diff --git a/docs/docs/Release-Notes.md b/docs/docs/Release-Notes.md
index 9ddf3116ca4b..4b9666716bc4 100644
--- a/docs/docs/Release-Notes.md
+++ b/docs/docs/Release-Notes.md
@@ -1,8 +1,260 @@
**NOTICE:** This software (or technical data) was produced for the U.S. Government under contract, and is subject to the
-Rights in Data-General Clause 52.227-14, Alt. IV (DEC 2007). Copyright 2024 The MITRE Corporation. All Rights Reserved.
+Rights in Data-General Clause 52.227-14, Alt. IV (DEC 2007). Copyright 2026 The MITRE Corporation. All Rights Reserved.
+
+# OpenMPF 10.0.x
+
+
10.0.0: January 2026
+
+
Documentation
+
+- Created a new [Artifact Extraction Guide](Artifact-Extraction-Guide/index.html).
+
+
No Language Left Behind (NLLB) Translation Component
+
+The No Language Left Behind component is based on [Meta's No Language Left Behind Project](https://ai.meta.com/research/no-language-left-behind/). The component translates input text from a given source language to English. The source language can be provided as a job property, or be indicated in the detection properties from a feed-forward track. By default, this component is configured to use the **`facebook/nllb-200-3.3B` model** [No Language Left Behind (NLLB)](https://huggingface.co/models?search=facebook/nllb). This provides the high translation quality, but also requires significant hardware resources. To accommodate smaller deployment enviroments, this component can use smaller NLLB models, such as [nllb-200-distilled-1.3B](https://huggingface.co/facebook/nllb-200-distilled-1.3B) or [nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M).
+
+Refer to the [README](https://github.com/openmpf/openmpf-components/blob/master/python/NllbTranslation/README.md) for details.
+
+
FastText Language Identification Component
+
+The FastText Language Detection Component utilizes the [GlotLID](https://github.com/cisnlp/GlotLID) language identification model
+and the [fastText](https://github.com/facebookresearch/fastText) library to perform language identification on text.
+
+Refer to the [README](https://github.com/openmpf/openmpf-components/blob/master/python/FastTextLanguageDetection/README.md) for details.
+
+
Subject Tracking API and OR-Tools Subject Component [Experimental]
+
+- The OR-Tools Subject Tracking component associates detection tracks of different types (e.g., face, person, vehicle) to create a subject and track it in video.
+
+- The component source can be found [here](https://github.com/openmpf-components/blob/master/python/OrToolsSubjectComponent).
+
+
Audit Logging
+
+We have added internal audit logging that may be used to audit events that occur during OpenMPF operation. Logging has been added to the Workflow Manager to record these types of events:
+
+- login/logout user information
+- create/read/update/download of data, configuration information, or action/pipeline information
+- rest API access
+- load UI content
+- TiesDB and S3 storage operations
+- Access to Hawtio
+
+The information is logged as a JSON string. The fields of the logging statement consist of:
+
+- `eid` : an event id signifying the type of event
+- `time` : a timestamp for the event in UTC with format YYYY-MM-DDThh:mm:ss.mmmZ
+- `tag` : a tag value, which could be used to categorize events. For this release, all event logging uses a single constant tag value.
+- `app` : the name of the application where the event occurred, which is a constant set to "openmpf"
+- `user` : the username of the user that initiated the activity being logged.
+- `op` : operation identifier, one of c(create), r(read/view), m(modify), d(delete), l(login)
+- `res` : operation result, one of a(allowed), d(denied), e(error)
+- `uri` : [optional] the full web or file path.
+- `bucket` : [optional] The string identifying the S3 bucket for object storage
+- `objectKey` : [optional] The name that identifies the object in the S3 bucket
+- `msg` : a message string
+
+- Here is example audit logging output when creating a job through the REST API:
+
+```text
+{"eid":200,"time":"2026-01-14T14:30:40.864Z","tag":"&B1E7-FFFF&","app":"openmpf","user":"admin","op":"c","res":"a","uri":"/rest/jobs","msg":"create job succeeded for Pipeline: OCV TINY YOLO VEHICLE DETECTION (WITH MARKUP) PIPELINE, Media URIs: [file:///opt/mpf/share/remote-media/car_video.MOV]"}
+```
+
+
+
+Audit logging is enabled by default, but can be disabled by setting the `audit.logging.enabled` system property to `false`.
+
+
Additional Quality Selection Properties
+
+- We have added the ability to perform feed-forward processing and artifact extraction based on component-defined properties, in addition to the standard `QUALITY_SELECTION_PROPERTY`.
+- Two new job properties have been added: `ARTIFACT_EXTRACTION_POLICY_BEST_DETECTION_PROP_NAMES_LIST` and `FEED_FORWARD_BEST_DETECTION_PROP_NAMES_LIST`. A detection component may choose to mark certain detections in a track as "best" by adding a custom property to those detections. For example, you could mark the detection whose bounding box has the largest area by adding a detection property named `BEST_SIZE` to that detection. If you want to extract the artifact for that detection, then you would add the string `BEST_SIZE` to the `ARTIFACT_EXTRACTION_POLICY_BEST_DETECTION_PROP_NAMES_LIST`. This would result in that detection artifact being extracted in addition to the others that would be extracted based on the `QUALITY_SELECTION_PROPERTY`.
+- Refer to the [Artifact Extraction Guide](Artifact-Extraction-Guide/index.html) and the [Feed-Forward Guide](Feed-Forward-Guide.md) for more details.
+
+
Python 3.12
+
+- The version of python that is used throughout OpenMPF has been upgraded from 3.8 to 3.12.
+
+
Feed-forward All Tracks [Experimental]
+
+- Previously, the Workflow Manager would feed forward each track from a previous pipeline stage to the next stage one track at a time. To support use cases where the next stage needs all of the tracks in one sub-job, we added a new job property `FEED_FORWARD_ALL_TRACKS`. When set to `true`, the workflow manager will pass all tracks generated in the current stage of the pipeline to the next.
+
+- A new class was added to the python component API:
+
+```text
+class AllVideoTracksJob(NamedTuple):
+ job_name: str
+ data_uri: str
+ start_frame: int
+ stop_frame: int
+ job_properties: Mapping[str, str]
+ media_properties: Mapping[str, str]
+ feed_forward_tracks: List[VideoTrack]
+```
+ and a new method: `get_detections_from_all_video_tracks()`.
+
+- This functionality is experimental and currently supported only with the python component API.
+
+- Refer to the [python component SDK](https://github.com/openmpf-python-sdk/blob/master/detection/api) for details.
+
+- An example illustrating the use of this feature can be found in the python SDK test component [test_component.py](https://github.com/openmpf-python-component-sdk/blob/master/detection/examples/PythonTestComponent/test_component.test_component.py).
+
+
Support for Supplying a Data URI for Media When Creating a Job
+
+- Users may now supply the media for an OpenMPF job using a data URI when creating a job through the REST API. The mediaURI field may contain a valid (properly encoded) URI to a single media source using one of the following URI schemes: `file:`, `http:`, `https:`, `data:`.
+
+- The data URI format is explained [here](https://en.wikipedia.org/wiki/Data_URI_scheme).
+
+Here is an example of a jobCreationRequest using a data URI for an image:
+```text
+{
+ "buildOutput": true,
+ "callbackMethod": "",
+ "callbackURL": "",
+ "externalId": "my-id",
+ "media": [
+ {
+ "mediaUri": "data:image/jpeg;base64,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"
+ }
+ ],
+ "pipelineName": "OCV YOLO OBJECT DETECTION PIPELINE",
+ "priority": 1
+}
+```
+
+
Features
+
+ - [[#1719](https://github.com/openmpf/openmpf/issues/1719)] Remove `OUTPUT_LAST_TASK_ONLY` and support `IS_ANNOTATOR` and `SUPPRESS_TRACKS` WFM properties
+ - [[#1796](https://github.com/openmpf/openmpf/issues/1796)] Translate specific CSV columns
+ - [[#1831](https://github.com/openmpf/openmpf/issues/1831)] Create a No Language Left Behind (NLLB) translation component
+ - [[#1832](https://github.com/openmpf/openmpf/issues/1832)] Add initial subject tracking implementation
+ - [[#1841](https://github.com/openmpf/openmpf/issues/1841)] Create new feature to allow feed-forward and artifact extraction selection based on a set of detection properties
+ - [[#1851](https://github.com/openmpf/openmpf/issues/1851)] Allow users to use JSON Path to specify which parts of a JSON document to process
+ - [[#1862](https://github.com/openmpf/openmpf/issues/1862)] Create a language identification component using FastText
+ - [[#1869](https://github.com/openmpf/openmpf/issues/1869)] Add support for using media selectors with multi-stage pipelines
+ - [[#1875](https://github.com/openmpf/openmpf/issues/1875)] Allow users to provide a data URI for media.
+ - [[#1889](https://github.com/openmpf/openmpf/issues/1889)] Update detection components to register using ActiveMQ.
+ - [[#1907](https://github.com/openmpf/openmpf/issues/1907)] Support feeding forward all tracks to one sub-job (for Python video jobs only)
+ - [[#1920](https://github.com/openmpf/openmpf/issues/1920)] Add audit logging of callbacks
+ - [[#1921](https://github.com/openmpf/openmpf/issues/1921)] Add audit logging for TiesDb
+ - [[#1979](https://github.com/openmpf/openmpf/issues/1979)] Add event ids, bucket keys, and URIs to audit logging
+ - [[#1933](https://github.com/openmpf/openmpf/issues/1933)] Add Custom SSO support
+
+
Updates
+
+ - [[#1085](https://github.com/openmpf/openmpf/issues/1085)] Some detection processing errors should result in a final `ERROR` status instead of `COMPLETE_WITH_ERRORS`
+ - [[#1269](https://github.com/openmpf/openmpf/issues/1269)] Improve `UNSUPPORTED_DATA_TYPE` message in JSON output object
+ - [[#1679](https://github.com/openmpf/openmpf/issues/1679)] Retire AzureOcrTextDetection component
+ - [[#1847](https://github.com/openmpf/openmpf/issues/1847)] Suppress the warning when a track does not contain the requested quality selection property
+ - [[#1854](https://github.com/openmpf/openmpf/issues/1854)] Update Jenkins build to generate SBOM
+ - [[#1857](https://github.com/openmpf/openmpf/issues/1857)] Optionally skip TiesDb check through Create Job page
+ - [[#1858](https://github.com/openmpf/openmpf/issues/1858)] Optionally give transient pipeline a name
+ - [[#1879](https://github.com/openmpf/openmpf/issues/1879)] Merge `COMPLETE_WITH_ERRORS` status into `ERROR`
+ - [[#1884](https://github.com/openmpf/openmpf/issues/1884)] Upgrade to Python 3.12
+ - [[#1956](https://github.com/openmpf/openmpf/issues/1956)] Initial TiesDB check failure should result in a warning, not an error
+
+
Bug Fixes
+
+ - [[#1916](https://github.com/openmpf/openmpf/issues/1916)] JSON button remains disabled on Job Status page when job ends in `ERROR`
+ - [[#1926](https://github.com/openmpf/openmpf/issues/1926)] Bad TiesDB URL results in multiple UI notifications and TiesDB status staying `IN PROGRESS`
+ - [[#1984](https://github.com/openmpf/openmpf/issues/1984)] Update CharsetDetectingReader to always use UTF-8 when the input only contains valid UTF-8 bytes
# OpenMPF 9.0.x
+
9.0.11: October 2025
+
+
Updates
+
+- [[#1911](https://github.com/openmpf/openmpf/issues/1911)] Update LlamaVideoSummarization to use TIMELINE_CHECK_ACCEPTABLE_THRESHOLD
+
+
+
+ - [[#1950](https://github.com/openmpf/openmpf/issues/1950)] HeifConverter fails for some avif files
+
+
9.0.9: August 2025
+
+
Features
+
+- [[#1940](https://github.com/openmpf/openmpf/issues/1940)] Record stats for the ffprobe process that determines PTS value
+
+
Bug Fixes
+
+- [[#1930](https://github.com/openmpf/openmpf/issues/1930)] Reading a file that uses the libaom-av1 decoder causes a crash during artifact extraction
+- [[#1943](https://github.com/openmpf/openmpf/issues/1943)] Memory leak in HeifConverter.cpp
+
+
9.0.8: June 2025
+
+
LLaMA Video Summarization Component
+
+- This component uses the VideoLLaMA3 model to generate a description of the activity in a video. It can describe a segment of the video, or create a summary of the entire video.
+
+- Refer to the [README](https://github.com/openmpf/openmpf-components/blob/master/python/LlamaVideoSummarization/README.md)
+ for details.
+
+
Features
+
+- [[#1883](https://github.com/openmpf/openmpf/issues/1883)] Create LLaMA video summarization component
+- [[#1888](https://github.com/openmpf/openmpf/issues/1888)] Enable segments to be specified by seconds
+
+
9.0.7: May 2025
+
+
Updates
+
+ - [[#1903](https://github.com/openmpf/openmpf/issues/1903)] Support AVIF images
+
+
9.0.6: December 2024
+
+
Bug Fixes
+
+ - [[#1865](https://github.com/openmpf/openmpf/issues/1865)] WTP models are not installed in AzureTranslation
+
+
9.0.3: July 2024
+
+
New Keyword Tagging Formats
+
+- The Keyword Tagging component was updated to use the Perl regex syntax, thereby enabling more regex features including look-behind/ahead.
+
+- Additional patterns were added to the tags file to enhance the base matching capability of the component:
+
+```text
+ phone numbers
+ emails
+ date formats
+ time
+ P.O Box
+ SSN
+ currency symbols
+```
+- Refer to the Keyword Tagging Component configuration file [text-tags.json](https://github.com/openmpf/openmpf-components/blob/master/cpp/KeywordTagging/plugin-files/config/text-tags.json) for a complete list of the regex expressions currently supported.
+
+
Updates
+
+ - [[#1834](https://github.com/openmpf/openmpf/issues/1834)] Make delay in AzureRead configurable and delay before first poll attempt
+ - [[#1770](https://github.com/openmpf/openmpf/issues/1770)] Update Keyword Tagging to support emails, dates, phone numbers, and other formats
+
+
9.0.2: July 2024
+
+
Bug Fixes
+
+ - [[#1828](https://github.com/openmpf/openmpf/issues/1828)] Track object provides incorrect parameters to the `ExemplarPolicyUtil.getExemplar()` function
+ - [[#1827](https://github.com/openmpf/openmpf/issues/1827)] `nlp_text_splitter` installation fails because of an issue with the thinc library
+
+
9.0.1: June 2024
+
+
Updates
+
+ - [[#1822](https://github.com/openmpf/openmpf/issues/1822)] Capitalize all tags produced by the KeywordTagging and TransformerTagging components
+
+
Bug Fixes
+
+ - [[#1819](https://github.com/openmpf/openmpf/issues/1819)] Workflow Manager splitterThreadPoolProfile does not properly re-use threads
+ - [[#1823](https://github.com/openmpf/openmpf/issues/1823)] TransformerTagging does not handle multiple feed-forward properties to process
+
9.0.0: May 2024
Documentation
diff --git a/docs/docs/index.md b/docs/docs/index.md
index 1afdbab03c17..7eaaef1cbbc3 100644
--- a/docs/docs/index.md
+++ b/docs/docs/index.md
@@ -21,6 +21,7 @@ A list of algorithms currently integrated into the OpenMPF as distributed proces
| Detection | Speech | Azure Cognitive Services Batch Transcription API
| Detection | Scene | OpenCV
| Detection | Classification | OpenCV DNN (GoogLeNet, Yahoo NSFW, vehicle color)
+| Detection | Classification | Clip
| Detection/Tracking | Classification | OpenCV DNN (YOLO)
| Detection/Tracking | Classification/Features | TensorRT (COCO classes)
| Detection | Text Region | EAST
@@ -29,8 +30,14 @@ A list of algorithms currently integrated into the OpenMPF as distributed proces
| Detection | Text (OCR) | Azure Cognitive Services Read API
| Detection | Form Structure (with OCR) | Azure Cognitive Services Form Recognizer API
| Detection | Keywords | Boost Regular Expressions
+| Detection | Known Phrases | Transformer Tagging
| Detection | Image (from document) | Apache Tika
-| Translation | Language | Azure Cognitive Services Translate API
+| Correction | Text | Natural Language Processing using cython_hunspell library
| Detection | Language | fastText with the GlotLID model
+| Translation | Language | Azure Cognitive Services Translate API
+| Translation | Language | No Language Left Behind (NLLB)
+| Translation | Language | Argos
+| Translation | Language | Whisper
+| Video Summarization | Activity | LLAMA3
The OpenMPF exposes data processing and job management web services via a User Interface (UI). These services allow users to upload media, create media processing jobs, determine the status of jobs, and retrieve the artifacts associated with completed jobs. The web services give application developers flexibility to use the OpenMPF in their preferred environment and programming language.
diff --git a/docs/site/Release-Notes/index.html b/docs/site/Release-Notes/index.html
index afd1f19843f1..b9752f20e954 100644
--- a/docs/site/Release-Notes/index.html
+++ b/docs/site/Release-Notes/index.html
@@ -61,6 +61,9 @@
Release Notes
NOTICE: This software (or technical data) was produced for the U.S. Government under contract, and is subject to the
-Rights in Data-General Clause 52.227-14, Alt. IV (DEC 2007). Copyright 2024 The MITRE Corporation. All Rights Reserved.
+Rights in Data-General Clause 52.227-14, Alt. IV (DEC 2007). Copyright 2026 The MITRE Corporation. All Rights Reserved.
+
No Language Left Behind (NLLB) Translation Component
+
+
The No Language Left Behind component is based on Meta's No Language Left Behind Project. The component translates input text from a given source language to English. The source language can be provided as a job property, or be indicated in the detection properties from a feed-forward track. By default, this component is configured to use the facebook/nllb-200-3.3B modelNo Language Left Behind (NLLB). This provides the high translation quality, but also requires significant hardware resources. To accommodate smaller deployment enviroments, this component can use smaller NLLB models, such as nllb-200-distilled-1.3B or nllb-200-distilled-600M.
The FastText Language Detection Component utilizes the GlotLID language identification model
+and the fastText library to perform language identification on text.
Subject Tracking API and OR-Tools Subject Component [Experimental]
+
+
+
+
The OR-Tools Subject Tracking component associates detection tracks of different types (e.g., face, person, vehicle) to create a subject and track it in video.
We have added internal audit logging that may be used to audit events that occur during OpenMPF operation. Logging has been added to the Workflow Manager to record these types of events:
+
+
login/logout user information
+
create/read/update/download of data, configuration information, or action/pipeline information
+
rest API access
+
load UI content
+
TiesDB and S3 storage operations
+
Access to Hawtio
+
+
The information is logged as a JSON string. The fields of the logging statement consist of:
+
+
eid : an event id signifying the type of event
+
time : a timestamp for the event in UTC with format YYYY-MM-DDThh:mm:ss.mmmZ
+
tag : a tag value, which could be used to categorize events. For this release, all event logging uses a single constant tag value.
+
app : the name of the application where the event occurred, which is a constant set to "openmpf"
+
user : the username of the user that initiated the activity being logged.
+
op : operation identifier, one of c(create), r(read/view), m(modify), d(delete), l(login)
+
res : operation result, one of a(allowed), d(denied), e(error)
+
uri : [optional] the full web or file path.
+
bucket : [optional] The string identifying the S3 bucket for object storage
+
objectKey : [optional] The name that identifies the object in the S3 bucket
+
+
msg : a message string
+
+
+
Here is example audit logging output when creating a job through the REST API:
+
+
+
{"eid":200,"time":"2026-01-14T14:30:40.864Z","tag":"&B1E7-FFFF&","app":"openmpf","user":"admin","op":"c","res":"a","uri":"/rest/jobs","msg":"create job succeeded for Pipeline: OCV TINY YOLO VEHICLE DETECTION (WITH MARKUP) PIPELINE, Media URIs: [file:///opt/mpf/share/remote-media/car_video.MOV]"}
+
+
Audit logging is enabled by default, but can be disabled by setting the audit.logging.enabled system property to false.
+
Additional Quality Selection Properties
+
+
+
We have added the ability to perform feed-forward processing and artifact extraction based on component-defined properties, in addition to the standard QUALITY_SELECTION_PROPERTY.
+
Two new job properties have been added: ARTIFACT_EXTRACTION_POLICY_BEST_DETECTION_PROP_NAMES_LIST and FEED_FORWARD_BEST_DETECTION_PROP_NAMES_LIST. A detection component may choose to mark certain detections in a track as "best" by adding a custom property to those detections. For example, you could mark the detection whose bounding box has the largest area by adding a detection property named BEST_SIZE to that detection. If you want to extract the artifact for that detection, then you would add the string BEST_SIZE to the ARTIFACT_EXTRACTION_POLICY_BEST_DETECTION_PROP_NAMES_LIST. This would result in that detection artifact being extracted in addition to the others that would be extracted based on the QUALITY_SELECTION_PROPERTY.
The version of python that is used throughout OpenMPF has been upgraded from 3.8 to 3.12.
+
+
Feed-forward All Tracks [Experimental]
+
+
+
+
Previously, the Workflow Manager would feed forward each track from a previous pipeline stage to the next stage one track at a time. To support use cases where the next stage needs all of the tracks in one sub-job, we added a new job property FEED_FORWARD_ALL_TRACKS. When set to true, the workflow manager will pass all tracks generated in the current stage of the pipeline to the next.
+
+
+
A new class was added to the python component API:
+
+
+
class AllVideoTracksJob(NamedTuple):
+ job_name: str
+ data_uri: str
+ start_frame: int
+ stop_frame: int
+ job_properties: Mapping[str, str]
+ media_properties: Mapping[str, str]
+ feed_forward_tracks: List[VideoTrack]
+
+
and a new method: get_detections_from_all_video_tracks().
+
+
+
This functionality is experimental and currently supported only with the python component API.
An example illustrating the use of this feature can be found in the python SDK test component test_component.py.
+
+
+
Support for Supplying a Data URI for Media When Creating a Job
+
+
+
+
Users may now supply the media for an OpenMPF job using a data URI when creating a job through the REST API. The mediaURI field may contain a valid (properly encoded) URI to a single media source using one of the following URI schemes: file:, http:, https:, data:.
This component uses the VideoLLaMA3 model to generate a description of the activity in a video. It can describe a segment of the video, or create a summary of the entire video.
