Swift
Swift
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.Chilkat Swift Downloads
func chilkatTest() {
var success: Bool = false
let rest = CkoRest()!
let authAws = CkoAuthAws()!
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(authProvider: authAws)
// URL: https://rekognition.us-west-2.amazonaws.com/
var bTls: Bool = true
var port: Int = 443
var bAutoReconnect: Bool = true
// Don't forget to change the region domain (us-west-2.amazonaws.com) to your particular region.
success = rest.connect(hostname: "rekognition.us-west-2.amazonaws.com", port: port, tls: bTls, autoReconnect: bAutoReconnect)
if success != true {
print("ConnectFailReason: \(rest.connectFailReason.intValue)")
print("\(rest.lastErrorText!)")
return
}
// 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.
let bdJpg = CkoBinData()!
success = bdJpg.loadFile(path: "qa_data/jpg/kid_blue_coat.jpg")
if success != true {
print("Failed to load the input JPG file.")
return
}
// 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:
// (image:https://example-code.com/images/kid_blue_coat.jpg/endImage)
// 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.
let sbJpg = CkoStringBuilder()!
bdJpg.getEncodedSb(encoding: "base64", sb: sbJpg)
let json = CkoJsonObject()!
json.updateSb(jsonPath: "Image.Bytes", sb: sbJpg)
json.updateString(jsonPath: "Attributes[0]", value: "ALL")
rest.addHeader(name: "Content-Type", value: "application/x-amz-json-1.1")
rest.addHeader(name: "X-Amz-Target", value: "RekognitionService.DetectFaces")
let sbRequestBody = CkoStringBuilder()!
json.emitSb(sb: sbRequestBody)
let sbResponseBody = CkoStringBuilder()!
success = rest.fullRequestSb(httpVerb: "POST", uriPath: "/", requestBody: sbRequestBody, responseBody: sbResponseBody)
if success != true {
print("\(rest.lastErrorText!)")
return
}
var respStatusCode: Int = rest.responseStatusCode.intValue
print("response status code = \(respStatusCode)")
if respStatusCode >= 400 {
print("Response Status Code = \(respStatusCode)")
print("Response Header:")
print("\(rest.responseHeader!)")
print("Response Body:")
print("\(sbResponseBody.getAsString()!)")
return
}
let jResp = CkoJsonObject()!
jResp.loadSb(sb: 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
var AgeRangeHigh: Int
var AgeRangeLow: Int
var BeardConfidence: String?
var BeardValue: Bool
var BoundingBoxHeight: String?
var BoundingBoxLeft: String?
var BoundingBoxTop: String?
var BoundingBoxWidth: String?
var Confidence: String?
var EyeglassesConfidence: String?
var EyeglassesValue: Bool
var EyesOpenConfidence: String?
var EyesOpenValue: Bool
var GenderConfidence: String?
var GenderValue: String?
var MouthOpenConfidence: String?
var MouthOpenValue: Bool
var MustacheConfidence: String?
var MustacheValue: Bool
var PosePitch: String?
var PoseRoll: String?
var PoseYaw: String?
var QualityBrightness: String?
var QualitySharpness: String?
var SmileConfidence: String?
var SmileValue: Bool
var SunglassesConfidence: String?
var SunglassesValue: Bool
var j: Int
var count_j: Int
var v_Type: String?
var X: String?
var Y: String?
var i: Int = 0
var count_i: Int = jResp.size(ofArray: "FaceDetails").intValue
while i < count_i {
jResp.i = i
AgeRangeHigh = jResp.int(of: "FaceDetails[i].AgeRange.High").intValue
AgeRangeLow = jResp.int(of: "FaceDetails[i].AgeRange.Low").intValue
BeardConfidence = jResp.string(of: "FaceDetails[i].Beard.Confidence")
BeardValue = jResp.bool(of: "FaceDetails[i].Beard.Value")
BoundingBoxHeight = jResp.string(of: "FaceDetails[i].BoundingBox.Height")
BoundingBoxLeft = jResp.string(of: "FaceDetails[i].BoundingBox.Left")
BoundingBoxTop = jResp.string(of: "FaceDetails[i].BoundingBox.Top")
BoundingBoxWidth = jResp.string(of: "FaceDetails[i].BoundingBox.Width")
Confidence = jResp.string(of: "FaceDetails[i].Confidence")
EyeglassesConfidence = jResp.string(of: "FaceDetails[i].Eyeglasses.Confidence")
EyeglassesValue = jResp.bool(of: "FaceDetails[i].Eyeglasses.Value")
EyesOpenConfidence = jResp.string(of: "FaceDetails[i].EyesOpen.Confidence")
EyesOpenValue = jResp.bool(of: "FaceDetails[i].EyesOpen.Value")
GenderConfidence = jResp.string(of: "FaceDetails[i].Gender.Confidence")
GenderValue = jResp.string(of: "FaceDetails[i].Gender.Value")
MouthOpenConfidence = jResp.string(of: "FaceDetails[i].MouthOpen.Confidence")
MouthOpenValue = jResp.bool(of: "FaceDetails[i].MouthOpen.Value")
MustacheConfidence = jResp.string(of: "FaceDetails[i].Mustache.Confidence")
MustacheValue = jResp.bool(of: "FaceDetails[i].Mustache.Value")
PosePitch = jResp.string(of: "FaceDetails[i].Pose.Pitch")
PoseRoll = jResp.string(of: "FaceDetails[i].Pose.Roll")
PoseYaw = jResp.string(of: "FaceDetails[i].Pose.Yaw")
QualityBrightness = jResp.string(of: "FaceDetails[i].Quality.Brightness")
QualitySharpness = jResp.string(of: "FaceDetails[i].Quality.Sharpness")
SmileConfidence = jResp.string(of: "FaceDetails[i].Smile.Confidence")
SmileValue = jResp.bool(of: "FaceDetails[i].Smile.Value")
SunglassesConfidence = jResp.string(of: "FaceDetails[i].Sunglasses.Confidence")
SunglassesValue = jResp.bool(of: "FaceDetails[i].Sunglasses.Value")
j = 0
count_j = jResp.size(ofArray: "FaceDetails[i].Emotions").intValue
while j < count_j {
jResp.j = j
Confidence = jResp.string(of: "FaceDetails[i].Emotions[j].Confidence")
v_Type = jResp.string(of: "FaceDetails[i].Emotions[j].Type")
j = j + 1
}
j = 0
count_j = jResp.size(ofArray: "FaceDetails[i].Landmarks").intValue
while j < count_j {
jResp.j = j
v_Type = jResp.string(of: "FaceDetails[i].Landmarks[j].Type")
X = jResp.string(of: "FaceDetails[i].Landmarks[j].X")
Y = jResp.string(of: "FaceDetails[i].Landmarks[j].Y")
j = j + 1
}
i = i + 1
}
}