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(Unicode C++) 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
#include <CkRestW.h>
#include <CkAuthAwsW.h>
#include <CkBinDataW.h>
#include <CkStringBuilderW.h>
#include <CkJsonObjectW.h>
void ChilkatSample(void)
{
CkRestW rest;
bool success;
CkAuthAwsW authAws;
authAws.put_AccessKey(L"AWS_ACCESS_KEY");
authAws.put_SecretKey(L"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(L"us-west-2");
authAws.put_ServiceName(L"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/
bool bTls = true;
int port = 443;
bool bAutoReconnect = true;
// Don't forget to change the region domain (us-west-2.amazonaws.com) to your particular region.
success = rest.Connect(L"rekognition.us-west-2.amazonaws.com",port,bTls,bAutoReconnect);
if (success != true) {
wprintf(L"ConnectFailReason: %d\n",rest.get_ConnectFailReason());
wprintf(L"%s\n",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.
CkBinDataW bdJpg;
success = bdJpg.LoadFile(L"qa_data/jpg/kid_blue_coat.jpg");
if (success != true) {
wprintf(L"Failed to load the input JPG file.\n");
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:
// 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.
CkStringBuilderW sbJpg;
bdJpg.GetEncodedSb(L"base64",sbJpg);
CkJsonObjectW json;
json.UpdateSb(L"Image.Bytes",sbJpg);
json.UpdateString(L"Attributes[0]",L"ALL");
rest.AddHeader(L"Content-Type",L"application/x-amz-json-1.1");
rest.AddHeader(L"X-Amz-Target",L"RekognitionService.DetectFaces");
CkStringBuilderW sbRequestBody;
json.EmitSb(sbRequestBody);
CkStringBuilderW sbResponseBody;
success = rest.FullRequestSb(L"POST",L"/",sbRequestBody,sbResponseBody);
if (success != true) {
wprintf(L"%s\n",rest.lastErrorText());
return;
}
int respStatusCode = rest.get_ResponseStatusCode();
wprintf(L"response status code = %d\n",respStatusCode);
if (respStatusCode >= 400) {
wprintf(L"Response Status Code = %d\n",respStatusCode);
wprintf(L"Response Header:\n");
wprintf(L"%s\n",rest.responseHeader());
wprintf(L"Response Body:\n");
wprintf(L"%s\n",sbResponseBody.getAsString());
return;
}
CkJsonObjectW jResp;
jResp.LoadSb(sbResponseBody);
jResp.put_EmitCompact(false);
wprintf(L"%s\n",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
// Chilkat functions returning "const char *" return a pointer to temporary internal memory owned and managed by Chilkat.
// See this example explaining how this memory should be used: const char * functions.
int AgeRangeHigh;
int AgeRangeLow;
const wchar_t *BeardConfidence = 0;
bool BeardValue;
const wchar_t *BoundingBoxHeight = 0;
const wchar_t *BoundingBoxLeft = 0;
const wchar_t *BoundingBoxTop = 0;
const wchar_t *BoundingBoxWidth = 0;
const wchar_t *Confidence = 0;
const wchar_t *EyeglassesConfidence = 0;
bool EyeglassesValue;
const wchar_t *EyesOpenConfidence = 0;
bool EyesOpenValue;
const wchar_t *GenderConfidence = 0;
const wchar_t *GenderValue = 0;
const wchar_t *MouthOpenConfidence = 0;
bool MouthOpenValue;
const wchar_t *MustacheConfidence = 0;
