Rodrigo Rodriguez
5ea171d126
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Split 20+ files over 1000 lines into focused subdirectories for better maintainability and code organization. All changes maintain backward compatibility through re-export wrappers. Major splits: - attendance/llm_assist.rs (2074→7 modules) - basic/keywords/face_api.rs → face_api/ (7 modules) - basic/keywords/file_operations.rs → file_ops/ (8 modules) - basic/keywords/hear_talk.rs → hearing/ (6 modules) - channels/wechat.rs → wechat/ (10 modules) - channels/youtube.rs → youtube/ (5 modules) - contacts/mod.rs → contacts_api/ (6 modules) - core/bootstrap/mod.rs → bootstrap/ (5 modules) - core/shared/admin.rs → admin_*.rs (5 modules) - designer/canvas.rs → canvas_api/ (6 modules) - designer/mod.rs → designer_api/ (6 modules) - docs/handlers.rs → handlers_api/ (11 modules) - drive/mod.rs → drive_handlers.rs, drive_types.rs - learn/mod.rs → types.rs - main.rs → main_module/ (7 modules) - meet/webinar.rs → webinar_api/ (8 modules) - paper/mod.rs → (10 modules) - security/auth.rs → auth_api/ (7 modules) - security/passkey.rs → (4 modules) - sources/mod.rs → sources_api/ (5 modules) - tasks/mod.rs → task_api/ (5 modules) Stats: 38,040 deletions, 1,315 additions across 318 files Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
877 lines
31 KiB
Rust
877 lines
31 KiB
Rust
//! Face API Service
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//!
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//! This module contains the main FaceApiService implementation with support for
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//! multiple providers: Azure Face API, AWS Rekognition, OpenCV, and InsightFace.
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use super::azure::AzureFaceResponse;
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use super::error::FaceApiError;
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use super::results::{FaceAnalysisResult, FaceDetectionResult, FaceVerificationResult};
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use super::types::{AnalysisOptions, DetectionOptions, FaceAttributeType, FaceSource, ImageSource, VerificationOptions};
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use crate::botmodels::{BoundingBox, DetectedFace, EmotionScores, FaceApiConfig, FaceApiProvider, FaceAttributes, FaceLandmarks, Gender, GlassesType, Point2D};
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use std::collections::HashMap;
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use std::sync::Arc;
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use tokio::sync::RwLock;
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use uuid::Uuid;
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/// Calculate cosine similarity between two embedding vectors
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fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
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if a.len() != b.len() || a.is_empty() {
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return 0.0;
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}
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let dot_product: f32 = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum();
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let norm_a: f32 = a.iter().map(|x| x * x).sum::<f32>().sqrt();
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let norm_b: f32 = b.iter().map(|x| x * x).sum::<f32>().sqrt();
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if norm_a == 0.0 || norm_b == 0.0 {
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return 0.0;
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}
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(dot_product / (norm_a * norm_b)).clamp(0.0, 1.0)
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}
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pub struct FaceApiService {
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config: FaceApiConfig,
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client: reqwest::Client,
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face_cache: Arc<RwLock<HashMap<Uuid, DetectedFace>>>,
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}
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impl FaceApiService {
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pub fn new(config: FaceApiConfig) -> Self {
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Self {
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config,
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client: reqwest::Client::new(),
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face_cache: Arc::new(RwLock::new(HashMap::new())),
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}
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}
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/// Detect faces in an image
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pub async fn detect_faces(
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&self,
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image: &ImageSource,
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options: &DetectionOptions,
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) -> Result<FaceDetectionResult, FaceApiError> {
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let start = std::time::Instant::now();
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match self.config.provider {
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FaceApiProvider::AzureFaceApi => {
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self.detect_faces_azure(image, options).await
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}
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FaceApiProvider::AwsRekognition => {
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self.detect_faces_aws(image, options).await
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}
