def detectFaces():
"""
Detect one face on an image.
"""
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V1)
imageWithOneFace = VLImage.load(filename=EXAMPLE_O)
pprint.pprint(
detector.detectOne(imageWithOneFace,
detect5Landmarks=False,
detect68Landmarks=False).asDict())
imageWithSeveralFaces = VLImage.load(filename=EXAMPLE_SEVERAL_FACES)
pprint.pprint(
detector.detectOne(imageWithSeveralFaces,
detect5Landmarks=False,
detect68Landmarks=False).asDict())
severalFaces = detector.detect([imageWithSeveralFaces],
detect5Landmarks=False,
detect68Landmarks=False)
pprint.pprint([face.asDict() for face in severalFaces[0]])
imageWithoutFace = VLImage.load(filename=EXAMPLE_WITHOUT_FACES)
pprint.pprint(
detector.detectOne(imageWithoutFace,
detect5Landmarks=False,
detect68Landmarks=False) is None)
severalFaces = detector.detect(
[ImageForDetection(imageWithSeveralFaces, Rect(1, 1, 300.0, 300.0))],
detect5Landmarks=False,
detect68Landmarks=False)
pprint.pprint(severalFaces)
def detectHumanBody():
"""
Detect one human body on an image.
"""
faceEngine = VLFaceEngine()
detector = faceEngine.createHumanDetector()
imageWithOneHuman = VLImage.load(filename=EXAMPLE_O)
pprint.pprint(
detector.detectOne(imageWithOneHuman, detectLandmarks=False).asDict())
imageWithSeveralHumans = VLImage.load(filename=EXAMPLE_SEVERAL_FACES)
pprint.pprint(
detector.detectOne(imageWithSeveralHumans,
detectLandmarks=False).asDict())
severalHumans = detector.detect([imageWithSeveralHumans],
detectLandmarks=True)
pprint.pprint([human.asDict() for human in severalHumans[0]])
imageWithoutHuman = VLImage.load(filename=EXAMPLE_WITHOUT_FACES)
pprint.pprint(
detector.detectOne(imageWithoutHuman, detectLandmarks=False) is None)
severalHumans = detector.detect(
[ImageForDetection(imageWithSeveralHumans, Rect(1, 1, 300.0, 300.0))])
pprint.pprint(severalHumans)
def estimateBackground():
"""
Example of a face detection background estimation.
"""
image = VLImage.load(filename=EXAMPLE_4)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
backgroundEstimator = faceEngine.createFaceDetectionBackgroundEstimator()
faceDetection = detector.detectOne(image)
#: single estimation
imageWithFaceDetection = ImageWithFaceDetection(image, faceDetection.boundingBox)
background = backgroundEstimator.estimate(imageWithFaceDetection)
pprint.pprint(background)
image2 = VLImage.load(filename=EXAMPLE_4)
faceDetection2 = detector.detectOne(image2)
#: batch estimation
imageWithFaceDetectionList = [
ImageWithFaceDetection(image, faceDetection.boundingBox),
ImageWithFaceDetection(image2, faceDetection2.boundingBox),
]
backgrounds = backgroundEstimator.estimateBatch(imageWithFaceDetectionList)
pprint.pprint(backgrounds)
async def asyncEstimateEyes():
"""
Async eyes estimation example.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
landMarks5Transformation = warper.makeWarpTransformationWithLandmarks(
faceDetection, "L5")
eyesEstimator = faceEngine.createEyeEstimator()
warpWithLandmarks = WarpWithLandmarks(warp, landMarks5Transformation)
eyes = await eyesEstimator.estimate(warpWithLandmarks, asyncEstimate=True)
pprint.pprint(eyes.asDict())
image2 = VLImage.load(filename=EXAMPLE_1)
faceDetection2 = detector.detectOne(image2)
warp2 = warper.warp(faceDetection2)
landMarks5Transformation2 = warper.makeWarpTransformationWithLandmarks(
faceDetection2, "L5")
task1 = eyesEstimator.estimateBatch(
[WarpWithLandmarks(warp, landMarks5Transformation)],
asyncEstimate=True)
task2 = eyesEstimator.estimateBatch(
[WarpWithLandmarks(warp2, landMarks5Transformation2)],
asyncEstimate=True)
for task in (task1, task2):
estimations = task.get()
pprint.pprint([estimation.asDict() for estimation in estimations])
def estimateHeadPose():
"""
