Configuration

SmartFace Embedded Stream Processor can be configured via settings.yaml file that comes as input of sfe_stream_processor executable.

./sfe_stream_processor /path/to/settings.yaml

The configuration of SFE Stream Processor includes settings of:

SFE Stream Processor configuration can be changed directly by editing the input settings.yaml file or using a simple web UI available by running sfe_settings_server executable. SFE Stream Processor automatically reloads the configuration when there is a change detected in settings.yaml.

An example settings.yaml is documented here

Connection

MQTT (MQ Telemetry Transport) protocol is used for communication between SFE Stream Processor running on edge device (camera or AI box) and SmartFace Platform running on server/cloud.

See below an example configuration:

connection:
  broker_address: 127.0.0.1
  broker_port: 1883
  client_id: 38-lilin
  topic: edge-stream
  keep_alive: 60
  timeout: 10
  tls_enable: false
  username: mqtt
  password: mqtt

Please find a detailed description of connection parameters below. All connection parameters are related to MQTT.

broker_address

Defines the IP address of a machine where the MQTT broker is running.

  • Use the IP address of a server/cloud where the SmartFace platform is running if you are using SmartFace for processing the edge stream.
  • Use 127.0.0.1 if you want to process the MQTT messages locally. You will also need an MQTT broker running on the device.

You can also use a hostname as broker_address if the DNS is enabled on your edge device.

broker_port

TCP/IP port 1883 is reserved with IANA for use with MQTT. TCP/IP port 8883 is also registered, for using MQTT over SSL. Use this port when TLS is enabled.

client_id

Client ID is a string that identifies the MQTT client connected to an MQTT broker. It should be unique for each camera. If you are processing multiple camera streams on one device, it should be unique for each camera stream. When using the SmartFace Platform for processing the MQTT message stream, the same client ID is used during the registration of a new edge stream.

topic

MQTT topic is a UTF-8 string that the broker uses to filter messages for each connected client.

Topic defines the root topic. The topic for a specific camera is then a combination of “topic” and “client_id” - <connection.topic>/<connection.client_id>, for example edge-stream/38-lilin.

For integration with the SmartFace Platform, the root topic should be set to edge-stream. This means the topic for a specific camera is edge-stream/<client_id>

keep_alive

MQTT connection keepalive represents the maximum time in seconds allowed to elapse between MQTT protocol packets sent by the client.

timeout

MQTT connection timeout in seconds. It limits how long the client will wait when trying to open a connection to an MQTT server before timing out.

tls_enable

You can enable TLS to encrypt communication between MQTT clients. The MQTT communication is not encrypted by default and it is highly recommended to use TLS, especially in public networks. TLS is required in case of cloud deployment of the SmartFace Platform.

username and password

MQTT supports username/password authentication to restrict access to an MQTT broker.

The MQTT broker used in the SmartFace Platform requires the following username and password:

  username: mqtt
  password: mqtt

You can set your username and password in the SmartFace Platform RabbitMQ interface running on port 15672, for example:

smartface-platform:15672

Face Detection

This section describes the configuration of face detection.

See below an example configuration:

face_detection:
  detection_threshold: 0.01
  landmarks_threshold: 0.5
  mask_status_threshold: 0.5
  max_detections: 10
  order_by: DetectionConfidence

detection_threshold

The threshold is used to filter low-confidence detections from processing. Detections below this threshold are not considered in tracking. The valid range is <0.0,1.0> The recommended value is 0.01.

landmarks_threshold

Face landmarks confidence threshold. Landmarks with confidence below the threshold are not annotated using the frame_output solver. The valid range is <0.0,1.0> The recommended value is 0.5.

mask_status_threshold

Face mask confidence threshold. Faces with mask confidence above the threshold are considered to be wearing a mask. Faces with mask confidence below the threshold are considered to be not wearing a mask. The valid range is <0.0,1.0> The recommended value is 0.5.

max_detections

Defines the maximum number of faces to be detected, tracked and processed per frame.

order_by

Detected faces can be ordered by FaceSize or DetectionConfidence.

Tracking

This section describes the configuration of tracking.

See below an example configuration:

tracking:
  threshold: 0.06
  stability: 0.85
  max_frames_lost: 25

threshold

Minimal detection confidence threshold for tracked faces. Faces with detection confidence below this threshold are filtered out by tracking and not propagated to landmarks detection and template extraction. The valid range is <0.0,1.0> The recommended value is 0.1.

stability

Stability is used to associate the tracklets with detections using a Kalman filter. Low stability may cause tracking to jump between detections. High Stability may cause more tracklets to be created. The valid range is <0.0,1.0> The recommended value is 0.85.

max_frames_lost

Defines the maximum number of frames to keep lost faces in tracking. max_frames_lost: 25

Face Template Extraction

This section describes the configuration of template extraction.