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FaceApiProvider::OpenCv => {
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self.detect_faces_opencv(image, options).await
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}
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FaceApiProvider::InsightFace => {
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self.detect_faces_insightface(image, options).await
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}
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}
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.map(|mut result| {
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result.processing_time_ms = start.elapsed().as_millis() as u64;
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result
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})
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}
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/// Verify if two faces are the same person
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pub async fn verify_faces(
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&self,
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face1: &FaceSource,
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face2: &FaceSource,
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options: &VerificationOptions,
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) -> Result<FaceVerificationResult, FaceApiError> {
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let start = std::time::Instant::now();
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match self.config.provider {
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FaceApiProvider::AzureFaceApi => {
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self.verify_faces_azure(face1, face2, options).await
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}
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FaceApiProvider::AwsRekognition => {
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self.verify_faces_aws(face1, face2, options).await
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}
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FaceApiProvider::OpenCv => {
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self.verify_faces_opencv(face1, face2, options).await
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}
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FaceApiProvider::InsightFace => {
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self.verify_faces_insightface(face1, face2, options).await
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}
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}
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.map(|mut result| {
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result.processing_time_ms = start.elapsed().as_millis() as u64;
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result
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})
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}
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/// Analyze face attributes
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pub async fn analyze_face(
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&self,
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source: &FaceSource,
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attributes: &[FaceAttributeType],
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options: &AnalysisOptions,
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) -> Result<FaceAnalysisResult, FaceApiError> {
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let start = std::time::Instant::now();
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match self.config.provider {
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FaceApiProvider::AzureFaceApi => {
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self.analyze_face_azure(source, attributes, options).await
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}
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FaceApiProvider::AwsRekognition => {
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self.analyze_face_aws(source, attributes, options).await
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}
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FaceApiProvider::OpenCv => {
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self.analyze_face_opencv(source, attributes, options).await
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}
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FaceApiProvider::InsightFace => {
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self.analyze_face_insightface(source, attributes, options).await
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}
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}
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.map(|mut result| {
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result.processing_time_ms = start.elapsed().as_millis() as u64;
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result
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})
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}
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// ========================================================================
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// Azure Face API Implementation
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// ========================================================================
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async fn detect_faces_azure(
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&self,
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image: &ImageSource,
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options: &DetectionOptions,
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) -> Result<FaceDetectionResult, FaceApiError> {
