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)
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]))
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 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 test_head_pose_batch_estimation(self):
"""
Batch estimating head pose test.
"""
image = VLImage.load(filename=GOST_HEAD_POSE_FACE)
detection = self.detector.detectOne(image, detect5Landmarks=True, detect68Landmarks=True)
imageWithFaceDetectionList = [
ImageWithFaceDetection(self.image, self.detection.boundingBox),
ImageWithFaceDetection(image, detection.boundingBox),
]
anglesList = self.headPoseEstimator.estimateBatch(imageWithFaceDetectionList)
assert isinstance(anglesList, list)
assert len(anglesList) == 2
for angles in anglesList:
self.assertHeadPose(angles)
def test_estimate_head_pose_by_bounding_box(self):
"""
Estimating head pose by bounding box test.
"""
angles = TestHeadPose.headPoseEstimator.estimateByBoundingBox(
ImageWithFaceDetection(self.image, self.detection.boundingBox))
self.assertHeadPose(angles)
def test_estimate_head_pose_by_bounding_box_from_other_image(self):
"""
Estimating head pose on image without faces by bounding box from other image.
"""
angles = self.headPoseEstimator.estimateByBoundingBox(
ImageWithFaceDetection(self.image, self.detection.boundingBox)
)
self.assertHeadPose(angles)
def test_estimate_head_pose_by_image_and_bounding_box_empty_bounding_box(self):
"""
Estimating head pose by image and empty bounding box
"""
fakeDetection = Detection(RectFloat(0.0, 0.0, 0.0, 0.0), 0.9)
bBox = BoundingBox(fakeDetection)
with pytest.raises(LunaSDKException) as exceptionInfo:
self.headPoseEstimator.estimateByBoundingBox(ImageWithFaceDetection(self.image, bBox))
self.assertLunaVlError(exceptionInfo, LunaVLError.InvalidDetection.format("Invalid detection"))
def test_estimate_head_pose_by_image_and_bounding_box_without_intersection(self):
"""
Estimating head pose by image and bounding box without intersection
"""
fakeDetection = Detection(RectFloat(3000.0, 3000.0, 100.0, 100.0), 0.9)
bBox = BoundingBox(fakeDetection)
with pytest.raises(LunaSDKException) as exceptionInfo:
self.headPoseEstimator.estimateByBoundingBox(ImageWithFaceDetection(self.image, bBox))
self.assertLunaVlError(exceptionInfo, LunaVLError.InvalidRect.format("Invalid rectangle"))
def test_estimate_background_by_image_and_bounding_box_without_intersection(
self):
"""
Estimating background by image and bounding box without intersection
"""
fakeDetection = Detection(RectFloat(3000.0, 3000.0, 100.0, 100.0), 0.9)
bBox = BoundingBox(fakeDetection)
with pytest.raises(LunaSDKException) as exceptionInfo:
self.backgroundEstimator.estimate(
ImageWithFaceDetection(VLImage.load(filename=ONE_FACE), bBox))
self.assertLunaVlError(
exceptionInfo, LunaVLError.InvalidRect.format("Invalid rectangle"))
def test_estimate_background_by_image_and_bounding_box_empty_bounding_box(
self):
"""
Estimating background by image and empty bounding box
"""
fakeDetection = Detection(RectFloat(0.0, 0.0, 0.0, 0.0), 0.9)
bBox = BoundingBox(fakeDetection)
with pytest.raises(LunaSDKException) as exceptionInfo:
self.backgroundEstimator.estimate(
ImageWithFaceDetection(VLImage.load(filename=ONE_FACE), bBox))
self.assertLunaVlError(
exceptionInfo,
LunaVLError.InvalidDetection.format("Invalid detection"))
def test_async_estimate_head_pose(self):
"""
Test async estimate head pose
"""
task = self.headPoseEstimator.estimate(self.detection.landmarks68, asyncEstimate=True)
self.assertAsyncEstimation(task, HeadPose)
task = self.headPoseEstimator.estimateBatch([self.detection] * 2, asyncEstimate=True)
self.assertAsyncBatchEstimation(task, HeadPose)
task = self.headPoseEstimator.estimateByBoundingBox(
ImageWithFaceDetection(self.image, self.detection.boundingBox), asyncEstimate=True
)
self.assertAsyncEstimation(task, HeadPose)
task = self.headPoseEstimator.estimateBy68Landmarks(self.detection.landmarks68, asyncEstimate=True)
self.assertAsyncEstimation(task, HeadPose)
async def asyncEstimateBackground():
"""
Example of an async background estimation.
"""
image = VLImage.load(filename=EXAMPLE_4)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
backgroundEstimator = faceEngine.createFaceDetectionBackgroundEstimator()
faceDetection = detector.detectOne(image)
# async estimation
imageWithFaceDetection = ImageWithFaceDetection(image, faceDetection.boundingBox)
backgrounds = await backgroundEstimator.estimate(imageWithFaceDetection, asyncEstimate=True)
pprint.pprint(backgrounds.asDict())
# run tasks and get results
task1 = backgroundEstimator.estimate(imageWithFaceDetection, asyncEstimate=True)
task2 = backgroundEstimator.estimate(imageWithFaceDetection, asyncEstimate=True)
for task in (task1, task2):
pprint.pprint(task.get().asDict())