See below an example configuration:

face_extraction:
  enable: true

enable

Enable face template extraction. The face template is used for identification.

If disabled (false), extraction is not performed, and face templates are not included in the FrameData. It means the template extraction has to be performed on the server to enable the identification.

Face Passive Liveness

This section describes the configuration of passive liveness evaluation.

See below an example configuration:

face_liveness_passive:
  enable: true
  strategy: OnEachExtractedFace
  conditions:
    - parameter: FaceSize
      lower_threshold: 30.0
    - parameter: FaceRelativeArea
      lower_threshold: 0.009
    - parameter: FaceRelativeAreaInImage
      lower_threshold: 0.9
    - parameter: YawAngle
      lower_threshold: -20.0
      upper_threshold: 20.0
    - parameter: PitchAngle
      lower_threshold: -20.0
      upper_threshold: 20.0
    - parameter: Sharpness
      lower_threshold: 0.6

enable

Enable face passive liveness evaluation.

If disabled (false), liveness evaluation is not performed, and liveness score is not included in the FrameData.

strategy

Defines strategy for liveness detection:

  • OnEachExtractedFace - If enabled, liveness is computed for each extracted face
  • OnEachIdentifiedFace - If enabled, liveness is computed for each recognized/identified face

conditions

Conditions defining minimal quality of the input face image for reliable liveness/spoof detections. The recommended thresholds are documented at Face Liveness Detection If the conditions are not met liveness score is not computed.

Face Identification

This section describes the configuration of face identification.

See below an example configuration:

face_identification:
  enable: true
  storage: /root/sfe/records_storage
  candidate_count: 1
  threshold: 0.40

enable

Enable face identification. If disabled (false), identification is not performed, and the identification results are not included in the FrameData.

storage

Defines the path to the directory on your file system to store the data for identification purposes.

candidate_count

Defines the maximum number of candidates returned from identification.

threshold

Defines the matching score threshold for identified candidates. Candidates with a matching score below this threshold are not considered positive matches and are not returned. The valid range is <0.0,1.0> The recommended value is 0.40.

Messaging

SFE Stream Processor sends messages containing face recognition analytics (FrameData) to the MQTT broker configured in the connection section.

See below an example configuration:

messaging:
  enable: true
  format: Protobuf
  strategy: OnNewAndInterval
  interval: 500
  allow_empty_messages: true

enable

Enable or disable sending messages from the SFE Stream Processor.

format

Defines the format of the message serialization. Supported message formats are Json, Yaml and Protobuf.

strategy

Defines the messaging strategy. Currently, the OnNewAndInterval strategy is supported. It means the message is sent when a new face is detected or the interval is expired.

interval

Defines the time interval in milliseconds for sending messages. The message is sent at least each <interval> milliseconds.

allow_empty_messages

By default, the message is sent only when:

  • a new face is detected or
  • at least one face is tracked This parameter enables sending empty messages. It is meant for debugging purposes only. An empty message means there are no detected or tracked faces

Crop image

This section describes the configuration of cropping.

See below an example configuration:

crop:
  enable: true
  size_extension: 2
  max_size: 50.0
  image_format: Raw

enable

Enable sending crop images of detected faces. Crop images can be used for post-processing or storage purposes on the server/cloud.

size_extension

Defines the size of the crop as an extension of the detection bounding box. The crop will be centered on the detection bounding box and the size will be multiplied by this value. Valid values are 0, 1, 2, 3, 4 and 5. You should use the correct value depending on the post-processing required on the server side, for example:

  • Template extraction requires size_extension=2
  • Passive liveness detection requires size extension=5

max_size

Defines the maximum size of the face in the crop area. Once a face is detected and its crop area is calculated using face_size_extension, this value will be used to determine the scale of the crop.

image_format

Specifies the format of the cropped images sent in a message. Supported image formats are Raw, Jpeg and Png.

Full frame

This section describes the configuration of full frames.

See below an example configuration:

full_frame:
  enable: false
  image_width: null
  image_height: null
  image_format: Raw

enable

Enable sending full-frame images. Please note sending a full-frame image requires a lot of bandwidth. That is why this parameter should be used for debugging purposes only.

image_width

The width of the full-frame image.

image_height

The height of the full-frame image.

image_format

Specifies the format of the full-frame image sent in a message. Supported image formats are Raw, Jpeg and Png.