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let endpoint = self.config.endpoint.as_ref()
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.ok_or(FaceApiError::ConfigError("Azure endpoint not configured".to_string()))?;
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let api_key = self.config.api_key.as_ref()
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.ok_or(FaceApiError::ConfigError("Azure API key not configured".to_string()))?;
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let mut return_params = vec!["faceId"];
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if options.return_landmarks.unwrap_or(false) {
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return_params.push("faceLandmarks");
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}
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let mut attributes = Vec::new();
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if options.return_attributes.unwrap_or(false) {
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attributes.extend_from_slice(&[
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"age", "gender", "smile", "glasses", "emotion",
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"facialHair", "headPose", "blur", "exposure", "noise", "occlusion"
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]);
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}
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let url = format!(
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"{}/face/v1.0/detect?returnFaceId={}&returnFaceLandmarks={}&returnFaceAttributes={}",
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endpoint,
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options.return_face_id,
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options.return_landmarks.unwrap_or(false),
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attributes.join(",")
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);
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let request = match image {
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ImageSource::Url(image_url) => {
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self.client
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.post(&url)
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.header("Ocp-Apim-Subscription-Key", api_key)
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.header("Content-Type", "application/json")
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.json(&serde_json::json!({ "url": image_url }))
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}
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ImageSource::Base64(data) => {
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let bytes = base64::Engine::decode(
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&base64::engine::general_purpose::STANDARD,
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data,
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).map_err(|e| FaceApiError::InvalidInput(e.to_string()))?;
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self.client
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.post(&url)
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.header("Ocp-Apim-Subscription-Key", api_key)
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.header("Content-Type", "application/octet-stream")
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.body(bytes)
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}
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ImageSource::Binary(bytes) => {
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self.client
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.post(&url)
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.header("Ocp-Apim-Subscription-Key", api_key)
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.header("Content-Type", "application/octet-stream")
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.body(bytes.clone())
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}
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_ => return Err(FaceApiError::InvalidInput("Unsupported image source for Azure".to_string())),
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};
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let response = request.send().await
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.map_err(|e| FaceApiError::NetworkError(e.to_string()))?;
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if !response.status().is_success() {
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let error_text = response.text().await.unwrap_or_default();
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return Err(FaceApiError::ApiError(error_text));
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}
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let azure_faces: Vec<AzureFaceResponse> = response.json().await
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.map_err(|e| FaceApiError::ParseError(e.to_string()))?;
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let faces = azure_faces
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.into_iter()
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.map(|af| af.into_detected_face())
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.collect();
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Ok(FaceDetectionResult::success(faces, 0))
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}
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async fn verify_faces_azure(
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&self,
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face1: &FaceSource,
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face2: &FaceSource,
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options: &VerificationOptions,
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) -> Result<FaceVerificationResult, FaceApiError> {
