tests/test_face_match.py
Rama master · réplica de lectura del repositorio original.
"""人脸比对（SFace）：模型的取得、余弦、不可用时的退路。真模型不进测试，不出网。"""
import hashlib
import tempfile
import unittest
from pathlib import Path
from unittest.mock import patch
from peach import face_detect, face_match
from peach.face_detect import Face, FaceModelUnavailable
class EnsureModelTests(unittest.TestCase):
"""与 YuNet 同一条取法：固定地址、校验 sha256，取不到就抛。"""
def test_a_matching_local_model_is_reused_without_network(self):
with tempfile.TemporaryDirectory() as temp:
target = Path(temp).resolve() / "sface.onnx"
payload = b"sface-bytes"
target.write_bytes(payload)
with patch.object(face_match, "MODEL_SHA256",
hashlib.sha256(payload).hexdigest()), \
patch.object(face_detect, "read_url") as opener:
self.assertEqual(face_match.ensure_model(target), target)
opener.assert_not_called()
def test_a_missing_model_without_network_is_reported_not_guessed(self):
with tempfile.TemporaryDirectory() as temp:
target = Path(temp).resolve() / "sface.onnx"
with self.assertRaises(FaceModelUnavailable) as caught:
face_match.ensure_model(target, allow_download=False)
self.assertIn("人脸比对模型", str(caught.exception))
def test_the_pinned_model_is_the_lfs_object(self):
self.assertEqual(len(face_match.MODEL_SHA256), 64)
self.assertIn("media.githubusercontent.com", face_match.MODEL_URL)
self.assertEqual(face_match.COSINE_THRESHOLD, 0.363)
class ModelLoadingTests(unittest.TestCase):
def setUp(self):
temporary = tempfile.TemporaryDirectory()
self.addCleanup(temporary.cleanup)
self.model = Path(temporary.name).resolve() / "中文模型" / "比对.onnx"
self.model.parent.mkdir()
self.payload = b"verified-sface-model"
self.model.write_bytes(self.payload)
digest = patch.object(face_match, "MODEL_SHA256", hashlib.sha256(self.payload).hexdigest())
digest.start()
self.addCleanup(digest.stop)
def test_unicode_model_path_is_read_as_onnx_bytes(self):
import cv2
import numpy
detector = object()
with patch.object(cv2, "FaceRecognizerSF") as recognizer:
embedder = face_match.FaceEmbedder(self.model, detector=detector, allow_download=False)
args = recognizer.create.call_args.args
self.assertEqual(args[0], "ONNX")
self.assertIsInstance(args[1], numpy.ndarray)
self.assertEqual((args[1].dtype, args[1].ndim, args[1].tobytes()),
(numpy.dtype("uint8"), 1, self.payload))
self.assertEqual((args[2].dtype, args[2].size), (numpy.dtype("uint8"), 0))
self.assertEqual(len(args), 3)
self.assertEqual(embedder.model_path, self.model)
self.assertIs(embedder._recognizer, recognizer.create.return_value)
self.assertIs(embedder._detector, detector)
def test_model_read_error_after_verification_reaches_the_caller(self):
import cv2
failure = PermissionError(13, "model read denied")
with patch.object(Path, "read_bytes", side_effect=[self.payload, failure]), \
patch.object(cv2, "FaceRecognizerSF") as recognizer, \
self.assertRaises(PermissionError) as caught:
face_match.FaceEmbedder(self.model, detector=object(), allow_download=False)
self.assertIs(caught.exception, failure)
recognizer.create.assert_not_called()
def test_an_onnx_import_error_reaches_the_caller(self):
import cv2
failure = cv2.error("invalid ONNX buffer")
with patch.object(cv2, "FaceRecognizerSF") as recognizer, self.assertRaises(cv2.error) as caught:
recognizer.create.side_effect = failure
face_match.FaceEmbedder(self.model, detector=object(), allow_download=False)
self.assertIs(caught.exception, failure)
