Zig
Zig
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 Zig Downloads
const std = @import("std");
const chilkat = @import("chilkat");
pub fn main(init: std.process.Init) !void {
const alloc = init.arena.allocator();
const rest = try chilkat.Rest.init();
defer rest.deinit();
const auth_aws = try chilkat.AuthAws.init();
defer auth_aws.deinit();
auth_aws.setAccessKey("AWS_ACCESS_KEY");
auth_aws.setSecretKey("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.)
auth_aws.setRegion("us-west-2");
auth_aws.setServiceName("rekognition");
// SetAuthAws causes Chilkat to automatically add the following headers: Authorization, X-Amz-Date
rest.setAuthAws(auth_aws) catch {};
// URL: https://rekognition.us-west-2.amazonaws.com/
const b_tls = true;
const port = 443;
const b_auto_reconnect = true;
// Don't forget to change the region domain (us-west-2.amazonaws.com) to your particular region.
rest.connect("rekognition.us-west-2.amazonaws.com", port, b_tls, b_auto_reconnect) catch {
std.debug.print("ConnectFailReason: {d}\n", .{rest.getConnectFailReason()});
std.debug.print("{s}\n", .{try rest.getLastErrorText(alloc)});
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.
const bd_jpg = try chilkat.BinData.init();
defer bd_jpg.deinit();
bd_jpg.loadFile("qa_data/jpg/kid_blue_coat.jpg") catch {
std.debug.print("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:
// (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.
const sb_jpg = try chilkat.StringBuilder.init();
defer sb_jpg.deinit();
bd_jpg.getEncodedSb("base64", sb_jpg) catch {};
const json = try chilkat.JsonObject.init();
defer json.deinit();
json.updateSb("Image.Bytes", sb_jpg) catch {};
json.updateString("Attributes[0]", "ALL") catch {};
rest.addHeader("Content-Type", "application/x-amz-json-1.1") catch {};
rest.addHeader("X-Amz-Target", "RekognitionService.DetectFaces") catch {};
const sb_request_body = try chilkat.StringBuilder.init();
defer sb_request_body.deinit();
json.emitSb(sb_request_body) catch {};
const sb_response_body = try chilkat.StringBuilder.init();
defer sb_response_body.deinit();
rest.fullRequestSb("POST", "/", sb_request_body, sb_response_body) catch {
std.debug.print("{s}\n", .{try rest.getLastErrorText(alloc)});
return;
};
const resp_status_code = rest.getResponseStatusCode();
std.debug.print("response status code = {d}\n", .{resp_status_code});
if (resp_status_code >= 400) {
std.debug.print("Response Status Code = {d}\n", .{resp_status_code});
std.debug.print("Response Header:\n", .{});
std.debug.print("{s}\n", .{try rest.getResponseHeader(alloc)});
std.debug.print("Response Body:\n", .{});
std.debug.print("{s}\n", .{try sb_response_body.getAsString(alloc)});
return;
}
const j_resp = try chilkat.JsonObject.init();
defer j_resp.deinit();
j_resp.loadSb(sb_response_body) catch {};
j_resp.setEmitCompact(false);
std.debug.print("{s}\n", .{try j_resp.emit(alloc)});
// 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 age_range_high: i32 = 0;
var age_range_low: i32 = 0;
var beard_confidence: [:0]const u8 = "";
var beard_value: bool = false;
var bounding_box_height: [:0]const u8 = "";
var bounding_box_left: [:0]const u8 = "";
var bounding_box_top: [:0]const u8 = "";
var bounding_box_width: [:0]const u8 = "";
var confidence: [:0]const u8 = "";
var eyeglasses_confidence: [:0]const u8 = "";
var eyeglasses_value: bool = false;
var eyes_open_confidence: [:0]const u8 = "";
var eyes_open_value: bool = false;
var gender_confidence: [:0]const u8 = "";
