EyesOpenValue = jResp.BoolOf("FaceDetails[i].EyesOpen.Value")
    GenderConfidence = jResp.stringOf("FaceDetails[i].Gender.Confidence")
    GenderValue = jResp.stringOf("FaceDetails[i].Gender.Value")
    MouthOpenConfidence = jResp.stringOf("FaceDetails[i].MouthOpen.Confidence")
    MouthOpenValue = jResp.BoolOf("FaceDetails[i].MouthOpen.Value")
    MustacheConfidence = jResp.stringOf("FaceDetails[i].Mustache.Confidence")
    MustacheValue = jResp.BoolOf("FaceDetails[i].Mustache.Value")
    PosePitch = jResp.stringOf("FaceDetails[i].Pose.Pitch")
    PoseRoll = jResp.stringOf("FaceDetails[i].Pose.Roll")
    PoseYaw = jResp.stringOf("FaceDetails[i].Pose.Yaw")
    QualityBrightness = jResp.stringOf("FaceDetails[i].Quality.Brightness")
    QualitySharpness = jResp.stringOf("FaceDetails[i].Quality.Sharpness")
    SmileConfidence = jResp.stringOf("FaceDetails[i].Smile.Confidence")
    SmileValue = jResp.BoolOf("FaceDetails[i].Smile.Value")
    SunglassesConfidence = jResp.stringOf("FaceDetails[i].Sunglasses.Confidence")
    SunglassesValue = jResp.BoolOf("FaceDetails[i].Sunglasses.Value")
    j = 0
    count_j = jResp.SizeOfArray("FaceDetails[i].Emotions")
    while j < count_j :
        jResp.put_J(j)
        Confidence = jResp.stringOf("FaceDetails[i].Emotions[j].Confidence")
        v_Type = jResp.stringOf("FaceDetails[i].Emotions[j].Type")
        j = j + 1

    j = 0
    count_j = jResp.SizeOfArray("FaceDetails[i].Landmarks")
    while j < count_j :
        jResp.put_J(j)
        v_Type = jResp.stringOf("FaceDetails[i].Landmarks[j].Type")
        X = jResp.stringOf("FaceDetails[i].Landmarks[j].X")
        Y = jResp.stringOf("FaceDetails[i].Landmarks[j].Y")
        j = j + 1

    i = i + 1


 

© 2000-2024 Chilkat Software, Inc. All Rights Reserved.