SDK Loop¶
SDK Task¶
A task that determinants a set of biometric features estimated from input images or samples. All features are divided into 3 groups:
face detection features: face detection, landmarks, eyes, gaze direction, ags.
samples(warp) features: emotions, mouth state, warp quality.
face attributes: age, gender, ethnicity, descriptor.
Face detection features are estimated on images, other features are estimated on warped images.
Task pipeline¶
SDK loop is a structure for task processing. There exist 3 stages of task processing:
detector stage
warp estimator stage
extractor stage
All features in estimated in the corresponding stage. Loop determines a pipeline for every task which is an order list of stages. Communication between stage handlers is done through queues. Loop sends a task to a queue which is corresponding first task pipeline stage. The task is put to a queue which corresponding next stage in the pipeline after stage processing. A task is put to a common results queue if it is failed or done. Loop gets a task from the results queue and transfers to a task customer.
Module realize Handlers task. This task logic based on luna-handlers service logic.
- class luna_handlers.sdk.sdk_loop.task.HandlersTask(data, params=None, **kwargs)[source]¶
Handler Task, correlate with luna-handlers APi logic (multiface policy, filtration logic, …)
- async estimateAggregatedFaceDescriptor(aggregatedSample, version, gcFilter)[source]¶
Estimate aggregated face descriptor :param aggregatedSample: aggregated face sample :param version: descriptor version :param gcFilter: optional gc filter from task
- Returns:
if sample is not filtered (after filtration) false: if sample is filtered, all source samples marks as filtered
- Return type:
true
- Return type:
bool
- estimateAggregatedLivenessV1(faceSamples, aggregatedSample, qualityThreshold, scoreThreshold, livenessFilter)[source]¶
Estimate aggregated livenessV1.
- Parameters:
faceSamples – source face samples
aggregatedSample – aggregated sample
qualityThreshold – quality threshold
scoreThreshold – score threshold
livenessFilter – liveness filter
- Returns:
if sample is not filtered (after filtration) false: if sample is filtered, all source samples marks as filtered
- Return type:
true
- Return type:
bool
- async estimateBodyAttributesFromImage(bodySamples, estimateAllRequiredTargets=False)[source]¶
Run body attributes estimations on samples which generated from detection. .. attribute:: bodySamples
face samples with detections
- Returns:
same body samples with estimation inside
- Return type:
list
[BodySample
]
- async estimateBodyWarpAttributes(bodySamplesOnImage, estimateAllRequiredTargets=False)[source]¶
Estimate body detection attributes
- Parameters:
bodySamplesOnImage – body samples with detections
- async estimateDetectionAttributes(image, estimateAllRequiredTargets=False)[source]¶
Estimate detection attributes All without face descriptotr, basic attributes, /detector of luna-handlers logic
- async estimateFaceAttributesFromImage(faceSamples, estimateAllRequiredTargets=False)[source]¶
Run face attributes estimations on samples which generated from detection. .. attribute:: faceSamples
face samples with detections
- Returns:
same face samples with estimation inside
- Return type:
list
[FaceSample
]
- async estimateFaceDetectionAttributes(faceSamplesOnImage)[source]¶
Estimate face detection attributes
- Parameters:
faceSamplesOnImage – face samples with detections
- async estimateFaceWarpAttributes(faceSamples, estimateAllRequiredTargets=False)[source]¶
Parallel estimate face warp attributes
- luna_handlers.sdk.sdk_loop.task.generateMultiEntitiesError(samples, image, error)[source]¶
Generete error for several detections on image
- Return type:
LoopError
Module contains base task
- class luna_handlers.sdk.sdk_loop.tasks.task.BaseTask(data, params=None, **kwargs)[source]¶
A base task class.
The task is an entity for a customization estimation business logic. Task determines what and how will be estimated. For example, user can realize task class with own a multiface policy processing logic, filtration, some else.
- monitoringPoint¶
execution task monitoring point (execution time, error, some else)
- Type:
- executionTime¶
task execution time
- Type:
float
- content¶
task content
- Type:
- result¶
task result
- Type:
- taskId¶
pseudo unique id for logging
- Type:
task id
- async classmethod close(closeEngine=True)[source]¶
Grace full shut down. :param closeEngine: close external engine or not
- filterByHeadPosePitch(sample)[source]¶
Filter sample by pitch angle logic.
