phase 7

tests/detect/test_brand_resolver.py

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+"""Tests for BrandResolver stage."""
+
+import numpy as np
+import pytest
+
+from detect.models import BoundingBox, Frame, TextCandidate
+from detect.profiles.base import BrandDictionary, ResolverConfig
+from detect.stages.brand_resolver import resolve_brands, _exact_match, _fuzzy_match
+
+
+DICTIONARY = BrandDictionary(brands={
+    "Nike": ["nike", "NIKE", "swoosh"],
+    "Adidas": ["adidas", "ADIDAS"],
+    "Coca-Cola": ["coca-cola", "coca cola", "coke", "COCA-COLA"],
+    "Emirates": ["emirates", "fly emirates", "EMIRATES"],
+})
+
+CONFIG = ResolverConfig(fuzzy_threshold=75)
+
+
+def _make_candidate(text: str, confidence: float = 0.9) -> TextCandidate:
+    dummy_frame = Frame(sequence=0, chunk_id=0, timestamp=1.0,
+                        image=np.zeros((10, 10, 3), dtype=np.uint8))
+    dummy_box = BoundingBox(x=0, y=0, w=10, h=10, confidence=0.8, label="text")
+    return TextCandidate(frame=dummy_frame, bbox=dummy_box, text=text, ocr_confidence=confidence)
+
+
+def test_exact_match():
+    assert _exact_match("Nike", DICTIONARY) == "Nike"
+    assert _exact_match("nike", DICTIONARY) == "Nike"
+    assert _exact_match("COCA-COLA", DICTIONARY) == "Coca-Cola"
+    assert _exact_match("fly emirates", DICTIONARY) == "Emirates"
+    assert _exact_match("unknown brand", DICTIONARY) is None
+
+
+def test_fuzzy_match():
+    brand, score = _fuzzy_match("Nik3", DICTIONARY, threshold=75)
+    assert brand == "Nike"
+    assert score >= 75
+
+    brand, score = _fuzzy_match("adldas", DICTIONARY, threshold=75)
+    assert brand == "Adidas"
+
+    brand, score = _fuzzy_match("xyzxyzxyz", DICTIONARY, threshold=75)
+    assert brand is None
+
+
+def test_resolve_exact():
+    candidates = [_make_candidate("Nike"), _make_candidate("EMIRATES")]
+    matched, unresolved = resolve_brands(candidates, DICTIONARY, CONFIG)
+    assert len(matched) == 2
+    assert len(unresolved) == 0
+    assert matched[0].brand == "Nike"
+    assert matched[1].brand == "Emirates"
+
+
+def test_resolve_fuzzy():
+    candidates = [_make_candidate("coca coIa")]  # OCR misread
+    matched, unresolved = resolve_brands(candidates, DICTIONARY, CONFIG)
+    assert len(matched) == 1
+    assert matched[0].brand == "Coca-Cola"
+
+
+def test_resolve_unresolved():
+    candidates = [_make_candidate("random garbage text")]
+    matched, unresolved = resolve_brands(candidates, DICTIONARY, CONFIG)
+    assert len(matched) == 0
+    assert len(unresolved) == 1
+
+
+def test_resolve_mixed():
+    candidates = [
+        _make_candidate("Nike"),
+        _make_candidate("unknown"),
+        _make_candidate("adldas"),
+    ]
+    matched, unresolved = resolve_brands(candidates, DICTIONARY, CONFIG)
+    assert len(matched) == 2  # Nike exact + Adidas fuzzy
+    assert len(unresolved) == 1
+
+
+def test_events_emitted(monkeypatch):
+    events = []
+    monkeypatch.setattr("detect.emit.push_detect_event",
+                        lambda job_id, etype, data: events.append((etype, data)))
+
+    candidates = [_make_candidate("Nike")]
+    resolve_brands(candidates, DICTIONARY, CONFIG, job_id="test-job")
+
+    event_types = [e[0] for e in events]
+    assert "log" in event_types
+    assert "detection" in event_types