Example of a head pose estimation.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
headPoseEstimator = faceEngine.createHeadPoseEstimator()
faceDetection = detector.detectOne(image, detect5Landmarks=False, detect68Landmarks=True)
#: estimate by 68 landmarks
angles = headPoseEstimator.estimateBy68Landmarks(faceDetection.landmarks68)
pprint.pprint(angles.asDict())
#: get frontal type
pprint.pprint(angles.getFrontalType())
#: estimate by detection
imageWithFaceDetection = ImageWithFaceDetection(image, faceDetection.boundingBox)
angles = headPoseEstimator.estimateByBoundingBox(imageWithFaceDetection)
angles.getFrontalType()
pprint.pprint(angles)
image2 = VLImage.load(filename=EXAMPLE_1)
faceDetection2 = detector.detectOne(image2, detect5Landmarks=False, detect68Landmarks=True)
#: batch estimate by detection
imageWithFaceDetectionList = [
ImageWithFaceDetection(image, faceDetection.boundingBox),
ImageWithFaceDetection(image2, faceDetection2.boundingBox),
]
anglesList = headPoseEstimator.estimateBatch(imageWithFaceDetectionList)
pprint.pprint(anglesList)
def detectFaces():
"""
Redect faces on images.
"""
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V1)
imageWithOneFace = VLImage.load(filename=EXAMPLE_O)
pprint.pprint(
detector.detectOne(imageWithOneFace,
detect5Landmarks=False,
detect68Landmarks=False).asDict())
detection = detector.detectOne(imageWithOneFace,
detect5Landmarks=False,
detect68Landmarks=False)
pprint.pprint(
detector.redetectOne(image=imageWithOneFace, detection=detection))
pprint.pprint(
detector.redetectOne(image=imageWithOneFace,
bBox=detection.boundingBox.rect))
imageWithSeveralFaces = VLImage.load(filename=EXAMPLE_SEVERAL_FACES)
severalFaces = detector.detect([imageWithSeveralFaces],
detect5Landmarks=False,
detect68Landmarks=False)
pprint.pprint(
detector.redetect(images=[
ImageForRedetection(
imageWithSeveralFaces,
[face.boundingBox.rect for face in severalFaces[0]]),
ImageForRedetection(imageWithOneFace,
[detection.boundingBox.rect]),
ImageForRedetection(imageWithOneFace, [Rect(0, 0, 1, 1)])
]))
def detectHumans():
"""
Redetect human body on images.
"""
faceEngine = VLFaceEngine()
detector = faceEngine.createHumanDetector()
imageWithOneHuman = VLImage.load(filename=EXAMPLE_O)
detection = detector.detectOne(imageWithOneHuman, detectLandmarks=False)
pprint.pprint(detector.redetectOne(image=imageWithOneHuman,
bBox=detection))
pprint.pprint(
detector.redetectOne(image=imageWithOneHuman,
bBox=detection.boundingBox.rect))
imageWithSeveralHumans = VLImage.load(filename=EXAMPLE_SEVERAL_FACES)
severalHumans = detector.detect([imageWithSeveralHumans],
detectLandmarks=False)
pprint.pprint(
detector.redetect(images=[
ImageForRedetection(
imageWithSeveralHumans,
[human.boundingBox.rect for human in severalHumans[0]]),
ImageForRedetection(imageWithOneHuman,
[detection.boundingBox.rect]),
ImageForRedetection(imageWithOneHuman, [Rect(0, 0, 1, 1)]),
]))
def estimateGazeDirection():
"""
Estimate gaze direction.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V1)
faceDetection = detector.detectOne(image, detect68Landmarks=True)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
landMarks5Transformation = warper.makeWarpTransformationWithLandmarks(faceDetection, "L5")
gazeEstimator = faceEngine.createGazeEstimator()
warpWithLandmarks5 = WarpWithLandmarks5(warp, landMarks5Transformation)
pprint.pprint(gazeEstimator.estimate(warpWithLandmarks5).asDict())
faceDetection2 = detector.detectOne(VLImage.load(filename=EXAMPLE_1), detect68Landmarks=True)
warp2 = warper.warp(faceDetection2)
landMarks5Transformation2 = warper.makeWarpTransformationWithLandmarks(faceDetection2, "L5")
warpWithLandmarks5List = [
WarpWithLandmarks5(warp, landMarks5Transformation),
