phase 10

tests/detect/test_checkpoint.py

@@ -0,0 +1,182 @@
+"""Tests for checkpoint serialization — round-trip without S3."""
+
+import numpy as np
+import pytest
+
+from detect.models import BoundingBox, BrandDetection, Frame, PipelineStats, TextCandidate
+from detect.checkpoint.serializer import (
+    serialize_state,
+    deserialize_state,
+    serialize_frame_meta,
+    serialize_text_candidate,
+    deserialize_text_candidate,
+)
+
+
+def _make_frame(seq: int = 0, w: int = 100, h: int = 80) -> Frame:
+    image = np.random.randint(0, 255, (h, w, 3), dtype=np.uint8)
+    return Frame(
+        sequence=seq,
+        chunk_id=0,
+        timestamp=float(seq) * 0.5,
+        image=image,
+        perceptual_hash=f"hash_{seq}",
+    )
+
+
+def _make_box(x=10, y=10, w=30, h=20) -> BoundingBox:
+    return BoundingBox(x=x, y=y, w=w, h=h, confidence=0.9, label="text")
+
+
+def _make_candidate(frame: Frame, text: str = "NIKE") -> TextCandidate:
+    box = _make_box()
+    return TextCandidate(frame=frame, bbox=box, text=text, ocr_confidence=0.85)
+
+
+def _make_detection(brand: str = "Nike", timestamp: float = 1.0) -> BrandDetection:
+    return BrandDetection(
+        brand=brand,
+        timestamp=timestamp,
+        duration=0.5,
+        confidence=0.92,
+        source="ocr",
+        content_type="soccer_broadcast",
+        frame_ref=0,
+    )
+
+
+# --- Frame metadata ---
+
+def test_serialize_frame_meta():
+    frame = _make_frame(seq=5)
+    meta = serialize_frame_meta(frame)
+
+    assert meta["sequence"] == 5
+    assert meta["timestamp"] == 2.5
+    assert meta["perceptual_hash"] == "hash_5"
+    assert "image" not in meta
+
+
+# --- TextCandidate ---
+
+def test_serialize_text_candidate():
+    frame = _make_frame()
+    candidate = _make_candidate(frame, text="EMIRATES")
+    data = serialize_text_candidate(candidate)
+
+    assert data["frame_sequence"] == 0
+    assert data["text"] == "EMIRATES"
+    assert data["ocr_confidence"] == 0.85
+    assert "bbox" in data
+
+
+def test_deserialize_text_candidate():
+    frame = _make_frame()
+    candidate = _make_candidate(frame, text="ADIDAS")
+
+    serialized = serialize_text_candidate(candidate)
+    frame_map = {frame.sequence: frame}
+
+    restored = deserialize_text_candidate(serialized, frame_map)
+
+    assert restored.text == "ADIDAS"
+    assert restored.ocr_confidence == 0.85
+    assert restored.frame is frame  # same object reference
+    assert restored.bbox.x == 10
+
+
+# --- Full state round-trip ---
+
+def test_state_round_trip():
+    frames = [_make_frame(seq=i) for i in range(3)]
+    filtered = frames[:2]
+
+    box = _make_box()
+    boxes_by_frame = {0: [box], 1: [box]}
+
+    candidates = [_make_candidate(frames[0], "NIKE"), _make_candidate(frames[1], "EMIRATES")]
+    unresolved = [_make_candidate(frames[2], "unknown")]
+    detections = [_make_detection("Nike", 0.5), _make_detection("Emirates", 1.0)]
+
+    stats = PipelineStats(
+        frames_extracted=3,
+        frames_after_scene_filter=2,
+        regions_detected=2,
+        regions_resolved_by_ocr=2,
+        cloud_llm_calls=1,
+        estimated_cloud_cost_usd=0.003,
+    )
+
+    state = {
+        "job_id": "test-123",
+        "video_path": "/tmp/test.mp4",
+        "profile_name": "soccer_broadcast",
+        "config_overrides": {"ocr": {"min_confidence": 0.3}},
+        "frames": frames,
+        "filtered_frames": filtered,
+        "boxes_by_frame": boxes_by_frame,
+        "text_candidates": candidates,
+        "unresolved_candidates": unresolved,
+        "detections": detections,
+        "stats": stats,
+    }
+
+    manifest = {f.sequence: f"s3://fake/frames/{f.sequence}.jpg" for f in frames}
+
+    # Serialize
+    serialized = serialize_state(state, manifest)
+
+    # Verify JSON-compatible (no numpy, no Frame objects)
+    import json
+    json_str = json.dumps(serialized, default=str)
+    assert len(json_str) > 0
+
+    # Deserialize with the original frames (simulating frame load from S3)
+    restored = deserialize_state(serialized, frames)
+
+    # Verify round-trip
+    assert restored["job_id"] == "test-123"
+    assert restored["video_path"] == "/tmp/test.mp4"
+    assert restored["profile_name"] == "soccer_broadcast"
+    assert restored["config_overrides"] == {"ocr": {"min_confidence": 0.3}}
+
+    assert len(restored["frames"]) == 3
+    assert len(restored["filtered_frames"]) == 2
+    assert len(restored["boxes_by_frame"]) == 2
+    assert len(restored["text_candidates"]) == 2
+    assert len(restored["unresolved_candidates"]) == 1
+    assert len(restored["detections"]) == 2
+
+    restored_stats = restored["stats"]
+    assert restored_stats.frames_extracted == 3
+    assert restored_stats.cloud_llm_calls == 1
+    assert restored_stats.estimated_cloud_cost_usd == 0.003
+
+    # TextCandidate frame references should point to actual Frame objects
+    tc = restored["text_candidates"][0]
+    assert tc.frame is frames[0]
+    assert tc.text == "NIKE"
+
+
+def test_state_round_trip_empty():
+    """Empty state should serialize/deserialize cleanly."""
+    state = {
+        "job_id": "empty-job",
+        "video_path": "",
+        "profile_name": "soccer_broadcast",
+        "frames": [],
+        "filtered_frames": [],
+        "boxes_by_frame": {},
+        "text_candidates": [],
+        "unresolved_candidates": [],
+        "detections": [],
+        "stats": PipelineStats(),
+    }
+
+    serialized = serialize_state(state, {})
+    restored = deserialize_state(serialized, [])
+
+    assert restored["job_id"] == "empty-job"
+    assert len(restored["frames"]) == 0
+    assert len(restored["detections"]) == 0
+    assert restored["stats"].frames_extracted == 0