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(Chilkat2-Python) Amazon Rekognition - Detect Faces in an Image
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
import sys
import chilkat2
rest = chilkat2.Rest()
authAws = chilkat2.AuthAws()
authAws.AccessKey = "AWS_ACCESS_KEY"
authAws.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.Region = "us-west-2"
authAws.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.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 = chilkat2.BinData()
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 = chilkat2.StringBuilder()
bdJpg.GetEncodedSb("base64",sbJpg)
json = chilkat2.JsonObject()
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 = chilkat2.StringBuilder()
json.EmitSb(sbRequestBody)
sbResponseBody = chilkat2.StringBuilder()
success = rest.FullRequestSb("POST","/",sbRequestBody,sbResponseBody)
if (success != True):
print(rest.LastErrorText)
sys.exit()
respStatusCode = rest.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 = chilkat2.JsonObject()
jResp.LoadSb(sbResponseBody)
jResp.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.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.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.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
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