Chilkat HOME .NET Core C# Android™ AutoIt C C# C++ Chilkat2-Python CkPython Classic ASP DataFlex Delphi ActiveX Delphi DLL Go Java Lianja Mono C# Node.js Objective-C PHP ActiveX PHP Extension Perl PowerBuilder PowerShell PureBasic Ruby SQL Server Swift 2 Swift 3,4,5... Tcl Unicode C Unicode C++ VB.NET VBScript Visual Basic 6.0 Visual FoxPro Xojo Plugin
(CkPython) Amazon Rekognition - Detect Faces in an ImageSee more Amazon Rekognition ExamplesDetects 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
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.