def redetectOne( # noqa: F811
self,
image: VLImage,
bBox: Union[Rect, HumanDetection],
detectLandmarks: bool = True,
asyncEstimate=False,
) -> Union[RedetectResult, AsyncTask[RedetectResult]]:
"""
Redetect human body on an image in area, restricted with image.bBox, bBox or detection.
Args:
image: image with a bounding box, or just VLImage. If VLImage provided, one of bBox or detection
should be defined.
bBox: detection bounding box
detectLandmarks: detect or not landmarks
Returns:
detection if human body found otherwise None if asyncEstimate is false otherwise async task
Raises:
LunaSDKException if an error occurs
"""
assertImageForDetection(image)
if isinstance(bBox, Rect):
coreBBox = Detection(bBox.coreRectF, 1.0)
else:
coreBBox = bBox.coreEstimation.detection
if asyncEstimate:
task = self._detector.asyncRedetectOne(
image.coreImage, coreBBox,
self._getDetectionType(detectLandmarks))
return AsyncTask(task,
partial(postProcessingRedetectOne, image=image))
error, detectRes = self._detector.redetectOne(
image.coreImage, coreBBox, self._getDetectionType(detectLandmarks))
return postProcessingRedetectOne(error, detectRes, image)
def redetectOne( # noqa: F811
self, image: VLImage, bBox: Union[Rect, HumanDetection]
) -> Union[None, HumanDetection]:
"""
Redetect human body on an image in area, restricted with image.bBox, bBox or detection.
Args:
image: image with a bounding box, or just VLImage. If VLImage provided, one of bBox or detection
should be defined.
bBox: detection bounding box
Returns:
detection if human body found otherwise None
Raises:
LunaSDKException if an error occurs
"""
assertImageForDetection(image)
if isinstance(bBox, Rect):
coreBBox = Detection(bBox.coreRectF, 1.0)
else:
coreBBox = bBox.coreEstimation.detection
error, detectRes = self._detector.redetectOne(image.coreImage, coreBBox, self._getDetectionType(True))
assertError(error)
if detectRes.isValid():
return HumanDetection(detectRes, image)
return None
def redetectOne( # noqa: F811
self,
image: VLImage,
bBox: Union[Rect, FaceDetection],
detect5Landmarks=True,
detect68Landmarks=False) -> Union[None, FaceDetection]:
"""
Redetect face on an image in area, restricted with image.bBox, bBox or detection.
Args:
image: image with a bounding box, or just VLImage. If VLImage provided, one of bBox or detection
should be defined.
bBox: detection bounding box
detect5Landmarks: detect or not landmarks5
detect68Landmarks: detect or not landmarks68
Returns:
detection if face found otherwise None
Raises:
LunaSDKException if an error occurs
"""
if isinstance(bBox, Rect):
coreBBox = Detection(bBox.coreRectF, 1.0)
else:
coreBBox = bBox.coreEstimation.detection
self._validateReDetectInput(image.coreImage, coreBBox)
error, detectRes = self._detector.redetectOne(
image.coreImage, coreBBox,
self._getDetectionType(detect5Landmarks, detect68Landmarks))
assertError(error)
if detectRes.isValid():
return FaceDetection(detectRes, image)
return None
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 _createCoreFaces(image: ImageForRedetection) -> List[Face]:
"""
Create core faces for redetection
Args:
image: image and bounding boxes for redetection
Returns:
Face object list. one object for one bbox
"""
return [
Face(image.image.coreImage, Detection(bBox.coreRectF, 1.0))
for bBox in image.bBoxes
]
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 getArgsForCoreRedetect(
images: List[ImageForRedetection]
) -> Tuple[List[CoreImage], List[List[Detection]]]:
"""
Create args for redetect for image list
Args:
images: list of images for redetection
Returns:
tuple: first - list core images
second - list detect area for corresponding images
"""
coreImages, detectAreas = [], []
for image in images:
coreImages.append(image.image.coreImage)
detectAreas.append(
[Detection(bbox.coreRect, 1.0) for bbox in image.bBoxes])
return coreImages, detectAreas
def redetectOne( # noqa: F811
self,
image: VLImage,
bBox: Union[Rect, FaceDetection],
detect5Landmarks=True,
detect68Landmarks=False,
asyncEstimate=False,
) -> Union[FaceRedetectResult, AsyncTask[FaceRedetectResult]]:
"""
Redetect face on an image in area, restricted with image.bBox, bBox or detection.
Args:
image: image with a bounding box, or just VLImage. If VLImage provided, one of bBox or detection
should be defined.
bBox: detection bounding box
detect5Landmarks: detect or not landmarks5
detect68Landmarks: detect or not landmarks68
asyncEstimate: estimate or run estimation in background
Returns:
detection if face found otherwise None if asyncEstimate is false otherwise async task
Raises:
LunaSDKException if an error occurs
"""
if isinstance(bBox, Rect):
coreBBox = Detection(bBox.coreRectF, 1.0)
else:
coreBBox = bBox.coreEstimation.detection
self._validateReDetectInput(image.coreImage, coreBBox)
if asyncEstimate:
task = self._detector.asyncRedetectOne(
image.coreImage, coreBBox,
self._getDetectionType(detect5Landmarks, detect68Landmarks))
return AsyncTask(task,
partial(postProcessingRedetectOne, image=image))
error, detectRes = self._detector.redetectOne(
image.coreImage, coreBBox,
self._getDetectionType(detect5Landmarks, detect68Landmarks))
return postProcessingRedetectOne(error, detectRes, image)