Natural Language Processing using cython_hunspell library
+
+
+
Detection
+
Language
+
fastText with the GlotLID model
+
+
Translation
Language
Azure Cognitive Services Translate API
-
Detection
+
Translation
Language
-
fastText with the GlotLID model
+
No Language Left Behind (NLLB)
+
+
+
Translation
+
Language
+
Argos
+
+
+
Translation
+
Language
+
Whisper
+
+
+
Video Summarization
+
Activity
+
LLAMA3
@@ -408,5 +443,5 @@
Overview
diff --git a/docs/site/search/search_index.json b/docs/site/search/search_index.json
index 1fdee4e561c5..8358854af3ee 100644
--- a/docs/site/search/search_index.json
+++ b/docs/site/search/search_index.json
@@ -2,19 +2,24 @@
"docs": [
{
"location": "/index.html",
- "text": "NOTICE:\n This software (or technical data) was produced for the U.S. Government under contract, and is subject to the\nRights in Data-General Clause 52.227-14, Alt. IV (DEC 2007). Copyright 2024 The MITRE Corporation. All Rights Reserved.\n\n\nOverview\n\n\nThere are numerous video and image exploitation capabilities available today. The Open Media Processing Framework (OpenMPF) provides a framework for chaining, combining, or replacing individual components for the purpose of experimentation and comparison.\n\n\nOpenMPF is a non-proprietary, scalable framework that permits practitioners and researchers to construct video, imagery, and audio exploitation capabilities using the available third-party components. Using OpenMPF, one can extract targeted entities in large-scale data environments, such as face and object detection.\n\n\nFor those developing new exploitation capabilities, OpenMPF exposes a set of Application Program Interfaces (APIs) for extending media analytics functionality. The APIs allow integrators to introduce new algorithms capable of detecting new targeted entity types. For example, a backpack detection algorithm could be integrated into an OpenMPF instance. OpenMPF does not restrict the number of algorithms that can operate on a given media file, permitting researchers, practitioners, and developers to explore arbitrarily complex composites of exploitation algorithms.\n\n\nA list of algorithms currently integrated into the OpenMPF as distributed processing components is shown here:\n\n\n\n\n\n\n\n\nOperation\n\n\nObject Type\n\n\nFramework\n\n\n\n\n\n\n\n\n\n\nDetection/Tracking\n\n\nFace\n\n\nLBP-Based OpenCV\n\n\n\n\n\n\nDetection/Tracking\n\n\nMotion\n\n\nMOG w/ STRUCK\n\n\n\n\n\n\nDetection/Tracking\n\n\nMotion\n\n\nSuBSENSE w/ STRUCK\n\n\n\n\n\n\nDetection/Tracking\n\n\nLicense Plate\n\n\nOpenALPR\n\n\n\n\n\n\nDetection\n\n\nSpeech\n\n\nSphinx\n\n\n\n\n\n\nDetection\n\n\nSpeech\n\n\nAzure Cognitive Services Batch Transcription API\n\n\n\n\n\n\nDetection\n\n\nScene\n\n\nOpenCV\n\n\n\n\n\n\nDetection\n\n\nClassification\n\n\nOpenCV DNN (GoogLeNet, Yahoo NSFW, vehicle color)\n\n\n\n\n\n\nDetection/Tracking\n\n\nClassification\n\n\nOpenCV DNN (YOLO)\n\n\n\n\n\n\nDetection/Tracking\n\n\nClassification/Features\n\n\nTensorRT (COCO classes)\n\n\n\n\n\n\nDetection\n\n\nText Region\n\n\nEAST\n\n\n\n\n\n\nDetection\n\n\nText (OCR)\n\n\nApache Tika\n\n\n\n\n\n\nDetection\n\n\nText (OCR)\n\n\nTesseract OCR\n\n\n\n\n\n\nDetection\n\n\nText (OCR)\n\n\nAzure Cognitive Services Read API\n\n\n\n\n\n\nDetection\n\n\nForm Structure (with OCR)\n\n\nAzure Cognitive Services Form Recognizer API\n\n\n\n\n\n\nDetection\n\n\nKeywords\n\n\nBoost Regular Expressions\n\n\n\n\n\n\nDetection\n\n\nImage (from document)\n\n\nApache Tika\n\n\n\n\n\n\nTranslation\n\n\nLanguage\n\n\nAzure Cognitive Services Translate API\n\n\n\n\n\n\nDetection\n\n\nLanguage\n\n\nfastText with the GlotLID model\n\n\n\n\n\n\n\n\nThe OpenMPF exposes data processing and job management web services via a User Interface (UI). These services allow users to upload media, create media processing jobs, determine the status of jobs, and retrieve the artifacts associated with completed jobs. The web services give application developers flexibility to use the OpenMPF in their preferred environment and programming language.",
+ "text": "NOTICE:\n This software (or technical data) was produced for the U.S. Government under contract, and is subject to the\nRights in Data-General Clause 52.227-14, Alt. IV (DEC 2007). Copyright 2024 The MITRE Corporation. All Rights Reserved.\n\n\nOverview\n\n\nThere are numerous video and image exploitation capabilities available today. The Open Media Processing Framework (OpenMPF) provides a framework for chaining, combining, or replacing individual components for the purpose of experimentation and comparison.\n\n\nOpenMPF is a non-proprietary, scalable framework that permits practitioners and researchers to construct video, imagery, and audio exploitation capabilities using the available third-party components. Using OpenMPF, one can extract targeted entities in large-scale data environments, such as face and object detection.\n\n\nFor those developing new exploitation capabilities, OpenMPF exposes a set of Application Program Interfaces (APIs) for extending media analytics functionality. The APIs allow integrators to introduce new algorithms capable of detecting new targeted entity types. For example, a backpack detection algorithm could be integrated into an OpenMPF instance. OpenMPF does not restrict the number of algorithms that can operate on a given media file, permitting researchers, practitioners, and developers to explore arbitrarily complex composites of exploitation algorithms.\n\n\nA list of algorithms currently integrated into the OpenMPF as distributed processing components is shown here:\n\n\n\n\n\n\n\n\nOperation\n\n\nObject Type\n\n\nFramework\n\n\n\n\n\n\n\n\n\n\nDetection/Tracking\n\n\nFace\n\n\nLBP-Based OpenCV\n\n\n\n\n\n\nDetection/Tracking\n\n\nMotion\n\n\nMOG w/ STRUCK\n\n\n\n\n\n\nDetection/Tracking\n\n\nMotion\n\n\nSuBSENSE w/ STRUCK\n\n\n\n\n\n\nDetection/Tracking\n\n\nLicense Plate\n\n\nOpenALPR\n\n\n\n\n\n\nDetection\n\n\nSpeech\n\n\nSphinx\n\n\n\n\n\n\nDetection\n\n\nSpeech\n\n\nAzure Cognitive Services Batch Transcription API\n\n\n\n\n\n\nDetection\n\n\nScene\n\n\nOpenCV\n\n\n\n\n\n\nDetection\n\n\nClassification\n\n\nOpenCV DNN (GoogLeNet, Yahoo NSFW, vehicle color)\n\n\n\n\n\n\nDetection\n\n\nClassification\n\n\nClip\n\n\n\n\n\n\nDetection/Tracking\n\n\nClassification\n\n\nOpenCV DNN (YOLO)\n\n\n\n\n\n\nDetection/Tracking\n\n\nClassification/Features\n\n\nTensorRT (COCO classes)\n\n\n\n\n\n\nDetection\n\n\nText Region\n\n\nEAST\n\n\n\n\n\n\nDetection\n\n\nText (OCR)\n\n\nApache Tika\n\n\n\n\n\n\nDetection\n\n\nText (OCR)\n\n\nTesseract OCR\n\n\n\n\n\n\nDetection\n\n\nText (OCR)\n\n\nAzure Cognitive Services Read API\n\n\n\n\n\n\nDetection\n\n\nForm Structure (with OCR)\n\n\nAzure Cognitive Services Form Recognizer API\n\n\n\n\n\n\nDetection\n\n\nKeywords\n\n\nBoost Regular Expressions\n\n\n\n\n\n\nDetection\n\n\nKnown Phrases\n\n\nTransformer Tagging\n\n\n\n\n\n\nDetection\n\n\nImage (from document)\n\n\nApache Tika\n\n\n\n\n\n\nCorrection\n\n\nText\n\n\nNatural Language Processing using cython_hunspell library\n\n\n\n\n\n\nDetection\n\n\nLanguage\n\n\nfastText with the GlotLID model\n\n\n\n\n\n\nTranslation\n\n\nLanguage\n\n\nAzure Cognitive Services Translate API\n\n\n\n\n\n\nTranslation\n\n\nLanguage\n\n\nNo Language Left Behind (NLLB)\n\n\n\n\n\n\nTranslation\n\n\nLanguage\n\n\nArgos\n\n\n\n\n\n\nTranslation\n\n\nLanguage\n\n\nWhisper\n\n\n\n\n\n\nVideo Summarization\n\n\nActivity\n\n\nLLAMA3\n\n\n\n\n\n\n\n\nThe OpenMPF exposes data processing and job management web services via a User Interface (UI). These services allow users to upload media, create media processing jobs, determine the status of jobs, and retrieve the artifacts associated with completed jobs. The web services give application developers flexibility to use the OpenMPF in their preferred environment and programming language.",
"title": "Home"
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"location": "/index.html#overview",
- "text": "There are numerous video and image exploitation capabilities available today. The Open Media Processing Framework (OpenMPF) provides a framework for chaining, combining, or replacing individual components for the purpose of experimentation and comparison. OpenMPF is a non-proprietary, scalable framework that permits practitioners and researchers to construct video, imagery, and audio exploitation capabilities using the available third-party components. Using OpenMPF, one can extract targeted entities in large-scale data environments, such as face and object detection. For those developing new exploitation capabilities, OpenMPF exposes a set of Application Program Interfaces (APIs) for extending media analytics functionality. The APIs allow integrators to introduce new algorithms capable of detecting new targeted entity types. For example, a backpack detection algorithm could be integrated into an OpenMPF instance. OpenMPF does not restrict the number of algorithms that can operate on a given media file, permitting researchers, practitioners, and developers to explore arbitrarily complex composites of exploitation algorithms. A list of algorithms currently integrated into the OpenMPF as distributed processing components is shown here: Operation Object Type Framework Detection/Tracking Face LBP-Based OpenCV Detection/Tracking Motion MOG w/ STRUCK Detection/Tracking Motion SuBSENSE w/ STRUCK Detection/Tracking License Plate OpenALPR Detection Speech Sphinx Detection Speech Azure Cognitive Services Batch Transcription API Detection Scene OpenCV Detection Classification OpenCV DNN (GoogLeNet, Yahoo NSFW, vehicle color) Detection/Tracking Classification OpenCV DNN (YOLO) Detection/Tracking Classification/Features TensorRT (COCO classes) Detection Text Region EAST Detection Text (OCR) Apache Tika Detection Text (OCR) Tesseract OCR Detection Text (OCR) Azure Cognitive Services Read API Detection Form Structure (with OCR) Azure Cognitive Services Form Recognizer API Detection Keywords Boost Regular Expressions Detection Image (from document) Apache Tika Translation Language Azure Cognitive Services Translate API Detection Language fastText with the GlotLID model The OpenMPF exposes data processing and job management web services via a User Interface (UI). These services allow users to upload media, create media processing jobs, determine the status of jobs, and retrieve the artifacts associated with completed jobs. The web services give application developers flexibility to use the OpenMPF in their preferred environment and programming language.",
+ "text": "There are numerous video and image exploitation capabilities available today. The Open Media Processing Framework (OpenMPF) provides a framework for chaining, combining, or replacing individual components for the purpose of experimentation and comparison. OpenMPF is a non-proprietary, scalable framework that permits practitioners and researchers to construct video, imagery, and audio exploitation capabilities using the available third-party components. Using OpenMPF, one can extract targeted entities in large-scale data environments, such as face and object detection. For those developing new exploitation capabilities, OpenMPF exposes a set of Application Program Interfaces (APIs) for extending media analytics functionality. The APIs allow integrators to introduce new algorithms capable of detecting new targeted entity types. For example, a backpack detection algorithm could be integrated into an OpenMPF instance. OpenMPF does not restrict the number of algorithms that can operate on a given media file, permitting researchers, practitioners, and developers to explore arbitrarily complex composites of exploitation algorithms. A list of algorithms currently integrated into the OpenMPF as distributed processing components is shown here: Operation Object Type Framework Detection/Tracking Face LBP-Based OpenCV Detection/Tracking Motion MOG w/ STRUCK Detection/Tracking Motion SuBSENSE w/ STRUCK Detection/Tracking License Plate OpenALPR Detection Speech Sphinx Detection Speech Azure Cognitive Services Batch Transcription API Detection Scene OpenCV Detection Classification OpenCV DNN (GoogLeNet, Yahoo NSFW, vehicle color) Detection Classification Clip Detection/Tracking Classification OpenCV DNN (YOLO) Detection/Tracking Classification/Features TensorRT (COCO classes) Detection Text Region EAST Detection Text (OCR) Apache Tika Detection Text (OCR) Tesseract OCR Detection Text (OCR) Azure Cognitive Services Read API Detection Form Structure (with OCR) Azure Cognitive Services Form Recognizer API Detection Keywords Boost Regular Expressions Detection Known Phrases Transformer Tagging Detection Image (from document) Apache Tika Correction Text Natural Language Processing using cython_hunspell library Detection Language fastText with the GlotLID model Translation Language Azure Cognitive Services Translate API Translation Language No Language Left Behind (NLLB) Translation Language Argos Translation Language Whisper Video Summarization Activity LLAMA3 The OpenMPF exposes data processing and job management web services via a User Interface (UI). These services allow users to upload media, create media processing jobs, determine the status of jobs, and retrieve the artifacts associated with completed jobs. The web services give application developers flexibility to use the OpenMPF in their preferred environment and programming language.",
"title": "Overview"
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"location": "/Release-Notes/index.html",
- "text": "NOTICE:\n This software (or technical data) was produced for the U.S. Government under contract, and is subject to the\nRights in Data-General Clause 52.227-14, Alt. IV (DEC 2007). Copyright 2024 The MITRE Corporation. All Rights Reserved.\n\n\nOpenMPF 9.0.x\n\n\n9.0.0: May 2024\n\n\n\nDocumentation\n\n\n\n\n\nCreated a new \nQuality Selection Guide\n.\n\n\n\n\nQuality Selection\n\n\n\n\n\nCan now specify a \nQUALITY_SELECTION_PROPERTY\n and \nQUALITY_SELECTION_THRESHOLD\n for choosing exemplars, artifacts,\n and controlling feed-forward behavior.\n\n\nThe following old job properties and old system properties are no longer supported. The tables show the new properties\n that should be used instead:\n\n\n\n\n\n\n\n\n\n\nOld Job Property\n\n\nNew Job Properties\n\n\n\n\n\n\n\n\n\n\nCONFIDENCE_THRESHOLD\n\n\nQUALITY_SELECTION_PROPERTY\nQUALITY_SELECTION_THRESHOLD\n\n\n\n\n\n\nARTIFACT_EXTRACTION_POLICY_TOP_CONFIDENCE_COUNT\n\n\nARTIFACT_EXTRACTION_POLICY_TOP_QUALITY_COUNT\n\n\n\n\n\n\nFEED_FORWARD_TOP_CONFIDENCE_COUNT\n\n\nFEED_FORWARD_TOP_QUALITY_COUNT\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nOld System Property\n\n\nNew System Properties\n\n\n\n\n\n\n\n\n\n\ndetection.confidence.threshold\n\n\ndetection.quality.selection.prop\ndetection.quality.selection.threshold\n\n\n\n\n\n\ndetection.artifact.extraction.policy.top.confidence.count\n\n\ndetection.artifact.extraction.policy.top.quality.count\n\n\n\n\n\n\n\n\n\n\nBy default, \nQUALITY_SELECTION_PROPERTY\n is set to the value of \ndetection.quality.selection.prop\n system property,\n which, by default, is \nCONFIDENCE\n. In most cases this preserves the previous behavior.\n\n\nBy default, \nQUALITY_SELECTION_THRESHOLD\n is set to the value of \ndetection.quality.selection.threshold\n system\n property, which, by default, is \n-Infinity\n. This setting disables the threshold. Previously, the default value of\n \ndetection.confidence.threshold\n was -1, which disabled the threshold for most components.\n\n\nComponents that previously used \nCONFIDENCE_THRESHOLD\n now have \nQUALITY_SELECTION_PROPERTY=CONFIDENCE\n. Also,\n \nQUALITY_SELECTION_THRESHOLD\n is set to the previous value of \nCONFIDENCE_THRESHOLD\n. For example, see \nthis\n commit\n\n for changes made to the OcvYoloDetection component.\n\n\nEXEMPLAR_POLICY\n is now set to \nQUALITY\n by default. This setting results in choosing the detection within each track\n with the maximum quality according to the \nQUALITY_SELECTION_PROPERTY\n. Previously, the selection was always made\n based on highest detection confidence.\n\n\nSimilarly, the new \nFEED_FORWARD_TOP_QUALITY_COUNT\n and \nARTIFACT_EXTRACTION_POLICY_TOP_QUALITY_COUNT\n properties use\n \nQUALITY_SELECTION_PROPERTY\n and \nQUALITY_SELECTION_THRESHOLD\n.\n\n\nRefer to the \nQuality Selection Guide\n for details.\n\n\n\n\nTransformer Tagging Component\n\n\n\n\n\nThis component uses a user-specified corpus JSON file to match known phrases against each sentence in the input text\n data.\n\n\nThe input text sentences that generate match scores above the threshold are called \"trigger sentences\". These\n sentences are grouped by \"tag\" based on which entry in the corpus they matched against.\n\n\nThe underlying \nall-mpnet-base-v2 model\n was trained\n on a variety of text data in order to understand the commonalities in phrasing, subject, and context.\n\n\nRefer to the \nREADME\n\n for details.\n\n\n\n\nKeyword Tagging Component Output\n\n\n\n\n\nUpdated the Keyword Tagging Component to generate output in the same format as the Transformer Tagging Component. For\n example, the output properties used to take the form \n TRIGGER WORDS\n and \n TRIGGER WORDS OFFSET\n:\n\n\n\n\nTEXT TRIGGER WORDS\nTEXT TRIGGER WORDS OFFSET\nTRANSLATION TRIGGER WORDS\nTRANSLATION TRIGGER WORDS OFFSET\n\n\n\n\n\nNow the output properties take the form \n TRIGGER WORDS\n and \n TRIGGER WORDS OFFSET\n:\n\n\n\n\nTEXT TRAVEL TRIGGER WORDS\nTEXT TRAVEL TRIGGER WORDS OFFSET\nTRANSLATION TRAVEL TRIGGER WORDS\nTRANSLATION TRAVEL TRIGGER WORDS OFFSET\n\n\n\n\n\nNotice that in the above example the new output properties include the word \nTRAVEL\n. If trigger words are detected\n for other tags, such as \nFINANCIAL\n and \nVEHICLE\n, those words will be used in separate \nTRIGGER WORDS\n and\n \nTRIGGER WORDS OFFSET\n output properties.\n\n\nThis change enables the job consumer to determine which trigger words are associated with each entry in the \nTAGS\n\n output property.\n\n\nRefer to the \"Outputs\" section of the\n \nREADME\n for details.\n\n\n\n\nReporting Component Processing Time\n\n\n\n\n\nThe JSON output object contains a new section for reporting component processing time in milliseconds. For example:\n\n\n\n\n\"timing\": {\n \"processingTime\": 1514,\n \"actions\": [\n {\n \"name\": \"OCV YOLO VEHICLE DETECTION ACTION\",\n \"processingTime\": 1431\n },\n {\n \"name\": \"TENSORFLOW VEHICLE COLOR DETECTION (WITH FF REGION) ACTION\",\n \"processingTime\": 83\n }\n ]\n},\n\n\n\n\n\nThis does not include the time sub-jobs spent waiting in queues, or processing time by the Workflow Manager, such as\n the time to perform media inspection.\n\n\nAlso, the above JSON is reported in the TiesDB job record within the \ndataObject\n field.\n\n\n\n\nNLP Text Splitter Utility\n\n\n\n\n\nThe new NLP Text Splitter utility uses spaCy or \nWhere's the Point (WtP)\n\n models for determining how to break up text into sentences.\n\n\nSupports both CPU processing and optional GPU processing.\n\n\nUpdated the Azure Translation Component to use this utility to ensure that translation requests are within the 50,000\n character limit.\n\n\nRefer to the\n \nREADME\n\n for details.\n\n\n\n\nCLIP Component Video Support\n\n\n\n\n\nThe CLIP Component now supports processing videos in addition to the previous ability to process images. Specify the\n batch size using \nDETECTION_FRAME_BATCH_SIZE\n.\n\n\nThe component also supports a new, larger, and more accurate \nViT-L/14\n model in addition to the previous \nViT-B/32\n\n model. Both models are supported via the optional Triton server as well as within the component itself for non-Triton\n deployments.\n\n\nRefer to the\n \nREADME\n\n for performance metrics.\n\n\nThe \nNUMBER_OF_TEMPLATES\n property has been renamed to \nTEMPLATE_TYPE\n and now accepts one of the following values:\n \nopenai_1\n, \nopenai_7\n, \nopenai_80\n.\n\n\n\n\nImport Root Certificates for Components\n\n\n\n\n\nCan now specify a \nMPF_CA_CERTS\n environment variable for component Docker services to import root certificates.\n\n\nMay be useful when components need to communicate with external web services.\n\n\nRefer to the \nREADME\n for details.\n\n\n\n\nDocker Secrets for Environment Variables\n\n\n\n\n\nCan now use Docker secrets for environment variables in the Docker compose file.\n\n\nThis prevents exposing information as plain text in \ndocker-compose.yml\n.\n\n\nMay be useful for environment variables like:\n\n\nWorkflow Manager username and password: \nWFM_USER\n and \nWFM_PASSWORD\n\n\nKeystore password when enabling Workflow Manager HTTPS: \nKEYSTORE_PASSWORD\n\n\nAzure credentials: \nMPF_PROP_ACS_URL\n and \nMPF_PROP_ACS_SUBSCRIPTION_KEY\n\n\n\n\n\n\nRefer to the\n \nREADME\n\n for details.\n\n\n\n\nFeatures\n\n\n\n\n\n[\n#1692\n] Create a TransformerTagging component\n\n\n[\n#1718\n] Support a \nQUALITY_SELECTION_PROP\n to specify how the WFM should choose an exemplar\n\n\n[\n#1754\n] Report amount of time components spent executing a job\n\n\n[\n#1756\n] Support \nMPF_CA_CERTS\n for components\n\n\n[\n#1771\n] Azure Translation: Identify character limits. Split text using NLP Text Splitter.\n\n\n[\n#1798\n] Add NLP Text Splitter to Python Component SDK\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#1694\n] Update CLIP component to support videos\n\n\n[\n#1706\n] Update KeywordTagging to work with TransformerTagging\n\n\n[\n#1745\n] Support using docker secrets for environment variables in \ndocker-compose.yml\n\n\n[\n#1769\n] Upgrade to proto3 and clean up \n.proto\n files\n\n\n[\n#1774\n] Update how TransformerTagging tokenizes sentences\n\n\n[\n#1785\n] Upgrade to OpenCV 4.9\n\n\n[\n#1786\n] Modify the behavior of Markup when \nCONFIDENCE\n is the bounding box label to be displayed\n\n\n[\n#1797\n] Further update Azure Translation and STT language maps\n\n\n[\n#1803\n] Upgrade Postgres client used by Workflow Manager\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1781\n] Markup boxes are not drawn when animation is disabled and there are gaps in a track\n\n\n[\n#1799\n] Keyword Tagging removes newlines so character offsets don't line up with original text\n\n\n\n\nOpenMPF 8.0.x\n\n\n8.0.4: May 2024\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1805\n] Workflow Manager incorrectly detects whether certain videos\n are constant or variable frame rate\n\n\n\n\n8.0.3: April 2024\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1788\n] Azure Speech and Translation: Update supported language\n mappings\n\n\n\n\n8.0.2: March 2024\n\n\n\nDocumentation\n\n\n\n\n\nUpdated the \nREST API\n with new \n[GET] /rest/queues\n and \n[GET] /rest/queues/{name}\n endpoints.