This page covers examples using ODE Pad Probe Handlers, ODE Triggers, ODE Actions, ODE Areas, ODE Acummulators, and ODE Heat Mappers.
- Occurrence Trigger with Monitor Action
- Always Trigger with Display Metadata Action and Source Display Types
- Minimum, Maximum, Range, and Summation Triggers, with Display Types and Actions
- Distance Trigger with Format BBox Action
- New Instance Triggers and Print Action
- Intersection Triggers with Format BBox and Print Actions
- Largest Trigger with Fill Surroundings Action
- Cross Trigger with Line Area and ODE Accumulator with Display Action
- New High and New Low Count Triggers with Fill Frame and Print Event Data Actions
- Occurrence Trigger with Area of Inclusion or Exclusion
- Instance Trigger with Capture Frame Action with a Mailer to Email Frame as Attachement
- Occurrence Trigger with ODE Head Mapper using RGBA Color Palettes
- Persistence Triggers with Format BBox Actions
- Persistence and Earliest Triggers with Customize Label and Display Actions
ode_occurrence_trigger_with_monitor_action.py- cpp example is still to be done
#
# This example demonstrates the use of an "ODE Monitor Action" -- added to an
# ODE Occurrence Trigger with the below criteria -- to monitor all
# ODE Occurrences
# - class id = PGIE_CLASS_ID_VEHICLE
# - inference-done-only = TRUE
# - minimum confidience = VEHICLE_MIN_CONFIDENCE
# - minimum width = VEHICLE_MIN_WIDTH
# - minimum height = VEHICLE_MIN_HEIGHT
#
# The ode_occurrence_monitor callback function (defined below) is added to the
# "Monitor Action" to be called with the ODE Occurrence event data for
# each detected object that meets the above criteria.
#
# The application can process the event data as needed. This examples simply
# prints all of the event data to console.
#
# The example uses a basic inference Pipeline consisting of:
# - A URI Source
# - Primary GST Inference Engine (PGIE)
# - IOU Tracker
# - Two Secondary GST Inference Engines (SGIEs)
# - On-Screen Display
# - Window Sink
# ode_always_trigger_display_source_info.py- cpp example is still to be done
#
# This Example demonstrates how to use an ODE Always Trigger to update the
# metadata of every frame to display specific information for each Source.
#
# 4 Sources are used, each with unique camara names.
#
# 3 Display Types are used to create the metadata to be added to each frame:
# * Source Stream Id
# * Source Name
# * Source Dimensions
#
# The 3 Display Types are added to an "Add Display Meta Action" which
# adds the metadata to a given frame.
#
# The ODE Action is added to an "Always Trigger" that always triggers once
# per frame in every batched frame (requires source=DSL_ODE_ANY_SOURCE).
#
# The ODE Trigger is added to a "ODE Pad Probe Handler" that is added
# to the sink (input) pad of the 2D Tiler. The ODE Handler is called with
# every batched frame that crosses over the Tilers sink pad.
#
# The example uses a basic inference Pipeline consisting of:
# - 4 URI Sources
# - Primary GST Inference Engine (PGIE)
# - IOU Tracker
# - 2D Tiler
# - On-Screen Display
# - Window Sink
# 
ode_always_trigger_display_source_info.py- cpp example is still to be done
#
# This example demonstrates how to use Minimum, Maximum, and Range Triggers.
#
# The triggers, upon meeting all criteria, will add a small rectangle (using
# a "Display Type" and "Add Display Metadata Action") on the Frame with the
# following colors indicating:
# Yellow = object count below Minimum
# Red = object count above Maximum
# Green = object count in range of Minimim to Maximum.
#
# An additional "Summation Trigger" with a "Display Action" will display the
# total number of objects next to the colored/filled indicator (rectangle)
#
# The ODE Triggers are added to an ODE Pad Probe Handler which is added to
# source (output) pad of the Tracker.
# The example uses a basic inference Pipeline consisting of:
# - A URI Source
# - Primary GST Inference Engine (PGIE)
# - IOU Tracker
# - On-Screen Display
# - Window Sink
# 
ode_distance_trigger_fill_object.py- cpp example is still to be done
#
# This example demonstrates the use of an ODE Distance Trigger to trigger on the
# occurrence of two objects of different class id that are closer that a
# minimum distance - specifically testing the distance between People and Vehicles.
# The bounding boxes for the two objects that are witin the minimim distance will.
# be filled (using a Format BBox Action) with a color for visual indication of
# the events.
# The Distance trigger is created with minimim distance critera as a percentage
# of the width of Class A in the A/B distance measurement. In this example,
# Class A will be the Person class and Class B the Vehicle class. ODE Occurrence
# will be triggered if the distance between any Person and Vehicle is measured to
# be less 250% of the width of the Person's BBox. Maximum is set to 0 == no maximum.
# Note: Class A and Class B can be set to the same Class Id or DSL_ODE_ANY_CLASS.
# test_point is DSL_BBOX_POINT_SOUTH == measuring from center points of bottom edges.
# test_method is DSL_DISTANCE_METHOD_PERCENT_WIDTH_A == % of Person's BBox width.
#
# The example uses a basic inference Pipeline consisting of:
# - A URI Source
# - Primary GST Inference Engine (PGIE)
# - IOU Tracker
# - On-Screen Display
# - Window Sink
# 
ode_instance_trigger_print_action.py- cpp example is still to be done
