90cce64cc8
test_engine: detection normalization, one-caller-at-a-time detection, method validation, undecodable payloads, crop isolation, classify_boxes accepting the detection dicts the API actually hands it, summary counting, empty-slot normalization across frame sizes, the still-image store (roundtrip, TTL, bound), catalog/stock behaviour, and template-image path resolution that refuses anything outside the catalog. test_tracking: per-session id numbering, the case that matters (constructing a second tracker must not reset a running session's ids), independent sessions, pan continuity, buffer expiry producing a new id, detection index mapping, and reset scoping. Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
284 lines
11 KiB
Python
284 lines
11 KiB
Python
"""
|
|
Contract tests for the recognition engine.
|
|
|
|
No YOLO weights and no network: a fake detector and a fake matcher stand in for
|
|
the model, so these tests defend the engine's own contracts - normalization,
|
|
locking, isolation between clients, empty-slot geometry, and the still-image
|
|
store.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
import os
|
|
import sys
|
|
import threading
|
|
import time
|
|
import unittest
|
|
|
|
current_dir = os.path.dirname(os.path.abspath(__file__))
|
|
if current_dir not in sys.path:
|
|
sys.path.insert(0, current_dir)
|
|
|
|
from engine import ( # noqa: E402
|
|
EngineSettings,
|
|
ImageExpired,
|
|
ImageStore,
|
|
RecognitionEngine,
|
|
UndecodableImage,
|
|
UnknownMethod,
|
|
identity_payload,
|
|
)
|
|
|
|
|
|
class FakeDetector:
|
|
"""Records concurrency and returns fixed boxes."""
|
|
|
|
weights_path = "fake.pt"
|
|
|
|
def __init__(self, boxes=None):
|
|
self.boxes = boxes if boxes is not None else [
|
|
{"id": 0, "box": [10, 10, 60, 90], "confidence": 0.9, "class_id": 0,
|
|
"width": 50, "height": 80, "center": [35, 50]},
|
|
{"id": 1, "box": [70, 10, 120, 90], "confidence": 0.8, "class_id": 0,
|
|
"width": 50, "height": 80, "center": [95, 50]},
|
|
]
|
|
self.concurrent = 0
|
|
self.max_concurrent = 0
|
|
self.calls = 0
|
|
self.lock = threading.Lock()
|
|
|
|
def detect(self, image, conf_threshold=0.25, iou_threshold=0.45):
|
|
with self.lock:
|
|
self.calls += 1
|
|
self.concurrent += 1
|
|
self.max_concurrent = max(self.max_concurrent, self.concurrent)
|
|
time.sleep(0.01)
|
|
with self.lock:
|
|
self.concurrent -= 1
|
|
return {"status": "success", "boxes": [dict(b) for b in self.boxes]}
|
|
|
|
def detect_empty_slots(self, image_shape, boxes, min_gap_ratio=0.75):
|
|
# One gap between the two boxes, expressed in the frame the engine hands
|
|
# over. Scaling with that frame is what makes the normalization test
|
|
# meaningful: the engine must translate the answer back to the client's
|
|
# pixel space.
|
|
height, width = int(image_shape[0]), int(image_shape[1])
|
|
x1, x2 = int(width * 0.30), int(width * 0.35)
|
|
return [{
|
|
"id": "empty_1",
|
|
"box": [x1, int(height * 0.10), x2, int(height * 0.90)],
|
|
"width": x2 - x1,
|
|
"height": int(height * 0.80),
|
|
"missing_items_est": 1,
|
|
"center": [(x1 + x2) // 2, height // 2],
|
|
"row_index": 1,
|
|
}]
|
|
|
|
|
|
class FakeMatcher:
|
|
def __init__(self):
|
|
self.calls = 0
|
|
|
|
def score_crop(self, crop, method="hybrid"):
|
|
self.calls += 1
|
|
time.sleep(0.01)
|
|
return {"sku_a": 40.0, "sku_b": 2.0}, {"sku_a": {"score": 40.0}}
|
|
|
|
def score_crop_fast(self, crop, shortlist=5):
|
|
return self.score_crop(crop)
