face-intel / tests /unit /test_conflicts.py
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Restructure + add reverse face search (PimEyes-style)
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"""Unit tests for confidence/conflicts.py."""
from __future__ import annotations
from dataclasses import dataclass
from typing import Optional
from confidence.conflicts import ConflictDetector
from models.providers import ProviderCapability
from providers.base import ProviderResult
@dataclass
class FakeBox:
detector: str
confidence: float
@dataclass
class FakeMatch:
query_face_index: int
best_match: Optional[str]
distance: float
recognizer: str
def _make_result(provider: str, capability: ProviderCapability,
normalized: dict, success: bool = True) -> ProviderResult:
return ProviderResult(
provider=provider,
capability=capability,
success=success,
elapsed_ms=10.0,
normalized=normalized,
)
class TestConflictDetector:
def test_no_conflicts_when_single_provider(self):
detector = ConflictDetector()
results = {
"haar": _make_result("haar", ProviderCapability.DETECTION,
{"num_faces": 1, "boxes": [{"x": 0, "y": 0, "w": 10, "h": 10}],
"confidences": [1.0]}),
}
boxes = [FakeBox(detector="haar", confidence=1.0)]
conflicts = detector.detect(results, boxes, [])
assert conflicts == []
def test_face_count_mismatch_detected(self):
detector = ConflictDetector()
results = {
"haar": _make_result("haar", ProviderCapability.DETECTION, {"num_faces": 1}),
"dnn": _make_result("dnn", ProviderCapability.DETECTION, {"num_faces": 2}),
}
conflicts = detector.detect(results, [], [])
assert len(conflicts) == 1
assert conflicts[0].kind == "face_count_mismatch"
assert "haar" in conflicts[0].providers
assert "dnn" in conflicts[0].providers
def test_face_count_agreement_no_conflict(self):
detector = ConflictDetector()
results = {
"haar": _make_result("haar", ProviderCapability.DETECTION, {"num_faces": 2}),
"dnn": _make_result("dnn", ProviderCapability.DETECTION, {"num_faces": 2}),
}
conflicts = detector.detect(results, [], [])
assert conflicts == []
def test_match_disagreement_detected(self):
detector = ConflictDetector()
matches = [
FakeMatch(query_face_index=0, best_match="alice", distance=0.3, recognizer="face_recognition"),
FakeMatch(query_face_index=0, best_match="bob", distance=0.4, recognizer="deepface"),
]
conflicts = detector.detect({}, [], matches)
assert len(conflicts) == 1
assert conflicts[0].kind == "match_disagreement"
def test_match_agreement_no_conflict(self):
detector = ConflictDetector()
matches = [
FakeMatch(query_face_index=0, best_match="alice", distance=0.3, recognizer="face_recognition"),
FakeMatch(query_face_index=0, best_match="alice", distance=0.4, recognizer="deepface"),
]
conflicts = detector.detect({}, [], matches)
assert conflicts == []
def test_quality_disagreement_detected(self):
detector = ConflictDetector()
results = {
"image_quality": _make_result("image_quality", ProviderCapability.IMAGE_ANALYSIS,
{"quality_score": 0.9}),
"image_properties": _make_result("image_properties", ProviderCapability.IMAGE_ANALYSIS,
{"quality_score": 0.4}),
}
conflicts = detector.detect(results, [], [])
assert len(conflicts) == 1
assert conflicts[0].kind == "quality_disagreement"
def test_format_mismatch_detected(self):
detector = ConflictDetector()
results = {
"exif": _make_result("exif", ProviderCapability.METADATA, {"format": "JPEG"}),
"xmp": _make_result("xmp", ProviderCapability.METADATA, {"format": "PNG"}),
}
conflicts = detector.detect(results, [], [])
assert len(conflicts) == 1
assert conflicts[0].kind == "format_mismatch"
def test_failed_results_ignored(self):
detector = ConflictDetector()
results = {
"haar": _make_result("haar", ProviderCapability.DETECTION, {"num_faces": 1}, success=False),
"dnn": _make_result("dnn", ProviderCapability.DETECTION, {"num_faces": 2}),
}
# Only dnn succeeded — no mismatch
conflicts = detector.detect(results, [], [])
# haar's normalized is not considered since success=False
# But the conflict detector looks at success=True results only
# So no conflict should be detected
assert conflicts == []