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Face Intelligence service — post-processes face detections into
structured intelligence: quality scores, pose, blur, best-face
selection, clustering, and duplicate elimination.
This service consumes the output of detection + recognition providers
and produces a FaceIntelligenceResult. It does NOT run providers itself
— it expects the caller to have already run detection (and optionally
recognition for embeddings).
Pure computation via cores.face.analysis — no external calls.
"""
from __future__ import annotations
import time
from typing import List, Optional
import numpy as np
from cores.face import (
blur_score, is_blurry, face_size, face_size_label,
estimate_pose_landmark, face_orientation, face_quality_score,
select_best_face, cluster_faces, find_duplicate_faces,
extract_face_crops,
)
from cores.vision.geometry import BBox
from models.jobs import JobRequest
from models.reports import (
FaceIntelligenceResult,
FaceQualityMetrics,
FaceCluster,
)
from pipeline import InputValidator, ImagePreprocessor, ImageHasher, FeatureExtractor
from utils.logging import execution_context, new_execution_id
class FaceIntelligenceService:
"""Post-processes face detections into structured intelligence."""
def __init__(
self,
validator: InputValidator,
preprocessor: ImagePreprocessor,
hasher: ImageHasher,
feature_extractor: FeatureExtractor,
) -> None:
self._validator = validator
self._preprocessor = preprocessor
self._hasher = hasher
self._feature_extractor = feature_extractor
async def analyze(self, request: JobRequest) -> dict:
"""Run face intelligence on an image.
Uses the feature extractor (which wraps a detection provider) to
detect faces, then computes quality/pose/cluster intelligence.
"""
eid = new_execution_id()
with execution_context(execution_id=eid, provider_id="face_intelligence_service"):
t0 = time.perf_counter()
vr = self._validator.validate(
image_url=request.image_url,
image_base64=request.image_base64,
)
if not vr.valid:
return {"success": False, "error": vr.error, "error_type": "ValidationError"}
if vr.source == "url":
pre = self._preprocessor.from_url(request.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,
)
face_crops = pipeline_output.face_crops
boxes = [fc.box for fc in face_crops]
total_faces = len(face_crops)
if total_faces == 0:
result = FaceIntelligenceResult(
total_faces=0,
best_face_index=None,
elapsed_ms=0.0,
)
elapsed = (time.perf_counter() - t0) * 1000.0
result.elapsed_ms = round(elapsed, 3)
return {"success": True, "face_intelligence": result.model_dump(),
"elapsed_ms": round(elapsed, 3)}
# Compute quality metrics per face
quality_metrics: List[FaceQualityMetrics] = []
blur_scores: List[float] = []
sizes: List[int] = []
pose_labels: List[str] = []
for i, fc in enumerate(face_crops):
face_img = fc.image
bbox = BBox(fc.box["x"], fc.box["y"], fc.box["w"], fc.box["h"])
blur = blur_score(face_img)
blur_scores.append(blur)
sizes.append(face_size(bbox))
# Pose estimation — use landmarks if available, else bbox
landmarks = None
# landmarks would come from the detector; for now use None
yaw, pitch, roll, pose_label = estimate_pose_landmark(landmarks)
pose_labels.append(pose_label)
orientation = face_orientation(roll)
qs = face_quality_score(face_img, bbox, blur=blur, pose_label=pose_label)
quality_metrics.append(FaceQualityMetrics(
quality_score=qs,
blur_score=round(blur, 4),
is_blurry=is_blurry(face_img),
face_size=face_size(bbox),
face_size_label=face_size_label(bbox),
pose_yaw=yaw,
pose_pitch=pitch,
pose_roll=roll,
pose_label=pose_label,
orientation=orientation,
is_best_face=False,
))
# Best face selection
best_idx = select_best_face(
[m.quality_score for m in quality_metrics],
sizes,
pose_labels,
)
if best_idx >= 0:
quality_metrics[best_idx].is_best_face = True
# Clustering — only if we have embeddings (from recognition provider)
# For now, we don't have embeddings here; clustering is optional
clusters: List[FaceCluster] = []
# If embeddings were attached to pipeline_output.gallery, use them
# (This would require the caller to have run recognition first)
# Duplicate face elimination by IoU
duplicate_indices = find_duplicate_faces(boxes)
elapsed = (time.perf_counter() - t0) * 1000.0
result = FaceIntelligenceResult(
total_faces=total_faces,
best_face_index=best_idx if best_idx >= 0 else None,
quality_metrics=quality_metrics,
clusters=clusters,
duplicate_face_indices=duplicate_indices,
elapsed_ms=round(elapsed, 3),
)
return {
"success": True,
"face_intelligence": result.model_dump(),
"elapsed_ms": round(elapsed, 3),
}
async def analyze_with_embeddings(
self,
image: np.ndarray,
boxes: List[dict],
embeddings: List[np.ndarray],
cluster_threshold: float = 0.6,
) -> FaceIntelligenceResult:
"""Analyze faces when embeddings are already available.
This is used when detection + recognition have already run, and we
want to add quality + clustering intelligence.
"""
t0 = time.perf_counter()
# Extract crops
crops = extract_face_crops(image, boxes)
quality_metrics: List[FaceQualityMetrics] = []
blur_scores: List[float] = []
sizes: List[int] = []
pose_labels: List[str] = []
for i, crop in enumerate(crops):
bbox = BBox(boxes[i]["x"], boxes[i]["y"], boxes[i]["w"], boxes[i]["h"])
blur = blur_score(crop)
blur_scores.append(blur)
sizes.append(face_size(bbox))
yaw, pitch, roll, pose_label = estimate_pose_landmark(None)
pose_labels.append(pose_label)
quality_metrics.append(FaceQualityMetrics(
quality_score=face_quality_score(crop, bbox, blur=blur, pose_label=pose_label),
blur_score=round(blur, 4),
is_blurry=is_blurry(crop),
face_size=face_size(bbox),
face_size_label=face_size_label(bbox),
pose_yaw=yaw,
pose_pitch=pitch,
pose_roll=roll,
pose_label=pose_label,
orientation=face_orientation(roll),
is_best_face=False,
))
best_idx = select_best_face(
[m.quality_score for m in quality_metrics], sizes, pose_labels
)
if best_idx >= 0:
quality_metrics[best_idx].is_best_face = True
# Cluster by embedding similarity
cluster_data = cluster_faces(embeddings, threshold=cluster_threshold)
clusters = [
FaceCluster(
cluster_id=c["cluster_id"],
face_indices=c["face_indices"],
representative_index=c["representative_index"],
num_faces=c["num_faces"],
avg_similarity=c["avg_similarity"],
)
for c in cluster_data
]
duplicate_indices = find_duplicate_faces(boxes)
elapsed = (time.perf_counter() - t0) * 1000.0
return FaceIntelligenceResult(
total_faces=len(crops),
best_face_index=best_idx if best_idx >= 0 else None,
quality_metrics=quality_metrics,
clusters=clusters,
duplicate_face_indices=duplicate_indices,
elapsed_ms=round(elapsed, 3),
)
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