WarpWithLandmarks5(warp2, landMarks5Transformation2),
]
estimations = gazeEstimator.estimateBatch(warpWithLandmarks5List)
pprint.pprint([estimation.asDict() for estimation in estimations])
def estimateFisheye():
"""
Example of a fisheye estimation.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
fishEstimator = faceEngine.createFisheyeEstimator()
faceDetection = detector.detectOne(image, detect5Landmarks=False, detect68Landmarks=True)
#: single estimation
imageWithFaceDetection = ImageWithFaceDetection(image, faceDetection.boundingBox)
fisheye = fishEstimator.estimate(imageWithFaceDetection)
pprint.pprint(fisheye)
image2 = VLImage.load(filename=EXAMPLE_1)
faceDetection2 = detector.detectOne(image2, detect5Landmarks=False, detect68Landmarks=True)
#: batch estimation
imageWithFaceDetectionList = [
ImageWithFaceDetection(image, faceDetection.boundingBox),
ImageWithFaceDetection(image2, faceDetection2.boundingBox),
]
fisheyeList = fishEstimator.estimateBatch(imageWithFaceDetectionList)
pprint.pprint(fisheyeList)
def estimateBasicAttributes():
"""
Estimate emotion from a warped image.
"""
image = VLImage.load(filename=EXAMPLE_SEVERAL_FACES)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V1)
faceDetections = detector.detect([image])[0]
warper = faceEngine.createWarper()
warps = [warper.warp(faceDetection) for faceDetection in faceDetections]
basicAttributesEstimator = faceEngine.createBasicAttributesEstimator()
pprint.pprint(
basicAttributesEstimator.estimate(warps[0].warpedImage,
estimateAge=True,
estimateGender=True,
estimateEthnicity=True).asDict())
pprint.pprint(
basicAttributesEstimator.estimateBasicAttributesBatch(
warps,
estimateAge=True,
estimateGender=True,
estimateEthnicity=True))
pprint.pprint(
basicAttributesEstimator.estimateBasicAttributesBatch(
warps,
estimateAge=True,
estimateGender=True,
estimateEthnicity=True,
aggregate=True))
def estimateAGS():
"""
Estimate face detection ags.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
agsEstimator = faceEngine.createAGSEstimator()
imageWithFaceDetection = ImageWithFaceDetection(image,
faceDetection.boundingBox)
pprint.pprint(
agsEstimator.estimate(imageWithFaceDetection=imageWithFaceDetection))
pprint.pprint(agsEstimator.estimate(faceDetection))
image2 = VLImage.load(filename=EXAMPLE_1)
faceDetection2 = detector.detectOne(image2)
imageWithFaceDetectionList = [
ImageWithFaceDetection(image, faceDetection.boundingBox),
ImageWithFaceDetection(image2, faceDetection2.boundingBox),
]
pprint.pprint(agsEstimator.estimateBatch(imageWithFaceDetectionList))
pprint.pprint(
agsEstimator.estimateBatch(detections=[faceDetection, faceDetection2]))
async def asyncEstimateBasicAttributes():
"""
Async estimate basic attributes.
"""
image = VLImage.load(filename=EXAMPLE_SEVERAL_FACES)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetections = detector.detect([image])[0]
warper = faceEngine.createFaceWarper()
warps = [warper.warp(faceDetection) for faceDetection in faceDetections]
basicAttributesEstimator = faceEngine.createBasicAttributesEstimator()
basicAttributes = await basicAttributesEstimator.estimate(
warps[0].warpedImage,
estimateAge=True,
estimateGender=True,
estimateEthnicity=True,
asyncEstimate=True)
pprint.pprint(basicAttributes.asDict())
task1 = basicAttributesEstimator.estimate(warps[0].warpedImage,
estimateAge=True,
estimateGender=True,
estimateEthnicity=True,
asyncEstimate=True)
task2 = basicAttributesEstimator.estimate(warps[0].warpedImage,
estimateAge=True,
estimateGender=True,
estimateEthnicity=True,
asyncEstimate=True)
for task in (task1, task2):
pprint.pprint(task.get().asDict())
def __init__(self,
startEstimators: Optional[List[FaceEstimator]] = None,
faceEngine: Optional[VLFaceEngine] = None):
"""
Init.