- Parameters:
sample – face sample
- Returns:
None if filter is None or head pose not estimated (body sample) otherwise filtration result
- Return type:
Optional
[FiltrationResult
,None
]
- filterByHeadPoseRoll(sample)[source]¶
Filter sample by roll angle logic.
- Parameters:
sample – face sample
- Returns:
None if filter is None or head pose not estimated (body sample) otherwise filtration result
- Return type:
Optional
[FiltrationResult
,None
]
- filterByHeadPoseYaw(sample)[source]¶
Filter sample by yaw angle logic.
- Parameters:
sample – face sample
- Returns:
None if filter is None or sample not estimated (body sample) otherwise filtration result
- Return type:
Optional
[FiltrationResult
,None
]
- filterFaceGS(sample)[source]¶
Filter sample by garbage score logic.
- Parameters:
sample – face sample
- Returns:
None if filter is None or head pose not estimated (body sample) otherwise filtration result
- Return type:
Optional
[FiltrationResult
,None
]
- filterSampleByLiveness(sample)[source]¶
Filter sample by livenessV1 logic.
- Parameters:
sample – face sample
- Returns:
None if filter is None or sample not estimated (body sample) otherwise filtration result
- Return type:
Optional
[FiltrationResult
,None
]
- filterSampleByMask(sample)[source]¶
Filter sample by mask logic :param sample: face sample
- Returns:
None if filter is None or mask not estimated (body sample) otherwise filtration result
- Return type:
Optional
[FiltrationResult
,None
]
- classmethod initialize(engine, monitoringPointClass=None, **kwargs)[source]¶
Task initialization. Setup globals: engine, monitoring point class
- Parameters:
engine – engine
monitoringPointClass – user defined monitoring class (for custom fields and tags)
**kwargs – some args for Task class realization
- monitoringPointClass¶
alias of
TaskMonitoringPoint
- class luna_handlers.sdk.sdk_loop.tasks.task.LivenessV1Params(qualityThreshold=0.5, scoreThreshold=None)[source]¶
Liveness v1 estimation params
- class luna_handlers.sdk.sdk_loop.tasks.task.TaskContent(images, params)[source]¶
Task content.
- params¶
task params
- images¶
source images for estimations
- class luna_handlers.sdk.sdk_loop.tasks.task.TaskEstimationParams(livenessv1=_Nothing.NOTHING, faceDescriptorVersion=None, bodyDescriptorVersion=None)[source]¶
Task estimation params
- class luna_handlers.sdk.sdk_loop.tasks.task.TaskMonitoringPoint(eventTime=None)[source]¶
Task monitoring point .. attribute:: executionTime
task execution time
- type:
float
- eventTime¶
start task timestamp
- Type:
float
- imageCount¶
task image count
- Type:
int
- class luna_handlers.sdk.sdk_loop.tasks.task.TaskParams(targets=None, filters=None, estimatorsParams=None, multifacePolicy=MultifacePolicy.allowed, useExifInfo=False, autoRotation=False, aggregate=False)[source]¶
A container for user defined task parameters. It is one place for all user parameters except images. These parameters are constant across a task group.
We suppose that user defines a set of interest parameters and create task with these parameters. User can to instance this structure and does not calculate to the requiredEstimations on each request.
