def assertQualityReply(qualityDict: dict):
"""
Validate quality reply
Args:
qualityDict: quality estimation result
"""
assert (
jsonValidator(schema=QUALITY_SCHEMA).validate(qualityDict) is
None), f"{qualityDict} does not match with schema {QUALITY_SCHEMA}"
def test_livenessv1_as_dict(self):
"""
Test liveness estimations as dict
"""
livenessDict = self.livenessEstimator.estimate(self.detection).asDict()
assert (
jsonValidator(schema=LIVENESSV1_SCHEMA).validate(livenessDict) is
None
), f"{livenessDict} does not match with schema {LIVENESSV1_SCHEMA}"
def test_estimate_glasses_as_dict(self):
"""
Test glasses estimations as dict
"""
glassesDict = TestGlasses.glassesEstimator.estimate(
self.warpNoGlasses).asDict()
assert (
jsonValidator(schema=GLASSES_SCHEMA).validate(glassesDict) is
None), f"{glassesDict} does not match with schema {GLASSES_SCHEMA}"
def test_estimate_credibility_as_dict(self):
"""
Test credibility credibility as dict
"""
credibilityCheck = TestCredibility.credibilityEstimator.estimate(
self.warp).asDict()
assert (
jsonValidator(schema=CREDIBILITY_SCHEMA).validate(credibilityCheck)
is None
), f"{credibilityCheck} does not match with schema {CREDIBILITY_SCHEMA}"
def test_bounding_box_as_dict(self):
"""
Test conversion bounding box to dictionary
"""
boundingBox = TestFaceDetector.defaultDetector.detectOne(
image=VLIMAGE_ONE_FACE).boundingBox.asDict()
assert (
jsonValidator(schema=REQUIRED_FACE_DETECTION).validate(boundingBox)
is None
), f"{boundingBox} does not match with schema {REQUIRED_FACE_DETECTION}"
def test_landmarks_as_dict(self):
"""
Test conversion landmarks to dictionary
"""
currentLandmarks5 = TestFaceDetector.defaultDetector.detectOne(
image=VLIMAGE_ONE_FACE).landmarks5.asDict()
assert (
jsonValidator(schema=LANDMARKS5).validate(currentLandmarks5) is
None
), f"{currentLandmarks5} does not match with schema {LANDMARKS5}"
def test_human_detection_as_dict(self):
"""
Test conversion result human detection to dictionary
"""
for case in self.landmarksCases:
with self.subTest(landmarks5=case.detectLandmarks):
detectAsDict = self.detector.detectOne(
image=VLIMAGE_ONE_FACE,
detectLandmarks=case.detectLandmarks).asDict()
assert (
jsonValidator(schema=REQUIRED_HUMAN_BODY_DETECTION
).validate(detectAsDict) is None
), f"{detectAsDict} does not match with schema {REQUIRED_HUMAN_BODY_DETECTION}"
def test_face_detection_as_dict(self):
"""
Test conversion result face detection to dictionary
"""
for case in self.landmarksCases:
with self.subTest(landmarks5=case.detect5Landmarks,
landmarks68=case.detect68Landmarks):
detectAsDict = TestFaceDetector.defaultDetector.detectOne(
image=VLIMAGE_ONE_FACE,
detect5Landmarks=case.detect5Landmarks,
detect68Landmarks=case.detect68Landmarks,
).asDict()
assert (
jsonValidator(
schema=REQUIRED_FACE_DETECTION).validate(detectAsDict)
is None
), f"{detectAsDict} does not match with schema {REQUIRED_FACE_DETECTION}"
