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Report merger — turns a dict of {provider_name: ProviderResult} into a
UnifiedFaceReport.
The merger:
1. Routes each ProviderResult to the correct collector based on capability.
2. Preserves every ProviderResult as Evidence (raw + normalized + timestamp).
3. Delegates confidence scoring to confidence.engine.
4. Delegates conflict detection to confidence.conflicts.
5. Attaches limitations collected from providers + system-wide.
"""
from __future__ import annotations
from typing import Dict, List
from confidence.engine import ConfidenceEngine
from confidence.conflicts import ConflictDetector
from models.reports import (
UnifiedFaceReport,
FaceDetection,
FaceMatch,
ImageAnalysisResult,
MetadataResult,
ForensicsResult,
OCRResult,
ObjectDetectionResult,
SceneResult,
NSFWResult,
AIDetectionResult,
EmbeddingResult,
Evidence,
ReportMetadata,
)
from normalization.schema import (
NormalizedBox,
NormalizedMatch,
NormalizedScrapeImage,
NormalizedReverseMatch,
NormalizedImageAnalysis,
NormalizedMetadata,
NormalizedForensics,
)
from pipeline.postprocessing import ResultPostprocessor
from providers.base import ProviderResult
class ReportMerger:
"""Merges provider results into a UnifiedFaceReport."""
def __init__(
self,
confidence_engine: ConfidenceEngine,
conflict_detector: ConflictDetector,
) -> None:
self._confidence = confidence_engine
self._conflicts = conflict_detector
def merge(
self,
results: Dict[str, ProviderResult],
image_hash: str,
job_id: str,
total_elapsed_ms: float,
kind: str = "detection",
) -> UnifiedFaceReport:
boxes = self._collect_boxes(results)
matches = self._collect_matches(results)
scraped = self._collect_scraped(results)
reverse_matches = self._collect_reverse(results)
image_analyses = self._collect_image_analyses(results)
metadata_extractions = self._collect_metadata(results)
forensics = self._collect_forensics(results)
evidence = self._build_evidence(results)
# Build FaceDetection objects with confidence
detections: List[FaceDetection] = []
for i, nbox in enumerate(boxes):
score = self._confidence.score_detection(nbox, results)
detections.append(FaceDetection(
box={"x": nbox.x, "y": nbox.y, "w": nbox.w, "h": nbox.h},
confidence=score,
landmarks=nbox.landmarks,
detected_by=[nbox.detector],
))
# Build FaceMatch objects
face_matches: List[FaceMatch] = []
for nm in matches:
score = self._confidence.score_match(nm)
face_matches.append(FaceMatch(
query_face_index=nm.query_face_index,
best_match=nm.best_match,
confidence=score,
distances=nm.distances,
))
# Build ImageAnalysisResult objects
image_analysis_models: List[ImageAnalysisResult] = []
for nia in image_analyses:
score = self._confidence.score_image_analysis(nia)
image_analysis_models.append(ImageAnalysisResult(
provider=nia.provider,
quality_score=nia.quality_score,
brightness=nia.brightness,
contrast=nia.contrast,
sharpness=nia.sharpness,
noise_level=nia.noise_level,
width=nia.width,
height=nia.height,
channels=nia.channels,
color_profile=nia.color_profile,
dominant_colors=nia.dominant_colors,
aspects=nia.aspects,
confidence=score,
))
# Build MetadataResult objects
metadata_models: List[MetadataResult] = []
for nm in metadata_extractions:
score = self._confidence.score_metadata(nm)
metadata_models.append(MetadataResult(
provider=nm.provider,
format=nm.format,
exif=nm.exif,
xmp=nm.xmp,
iptc=nm.iptc,
gps=nm.gps,
camera_make=nm.camera_make,
camera_model=nm.camera_model,
software=nm.software,
capture_time=nm.capture_time,
confidence=score,
))
# Build ForensicsResult objects
forensics_models: List[ForensicsResult] = []
for nf in forensics:
score = self._confidence.score_forensics(nf)
forensics_models.append(ForensicsResult(
provider=nf.provider,
integrity_score=nf.integrity_score,
is_duplicate=nf.is_duplicate,
duplicate_of=nf.duplicate_of,
similarity_score=nf.similarity_score,
manipulation_indicators=nf.manipulation_indicators,
ela_score=nf.ela_score,
noise_inconsistency=nf.noise_inconsistency,
details=nf.details,
confidence=score,
))
# Detect cross-provider conflicts
conflicts = self._conflicts.detect(results, boxes, matches)
# Aggregate limitations
limitations: List[str] = []
for r in results.values():
if not r.success and r.error:
limitations.append(f"{r.provider}: {r.error}")
if not any(r.success for r in results.values()) and results:
limitations.append("All providers failed for this job")
# Collect new capability results
ocr_results = self._collect_ocr(results)
object_detections = self._collect_object_detections(results)
scene_results = self._collect_scene(results)
nsfw_results = self._collect_nsfw(results)
ai_detection_results = self._collect_ai_detection(results)
embedding_results = self._collect_embeddings(results)
# Metadata
succeeded = [r.provider for r in results.values() if r.success]
failed = [r.provider for r in results.values() if not r.success]
metadata = ReportMetadata(
job_id=job_id,
image_hash=image_hash,
total_elapsed_ms=total_elapsed_ms,
providers_invoked=list(results.keys()),
providers_succeeded=succeeded,
providers_failed=failed,
limitations=limitations,
)
report = UnifiedFaceReport(
metadata=metadata,
detections=detections,
matches=face_matches,
scraped_images=[s.__dict__ for s in scraped],
reverse_matches=[r.__dict__ for r in reverse_matches],
image_analyses=image_analysis_models,
metadata_extractions=metadata_models,
forensics=forensics_models,
ocr_results=ocr_results,
object_detections=object_detections,
scene_results=scene_results,
nsfw_results=nsfw_results,
ai_detection_results=ai_detection_results,
embedding_results=embedding_results,
evidence=evidence,
conflicts=conflicts,
overall_confidence=self._confidence.score_overall(
report_detections=detections,
matches=face_matches,
conflicts=conflicts,
image_analyses=image_analysis_models,
forensics=forensics_models,
),
)
return report
# ------------------------------------------------------------------ #
# Collectors — one per capability
# ------------------------------------------------------------------ #
def _collect_boxes(self, results: Dict[str, ProviderResult]) -> List[NormalizedBox]:
out: List[NormalizedBox] = []
for r in results.values():
if not r.success or r.capability.value != "detection":
continue
norm = r.normalized
boxes = norm.get("boxes", [])
confs = norm.get("confidences", [1.0] * len(boxes))
lms = norm.get("landmarks")
for i, b in enumerate(boxes):
lm = lms[i] if isinstance(lms, list) and i < len(lms) else (lms if isinstance(lms, dict) else None)
out.append(NormalizedBox(
x=b["x"], y=b["y"], w=b["w"], h=b["h"],
confidence=float(confs[i]) if i < len(confs) else 1.0,
detector=r.provider,
landmarks=lm,
))
return out
def _collect_matches(self, results: Dict[str, ProviderResult]) -> List[NormalizedMatch]:
out: List[NormalizedMatch] = []
for r in results.values():
if not r.success or r.capability.value != "recognition":
continue
for m in r.normalized.get("matches", []):
out.append(NormalizedMatch(
query_face_index=m.get("query_face_index", 0),
best_match=m.get("best_match"),
distance=m.get("distance", 1.0),
distances=m.get("distances", {}),
recognizer=r.provider,
))
return out
def _collect_scraped(self, results: Dict[str, ProviderResult]) -> List[NormalizedScrapeImage]:
out: List[NormalizedScrapeImage] = []
for r in results.values():
if not r.success or r.capability.value != "scraping":
continue
for img in r.normalized.get("images", []):
out.append(NormalizedScrapeImage(
url=img.get("url", ""),
alt=img.get("alt", ""),
source_page=img.get("source_page", ""),
scraper=r.provider,
width=img.get("width"),
height=img.get("height"),
))
deduped = ResultPostprocessor.dedupe_images([s.__dict__ for s in out])
return [NormalizedScrapeImage(**d) for d in deduped]
def _collect_reverse(self, results: Dict[str, ProviderResult]) -> List[NormalizedReverseMatch]:
out: List[NormalizedReverseMatch] = []
for r in results.values():
if not r.success or r.capability.value != "reverse_search":
continue
for m in r.normalized.get("results", []):
out.append(NormalizedReverseMatch(
image_url=m.get("image_url", ""),
source_page=m.get("source_page", ""),
title=m.get("title", ""),
snippet=m.get("snippet", ""),
thumbnail=m.get("thumbnail", ""),
provider=r.provider,
))
return out
def _collect_image_analyses(self, results: Dict[str, ProviderResult]) -> List[NormalizedImageAnalysis]:
out: List[NormalizedImageAnalysis] = []
for r in results.values():
if not r.success or r.capability.value != "image_analysis":