Args:
startEstimators: list of estimators which will be initiate now
faceEngine: faceengine, factory for estimators
"""
if faceEngine is None:
self._faceEngine = VLFaceEngine()
else:
self._faceEngine = faceEngine
self._basicAttributesEstimator = None
self._eyeEstimator = None
self._emotionsEstimator = None
self._gazeDirectionEstimator = None
self._mouthStateEstimator = None
self._warpQualityEstimator = None
self._headPoseEstimator = None
self._AGSEstimator = None
self._descriptorEstimator = None
self.warper = self._faceEngine.createWarper()
if startEstimators:
for estimator in set(startEstimators):
self.initEstimator(estimator)
async def asyncEstimateMedicalMask():
"""
Async medical mask estimation example
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
medicalMaskEstimator = faceEngine.createMaskEstimator()
# Estimate from detection
pprint.pprint(medicalMaskEstimator.estimate(faceDetection).asDict())
# Estimate from wrap
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
mask = await medicalMaskEstimator.estimate(warp.warpedImage,
asyncEstimate=True)
pprint.pprint(mask.asDict())
warp2 = warper.warp(detector.detectOne(VLImage.load(filename=EXAMPLE_1)))
task1 = medicalMaskEstimator.estimate(warp.warpedImage, asyncEstimate=True)
task2 = medicalMaskEstimator.estimate(warp2.warpedImage,
asyncEstimate=True)
for task in (task1, task2):
pprint.pprint(task.get())
def estimateRedEye():
"""
Red-eye estimation example.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
landMarks5Transformation = warper.makeWarpTransformationWithLandmarks(
faceDetection, "L5")
redEyeEstimator = faceEngine.createRedEyeEstimator()
warpWithLandmarks = WarpWithLandmarks5(warp, landMarks5Transformation)
pprint.pprint(redEyeEstimator.estimate(warpWithLandmarks).asDict())
image2 = VLImage.load(filename=EXAMPLE_1)
faceDetection2 = detector.detectOne(image2)
warp2 = warper.warp(faceDetection2)
landMarks5Transformation2 = warper.makeWarpTransformationWithLandmarks(
faceDetection2, "L5")
warpWithLandmarksList = [
WarpWithLandmarks5(warp, landMarks5Transformation),
WarpWithLandmarks5(warp2, landMarks5Transformation2),
]
estimations = redEyeEstimator.estimateBatch(warpWithLandmarksList)
pprint.pprint([estimation.asDict() for estimation in estimations])
def test_get_launch_options(self):
"""Get launch options from VLFaceEngine"""
runtime = RuntimeSettingsProvider()
for device in DeviceClass:
with self.subTest(device):
runtime.runtimeSettings.deviceClass = device
fe = VLFaceEngine(runtimeConf=runtime)
assert fe.getLaunchOptions(None).deviceClass == device
def createWarp():
"""
Create face warp from detection.
"""
faceEngine = VLFaceEngine()
image = VLImage.load(filename=EXAMPLE_O)
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
pprint.pprint(warp.warpedImage.rect)
def createWarp():
"""
Create human body warp from human detection.
"""
faceEngine = VLFaceEngine()
image = VLImage.load(filename=EXAMPLE_O)
detector = faceEngine.createHumanDetector()
humanDetection = detector.detectOne(image)
warper = faceEngine.createHumanWarper()
warp = warper.warp(humanDetection)
pprint.pprint(warp.warpedImage.rect)
def estimateGlasses():
"""
Create warp to detection.