Solvers

This section describes the configuration of solvers.

See below an example configuration:

solvers:
  frame_input: 
    solver: /emmc/plugin/Inno/solver/camera-input.ambarella.solver.0.7.3
    parameters: []
  frame_output:
    solver: null
    parameters: []
  face_detection:
    solver: /emmc/plugin/Inno/solver/face_detect_accurate_mask_w1280h720_perf.ambarella.solver.0.7.3
    parameters: []
  face_landmarks:
    solver: /emmc/plugin/Inno/solver/face_landmarks_0.25_acc.ambarella.solver.0.7.3
    parameters: []
  face_extraction:
    solver: /emmc/plugin/Inno/solver/face_template_extract_accurate_mask_perf.ambarella.solver.0.7.3
    parameters: []
  face_liveness_passive:
    solver: null
    parameters: []

For each solver, you can also define solver-specific parameters as an array of name and value pairs. Support solver parameters are documented in SFE Solver.

To apply changes in solver configuration it is required to restart the SFE Stream Processor.

frame_input

Defines the relative or full path to frame input solver.

The frame input solver is responsible for loading the frame data from various video sources.

See also SmartFace Embedded Input and Output Solvers for more information on how to configure input solver properly.

frame_output

For debugging purposes only.

Defines the relative or full path to the frame output solver. Can be used for the output stream annotations.

face_detection

Defines the relative or full path to face detection solver.

face_landmarks

Defines the relative or full path to face landmarks solver.

face_extraction

Defines the relative or full path to face template extraction solver.

face_liveness_passive

Defines the relative or full path to face passive liveness solver.

License

In this section, you can set the license to be used for the initialization of the SFE Stream Processor

See below an example configuration:

license:
  data: ewogICAgInZlcnNpb24iOiAiMi4xIiwKICAgI...

To apply changes in License data it is required to restart the SFE Stream Processor.

data

Base64 encoded license data.

The default value is null, which means the license file from the file system or ILICENSE_DATA environment variable is used. For more information, see also Licensing documentation.

Log

This section describes the configuration of logging.

See below an example configuration:

log:
  level: Info
  path: /emmc/plugin/Inno/log/sfe_stream_processor.log

To apply changes in logging configuration it is required to restart the SFE Stream Processor.

level

Specifies the log level. Supported values are:

  • Off - logging is turned off
  • Error - only error messages are logged
  • Warn - additionally warning messages are logged
  • Info - additionally info messages are logged
  • Debug - additionally debug messages are logged
  • Trace - additionally trace messages are logged,

The default log level is Info

path

Defines the path to a log file. When no path is set, standard error (stderr) is used.

By default, no log file path is set.

SFE Stream processor configuration example

# MQTT Connection
connection:
  # Address of the MQTT broker server
  broker_address: 10.11.82.88
  # Port of the MQTT broker server (default: 1883)
  broker_port: 1883
  # Client ID of the MQTT client, when empty a random UUID will be generated on startup
  client_id: 38-lilin
  # MQTT root topic path
  topic: edge-stream
  # Connection keep alive in seconds
  keep_alive: 60
  # Connection timeout in seconds
  timeout: 10
  # Enable TLS
  tls_enable: false
  # Username for authentication
  username: mqtt
  # Password for authentication
  password: mqtt

# Face detection
face_detection:
  # The threshold is used to filter low-confidence detections from processing. Detections below this threshold are not considered in tracking.
  detection_threshold: 0.01
  # Threshold for face landmarks detection, detection confidence must be higher than this value
  landmarks_threshold: 0.5
  # Threshold for face mask detection, detection confidence must be higher than this value
  mask_status_threshold: 0.5
  # Limit the number of faces to be detected
  max_detections: 10
  # Detected faces are order by (FaceSize, DetectionConfidence)
  order_by: DetectionConfidence

# Face tracking
tracking:
  # Minimal detection confidence threshold for tracked faces. Faces with detection confidence below this threshold are filtered out by tracking and not propagated to landmarks detection and template extraction.
  threshold: 0.1
  # Stability of tracking
  #   low stability may cause tracking to jump between detections
  #   high Stability may cause more tracklets to be created
  stability: 0.85
  # Maximum number of frames a face can be lost before it is removed from the tracking list
  max_frames_lost: 25