- targets¶
user defined targets
- Type:
set[LoopEstimations]
- filters¶
user defined filters
- Type:
Filters
- useExifInfo¶
use exif or not for correct loading image
- Type:
bool
- estimatorsParams¶
params for estimations (thresholds and etc)
- Type:
- autoRotation¶
try detect rotated images or not
- Type:
bool
- aggregate¶
estimate or not aggregated attributes
- Type:
bool
- multifacePolicy¶
multiface policy
- requiredEstimations¶
predicted required estimations for correct processing all params
- Type:
set[LoopEstimations]
- class luna_handlers.sdk.sdk_loop.tasks.task.TaskResult(images, aggregatedSample)[source]¶
Task result container .. attribute:: images
loaded images, None if image load is failed by unexpected reason
- type:
Optional[list[Image]]
- aggregatedSample¶
aggregated sample
- Type:
Optional[AggregatedSample]
- error¶
processing task error
- Type:
Optional[LoopError]
- class luna_handlers.sdk.sdk_loop.tasks.task.TaskState(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Task state enum
- luna_handlers.sdk.sdk_loop.tasks.task.getNotFilteredSamples(images)[source]¶
Helper, get non filtered face and bodies samples from images
- Return type:
tuple
[list
[FaceSample
],list
[BodySample
]]
- luna_handlers.sdk.sdk_loop.tasks.task.predictRequiredEstimations(targets, filters=None, needExifInfo=False)[source]¶
Predict required estimations for correct filters work, autorotation and etc :param targets: user targets :param filters: user filters :param needExifInfo: need or not exif
- Returns:
set all estimations.
- Return type:
set
[LoopEstimations
]
Module contains human sample models
- class luna_handlers.sdk.sdk_loop.models.sample.AggregatedSample(face=_Nothing.NOTHING, body=_Nothing.NOTHING)[source]¶
An Aggregated human sample - union of aggregated face sample and aggregated body samples
- class luna_handlers.sdk.sdk_loop.models.sample.Sample(face=None, body=None)[source]¶
A human sample - union of face sample and body samples
Module contains face sample models
- class luna_handlers.sdk.sdk_loop.models.face_sample.AggregateAttributesCounter(score, index, count=1, operator=ScoreOperator.concat)[source]¶
Container class for counting aggregate attributes.
- class luna_handlers.sdk.sdk_loop.models.face_sample.AggregatedEmotions(emotions)[source]¶
Aggregated emotions
- predominateEmotion¶
predominant aggregated emotion
- Type:
Emotion
- anger¶
aggregated anger score
- Type:
float
- disgust¶
aggregated disgust score
- Type:
float
- fear¶
aggregated fear score
- Type:
float
- happiness¶
aggregated happiness score
- Type:
float
- neutral¶
aggregated neutral score
- Type:
float
- sadness¶
aggregated sadness score
- Type:
float
- surprise¶
aggregated surprise score
- Type:
float
- class luna_handlers.sdk.sdk_loop.models.face_sample.AggregatedFaceSample(samples=_Nothing.NOTHING, liveness=None, descriptor=None, basicAttributes=None, filters=_Nothing.NOTHING, mask=None, emotions=None)[source]¶
Aggregated face sample contaiter
- class luna_handlers.sdk.sdk_loop.models.face_sample.AggregatedMask(masks)[source]¶
Aggregated mask
- predominateMask¶
predominant aggregated mask
- Type:
MaskState
- medicalMask¶
aggregated mask score
- Type:
float
- occluded¶
aggregated occluded score
- Type:
float
- missing¶
aggregated missing score
- Type:
float
- class luna_handlers.sdk.sdk_loop.models.face_sample.FaceSample(detection=None, eyes=None, gaze=None, emotions=None, mouthState=None, basicAttributes=None, warpQuality=None, mask=None, glasses=None, headPose=None, transformedLandmarks5=None, livenessV1=None, warp=None, descriptor=None, headwear=None, fisheye=None, redEyes=None, eyebrowExpression=None, naturalLight=None, detectionBackground=None, imageColorType=None, filters=_Nothing.NOTHING, dynamicRange=None, portraitStyle=None)[source]¶
Face attributes containers
- class luna_handlers.sdk.sdk_loop.models.face_sample.ScoreOperator(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Score operator enum.
Module contains body sample models
- class luna_handlers.sdk.sdk_loop.models.body_sample.AggregatedBodySample(samples=_Nothing.NOTHING, descriptor=None, attributes=None, filters=_Nothing.NOTHING)[source]¶
Aggregated body sample contaiter
- class luna_handlers.sdk.sdk_loop.models.body_sample.BodySample(detection=None, descriptor=None, warp=None, attributes=None, filters=_Nothing.NOTHING)[source]¶
Body attributes containers
Module contains models for work with images
- class luna_handlers.sdk.sdk_loop.models.image.Image(origin, sdkImage=None, error=None, exif=None, faceBoxes=None, bodyBoxes=None, orientation=None, peopleCount=None, samples=_Nothing.NOTHING, pillowImage=None, ndarray=None, exifOrientation=None, rawImage=None)[source]¶
A structure for work with image.