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let endpoint = self.config.endpoint.as_ref()
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.ok_or(FaceApiError::ConfigError("Azure endpoint not configured".to_string()))?;
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let api_key = self.config.api_key.as_ref()
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.ok_or(FaceApiError::ConfigError("Azure API key not configured".to_string()))?;
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// Get face IDs (may need to detect first)
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let face1_id = self.get_or_detect_face_id(face1).await?;
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let face2_id = self.get_or_detect_face_id(face2).await?;
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let url = format!("{}/face/v1.0/verify", endpoint);
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let response = self.client
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.post(&url)
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.header("Ocp-Apim-Subscription-Key", api_key)
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.header("Content-Type", "application/json")
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.json(&serde_json::json!({
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"faceId1": face1_id.to_string(),
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"faceId2": face2_id.to_string()
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}))
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.send()
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.await
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.map_err(|e| FaceApiError::NetworkError(e.to_string()))?;
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if !response.status().is_success() {
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let error_text = response.text().await.unwrap_or_default();
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return Err(FaceApiError::ApiError(error_text));
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}
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let result: super::azure::AzureVerifyResponse = response.json().await
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.map_err(|e| FaceApiError::ParseError(e.to_string()))?;
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Ok(FaceVerificationResult::match_found(
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result.confidence,
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options.confidence_threshold as f64,
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0,
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).with_face_ids(face1_id, face2_id))
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}
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async fn analyze_face_azure(
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&self,
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source: &FaceSource,
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attributes: &[FaceAttributeType],
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options: &AnalysisOptions,
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) -> Result<FaceAnalysisResult, FaceApiError> {
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let detect_options = DetectionOptions {
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return_face_id: true,
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return_landmarks: Some(options.return_landmarks),
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return_attributes: Some(!attributes.is_empty()),
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..Default::default()
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};
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let image = match source {
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FaceSource::Image(img) => img.clone(),
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FaceSource::DetectedFace(face) => {
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return Ok(FaceAnalysisResult::success(*face.clone(), 0));
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}
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_ => return Err(FaceApiError::InvalidInput("Cannot analyze from face ID alone".to_string())),
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};
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let result = self.detect_faces_azure(&image, &detect_options).await?;
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if let Some(face) = result.faces.into_iter().next() {
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Ok(FaceAnalysisResult::success(face, 0))
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} else {
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Err(FaceApiError::NoFaceFound)
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}
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}
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// ========================================================================
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// AWS Rekognition Implementation
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// ========================================================================
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async fn detect_faces_aws(
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&self,
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image: &ImageSource,
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options: &DetectionOptions,
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) -> Result<FaceDetectionResult, FaceApiError> {
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use std::time::Instant;
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let start = Instant::now();
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// Get image bytes
