# Copyright 2021 The KubeEdge Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List
from sedna.core.multi_edge_inference.components import BaseService
from sedna.core.multi_edge_inference.plugins \
import PLUGIN, PluggableModel, PluggableNetworkService
from sedna.core.multi_edge_inference.plugins.registered \
import Feature_Extraction, VideoAnalytics_I
[docs]class FEService(BaseService):
"""
In MultiEdgeInference, the Feature Extraction component
is deployed in the edge or the cloud and it used to extract
ReID features from frames received by the ObjectDetector component
and send back to it the enriched data using Kafka or REST API.
Parameters
----------
consumer_topics : List
A list of Kafka topics used to communicate with the Object
Detector service (to receive data from it).
This is accessed only if the Kafka backend is in use.
producer_topics : List
A list of Kafka topics used to communicate with the Object
Detector service (to send data to it).
This is accessed only if the Kafka backend is in use.
plugins : List
A list of PluggableNetworkService. It can be left empty
as the FeatureExtraction service is already preconfigured
to connect to the correct network services.
models : List
A list of PluggableModel. By passing a specific instance
of the model, it is possible to customize the FeatureExtraction
component to, for example, extract differently the objects
features.
timeout: int
It sets a timeout condition to terminate the main fetch loop
after the specified amount of seconds has passed since we
received the last frame.
asynchronous: bool
If True, the AI processing will be decoupled from the data
acquisition step. If False, the processing will be sequential.
In general, set it to True when ingesting a stream (e.g., RTSP)
and to False when reading from disk (e.g., a video file).
Examples
--------
model = FeatureExtractionAI() # A class implementing the PluggableModel
abstract class (example pedestrian_tracking/feature_extraction/worker.py)
fe_service = FEService(models=[model], asynchronous=False)
Notes
-----
For the parameters described above, only 'models' has to be defined, while
for others the default value will work in most cases.
"""
def __init__(
self,
consumer_topics=["object_detection"],
producer_topics=["enriched_object"],
plugins: List[PluggableNetworkService] = [],
models: List[PluggableModel] = [],
timeout=10,
asynchronous=False
):
merged_plugins = \
[VideoAnalytics_I(), Feature_Extraction(wrapper=self)] + plugins
super().__init__(
consumer_topics,
producer_topics,
merged_plugins,
models,
timeout,
asynchronous)
[docs] def process_data(self, ai, data, **kwargs):
for ai in self.models:
result = ai.inference(data)
if result != []:
if self.kafka_enabled:
for d in result:
self.producer.write_result(d)
else:
plg = self.get_plugin(PLUGIN.VIDEO_ANALYTICS_I)
plg.plugin_api.transmit(result, **kwargs)
[docs] def update_operational_mode(self, status):
pass
[docs] def get_target_features(self, ldata):
# TODO: Fix this workaround, we need a function to select a model
# based on its name
fe_ai = self.models[0]
return fe_ai.get_target_features(ldata)