def estimate(
warp: Union[HumanWarp, HumanWarpedImage, FaceWarp, FaceWarpedImage],
descriptorFactory: BaseDescriptorFactory,
coreEstimator: IDescriptorExtractorPtr,
descriptor: Optional[BaseDescriptor] = None,
asyncEstimate: bool = False,
) -> Union[BaseDescriptor, AsyncTask[BaseDescriptor]]:
"""
Estimate a face descriptor or a human descriptor from the warped image.
Args:
warp: warped image
descriptor: descriptor for saving extract result
descriptorFactory: descriptor factory
coreEstimator: descriptor extractor
asyncEstimate: estimate or run estimation in background
Returns:
estimated descriptor if asyncEstimate is false otherwise async task
Raises:
LunaSDKException: if estimation failed
"""
if descriptor is None:
descriptor = descriptorFactory.generateDescriptor()
coreDescriptor = descriptor.coreEstimation
else:
coreDescriptor = descriptor.coreEstimation
if asyncEstimate:
task = coreEstimator.asyncExtractFromWarpedImage(
warp.warpedImage.coreImage, coreDescriptor)
return AsyncTask(task, partial(postProcessing, descriptor=descriptor))
error, optionalGS = coreEstimator.extractFromWarpedImage(
warp.warpedImage.coreImage, coreDescriptor)
return postProcessing(error, optionalGS, descriptor)
def estimate(
warp: Union[HumanWarp, HumanWarpedImage, FaceWarp, FaceWarpedImage],
descriptorFactory: BaseDescriptorFactory,
coreEstimator: IDescriptorExtractorPtr,
descriptor: Optional[BaseDescriptor] = None,
) -> BaseDescriptor:
"""
Estimate a face descriptor or a human descriptor from the warped image.
Args:
warp: warped image
descriptor: descriptor for saving extract result
descriptorFactory: descriptor factory
coreEstimator: descriptor extractor
Returns:
estimated descriptor
Raises:
LunaSDKException: if estimation failed
"""
if descriptor is None:
descriptor = descriptorFactory.generateDescriptor()
coreDescriptor = descriptor.coreEstimation
else:
coreDescriptor = descriptor.coreEstimation
error, optionalGS = coreEstimator.extractFromWarpedImage(
warp.warpedImage.coreImage, coreDescriptor)
if error.isError:
raise LunaSDKException(LunaVLError.fromSDKError(error))
descriptor.garbageScore = optionalGS
return descriptor
def estimateDescriptorsBatch(
warps: Union[List[Union[HumanWarp, HumanWarpedImage]],
List[Union[FaceWarp, FaceWarpedImage]]],
descriptorFactory: Type[BaseDescriptorFactory],
coreEstimator: IDescriptorExtractorPtr,
aggregate: bool = False,
descriptorBatch: Optional[BaseDescriptorBatch] = None,
) -> Tuple[BaseDescriptorBatch, Union[BaseDescriptor, None]]:
"""
Estimate a batch of descriptors from warped images.
Args:
warps: warped images
aggregate: whether to estimate aggregate descriptor or not
descriptorBatch: optional batch for saving descriptors
descriptorFactory: descriptor factory
coreEstimator: descriptor extractor
Returns:
tuple of batch and the aggregate descriptors (or None)
Raises:
LunaSDKException: if estimation failed
"""
if descriptorBatch is None:
descriptorBatch = descriptorFactory.generateDescriptorsBatch(
len(warps))
if aggregate:
aggregatedDescriptor = descriptorFactory.generateDescriptor()
error, optionalGSAggregateDescriptor, scores = coreEstimator.extractFromWarpedImageBatch(
[warp.warpedImage.coreImage for warp in warps],
descriptorBatch.coreEstimation,
aggregatedDescriptor.coreEstimation,
len(warps),
)
if error.isError:
raise LunaSDKException(LunaVLError.fromSDKError(error))
aggregatedDescriptor.garbageScore = optionalGSAggregateDescriptor
else:
aggregatedDescriptor = None
error, scores = coreEstimator.extractFromWarpedImageBatch(
[warp.warpedImage.coreImage for warp in warps],
descriptorBatch.coreEstimation, len(warps))
if error.isError:
raise LunaSDKException(LunaVLError.fromSDKError(error))
descriptorBatch.scores = scores
return descriptorBatch, aggregatedDescriptor
def estimateDescriptorsBatch(
warps: Union[List[Union[HumanWarp, HumanWarpedImage]],
List[Union[FaceWarp, FaceWarpedImage]]],
descriptorFactory: BaseDescriptorFactory,
coreEstimator: IDescriptorExtractorPtr,
aggregate: bool = False,
descriptorBatch: Optional[BaseDescriptorBatch] = None,
asyncEstimate: bool = False,
) -> Union[Tuple[BaseDescriptorBatch, Union[BaseDescriptor, None]],
AsyncTask[Tuple[BaseDescriptorBatch, Union[BaseDescriptor,
None]]], ]:
"""
Estimate a batch of descriptors from warped images.
