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FabledCurator/tests/test_ccip.py
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feat(ml): CCIP character matches ground to the matched figure region (#133 step 2)
match_image now tracks WHICH query figure produced the winning cosine per
character (argmax over the per-figure best-reference sim) and attaches its bbox as
grounding {bbox,kind:'figure',detector}. SuggestionService carries it: a CCIP-only
character hit grounds to its figure; a 'both' hit keeps the head's localized crop
if it had one, else falls back to the CCIP figure — so corroborated characters
stay grounded. Test: a character match carries the matched figure's bbox+kind.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM
2026-07-05 23:18:41 -04:00

148 lines
5.5 KiB
Python

"""CCIP few-shot character matcher (#114). numpy cosine on stored vectors — no
model needed, so it runs in CI with synthetic CCIP vectors."""
import pytest
from sqlalchemy import select
from backend.app.models import ImageRecord, ImageRegion, TagKind
from backend.app.models.tag import image_tag
from backend.app.services.ml.ccip import match_image
from backend.app.services.tag_service import TagService
pytestmark = pytest.mark.integration
def _ccip(slot: int) -> list[float]:
v = [0.0] * 768
v[slot] = 1.0
return v
async def _img(db, sha) -> ImageRecord:
img = ImageRecord(
path=f"/images/{sha}.jpg", sha256=sha, size_bytes=1, mime="image/jpeg",
width=1, height=1, origin="imported_filesystem", integrity_status="unknown",
)
db.add(img)
await db.flush()
return img
async def _figure(db, image_id, ccip):
db.add(ImageRegion(
image_record_id=image_id, kind="figure",
rx=0.0, ry=0.0, rw=1.0, rh=1.0,
ccip_embedding=ccip, embedding_version="ccip-test",
))
async def _tag_image(db, image_id, tag_id):
await db.execute(image_tag.insert().values(
image_record_id=image_id, tag_id=tag_id, source="manual",
))
@pytest.mark.asyncio
async def test_matches_same_character_across_images(db):
raven = await TagService(db).find_or_create("Raven", TagKind.character)
ref = await _img(db, "a" * 64) # a tagged example = a prototype
await _figure(db, ref.id, _ccip(0))
await _tag_image(db, ref.id, raven.id)
query = await _img(db, "b" * 64) # untagged, near-identical figure
await _figure(db, query.id, _ccip(0))
await db.commit()
matches = await match_image(db, query.id)
m = next(x for x in matches if x["tag_id"] == raven.id)
assert m["source"] == "ccip" and m["category"] == "character"
assert m["score"] > 0.9
# #1206: the match grounds to the figure region that matched (hover → the
# figure box lights up), so a character suggestion is localized too.
assert m["grounding"]["bbox"] == pytest.approx([0.0, 0.0, 1.0, 1.0])
assert m["grounding"]["kind"] == "figure"
@pytest.mark.asyncio
async def test_no_match_for_different_character(db):
raven = await TagService(db).find_or_create("Raven", TagKind.character)
ref = await _img(db, "c" * 64)
await _figure(db, ref.id, _ccip(0))
await _tag_image(db, ref.id, raven.id)
query = await _img(db, "d" * 64)
await _figure(db, query.id, _ccip(5)) # orthogonal → not Raven
await db.commit()
assert await match_image(db, query.id) == []
@pytest.mark.asyncio
async def test_excludes_already_applied_character(db):
raven = await TagService(db).find_or_create("Raven", TagKind.character)
ref = await _img(db, "e" * 64)
await _figure(db, ref.id, _ccip(0))
await _tag_image(db, ref.id, raven.id)
query = await _img(db, "f" * 64)
await _figure(db, query.id, _ccip(0))
await _tag_image(db, query.id, raven.id) # already tagged → no re-suggest
await db.commit()
assert all(m["tag_id"] != raven.id for m in await match_image(db, query.id))
@pytest.mark.asyncio
async def test_no_figure_vectors_means_no_match(db):
query = await _img(db, "g" * 64)
await db.commit()
assert await match_image(db, query.id) == []
@pytest.mark.asyncio
async def test_threshold_gates_borderline_match(db):
# A figure ~0.9 cosine from the reference: matched at 0.85, dropped at 0.95.
raven = await TagService(db).find_or_create("Raven", TagKind.character)
ref = await _img(db, "h" * 64)
await _figure(db, ref.id, _ccip(0)) # e0
await _tag_image(db, ref.id, raven.id)
near = [0.0] * 768
near[0], near[1] = 0.9, 0.4359 # |·|=1, cos(e0)=0.9
query = await _img(db, "i" * 64)
await _figure(db, query.id, near)
await db.commit()
assert any(m["tag_id"] == raven.id for m in await match_image(db, query.id, 0.85))
assert await match_image(db, query.id, 0.95) == []
@pytest.mark.asyncio
async def test_multi_character_image_not_used_as_reference(db):
# A figure on a 2-character image is ambiguous (tag is image-level), so it
# must NOT seed either character's prototypes — else it'd match both.
raven = await TagService(db).find_or_create("Raven", TagKind.character)
daphne = await TagService(db).find_or_create("Daphne", TagKind.character)
multi = await _img(db, "j" * 64)
await _figure(db, multi.id, _ccip(0))
await _tag_image(db, multi.id, raven.id)
await _tag_image(db, multi.id, daphne.id)
query = await _img(db, "k" * 64)
await _figure(db, query.id, _ccip(0)) # identical to the ambiguous figure
await db.commit()
assert await match_image(db, query.id) == [] # no clean references → nothing
@pytest.mark.asyncio
async def test_auto_apply_tags_confident_match(db):
raven = await TagService(db).find_or_create("Raven", TagKind.character)
ref = await _img(db, "l" * 64)
await _figure(db, ref.id, _ccip(0))
await _tag_image(db, ref.id, raven.id) # single-character reference
query = await _img(db, "m" * 64)
await _figure(db, query.id, _ccip(0)) # identical → cosine 1.0
await db.commit()
from backend.app.tasks.ml import scheduled_ccip_auto_apply
assert "applied=" in scheduled_ccip_auto_apply() # sync task, own session
rows = (await db.execute(
select(image_tag.c.tag_id, image_tag.c.source).where(
image_tag.c.image_record_id == query.id
)
)).all()
assert (raven.id, "ccip_auto") in [(t, s) for t, s in rows]