\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#1776\n] Add REST endpoint for retrieving the ActiveMQ message counts\n for each queue\n\n\n\n\n8.0.1: March 2024\n\n\n\nUpdates\n\n\n\n\n\n[\n#1768\n] Add Option to Merge Text Sections in TikaTextDetection\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1763\n] Media inspections fails when ffprobe does not specify a\n stream \"codec_type\"\n\n\n\n\n8.0.0: December 2023\n\n\n\nDocumentation\n\n\n\n\n\nCreated a new \nOpenID Connect Guide\n.\n\n\nUpdated the \nAdmin Guide\n and \nUser Guide\n to remove\n \n/workflow-manager\n from the Workflow Manager base URL. The Admin Guide includes a section for the new Hawtio web\n console.\n\n\nUpdated the \nREST API\n to use path parameters for pipelines, tasks, actions, and algorithms\n endpoints.\n\n\nUpdated the \nComponent Descriptor Reference\n with \nalgorithm.trackType\n.\n\n\nUpdated the \nC++ Batch Component API\n, \nPython Batch Component\n API\n, and \nJava Batch Component API\n to\n remove the ability to get the detection type since track type is now specified in \ndescriptor.json\n.\n\n\nCreated a new \nTrigger Guide\n.\n\n\nCreated a new \nRoll Up Guide\n.\n\n\n\n\nOpenID-Connect (OIDC) Authentication\n\n\n\n\n\nThe Workflow Manager can now optionally use an OpenID Connect (OIDC) provider to handle authentication for users of\n the web UI and clients of the REST API. The URI for the OIDC provider is specified using the \nOIDC_ISSUER_URI\n\n environment variable.\n\n\nWhen enabled, OIDC is used to authenticate components when they register with the Workflow Manager.\n\n\nWhen \nCALLBACK_USE_OIDC\n is set to \ntrue\n, the Workflow Manager will send a token in job request callbacks.\n\n\nWhen \nTIES_DB_USE_OIDC\n is set to \ntrue\n, the Workflow Manager will send a token when posting to a TiesDb server.\n\n\nWhen OIDC is not enabled, the Workflow Manager uses basic authentication with usernames and passwords, as in previous\n versions of OpenMPF.\n\n\nRefer to the \nOpenID Connect Guide\n for more information on the various OIDC\n environment variables and a Keycloak example.\n\n\n\n\nEmbedded ActiveMQ Broker and Hawtio\n\n\n\n\n\nActiveMQ is now part of the Workflow Manager Spring Boot web application and is no longer run as a separate Docker\n service. This enables ActiveMQ to integrate with Spring Security so it can be protected by the Workflow Manager's OIDC\n support.\n\n\nThe Workflow Manager is the sender or recipient of all ActiveMQ messages, so embedding ActiveMQ in the Workflow\n Manager prevents a network hop on all messages.\n\n\nThe ActiveMQ management page has been replaced by \nHawtio\n, which is more feature rich and can be\n used to monitor the state of the ActiveMQ queues used for communication between the Workflow Manager and the\n components. The Hawtio web console can be accessed by selecting \"Hawtio\" from the \"Configuration\" dropdown menu in the\n top menu bar of the web UI.\n\n\nImportantly, the base URL for the Workflow Manager is now http://localhost:8080 instead of\n http://localhost:8080/workflow-manager. \n/workflow-manager\n is no longer part of the path. This change was made to\n enable Hawtio integration.\n\n\n\n\nREST API Updates\n\n\n\n\n\nThe following changes have been made to the REST endpoints to address a limitation with Swagger (OpenAPI). These\n changes enable the REST endpoints to properly show up in the Swagger page, which is accessed by selecting \"REST API\"\n from the \"Configuration\" dropdown menu in the top menu bar of the web UI.\n\n\n\n\n\n\n\n\n\n\nOld REST Endpoint\n\n\nNew REST Endpoint\n\n\n\n\n\n\n\n\n\n\n[GET] /rest/pipelines?name={name}\n\n\n[GET] /rest/pipelines/{name}\n\n\n\n\n\n\n[GET] /rest/tasks?name={name}\n\n\n[GET] /rest/tasks/{name}\n\n\n\n\n\n\n[GET] /rest/actions?name={name}\n\n\n[GET] /rest/actions/{name}\n\n\n\n\n\n\n[GET] /rest/algorithms?name={name}\n\n\n[GET] /rest/algorithms/{name}\n\n\n\n\n\n\n[DELETE] /rest/pipelines?name={name}\n\n\n[DELETE] /rest/pipelines/{name}\n\n\n\n\n\n\n[DELETE] /rest/tasks?name={name}\n\n\n[DELETE] /rest/tasks/{name}\n\n\n\n\n\n\n[DELETE] /rest/actions?name={name}\n\n\n[DELETE] /rest/actions/{name}\n\n\n\n\n\n\n\n\n\n\nIn general, the name is now specified as part of the URL path instead of as a URL parameter.\n\n\n/\n and \n;\n characters are no longer allowed in these names.\n\n\n\n\nPackaging and Deployment\n\n\n\n\n\nEach component's \ndescriptor.json\n now requires an \nalgorithm.trackType\n field. This is used by the Workflow Manager\n to determine the kind of tracks that may be generated by the component (e.g. \nFACE\n, \nTEXT\n, \nCLASS\n, etc.). This is\n now used in place of the component API calls that were used to get the detection type. \n\n\n\n\nComponent API Updates\n\n\n\n\n\nThe following changes were made since the track type is now part of each component's \ndescriptor.json\n:\n\n\nRemoved \nGetDetectionType()\n from the CPP Component API.\n\n\nRemoved \ndetection_type\n from the Python Component API.\n\n\nRemoved \ngetDetectionType()\n from the Java Component API.\n\n\n\n\n\n\n\n\nChanges to JSON Output Object\n\n\n\n\n\nNew JSON output objects use \naction\n instead of \nsource\n in the track type group. Also, \nsource\n is removed from each track.\n\n\nConsider this example of the old JSON output:\n\n\n\n\n\"output\": {\n \"FACE\": [\n {\n \"source\": \"+#MOG MOTION DETECTION (WITH AUTO-ORIENTATION) PREPROCESSOR ACTION#OCV FACE DETECTION (WITH AUTO-ORIENTATION) ACTION\",\n \"algorithm\": \"FACECV\",\n \"tracks\": [\n {\n \"id\": \"4bcba9b95b92a5115b7da1097fcffa962480d0b4424a656772bef12161d775c1\",\n \"startOffsetFrame\": 0,\n \"stopOffsetFrame\": 0,\n \"startOffsetTime\": 0,\n \"stopOffsetTime\": 0,\n \"type\": \"FACE\",\n \"source\": \"+#MOG MOTION DETECTION (WITH AUTO-ORIENTATION) PREPROCESSOR ACTION#OCV FACE DETECTION (WITH AUTO-ORIENTATION) ACTION\",\n \"confidence\": 8.799637,\n ...\n\n\n\n\n\nThe corresponding new JSON output is:\n\n\n\n\n\"output\": {\n \"FACE\": [\n {\n \"action\": \"OCV FACE DETECTION (WITH AUTO-ORIENTATION) ACTION\",\n \"algorithm\": \"FACECV\",\n \"tracks\": [\n {\n \"id\": \"4bcba9b95b92a5115b7da1097fcffa962480d0b4424a656772bef12161d775c1\",\n \"startOffsetFrame\": 0,\n \"stopOffsetFrame\": 0,\n \"startOffsetTime\": 0,\n \"stopOffsetTime\": 0,\n \"type\": \"FACE\",\n \"confidence\": 8.799637,\n ...\n\n\n\nTrigger Support\n\n\n\n\n\nA \nTRIGGER\n property can now be added to any action in a pipeline. It will only be used if \nFEED_FORWARD_TYPE\n is\n provided and set to something other than \nNONE\n. The \nTRIGGER\n property is used to conditionally control if the\n Workflow Manager executes that action. Each feed-forward track that is not executed is passed to the next stage of the\n pipeline. This results in skipping untriggered actions.\n\n\nThe value of \nTRIGGER\n takes the form \n=[;...]\n. For example, if the value is\n \nCLASSIFICATION=car\n then the Workflow Manager would only execute the associated action using feed-forward tracks from\n the previous stage in the pipeline if those tracks have the \nCLASSIFICATION\n track property with a value of \ncar\n.\n This could be useful to skip a license plate detection action. To enable the action to trigger on more than just \ncar\n\n tracks you can provide a list of valid values. For example, \nCLASSIFICATION=car;truck;bus\n.\n\n\nThe \nTrigger Guide\n goes into more detail and provides an example of a pipeline with\n multiple speech-to-text stages. \nTRIGGER\n is used to select which speech-to-text algorithm is executed based on the\n detected language in the media.\n\n\n\n\nRoll Up Support\n\n\n\n\n\nThe Workflow Manager can be configured to replace the values of track and detection properties\n after receiving tracks and detections from a component. For example, the \nCLASSIFICATION\n property\n may be set to \"car\", \"bus\", and \"truck\". Those can be rolled up into \"vehicle\".\n\n\nTo use this feature, set the \nROLL_UP_FILE\n property to the path of a JSON file that matches\n the format of this example:\n\n\n\n\n[\n {\n \"propertyToProcess\": \"CLASSIFICATION\",\n \"originalPropertyCopy\": \"ORIGINAL CLASSIFICATION\",\n \"groups\": [\n {\n \"rollUp\": \"vehicle\",\n \"members\": [\n \"truck\",\n \"car\",\n \"bus\"\n ]\n }\n ]\n }\n]\n\n\n\n\n\nRefer to the \nRoll Up Guide\n for an explanation and more details.\n\n\n\n\nChanged All \"whitelist\" References to \"allow list\"\n\n\n\n\n\nIn an effort to be more culturally sensitive, all references to \"whitelist\" have been removed or renamed to \"allow\n list\".\n\n\nThe \nwhitelist.\n prefix has been removed from the entries in the \nmediaType.properties\n file. For example,\n \nwhitelist.image/gif=VIDEO\n is now \nimage/gif=VIDEO\n.\n\n\nThe OcvDnnDetection component \nFEED_FORWARD_WHITELIST_FILE\n property has been renamed to\n \nFEED_FORWARD_ALLOW_LIST_FILE\n.\n\n\nThe OcvYoloDetection component \nCLASS_WHITELIST_FILE\n property has been renamed to \nCLASS_ALLOW_LIST_FILE\n.\n\n\n\n\nArgos Translation Component\n\n\n\n\n\nThis new component utilizes \nArgos Translate\n to translate input\n text from a given source language to English. It can be used in a feed-forward pipeline to process tracks with\n language and/or script identifiers from an upstream stage.\n\n\nRefer to the \nREADME\n for\n details.\n\n\n\n\nWhisper Speech-to-Text and Translation Component\n\n\n\n\n\nThis new component utilizes \nOpenAI Whisper\n to perform language detection,\n speech-to-text transcription, or speech translation.\n\n\nIf multiple languages are spoken in a single piece of media, language detection will detect only one of them.\n\n\nNote that Whisper is not designed to return a transcription in the source language when performing translation, so we\n implemented the component to perform an additional transcribe call when configured to perform translation.\n\n\nRefer to the \nREADME\n\n for details.\n\n\n\n\nContrastive Language\u2013Image Pre-training (CLIP) Component\n\n\n\n\n\nThis new component utilizes \nCLIP\n to classify images using the 80 COCO classes, 1000\n ImageNet classes, or a list of user-provided classes. It can run on a CPU or GPU, and can make calls to an NVIDIA\n Triton inference server.\n\n\nClassification is performed by taking the class names and filling in one or more text prompts. For example, \"a photo\n of {}\", where \"{}\" can be \"dog\" or \"cat\". An embedding is generated using the text prompt(s) for each class and\n compared against the image embedding to get a match score. Optionally, users can provide a list of their own text\n prompts.\n\n\nOpenAI trained the CLIP model using a wide variety of images and their respective captions from the Internet. This may\n make it suitable for a wide variety of classification tasks without further training (known as zero-shot\n classification). For example, a user could make up a list of classes for arbitrary objects like \"walrus\", \"paperclip\",\n \"pizza\", etc., and use the default text prompts.\n\n\nIt is also possible to use CLIP to classify concepts like scenes and sentiment. For example, using a text prompt of \"a\n {} scene\" where the classes are \"safe\", \"violent\", and \"dangerous\".\n\n\nOptionally, the CLIP component can return the image embedding as the track \nFEATURE\n. For example, this can be used\n for search and retrieval tasks by comparing it to other embeddings enrolled in a database.\n\n\nRefer to the \nREADME\n for\n details.\n\n\n\n\nFeatures\n\n\n\n\n\n[\n#1547\n] Create Argos translation component\n\n\n[\n#1574\n] Update the WFM to support an optional \nTRIGGER\n property on any action\n\n\n[\n#1598\n] Create a Whisper component for speech-to-text and and translation\n\n\n[\n#1644\n] Create CLIP component for processing images\n\n\n[\n#1704\n] Update Workflow Manager to authenticate users and REST clients using OIDC\n\n\n[\n#1730\n] Update Workflow Manager to optionally use OIDC when sending callbacks and posting to TiesDb\n\n\n[\n#1733\n] Update Workflow Manager to use an embedded ActiveMQ broker\n\n\n[\n#1793\n] Add Roll Up support to Workflow Manager\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#799\n] Avoid unnecessary serialization between Camel routes\n\n\n[\n#949\n] Change \n/pipelines?name=MYPIPELINE\n REST endpoint to \n/pipelines/MYPIPELINE\n\n\n[\n#1643\n] Remove \nLONG_SPEAKER_ID\n and instead only use \nSPEAKER_ID\n\n\n[\n#1645\n] Refactor camel code\n\n\n[\n#1705\n] Change all references to \"whitelist\" to \"allow list\" and \"blacklist\" to \"block list\"\n\n\n[\n#1759\n] Disable markup animation by default\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1642\n] \nInProgressBatchJobsService.setProcessedAction\n is now called when a previous task produces no tracks\n\n\n[\n#1755\n] The Workflow Manager logs page does not properly handle multi-byte characters\n\n\n\n\nOpenMPF 7.2.x\n\n\n7.2.6: January 2024\n\n\n\nDocumentation\n\n\n\n\n\nCreated a new \nHealth Check Guide\n.\n\n\n\n\nHealth Check Support\n\n\n\n\n\nThe C++ and Python component executors can be configured to run health checks on components prior to running jobs.\n Health checks are configured using environment variables:\n\n\nHEALTH_CHECK\n: When set to \"ENABLED\", the component executor will run health checks.\n\n\nHEALTH_CHECK_TIMEOUT\n: When set to a positive integer, specifies the minimum number of seconds between health\n checks. When absent or set to 0, a health check will run before every job.\n\n\nHEALTH_CHECK_RETRY_MAX_ATTEMPTS\n: When set to a positive integer, specifies the number of consecutive health\n check failures that will cause the component service to exit. When absent or set to 0, the component service will\n never exit because of a failed health check.\n\n\n\n\n\n\nAlso, an INI file must be provided at \n$MPF_HOME/plugins//health/health-check.ini\n. For example:\n\n\n\n\nmedia=$MPF_HOME/plugins/OcvFaceDetection/health/meds_faces_image.png\nmin_num_tracks=2\nmedia_type=IMAGE\n\n[job_properties]\nJOB PROP1=VALUE1\nJOB PROP2=VALUE2\n\n[media_properties]\nMEDIA PROP=MEDIA VALUE\n\n\n\n\n\nRefer to the \nHealth Check Guide\n for an explanation and more details.\n\n\n\n\nFeatures\n\n\n\n\n\n[\n#1731\n] Implement health checks for C++ and Python components\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#1727\n] Update ffmpeg to 6.1\n\n\n\n\n7.2.5: November 2023\n\n\n\nUpdates\n\n\n\n\n\n[\n#1715\n] Upgrade ActiveMQ to 5.17.6\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1711\n] When selecting detections with the highest confidence,\n Workflow Manager should consistently handle detections with equal confidence\n\n\n\n\n7.2.4: September 2023\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1707\n] Fix bug where TiesDB check status reports\n \nNO_TIES_DB_URL_IN_JOB\n instead of \nMEDIA_MIME_TYPES_ABSENT\n\n\n\n\n7.2.3: June 2023\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1697\n] Prevent OcvYoloDetection component from deadlocking on\n strange frame sizes when using Triton\n\n\n\n\n7.2.2: June 2023\n\n\n\nUpdates\n\n\n\n\n\n[\n#1693\n] Add property to enable/disable SAS in AzureSpeech\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1695\n] Fix memory leak in KeywordTagging component\n\n\n\n\n7.2.1: June 2023\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1678\n] Fix bug where ffmpeg hangs when processing some kinds of\n unsupported/corrupted media\n\n\n\n\n7.2.0: May 2023\n\n\n\nDocumentation\n\n\n\n\n\nCreated a new \nTiesDb Guide\n.\n\n\nUpdated the \nComponent Descriptor Reference\n with \noutputChangedCounter\n.\n\n\nUpdated the \nREST API\n with a new \n[POST] /rest/jobs/tiesdbrepost\n endpoint.\n\n\nUpdated the REST API \n[POST] /rest/jobs\n response with \ntiesDbCheckStatus\n and \noutputObjectUri\n.\n\n\n\n\nTiesDb Re-Post\n\n\n\n\n\nAdded a new \n[POST] /rest/jobs/tiesdbrepost\n endpoint that accepts an array of job ids as an input and will attempt to\n re-post the job assertions (records) to TiesDb for each one. \n\n\nAdded a \"TiesDb\" column to the Job Status page. If there is a problem posting a record to the TiesDb server the column\n will contain an \"ERROR\" button. Clicking on it will provide a description of the error and a button that can be used\n to re-post the associated job records.\n\n\n\n\nTiesDb Checking\n\n\n\n\n\nIf the \nTIES_DB_URL\n job property or \nties.db.url\n system property is set when submitting a job creation request, \n then the Workflow Manager will attempt to check TiesDb for existing job results before running the job again.\n\n\nThe Workflow Manager will attempt to use the most-recently-created job results, preferring jobs that completed without\n errors or warnings, and preferring jobs that completed with warnings over completed with errors.\n\n\nTo prevent this check, set \nSKIP_TIES_DB_CHECK=true\n. That will force the job to run and attempt to post the new\n job results to TiesDb.\n\n\nWhen using TiesDb, we strongly recommend providing both the \nMEDIA_HASH\n and \nMIME_TYPE\n in the \nmedia.metadata\n map\n in the job request. This will enable the Workflow Manager to skip media inspection. When using S3 object storage, this\n means that the Workflow Manager will not need to download the media before checking TiesDb for existing job records.\n\n\nThe \n[POST] /rest/jobs\n response now contains a \ntiesDbCheckStatus\n and \noutputObjectUri\n field. \ntiesDbCheckStatus\n\n will be set to one of the following values:\n\n\nNOT_REQUESTED\n\n\nNO_TIES_DB_URL_IN_JOB\n\n\nMEDIA_HASHES_ABSENT\n\n\nMEDIA_MIME_TYPES_ABSENT\n\n\nNO_MATCH\n\n\nFOUND_MATCH\n\n\n\n\n\n\nWhen there is a \nFOUND_MATCH\n, the \noutputObjectUri\n will be set to the URI of the old TiesDb record if S3 copy is\n not enabled.\n\n\nBy default, the \nties.db.s3.copy.enabled\n system property is set to \ntrue\n. This means that the Workflow Manager will\n attempt to copy all of the artifacts, markup, and derivative media associated with the job in TiesDb from the S3\n locations associated with the old job to the new S3 location specified in the new job. A new JSON output object will\n be generated. To disable this behavior set the system property, or \nTIES_DB_S3_COPY_ENABLED\n, to \nfalse\n. Then the\n Workflow Manager will simply provide a link to the old JSON as the result of the new job.\n\n\nIf there is a problem copying between S3 locations, the \"TiesDb\" column to the Job Status page will show a\n \"COPY ERROR\" button. Clicking on it will provide a description of the error.\n\n\n\n\nTiesDb Linked Media\n\n\n\n\n\nAdded support for \nLINKED_MEDIA_HASH\n in the \nmedia.properties\n section of the job creation request. When specified,\n the value of \nLINKED_MEDIA_HASH\n will be used instead of the actual media hash when creating a record in TiesDb,\n and also when looking for existing records in TiesDb.\n\n\nThis feature can be used to submit a transcoded (or thumbnail) version of an image to process instead of the source\n image. For example, the source image may be in a format not supported by OpenMPF. In this case, the value of\n \nLINKED_MEDIA_HASH\n can be set to the source image, but the rest of the job creation request would specify\n the \nmedia.mediaUri\n and \nmedia.metadata\n for the transcoded version of that image.\n\n\n\n\nOutput Changed Counter\n\n\n\n\n\nAdded the \noutput.changed.counter\n system property to the Workflow Manager and \noutputChangedCounter\n field to each\n component's \ndescriptor.json\n. These values are used when calculating the hash for a job when its record is posted to\n TiesDb, and also when checking TiesDb for existing records when a new job is submitted.\n\n\nIf the Workflow Manager is updated for any reason that should invalidate pre-existing job results, such as a\n change to the fields in the JSON output object, or significant improvements to track merging, for example, then the\n value of \noutput.changed.counter\n should be incremented by one. This will ensure that records in TiesDb will not be\n used so that all future jobs will need to be (re)run at least once until the counter is incremented again.\n\n\nThe same is true for each component. If a component is updated for any reason that should invalidate\n pre-existing job results, such as changes to input or output properties, or substantial improvements to the algorithm,\n then the value of \noutputChangedCounter\n should be incremented by one.\n\n\n\n\nChanges to JSON Output Object\n\n\n\n\n\nNew JSON output objects will include \ntiesDbSourceJobId\n and \ntiesDbSourceMediaPath\n when the Workflow Manager can use\n previous job results stored in TiesDB. Note that the Workflow Manager will not generate a new JSON output object\n unless \nS3_RESULTS_BUCKET\n is set to a valid value, S3 access and secret keys are provided, and\n \nTIES_DB_S3_COPY_ENABLED=true\n.\n\n\n\n\nffprobe for Media Inspection\n\n\n\n\n\nThe Workflow Manager media inspection behavior now uses \nffprobe\n with \n-print_format json\n to return more precise\n \nFPS\n values for the \nmedia.mediaMetadata\n in the JSON output object. For example, the previous version of the\n Workflow Manager would return \n29.97\n, where the new version will return \n29.97002997002997\n. In multi-hour-long\n vidoes this can prevent cases where the last few frames were being ignored.\n\n\nThe previous version of the Workflow Manager was using both \nffmpeg\n and OpenCV to determine the number of frames in\n a video. We removed the OpenCV frame counter in this version because the \nffprobe\n approach is more accurate.