bool MustacheValue;
const wchar_t *PosePitch = 0;
const wchar_t *PoseRoll = 0;
const wchar_t *PoseYaw = 0;
const wchar_t *QualityBrightness = 0;
const wchar_t *QualitySharpness = 0;
const wchar_t *SmileConfidence = 0;
bool SmileValue;
const wchar_t *SunglassesConfidence = 0;
bool SunglassesValue;
int j;
int count_j;
const wchar_t *v_Type = 0;
const wchar_t *X = 0;
const wchar_t *Y = 0;
int i = 0;
int count_i = jResp.SizeOfArray(L"FaceDetails");
while (i < count_i) {
jResp.put_I(i);
AgeRangeHigh = jResp.IntOf(L"FaceDetails[i].AgeRange.High");
AgeRangeLow = jResp.IntOf(L"FaceDetails[i].AgeRange.Low");
BeardConfidence = jResp.stringOf(L"FaceDetails[i].Beard.Confidence");
BeardValue = jResp.BoolOf(L"FaceDetails[i].Beard.Value");
BoundingBoxHeight = jResp.stringOf(L"FaceDetails[i].BoundingBox.Height");
BoundingBoxLeft = jResp.stringOf(L"FaceDetails[i].BoundingBox.Left");
BoundingBoxTop = jResp.stringOf(L"FaceDetails[i].BoundingBox.Top");
BoundingBoxWidth = jResp.stringOf(L"FaceDetails[i].BoundingBox.Width");
Confidence = jResp.stringOf(L"FaceDetails[i].Confidence");
EyeglassesConfidence = jResp.stringOf(L"FaceDetails[i].Eyeglasses.Confidence");
EyeglassesValue = jResp.BoolOf(L"FaceDetails[i].Eyeglasses.Value");
EyesOpenConfidence = jResp.stringOf(L"FaceDetails[i].EyesOpen.Confidence");
EyesOpenValue = jResp.BoolOf(L"FaceDetails[i].EyesOpen.Value");
GenderConfidence = jResp.stringOf(L"FaceDetails[i].Gender.Confidence");
GenderValue = jResp.stringOf(L"FaceDetails[i].Gender.Value");
MouthOpenConfidence = jResp.stringOf(L"FaceDetails[i].MouthOpen.Confidence");
MouthOpenValue = jResp.BoolOf(L"FaceDetails[i].MouthOpen.Value");
MustacheConfidence = jResp.stringOf(L"FaceDetails[i].Mustache.Confidence");
MustacheValue = jResp.BoolOf(L"FaceDetails[i].Mustache.Value");
PosePitch = jResp.stringOf(L"FaceDetails[i].Pose.Pitch");
PoseRoll = jResp.stringOf(L"FaceDetails[i].Pose.Roll");
PoseYaw = jResp.stringOf(L"FaceDetails[i].Pose.Yaw");
QualityBrightness = jResp.stringOf(L"FaceDetails[i].Quality.Brightness");
QualitySharpness = jResp.stringOf(L"FaceDetails[i].Quality.Sharpness");
SmileConfidence = jResp.stringOf(L"FaceDetails[i].Smile.Confidence");
SmileValue = jResp.BoolOf(L"FaceDetails[i].Smile.Value");
SunglassesConfidence = jResp.stringOf(L"FaceDetails[i].Sunglasses.Confidence");
SunglassesValue = jResp.BoolOf(L"FaceDetails[i].Sunglasses.Value");
j = 0;
count_j = jResp.SizeOfArray(L"FaceDetails[i].Emotions");
while (j < count_j) {
jResp.put_J(j);
Confidence = jResp.stringOf(L"FaceDetails[i].Emotions[j].Confidence");
v_Type = jResp.stringOf(L"FaceDetails[i].Emotions[j].Type");
j = j + 1;
}
j = 0;
count_j = jResp.SizeOfArray(L"FaceDetails[i].Landmarks");
while (j < count_j) {
jResp.put_J(j);
v_Type = jResp.stringOf(L"FaceDetails[i].Landmarks[j].Type");
X = jResp.stringOf(L"FaceDetails[i].Landmarks[j].X");
Y = jResp.stringOf(L"FaceDetails[i].Landmarks[j].Y");
j = j + 1;
}
i = i + 1;
}
}
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