continue
n = r.normalized
out.append(NormalizedImageAnalysis(
provider=r.provider,
quality_score=n.get("quality_score"),
brightness=n.get("brightness"),
contrast=n.get("contrast"),
sharpness=n.get("sharpness"),
noise_level=n.get("noise_level"),
width=n.get("width"),
height=n.get("height"),
channels=n.get("channels"),
color_profile=n.get("color_profile"),
dominant_colors=n.get("dominant_colors", []),
aspects=n.get("aspects", {}),
))
return out
def _collect_metadata(self, results: Dict[str, ProviderResult]) -> List[NormalizedMetadata]:
out: List[NormalizedMetadata] = []
for r in results.values():
if not r.success or r.capability.value != "metadata":
continue
n = r.normalized
out.append(NormalizedMetadata(
provider=r.provider,
format=n.get("format"),
exif=n.get("exif", {}),
xmp=n.get("xmp", {}),
iptc=n.get("iptc", {}),
gps=n.get("gps"),
camera_make=n.get("camera_make"),
camera_model=n.get("camera_model"),
software=n.get("software"),
capture_time=n.get("capture_time"),
))
return out
def _collect_forensics(self, results: Dict[str, ProviderResult]) -> List[NormalizedForensics]:
out: List[NormalizedForensics] = []
for r in results.values():
if not r.success or r.capability.value != "forensics":
continue
n = r.normalized
out.append(NormalizedForensics(
provider=r.provider,
integrity_score=n.get("integrity_score"),
is_duplicate=n.get("is_duplicate"),
duplicate_of=n.get("duplicate_of"),
similarity_score=n.get("similarity_score"),
manipulation_indicators=n.get("manipulation_indicators", []),
ela_score=n.get("ela_score"),
noise_inconsistency=n.get("noise_inconsistency"),
details=n.get("details", {}),
))
return out
def _collect_ocr(self, results: Dict[str, ProviderResult]) -> List[OCRResult]:
out: List[OCRResult] = []
for r in results.values():
if not r.success or r.capability.value != "ocr":
continue
n = r.normalized
out.append(OCRResult(
provider=r.provider,
text_blocks=n.get("text_blocks", []),
full_text=n.get("full_text", ""),
language=n.get("language"),
))
return out
def _collect_object_detections(self, results: Dict[str, ProviderResult]) -> List[ObjectDetectionResult]:
out: List[ObjectDetectionResult] = []
for r in results.values():
if not r.success or r.capability.value != "object_detection":
continue
n = r.normalized
out.append(ObjectDetectionResult(
provider=r.provider,
objects=n.get("objects", []),
model=n.get("model"),
))
return out
def _collect_scene(self, results: Dict[str, ProviderResult]) -> List[SceneResult]:
out: List[SceneResult] = []
for r in results.values():
if not r.success or r.capability.value != "scene_recognition":
continue
n = r.normalized
out.append(SceneResult(
provider=r.provider,
labels=n.get("labels", []),
))
return out
def _collect_nsfw(self, results: Dict[str, ProviderResult]) -> List[NSFWResult]:
out: List[NSFWResult] = []
for r in results.values():
if not r.success or r.capability.value != "nsfw_detection":
continue
n = r.normalized
out.append(NSFWResult(
provider=r.provider,
is_nsfw=n.get("is_nsfw", False),
labels=n.get("labels", []),
))
return out
def _collect_ai_detection(self, results: Dict[str, ProviderResult]) -> List[AIDetectionResult]:
out: List[AIDetectionResult] = []
for r in results.values():
if not r.success or r.capability.value != "ai_image_detection":
continue
n = r.normalized
out.append(AIDetectionResult(
provider=r.provider,
is_ai_generated=n.get("is_ai_generated", False),
))
return out
def _collect_embeddings(self, results: Dict[str, ProviderResult]) -> List[EmbeddingResult]:
out: List[EmbeddingResult] = []
for r in results.values():
if not r.success or r.capability.value != "embedding":
continue
n = r.normalized
out.append(EmbeddingResult(
provider=r.provider,
embedding=n.get("embedding", []),
model=n.get("model"),
dimensions=n.get("dimensions", 0),
))
return out
def _build_evidence(self, results: Dict[str, ProviderResult]) -> List[Evidence]:
out: List[Evidence] = []
for r in results.values():
out.append(Evidence(
provider=r.provider,
capability=r.capability.value,
raw=r.raw,
normalized=r.normalized,
elapsed_ms=r.elapsed_ms,
success=r.success,
error=r.error,
error_type=r.error_type,
metadata=r.metadata,
retry_count=r.retry_count,
))
return out
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