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let image_bytes = self.get_image_bytes(image).await?;
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// Check if AWS credentials are configured
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let aws_region = std::env::var("AWS_REGION").unwrap_or_else(|_| "us-east-1".to_string());
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let _aws_key = std::env::var("AWS_ACCESS_KEY_ID")
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.map_err(|_| FaceApiError::ConfigError("AWS_ACCESS_KEY_ID not configured".to_string()))?;
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let _aws_secret = std::env::var("AWS_SECRET_ACCESS_KEY")
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.map_err(|_| FaceApiError::ConfigError("AWS_SECRET_ACCESS_KEY not configured".to_string()))?;
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// Use simulation for face detection
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// In production with aws-sdk-rekognition crate, this would call the real API
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let faces = self.simulate_face_detection(&image_bytes, options).await;
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// Cache detected faces
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for face in &faces {
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self.face_cache.write().await.insert(face.id, face.clone());
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}
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let processing_time = start.elapsed().as_millis() as u64;
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log::info!(
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"AWS Rekognition: Detected {} faces in {}ms (region: {})",
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faces.len(),
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processing_time,
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aws_region
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);
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Ok(FaceDetectionResult::success(faces, processing_time))
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}
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async fn verify_faces_aws(
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&self,
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face1: &FaceSource,
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face2: &FaceSource,
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options: &VerificationOptions,
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) -> Result<FaceVerificationResult, FaceApiError> {
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use std::time::Instant;
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let start = Instant::now();
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// Get face IDs or detect faces
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let face1_id = self.get_or_detect_face_id(face1).await?;
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let face2_id = self.get_or_detect_face_id(face2).await?;
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// Get embeddings from cache
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let cache = self.face_cache.read().await;
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let embedding1 = cache.get(&face1_id)
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.and_then(|f| f.embedding.clone())
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.ok_or(FaceApiError::InvalidInput("No embedding for face 1".to_string()))?;
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let embedding2 = cache.get(&face2_id)
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.and_then(|f| f.embedding.clone())
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.ok_or(FaceApiError::InvalidInput("No embedding for face 2".to_string()))?;
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drop(cache);
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// Calculate cosine similarity between embeddings
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let similarity = cosine_similarity(&embedding1, &embedding2);
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let threshold = options.threshold.unwrap_or(0.8) as f32;
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let is_match = similarity >= threshold;
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let processing_time = start.elapsed().as_millis() as u64;
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log::info!(
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"AWS Rekognition verify: similarity={:.3}, threshold={:.3}, match={}",
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similarity,
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threshold,
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is_match
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);
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Ok(FaceVerificationResult::match_found(
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similarity as f64,
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threshold as f64,
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processing_time,
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).with_face_ids(face1_id, face2_id))
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}
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async fn analyze_face_aws(
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&self,
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source: &FaceSource,
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attributes: &[FaceAttributeType],
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_options: &AnalysisOptions,
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) -> Result<FaceAnalysisResult, FaceApiError> {
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use std::time::Instant;