\n The \nffprobe\n command replaces the old \nffmpeg\n command. \n\n\n\n\nWeb User Interface\n\n\n\n\n\nUpdated the Job Status page to be more efficient. Searching a database of hundreds of thousands of jobs takes a long\n time. By limiting the search to one page of results at a time the UI is more responsive.\n\n\nRemoved timeout and bootout. The user session will no longer automatically end due to time out, or due to the same\n user logging in from a different host or browser. These behaviors were deemed too disruptive by end users.\n\n\nUpdated the Job Status page to include a \"TiesDb\" column that reports TiesDb status, such as when posting records\n to TiesDb and when retrieving existing records.\n\n\n\n\nFeatures\n\n\n\n\n\n[\n#1438\n] Create a REST endpoint that will attempt to re-post to TiesDb\n\n\n[\n#1613\n] Check TiesDb before running a job\n\n\n[\n#1650\n] Create TiesDb records for thumbnail jobs under the parent media\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#1342\n] Use ffprobe to get FPS during media inspection\n\n\n[\n#1564\n] Use ffprobe's JSON output instead of regexes during media inspection\n\n\n[\n#1601\n] Update the Workflow Manager jobs table to be more efficient\n\n\n[\n#1611\n] Remove Workflow Manager timeout and bootout behavior\n\n\n\n\nOpenMPF 7.1.x\n\n\n7.1.12: March 2023\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1667\n] Handle Webp files with extra data at the end that cause components to crash\n\n\n\n\n7.1.10: March 2023\n\n\n\nUpdates\n\n\n\n\n\n[\n#1662\n] Monitor StorageBackend\n\n\n\n\n7.1.9: February 2023\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1675\n] Prevent upgrade of cudnn in yolo server dockerfile\n\n\n\n\n7.1.8: February 2023\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1649\n] Install specific version of libcudnn8 in Docker build\n\n\n\n\n7.1.7: February 2023\n\n\n\nUpdates\n\n\n\n\n\n[\n#1674\n] Update \nSPEAKER_ID\n logic, set \nLONG_SPEAKER_ID=0\n\n\n\n\n7.1.5: January 2023\n\n\n\nFeatures\n\n\n\n\n\n[\n#1542\n] Update Azure Speech Detection component to select transcription language based on feed-forward track\n\n\n[\n#1543\n] Update audio transcoder to accept subsegments\n\n\n[\n#1605\n] Update Azure Translation to use detected language from upstream\n\n\n\n\n7.1.1: December 2022\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1634\n] Update version numbers to 7.1\n\n\n\n\n7.1.0: December 2022\n\n\n\nDocumentation\n\n\n\n\n\nUpdated the Object Storage Guide with \nS3_UPLOAD_OBJECT_KEY_PREFIX\n.\n\n\nUpdated the Markup Guide with \nMARKUP_TEXT_LABEL_MAX_LENGTH\n.\n\n\n\n\nExemplar Selection Policy\n\n\n\n\n\nThe policy for selecting the exemplar detection for each track can now be set using the \nEXEMPLAR_POLICY\n job property\n with following values:\n\n\nCONFIDENCE\n: Select the detection with the maximum confidence. If some confidences are the same, select the\n detection with the lower frame number. This is the default setting.\n\n\nFIRST\n: Select the detection with the lowest frame number\n\n\nLAST\n: Select the detection with the highest frame number\n\n\nMIDDLE\n: Select the detection with the frame number closest to the middle frame of the track, preferring the\n detection with the lower frame number if there is an even number of frames\n\n\n\n\n\n\n\n\nAutomatic Rotation and Horizontal Flip Enabled by Default\n\n\n\n\n\nIt is no longer necessary to explicitly set \nAUTO_ROTATE\n and \nAUTO_FLIP\n to true since that is now the default value.\n\n\nThese properties affect all video and image components that use the MPFImageReader and MPFVideoCapture tools. When\n true, if the image has EXIF data, or there is metadata associated with a video that ffmpeg understands, the tools will\n use that information to properly orient the frames before returning the frames to the component for processing.\n\n\n\n\nSupport S3 Object Storage Key Prefix\n\n\n\n\n\nSet the \nS3_UPLOAD_OBJECT_KEY_PREFIX\n job property or \ns3.upload.object.key.prefix\n system property to add a prefix to\n object keys when the Workflow Manager uploads objects to the S3 object store. This affects the JSON output object,\n artifacts, markup files, and derivative media.\n\n\nSpecifically, the Workflow Manager will upload objects to\n \n///\n.\n\n\nFor example, if you wish to add \"work/\" to the object key, then set \nS3_UPLOAD_OBJECT_KEY_PREFIX=work/\n.\n\n\n\n\nFeatures\n\n\n\n\n\n[\n#1526\n] Allow markup to display more than 10 characters in the text\n part of the label\n\n\n[\n#1527\n] Enable the Workflow Manager to select the middle detection\n as the exemplar\n\n\n[\n#1566\n] Make \nAUTO_ROTATE\n and \nAUTO_FLIP\n true by default\n\n\n[\n#1569\n] Modify C++ and Python component executor to automatically\n add the job name to log messages\n\n\n[\n#1621\n] Make S3 object keys used for upload configurable\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#1602\n] Update Workflow Manager to use Spring Boot\n\n\n[\n#1631\n] Update byte-buddy, Mockito, and Hibernate versions to\n resolve build issue. Most notably, update Hibernate to 5.6.14.\n\n\n[\n#1632\n] Update ActiveMQ to 5.17.3\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1581\n] Don't change track start and end frame when\n \nFEED_FORWARD_TOP_CONFIDENCE_COUNT\n is disabled\n\n\n[\n#1595\n] Work around how Ubuntu only recognizes certificate files\n that end in .crt\n\n\n[\n#1610\n] Prevent premature pipeline creation when using web UI\n\n\n[\n#1612\n] At startup, prevent Workflow Manager from consuming from\n queues before purging them\n\n\n\n\nOpenMPF 7.0.x\n\n\n7.0.3: September 2022\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1561\n] Fix logging for Python components when running through CLI\n runner\n\n\n[\n#1583\n] Can now properly view media while job is in progress\n\n\n[\n#1587\n] Fix bugs in amq_detection_component's use of select\n\n\n\n\n7.0.2: August 2022\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1562\n] Fix bug where an ffmpeg change prevented detecting video\n rotation\n\n\n\n\n7.0.0: July 2022\n\n\n\nDocumentation\n\n\n\n\n\nUpdated the Development Environment Guide by replacing steps for CentOS 7 with Ubuntu 20.04.\n\n\nAdded the Derivative Media Guide.\n\n\nUpdated the Batch Component APIs with revised error codes.\n\n\nUpdated the Python Batch Component API and Python base Docker image README with instructions for\n using \npyproject.toml\n and \nsetup.cfg\n.\n\n\nUpdated the Admin Guide and User Guide with images that show the new TiesDb and Callback columns in the job status UI.\n\n\nUpdated the REST API with the \npipelineDefinition\n, \nframeRanges\n, and \ntimeRanges\n fields now supported by the\n \n[POST] /rest/jobs\n endpoint.\n\n\nUpdated the OcvYoloDetection component README with information on using the NVIDIA Triton inference server.\n\n\nUpdated the Markup Guide with \nMARKUP_ANIMATION_ENABLED\n and \nMARKUP_LABELS_TRACK_INDEX_ENABLED\n.\n\n\nUpdated the Contributor Guide with new steps for generating documentation.\n\n\n\n\nTransition from CentOS 7 to Ubuntu 20.04\n\n\n\n\n\nAll the Docker images that previously used CentOS 7 as a base now use Ubuntu 20.04.\n\n\nWe decided not to use CentOS 8, which is a version of CentOS Stream, due to concerns about stability.\n\n\nAlso, Ubuntu is a very common OS within the AI and ML space, and has significant community support.\n\n\n\n\nUse Job Id that Enables Load Balancing\n\n\n\n\n\nThe Workflow Manager can now optionally accept job ids of the form \n-\n through\n the REST endpoints, where \n\n is the same as the shorter id used in previous releases. The\n \n-\n prefix enables better tracking and separation of jobs run across multiple\n Workflow Manager instances in a cluster.\n\n\nThe prefix can be set in the \ndocker-compose.yml\n file by assigning \n{{.Node.Hostname}}\n to the \nNODE_HOSTNAME\n\n environment variable for the Workflow Manager service, or hard-coding \nNODE_HOSTNAME\n to the desired hostname.\n\n\nThe shorter version of the id can still be used in REST requests, but the longer id will always be returned by the\n Workflow Manager when responding to those requests.\n\n\nThe shorter id will always be used internally by the Workflow Manager, meaning the job status web UI and log messages\n will all use the shorter job id. \n\n\n\n\nSupport for Derivative Media\n\n\n\n\n\nThe TikaImageDetection component now returns \nMEDIA\n tracks instead of \nIMAGE\n tracks when extracting images from\n documents, such as PDFs, Word documents, and PowerPoint slides. The document is considered the \"source\", or \"parent\",\n media, and the images are considered the \"derivative\", or \"child\", media.\n\n\nActions can now be configured with \nSOURCE_MEDIA_ONLY=true\n or \nDERIVATIVE_MEDIA_ONLY=true\n, which will result in only\n performing the action on that kind of media. Feed forward can still be used to pass track information from one stage\n to another. The tracks will skip the stages (actions) that don't apply.\n\n\nThis enables complex pipelines like one that extracts text from a PDF using TikaTextDetection, OCRs embedded images\n using EastTextDetection and TesseractOCRTextDetection, and runs all of the \nTEXT\n tracks through KeywordTagging.\n\n\nAdded the following pipelines to the TikaImageDetection component:\n\n\nTIKA IMAGE DETECTION WITH DERIVATIVE MEDIA TESSERACT OCR PIPELINE\n\n\nTIKA IMAGE DETECTION WITH DERIVATIVE MEDIA TESSERACT OCR AND KEYWORD TAGGING PIPELINE\n\n\nTIKA IMAGE DETECTION WITH DERIVATIVE MEDIA TESSERACT OCR (WITH EAST REGIONS) AND KEYWORD TAGGING PIPELINE\n\n\nTIKA IMAGE DETECTION WITH DERIVATIVE MEDIA TESSERACT OCR (WITH EAST REGIONS) AND KEYWORD TAGGING AND MARKUP PIPELINE\n\n\nTIKA IMAGE DETECTION WITH DERIVATIVE MEDIA OCV FACE PIPELINE\n\n\nTIKA IMAGE DETECTION WITH DERIVATIVE MEDIA OCV FACE AND MARKUP PIPELINE\n\n\n\n\n\n\n\n\nReport when Job Callbacks and TiesDb POSTs Fail\n\n\n\n\n\nThe job status UI displays two new columns, one that indicates the status of posting to TiesDB, and one that indicates\n the status of posting the job callback to the job producer.\n\n\nAdditionally, the \n[GET] /rest/jobs/{id}\n endpoint now includes a \ntiesDbStatus\n and \ncallbackStatus\n field.\n\n\nNote that, by design, the JSON output itself does not contain these statuses.\n\n\n\n\nAllow Pipelines to be Specified in a Job Request\n\n\n\n\n\nOptionally, the \npipelineDefinition\n field can be provided instead of the \npipelineName\n field when using the\n \n[POST] /rest/jobs\n endpoint in order to specify a pipeline on the fly for that specific job run. It will not be saved\n for later reuse.\n\n\nThe format of the pipeline definition is similar to that in a \ndescriptor.json\n file, with separate sections for\n defining \ntasks\n and \nactions\n. Pre-existing tasks and actions known to the Workflow Manager can be specified in the\n definition. They do not need to be defined again.\n\n\nThis feature is a convenient alternative to creating persistent definitions using the \n[POST] /rest/pipelines\n,\n \n[POST] /rest/tasks\n, and \n[POST] /rest/actions\n endpoints. For example, this feature could be used to quickly add or\n remove a motion preprocessing stage from a pipeline.\n\n\n\n\nAllow User-Specified Segment Boundaries\n\n\n\n\n\nOptionally, multiple \nframeRanges\n and/or \ntimeRanges\n fields can be provided when using the \n[POST] /rest/jobs\n\n endpoint in order to manually specify segment boundaries. These values will override the normal segmenting behavior of\n the Workflow Manager.\n\n\nNote that overlapping ranges will be combined and large ranges may still be split up according to the value of\n \nTARGET_SEGMENT_LENGTH\n and \nVFR_TARGET_SEGMENT_LENGTH\n.\n\n\nNote that \nframeRanges\n is specified using the frame number and \ntimeRanges\n is specified in milliseconds.\n\n\n\n\nAdd Triton Inference Server support to YOLO component\n\n\n\n\n\nThe OcvYoloDetection component now supports the ability to send requests to an NVIDIA Triton Inference Server by\n setting \nENABLE_TRITON=true\n. If set to false, the component will process jobs using OpenCV DNN on the local host\n running the Docker service, as per normal.\n\n\nBy default \nTRITON_SERVER=ocv-yolo-detection-server:8001\n, which\n corresponds to the \nocv-yolo-detection-server\n entry in your \ndocker-compose.yml\n file. Refer to the example entry\n within \ndocker-compose.components.yml\n\n . That entry uses a pre-built and pre-configured version of the Triton server.\n\n\nThe Triton server runs the YOLOv4 model within the TensorRT framework, which performs a warmup operation when the\n server starts up to determine which optimizations to enable for the available GPU hardware. \n*.engine\n files are\n generated within the \nyolo_engine_file\n Docker volume for later reuse.\n\n\nTo further improve inferencing speed, shared memory can be configured between the \nocv-yolo-detection\n client service and the\n \nocv-yolo-detection-server\n service if they are running on the same host. Set \nTRITON_USE_SHM=true\n and configure the\n server with a \n/dev/shm:/dev/shm\n Docker volume.\n\n\nDepending on the available GPU hardware, the Triton server can achieve speeds that are 5x faster than OpenCV DNN with\n tracking enabled, no shared memory, and nearly 9x faster with tracking disabled, with shared memory. Our tests used a\n single RTX 2080 GPU.\n\n\n\n\nRemoved Unused and Redundant Error Codes\n\n\n\n\n\nThe error codes shown on the left were redundant and replaced with the corresponding error codes on the right:\n\n\n\n\n\n\n\n\n\n\nOld Error Code\n\n\nNew Error Code\n\n\n\n\n\n\n\n\n\n\nMPF_IMAGE_READ_ERROR\n\n\nMPF_COULD_NOT_READ_MEDIA\n\n\n\n\n\n\nMPF_BOUNDING_BOX_SIZE_ERROR\n\n\nMPF_BAD_FRAME_SIZE\n\n\n\n\n\n\nMPF_JOB_PROPERTY_IS_NOT_INT\n\n\nMPF_INVALID_PROPERTY\n\n\n\n\n\n\nMPF_JOB_PROPERTY_IS_NOT_FLOAT\n\n\nMPF_INVALID_PROPERTY\n\n\n\n\n\n\nMPF_INVALID_FRAME_INTERVAL\n\n\nMPF_INVALID_PROPERTY\n\n\n\n\n\n\nMPF_DETECTION_TRACKING_FAILED\n\n\nMPF_OTHER_DETECTION_ERROR_TYPE\n\n\n\n\n\n\n\n\nAlso, the following error codes are no longer being used and have been removed:\n\n\n\n\nMPF_UNRECOGNIZED_DATA_TYPE\n\n\nAll media types can now be processed since we support the \nUNKNOWN\n (a.k.a. \"generic\")\n media type\n\n\n\n\n\n\nMPF_INVALID_DATAFILE_URI\n\n\nThe Workflow Manager will reject a job with an invalid media URI before it gets to a\n component\n\n\n\n\n\n\nMPF_INVALID_START_FRAME\n\n\nMPF_INVALID_STOP_FRAME\n\n\nMPF_INVALID_ROTATION\n\n\n\n\nMarkup Improvements\n\n\n\n\n\nBy default, the Markup component draws bounding boxes to fill in the gaps between detections in each track by\n interpolating the box size and position. This can now be disabled by setting the job property\n \nMARKUP_ANIMATION_ENABLED=false\n, or the system property \nmarkup.video.animation.enabled=false\n.\n Disabling this feature can be useful to prevent floating boxes from cluttering the marked-up frames.\n\n\nThe Markup component will now start each bounding box label with a track index like \n[0]\n that can be used to\n correlate the box with the track in the JSON output object. The JSON output now contains an \nindex\n field for every\n track, relative to each piece of media, that is simply an integer that starts at 0 and counts upward. This can be\n disabled by setting the job property \nMARKUP_LABELS_TRACK_INDEX_ENABLED=false\n, or the system property\n \nmarkup.labels.track.index.enabled=false\n.\n\n\n\n\nChanges to JSON Output Object\n\n\n\n\n\nComponents that generate \nMEDIA\n tracks will result in new derivative \nmedia\n entries in the JSON output file. This\n means it's possible to provide a single piece of media as an input and have more than one \nmedia\n entry in the JSON\n output. The output will always include the original media.\n\n\nEach \nmedia\n entry in the JSON output now contains a \nparentMediaId\n in addition to the \nmediaId\n. The \nparentMediaId\n\n for original source media will always be set to -1; otherwise, for derivative media, the \nparentMediaId\n is set the\n \nmediaId\n of the source media from which the child media was derived.\n\n\nEach \nmedia\n entry also contains a new \nframeRanges\n and \ntimeRanges\n collection.\n\n\nThe JSON output file also contains a new \nindex\n field for every track, relative to each piece of media.\n\n\n\n\nFeatures\n\n\n\n\n\n[\n#792\n] Perform detection on images extracted from PDFs\n\n\n[\n#1283\n] Add user-specified segment boundaries\n\n\n[\n#1374\n] Transition from CentOS 7 to Ubuntu 20.04\n\n\n[\n#1396\n] Report when job callbacks and TiesDb POSTs fail\n\n\n[\n#1398\n] Add Triton Inference Server support to YOLO component\n\n\n[\n#1428\n] Allow pipelines to be specified in a job request\n\n\n[\n#1454\n] Transition from Clair scans to Trivy scans\n\n\n[\n#1485\n] Use \npyproject.toml\n and \nsetup.cfg\n instead of \nsetup.py\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#803\n] Update Tika Image Detection to generate one track per piece of extracted media\n\n\n[\n#808\n] Update Tika Text Detection component to not use leading zeros for \nPAGE_NUM\n\n\n[\n#1105\n] Remove dependency on QT from C++ SDK\n\n\n[\n#1282\n] Use job id that enables load balancing\n\n\n[\n#1303\n] Update Tika Image Detection to return \nMEDIA\n tracks\n\n\n[\n#1319\n] Review existing error codes and remove unused or redundant error codes\n\n\n[\n#1384\n] Update Apache Tika to 2.4.1 for TikaImageDetection and TikaTextDetection Components\n\n\n[\n#1436\n] CLI Runner should initialize a component once when handling multiple jobs\n\n\n[\n#1465\n] Remove YoloV3 support from OcvYoloDetection component\n\n\n[\n#1513\n] Update to Spring 5.3.18\n\n\n[\n#1528\n] CLI runner should also sort by startOffsetTime\n\n\n[\n#1540\n] Upgrade to Java 17\n\n\n[\n#1549\n] Allow markup animation to be disabled\n\n\n[\n#1550\n] Add track index to markup\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1372\n] Tika Image Detection no longer misses images in PowerPoint and Word documents\n\n\n[\n#1449\n] Simon data is now refreshed when clicking the Processes tab\n\n\n[\n#1495\n] Fix bug where invalid CSRF token found for \n/workflow-manager/login\n\n\n\n\nOpenMPF 6.3.x\n\n\n6.3.14: May 2022\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1530\n] Fix S3 code memory leak\n\n\n\n\n6.3.12: April 2022\n\n\n\nUpdates\n\n\n\n\n\n[\n#1519\n] Upgrade to OpenCV 4.5.5\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1520\n] S3 code now retries on most 400 errors\n\n\n\n\n6.3.11: April 2022\n\n\n\nDocumentation\n\n\n\n\n\nUpdated the Object Storage Guide with \nS3_SESSION_TOKEN\n, \nS3_USE_VIRTUAL_HOST\n, \nS3_HOST\n, and \nS3_REGION\n.\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#1496\n] Update S3 client code\n\n\n[\n#1514\n] Update Tomcat to 8.5.78\n\n\n\n\n6.3.10: March 2022\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1486\n] Fix bug where \nMOVING\n was being added to immutable map twice\n\n\n[\n#1498\n] Can now provide media metadata when frameTimeInfo is missing\n\n\n[\n#1501\n] MPFVideoCapture now properly reads frames from videos with rotation metadata\n\n\n[\n#1502\n] Detections with \nHORIZONTAL_FLIP\n will no longer result in illformed detections and incorrectly padded regions\n\n\n[\n#1503\n] Videos with rotation metadata will no longer result in corrupt markup\n\n\n\n\n6.3.8: January 2022\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1469\n] \nTENSORFLOW VEHICLE COLOR DETECTION\n pipelines no longer refer to YOLO tasks that no longer exist\n\n\n\n\n6.3.7: January 2022\n\n\n\nUpdates\n\n\n\n\n\n[\n#1466\n] Upgrade log4j to 2.17.1\n\n\n\n\n6.3.6: December 2021\n\n\n\nUpdates\n\n\n\n\n\n[\n#1457\n] Upgrade log4j to 2.16.0\n\n\n\n\n6.3.5: November 2021\n\n\n\nUpdates\n\n\n\n\n\n[\n#1451\n] Make concurrent callbacks configurable\n\n\n\n\n6.3.4: November 2021\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1441\n] Modify AdminStatisticsController so that it doesn't hold all jobs in memory at once\n\n\n\n\n6.3.3: October 2021\n\n\n\nFeatures\n\n\n\n\n\n[\n#1425\n] Make protobuf size limit configurable\n\n\n\n\n6.3.2: October 2021\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1420\n] Sphinx component no longer omits audio at end of video files\n\n\n[\n#1422\n] Media inspection now correctly calculates milliseconds from ffmpeg duration\n\n\n\n\n6.3.1: September 2021\n\n\n\nFeatures\n\n\n\n\n\n[\n#1404\n] Improve OcvDnnDetection vehicle color detection\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#1251\n] Add version to JSON output object\n\n\n[\n#1272\n] Update Keyword Tagging to work on multiple inputs\n\n\n[\n#1350\n] Retire old components to the graveyard: DlibFaceDetection, DarknetDetection, and OcvPersonDetection\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1010\n] \nmpf.output.objects.enabled\n now behaves as expected\n\n\n[\n#1271\n] Azure speech component no longer omits audio at end of video files\n\n\n[\n#1389\n] NLP text correction component now properly reads the value of \nFULL_TEXT_CORRECTION_OUTPUT\n\n\n[\n#1403\n] Corrected README to state that the Azure Speech Component doesn't support v2 of the API\n\n\n[\n#1406\n] Speech detections in videos are no longer dropped if using keyword tagging\n\n\n[\n#1411\n] Exception no longer occurs when adding \nSHRUNK_TO_NOTHING=TRUE\n to an immutable map in multiple pipeline stages\n\n\n[\n#1413\n] Speech detections in videos are no longer dropped if using translation\n\n\n\n\n6.3.0: September 2021\n\n\n\nDocumentation\n\n\n\n\n\nUpdated the API documents, Development Environment Guide, Node Guide, Install Guide, User Guide, Admin Guide, and\n others to clarify the difference between Docker and non-Docker behaviors.\n\n\nTransformed Packaging and Registering a Component document into Component Descriptor Reference.\n\n\nSplit Media Segmentation Guide from User Guide.\n\n\nUpdated and renamed the Workflow Manager document to Workflow Manager Architecture.\n\n\nUpdated the various Docker guides to clarify the difference between building Docker images from scratch versus\n building them using pre-built base images on Docker Hub, emphasizing the latter.