The image contains all temporary entities for estimation targets and provides conversation between them. The image contains estimation results also.
- classmethod exifTranspose(image)[source]¶
Based on genuine function from pillow https://pillow.readthedocs.io/en/latest/_modules/PIL/ImageOps.html#exif_transpose
The only difference is that manipulation is being done inplace, allowing us to leverage internal caching of exif data.
NOTE: Transposed image might have incorrect values in particular tags. For example, orientation, length, width.
- Return type:
- extractExif()[source]¶
Extract exif from image.
Set an error if load image is failed. If image contains an error will be return empty dict
- Returns:
extracted exif tags
- Return type:
dict
- getAsNumpy()[source]¶
Convert image to numpy array.
If occured error function will set up in the image.
- Return type:
Optional
[ndarray
,None
]
- getAsPillow()[source]¶
Get image data as pillow object.
- Returns:
PIL image
- Return type:
Optional
[Image
,None
]
- property rawImage: bytes | bytearray | Image | ndarray¶
Get data for load
- Return type:
Union
[bytes
,bytearray
,Image
,ndarray
]
- class luna_handlers.sdk.sdk_loop.models.image.ImageType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Image type enum
- class luna_handlers.sdk.sdk_loop.models.image.InputImage(body, imageType, filename='', faceBoxes=None, bodyBoxes=None, pillowImage=None)[source]¶
Container for input image
- luna_handlers.sdk.sdk_loop.models.image.getAsPillow(data)[source]¶
Get image data as pillow object.
- Returns:
PIL image if image was loaded success otherwise error
- Return type:
Union
[Image
,LoopError
]
- luna_handlers.sdk.sdk_loop.models.image.getTransposeParam(method)[source]¶
Get transpose param by exif orientation :param method: method from a image
- Returns:
tuple< rotation, neeed or not to flip the image>
- Return type:
tuple
[RotationAngle
,bool
]
Module contains monitoring point protocol
- class luna_handlers.sdk.sdk_loop.monitoring_utils.monitoring.MonitoringPoint(*args, **kwargs)[source]¶
Protocol for a monitoring point.
Module contains base class for estimation monitoring points
- class luna_handlers.sdk.sdk_loop.monitoring_utils.estimation_monitoring.EstimationPoint(eventTime=None, executionTime=0, batchSize=0)[source]¶
Estimation monitoring point, implementation Monitoring point protocol
- executionTime¶
execution estimations time (batch)
- Type:
float
- batchSize¶
batch size for estimations
- Type:
int
- eventTime¶
event time, timestamp
- Type:
float
Module contains global monitoring pont storages
- class luna_handlers.sdk.sdk_loop.monitoring_utils.storages.Storage(name, enable=True)[source]¶
Container for monitoring points
- name¶
storage name
- enable¶
enable or not monitoring point storing
- _values¶
map - keys - series name, values - list monitoring points
- popValues()[source]¶
pop points from the storage
- Return type:
ContextManager
[dict
[str
,list
[MonitoringPoint
]]]
Helpers enums.