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let start = Instant::now();
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let face_id = self.get_or_detect_face_id(source).await?;
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// Simulate face analysis - in production, call AWS Rekognition DetectFaces with Attributes
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let mut result_attributes = FaceAttributes {
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age: None,
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gender: None,
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emotion: None,
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smile: None,
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glasses: None,
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facial_hair: None,
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head_pose: None,
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blur: None,
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exposure: None,
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noise: None,
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occlusion: None,
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};
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// Populate requested attributes with simulated data
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for attr in attributes {
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match attr {
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FaceAttributeType::Age => {
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result_attributes.age = Some(25.0 + (face_id.as_u128() % 40) as f32);
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}
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FaceAttributeType::Gender => {
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result_attributes.gender = Some(if face_id.as_u128() % 2 == 0 {
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Gender::Male
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} else {
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Gender::Female
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});
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}
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FaceAttributeType::Emotion => {
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result_attributes.emotion = Some(EmotionScores {
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neutral: 0.7,
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happiness: 0.2,
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sadness: 0.02,
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anger: 0.01,
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surprise: 0.03,
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fear: 0.01,
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disgust: 0.01,
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contempt: 0.02,
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});
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}
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FaceAttributeType::Smile => {
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result_attributes.smile = Some(0.3 + (face_id.as_u128() % 70) as f32 / 100.0);
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}
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FaceAttributeType::Glasses => {
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result_attributes.glasses = Some(if face_id.as_u128() % 3 == 0 {
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GlassesType::ReadingGlasses
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} else {
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GlassesType::NoGlasses
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});
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}
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_ => {}
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}
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}
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let processing_time = start.elapsed().as_millis() as u64;
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let detected_face = DetectedFace {
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id: face_id,
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bounding_box: BoundingBox {
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left: 100.0,
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top: 80.0,
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width: 120.0,
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height: 150.0,
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},
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confidence: 0.95,
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landmarks: None,
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attributes: Some(result_attributes.clone()),
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embedding: None,
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};
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Ok(FaceAnalysisResult::success(detected_face, processing_time))
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}
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|
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// ========================================================================
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// OpenCV Implementation (Local Processing)
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// ========================================================================
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|
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async fn detect_faces_opencv(
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&self,
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image: &ImageSource,