|
|
|
|
def classify_crop(self, crop, method="hybrid"):
|
|
from identity import decide_identity
|
|
|
|
scores, details = self.score_crop(crop, method=method)
|
|
return decide_identity(scores, details=details, method=method)
|
|
|
|
def generate_match_visualization(self, crop, sku, method="surf", max_matches=30):
|
|
return crop
|
|
|
|
|
|
class FakeInventory:
|
|
def __init__(self):
|
|
self.products = {
|
|
"sku_a": {"sku_id": "sku_a", "product_name": "Alpha", "price": 100.0,
|
|
"number_in": 10, "number_out": 2, "number_on": 8,
|
|
"receipt_date": "2026-01-01", "expiration_date": "2026-12-31",
|
|
"local_image_path": None},
|
|
"sku_b": {"sku_id": "sku_b", "product_name": "Beta", "price": 200.0,
|
|
"number_in": 5, "number_out": 1, "number_on": 4,
|
|
"receipt_date": "2026-01-02", "expiration_date": "2026-12-30",
|
|
"local_image_path": None},
|
|
}
|
|
self.lock = threading.Lock()
|
|
|
|
def list_all_skus(self):
|
|
return list(self.products)
|
|
|
|
def get_product(self, sku_id):
|
|
return self.products.get(sku_id)
|
|
|
|
def update_stock(self, sku_id, delta):
|
|
with self.lock:
|
|
product = self.products.get(sku_id)
|
|
if product is None:
|
|
return False
|
|
product["number_on"] = max(0, product["number_on"] + delta)
|
|
if delta < 0:
|
|
product["number_out"] += abs(delta)
|
|
else:
|
|
product["number_in"] += delta
|
|
return True
|
|
|
|
|
|
def make_engine(**overrides):
|
|
settings = EngineSettings(device="cpu", classify_workers=4, **overrides)
|
|
detector = FakeDetector()
|
|
matcher = FakeMatcher()
|
|
inventory = FakeInventory()
|
|
engine = RecognitionEngine(detector, matcher, inventory, settings=settings)
|
|
return engine, detector, matcher, inventory
|
|
|
|
|
|
def make_image(width=200, height=120):
|
|
import numpy as np
|
|
|
|
return np.zeros((height, width, 3), dtype="uint8")
|
|
|
|
|
|
class EngineBasicsTests(unittest.TestCase):
|
|
def test_detect_normalizes_and_reindexes(self):
|
|
engine, _, _, _ = make_engine()
|
|
boxes = engine.detect(make_image())
|
|
self.assertEqual(len(boxes), 2)
|
|
self.assertEqual([b["id"] for b in boxes], [0, 1])
|
|
self.assertEqual(boxes[0]["width"], 50)
|
|
self.assertEqual(boxes[0]["center"], [35, 50])
|
|
|
|
def test_detector_calls_never_overlap(self):
|
|
engine, detector, _, _ = make_engine()
|
|
threads = [threading.Thread(target=engine.detect, args=(make_image(),)) for _ in range(8)]
|
|
for thread in threads:
|
|
thread.start()
|
|
for thread in threads:
|
|
thread.join()
|
|
self.assertEqual(detector.calls, 8)
|
|
self.assertEqual(detector.max_concurrent, 1, "the model must be used by one caller at a time")
|
|
|
|
def test_unknown_method_is_rejected(self):
|
|
engine, _, _, _ = make_engine()
|
|
with self.assertRaises(UnknownMethod):
|
|
engine.score_crop(make_image(), method="not-a-method")
|
|
|
|
def test_undecodable_payload_raises(self):
|
|
engine, _, _, _ = make_engine()
|
|
with self.assertRaises(UndecodableImage):
|
|
engine.decode_image(b"this is not an image")
|
|
|
|
def test_crop_is_a_copy(self):
|
|
engine, _, _, _ = make_engine()
|
|
image = make_image()
|
|
crop = engine.crop(image, [0, 0, 10, 10])
|
|
crop[0, 0, 0] = 255
|
|
self.assertEqual(int(image[0, 0, 0]), 0)
|
|
|
|
|
|
class EngineClassificationTests(unittest.TestCase):
|
|
def test_classify_boxes_accepts_detection_dicts_without_size_fields(self):
|
|
"""The API hands classify_boxes stored detections, which carry no width/height."""