- class luna_handlers.sdk.sdk_loop.enums.Estimators(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Available loop estimators
- basicAttributes = 'basicAttributes'¶
basic attributes (age, gender, ethnicity)
- bodyAttributes = 'bodyAttributes'¶
body attributes
- bodyDescriptor = 'bodyDescriptor'¶
body descriptors
- bodyDetection = 'bodyDetection'¶
body detection
- bodyWarp = 'bodyWarp'¶
body warp
- dynamicRange = 'dynamicRange'¶
dynamic range
- emotions = 'emotions'¶
emotions
- eyebrowExpression = 'eyebrow_expression'¶
eyebrow expression
- eyes = 'eyes'¶
eyes state, points
- faceDescriptor = 'faceDescriptor'¶
face descriptor
- faceDetection = 'faceDetection'¶
face detection
- faceDetectionBackground = 'face_detection_background'¶
face detection background
- faceLandmarks5 = 'face_landmarks5'¶
face landmarks5
- faceLandmarks68 = 'face_landmarks68'¶
face landmarks68
- faceNaturalLight = 'face_natural_light'¶
face natural light
- faceWarp = 'faceWarp'¶
face warp
- fisheye = 'fisheye'¶
fisheye
- gazeDirection = 'gaze'¶
gaze direction
- glasses = 'glasses'¶
glasses
- headPose = 'headPose'¶
head pose
- headwear = 'headwear'¶
headwear
- humanDetection = 'humaDetection'¶
human detection (face and body)
- imageColorType = 'image_color_type'¶
image color type
- imageOrientation = 'imageOrientation'¶
image orientation
- livenessV1 = 'livenessV1'¶
livenessv1
- mask = 'mask'¶
mask
- mouthState = 'mouthState'¶
mouth state
- peopleCount = 'peopleCount'¶
people count
- quality = 'warpQuality'¶
warp quality
- redEyes = 'red_eyes'¶
red-eyes
- class luna_handlers.sdk.sdk_loop.enums.LoopEstimations(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
SDK Loop estimations enum. Name - estimation target.
- basicAttributes = 'basic_attributes'¶
basic attributes (age, gender, ethnicity)
- bodyAttributes = 'body_attributes'¶
body attributes
- bodyDescriptor = 'body_descriptor'¶
body descriptor
- bodyDetection = 'body_detection'¶
body detection
- bodyLandmarks17 = 'body_landmarks17'¶
body landmarks
- bodyWarp = 'body_warp'¶
body warp
- dynamicRange = 'dynamic_range'¶
dynamic range
- emotions = 'emotions'¶
emotions
- eyebrowExpression = 'eyebrow_expression'¶
eyebrow expression
- eyes = 'eyes'¶
eyes state, points
- faceDescriptor = 'face_descriptor'¶
face descriptor
- faceDetection = 'face_detection'¶
face detection
- faceDetectionBackground = 'face_detection_background'¶
face detection background
- faceLandmarks5 = 'face_landmarks5'¶
face landmarks68
- faceLandmarks68 = 'face_landmarks68'¶
face landmarks68
- faceNaturalLight = 'face_natural_light'¶
face natural light
- faceWarp = 'face_warp'¶
face warp
- faceWarpQuality = 'face_warp_quality'¶
face warp quality
- fisheye = 'fisheye'¶
fisheye
- gaze = 'gaze'¶
gaze direction
- glasses = 'glasses'¶
glasses
- headPose = 'head_pose'¶
head pose
- headwear = 'headwear'¶
headwear
- imageColorType = 'image_color_type'¶
image color type
- imageOrientation = 'image_orientation'¶
image orientation
- livenessV1 = 'livenessV1'¶
livenessV1
- mask = 'mask'¶
mask
- mouthAttributes = 'mouth_state'¶
mouth state
- peopleCount = 'people_count'¶
people count
- redEyes = 'red_eyes'¶
red-eyes
- class luna_handlers.sdk.sdk_loop.enums.MultifacePolicy(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Multiple face detection policy enum.
- allowed = 1¶
multiple face detection allowed
- getBest = 2¶
get best detection from the image
- notAllowed = 0¶
multiple face detection not allowed
Module contains face detector estimator realization Estimation landmakrks68 is very slow therefore we logical divide detection with and without landmarks17
- class luna_handlers.sdk.sdk_loop.estimators.face_estimators.face_detector.FaceDetMonitoringPoint(imageWidth, imageHeight, executionTime=0, batchSize=0)[source]¶
Face detector monitoring point. We get first (random) image for monitoring an image size and a body detection size.