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options: &DetectionOptions,
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) -> Result<FaceDetectionResult, FaceApiError> {
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use std::time::Instant;
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let start = Instant::now();
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|
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// Get image bytes for local processing
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let image_bytes = self.get_image_bytes(image).await?;
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// OpenCV face detection simulation
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// In production, this would use opencv crate with Haar cascades or DNN
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let faces = self.simulate_face_detection(&image_bytes, options).await;
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|
|
let processing_time = start.elapsed().as_millis() as u64;
|
|
|
|
log::info!(
|
|
"OpenCV: Detected {} faces locally in {}ms",
|
|
faces.len(),
|
|
processing_time
|
|
);
|
|
|
|
Ok(FaceDetectionResult::success(faces, processing_time))
|
|
}
|
|
|
|
async fn verify_faces_opencv(
|
|
&self,
|
|
face1: &FaceSource,
|
|
face2: &FaceSource,
|
|
_options: &VerificationOptions,
|
|
) -> Result<FaceVerificationResult, FaceApiError> {
|
|
use std::time::Instant;
|
|
let start = Instant::now();
|
|
|
|
let face1_id = self.get_or_detect_face_id(face1).await?;
|
|
let face2_id = self.get_or_detect_face_id(face2).await?;
|
|
|
|
// Local face verification using feature comparison
|
|
// In production, use LBPH, Eigenfaces, or DNN embeddings
|
|
let similarity = if face1_id == face2_id {
|
|
1.0
|
|
} else {
|
|
0.5 + (face1_id.as_u128() % 50) as f32 / 100.0
|
|
};
|
|
|
|
let is_match = similarity >= 0.75;
|
|
let processing_time = start.elapsed().as_millis() as u64;
|
|
|
|
Ok(FaceVerificationResult {
|
|
success: true,
|
|
is_match,
|
|
confidence: similarity as f64,
|
|
threshold: 0.75,
|
|
face1_id: Some(face1_id),
|
|
face2_id: Some(face2_id),
|
|
processing_time_ms: processing_time,
|
|
error: None,
|
|
})
|
|
}
|
|
|
|
async fn analyze_face_opencv(
|
|
&self,
|
|
source: &FaceSource,
|
|
attributes: &[FaceAttributeType],
|
|
_options: &AnalysisOptions,
|
|
) -> Result<FaceAnalysisResult, FaceApiError> {
|
|
use std::time::Instant;
|
|
let start = Instant::now();
|
|
|
|
let face_id = self.get_or_detect_face_id(source).await?;
|
|
|
|
// Local analysis - OpenCV can do basic attribute detection
|
|
let mut result_attributes = FaceAttributes {
|
|
age: None,
|
|
gender: None,
|
|
emotion: None,
|
|
smile: None,
|
|
glasses: None,
|
|
facial_hair: None,
|
|
head_pose: None,
|
|
blur: None,
|
|
exposure: None,
|
|
noise: None,
|
|
occlusion: None,
|
|
};
|
|
|
|
for attr in attributes {
|
|
match attr {
|
|
FaceAttributeType::Age => {
|
|
// Age estimation using local model
|
|
result_attributes.age = Some(30.0 + (face_id.as_u128() % 35) as f32);
|
|
}
|
|
FaceAttributeType::Gender => {
|
|
result_attributes.gender = Some(if face_id.as_u128() % 2 == 0 {
|
|
Gender::Male
|
|
} else {
|
|
Gender::Female
|
|
});
|
|
}
|
|
_ => {
|
|
// Other attributes require more advanced models
|
|
}
|
|
}
|
|
}
|
|
|
|
let processing_time = start.elapsed().as_millis() as u64;
|
|
|
|
let detected_face = DetectedFace {
|
|
id: face_id,
|
|
bounding_box: BoundingBox {
|
|
left: 100.0,
|
|
top: 80.0,
|
|
width: 120.0,
|
|
height: 150.0,
|
|
},
|
|
confidence: 0.85,
|
|
landmarks: None,
|
|
attributes: Some(result_attributes),
|
|
embedding: None,
|
|
};
|
|
|
|
Ok(FaceAnalysisResult::success(detected_face, processing_time))
|
|
}
|
|
|
|
// ========================================================================
|
|
// InsightFace Implementation (Deep Learning)
|
|
// ========================================================================
|
|
|
|
async fn detect_faces_insightface(
|
|
&self,
|
|
image: &ImageSource,
|
|
options: &DetectionOptions,
|
|
) -> Result<FaceDetectionResult, FaceApiError> {
|
|
use std::time::Instant;
|
|
let start = Instant::now();
|
|
|
|
let image_bytes = self.get_image_bytes(image).await?;
|
|
|
|
// InsightFace uses RetinaFace for detection - very accurate
|
|
// In production, call Python InsightFace via FFI or HTTP service
|
|
let faces = self.simulate_face_detection(&image_bytes, options).await;
|
|
|
|
let processing_time = start.elapsed().as_millis() as u64;
|
|
|
|
log::info!(
|
|
"InsightFace: Detected {} faces using RetinaFace in {}ms",
|
|
faces.len(),
|
|
processing_time
|
|
);
|
|
|
|
Ok(FaceDetectionResult::success(faces, processing_time))
|
|
}
|
|
|
|
async fn verify_faces_insightface(
|
|
&self,
|
|
face1: &FaceSource,
|
|
face2: &FaceSource,
|
|
_options: &VerificationOptions,
|
|
) -> Result<FaceVerificationResult, FaceApiError> {
|
|
use std::time::Instant;
|
|
let start = Instant::now();
|
|
|
|
let face1_id = self.get_or_detect_face_id(face1).await?;
|
|
let face2_id = self.get_or_detect_face_id(face2).await?;
|
|
|
|
// InsightFace ArcFace provides high-accuracy verification
|
|
let similarity = if face1_id == face2_id {
|
|
1.0
|
|
} else {
|
|
// Simulate ArcFace cosine similarity
|
|
0.4 + (face1_id.as_u128() % 60) as f32 / 100.0
|
|
};
|
|
|
|
let is_match = similarity >= 0.68; // ArcFace threshold
|
|
let processing_time = start.elapsed().as_millis() as u64;
|
|
|
|
Ok(FaceVerificationResult {
|
|
success: true,
|
|
is_match,
|
|
confidence: similarity as f64,
|
|
threshold: 0.68,
|
|
face1_id: Some(face1_id),
|
|
face2_id: Some(face2_id),
|
|
processing_time_ms: processing_time,
|
|
error: None,
|
|
})
|
|
}
|
|
|
|
async fn analyze_face_insightface(
|
|
&self,
|
|
source: &FaceSource,
|
|
attributes: &[FaceAttributeType],
|
|
_options: &AnalysisOptions,
|
|
) -> Result<FaceAnalysisResult, FaceApiError> {
|
|
use std::time::Instant;
|
|
let start = Instant::now();
|
|
|
|
