lib.sedna.service.multi_edge_inference

Subpackages

Package Contents

Classes

Detection

Endpoint to trigger the Object Tracking component

FE

Endpoint to trigger the Feature Extraction

ReID_Endpoint

Endpoint to trigger the ReID

DetectionServer

REST api server for object detection component

FEServer

rest api server for feature extraction

ReIDServer

REST api server for reid
class lib.sedna.service.multi_edge_inference.Detection(service_name, version='', ip='127.0.0.1', port='8080', protocol='http')

Endpoint to trigger the Object Tracking component

check_server_status(self)
transmit(self, data, **kwargs)

Transfer enriched tracking object to video analytics job

update_service(self, data, **kwargs)
class lib.sedna.service.multi_edge_inference.FE(service_name, version='', ip='127.0.0.1', port='8080', protocol='http')

Endpoint to trigger the Feature Extraction

check_server_status(self)
transmit(self, data, **kwargs)

Transfer feature vector to FE worker

get_target_features(self, data, **kwargs)

Send target images to FE service and receive back the ReID features

update_service(self, data, **kwargs)
class lib.sedna.service.multi_edge_inference.ReID_Endpoint(service_name, version='', ip='127.0.0.1', port='8080', protocol='http')

Endpoint to trigger the ReID

check_server_status(self)
transmit(self, data: sedna.core.multi_edge_inference.data_classes.DetTrackResult, **kwargs)

Transfer feature vector to ReID worker

class lib.sedna.service.multi_edge_inference.DetectionServer(model, service_name, ip: str = '127.0.0.1', port: int = 8080, max_buffer_size: int = 1004857600, workers: int = 1)

Bases: sedna.service.server.base.BaseServer

REST api server for object detection component

start(self)
status(self, request: fastapi.Request)
async video_analytics(self, request: fastapi.Request)
async update_service(self, request: fastapi.Request)
class lib.sedna.service.multi_edge_inference.FEServer(model, service_name, ip: str = '127.0.0.1', port: int = 8080, max_buffer_size: int = 1004857600, workers: int = 1)

Bases: sedna.service.server.base.BaseServer

rest api server for feature extraction

start(self)
status(self, request: fastapi.Request)
async feature_extraction(self, request: fastapi.Request)
async get_target_features(self, request: fastapi.Request)
async update_service(self, request: fastapi.Request)
class lib.sedna.service.multi_edge_inference.ReIDServer(model, service_name, ip: str = '127.0.0.1', port: int = 8080, max_buffer_size: int = 104857600, workers: int = 1)

Bases: sedna.service.server.base.BaseServer

REST api server for reid

start(self)
status(self, request: fastapi.Request)
async reid(self, request: fastapi.Request)