- detectionCount¶
detection count on all images from batch
- imageWidth¶
first image width from batch
- imageHeight¶
first image height from batch
- detectionWidth¶
first body detection width on first image from batch
- detectionHeight¶
first body detection height on first image from batch
- class luna_handlers.sdk.sdk_loop.estimators.face_estimators.face_detector.FaceDetV3Settings(redetectFaceTargetSize=None, minFaceSize=None, redetectTensorSize=None, redetectScoreThreshold=None, scoreThreshold=None)[source]¶
Face detector version 3 settings
- class luna_handlers.sdk.sdk_loop.estimators.face_estimators.face_detector.FaceDetectionEstimationBroker(optimalBatchSize, estimatorSettings, workerClass, workerCount=1, queueName='', **kwargs)[source]¶
Face detection without landmarks68 broker
- class luna_handlers.sdk.sdk_loop.estimators.face_estimators.face_detector.FaceDetectionEstimationBrokerMixin[source]¶
Face detector creation mixin
- class luna_handlers.sdk.sdk_loop.estimators.face_estimators.face_detector.FaceDetectionQueueEstimator(taskQueue, estimator, maxBatchSize, settings, estimationName='')[source]¶
Face detection estimator without landmarks68 estimator
- class luna_handlers.sdk.sdk_loop.estimators.face_estimators.face_detector.FaceDetectorSettings(launchOptions=<factory>, detectorSettings=<factory>, maxFacesCount=128)[source]¶
Face detector settings
- class luna_handlers.sdk.sdk_loop.estimators.face_estimators.face_detector.FaceReDetMonitoringPointBase(bboxWidth, bboxHeight, imageWidth, imageHeight, executionTime=0, batchSize=0)[source]¶
Face detector monitoring point. We get first (random) image for monitoring an image size, bbox and a face detection size.
- bboxWidth¶
first bbox width on first image from batch
- bboxHeight¶
first bbox height on first image from batch
- class luna_handlers.sdk.sdk_loop.estimators.face_estimators.face_detector.FaceReDetectionEstimationBroker(optimalBatchSize, estimatorSettings, workerClass, workerCount=1, queueName='', **kwargs)[source]¶
Face ReDetection with landmarks68 broker
- class luna_handlers.sdk.sdk_loop.estimators.face_estimators.face_detector.FaceReDetectionQueueEstimator(taskQueue, estimator, maxBatchSize, settings, estimationName='')[source]¶
Body ReDetection estimator without landmarks65 estimation
Module contains face warp estimator realization
- class luna_handlers.sdk.sdk_loop.estimators.face_estimators.face_warp_estimator.FaceWarpEstimationBroker(optimalBatchSize, estimatorSettings, workerClass, workerCount=1, queueName='', **kwargs)[source]¶
Face warp broker
- class luna_handlers.sdk.sdk_loop.estimators.face_estimators.face_warp_estimator.FaceWarpFaceWarpQueueEstimator(taskQueue, estimator, maxBatchSize, settings, estimationName='')[source]¶
Face warp estimator
- class luna_handlers.sdk.sdk_loop.estimators.face_estimators.face_warp_estimator.FaceWarpMonitoringPoint(eventTime=None, executionTime=0, batchSize=0)[source]¶
Face warp estimation monitoring
- class luna_handlers.sdk.sdk_loop.estimators.face_estimators.face_warp_estimator.FaceWarperSettings(launchOptions=<factory>)[source]¶
Face warp estimator settings
Module contains body detector estimator realization. Estimation landmakrks17 is very slow therefore we logical divide detection with and without landmarks17
- class luna_handlers.sdk.sdk_loop.estimators.body_estimators.body_detector.BodyBrokerDetectorMixin[source]¶
Body detection base broker
- class luna_handlers.sdk.sdk_loop.estimators.body_estimators.body_detector.BodyDetMonitoringPoint(imageWidth, imageHeight, estimate17Landmarks, executionTime=0, batchSize=0)[source]¶
Body detector monitoring point. We get first (random) image for monitoring an image size and a body detection size.