\n\n\nUpdated the Contributor Guide to document the hotfix pull request process.\n\n\n\n\nTiesDb Integration\n\n\n\n\n\nTiesDb is a PostgreSQL DB with a RESTful API that stores media metadata. The metadata entries are queried using the\n hash (sha256, md5) of the media file. TIES stands\n for \nTriage Import Export Schema\n. TiesDb is deployed and managed externally to\n OpenMPF. For more information please contact us.\n\n\nWhen a job completes, OpenMPF can post assertions to media entries that exist in TiesDb. In general, one assertion is\n generated for each algorithm run on a piece of media. It contains the job status, algorithm name, detection\n type (\nFACE\n, \nTEXT\n, \nMOTION\n, etc.), and number of tracks generated, as well as a link to the full JSON output\n object.\n\n\nEach assertion serves as a lasting record so that job producers may first check TiesDb to see if an algorithm was run\n on a piece of media before submitting the same job to OpenMPF again.\n\n\nTo enable TiesDb support, set the \nTIES_DB_URL\n job property or \nties.db.url\n system property to\n the \n://:\n part of the URL. The Workflow Manager will append\n the \n/api/db/supplementals?sha256Hash=\n part. Here is an example of a TiesDb POST:\n\n\n\n\n{\n \"dataObject\": {\n \"sha256OutputHash\": \"1f8f2a8b2f5178765dd4a2e952f97f5037c290ee8d011cd7e92fb8f57bc75f17\",\n \"outputType\": \"FACE\",\n \"algorithm\": \"FACECV\",\n \"processDate\": \"2021-09-09T21:37:30.516-04:00\",\n \"pipeline\": \"OCV FACE DETECTION PIPELINE\",\n \"outputUri\": \"file:///home/mpf/git/openmpf-projects/openmpf/trunk/install/share/output-objects/1284/detection.json\",\n \"jobStatus\": \"COMPLETE\",\n \"jobId\": 1284,\n \"systemVersion\": \"6.3\",\n \"trackCount\": 1,\n \"systemHostname\": \"openmpf-master\"\n },\n \"system\": \"OpenMPF\",\n \"securityTag\": \"UNCLASSIFIED\",\n \"informationType\": \"OpenMPF FACE\",\n \"assertionId\": \"4874829f666d79881f7803207c7359dc781b97d2c68b471136bf7235a397c5cd\"\n}\n\n\n\nNatural Language Processing (NLP) Text Correction Component\n\n\n\n\n\nThis component utilizes the \nCyHunspell\n library, which is a Python\n port of the \nHunspell\n spell-checking library, to perform post-processing\n correction of OCR text. In general, it's intended to be used in a pipeline after a component like\n TesseractOCRTextDetection that generates \nTEXT\n tracks. These tracks are then fed-forward into NlpTextCorrection,\n which will add a \nCORRECTED TEXT\n property to the existing tracks.\n The \nTESSERACT OCR TEXT DETECTION WITH NLP TEXT CORRECTION PIPELINE\n performs this behavior. The component can also\n run on its own to process plain text files. Refer to\n the \nREADME\n for details.\n\n\n\n\nAzure Cognitive Services (ACS) Read Component\n\n\n\n\n\nThis component utilizes\n the \nAzure Cognitive Services Read Detection REST endpoint\n\n to extract formatted text from documents (PDFs), images, and videos. Refer to\n the \nREADME\n for\n details.\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#1151\n] Now supports \nIN_PROGRESS_WITH_WARNINGS\n status\n\n\n[\n#1234\n] Now sorts JSON output object media by media id\n\n\n[\n#1341\n] Added job id to all batch-job-specific Workflow Manager log\n messages\n\n\n[\n#1349\n] Improved reporting and recording job status\n\n\n[\n#1353\n] Updated the Workflow Manager to remove and warn about\n zero-size detections\n\n\n[\n#1382\n] Updated Tika version to 1.27 for TikaImageDetection and\n TikaTextDetection components\n\n\n[\n#1387\n] Markup can now be configured in a\n component's \ndescriptor.json\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1080\n] Batch jobs no longer prematurely set to 100% completion\n during artifact extraction\n\n\n[\n#1106\n] When a job ends in \nERROR\n or \nCANCELLED_BY_SHUTDOWN\n the\n job status UI now shows an End Date\n\n\n[\n#1158\n] JSON output object URI no longer changes when callback fails\n\n\n[\n#1317\n] TikaTextDetection no longer generates first PDF track\n at \nPAGE_NUM\n 2\n\n\n[\n#1337\n] Now using \nMPF_BAD_FRAME_SIZE\n instead\n of \nMPF_DETECTION_FAILED\n for OpenCV empty/resize exception\n\n\n[\n#1359\n] Image detection tracks no longer\n have \nendOffsetFrameInclusive\n set to 1\n\n\n[\n#1373\n] When uploading large files through the Workflow Manager web\n UI, now more than the first 865032704 bytes get written\n\n\n[\n#1379\n] TikaImageDetection component now avoids conflicts by no\n longer using the same path when extracting images for jobs with multiple pieces of media\n\n\n[\n#1386\n] FeedForwardFrameCropper in the Python SDK now handles\n negative coordinates properly\n\n\n[\n#1391\n] If a job is configured to upload markup and markup fails,\n the job no longer gets stuck\n\n\n\n\nKnown Issues\n\n\n\n\n\n[\n#1372\n] TikaImageDetection misses images in PowerPoint and Word\n documents\n\n\n[\n#1389\n] NlpTextCorrection does not properly read the value\n of \nFULL_TEXT_CORRECTION_OUTPUT\n\n\n\n\nOpenMPF 6.2.x\n\n\n6.2.5: July 2021\n\n\n\nUpdates\n\n\n\n\n\n[\n#1367\n] Enable cross-origin resource sharing on Workflow Manager\n\n\n\n\n6.2.4: June 2021\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1356\n] AzureSpeech now properly reports when media is missing audio stream\n\n\n[\n#1357\n] AzureSpeech now handles case where speaker id is not present\n\n\n\n\n6.2.2: June 2021\n\n\n\nUpdates\n\n\n\n\n\n[\n#1333\n] Combine media name and job id into one WFM log line\n\n\n[\n#1336\n] Remove duplicate \"Setting status of job to COMPLETE\" Workflow Manager log line and other improvements\n\n\n[\n#1338\n] Update OpenCV DNN Detection component to optionally use feed-forward confidence values\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1237\n] Fixed jQuery DataTables bug: \"int parameter 'draw' is present but cannot be translated into a null value\"\n\n\n[\n#1254\n] Jobs table no longer flickers when polling is enabled and the search box is used\n\n\n[\n#1308\n] Prevent OCV YOLO Tracking from generating zero-sized detections\n\n\n[\n#1313\n] Fix JSON output object timestamps for variable frame rate videos\n\n\n\n\n6.2.1: May 2021\n\n\n\nUpdates\n\n\n\n\n\n[\n#1330\n] Return error codes for \nmodels_ini_parser.py\n exceptions\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1331\n] Decoding certain heic images no longer causes Workflow Manager to segfault\n\n\n\n\n6.2.0: May 2021\n\n\n\nTesseract OCR Text Detection Component Support for Videos\n\n\n\n\n\nThe component can now process videos in addition to images and PDFs. Each video frame is processed sequentially.\n The \nMAX_PARALLEL_SCRIPT_THREADS\n property determines how many threads to use to process each frame, one thread per\n language or script.\n\n\nNote that for videos without much text, it may be faster to disable threading by\n setting \nMAX_PARALLEL_SCRIPT_THREADS=1\n. This will allow the component to reuse TessAPI instances instead of creating\n new ones for every frame. Please refer to the Known Issues section.\n\n\nResolved issues: \n#1285\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#1086\n] Added support for \nCOULD_NOT_OPEN_MEDIA\n\n and \nCOULD_NOT_READ_MEDIA\n error types\n\n\n[\n#1159\n] Split \nIssueCodes.REMOTE_STORAGE\n\n into \nREMOTE_STORAGE_DOWNLOAD\n and \nREMOTE_STORAGE_UPLOAD\n\n\n[\n#1250\n] Modified \n/rest/jobs/{id}\n to include the job's media\n\n\n[\n#1312\n] Created \nNETWORK_ERROR\n error code for when a component\n can't connect to an external server. Updated Python HTTP retry code to return \nNETWORK_ERROR\n. This affects the Azure\n components.\n\n\n\n\nKnown Issues\n\n\n\n\n\n[\n#1008\n] Use global TessAPI instances with parallel processing\n\n\n\n\nOpenMPF 6.1.x\n\n\n6.1.6: May 2021\n\n\n\nHandle Variable Frame Rate Videos\n\n\n\n\n\nThe Workflow Manager will attempt to detect if a video is constant frame rate (CFR) or variable frame rate (VFR)\n during media inspection. If no determination can be made, it will default to VFR behavior. If CFR, the JSON output\n object will have a \nHAS_CONSTANT_FRAME_RATE=true\n property in the \nmediaMetadata\n field.\n\n\nWhen \nMPFVideoCapture\n handles a CFR video it will use OpenCV to set the frame position, unless the position is within\n 16 frames of the current position, in which case it will iteratively use OpenCV \ngrab()\n to advance to the desired\n frame.\n\n\nWhen \nMPFVideoCapture\n handles a VFR video it will always iteratively use OpenCV \ngrab()\n to advance to the desired\n frame because setting the frame position directly has been shown to not work correctly on VFR videos.\n\n\nWhen a video is split into multiple segments, \nMPFVideoCapture\n must iteratively use \ngrab()\n to advance from frame 0\n to the start of the segment. This introduces performance overhead. To mitigate this we recommend using larger video\n segments than those used for CFR videos.\n\n\nIn addition to the existing \nTARGET_SEGMENT_LENGTH\n and \nMIN_SEGMENT_LENGTH\n job\n properties (\ndetection.segment.target.length\n and \ndetection.segment.minimum.length\n system properties) for CFR\n videos, the Workflow Manager now supports the \nVFR_TARGET_SEGMENT_LENGTH\n and \nVFR_MIN_SEGMENT_LENGTH\n job\n properties (\ndetection.vfr.segment.target.length\n and \ndetection.vfr.segment.minimum.length\n system properties) for\n VFR videos.\n\n\nNote that the timestamps associated with tracks and detections in a VFR video may be wrong. Please refer to the Known\n Issues section.\n\n\nResolved issues: \n#1307\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#1287\n] Updated Tika Text Detection Component to break up large\n chunks of text. The component now generates tracks with both a \nPAGE_NUM\n property and \nSECTION_NUM\n property. Please\n refer to\n the \nREADME\n.\n\n\n\n\nKnown Issues\n\n\n\n\n\n[\n#1313\n] Incorrect JSON output object timestamps for variable frame\n rate videos\n\n\n[\n#1317\n] Tika Text Detection component generates first PDF track\n at \nPAGE_NUM\n 2\n\n\n\n\n6.1.5: April 2021\n\n\n\nUpdates\n\n\n\n\n\n[\n#1300\n] Parallelized S3 artifact upload. Use\n the \ndetection.artifact.extraction.parallel.upload.count\n system property to configure the number of parallel uploads.\n\n\n\n\n6.1.4: April 2021\n\n\n\nUpdates\n\n\n\n\n\n[\n#1299\n] Improved artifact extraction performance when there is no\n rotation or flip\n\n\n\n\n6.1.3: April 2021\n\n\n\nUpdates\n\n\n\n\n\n[\n#1295\n] Improved artifact extraction and markup JNI memory\n utilization\n\n\n[\n#1297\n] Limited Workflow Manager IO threads to a reasonable number\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1296\n] Fixed ActiveMQ job priorities\n\n\n\n\n6.1.2: April 2021\n\n\n\nUpdates\n\n\n\n\n\n[\n#1294\n] Limited ffmpeg threads to a reasonable number\n\n\n\n\n6.1.1: April 2021\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1292\n] Don't skip artifact extraction for failed media\n\n\n\n\n6.1.0: April 2021\n\n\n\nOpenMPF Command Line Runner\n\n\n\n\n\nThe Command Line Runner allows users to run jobs with a single component without the Workflow Manager.\n\n\nIt outputs results in a JSON structure that is a subset of the regular OpenMPF output.\n\n\nIt only supports C++ and Python components.\n\n\nSee the\n \nREADME\n\n for more information.\n\n\n\n\nC++ Batch Component API\n\n\n\n\n\nComponent code should no longer configure Log4CXX. The component executor now handles configuring Log4CXX. Component\n code should call \nlog4cxx::Logger::getLogger(\"\")\n\n to get access to the logger. Calls to \nlog4cxx::xml::DOMConfigurator::configure(logconfig_file);\n\n should be removed.\n\n\n\n\nPython Batch Component API \n\n\n\n\n\nComponent code should no longer configure logging. The component executor now handles configuring logging. Calls\n to \nmpf.configure_logging\n should be replaced with\n \nlogging.getLogger('')\n.\n\n\n\n\nDocker Component Base Images\n\n\n\n\n\n\n\nIn order to support running a component through the CLI runner, C++ component developers should set\n the \nLD_LIBRARY_PATH\n environment variable in the final stage of their Dockerfiles. It should generally be set\n like: \nENV LD_LIBRARY_PATH $PLUGINS_DIR//lib\n.\n\n\n\n\n\n\nBecause of the logging changes mentioned above, components no longer need to set the\n \nCOMPONENT_LOG_NAME\n environment variable in their Dockerfiles.\n\n\n\n\n\n\nAdded the\n \nopenmpf_python_executor_ssb\n base image\n\n . It can be used instead of \nopenmpf_python_component_build\n and \nopenmpf_python_executor\n to simplify Dockerfiles for\n Python components that are pure Python and have no build time dependencies.\n\n\n\n\n\n\nLabel Moving vs. Non-Moving Tracks\n\n\n\n\n\nThe Workflow Manager can now identify whether a track is moving or non-moving. This is determined by calculating the\n average bounding box for a track by averaging the size and position of all the detections in the track. Then, for each\n detection in the track, the intersection over union (IoU) is calculated between that detection and the average\n detection. If the IoU for at least \nMOVING_TRACK_MIN_DETECTIONS\n number of detections is less than or equal to\n \nMOVING_TRACK_MAX_IOU\n, then the track is considered a moving track.\n\n\nAdded the following Workflow Manager job properties. These can be set for any video job:\n\n\nMOVING_TRACK_LABELS_ENABLED\n: When set to true, attempt to label tracks as either moving or non-moving objects.\n Each track will have a \nMOVING\n property set to \nTRUE\n or \nFALSE\n.\n\n\nMOVING_TRACKS_ONLY\n: When set to true, remove any tracks that were marked as not moving.\n\n\nMOVING_TRACK_MAX_IOU\n: The maximum IoU overlap between detection bounding boxes and the average per-track\n bounding box for objects to be considered moving. Value is expected to be between 0 and 1. Note that the lower\n IoU, the more likely the object is moving.\n\n\nMOVING_TRACK_MIN_DETECTIONS\n: The minimum number of moving detections for a track to be labeled as moving.\n\n\n\n\n\n\n\n\nMarkup Improvements\n\n\n\n\n\nUsers can now watch videos directly in the OpenMPF web UI within the media pop-up dialog for each job. Most modern web\n browsers support videos encoded in VP9 and H.264. If a video cannot be played, users have the option to download it\n and play it using a stand-alone media player.\n\n\nTo set the markup encoder use \nMARKUP_VIDEO_ENCODER\n. The default encoder has changed from \nmjpeg\n to \nvp9\n. As a\n result, it will take longer to generate marked up videos, but they will be higher quality and can be viewed in the web\n UI.\n\n\nEach bounding box in the marked up media is now labeled. By default, the label shows the track-level \nCLASSIFICATION\n\n and associated confidence value. The information shown in the label can be changed by\n setting \nMARKUP_LABELS_TEXT_PROP_TO_SHOW\n and \nMARKUP_LABELS_NUMERIC_PROP_TO_SHOW\n. To show information for each\n individual detection, rather than the entire track, set \nMARKUP_LABELS_FROM_DETECTIONS=TRUE\n.\n\n\nExemplar detections in video tracks include a star icon in their label.\n\n\nOptionally, set \nMARKUP_VIDEO_MOVING_OBJECT_ICONS_ENABLED=TRUE\n to show icons that represent if the track is moving or\n non-moving.\n\n\nOptionally, set \nMARKUP_VIDEO_BOX_SOURCE_ICONS_ENABLED=TRUE\n to show icons that represent the source of the detection.\n For example, if the box is the result of an algorithm detection, tracking performing gap fill, or Workflow Manager\n animation.\n\n\nEach frame of a marked-up video now has a frame number in the upper right corner.\n\n\nPlease refer to the \nMarkup Guide\n for the complete set of markup properties, icon definitions, and\n encoder considerations.\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#1181\n] Updated the Tesseract OCR Text Detection component from\n Tesseract version 4.0.0 to 4.1.1\n\n\n[\n#1232\n] Updated the Azure Speech Detection component from Azure\n Batch Transcription version 2.0 to 3.0\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1187\n] EXIF orientation is now preserved during markup and artifact\n extraction\n\n\n[\n#1257\n] Updated \nOUTPUT_LAST_TASK_ONLY\n to work on all media types\n\n\n\n\nOpenMPF 6.0.x\n\n\n6.0.11: March 2021\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1284\n] Updated the Azure Translation component to count emoji as 2\n characters\n\n\n\n\n6.0.10: March 2021\n\n\n\nUpdates\n\n\n\n\n\n[\n#1270\n] The Azure Cognitive Services components now retry HTTP\n requests\n\n\n\n\n6.0.9: March 2021\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1273\n] Setting \nTRANSLATION\n to the empty string no longer prevents\n Keyword Tagging\n\n\n\n\n6.0.6: March 2021\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1265\n] Updated the Tika Text Detection component to handle\n spreadsheets\n\n\n[\n#1268\n] Updated the Tika Text Detection component to remove metadata\n\n\n\n\n6.0.5: February 2021\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1266\n] The Azure Translation component now handles the final\n segment correctly when guessing sentence breaks\n\n\n\n\n6.0.4: February 2021\n\n\n\nUpdates\n\n\n\n\n\n[\n#1264\n] Updated the Azure Translation component to handle large\n amounts of text\n\n\n[\n#1269\n] AzureTranslation no longer tries to translate text that is\n already in the \nTO_LANGUAGE\n\n\n\n\n6.0.3: February 2021\n\n\n\nOpenCV YOLO Detection Component\n\n\n\n\n\nThis new component utilizes the OpenCV Deep Neural Networks (DNN) framework to detect and classify objects in images\n and videos using Darknet YOLOv4 models trained on the COCO dataset. It supports both CPU and GPU modes of operation.\n Tracking is performed using a combination of intersection over union, pixel difference after Fast Fourier transform (\n FFT) phase correlation, Kalman filtering, and OpenCV MOSSE tracking. Refer to\n the \nREADME\n for details.\n\n\n\n\n6.0.2: January 2021\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1249\n] FFmpeg no longer reports different frame counts for the same\n piece of media\n\n\n\n\n6.0.1: December 2020\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1238\n] The JSON output object is now generated when remote media\n cannot be downloaded.\n\n\n\n\n6.0.0: December 2020\n\n\n\nUpgrade to OpenCV 4.5.0\n\n\n\n\n\nUpdated core framework and components from OpenCV 3.4.7 to OpenCV 4.5.0.\n\n\nOpenCV is now built with CUDA support, including cuDNN (CUDA Deep Neural Network library) and cuBLAS (CUDA Basic\n Linear Algebra Subroutines library). All C++ components that use the base C++ builder and executor Docker images have\n CUDA support built in, giving developers the option to make use of it.\n\n\nAdded GPU support to the OcvDnnDetection component.\n\n\n\n\nAzure Cognitive Services (ACS) Translation Component\n\n\n\n\n\nThis new component utilizes\n the \nAzure Cognitive Services Translator REST endpoint\n\n to translate text from one language (locale) to another. Generally, it's intended to operate on feed-forward tracks\n that contain detections with \nTEXT\n and \nTRANSCRIPT\n properties. It can also operate on plain text file inputs. Refer\n to the \nREADME\n for\n details.\n\n\n\n\nInteroperability Package\n\n\n\n\n\nAdded \nalgorithm\n field to the element that describes a collection of tracks generated by an action in the JSON output\n object. For example:\n\n\n\n\n\"output\": {\n \"FACE\": [{\n \"source\": \"+#MOG MOTION DETECTION PREPROCESSOR ACTION#OCV FACE DETECTION ACTION\",\n \"algorithm\": \"FACECV\",\n \"tracks\": [{ ... }],\n ...\n },\n\n\n\nMerge Tasks in JSON Output Object\n\n\n\n\n\nThe output of two tasks in the JSON output object can be merged by setting the \nOUTPUT_MERGE_WITH_PREVIOUS_TASK\n\n property to true. This is a Workflow Manager property and can be set on any task in any pipeline, although it has no\n effect when set on the first task or the Markup task.\n\n\nWhen the output of two tasks are merged, the tracks for the previous task will not be shown in the JSON output object,\n and no artifacts are generated for it. The task will be listed under \nTRACKS MERGED\n, if it's not already listed\n under \nTRACKS SUPPRESSED\n due to the \nmpf.output.objects.last.task.only\n system property setting,\n or \nOUTPUT_LAST_TASK_ONLY\n property. The tracks associated with the second task will inherit the detection type and\n algorithm of the previous task.\n\n\nFor example, the \nTESSERACT OCR TEXT DETECTION WITH KEYWORD TAGGING PIPELINE\n is defined as\n the \nTESSERACT OCR TEXT DETECTION TASK\n followed by the \nKEYWORD TAGGING (WITH FF REGION) TASK\n. The second task\n sets \nOUTPUT_MERGE_WITH_PREVIOUS_TASK\n to true. The resulting JSON output object contains one set of keyword-tagged\n OCR tracks that have the \nTEXT\n detection type and \nTESSERACTOCR\n algorithm (both inherited from\n the \nTESSERACT OCR TEXT DETECTION TASK\n):\n\n\n\n\n\"output\": {\n \"TRACKS MERGED\": [{\n \"source\": \"+#TESSERACT OCR TEXT DETECTION ACTION\",\n \"algorithm\": \"TESSERACTOCR\"\n }],\n \"TEXT\": [{\n \"source\": \"+#TESSERACT OCR TEXT DETECTION ACTION#KEYWORD TAGGING (WITH FF REGION) ACTION\",\n \"algorithm\": \"TESSERACTOCR\",\n \"tracks\": [{\n \"type\": \"TEXT\",\n \"trackProperties\": {\n \"TAGS\": \"ANIMAL\",\n \"TEXT\": \"The quick brown fox\",\n \"TEXT_LANGUAGE\": \"script/Latin\",\n \"TRIGGER_WORDS\": \"fox\",\n \"TRIGGER_WORDS_OFFSET\": \"16-18\"\n ...\n\n\n\n\n\nNote that you can use the \nOUTPUT_MERGE_WITH_PREVIOUS_TASK\n setting on multiple tasks. For example, if you set it as a\n job property it will be applied to all tasks (with the exception of Markup - in which case the task before Markup is\n used), so you will only get the output of the last task in the pipeline. The last task will inherit the detection type\n and algorithm of the first task in the pipeline.\n\n\n\n\nTesseract Custom Dictionaries\n\n\n\n\n\nThe Tesseract component Docker image now contains an \n/opt/mpf/tessdata_model_updater\n binary that you can use to\n update \n*.traineddata\n models with a custom dictionary, as well as extract files from existing models. Refer to\n the \nDICTIONARIES\n\n guide to learn how to use the tool.