|
|
engine, _, _, _ = make_engine()
|
|
detections = [
|
|
{"id": 0, "box": [10, 10, 60, 90], "confidence": 0.9, "identity": identity_payload(None)},
|
|
{"id": 1, "box": [70, 10, 120, 90], "confidence": 0.8, "identity": identity_payload(None)},
|
|
]
|
|
rows = engine.classify_boxes(make_image(), detections, method="hybrid")
|
|
self.assertEqual(len(rows), 2)
|
|
self.assertEqual(rows[0]["identity"]["state"], "recognized")
|
|
self.assertEqual(rows[0]["identity"]["sku_id"], "sku_a")
|
|
|
|
def test_parallel_classification_matches_sequential(self):
|
|
engine, _, matcher, _ = make_engine()
|
|
boxes = [
|
|
{"id": index, "box": [10 + index, 10, 60 + index, 90], "confidence": 0.9,
|
|
"width": 50, "height": 80, "center": [35, 50]}
|
|
for index in range(12)
|
|
]
|
|
parallel = engine.classify_boxes(make_image(), boxes, method="hybrid")
|
|
sequential = [engine.score_crop(engine.crop(make_image(), box["box"]), "hybrid") for box in boxes]
|
|
self.assertEqual(len(parallel), 12)
|
|
self.assertEqual(len(sequential), 12)
|
|
for row in parallel:
|
|
self.assertEqual(row["identity"]["sku_id"], "sku_a")
|
|
|
|
def test_summary_counts_states(self):
|
|
engine, _, _, _ = make_engine()
|
|
detections = [
|
|
{"identity": {"state": "recognized", "sku_id": "sku_a"}},
|
|
{"identity": {"state": "recognized", "sku_id": "sku_a"}},
|
|
{"identity": {"state": "needs_confirmation", "sku_id": None}},
|
|
{"identity": {"state": "unrecognized", "sku_id": None}},
|
|
]
|
|
summary = engine.summarize(detections, [])
|
|
self.assertEqual(summary["recognized"], 2)
|
|
self.assertEqual(summary["needs_confirmation"], 1)
|
|
self.assertEqual(summary["unrecognized"], 1)
|
|
self.assertEqual(summary["by_sku"], [{"sku_id": "sku_a", "facings": 2}])
|
|
|
|
|
|
class EmptySlotNormalizationTests(unittest.TestCase):
|
|
def test_slots_scale_with_the_frame(self):
|
|
"""The detector's thresholds are absolute pixels, so a half-size frame must
|
|
produce the same slots (scaled) as the reference-size frame."""
|
|
engine, _, _, _ = make_engine()
|
|
boxes_small = [
|
|
{"box": [5, 5, 30, 45]},
|
|
{"box": [35, 5, 60, 45]},
|
|
]
|
|
slots_small = engine.empty_slots((60, 100), boxes_small)
|
|
boxes_big = [
|
|
{"box": [10, 10, 60, 90]},
|
|
{"box": [70, 10, 120, 90]},
|
|
]
|
|
slots_big = engine.empty_slots((120, 200), boxes_big)
|
|
self.assertEqual(len(slots_small), 1)
|
|
self.assertEqual(len(slots_big), 1)
|
|
# the small frame is scaled up to the reference width, so the returned
|
|
# slot comes back in the small frame's own pixels
|
|
self.assertLess(slots_small[0]["box"][2], slots_big[0]["box"][2])
|
|
|
|
|
|
class ImageStoreTests(unittest.TestCase):
|
|
def test_roundtrip_and_expiry(self):
|
|
store = ImageStore(max_items=2, ttl_s=0.05, max_bytes=10_000_000)
|
|
stored = store.put(make_image())
|
|
self.assertEqual(store.get(stored.image_id).width, 200)
|
|
time.sleep(0.08)
|
|
with self.assertRaises(ImageExpired):
|
|
store.get(stored.image_id)
|
|
|
|
def test_bounded_by_count(self):
|
|
store = ImageStore(max_items=2, ttl_s=60, max_bytes=10_000_000)
|
|
ids = [store.put(make_image()).image_id for _ in range(4)]
|
|
with self.assertRaises(ImageExpired):
|
|
store.get(ids[0])
|
|
self.assertEqual(store.get(ids[-1]).width, 200)
|
|
|
|
|
|
class CatalogTests(unittest.TestCase):
|
|
def test_catalog_and_stock_update(self):
|
|
engine, _, _, _ = make_engine()
|
|
catalog = engine.catalog()
|
|
self.assertEqual(len(catalog), 2)
|
|
self.assertEqual(catalog[0]["image_url"], "/v1/catalog/sku_a/image")
|
|
product = engine.update_stock("sku_a", -1)
|
|
self.assertEqual(product["number_on"], 7)
|
|
self.assertEqual(product["number_out"], 3)
|
|
self.assertIsNone(engine.update_stock("nope", -1))
|
|
|
|
def test_catalog_image_path_is_resolved_through_inventory(self):
|
|
engine, _, _, _ = make_engine()
|
|
self.assertIsNone(engine.catalog_image_path("../../etc/passwd"))
|
|
self.assertIsNone(engine.catalog_image_path("sku_a"))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|