# Face extraction
face_extraction:
  # Enable face extraction, if disabled identification will not work
  enable: true

# Face passive liveness:
face_liveness_passive:
  # Enable face passive liveness calculation, if disable liveness score is not calculated
  enable: false
  # Face liveness strategy
  strategy: OnEachExtractedFace
  # Liveness conditions for reliable liveness detection
  conditions:
    # Minimal face size
    - parameter: FaceSize
      lower_threshold: 30.0
    # Minimal face relative area
    - parameter: FaceRelativeArea
      lower_threshold: 0.009
    # Minimal face relative area in image
    - parameter: FaceRelativeAreaInImage
      lower_threshold: 0.9
    # Yaw angle range
    - parameter: YawAngle
      lower_threshold: -20.0
      upper_threshold: 20.0
    # Pitch angle range
    - parameter: PitchAngle
      lower_threshold: -20.0
      upper_threshold: 20.0
    # Minimal sharpness 
    - parameter: Sharpness
      lower_threshold: 0.6

# Face identification
face_identification:
  # Enable face identification
  enable: true
  # Path to the face database
  storage: /emmc/plugin/Inno/records_storage.bin
  # Maximum number of candidates to be returned by the identification
  candidate_count: 10
  # Threshold for face identification, identification confidence must be higher than this value
  threshold: 0.40

# Message strategy and format
messaging:
  # Enable message publishing
  enable: true
  # Message format to be used
  #   Json: Json format
  #   Protobuf: Protobuf format
  #   Yaml: Yaml format
  format: Protobuf
  # Publishing strategy:
  #   OnNewAndInterval: Publishes a message when a new face is detected and every interval
  strategy: OnNewAndInterval
  # Publising interval in milliseconds between messages
  interval: 250
  # Publish a message even when no faces are detected
  allow_empty_messages: false

# Include face image in the message
crop:
  # Enable face image in message
  enable: true
  # Border around the face in face width units
  size_extension: 2
  # Max width of the face image in the message in pixels
  max_size: 50.0
  # Image format of the face image in the message
  #   Jpeg: Jpeg format
  #   Png: Png format
  #   Raw: Raw BGR format
  image_format: Raw

# Include full input frame in the message
full_frame:
  # Enable full frame in message
  enable: false
  # Width of the full frame in the message, if null the original frame width will be used
  image_width: null
  # Height of the full frame in the message, if null the original frame height will be used
  image_height: null
  # Image format of the full frame in the message
  #   Jpeg: Jpeg format
  #   Png: Png format
  #   Raw: Raw BGR format
  image_format: Raw

# Solvers to be used
solvers:
  # Solver used to aquire the input frame
  frame_input: 
    solver: ./solver/gstreamer_input.cpu.solver
    parameters:
      - name: gst_pipeline
        value: "rtspsrc location=rtsp://root:Innovatrics1@192.168.17.40:554/axis-media/media.amp latency=30 ! rtph264depay ! h264parse ! openh264dec ! videorate ! videoconvert ! videoscale ! video/x-raw,format=BGR,framerate=10/1,width=1280,height=720"
      - name: gst_width
        value: 1280
      - name: gst_height
        value: 720
  # Solver used to output the frame for preview and debugging
  frame_output:
    solver: null
    parameters: []
  # Solver used to detect faces
  face_detection:
    solver: ./solver/face_detect_accurate_mask.onnxrt.solver
    parameters:
    - name: runtime_provider
      value: cpu
    - name: intra_threads
      value: '6'
    - name: inter_threads
      value: '1'
  # Solver used to detect face landmarks
  face_landmarks:
    solver: ./solver/face_landmarks_0.25.onnxrt.solver
    parameters:
    - name: runtime_provider
      value: cpu
    - name: intra_threads
      value: '6'
    - name: inter_threads
      value: '1'
  # Solver used to exract face templates
  face_extraction:
    solver: ./solver/face_template_extract_accurate_mask.onnxrt.solver
    parameters:
    - name: runtime_provider
      value: cpu
    - name: intra_threads
      value: '6'
    - name: inter_threads
      value: '1'
  # Solver used to calculate passive liveness score
  face_liveness_passive:
    solver: ./solver/face_liveness_passive_distant.onnxrt.solver
    parameters:
    - name: runtime_provider
      value: cpu
    - name: intra_threads
      value: '6'
    - name: inter_threads
      value: '1'

license:
  data: null
log:
  level: Info
  path: /emmc/plugin/Inno/log/sfe_stream_processor.log

SFE Settings server

You can use the SFE Settings Server to modify the configuration using Web UI. It changes the content of settings.yaml file which comes as an input argument. You have to configure the IP address and port where the settings UI is available. See below an example of how to run the SFE Setting Server.

./sfe_settings_server --address 0.0.0.0 --port 8592 --settings-file settings.yaml

Then you can use the following URL (IP address of the camera and specified port) to access and modify the configuration.

