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FaceIndexService — high-level reverse face search service.
Handles:
- Image upload + validation
- Face detection (using the feature extractor)
- Embedding generation (via InsightFace provider if available)
- Enrollment into the FaceIndex
- Search against the FaceIndex
- Crawler for seeding the index with public faces
"""
from __future__ import annotations
import base64
import io
import time
import uuid
from typing import Optional
import numpy as np
from loguru import logger
from config.settings import Settings
from pipeline import InputValidator, ImagePreprocessor, ImageHasher, FeatureExtractor
from storage.face_index import FaceIndex
from utils.logging import execution_context, new_execution_id
class FaceIndexService:
"""High-level service for the reverse face search index."""
def __init__(
self,
settings: Settings,
validator: InputValidator,
preprocessor: ImagePreprocessor,
hasher: ImageHasher,
feature_extractor: FeatureExtractor,
face_index: FaceIndex,
) -> None:
self._settings = settings
self._validator = validator
self._preprocessor = preprocessor
self._hasher = hasher
self._feature_extractor = feature_extractor
self._index = face_index
# ------------------------------------------------------------------ #
# Enrollment
# ------------------------------------------------------------------ #
async def enroll(
self,
image_url: Optional[str] = None,
image_base64: Optional[str] = None,
image_bytes: Optional[bytes] = None,
name: Optional[str] = None,
source_url: Optional[str] = None,
metadata: Optional[dict] = None,
) -> dict:
"""
Enroll a face into the searchable index.
Accepts an image (URL, base64, or raw bytes) + optional metadata.
Detects the largest face in the image, computes its embedding,
and adds it to the FaceIndex.
Returns dict with face_id + status.
"""
eid = new_execution_id()
with execution_context(execution_id=eid, provider_id="face_index_service.enroll"):
t0 = time.perf_counter()
# Validate input
if image_bytes:
vr = self._validator.validate(image_bytes=image_bytes)
else:
vr = self._validator.validate(
image_url=image_url, image_base64=image_base64,
)
if not vr.valid:
return {"success": False, "error": vr.error, "error_type": "ValidationError"}
# Preprocess
if vr.source == "url":
pre = self._preprocessor.from_url(image_url)
else:
pre = self._preprocessor.from_bytes(vr.image_bytes, vr.source)
img_hash = self._hasher.hash(pre.image)
pipeline_output = self._feature_extractor.extract(
pre.image, img_hash, pre.width, pre.height, pre.source,
original_bytes=pre.original_bytes,
original_format=pre.original_format,
)
# Check if any faces were detected
if not pipeline_output.face_crops:
return {
"success": False,
"error": "No faces detected in image",
"error_type": "NoFaceDetected",
}
# Use the largest face (highest area)
largest = max(
pipeline_output.face_crops,
key=lambda c: c.width * c.height if hasattr(c, "width") else 0,
)
embedding = getattr(largest, "embedding", None)
if embedding is None:
# Fallback: try to compute embedding via feature extractor
embedding = self._feature_extractor.compute_embedding(largest.image)
if embedding is None:
return {
"success": False,
"error": "Could not compute face embedding (InsightFace provider not available?)",
"error_type": "EmbeddingFailed",
}
# Enroll into index
face_id = self._index.enroll(
embedding=embedding,
name=name,
source_url=source_url,
metadata=metadata,
thumbnail_path=None, # could save crop here in future
)
elapsed = (time.perf_counter() - t0) * 1000.0
return {
"success": True,
"face_id": face_id,
"embedding_dim": self._index.EMBEDDING_DIM,
"elapsed_ms": round(elapsed, 3),
}
# ------------------------------------------------------------------ #
# Search
# ------------------------------------------------------------------ #
async def search(
self,
image_url: Optional[str] = None,
image_base64: Optional[str] = None,
image_bytes: Optional[bytes] = None,
top_k: Optional[int] = None,
threshold: Optional[float] = None,
) -> dict:
"""
Search the index for faces matching the query image.
Returns dict with matches sorted by similarity (descending).
"""
eid = new_execution_id()
with execution_context(execution_id=eid, provider_id="face_index_service.search"):
t0 = time.perf_counter()
# Validate input
if image_bytes:
vr = self._validator.validate(image_bytes=image_bytes)
else:
vr = self._validator.validate(
image_url=image_url, image_base64=image_base64,
)
if not vr.valid:
return {"success": False, "error": vr.error, "error_type": "ValidationError"}
# Preprocess
if vr.source == "url":
pre = self._preprocessor.from_url(image_url)
else:
pre = self._preprocessor.from_bytes(vr.image_bytes, vr.source)
img_hash = self._hasher.hash(pre.image)
pipeline_output = self._feature_extractor.extract(
pre.image, img_hash, pre.width, pre.height, pre.source,
original_bytes=pre.original_bytes,
original_format=pre.original_format,
)
if not pipeline_output.face_crops:
return {
"success": True,
"query_face_detected": False,
"matches": [],
"elapsed_ms": round((time.perf_counter() - t0) * 1000, 3),
}
# Use the largest face
largest = max(
pipeline_output.face_crops,
key=lambda c: c.width * c.height if hasattr(c, "width") else 0,
)
embedding = getattr(largest, "embedding", None)
if embedding is None:
embedding = self._feature_extractor.compute_embedding(largest.image)
if embedding is None:
return {
"success": False,
"error": "Could not compute face embedding",
"error_type": "EmbeddingFailed",
}
# Search the index
matches = self._index.search(
embedding,
top_k=top_k or self._settings.face_index_top_k,
threshold=threshold if threshold is not None else self._settings.face_index_threshold,
)
elapsed = (time.perf_counter() - t0) * 1000.0
return {
"success": True,
"query_face_detected": True,
"num_matches": len(matches),
"matches": matches,
"elapsed_ms": round(elapsed, 3),
}
# ------------------------------------------------------------------ #
# CRUD passthrough
# ------------------------------------------------------------------ #
def list(self, limit: int = 50, offset: int = 0, name: Optional[str] = None) -> list[dict]:
return self._index.list(limit=limit, offset=offset, name=name)
def get(self, face_id: str) -> Optional[dict]:
return self._index.get(face_id)
def delete(self, face_id: str) -> bool:
return self._index.delete(face_id)
def stats(self) -> dict:
return self._index.stats()
def clear(self) -> int:
return self._index.clear()
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