#
# This example demonstrates the use of two ODE Instance Triggers -- one for
# the Person class and the other for the Vehicle class -- to trigger on new
# Object instances as identified by an IOU Tracker. A Print Action is added
# to the Instance Triggers to print out the event data for each new object.
#
# The example uses a basic inference Pipeline consisting of:
# - A URI Source
# - Primary GST Inference Engine (PGIE)
# - IOU Tracker
# - On-Screen Display
# - Window Sink
# An example of the Object Dection Event (ODE) data printed to the console by the ODE Print Trigger
Trigger Name : person-instance-trigger
Unique ODE Id : 249
NTP Timestamp : 2024-09-14 14:13:10.084472
Source Data : ------------------------
Inference : Yes
Source Id : 0x00000000
Batch Id : 0
Pad Index : 0
Frame : 18
Width : 1920
Heigh : 1080
Object Data : ------------------------
Obj ClassId : 2
Infer Id : 1
Tracking Id : 1
Label : person
Infer Conf : 0.260092
Track Conf : 1
Persistence : 0
Direction : 0
Left : 405
Top : 464
Width : 65
Height : 240
Criteria : ------------------------
Class Id : 2
Infer Id : -1
Min Infer Conf : 0
Min Track Conf : 0
Min Frame Count : 1 out of 1
Min Width : 0
Min Height : 0
Max Width : 0
Max Height : 0
Inference : No
ode_intersection_trigger_min_max_dimensions.py- cpp example is still to be done
#
# This example is used to demonstrate the Use of Two Intersection Triggers,
# one for the Vehicle class the other for the Person class. A "Format BBox"
# action will be used to shade the background of the Objects intersecting.
# Person intersecting with Person and Vehicle intersecting with Vehicle.
#
# Min and Max Dimensions will set as addional criteria for the Preson and
# Vehicle Triggers respecively
#
# The example uses a basic inference Pipeline consisting of:
# - A URI Source
# - Primary GST Inference Engine (PGIE)
# - IOU Tracker
# - On-Screen Display
# - Window Sink
# 
ode_largest_object_fill_surroundings.py- cpp example is still to be done
#
# This example demonstrates the used of a "Largest Object Trigger" and "Fill
# Surroundings Action" to continuosly highlight the largest object in the Frame
# as measured by bounding box area.
#
# The example uses a basic inference Pipeline consisting of:
# - A URI Source
# - Primary GST Inference Engine (PGIE)
# - IOU Tracker
# - On-Screen Display
# - Window Sink
# 
ode_line_cross_object_capture_overlay_image.py- cpp example is still to be done
#
# This example demonstrates the use of an ODE Cross Trigger with an ODE Line Area
# and ODE Accumulator to accumulate occurrences of an object (person) crossing
# the line. The Accumulator uses an ODE Display Action to add the current counts
# of the IN and OUT crossings as display-metadata to each frame.
#
# The bounding box and historical trace of each object (tracked by the "Cross
# Trigger") is assigned a new random RGBA color and added as display-metadata
# to each frame.
#
# An ODE Capture Object Action with an Image Render Player is added to the Cross
# Trigger to capture and render an image of each object (person) that crosses the
# line. Each image is displayed for 3 seconds. All files are written to the current
# directory (configurable).
#
# The example uses a basic inference Pipeline consisting of:
# - A File Source
# - Primary GST Inference Engine (PGIE)
# - IOU Tracker
# - On-Screen Display
# - Window Sink
# 
ode_new_high_new_low_triggers_fill_frame.py- cpp example is still to be done
#
# This example demonstrates the use of the New-High and New-Low Count Triggers
# that trigger on new high and low object counts respectively. The frame is
# filled with a full color for a (brief) visual indication on each new occurrence.
# A print Action is used to print the event data to the console as well.
#
# The example uses a basic inference Pipeline consisting of:
# - A URI Source
# - Primary GST Inference Engine (PGIE)
# - IOU Tracker
# - On-Screen Display
# - Window Sink
# ode_occurrence_polygon_area_inclussion_exclusion.py- cpp example is still to be done
#
# This example demonstrates the use of a Polygon Area for Inclusion
# or Exclusion criteria for ODE occurrence.
#
# A "Polygon Display Type" is used to create either an ODE Area of Inclusion or
# Exclusion based on the AREA_TYPE variable defined below.
#
# The ODE Area is then added to an ODE Occurrence Trigger to be used as criteria
# for ODE occurrence.
#
# A "Format BBox Action" is used to fill each detected object that triggers
# occurrence with an opaque red color for visual confirmation.
#
# The example uses a basic inference Pipeline consisting of:
# - A URI Source
# - Primary GST Inference Engine (PGIE)
# - IOU Tracker
# - On-Screen Display
# - Window Sink
# 
ode_instance_frame_capture_email_attachment.py- cpp example is still to be done