10.11.80.38:8592

Scroll down and click the Save button to save and apply the changes. The SFE Stream Processor listens to changes in settings.yaml and reloads the configuration if necessary.

To apply changes in solvers, logging and license configuration it is required to restart the SFE Stream Processor

SFE Settings Server also displays the latest logging information based on the configured log level.

GStreamer pipelines

Generic rules for using GStreamer pipelines

In this article, there are multiple examples of the GStreamer pipeline and they can be used in 2 different ways.

Using gst-launch-1.0

The gst-launch-1.0 tool should be used for pipeline validation. You need to visualize the pipeline output first, then you can pass it to the sfe_stream_processor. If you’re trying to run a Gstreamer pipeline from this article with gst-launch-1.0, you should modify the example pipeline as follows:

$ gst-launch-1.0 <here goes example pipeline of your choice> \
! videoconvert ! xvimagesink

Parts of the modified pipeline:

  • first, you prepend gst-launch-1.0
  • then you place your pipeline right after the gst-launch-1.0
  • videoconvert element goes right after the example pipeline (converts frames to the correct format for pipeline sink)
  • the last element is a pipeline sink (recommended is xvimagesink)

Using sfe_stream_processor

In the SFE stream processor, you have to write down a GStreamer pipeline in settings.yaml file (this is the file the sfe_stream_processor takes as its input). The YAML file contains lots of different fields but here, we focus only on frame_input section:

frame_input:
    solver: ./solver/gstreamer_input.cpu.solver
    parameters:
      - name: gst_pipeline
        value: "here goes example pipeline"
	  - name: gst_width
        value: <frame_width>
      - name: gst_height
        value: <frame_height>

If we consider a concrete example:

frame_input:
    solver: ./solver/gstreamer_input.cpu.solver
    parameters:
      - name: gst_pipeline
        value: "rtspsrc location=rtsp://root:Innovatrics1@192.168.17.40:554/axis-media/media.amp latency=30 ! rtph264depay ! h264parse ! openh264dec ! videorate ! videoconvert ! videoscale ! video/x-raw,format=BGR,framerate=10/1,width=1280,height=720"
      - name: gst_width
        value: 1280
      - name: gst_height
        value: 720

See also GStreamer input solver for more information and parameters.

There are a few rules to follow while designing your pipeline:

  • the pipeline MUST convert the video source to BGR format
  • width/height properties MUST specify frame resolution going out of your pipeline
  • sink elements MUST NOT be specified
  • in most cases, the last part of your pipeline should contain videoconvert ! video/x-raw,format=BGR

Example pipelines

Pipeline using MP4 video file

filesrc location=test.mp4 \
! qtdemux ! h264parse ! avdec_h264 \
! videorate drop-only=true \
! videoconvert ! video/x-raw,format=BGR,framerate=10/1

Pipeline using MP4 video file and Intel VA-API elements

filesrc location=test.mp4 \
! qtdemux ! vaapidecodebin \
! queue leaky=no max-size-buffers=5 max-size-bytes=0 max-size-time=0 \
! vaapipostproc ! videorate \
! video/x-raw,framerate=10/1,width=1280,height=720 \
! queue leaky=no max-size-buffers=5 max-size-bytes=0 max-size-time=0 \
! videoconvert ! video/x-raw,format=BGR

Pipeline using RTSP stream

rtspsrc location=rtsp://aa.bb.cc.dd/strm latency=1 \
! queue ! rtph264depay ! h264parse ! avdec_h264 \
! videorate ! videoconvert ! videoscale \
! video/x-raw,format=BGR,width=1280,height=720,framerate=10/1

Pipeline using USB camera

v4l2src device=/dev/video0 ! video/x-raw,framerate=10/1,width=1280,height=720 \
! videoconvert ! video/x-raw,format=BGR

Note: You may be interested in using another GStreamer tool gst-device-monitor-1.0 that lists all input devices. Simplified example output of this tool run on a laptop with Webcam:

Device found:

	name  : Integrated_Webcam_HD: Integrate
	class : Video/Source
	caps  : video/x-raw, format=YUY2, width=640, height=480, pixel-aspect-ratio=1/1, framerate=30/1
			...
	        image/jpeg, width=1280, height=720, pixel-aspect-ratio=1/1, framerate=30/1
			...
	properties:
		...
		device.subsystem = video4linux
		device.product.name = Integrated_Webcam_HD: Integrate
		...
		device.path = /dev/video0
		...
	gst-launch-1.0 v4l2src ! ...