Args:
warps: warped images
aggregate: whether to estimate aggregate descriptor or not
descriptorBatch: optional batch for saving descriptors
descriptorFactory: descriptor factory
coreEstimator: descriptor extractor
asyncEstimate: estimate or run estimation in background
Returns:
tuple of batch and the aggregate descriptors (or None) if asyncEstimate is false otherwise async task
Raises:
LunaSDKException: if estimation failed
"""
if descriptorBatch is None:
descriptorBatch = descriptorFactory.generateDescriptorsBatch(
len(warps))
coreImages = [warp.warpedImage.coreImage for warp in warps]
validateInputByBatchEstimator(coreEstimator, coreImages)
if aggregate:
aggregatedDescriptor = descriptorFactory.generateDescriptor()
if asyncEstimate:
task = coreEstimator.asyncExtractFromWarpedImageBatch(
coreImages, descriptorBatch.coreEstimation,
aggregatedDescriptor.coreEstimation)
return AsyncTask(
task,
postProcessing=partial(
postProcessingBatchWithAggregation,
descriptorBatch=descriptorBatch,
aggregatedDescriptor=aggregatedDescriptor,
),
)
error, optionalGSAggregateDescriptor, scores = coreEstimator.extractFromWarpedImageBatch(
coreImages, descriptorBatch.coreEstimation,
aggregatedDescriptor.coreEstimation)
return postProcessingBatchWithAggregation(
error,
optionalGSAggregateDescriptor,
scores,
descriptorBatch=descriptorBatch,
aggregatedDescriptor=aggregatedDescriptor,
)
if asyncEstimate:
task = coreEstimator.asyncExtractFromWarpedImageBatch(
coreImages, descriptorBatch.coreEstimation)
return AsyncTask(task,
postProcessing=partial(
postProcessingBatch,
descriptorBatch=descriptorBatch))
error, scores = coreEstimator.extractFromWarpedImageBatch(
coreImages, descriptorBatch.coreEstimation)
return postProcessingBatch(error, scores, descriptorBatch=descriptorBatch)
def estimateDescriptorsBatch(
warps: Union[List[Union[HumanWarp, HumanWarpedImage]],
List[Union[FaceWarp, FaceWarpedImage]]],
descriptorFactory: Type[BaseDescriptorFactory],
coreEstimator: IDescriptorExtractorPtr,
aggregate: bool = False,
descriptorBatch: Optional[BaseDescriptorBatch] = None,
) -> Tuple[BaseDescriptorBatch, Union[BaseDescriptor, None]]:
"""
Estimate a batch of descriptors from warped images.
Args:
warps: warped images
aggregate: whether to estimate aggregate descriptor or not
descriptorBatch: optional batch for saving descriptors
descriptorFactory: descriptor factory
coreEstimator: descriptor extractor
Returns:
tuple of batch and the aggregate descriptors (or None)
Raises:
LunaSDKException: if estimation failed
"""
def getErrorsExtractingOneByOne() -> List[LunaVLError]:
"""
Extract descriptor without batching and collect errors
Returns:
list with errors
"""
errors = []
for idx, warp in enumerate(warps):
if len(descriptorBatch):
coreDescriptor = descriptorBatch[idx].coreEstimation
else:
descriptor = descriptorFactory.generateDescriptor()
coreDescriptor = descriptor.coreEstimation
errorOne, _ = coreEstimator.extractFromWarpedImage(
warp.warpedImage.coreImage, coreDescriptor)
if errorOne.isError:
errors.append(LunaVLError.fromSDKError(errorOne))
else:
errors.append(LunaVLError.Ok.format(
LunaVLError.Ok.description))
return errors
if descriptorBatch is None:
descriptorBatch = descriptorFactory.generateDescriptorsBatch(
len(warps))
if aggregate:
aggregatedDescriptor = descriptorFactory.generateDescriptor()
error, optionalGSAggregateDescriptor, scores = coreEstimator.extractFromWarpedImageBatch(
[warp.warpedImage.coreImage for warp in warps],
descriptorBatch.coreEstimation,
aggregatedDescriptor.coreEstimation,
len(warps),
)
if error.isError:
errors = getErrorsExtractingOneByOne()
raise LunaSDKException(
LunaVLError.BatchedInternalError.format(
LunaVLError.fromSDKError(error).detail), errors)
aggregatedDescriptor.garbageScore = optionalGSAggregateDescriptor
else:
aggregatedDescriptor = None
error, scores = coreEstimator.extractFromWarpedImageBatch(
[warp.warpedImage.coreImage for warp in warps],
descriptorBatch.coreEstimation, len(warps))
if error.isError:
errors = getErrorsExtractingOneByOne()
raise LunaSDKException(
LunaVLError.BatchedInternalError.format(
LunaVLError.fromSDKError(error).detail), errors)
descriptorBatch.scores = scores
return descriptorBatch, aggregatedDescriptor