\n\n\nIn general, legacy \n*.traineddata\n models are more influenced by words in their dictionary than more modern\n LSTM \n*.traineddata\n models. Also, refer to the known issue below.\n\n\n\n\nKnown Issues\n\n\n\n\n\n[\n#1243\n] Unpacking a \n*.traineddata\n model, for example, in order to\n modify its dictionary, and then repacking it may result in dropping some of the words present in the original\n dictionary file. This may be due to some kind of compression or filtering. It's unknown what effect this has on OCR\n results.\n\n\n\n\nOpenMPF 5.1.x\n\n\n5.1.3: December 2020\n\n\n\nSetting Properties as Docker Environment Variables\n\n\n\n\n\nAny property that can be set as a job property can now be set as a Docker environment variable by prefixing it\n with \nMPF_PROP_\n. For example, setting the \nMPF_PROP_TRTIS_SERVER\n environment variable in the \ntrtis-detection\n\n service in your \ndocker-compose.yml\n file will have the same effect as setting the \nTRTIS_SERVER\n job property.\n\n\nProperties set in this way will take precedence over all other property types (job, algorithm, media, etc). It is not\n possible to change the value of properties set via environment variables at runtime and therefore they should only be\n used to specify properties that will not change throughout the entire lifetime of the service.\n\n\n\n\nUpdates\n\n\n\n\n\nThe \nmpf.output.objects.censored.properties\n system property can be used to prevent properties from being shown in\n JSON output objects. The value for these properties will appear as \n\n.\n\n\nThe Azure Speech Detection component now retries without diarization when diarization is not supported by the selected\n locale.\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1230\n] The Azure Speech Detection component now uses a UUID for the\n recording id associated with a piece of media in order to prevent deleting a piece of media while it's in use.\n\n\n\n\n5.1.1: December 2020\n\n\n\nUpdates\n\n\n\n\n\nOnly generate \nFRAME_COUNT\n warning when the frame difference is > 1. This can be configured using\n the \nwarn.frame.count.diff\n system property.\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1209\n] The Keyword Tagging component now generates video tracks in\n the JSON output object.\n\n\n[\n#1212\n] The Keyword Tagging component now preserves the detection\n bounding box and confidence.\n\n\n\n\n5.1.0: November 2020\n\n\n\nMedia Inspection Improvements\n\n\n\n\n\nThe Workflow Manager will now handle video files that don't have a video stream as an \nAUDIO\n type, and handle video\n files that don't have a video or audio stream as an \nUNKNOWN\n type. The JSON output object contains a\n new \nmedia.mediaType\n field that will be set to \nVIDEO\n, \nAUDIO\n, \nIMAGE\n, or \nUNKNOWN\n.\n\n\nThe Workflow Manager now configures Tika\n with \ncustom MIME type support\n\n . Currently, this enables the detection of \nvideo/vnd.dlna.mpeg-tts\n and \nimage/jxr\n MIME types.\n\n\nIf the Workflow Manager cannot use Tika to determine the media MIME type then it will fall back to using the\n Linux \nfile\n command with\n a \ncustom magicfile\n\n .\n\n\nOpenMPF now supports Apple-optimized PNGs and HEIC images. Refer to the Bug Fixes section below.\n\n\n\n\nEAST Text Region Detection Component Improvements\n\n\n\n\n\nThe \nTEMPORARY_PADDING\n property has been separated into \nTEMPORARY_PADDING_X\n and \nTEMPORARY_PADDING_Y\n so that X and\n Y padding can be configured independently.\n\n\nThe \nMERGE_MIN_OVERLAP\n property has been renamed to \nMERGE_OVERLAP_THRESHOLD\n so that setting it to a value of 0 will\n merge all regions that touch, regardless of how small the amount of overlap.\n\n\nRefer to\n the \nREADME\n\n for details.\n\n\n\n\nMPFVideoCapture and MPFImageReader Tool Improvements\n\n\n\n\n\nThese tools now support a \nROTATION_FILL_COLOR\n property for setting the fill color for pixels near the corners and\n edges of frames when performing non-orthogonal rotations. Previously, the color was hardcoded to \nBLACK\n. That is\n still the default setting for most components. Now the color can be set to \nWHITE\n, which is the default setting for\n the Tesseract component.\n\n\nThese tools now support a \nROTATION_THRESHOLD\n property for adjusting the threshold at which the frame transformer\n performs rotation. Previously, the value was hardcoded to 0.1 degrees. That is still the default value. Rotation is\n not performed on any \nROTATION\n value less than that threshold. The motivation is that some algorithms detect small\n rotations (for example, on structured text) when there is no rotation. In such cases rotating the frame results in\n fewer detections.\n\n\nOpenMPF now uses FFmpeg when counting video frames. Refer to the Bug Fixes section below.\n\n\n\n\nAzure Cognitive Services (ACS) Form Detection Component\n\n\n\n\n\nThis new component utilizes\n the \nAzure Cognitive Services Form Detection REST endpoint\n\n to extract formatted text from documents (PDFs) and images. Refer to\n the \nREADME\n for\n details.\n\n\nThis component is capable of performing detections using a specified ACS endpoint URL. For example, different\n endpoints support receipt detection, business card detection, layout analysis, and support for custom models trained\n with or without labeled data.\n\n\nThis component may output the following detection properties depending on the endpoint, model, and media being\n processed: \nTEXT\n, \nTABLE_CSV_OUTPUT\n, \nKEY_VALUE_PAIRS_JSON\n, and \nDOCUMENT_JSON_FIELDS\n.\n\n\n\n\nKeyword Tagging Component\n\n\n\n\n\nThis new component performs the same keyword tagging behavior that was previously part of the Tesseract component, but\n does so on feed-forward tracks that generate detections with \nTEXT\n and \nTRANSCRIPT\n properties. Refer to\n the \nREADME\n for details.\n\n\nIn addition to the Tesseract component, keyword tagging behavior has been removed from the Tika Text component and ACS\n OCR component.\n\n\nExample pipelines have been added to the following components which make use of a final Keyword Tagging component\n stage:\n\n\nTesseract\n\n\nTika Text\n\n\nACS OCR\n\n\nSphinx\n\n\nACS Speech\n\n\n\n\n\n\n\n\nOptionally Skip Media Inspection\n\n\n\n\n\nThe Workflow Manager will skip media inspection if all of the required media metadata is provided in the job request.\n The \nMEDIA_HASH\n and \nMIME_TYPE\n fields are always required. Depending on the media data type, other fields may be\n required or optional:\n\n\nImages\n\n\nRequired: \nFRAME_WIDTH\n, \nFRAME_HEIGHT\n\n\nOptional: \nHORIZONTAL_FLIP\n, \nROTATION\n\n\n\n\n\n\nVideos\n\n\nRequired: \nFRAME_WIDTH\n, \nFRAME_HEIGHT\n, \nFRAME_COUNT\n, \nFPS\n, \nDURATION\n\n\nOptional: \nHORIZONTAL_FLIP\n, \nROTATION\n\n\n\n\n\n\nAudio files\n\n\nRequired: \nDURATION\n\n\n\n\n\n\n\n\n\n\n\n\nUpdates\n\n\n\n\n\nUpdate OpenMPF Python SDK exception handling for Python 3. Now instead of raising an \nEnvironmentError\n, which has\n been deprecated in Python 3, the SDK will raise an \nmpf.DetectionError\n or allow the underlying exception to be\n thrown.\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1028\n] OpenMPF can now properly handle Apple-optimized PNGs, which\n have a non-standard data chunk named CgBI before the IHDR chunk. The Workflow Manager\n uses \npngdefry\n to convert the image into a standard PNG for processing. Before\n this fix, Tika would throw an error when trying to determine the MIME type of the Apple-optimized PNG.\n\n\n[\n#1130\n] OpenMPF can now properly handle HEIC images. The Workflow\n Manager uses \nlibheif\n to convert the image into a standard PNG for processing.\n Before this fix, the HEIC image was sometimes falsely identified as a video and the Workflow Manager would fail to\n count the number of frames.\n\n\n[\n#1171\n] The MIME type in the JSON output object is no longer null\n when there is a frame counting exception.\n\n\n[\n#1192\n] When processing videos, the frame count is now obtained from\n both OpenCV and FFmpeg. The lower of the two is used. If they don't match, a \nFRAME_COUNT\n warning is generated.\n Before this fix, on some videos OpenCV would return frame counts that were magnitudes higher than the frames that\n could actually be read. This resulted in failing to process many video segments with a \nBAD_FRAME_SIZE\n error.\n\n\n\n\nOpenMPF 5.0.x\n\n\n5.0.9: October 2020\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1200\n] The MPFVideoCapture and MPFImageReader tools now properly\n handle cropping to frame regions when the region coordinates fall outside of the frame boundary. There was a bug that\n would result in an OpenCV error. Note that the bug only occurred when cropping was not performed with rotation or\n flipping.\n\n\n\n\n5.0.8: October 2020\n\n\n\nUpdates\n\n\n\n\n\nThe Tesseract component now supports a \nTESSDATA_MODELS_SUBDIRECTORY\n property. The component will look for tessdata\n files in \n/\n. This allows users to easily switch between \ntessdata\n\n , \ntessdata_best\n, and \ntessdata_fast\n subdirectories.\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1199\n] Added missing synchronized to InProgressBatchJobsService,\n which was resulting in some jobs staying \nIN_PROGRESS\n indefinitely.\n\n\n\n\n5.0.7: September 2020\n\n\n\nTensorRT Inference Server (TRTIS) Object Detection Component\n\n\n\n\n\nThis new component detects objects in images and videos by making use of\n an \nNVIDIA TensorRT Inference Server\n (\n TRTIS), and calculates features that can later be used by other systems to recognize the same object in other media.\n We provide support for running the server as a separate service during a Docker deployment, but an external server\n instance can be used instead.\n\n\nBy default, the ip_irv2_coco model is supported and will optionally classify detected objects\n using \nCOCO labels\n\n . Additionally, features can be generated for whole frames, automatically-detected object regions, and user-specified\n regions. Refer to the \nREADME\n\n .\n\n\n\n\n5.0.6: August 2020\n\n\n\nEnable OcvDnnDetection to Annotate Feed-forward Detections\n\n\n\n\n\nThe OcvDnnDetection component can now by configured to operate only on certain feed-forward detections and annotate\n them with supplementary information. For example, the following pipeline can be configured to generate detections that\n have both \nCLASSIFICATION\n and \nCOLOR\n detection properties:\n\n\n\n\nDarknetDetection (person + vehicle) --> OcvDnnDetection (vehicle color)\n\n\n\n\n\nFor example:\n\n\n\n\n \"detectionProperties\": {\n \"CLASSIFICATION\": \"car\",\n \"CLASSIFICATION CONFIDENCE LIST\": \"0.397336\",\n \"CLASSIFICATION LIST\": \"car\",\n \"COLOR\": \"blue\",\n \"COLOR CONFIDENCE LIST\": \"0.93507; 0.055744\",\n \"COLOR LIST\": \"blue; gray\"\n }\n\n\n\n\n\nThe OcvDnnDetection component now supports the following properties:\n\n\nCLASSIFICATION_TYPE\n: Set this value to change the \nCLASSIFICATION*\n part of each output property name to\n something else. For example, setting it to \nCOLOR\n will generate \nCOLOR\n, \nCOLOR LIST\n,\n and \nCOLOR CONFIDENCE LIST\n. When handling feed-foward detections, the pre-existing \nCLASSIFICATION*\n properties\n will be carried over and the \nCOLOR*\n properties will be added to the detection.\n\n\nFEED_FORWARD_WHITELIST_FILE\n: When \nFEED_FORWARD_TYPE\n is provided and not set to \nNONE\n, only feed-forward\n detections with class names contained in the specified file will be processed. For, example, a file with only \"\n car\" in it will result in performing the exclude behavior (below) for all feed-foward detections that do not have\n a \nCLASSIFICATION\n of \"car\".\n\n\nFEED_FORWARD_EXCLUDE_BEHAVIOR\n: Specifies what to do when excluding detections not specified in\n the \nFEED_FORWARD_WHITELIST_FILE\n. Acceptable values are:\n\n\nPASS_THROUGH\n: Return the excluded detections, without modification, along with any annotated detections.\n\n\nDROP\n: Don't return the excluded detections. Only return annotated detections.\n\n\n\n\n\n\n\n\n\n\n\n\nUpdates\n\n\n\n\n\nMake interop package work with Java 8 to better support exernal job producers and consumers.\n\n\n\n\n5.0.5: August 2020\n\n\n\nUpdates\n\n\n\n\n\nConfigure Camel not to auto-acknowledge messages. Users can now see the number of pending messages in the ActiveMQ\n management console for queues consumed by the Workflow Manager.\n\n\nImprove Tesseract OSD fallback behavior. This prevents selecting the OSD rotation from the fallback pass without the\n OSD script from the fallback pass.\n\n\n\n\n5.0.4: August 2020\n\n\n\nUpdates\n\n\n\n\n\nRetry job callbacks when they fail. The Workflow Manager now supports the \nhttp.callback.timeout.ms\n\n and \nhttp.callback.retries\n system properties.\n\n\nDrop \"duplicate paged in from cursor\" DLQ messages.\n\n\n\n\n5.0.3: July 2020\n\n\n\nUpdates\n\n\n\n\n\nUpdate ActiveMQ to 5.16.0.\n\n\n\n\n5.0.2: July 2020\n\n\n\nUpdates\n\n\n\n\n\nDisable video segmentation for ACS Speech Detection to prevent issues when generating speaker ids.\n\n\n\n\n5.0.1: July 2020\n\n\n\nUpdates\n\n\n\n\n\nUpdated Tessseract component with \nMAX_PIXELS\n setting to prevent processing large images.\n\n\n\n\n5.0.0: June 2020\n\n\n\nDocumentation\n\n\n\n\n\nUpdated the openmpf-docker repo \nREADME\n\n and \nSWARM\n guides to describe the new build process,\n which now includes automatically copying the openmpf repo source code into the openmpf-build image instead of using\n various bind mounts, and building all of the component base builder and executor images.\n\n\nUpdated the openmpf-docker repo \nREADME\n with the\n following sections:\n\n\nHow\n to \nUse Kibana for Log Viewing and Aggregation\n\n\nHow\n to \nRestrict Media Types that a Component Can Process\n\n\nHow\n to \nImport Root Certificates for Additional Certificate Authorities\n\n\n\n\n\n\nUpdated the \nCONTRIBUTING\n guide for Docker\n deployment with information on the new build process and component base builder and executor images.\n\n\nUpdated the \nInstall Guide\n with a pointer to the \"Quick Start\" section on DockerHub.\n\n\nUpdated the \nREST API\n with the new endpoints for getting, deleting, and creating actions, tasks, and\n pipelines, as well as a change to the \n[GET] /rest/info\n endpoint.\n\n\nUpdated the \nC++ Batch Component API\n to describe changes to the \nGetDetection()\n calls,\n which now return a collection of detections or tracks instead of an error code, and to describe improvements to\n exception handling.\n\n\nUpdated the \nC++ Batch Component API\n\n , \nPython Batch Component API\n,\n and \nJava Batch Component API\n with \nMIME_TYPE\n, \nFRAME_WIDTH\n, and \nFRAME_HEIGHT\n media\n properties.\n\n\nUpdated the \nPython Batch Component API\n with information on Python3 and the\n simplification of using a \ndict\n for some of the data members.\n\n\n\n\nJSON Output Object\n\n\n\n\n\nRenamed \nstages\n to \ntasks\n for clarity and consistency with the rest of the code.\n\n\nThe \nmedia\n element no longer contains a \nmessage\n field.\n\n\nEach \ndetectionProcessingError\n element now contains a \ncode\n field.\n\n\nErrors and warnings are now grouped by \nmediaId\n and summarized using a \ndetails\n element that contains a \nsource\n\n , \ncode\n, and \nmessage\n field. Refer\n to \nthis comment\n for an example of the JSON\n structure. Note that errors and warnings generated by the Workflow Manager do not have a \nmediaId\n.\n\n\nWhen an error or warning occurs in multiple frames of a video for a single piece of media it will be represented\n in one \ndetails\n element and the \nmessage\n will list the frame ranges.\n\n\n\n\n\n\n\n\nInteroperability Package\n\n\n\n\n\nRenamed \nJsonStage.java\n to \nJsonTask.java\n.\n\n\nRemoved \nJsonJobRequest.java\n.\n\n\nModified \nJsonDetectionProcessingError.java\n by removing the \nstartOffset\n and \nstopOffset\n fields and adding the\n following new fields: \nstartOffsetFrame\n, \nstopOffsetFrame\n, \nstartOffsetTime\n, \nstopOffsetTime\n, and \ncode\n.\n\n\nUpdated \nJsonMediaOutputObject.java\n by removing \nmessage\n field.\n\n\nAdded \nJsonMediaIssue.java\n and \nJsonIssueDetails.java\n.\n\n\n\n\nPersistent Database\n\n\n\n\n\nThe \ninput_object\n column in the \njob_request\n table has been renamed to \njob\n and the content now contains a\n serialized form of \nBatchJob.java\n instead of \nJsonJobRequest.java\n.\n\n\n\n\nC++ Batch Component API\n\n\n\n\n\nThe \nGetDetection()\n calls now return a collection instead of an error code:\n\n\nstd::vector GetDetections(const MPFImageJob &job)\n\n\nstd::vector GetDetections(const MPFVideoJob &job)\n\n\nstd::vector GetDetections(const MPFAudioJob &job)\n\n\nstd::vector GetDetections(const MPFGenericJob &job)\n\n\n\n\n\n\nMPFDetectionException\n can now be constructed with a \nwhat\n parameter representing a descriptive error message:\n\n\nMPFDetectionException(MPFDetectionError error_code, const std::string &what = \"\")\n\n\nMPFDetectionException(const std::string &what)\n\n\n\n\n\n\n\n\nPython Batch Component API\n\n\n\n\n\nSimplified the \ndetection_properties\n and \nframe_locations\n data members to use a Python \ndict\n instead of a custom\n data type.\n\n\n\n\nFull Docker Conversion\n\n\n\n\n\nEach component is now encapsulated in its own Docker image which self-registers with the Workflow Manager at runtime.\n This deconflicts component dependencies, and allows for greater flexibility when deciding which components to deploy\n at runtime.\n\n\nThe Node Manager image has been removed. For Docker deployments, component services should be managed using Docker\n tools external to OpenMPF.\n\n\nIn Docker deployments, streaming job REST endpoints are disabled, the Nodes web page is no longer available, component\n tar.gz packages cannot be registered through the Component Registration web page, and the \nmpf\n command line script\n can now only be run on the Workflow Manager container to modify user settings. The preexisting features are now\n reserved for non-Docker deployments and development environments.\n\n\nThe OpenMPF Docker stack can optionally be deployed with \nKibana\n (which depends on\n Elasticsearch and Filebeat) for viewing log files. Refer to the\n openmpf-docker \nREADME\n\n .\n\n\n\n\nDocker Component Base Images\n\n\n\n\n\nA base builder image and executor image are provided for\n C++ (\nREADME\n),\n Python (\nREADME\n), and\n Java (\nREADME\n) component\n development. Component developers can also refer to the Dockerfile in the source code for each component as reference\n for how to make use of the base images.\n\n\n\n\nRestrict Media Types that a Component Can Process\n\n\n\n\n\nEach component service now supports an optional \nRESTRICT_MEDIA_TYPES\n Docker environment variable that specifies the\n types of media that service will process. For example, \nRESTRICT_MEDIA_TYPES: VIDEO,IMAGE\n will process both videos\n and images, while \nRESTRICT_MEDIA_TYPES: IMAGE\n will only process images. If not specified, the service will process\n all of the media types it natively supports. For example, this feature can be used to ensure that some services are\n always available to process images while others are processing long videos.\n\n\n\n\nImport Additional Root Certificates into the Workflow Manager\n\n\n\n\n\nAdditional root certificates can be imported into the Workflow Manager at runtime by adding an entry\n for \nMPF_CA_CERTS\n to the workflow-manager service's environment variables in \ndocker-compose.core.yml\n\n . \nMPF_CA_CERTS\n must contain a colon-delimited list of absolute file paths. Of note, a root certificate may be used\n to trust the identity of a remote object storage server.\n\n\n\n\nDockerHub\n\n\n\n\n\nPushed prebuilt OpenMPF Docker images to \nDockerHub\n. Refer to the \"Quick Start\"\n section of the OpenMPF Workflow Manager\n image \ndocumentation\n.\n\n\n\n\nVersion Updates\n\n\n\n\n\nUpdated from Oracle Java 8 to OpenJDK 11, which required updating to Tomcat 8.5.41. We now\n use \nCargo\n to run integration tests.\n\n\nUpdated OpenCV from 3.0.0 to 3.4.7 to update Deep Neural Networks (DNN) support.\n\n\nUpdated Python from 2.7 to 3.8.2.\n\n\n\n\nFFmpeg\n\n\n\n\n\nWe are no longer building separate audio and video encoders and decoders for FFmpeg. Instead, we are using the\n built-in decoders that come with FFmpeg by default. This simplifies the build process and redistribution via Docker\n images.\n\n\n\n\nArtifact Extraction\n\n\n\n\n\nThe \nARTIFACT_EXTRACTION_POLICY\n property can now be assigned a value of \nNONE\n, \nVISUAL_TYPES_ONLY\n, \nALL_TYPES\n,\n or \nALL_DETECTIONS\n.\n\n\nWith the \nVISUAL_TYPES_ONLY\n or \nALL_TYPES\n policy, artifacts will be extracted according to\n the \nARTIFACT_EXTRACTION_POLICY*\n properties. With the \nNONE\n and \nALL_DETECTIONS\n policies, those settings are\n ignored.\n\n\nNote that previously \nNONE\n, \nVISUAL_EXEMPLARS_ONLY\n, \nEXEMPLARS_ONLY\n, \nALL_VISUAL_DETECTIONS\n,\n and \nALL_DETECTIONS\n were supported.\n\n\n\n\n\n\nThe following \nARTIFACT_EXTRACTION_POLICY*\n properties are now supported:\n\n\nARTIFACT_EXTRACTION_POLICY_EXEMPLAR_FRAME_PLUS\n: Extract the exemplar frame from the track, plus this many frames\n before and after the exemplar.\n\n\nARTIFACT_EXTRACTION_POLICY_FIRST_FRAME\n: If true, extract the first frame from the track.\n\n\nARTIFACT_EXTRACTION_POLICY_MIDDLE_FRAME\n: If true, extract the frame with a detection that is closest to the\n middle frame from the track.\n\n\nARTIFACT_EXTRACTION_POLICY_LAST_FRAME\n: If true, extract the last frame from the track.\n\n\nARTIFACT_EXTRACTION_POLICY_TOP_CONFIDENCE_COUNT\n: Sort the detections in a track by confidence and then extract\n this many detections, starting with those which have the highest confidence.\n\n\nARTIFACT_EXTRACTION_POLICY_CROPPING\n: If true, an artifact will be extracted for each detection in each frame\n that is selected according to the other \nARTIFACT_EXTRACTION_POLICY*\n properties. The extracted artifact will be\n cropped to the width and height of the detection bounding box, and the artifact will be rotated according to the\n detection \nROTATION\n property. If false, the artifact extraction behavior is unchanged from the previous release:\n the entire frame will be extracted without any rotation.