def setup_class(cls):
super().setup_class()
cls.maskEstimator = cls.faceEngine.createMaskEstimator()
cls.warpImageMedicalMask = FaceWarpedImage(VLImage.load(filename=FACE_WITH_MASK))
cls.warpImageMissing = FaceWarpedImage(VLImage.load(filename=WARP_CLEAN_FACE))
cls.warpImageOccluded = FaceWarpedImage(VLImage.load(filename=OCCLUDED_FACE))
def setup_class(cls):
super().setup_class()
cls.glassesEstimator = cls.faceEngine.createGlassesEstimator()
cls.warpNoGlasses = FaceWarpedImage(
VLImage.load(filename=WARP_CLEAN_FACE))
cls.warpEyeGlasses = FaceWarpedImage(
VLImage.load(filename=WARP_FACE_WITH_EYEGLASSES))
cls.warpSunGlasses = FaceWarpedImage(
VLImage.load(filename=WARP_FACE_WITH_SUNGLASSES))
def test_batch_estimate_with_success_and_error(self):
"""
Test batch estimate with good and bad warp.
"""
badWarp = FaceWarpedImage(VLImage.load(filename=WARP_CLEAN_FACE))
badWarp.coreImage = VLIMAGE_SMALL.coreImage
with pytest.raises(LunaSDKException) as exceptionInfo:
self.estimator.estimateBasicAttributesBatch(
warps=[self._warp, badWarp],
estimateAge=True,
estimateGender=True,
estimateEthnicity=True,
aggregate=False,
)
self.assertLunaVlError(exceptionInfo, LunaVLError.BatchedInternalError.format("Failed validation."))
assert len(exceptionInfo.value.context) == 2, "Expect two errors in exception context"
self.assertReceivedAndRawExpectedErrors(exceptionInfo.value.context[0], LunaVLError.Ok)
self.assertReceivedAndRawExpectedErrors(exceptionInfo.value.context[1], LunaVLError.InvalidImageSize)
def test_batch_detect_with_success_and_error(self):
"""
Test batch detection with success and error using FACE_DET_V3 (there is no error with other detector)
"""
badWarp = FaceWarpedImage(VLImage.load(filename=WARP_CLEAN_FACE))
badWarp.coreImage = VLIMAGE_SMALL.coreImage
for detector in self.detectors:
with self.subTest(detectorType=detector.detectorType):
with pytest.raises(LunaSDKException) as exceptionInfo:
detector.detect(
images=[VLIMAGE_ONE_FACE, VLIMAGE_BAD_IMAGE])
self.assertLunaVlError(exceptionInfo,
LunaVLError.BatchedInternalError)
assert len(exceptionInfo.value.context
) == 2, "Expect two errors in exception context"
self.assertReceivedAndRawExpectedErrors(
exceptionInfo.value.context[0], LunaVLError.Ok)
self.assertReceivedAndRawExpectedErrors(
exceptionInfo.value.context[1], LunaVLError.Internal)
def test_extract_descriptors_batch_positive_and_negative(self):
"""
Test estimate descriptor batch with one good warp and one bad (expected error).
"""
for case in self.cases:
for planVersion in case.versions:
extractor = case.extractorFactory(descriptorVersion=planVersion)
for kw in (dict(), dict(descriptorBatch=self.getBatch(planVersion, len(case.warps), case.type))):
for aggregate in (True, False):
with self.subTest(
type=case.type, plan_version=planVersion, aggregate=aggregate, external_descriptor=bool(kw)
):
badWarp = FaceWarpedImage(VLImage.load(filename=WARP_CLEAN_FACE))
badWarp.coreImage = VLIMAGE_SMALL.coreImage
with pytest.raises(LunaSDKException) as exceptionInfo:
extractor.estimateDescriptorsBatch([case.warps[0], badWarp], aggregate=aggregate, **kw)
assert len(exceptionInfo.value.context) == 2, "Expect two errors in exception context"
self.assertReceivedAndRawExpectedErrors(exceptionInfo.value.context[0], LunaVLError.Ok)
self.assertReceivedAndRawExpectedErrors(
exceptionInfo.value.context[1], LunaVLError.InvalidImageSize
)
def setup_class(cls):
super().setup_class()
cls.maskEstimator = cls.faceEngine.createMaskEstimator()
cls.medicalMaskWarpNProperties = WarpNExpectedProperties(
FaceWarpedImage(VLImage.load(filename=FACE_WITH_MASK)),
MaskProperties(0.000, 0.999, 0.000))
cls.missingMaskWarpNProperties = WarpNExpectedProperties(
FaceWarpedImage(VLImage.load(filename=WARP_CLEAN_FACE)),
MaskProperties(0.998, 0.002, 0.000))
cls.occludedMaskWarpNProperties = WarpNExpectedProperties(
FaceWarpedImage(VLImage.load(filename=OCCLUDED_FACE)),
MaskProperties(0.260, 0.669, 0.071) # TODO: bug
)
cls.imageMedicalMask = VLImage.load(filename=FULL_FACE_WITH_MASK)
cls.warpImageMedicalMask = FaceWarpedImage(
VLImage.load(filename=FACE_WITH_MASK))
cls.imageMissing = VLImage.load(filename=FULL_FACE_NO_MASK)
cls.warpImageMissing = FaceWarpedImage(
VLImage.load(filename=WARP_CLEAN_FACE))
cls.imageOccluded = VLImage.load(filename=FULL_OCCLUDED_FACE)
cls.warpImageOccluded = FaceWarpedImage(
VLImage.load(filename=OCCLUDED_FACE))
cls.largeImage = VLImage.load(filename=LARGE_IMAGE)