var gender_value: [:0]const u8 = "";
var mouth_open_confidence: [:0]const u8 = "";
var mouth_open_value: bool = false;
var mustache_confidence: [:0]const u8 = "";
var mustache_value: bool = false;
var pose_pitch: [:0]const u8 = "";
var pose_roll: [:0]const u8 = "";
var pose_yaw: [:0]const u8 = "";
var quality_brightness: [:0]const u8 = "";
var quality_sharpness: [:0]const u8 = "";
var smile_confidence: [:0]const u8 = "";
var smile_value: bool = false;
var sunglasses_confidence: [:0]const u8 = "";
var sunglasses_value: bool = false;
var j: i32 = 0;
var count_j: i32 = 0;
var v_type: [:0]const u8 = "";
var x: [:0]const u8 = "";
var y: [:0]const u8 = "";
var i: i32 = 0;
const count_i = j_resp.sizeOfArray("FaceDetails");
while (i < count_i) {
j_resp.setI(i);
age_range_high = j_resp.intOf("FaceDetails[i].AgeRange.High");
age_range_low = j_resp.intOf("FaceDetails[i].AgeRange.Low");
beard_confidence = try j_resp.stringOf(alloc, "FaceDetails[i].Beard.Confidence");
beard_value = j_resp.boolOf("FaceDetails[i].Beard.Value");
bounding_box_height = try j_resp.stringOf(alloc, "FaceDetails[i].BoundingBox.Height");
bounding_box_left = try j_resp.stringOf(alloc, "FaceDetails[i].BoundingBox.Left");
bounding_box_top = try j_resp.stringOf(alloc, "FaceDetails[i].BoundingBox.Top");
bounding_box_width = try j_resp.stringOf(alloc, "FaceDetails[i].BoundingBox.Width");
confidence = try j_resp.stringOf(alloc, "FaceDetails[i].Confidence");
eyeglasses_confidence = try j_resp.stringOf(alloc, "FaceDetails[i].Eyeglasses.Confidence");
eyeglasses_value = j_resp.boolOf("FaceDetails[i].Eyeglasses.Value");
eyes_open_confidence = try j_resp.stringOf(alloc, "FaceDetails[i].EyesOpen.Confidence");
eyes_open_value = j_resp.boolOf("FaceDetails[i].EyesOpen.Value");
gender_confidence = try j_resp.stringOf(alloc, "FaceDetails[i].Gender.Confidence");
gender_value = try j_resp.stringOf(alloc, "FaceDetails[i].Gender.Value");
mouth_open_confidence = try j_resp.stringOf(alloc, "FaceDetails[i].MouthOpen.Confidence");
mouth_open_value = j_resp.boolOf("FaceDetails[i].MouthOpen.Value");
mustache_confidence = try j_resp.stringOf(alloc, "FaceDetails[i].Mustache.Confidence");
mustache_value = j_resp.boolOf("FaceDetails[i].Mustache.Value");
pose_pitch = try j_resp.stringOf(alloc, "FaceDetails[i].Pose.Pitch");
pose_roll = try j_resp.stringOf(alloc, "FaceDetails[i].Pose.Roll");
pose_yaw = try j_resp.stringOf(alloc, "FaceDetails[i].Pose.Yaw");
quality_brightness = try j_resp.stringOf(alloc, "FaceDetails[i].Quality.Brightness");
quality_sharpness = try j_resp.stringOf(alloc, "FaceDetails[i].Quality.Sharpness");
smile_confidence = try j_resp.stringOf(alloc, "FaceDetails[i].Smile.Confidence");
smile_value = j_resp.boolOf("FaceDetails[i].Smile.Value");
sunglasses_confidence = try j_resp.stringOf(alloc, "FaceDetails[i].Sunglasses.Confidence");
sunglasses_value = j_resp.boolOf("FaceDetails[i].Sunglasses.Value");
j = 0;
count_j = j_resp.sizeOfArray("FaceDetails[i].Emotions");
while (j < count_j) {
j_resp.setJ(j);
confidence = try j_resp.stringOf(alloc, "FaceDetails[i].Emotions[j].Confidence");
v_type = try j_resp.stringOf(alloc, "FaceDetails[i].Emotions[j].Type");
j = j + 1;
}
j = 0;
count_j = j_resp.sizeOfArray("FaceDetails[i].Landmarks");
while (j < count_j) {
j_resp.setJ(j);
v_type = try j_resp.stringOf(alloc, "FaceDetails[i].Landmarks[j].Type");
x = try j_resp.stringOf(alloc, "FaceDetails[i].Landmarks[j].X");
y = try j_resp.stringOf(alloc, "FaceDetails[i].Landmarks[j].Y");
j = j + 1;
}
i = i + 1;
}
}