\n\n\n\n\n\n\nFor clarity, \nOUTPUT_EXEMPLARS_ONLY\n has been renamed to \nOUTPUT_ARTIFACTS_AND_EXEMPLARS_ONLY\n. Extracted artifacts\n will always be reported in the JSON output object.\n\n\nThe \nmpf.output.objects.exemplars.only\n system property has been renamed\n to \nmpf.output.objects.artifacts.and.exemplars.only\n. It works the same as before with the exception that if an\n artifact is extracted for a detection then that detection will always be represented in the JSON output object,\n whether it's an exemplar or not.\n\n\nThe \nmpf.output.objects.last.stage.only\n system property has been renamed to \nmpf.output.objects.last.task.only\n. It\n works the same as before with the exception that when set to true artifact extraction is skipped for all tasks but the\n last task.\n\n\n\n\nREST Endpoints\n\n\n\n\n\nModified \n[GET] /rest/info\n. Now returns output like \n{\"version\": \"4.1.0\", \"dockerEnabled\": true}\n.\n\n\nAdded the following REST endpoints for getting, removing, and creating actions, tasks, and pipelines. Refer to\n the \nREST API\n for more information:\n\n\n[GET] /rest/actions\n, \n[GET] /rest/tasks\n, \n[GET] /rest/pipelines\n\n\n[DELETE] /rest/actions\n, \n[DELETE] /rest/tasks\n, \n[DELETE] /rest/pipelines\n\n\n[POST] /rest/actions\n , \n[POST] /rest/tasks\n, \n[POST] /rest/pipelines\n\n\n\n\n\n\nAll of the endpoints above are new with the exception of \n[GET] /rest/pipelines\n. The endpoint has changed since the\n last version of OpenMPF. Some fields in the response JSON have been removed and renamed. Also, it now returns a\n collection of tasks for each pipelines. Refer to the REST API.\n\n\n[GET] /rest/algorithms\n can be used to get information about algorithms. Note that algorithms are tied to registered\n components, so to remove an algorithm you must unregister the associated component. To add an algorithm, start the\n associated component's Docker container so it self-registers with the Workflow Manager.\n\n\n\n\nIncomplete Actions, Tasks, and Pipelines\n\n\n\n\n\nThe previous version of OpenMPF would generate an error when attempting to register a component that included actions,\n tasks, or pipelines that depend on algorithms, actions, or tasks that are not yet registered with the Workflow\n Manager. This required components to be registered in a specific order. Also, when unregistering a component, it\n required the components which depend on it to be unregistered. These dependency checks are no longer enforced.\n\n\nIn general, the Workflow Manager now appropriately handles incomplete actions, tasks, and pipelines by checking if all\n of the elements are defined before executing a job, and then preserving that information in memory until the job is\n complete. This allows components to be registered and removed in an arbitrary order without affecting the state of\n other components, actions, tasks, or pipelines. This also allows actions and tasks to be removed using the new REST\n endpoints and then re-added at a later time while still preserving the elements that depend on them.\n\n\nNote that unregistering a component while a job is running will cause it to stall. Please ensure that no jobs are\n using a component before unregistering it.\n\n\n\n\nPython Arbitrary Rotation\n\n\n\n\n\nThe Python MPFVideoCapture and MPFImageReader tools now support \nROTATION\n values other than 0, 90, 180, and 270\n degrees. Users can now specify a clockwise \nROTATION\n job property in the range [0, 360). Values outside that range\n will be normalized to that range. Floating point values are accepted. This is similar to the existing support\n for \nC++ arbitrary rotation\n.\n\n\n\n\nOpenCV Deep Neural Networks (DNN) Detection Component\n\n\n\n\n\nThis new component replaces the old CaffeDetection component. It supports the same GoogLeNet and Yahoo Not Suitable\n For Work (NSFW) models as the old component, but removes support for the Rezafuad vehicle color detection model in\n favor of a custom TensorFlow vehicle color detection model. In our tests, the new model has proven to be more\n generalizable and provide more accurate results on never-before-seen test data. Refer to\n the \nREADME\n.\n\n\n\n\nAzure Cognitive Services (ACS) Speech Detection Component\n\n\n\n\n\nThis new component utilizes\n the \nAzure Cognitive Services Batch Transcription REST endpoint\n\n to transcribe speech from audio and video files. Refer to\n the \nREADME\n.\n\n\n\n\nTesseract OCR Text Detection Component\n\n\n\n\n\nText tagging has been simplified to only support regular expression searches. Whole keyword searches are a subset of\n regular expression searches, and are therefore still supported. Also, the \ntext-tags.json\n file format has been\n updated to allow for specifying case-sensitive regular expression searches.\n\n\nAdditionally, the \nTRIGGER_WORDS\n and \nTRIGGER_WORDS_OFFSET\n detection properties are now supported, which list the\n OCR'd words that resulted in adding a \nTAG\n to the detection, and the character offset of those words within the\n OCR'd \nTEXT\n, respectively.\n\n\nKey changes to tagging output and \ntext-tags.json\n format are outlined below. Refer to\n the \nREADME\n\n for more information:\n\n\nRegex patterns should now be entered in the format \n{\"pattern\": \"regex_pattern\"}\n. Users can add and toggle\n the \n\"caseSensitive\"\n regex flag for each pattern.\n\n\nFor example: \n{\"pattern\": \"(\\\\b)bus(\\\\b)\", \"caseSensitive\": true}\n enables case-sensitive regex pattern\n matching.\n\n\nBy default, each regex pattern, including those in the legacy format, will be case-insensitive.\n\n\n\n\n\n\nAs part of the text tagging update, the \nTAGS\n outputs are now separated by semicolons \n;\n rather than commas \n,\n\n to be consistent with the delimiters for \nTRIGGER_WORDS\n and \nTRIGGER_WORDS_OFFSET\n output patterns.\n\n\nBecause semicolons can be part of the trigger word itself, those semicolons will be encapsulated in brackets.\n\n\nFor example, \ndetected trigger with a ;\n in the OCR'd \nTEXT\n is reported\n as \nTRIGGER_WORDS=detected trigger with a [;]; some other trigger\n.\n\n\n\n\n\n\nCommas are now used to group each set of \nTRIGGER_WORDS_OFFSET\n with its respective \nTRIGGER_WORDS\n output.\n Both \nTAGS\n and \nTRIGGER_WORDS\n are separated by semicolons only.\n\n\nFor example: \nTRIGGER_WORDS=trigger1; trigger2\n, \nTRIGGER_WORDS_OFFSET=0-5, 6-10; 12-15\n, means\n that \ntrigger1\n occurs twice in the text at the index ranges 0-5 and 6-10, and \ntrigger2\n occurs at index\n range 12-15.\n\n\n\n\n\n\n\n\n\n\nRegex tagging now follows the C++ ECMAS format (\n see \nexamples here\n) after resolving JSON string conversion\n for regex tags.\n\n\nAs a result the regex patterns \n\\b\n and \n\\p\n in the text tagging file must now be written as \n\\\\b\n and \n\\\\p\n,\n respectively, to match the format of other regex character patterns (ex. \n\\\\d\n, \n\\\\w\n, \n\\\\s\n, etc.).\n\n\n\n\n\n\nThe \nMAX_PARALLEL_SCRIPT_THREADS\n and \nMAX_PARALLEL_PAGE_THREADS\n properties are now supported. When processing\n images, the first property is used to determine how many threads to run in parallel. Each thread performs OCR using a\n different language or script model. When processing PDFs, the second property is used to determine how many threads to\n run in parallel. Each thread performs OCR on a different page of the PDF.\n\n\nThe \nENABLE_OSD_FALLBACK\n property is now supported. If enabled, an additional round of OSD is performed when the\n first round fails to generate script predictions that are above the OSD score and confidence thresholds. In the second\n pass, the component will run OSD on multiple copies of the input text image to get an improved prediction score\n and \nOSD_FALLBACK_OCCURRED\n detection property will be set to true.\n\n\nIf any OSD-detected models are missing, the new \nMISSING_LANGUAGE_MODELS\n detection property will list the missing\n models.\n\n\n\n\nTika Text Detection Component\n\n\n\n\n\nThe Tika text detection component now supports text tagging in the same way as the Tesseract component. Refer to\n the \nREADME\n.\n\n\n\n\nOther Improvements\n\n\n\n\n\nSimplified component \ndescriptor.json\n files by moving the specification of common properties, such\n as \nCONFIDENCE_THRESHOLD\n, \nFRAME_INTERVAL\n, \nMIN_SEGMENT_LENGTH\n, etc., to a single \nworkflow-properties.json\n file.\n Now when the Workflow Manager is updated to support new features, the component \ndescriptor.json\n file will not need\n to be updated.\n\n\nUpdated the Sphinx component to return \nTRANSCRIPT\n instead of \nTRANSCRIPTION\n, which is grammatically correct.\n\n\nWhitespace is now trimmed from property names when jobs are submitted via the REST API.\n\n\nThe Darknet Docker image now includes the YOLOv3 model weights.\n\n\nThe C++ and Python ModelsIniParser now allows users to specify optional fields.\n\n\nWhen a job completion callback fails, but otherwise the job is successful, the final state of the job will\n be \nCOMPLETE_WITH_WARNINGS\n.\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#772\n] Can now create a custom pipeline with long action names using\n the Pipelines 2 UI.\n\n\n[\n#812\n] Now properly setting the start and stop index for elements in\n the \ndetectionProcessingErrors\n collection in the JSON output object. Errors reported for each job segment will now\n appear in the collection.\n\n\n[\n#941\n] Tesseract component no longer segfaults when handling corrupt\n media.\n\n\n[\n#1005\n] Fixed a bug that caused a NullPointerException when\n attempting to get output object JSON via REST before a job completes.\n\n\n[\n#1035\n] The search bar in the Job Status UI can once again for used\n to search for job id.\n\n\n[\n#1104\n] Fixed C++/Python component executor memory leaks.\n\n\n[\n#1108\n] Fixed a bug when handling frames and detections that are\n horizontally flipped. This affected both markup and feed-forward behaviors.\n\n\n[\n#1119\n] Fixed Tesseract component memory leaks and uninitialized\n read issues.\n\n\n\n\nKnown Issues\n\n\n\n\n\n[\n#1028\n] Media inspection fails to handle Apple-optimized PNGs with\n the CgBI data chunk before the IHDR chunk.\n\n\n[\n#1109\n] We made the search bar in the Job Status UI more efficient\n by shifting it to a database query, but in doing so introduced a bug where the search operates on UTC time instead of\n local system time.\n\n\n[\n#1010\n] \nmpf.output.objects.enabled\n does not behave as expected for\n batch jobs. A user would expect it to control whether the JSON output object is generated, but it's generated\n regardless of that setting.\n\n\n[\n#1032\n] Jobs fail on corrupt QuickTime videos. For these videos, the\n OpenCV-reported frame count is more than twice the actual frame count.\n\n\n[\n#1106\n] When a job ends in ERROR the job status UI does not show an\n End Date.\n\n\n\n\nOpenMPF 4.1.x\n\n\n4.1.14: June 2020\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1120\n] The node-manager Docker image now correctly installs CUDA\n libraries so that GPU-enabled components on that image can run on the GPU.\n\n\n[\n#1064\n] Fixed memory leaks in the Darknet component for various\n network types, and when using GPU resources. This bug covers everything not addressed\n by \n#1062\n.\n\n\n\n\n4.1.13: June 2020\n\n\n\nUpdates\n\n\n\n\n\nUpdated the OpenCV build and media inspection process to properly handle webp images.\n\n\n\n\n4.1.12: May 2020\n\n\n\nUpdates\n\n\n\n\n\nUpdated JDK from \njdk-8u181-linux-x64.rpm\n to \njdk-8u251-linux-x64.rpm\n.\n\n\n\n\n4.1.11: May 2020\n\n\n\nTesseract OCR Text Detection Component\n\n\n\n\n\nAdded \nINVALID_MIN_IMAGE_SIZE\n job property to filter out images with extremely low width or height.\n\n\nUpdated image rescaling behavior to account for image dimension limits.\n\n\nFixed handling of \nnullptr\n returns from Tesseract API OCR calls.\n\n\n\n\n4.1.8: May 2020\n\n\n\nAzure Cognitive Services (ACS) OCR Component\n\n\n\n\n\nThis new component utilizes\n the \nACS OCR REST endpoint\n\n to extract text from images and videos. Refer to\n the \nREADME\n.\n\n\n\n\n4.1.6: April 2020\n\n\n\nUpdates\n\n\n\n\n\nNow silently discarding ActiveMQ DLQ \"Suppressing duplicate delivery on connection\" messages in addition to \"duplicate\n from store\" messages.\n\n\n\n\n4.1.5: March 2020\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1062\n] Fixed a memory leak in the Darknet component that occurred\n when running jobs on CPU resources with the Tiny YOLO model.\n\n\n\n\nKnown Issues\n\n\n\n\n\n[\n#1064\n] The Darknet component has memory leaks for various network\n types, and potentially when using GPU resources. This bug covers everything not addressed\n by \n#1062\n.\n\n\n\n\n4.1.4: March 2020\n\n\n\nUpdates\n\n\n\n\n\nUpdated from Hibernate 5.0.8 to 5.4.12 to support schema-based multitenancy. This allows multiple instances of OpenMPF\n to use the same PostgreSQL database as long as each instance connects to the database as a separate user, and the\n database is configured appropriately. This also required updating Tomcat from 7.0.72 to 7.0.76.\n\n\n\n\nJSON Output Object\n\n\n\n\n\nUpdated the Workflow Manager to include an \noutputobjecturi\n in GET callbacks, and \noutputObjectUri\n in POST\n callbacks, when jobs complete. This URI specifies a file path, or path on the object storage server, depending on\n where the JSON output object is located.\n\n\n\n\nInteroperability Package\n\n\n\n\n\nUpdated \nJsonCallbackBody.java\n to contain an \noutputObjectUri\n field.\n\n\n\n\n4.1.3: February 2020\n\n\n\nFeatures\n\n\n\n\n\nAdded support for \nDETECTION_PADDING_X\n and \nDETECTION_PADDING_Y\n optional job properties. The value can be a\n percentage or whole-number pixel value. When positive, each detection region in each track will be expanded. When\n negative, the region will shrink. If the detection region is shrunk to nothing, the shrunk dimension(s) will be set to\n a value of 1 pixel and the \nSHRUNK_TO_NOTHING\n detection property will be set to true.\n\n\nAdded support for \nDISTANCE_CONFIDENCE_WEIGHT_FACTOR\n and \nSIZE_CONFIDENCE_WEIGHT_FACTOR\n SuBSENSE algorithm\n properties. Increasing the value of the first property will generate detection confidence values that favor being\n closer to the center frame of a track. Increasing the value of the second property will generate detection confidence\n values that favor large detection regions.\n\n\n\n\n4.1.1: January 2020\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1016\n] Fixed a bug that caused a deadlock situation when the media\n inspection process failed quickly when processing many jobs using a pipeline with more than one stage.\n\n\n\n\n4.1.0: July 2019\n\n\n\nDocumentation\n\n\n\n\n\nUpdated the \nC++ Batch Component API\n to describe the \nROTATION\n\n detection property. See the \nC++ Arbitrary Rotation\n section below.\n\n\nUpdated the \nREST API\n with new component registration REST endpoints. See\n the \nComponent Registration REST Endpoints\n section below.\n\n\nAdded a \nREADME\n for\n the EAST text region detection component. See\n the \nEAST Text Region Detection Component\n section below.\n\n\nUpdated the Tesseract OCR text detection\n component \nREADME\n\n . See the \nTesseract OCR Text Detection Component\n section below.\n\n\nUpdated the openmpf-docker repo \nREADME\n\n and \nSWARM\n guide to describe the new streamlined\n approach to using \ndocker-compose config\n. See the \nDocker Deployment\n section below.\n\n\nFixed the description of \nMIN_SEGMENT_LENGTH\n and associated examples in\n the \nUser Guide\n for\n issue \n#891\n.\n\n\nUpdated the \nJava Batch Component API\n with information on how to use Log4j2.\n Related to resolving issue \n#855\n.\n\n\nUpdated the \nInstall Guide\n to point to the\n Docker \nREADME\n.\n\n\nTransformed the Build Guide into a \nDevelopment Environment Guide\n.\n\n\n\n\n\n\nC++ Arbitrary Rotation\n\n\n\n\n\nThe C++ MPFVideoCapture and MPFImageReader tools now support \nROTATION\n values other than 0, 90, 180, and 270 degrees.\n Users can now specify a clockwise \nROTATION\n job property in the range [0, 360). Values outside that range will be\n normalized to that range. Floating point values are accepted.\n\n\nWhen using those tools to read frame data, they will automatically correct for rotation so that the returned frame is\n horizontally oriented toward the normal 3 o'clock position.\n\n\nWhen \nFEED_FORWARD_TYPE=REGION\n, these tools will look for a \nROTATION\n detection property in the feed-forward\n detections and automatically correct for rotation. For example, a detection property of \nROTATION=90\n represents\n that the region is rotated 90 degrees counter clockwise, and therefore must be rotated 90 degrees clockwise to\n correct for it.\n\n\nWhen \nFEED_FORWARD_TYPE=SUPERSET_REGION\n, these tools will properly account for the \nROTATION\n detection property\n associated with each feed-forward detection when calculating the bounding box that encapsulates all of those\n regions.\n\n\nWhen \nFEED_FORWARD_TYPE=FRAME\n, these tools will rotate the frame according to the \nROTATION\n job property. It's\n important to note that for rotations other than 0, 90, 180, and 270 degrees the rotated frame dimensions will be\n larger than the original frame dimensions. This is because the frame needs to be expanded to encapsulate the\n entirety of the original rotated frame region. Black pixels are used to fill the empty space near the edges of the\n original frame.\n\n\n\n\n\n\nThe Markup component now places a colored dot at the upper-left corner of each detection region so that users can\n determine the rotation of the region relative to the entire frame.\n\n\n\n\n\n\nComponent Registration REST Endpoints\n\n\n\n\n\nAdded a \n[POST] /rest/components/registerUnmanaged\n endpoint so that components running as separate Docker containers\n can self-register with the Workflow Manager.\n\n\nSince these components are not managed by the Node Manager, they are considered unmanaged OpenMPF components.\n These components are not displayed in Nodes web UI and are tagged as unmanaged in the Component Registration web\n UI where they can only be removed.\n\n\nNote that components uploaded to the Component Registration web UI as .tar.gz files are considered managed\n components.\n\n\n\n\n\n\nAdded a \n[DELETE] /rest/components/{componentName}\n endpoint that can be used to remove managed and unmanaged\n components.\n\n\n\n\nPython Component Executor Docker Image\n\n\n\n\n\nComponent developers can now use a Python component executor Docker image to write a Python component for OpenMPF that\n can be encapsulated within a Docker container. This isolates the build and execution environment from the rest of\n OpenMPF. For more information, see\n the \nREADME\n.\n\n\nComponents developed with this image are not managed by the Node Manager; rather, they self-register with the Workflow\n Manager and their lifetime is determined by their own Docker container.\n\n\n\n\n\n\nDocker Deployment\n\n\n\n\n\nStreamlined single-host \ndocker-compose up\n deployments and multi-host \ndocker stack deploy\n swarm deployments. Now\n users are instructed to create a single \ndocker-compose.yml\n file for both types of deployments.\n\n\nRemoved the \ndocker-generate-compose-files.sh\n script in favor of allowing users the flexibility of combining\n multiple \ndocker-compose.*.yml\n files together using \ndocker-compose config\n. See\n the \nGenerate docker-compose.yml\n\n section of the README.\n\n\nComponents based on the Python component executor Docker image can now be defined and configured directly\n in \ndocker-compose.yml\n.\n\n\nOpenMPF Docker images now make use of Docker labels.\n\n\n\n\n\n\nEAST Text Region Detection Component\n\n\n\n\n\nThis new component uses the Efficient and Accurate Scene Text (EAST) detection model to detect text regions in images\n and videos. It reports their location, angle of rotation, and text type (\nSTRUCTURED\n or \nUNSTRUCTURED\n), and supports\n a variety of settings to control the behavior of merging text regions into larger regions. It does not perform OCR on\n the text or track detections across video frames. Thus, each video track is at most one detection long. For more\n information, see\n the \nREADME\n.\n\n\nOptionally, this component can be built as a Docker image using the Python component executor Docker image, allowing\n it to exist apart from the Node Manager image.\n\n\n\n\n\n\nTesseract OCR Text Detection Component\n\n\n\n\n\nUpdated to support reading tessdata \n*.traineddata\n files at a specified \nMODELS_DIR_PATH\n. This allows users to\n install new \n*.traineddata\n files post deployment.\n\n\nUpdated to optionally perform Tesseract Orientation and Script Detection (OSD). When enabled, the component will\n attempt to use the orientation results of OSD to automatically rotate the image, as well as perform OCR using the\n scripts detected by OSD.\n\n\nUpdated to optionally rotate a feed-forward text region 180 degrees to account for upside-down text.\n\n\nNow supports the following preprocessing properties for both structured and unstructured text:\n\n\nText sharpening\n\n\nText rescaling\n\n\nOtsu image thresholding\n\n\nAdaptive thresholding\n\n\nHistogram equalization\n\n\nAdaptive histogram equalization (also known as Contrast Limited Adaptive Histogram Equalization (CLAHE))\n\n\n\n\n\n\nWill use the \nTEXT_TYPE\n detection property in feed-forward regions provided by the EAST component to determine which\n preprocessing steps to perform.\n\n\nFor more information on these new features, see\n the \nREADME\n.\n\n\nRemoved gibberish and string filters since they only worked on English text.\n\n\n\n\nActiveMQ Profiles\n\n\n\n\n\nThe ActiveMQ Docker image now supports custom profiles. The container selects an \nactivemq.xml\n and \nenv\n file to use\n at runtime based on the value of the \nACTIVE_MQ_PROFILE\n environment variable. Among others, these files contain\n configuration settings for Java heap space and component queue memory limits.