def test_mismatched_model_bytes_never_reach_opencv(self):
import cv2
self.model.write_bytes(b"partial-model")
with patch.object(cv2, "FaceRecognizerSF") as recognizer, self.assertRaises(FaceModelUnavailable):
face_match.FaceEmbedder(self.model, detector=object(), allow_download=False)
recognizer.create.assert_not_called()
class CosineTests(unittest.TestCase):
def test_same_direction_is_one_and_orthogonal_is_zero(self):
self.assertAlmostEqual(face_match.cosine((1, 2, 3), (2, 4, 6)), 1.0)
self.assertAlmostEqual(face_match.cosine((1, 0), (0, 1)), 0.0)
self.assertEqual(face_match.cosine((0, 0), (1, 1)), 0.0)
class MatcherTests(unittest.TestCase):
def test_an_unavailable_model_is_reported_once_and_compares_nothing(self):
built = []
def broken():
built.append(1)
raise FaceModelUnavailable("缺少人脸比对模型：sface.onnx")
with patch.object(face_match, "FaceEmbedder", side_effect=broken):
matcher = face_match.FaceMatcher()
self.assertEqual(built, [], "用不上就不去取模型")
self.assertIsNone(matcher.embedding(b"anything"))
self.assertIsNone(matcher.embedding(b"again"))
self.assertEqual(built, [1])
self.assertIn("缺少人脸比对模型", matcher.unavailable)
def test_a_face_without_landmarks_gives_no_feature(self):
"""摆正要五个关键点；框里没带就不比，不拿没摆正的脸去算分。"""
import numpy
class Detector:
def detect(self, _image):
return [Face(cx=0.5, cy=0.5, width=0.3, height=0.3, score=0.9)]
embedder = face_match.FaceEmbedder.__new__(face_match.FaceEmbedder)
embedder._detector = Detector()
embedder._recognizer = None
self.assertIsNone(embedder.embed(numpy.zeros((400, 400, 3), numpy.uint8)))
class SmallFaceCropTests(unittest.TestCase):
"""全身照上的小脸先裁出来放大再摆正；大脸和裁出来检不到的都照整图走。"""
POINTS = (0.4, 0.4, 0.6, 0.4, 0.5, 0.5, 0.42, 0.6, 0.58, 0.6)
def embedder(self, faces_on_crop):
import cv2
import numpy
seen = []
class Detector:
def __init__(self, first):
self.first = first
def detect(self, image):
seen.append(image.shape[:2])
return self.first if len(seen) == 1 else faces_on_crop
class Recognizer:
def alignCrop(self, image, _box):
seen.append(("aligned", image.shape[:2]))
return image
def feature(self, _aligned):
return numpy.ones((1, 4), numpy.float32)
embedder = face_match.FaceEmbedder.__new__(face_match.FaceEmbedder)
embedder._cv2 = cv2
embedder._recognizer = Recognizer()
return embedder, Detector, seen
def run_on(self, face_width, faces_on_crop):
import numpy
embedder, Detector, seen = self.embedder(faces_on_crop)
embedder._detector = Detector([Face(cx=0.5, cy=0.2, width=face_width, height=face_width,
score=0.9, landmarks=self.POINTS)])
feature = embedder.embed(numpy.zeros((1000, 800, 3), numpy.uint8))
return feature, seen
def test_a_small_face_is_aligned_on_the_enlarged_crop(self):
side = face_match.FACE_CROP_SIDE
feature, seen = self.run_on(0.1, [Face(cx=0.5, cy=0.5, width=0.5, height=0.5,
score=0.9, landmarks=self.POINTS)])
self.assertEqual(feature, (1.0, 1.0, 1.0, 1.0))
self.assertEqual(seen, [(1000, 800), (side, side), ("aligned", (side, side))])
def test_a_large_face_is_aligned_on_the_whole_picture(self):
_feature, seen = self.run_on(0.2, [])
self.assertEqual(seen, [(1000, 800), ("aligned", (1000, 800))])
def test_a_crop_without_a_usable_face_falls_back_to_the_whole_picture(self):
_feature, seen = self.run_on(0.1, [Face(cx=0.5, cy=0.5, width=0.5, height=0.5, score=0.9)])
side = face_match.FACE_CROP_SIDE
self.assertEqual(seen, [(1000, 800), (side, side), ("aligned", (1000, 800))])
if __name__ == "__main__":
unittest.main()