#
# This example demostrates the use of an "ODE Occurrence Trigger" to trigger
# on every occurrence of every Person within a Polygon ODE Inclusion Area.
# The Trigger uses a "Format BBox Action" to fill each occurrence with
# an opaque red color for visual confirmation while the Person is in the Area.
#
# An Instance Trigger is then used to Trigger on every new Instance detected in
# the same ODE Area.. i.e. when the Person is first detected in the Area and only
# once.
# This Trigger uses a "Frame Capture Action" to capture and encode the frame
# and save it to file. The Action then uses a Mailer component to mail the
# image as an attachment using DSL's SMTP services.
#
# IMPORTANT! it is STRONGLY advised that you create a new, free Gmail account --
# that is seperate/unlinked from all your other email accounts -- strictly for
# the purpose of sending ODE Event data uploaded from DSL. Then, add your
# Personal email address as a "To" address to receive the emails.
#
# Gmail considers regular email programs (i.e Outlook, etc.) and non-registered
# third-party apps to be "less secure". The email account used for sending email
# must have the "Allow less secure apps" option turned on. Once you've created
# this new account, you can go to the account settings and enable Less secure
# app access. see https://myaccount.google.com/lesssecureapps
#
# The example uses a basic inference Pipeline consisting of:
# - A URI Source
# - Primary GST Inference Engine (PGIE)
# - IOU Tracker
# - On-Screen Display
# - Window Sink
# ode_occurrence_trigger_with_heat_mapper.py- cpp example is still to be done
#
# This example demonstrates the use of an ODE Heat-Mapper added to an
# ODE Occurrence trigger that triggers on every Person occurrence.
# The occurrence data is mapped/ovelaid on everyframe. The example creates
# all 5 predefined RGBA Color Palettes - Spectral, Red, Green, Blue, and Grey.
# The ODE Heat-Mapper is created with the Spectral palette, but can be updated
# at runtime by pressing the 'N' key.
#
# Several keys, bound to the Window Sink, are mapped to the ODE Heat Mapper services
# - 'N' key maps to 'next' color palette with - dsl_ode_heat_mapper_color_palette_set
# - 'C' key maps to 'clear' heat-map metrics - dsl_ode_heat_mapper_metrics_clear
# - 'P' key maps to 'print' heat-map metrics - dsl_ode_heat_mapper_metrics_print
# - 'L' key maps to 'log' heat-map metrics - dsl_ode_heat_mapper_metrics_log
# - 'G' key maps to 'get' heat-map metrics - dsl_ode_heat_mapper_metrics_get
#
#
# The example uses a basic inference Pipeline consisting of:
# - A URI Source
# - Primary GST Inference Engine (PGIE)
# - IOU Tracker
# - On-Screen Display
# - Window Sink
# 
ode_persistence_trigger_fill_tracked_objects.py- cpp example is still to be done
#
# This example demonstrates the use of three ODE Persistence Triggers to trigger on
# all tracked Objects - as identified by an IOU Tracker - that persist accross consecutive
# frames for a specifid period of time. Each trigger specifies a range of minimum and
# maximum times of persistence.
# Trigger 1: 0 - 3 seconds - action = fill object with opaque green color
# Trigger 2: 3 - 6 seconds - action = fill object with opaque yellow color
# Trigger 3: 6 - 0 seconds - action = fill object with opaque red color
# This will have the effect of coloring an object by its time in view
#
# The example uses a basic inference Pipeline consisting of:
# - A URI Source
# - Primary GST Inference Engine (PGIE)
# - IOU Tracker
# - On-Screen Display
# - Window Sink
# ode_persistence_and_earliest_triggers_custom_labels.py- cpp example is still to be done
#
# This script demonstrates the use of a Persistence Trigger to trigger on each Vehicle
# that is tracked for more than one frame -- to calculate the time of Object persistence
# from the first frame the object was detected.
#
# The Tracked Object's label is then "customized" to show the tracking Id and time of
# persistence for each tracked Vehicle.
#
# The script also creates an Earliest Trigger to trigger on the Vehicle that appeared
# the earliest -- i.e. the object with greatest persistence value -- and displays that
# Object's persistence using an ODE Display Action.
#
# The example uses a basic inference Pipeline consisting of:
# - A URI Source
# - Primary GST Inference Engine (PGIE)
# - IOU Tracker
# - On-Screen Display
# - Window Sink
#