cls.detector = cls.faceEngine.createFaceDetector(
DetectorType.FACE_DET_V3)
def test_descriptor_batch_low_threshold_aggregation(self):
"""
Test descriptor batch with low threshold warps with aggregation
"""
faceWarp = FaceWarpedImage.load(filename=BAD_THRESHOLD_WARP)
for descriptorVersion in EFDVa:
with self.subTest(planVersion=descriptorVersion):
extractor = self.faceEngine.createFaceDescriptorEstimator(descriptorVersion)
descriptorBatch = self.getBatch(descriptorVersion, 2, DescriptorType.face)
_, descriptor = extractor.estimateDescriptorsBatch(
[faceWarp] * 2, aggregate=1, descriptorBatch=descriptorBatch
)
assert descriptor.garbageScore < 0.6, "Expected low gs"
def setup_class(cls):
super().setup_class()
cls.warper = cls.faceEngine.createFaceWarper()
cls.mouthEstimator = cls.faceEngine.createMouthEstimator()
CaseWarp = namedtuple("CaseWarp", ("warp", "detector"))
cls.warpList = []
for detector in cls.detectors:
detection = detector.detectOne(VLIMAGE_ONE_FACE)
cls.warpList.append(
CaseWarp(cls.warper.warp(detection),
detector.detectorType.name))
cls.warpList.append(
CaseWarp(FaceWarpedImage(VLImage.load(filename=WARP_WHITE_MAN)),
"None"))
def test_descriptor_batch_bad_threshold_aggregation(self):
"""
Test descriptor batch with bad threshold warps with aggregation
"""
faceWarp = FaceWarpedImage.load(filename=BAD_THRESHOLD_WARP)
for descriptorVersion in [56]:
with self.subTest(planVersion=descriptorVersion):
extractor = self.faceEngine.createFaceDescriptorEstimator(descriptorVersion)
descriptorBatch = self.getBatch(descriptorVersion, 2, DescriptorType.face)
with pytest.raises(LunaSDKException) as exceptionInfo:
extractor.estimateDescriptorsBatch([faceWarp] * 2, aggregate=1, descriptorBatch=descriptorBatch)
self.assertLunaVlError(
exceptionInfo,
LunaVLError.BatchedInternalError.format(
"Cant aggregate descriptors - all images'a GSs are less the threashold"
),
)
from lunavl.sdk.image_utils.image import VLImage
from tests.base import BaseTestClass
from tests.detect_test_class import VLIMAGE_SMALL
from tests.resources import WARP_WHITE_MAN, HUMAN_WARP, WARP_CLEAN_FACE, BAD_THRESHOLD_WARP
EFDVa = EXISTENT_FACE_DESCRIPTOR_VERSION_ABUNDANCE = [46, 52, 54, 56]
EHDVa = EXISTENT_HUMAN_DESCRIPTOR_VERSION_ABUNDANCE = [DHDV]
class DescriptorType(Enum):
face = "face"
human = "human"
faceWarp = FaceWarpedImage.load(filename=WARP_WHITE_MAN)
faceWarps = [faceWarp] * 3
humanWarp = HumanWarpedImage.load(filename=HUMAN_WARP)
humanWarps = [humanWarp] * 3
class DescriptorCase(NamedTuple):
descriptor: BaseDescriptor
aggregatedDescriptor: BaseDescriptor
descriptorBatch: BaseDescriptorBatch
type: DescriptorType
estimator: Union[FaceDescriptorEstimator, HumanDescriptorEstimator]
class ExtractorCase(NamedTuple):
type: DescriptorType
def setUpClass(cls) -> None:
""" Load warps. """
cls._warp = FaceWarpedImage.load(filename=WARP_ONE_FACE)
cls._warp2 = FaceWarpedImage.load(filename=WARP_CLEAN_FACE)
def setup_class(cls):
super().setup_class()
cls.credibilityEstimator = cls.faceEngine.createCredibilityEstimator()
cls.warp = FaceWarpedImage(VLImage.load(filename=WARP_CLEAN_FACE))
FaceDescriptor,
FaceDescriptorBatch,
)
from lunavl.sdk.errors.errors import LunaVLError
from lunavl.sdk.errors.exceptions import LunaSDKException
from lunavl.sdk.estimators.face_estimators.facewarper import FaceWarpedImage
from lunavl.sdk.indexes.base import IndexResult
from lunavl.sdk.indexes.builder import IndexBuilder
from lunavl.sdk.indexes.stored_index import DynamicIndex, DenseIndex, IndexType
from lunavl.sdk.estimators.face_estimators.face_descriptor import FaceDescriptorEstimator
from tests.base import BaseTestClass
from tests.resources import WARP_WHITE_MAN, WARP_ONE_FACE, WARP_CLEAN_FACE
EFDVa = EXISTENT_FACE_DESCRIPTOR_VERSION_ABUNDANCE = [54, 56, 57, 58]
faceWarp = FaceWarpedImage.load(filename=WARP_WHITE_MAN)
faceWarps = [FaceWarpedImage.load(filename=WARP_CLEAN_FACE), FaceWarpedImage.load(filename=WARP_ONE_FACE)]
currDir = os.path.dirname(__file__)
pathToStoredIndex = os.path.join(currDir, "data", "stored.index")
nonDefaultDynamicIndex = os.path.join(currDir, "data", "descriptor57.index")
class TestIndexFunctionality(BaseTestClass):
"""Test of indexes."""
descriptorVersion: int
nonDefaultDescriptorVersion: int
defaultFaceEstimator: FaceDescriptorEstimator
faceDescriptor: FaceDescriptor
nonDefaultFaceDescriptor: FaceDescriptor
faceDescriptorBatch: FaceDescriptorBatch