"""
image = VLImage.load(filename=EXAMPLE_3)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
glassesEstimator = faceEngine.createGlassesEstimator()
pprint.pprint(glassesEstimator.estimate(warp.warpedImage).asDict())
def estimateAGS():
"""
Estimate face detection ags.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V1)
faceDetection = detector.detectOne(image)
agsEstimator = faceEngine.createAGSEstimator()
pprint.pprint(agsEstimator.estimate(image=image, boundingBox=faceDetection.boundingBox))
pprint.pprint(agsEstimator.estimate(faceDetection))
def estimateCredibility():
"""
Estimate credibility of a person
"""
image = VLImage.load(filename=EXAMPLE_1)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
credibilityEstimator = faceEngine.createCredibilityEstimator()
pprint.pprint(credibilityEstimator.estimate(warp.warpedImage).asDict())
def estimateOrientationMode():
"""
Example of a orientation mode estimation.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
orientationModeEstimator = faceEngine.createOrientationModeEstimator()
#: estimate
pprint.pprint(orientationModeEstimator.estimate(image))
image2 = VLImage.load(filename=EXAMPLE_1)
#: estimate batch
pprint.pprint(orientationModeEstimator.estimateBatch([image, image2]))
def estimateImageColorType():
"""
Estimate image color type from a warped image.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
imageColorTypeEstimator = faceEngine.createImageColorTypeEstimator()
pprint.pprint(imageColorTypeEstimator.estimate(warp.warpedImage).asDict())
def estimateEmotion():
"""
Estimate emotion from a warped image.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V1)
faceDetection = detector.detectOne(image)
warper = faceEngine.createWarper()
warp = warper.warp(faceDetection)
emotionEstimator = faceEngine.createEmotionEstimator()
pprint.pprint(emotionEstimator.estimate(warp.warpedImage).asDict())
def estimateHeadwear():
"""
Estimate headwear from a warped image.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
eyebrowEstimator = faceEngine.createHeadwearEstimator()
pprint.pprint(eyebrowEstimator.estimate(warp.warpedImage).asDict())
def estimateWarpQuality():
"""
Create warp from detection.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
qualityEstimator = faceEngine.createWarpQualityEstimator()
pprint.pprint(qualityEstimator.estimate(warp.warpedImage).asDict())
def matchDescriptors():
"""
Match raw descriptors.
"""
faceEngine = VLFaceEngine()
version = 54
matcher = faceEngine.createFaceMatcher(descriptorVersion=version)
magicPrefix = b"dp\x00\x00" + version.to_bytes(length=4,
byteorder="little")
descriptor1 = magicPrefix + bytes([126, 128] * 256) # length is 8 + 512
descriptor2 = magicPrefix + bytes([128, 126] * 256) # length is 8 + 512
print(matcher.match(descriptor1, descriptor2))
print(matcher.match(descriptor1, [descriptor2, descriptor1]))
def createWarp():
"""
Create warp from detection.
"""
faceEngine = VLFaceEngine()
image = VLImage.load(
url=
"https://cdn1.savepice.ru/uploads/2019/4/15/194734af15c4fcd06dec6db86bbeb7cd-full.jpg"
)
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V1)
faceDetection = detector.detectOne(image)
warper = faceEngine.createWarper()
warp = warper.warp(faceDetection)
pprint.pprint(warp.warpedImage.rect)
async def asyncRotateNEstimateImage():
"""
Async example of image rotation.
"""
nonRotatedImage = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
orientationModeEstimator = faceEngine.createOrientationModeEstimator()
#: rotate & estimate | not rotated
image = VLImage.rotate(nonRotatedImage, RotationAngle.ANGLE_0)
orientation = await orientationModeEstimator.estimate(image,
asyncEstimate=True)
pprint.pprint(orientation)
task1 = orientationModeEstimator.estimate(image, asyncEstimate=True)
task2 = orientationModeEstimator.estimate(image, asyncEstimate=True)
for task in (task1, task2):
pprint.pprint(task.get())
def estimateLiveness():
"""
Estimate liveness.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image, detect68Landmarks=True)
livenessEstimator = faceEngine.createLivenessV1Estimator()
pprint.pprint(livenessEstimator.estimate(faceDetection, qualityThreshold=0.5).asDict())
faceDetection2 = detector.detectOne(VLImage.load(filename=EXAMPLE_1), detect68Landmarks=True)
pprint.pprint(livenessEstimator.estimateBatch([faceDetection, faceDetection2]))