- detectionCount¶
detection count on all images from batch
- imageWidth¶
first image width from batch
- imageHeight¶
first image height from batch
- detectionWidth¶
first body detection width on first image from batch
- detectionHeight¶
first body detection height on first image from batch
- estimate17Landmarks¶
estimate or not 17Landmarks
- class luna_handlers.sdk.sdk_loop.estimators.body_estimators.body_detector.BodyDetection17EstimationBroker(optimalBatchSize, estimatorSettings, workerClass, workerCount=1, queueName='', **kwargs)[source]¶
Body detection with landmarks17 broker
- class luna_handlers.sdk.sdk_loop.estimators.body_estimators.body_detector.BodyDetection17QueueEstimator(taskQueue, estimator, maxBatchSize, settings, estimationName='')[source]¶
Body detection estimator with landmarks17 estimation
- class luna_handlers.sdk.sdk_loop.estimators.body_estimators.body_detector.BodyDetectionEstimationBroker(optimalBatchSize, estimatorSettings, workerClass, workerCount=1, queueName='', **kwargs)[source]¶
Body detection without landmarks17 broker
- class luna_handlers.sdk.sdk_loop.estimators.body_estimators.body_detector.BodyDetectionQueueEstimator(taskQueue, estimator, maxBatchSize, settings, estimationName='')[source]¶
Body detection estimator without landmarks17 estimator
- class luna_handlers.sdk.sdk_loop.estimators.body_estimators.body_detector.BodyDetectorSettings(launchOptions=<factory>, maxBodiesCount=128)[source]¶
Body detector settings
- class luna_handlers.sdk.sdk_loop.estimators.body_estimators.body_detector.BodyReDetMonitoringPointBase(bboxWidth, bboxHeight, imageWidth, imageHeight, estimate17Landmarks, executionTime=0, batchSize=0)[source]¶
Body detector monitoring point. We get first (random) image for monitoring an image size, bbox and a body detection size.
- bboxWidth¶
first bbox width on first image from batch
- bboxHeight¶
first bbox height on first image from batch
- class luna_handlers.sdk.sdk_loop.estimators.body_estimators.body_detector.BodyReDetection17EstimationBroker(optimalBatchSize, estimatorSettings, workerClass, workerCount=1, queueName='', **kwargs)[source]¶
Body ReDetection with landmarks17 broker
- class luna_handlers.sdk.sdk_loop.estimators.body_estimators.body_detector.BodyReDetection17QueueEstimator(taskQueue, estimator, maxBatchSize, settings, estimationName='')[source]¶
Body ReDetection estimator with landmarks17 estimation
- class luna_handlers.sdk.sdk_loop.estimators.body_estimators.body_detector.BodyReDetectionEstimationBroker(optimalBatchSize, estimatorSettings, workerClass, workerCount=1, queueName='', **kwargs)[source]¶
Body ReDetection without landmarks17 broker
- class luna_handlers.sdk.sdk_loop.estimators.body_estimators.body_detector.BodyReDetectionQueueEstimator(taskQueue, estimator, maxBatchSize, settings, estimationName='')[source]¶
Body ReDetection estimator without landmarks17 estimator
Module contains human detector estimator realization.
- class luna_handlers.sdk.sdk_loop.estimators.others_estimators.human_detector.HumanDetMonitoringPoint(imageWidth, imageHeight, executionTime=0, batchSize=0)[source]¶
Human detector monitoring point. We get first (random) image and detection for monitoring an image size and detections size.
- detectionCount¶
human detection count on all images from batch
- faceDetectionCount¶
face detection count on all images from batch
- bodyDetectionCount¶
face detection count on all images from batch
- imageWidth¶
first image width from batch
- imageHeight¶
first image height from batch
- bodyDetectionWidth¶
first body detection width on first image from batch
- bodyDetectionHeight¶
first body detection height on first image from batch
- faceDetectionWidth¶
first face detection width on first image from batch
- faceDetectionHeight¶
first face detection height on first image from batch
- class luna_handlers.sdk.sdk_loop.estimators.others_estimators.human_detector.HumanDetectionEstimationBroker(optimalBatchSize, estimatorSettings, workerClass, workerCount=1, queueName='', **kwargs)[source]¶
Human detection base broker
- class luna_handlers.sdk.sdk_loop.estimators.others_estimators.human_detector.HumanDetectionQueueEstimator(taskQueue, estimator, maxBatchSize, settings, estimationName='')[source]¶
Body detection estimator without landmarks17 estimator
- class luna_handlers.sdk.sdk_loop.estimators.others_estimators.human_detector.HumanDetectorSettings(launchOptions=<factory>, minFaceSize=None, faceThreshold=None, bodyThreshold=None, associationThreshold=None)[source]¶
Human detector settings