let face_id = self.get_or_detect_face_id(source).await?;
|
|
|
|
// InsightFace provides comprehensive attribute analysis
|
|
let mut result_attributes = FaceAttributes {
|
|
age: None,
|
|
gender: None,
|
|
emotion: None,
|
|
smile: None,
|
|
glasses: None,
|
|
facial_hair: None,
|
|
head_pose: None,
|
|
blur: None,
|
|
exposure: None,
|
|
noise: None,
|
|
occlusion: None,
|
|
};
|
|
|
|
for attr in attributes {
|
|
match attr {
|
|
FaceAttributeType::Age => {
|
|
// InsightFace age estimation is very accurate
|
|
result_attributes.age = Some(28.0 + (face_id.as_u128() % 42) as f32);
|
|
}
|
|
FaceAttributeType::Gender => {
|
|
result_attributes.gender = Some(if face_id.as_u128() % 2 == 0 {
|
|
Gender::Male
|
|
} else {
|
|
Gender::Female
|
|
});
|
|
}
|
|
FaceAttributeType::Emotion => {
|
|
result_attributes.emotion = Some(EmotionScores {
|
|
neutral: 0.65,
|
|
happiness: 0.25,
|
|
sadness: 0.03,
|
|
anger: 0.02,
|
|
surprise: 0.02,
|
|
fear: 0.01,
|
|
disgust: 0.01,
|
|
contempt: 0.01,
|
|
});
|
|
}
|
|
FaceAttributeType::Smile => {
|
|
result_attributes.smile = Some(0.4 + (face_id.as_u128() % 60) as f32 / 100.0);
|
|
}
|
|
FaceAttributeType::Glasses => {
|
|
result_attributes.glasses = Some(if face_id.as_u128() % 4 == 0 {
|
|
GlassesType::ReadingGlasses
|
|
} else {
|
|
GlassesType::NoGlasses
|
|
});
|
|
}
|
|
_ => {}
|
|
}
|
|
}
|
|
|
|
let processing_time = start.elapsed().as_millis() as u64;
|
|
|
|
let detected_face = DetectedFace {
|
|
id: face_id,
|
|
bounding_box: BoundingBox {
|
|
left: 100.0,
|
|
top: 80.0,
|
|
width: 120.0,
|
|
height: 150.0,
|
|
},
|
|
confidence: 0.92,
|
|
landmarks: None,
|
|
attributes: Some(result_attributes),
|
|
embedding: None,
|
|
};
|
|
|
|
Ok(FaceAnalysisResult::success(detected_face, processing_time))
|
|
}
|
|
|
|
// ========================================================================
|
|
// Helper Methods for Provider Implementations
|
|
// ========================================================================
|
|
|
|
async fn get_image_bytes(&self, source: &ImageSource) -> Result<Vec<u8>, FaceApiError> {
|
|
match source {
|
|
ImageSource::Variable(var) => {
|
|
Err(FaceApiError::InvalidInput(format!("Variable image source '{}' not supported in this context", var)))
|
|
}
|
|
ImageSource::Url(url) => {
|
|
let client = reqwest::Client::new();
|
|
let response = client
|
|
.get(url)
|
|
.send()
|
|
.await
|
|
.map_err(|e| FaceApiError::NetworkError(e.to_string()))?;
|
|
let bytes = response
|
|
.bytes()
|
|
.await
|
|
.map_err(|e| FaceApiError::NetworkError(e.to_string()))?;
|
|
Ok(bytes.to_vec())
|
|
}
|
|
ImageSource::Base64(data) => {
|
|
use base64::Engine;
|
|
base64::engine::general_purpose::STANDARD
|
|
.decode(data)
|
|
.map_err(|e| FaceApiError::ParseError(e.to_string()))
|
|
}
|
|
ImageSource::Bytes(bytes) | ImageSource::Binary(bytes) => Ok(bytes.clone()),
|
|
ImageSource::FilePath(path) => {
|
|
std::fs::read(path).map_err(|e| FaceApiError::InvalidInput(e.to_string()))
|
|
}
|
|
}
|
|
}
|
|
|
|
async fn simulate_face_detection(
|
|
&self,
|
|
image_bytes: &[u8],
|
|
options: &DetectionOptions,
|
|
) -> Vec<DetectedFace> {
|
|
// Simulate detection based on image size/content
|
|
// In production, actual detection algorithms would be used
|
|
let num_faces = if image_bytes.len() > 100_000 {
|
|
(image_bytes.len() / 500_000).min(5).max(1)
|
|
} else {
|
|
1
|
|
};
|
|
|
|
let max_faces = options.max_faces.unwrap_or(10);
|
|
let num_faces = num_faces.min(max_faces);
|
|
|
|
(0..num_faces)
|
|
.map(|i| {
|
|
let face_id = Uuid::new_v4();
|
|
DetectedFace {
|
|
id: face_id,
|
|
bounding_box: BoundingBox {
|
|
left: 100.0 + (i as f32 * 150.0),
|
|
top: 80.0 + (i as f32 * 20.0),
|
|
width: 120.0,
|
|
height: 150.0,
|
|
},
|
|
confidence: 0.95 - (i as f64 * 0.05),
|
|
landmarks: if options.return_landmarks.unwrap_or(false) {
|
|
Some(FaceLandmarks {
|
|
left_eye: Point2D { x: 140.0, y: 120.0 },
|
|
right_eye: Point2D { x: 180.0, y: 120.0 },
|
|
nose_tip: Point2D { x: 160.0, y: 150.0 },
|
|
mouth_left: Point2D { x: 145.0, y: 175.0 },
|
|
mouth_right: Point2D { x: 175.0, y: 175.0 },
|
|
left_eyebrow_left: None,
|
|
left_eyebrow_right: None,
|
|
right_eyebrow_left: None,
|
|
right_eyebrow_right: None,
|
|
})
|
|
} else {
|
|
None
|
|
},
|
|
attributes: if options.return_attributes.unwrap_or(false) {
|
|
Some(FaceAttributes {
|
|
age: Some(25.0 + (face_id.as_u128() % 40) as f32),
|
|
gender: Some(if face_id.as_u128() % 2 == 0 {
|
|
Gender::Male
|
|
} else {
|
|
Gender::Female
|
|
}),
|
|
emotion: None,
|
|
smile: Some(0.5),
|
|
glasses: Some(GlassesType::NoGlasses),
|
|
facial_hair: None,
|
|
head_pose: None,
|
|
blur: None,
|
|
exposure: None,
|
|
noise: None,
|
|
occlusion: None,
|
|
})
|
|
} else {
|
|
None
|
|
},
|
|
embedding: None,
|
|
}
|
|
})
|
|
.collect()
|
|
}
|
|
|
|
fn _generate_landmarks(&self) -> HashMap<String, (f32, f32)> {
|
|
let mut landmarks = HashMap::new();
|
|
landmarks.insert("left_eye".to_string(), (140.0, 120.0));
|
|
landmarks.insert("right_eye".to_string(), (180.0, 120.0));
|
|
landmarks.insert("nose_tip".to_string(), (160.0, 150.0));
|
|
landmarks.insert("mouth_left".to_string(), (145.0, 175.0));
|
|
landmarks.insert("mouth_right".to_string(), (175.0, 175.0));
|
|
landmarks
|
|
}
|
|
|
|
// ========================================================================
|
|
// Helper Methods
|
|
// ========================================================================
|
|
|
|
async fn get_or_detect_face_id(&self, source: &FaceSource) -> Result<Uuid, FaceApiError> {
|
|
match source {
|
|
FaceSource::FaceId(id) => Ok(*id),
|
|
FaceSource::DetectedFace(face) => Ok(face.id),
|
|
FaceSource::Image(image) => {
|
|
let result = self.detect_faces(image, &DetectionOptions::default()).await?;
|
|
result.faces.first()
|
|
.map(|f| f.id)
|
|
.ok_or(FaceApiError::NoFaceFound)
|
|
}
|
|
FaceSource::Embedding(_) => {
|
|
Err(FaceApiError::InvalidInput("Cannot get face ID from embedding".to_string()))
|
|
}
|
|
}
|
|
}
|
|
}
|