\n\n\nThis release only supports a \ndefault\n profile setting, as defined by \nactivemq-default.xml\n and \nenv.default\n;\n however, developers are free to add other \nactivemq-.xml\n and \nenv.\n files to the ActiveMQ Docker\n image to suit their needs.\n\n\n\n\nDisabled ActiveMQ Prefetch\n\n\n\n\n\nDisabled ActiveMQ prefetching on all component queues. Previously, a prefetch value of one was resulting in situations\n where one component service could be dispatched two sub-jobs, thereby starving other available component services\n which could process one of those sub-jobs in parallel.\n\n\n\n\nSearch Region Percentages\n\n\n\n\n\nIn addition to using exact pixel values, users can now use percentages for the following properties when specifying\n search regions for C++ and Python components:\n\n\nSEARCH_REGION_TOP_LEFT_X_DETECTION\n\n\nSEARCH_REGION_TOP_LEFT_Y_DETECTION\n\n\nSEARCH_REGION_BOTTOM_RIGHT_X_DETECTION\n\n\nSEARCH_REGION_BOTTOM_RIGHT_Y_DETECTION\n\n\n\n\n\n\nFor example, setting \nSEARCH_REGION_TOP_LEFT_X_DETECTION=50%\n will result in components only processing the right half\n of an image or video.\n\n\nOptionally, users can specify exact pixel values of some of these properties and percentages for others.\n\n\n\n\nOther Improvements\n\n\n\n\n\nIncreased the number of ActiveMQ maxConcurrentConsumers for the \nMPF.COMPLETED_DETECTIONS\n queue from 30 to 60.\n\n\nThe Create Job web UI now only displays the content of the \n$MPF_HOME/share/remote-media\n directory instead of all\n of \n$MPF_HOME/share\n, which prevents the Workflow Manager from indexing generated JSON output files, artifacts, and\n markup. Indexing the latter resulted in Java heap space issues for large scale production systems. This is a\n mitigation for issue \n#897\n.\n\n\nThe Job Status web UI now makes proper use of pagination in SQL/Hibernate through the Workflow Manager to avoid\n retrieving the entire jobs table, which was inefficient.\n\n\nThe Workflow Manager will now silently discard all duplicate messages in the ActiveMQ Dead Letter Queue (DLQ),\n regardless of destination. Previously, only messages destined for component sub-job request queues were discarded.\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#891\n] Fixed a bug where the Workflow Manager media segmenter\n generated short segments that were minimally \nMIN_SEGMENT_LENGTH+1\n in size instead of \nMIN_SEGMENT_LENGTH\n.\n\n\n[\n#745\n] In environments where thousands of jobs are processed, users\n have observed that, on occasion, pending sub-job messages in ActiveMQ queues are not processed until a new job is\n created. This seems to have been resolved by disabling ActiveMQ prefetch behavior on component queues.\n\n\n[\n#855\n] A logback circular reference suppressed exception no longer\n throws a StackOverflowError. This was resolved by transitioning the Workflow Manager and Java components from the\n Logback framework to Log4j2.\n\n\n\n\nKnown Issues\n\n\n\n\n\n[\n#897\n] OpenMPF will attempt to index files located\n in \n$MPF_HOME/share\n as soon as the webapp is started by Tomcat. This is so that those files can be listed in a\n directory tree in the Create Job web UI. The main problem is that once a file gets indexed it's never removed from the\n cache, even if the file is manually deleted, resulting in a memory leak.\n\n\n\n\nLate Additions: November 2019\n\n\n\n\n\nUser names, roles, and passwords can now be set by using an optional \nuser.properties\n file. This allows\n administrators to override the default OpenMPF users that come preconfigured, which may be a security risk. Refer to\n the \"Configure Users\" section of the\n openmpf-docker \nREADME\n for\n more information.\n\n\n\n\nLate Additions: December 2019\n\n\n\n\n\nTransitioned from using a mySQL persistent database to PostgreSQL to support users that use an external PostgreSQL\n database in the cloud.\n\n\nUpdated the EAST component to support a \nTEMPORARY_PADDING\n and \nFINAL_PADDING\n property. The first property\n determines how much padding is added to detections during the non-maximum suppression or merging step. This padding is\n effectively removed from the final detections. The second property is used to control the final amount of padding on\n the output regions. Refer to\n the \nREADME\n.\n\n\n\n\nOpenMPF 4.0.x\n\n\n4.0.0: February 2019\n\n\n\nDocumentation\n\n\n\n\n\nAdded an \nObject Storage Guide\n with information on how to configure OpenMPF to work\n with a custom NGINX object storage server, and how to run jobs that use an S3 object storage server. Note that the\n system properties for the custom NGINX object storage server have changed since the last release.\n\n\n\n\nUpgrade to Tesseract 4.0\n\n\n\n\n\nBoth the Tesseract OCR Text Detection Component and OpenALPR License Plate Detection Components have been updated to\n use the new version of Tesseract.\n\n\nAdditionally, Leptonica has been upgraded from 1.72 to 1.75.\n\n\n\n\nDocker Deployment\n\n\n\n\n\nThe Docker images now use the yum package manager to install ImageMagick6 from a public RPM repository instead of\n downloading the RPMs directly from imagemagick.org. This resolves an issue with the OpenMPF Docker build where RPMs\n on \nimagemagick.org\n were no longer available.\n\n\n\n\nTesseract OCR Text Detection Component\n\n\n\n\n\nUpdated to allow the user to set a \nTESSERACT_OEM\n property in order to select an OCR engine mode (OEM).\n\n\n\"script/Latin\" can now be specified as the \nTESSERACT_LANGUAGE\n. When selected, Tesseract will select all Latin\n characters, which can be from different Latin languages.\n\n\n\n\nCeph S3 Object Storage\n\n\n\n\n\nAdded support for downloading files from, and uploading files to, an S3 object storage server. The following job\n properties can be provided: \nS3_ACCESS_KEY\n, \nS3_SECRET_KEY\n, \nS3_RESULTS_BUCKET\n, \nS3_UPLOAD_ONLY\n.\n\n\nAt this time, only support for Ceph object storage has been tested. However, the Workflow Manager uses the AWS SDK for\n Java to communicate with the object store, so it is possible that other S3-compatible storage solutions may work as\n well.\n\n\n\n\nISO-8601 Timestamps\n\n\n\n\n\nAll timestamps in the JSON output object, and streaming video callbacks, are now in the ISO-8601 format (e.g. \"\n 2018-12-19T12:12:59.995-05:00\"). This new format includes the time zone, which makes it possible to compare timestamps\n generated between systems in different time zones.\n\n\nThis change does not affect the track and detection start and stop offset times, which are still reported in\n milliseconds since the start of the video.\n\n\n\n\nReduced Redis Usage\n\n\n\n\n\nThe Workflow Manager has been refactored to reduce usage of the Redis in-memory database. In general, Redis is not\n necessary for storing job information and only resulted in introducing potential delays in accessing that data over\n the network stack.\n\n\nNow, only track and detection data is stored in Redis for batch jobs. This reduces the amount of memory the Workflow\n Manager requires of the Java Virtual Machine. Compared to the other job information, track and detection data can\n potentially be relatively much larger. In the future, we plan to store frame data in Redis for streaming jobs as well.\n\n\n\n\nCaffe Vehicle Color Estimation\n\n\n\n\n\nThe Caffe\n Component \nmodels.ini\n\n file has been updated with a \"vehicle_color\" section with links for downloading\n the \nReza Fuad Rachmadi's Vehicle Color Recognition Using Convolutional Neural Network\n\n model files.\n\n\nThe following pipelines have been added. These require the above model files to be placed\n in \n$MPF_HOME/share/models/CaffeDetection\n:\n\n\nCAFFE REZAFUAD VEHICLE COLOR DETECTION PIPELINE\n\n\nCAFFE REZAFUAD VEHICLE COLOR DETECTION (WITH FF REGION FROM TINY YOLO VEHICLE DETECTOR) PIPELINE\n\n\nCAFFE REZAFUAD VEHICLE COLOR DETECTION (WITH FF REGION FROM YOLO VEHICLE DETECTOR) PIPELINE\n\n\n\n\n\n\n\n\nTrack Merging and Minimum Track Length\n\n\n\n\n\nThe following system properties now have \"video\" in their names:\n\n\ndetection.video.track.merging.enabled\n\n\ndetection.video.track.min.gap\n\n\ndetection.video.track.min.length\n\n\ndetection.video.track.overlap.threshold\n\n\n\n\n\n\nThe above properties can be overridden by the following job properties, respectively. These have not been renamed\n since the last release:\n\n\nMERGE_TRACKS\n\n\nMIN_GAP_BETWEEN_TRACKS\n\n\nMIN_TRACK_LENGTH\n\n\nMIN_OVERLAP\n\n\n\n\n\n\nThese system and job properties now only apply to video media. This resolves an issue where users had\n set \ndetection.track.min.length=5\n, which resulted in dropping all image media tracks. By design, each image track can\n only contain a single detection.\n\n\n\n\nBug Fixes\n\n\n\n\n\nFixed a bug where the Docker entrypoint scripts appended properties to the end\n of \n$MPF_HOME/share/config/mpf-custom.properties\n every time the Docker deployment was restarted, resulting in entries\n like \ndetection.segment.target.length=5000,5000,5000\n.\n\n\nUpgrading to Tesseract 4 fixes a bug where, when specifying \nTESSERACT_LANGUAGE\n, if one of the languages is Arabic,\n then Arabic must be specified last. Arabic can now be specified first, for example: \nara+eng\n.\n\n\nFixed a bug where the minimum track length property was being applied to image tracks. Now it's only applied to video\n tracks.\n\n\nFixed a bug where ImageMagick6 installation failed while building Docker images.\n\n\n\n\nOpenMPF 3.0.x\n\n\n3.0.0: December 2018\n\n\n\n\n\nNOTE:\n The \nBuild Guide\n and \nInstall Guide\n are outdated. The old process for manually configuring a Build VM, using it to build an OpenMPF package, and installing that package, is deprecated in favor of Docker containers. Please refer to the openmpf-docker \nREADME\n.\n\n\nNOTE:\n Do not attempt to register or unregister a component through the Nodes UI in a Docker deployment. It may appear to succeed, but the changes will not affect the child Node Manager containers, only the Workflow Manager container. Also, do not attempt to use the \nmpf\n command line tools in a Docker deployment.\n\n\n\n\nDocumentation\n\n\n\n\n\nAdded a \nREADME\n\n , \nSWARM\n guide,\n and \nCONTRIBUTING\n guide for Docker deployment.\n\n\nUpdated the \nUser Guide\n with information on how track\n properties and track confidence are handled when merging tracks.\n\n\nAdded README files for new components. Refer to the component sections below.\n\n\n\n\nDocker Support\n\n\n\n\n\nOpenMPF can now be built and distributed as 5 Docker images: openmpf_workflow_manager, openmpf_node_manager,\n openmpf_active_mq, mysql_database, and redis.\n\n\nThese images can be deployed on a single host using \ndocker-compose up\n.\n\n\nThey can also be deployed across multiple hosts in a Docker swarm cluster using \ndocker stack deploy\n.\n\n\nGPU support is enabled through the NVIDIA Docker runtime.\n\n\nBoth HTTP and HTTPS deployments are supported.\n\n\n\n\n\n\nJSON Output Object\n\n\n\n\n\nAdded a \ntrackProperties\n field at the track level that works in much the same way as the \ndetectionProperties\n field\n at the detection level. Both are maps that contain zero or more key-value pairs. The component APIs have always\n supported the ability to return track-level properties, but they were never represented in the JSON output object,\n until now.\n\n\nSimilarly, added a track \nconfidence\n field. The component APIs always supported setting it, but the value was never\n used in the JSON output object, until now.\n\n\nAdded \njobErrors\n and\njobWarnings\n fields. The \njobErrors\n field will mention that there are items\n in \ndetectionProcessingErrors\n fields.\n\n\nThe \noffset\n, \nstartOffset\n, and \nstopOffset\n fields have been removed in favor of the existing \noffsetFrame\n\n , \nstartOffsetFrame\n, and \nstopOffsetFrame\n fields, respectively. They were redundant and deprecated.\n\n\nAdded a \nmpf.output.objects.exemplars.only\n system property, and \nOUTPUT_EXEMPLARS_ONLY\n job property, that can be set\n to reduce the size of the JSON output object by only recording the track exemplars instead of all of the detections in\n each track.\n\n\nAdded a \nmpf.output.objects.last.stage.only\n system property, and \nOUTPUT_LAST_STAGE_ONLY\n job property, that can be\n set to reduce the size of the JSON output object by only recording the detections for the last non-markup stage of a\n pipeline.\n\n\n\n\nDarknet Component\n\n\n\n\n\nThe Darknet component can now support processing streaming video.\n\n\nIn batch mode, video frames are prefetched, decoded, and stored in a buffer using a separate thread from the one that\n performs the detection. The size of the prefetch buffer can be configured by setting \nFRAME_QUEUE_CAPACITY\n.\n\n\nThe Darknet component can now perform basic tracking and generate video tracks with multiple detections. Both the\n default detection mode and preprocessor detection mode are supported.\n\n\nThe Darknet component has been updated to support the full and tiny YOLOv3 models. The YOLOv2 models are no longer\n supported.\n\n\n\n\nTesseract OCR Text Detection Component\n\n\n\n\n\nThis new component extracts text found in an image and reports it as a single-detection track.\n\n\nPDF documents can also be processed with one track detection per page.\n\n\nUsers may set the language of each track using the \nTESSERACT_LANGUAGE\n property as well as adjust other image\n preprocessing properties for text extraction.\n\n\nRefer to\n the \nREADME\n.\n\n\n\n\nOpenCV Scene Change Detection Component\n\n\n\n\n\nThis new component detects and segments a given video by scenes. Each scene change is detected using histogram\n comparison, edge comparison, brightness (fade outs), and overall hue/saturation/value differences between adjacent\n frames.\n\n\nUsers can toggle each type of of scene change detection technique as well as threshold properties for each detection\n method.\n\n\nRefer to the \nREADME\n.\n\n\n\n\nTika Text Detection Component\n\n\n\n\n\nThis new component extracts text contained in documents and performs language detection. 71 languages and most\n document formats (.txt, .pptx, .docx, .doc, .pdf, etc.) are supported.\n\n\nRefer to the \nREADME\n.\n\n\n\n\nTika Image Detection Component\n\n\n\n\n\nThis new component extracts images embedded in document formats (.pdf, .ppt, .doc) and stores them on disk in a\n specified directory.\n\n\nRefer to the \nREADME\n.\n\n\n\n\nTrack-Level Properties and Confidence\n\n\n\n\n\nRefer to the addition of track-level properties and confidence in the \nJSON Output Object\n\n section.\n\n\nComponents have been updated to return meaningful track-level properties. Caffe and Darknet include \nCLASSIFICATION\n,\n OALPR includes the exemplar \nTEXT\n, and Sphinx includes the \nTRANSCRIPTION\n.\n\n\nThe Workflow Manager will now populate the track-level confidence. It is the same as the exemplar confidence, which is\n the max of all of the track detections.\n\n\n\n\nCustom NGINX HTTP Object Storage\n\n\n\n\n\nAdded \nhttp.object.storage.*\n system properties for configuring an optional custom NGINX object storage server on\n which to store generated detection artifacts, JSON output objects, and markup files.\n\n\nWhen a file cannot be uploaded to the server, the Workflow Manager will fall back to storing it in \n$MPF_HOME/share\n,\n which is the default behavior when an object storage server is not specified.\n\n\nIf and when a failure occurs, the JSON output object will contain a descriptive message in the \njobWarnings\n field,\n and, if appropriate, the \nmarkupResult.message\n field. If the job completes without other issues, the final status\n will be \nCOMPLETE_WITH_WARNINGS\n.\n\n\nThe NGINX storage server runs custom server-side code which we can make available upon request. In the future, we plan\n to support more common storage server solutions, such as Amazon S3.\n\n\n\n\n\n\nActiveMQ\n\n\n\n\n\nThe \nMPF_OUTPUT\n queue is no longer supported and has been removed. Job producers can specify a callback URL when\n creating a job so that they are alerted when the job is complete. Users observed heap space issues with ActiveMQ after\n running thousands of jobs without consuming messages from the \nMPF_OUTPUT\n queue.\n\n\nThe Workflow Manager will now silently discard duplicate sub-job request messages in the ActiveMQ Dead Letter Queue (\n DLQ). This fixes a bug where the Workflow Manager would prematurely terminate jobs corresponding to the duplicate\n messages. It's assumed that ActiveMQ will only place a duplicate message in the DLQ if the original message, or\n another duplicate, can be delivered.\n\n\n\n\nNode Auto-Configuration\n\n\n\n\n\nAdded the \nnode.auto.config.enabled\n, \nnode.auto.unconfig.enabled\n, and \nnode.auto.config.num.services.per.component\n\n system properties for automatically managing the configuration of services when nodes join and leave the OpenMPF\n cluster.\n\n\nDocker will assign a a hostname with a randomly-generated id to containers in a swarm deployment. The above properties\n allow the Workflow Manager to automatically discover and configure services on child Node Manager components, which is\n convenient since the hostname of those containers cannot be known in advance, and new containers with new hostnames\n are created when the swarm is restarted.\n\n\n\n\nJob Status Web UI\n\n\n\n\n\nAdded the \nweb.broadcast.job.status.enabled\n and \nweb.job.polling.interval\n system properties that can be used to\n configure if the Workflow Manager automatically broadcasts updates to the Job Status web UI. By default, the\n broadcasts are enabled.\n\n\nIn a production environment that processes hundreds of jobs or more at the same time, this behavior can result in\n overloading the web UI, causing it to slow down and freeze up. To prevent this, set \nweb.broadcast.job.status.enabled\n\n to \nfalse\n. If \nweb.job.polling.interval\n is set to a non-zero value, the web UI will poll for updates at that\n interval (specified in milliseconds).\n\n\nTo disable broadcasts and polling, set \nweb.broadcast.job.status.enabled\n to \nfalse\n and \nweb.job.polling.interval\n to\n a zero or negative value. Users will then need to manually refresh the Job Status web page using their web browser.\n\n\n\n\nOther Improvements\n\n\n\n\n\nNow using variable-length text fields in the mySQL database for string data that may exceed 255 characters.\n\n\nUpdated the MPFImageReader tool to use OpenCV video capture behind the scenes to support reading data from HTTP URLs.\n\n\nPython components can now include pre-built wheel files in the plugin package.\n\n\nWe now use a \nJenkinsfile\n Groovy script for our\n Jenkins build process. This allows us to use revision control for our continuous integration process and share that\n process with the open source community.\n\n\nAdded \nremote.media.download.retries\n and \nremote.media.download.sleep\n system properties that can be used to\n configure how the Workflow Manager will attempt to retry downloading remote media if it encounters a problem.\n\n\nArtifact extraction now uses MPFVideoCapture, which employs various fallback strategies for extracting frames in cases\n where a video is not well-formed or corrupted. For components that use MPFVideoCapture, this enables better\n consistency between the frames they process and the artifacts that are later extracted.\n\n\n\n\nBug Fixes\n\n\n\n\n\nJobs now properly end in \nERROR\n if an invalid media URL is provided or there is a problem accessing remote media.\n\n\nJobs now end in \nCOMPLETE_WITH_ERRORS\n when a detection splitter error occurs due to missing system properties.\n\n\nComponents can now include their own version of the Google Protobuf library. It will not conflict with the version\n used by the rest of OpenMPF.\n\n\nThe Java component executor now sets the proper job id in the job name instead of using the ActiveMQ message request\n id.\n\n\nThe Java component executor now sets the run directory using \nsetRunDirectory()\n.\n\n\nActions can now be properly added using an \"extras\" component. An extras component only includes a \ndescriptor.json\n\n file and declares Actions, Tasks, and Pipelines using other component algorithms.\n\n\nRefer to the items listed in the \nActiveMQ\n section.\n\n\nRefer to the addition of track-level properties and confidence in the \nJSON Output Object\n\n section.\n\n\n\n\nKnown Issues\n\n\n\n\n\n[\n#745\n] In environments where thousands of jobs are processed, users\n have observed that, on occasion, pending sub-job messages in ActiveMQ queues are not processed until a new job is\n created. The reason is currently unknown.\n\n\n[\n#544\n] Image artifacts retain some permissions from source files\n available on the local host. This can result in some of the image artifacts having executable permissions.\n\n\n[\n#604\n] The Sphinx component cannot be unregistered\n because \n$MPF_HOME/plugins/SphinxSpeechDetection/lib\n is owned by root on a deployment machine.\n\n\n[\n#623\n] The Nodes UI does not work correctly\n when \n[POST] /rest/nodes/config\n is used at the same time. This is because the UI's state is not automatically updated\n to reflect changes made through the REST endpoint.\n\n\n[\n#783\n] The Tesseract OCR Text Detection Component has\n a \nknown issue\n because it uses Tesseract 3. If a combination\n of languages is specified using \nTESSERACT_LANGUAGE\n, and one of the languages is Arabic, then Arabic must be\n specified last. For example, for English and Arabic, \neng+ara\n will work, but \nara+eng\n will not.\n\n\n[\n#784\n] Sometimes services do not start on OpenMPF nodes, and those\n services cannot be started through the Nodes web UI. This is not a Docker-specific problem, but it has been observed\n in a Docker swarm deployment when auto-configuration is enabled. The workaround is to restart the Docker swarm\n deployment, or remove the entire node in the Nodes UI and add it again.\n\n\n\n\nOpenMPF 2.1.x\n\n\n2.1.0: June 2018\n\n\n\n\n\nNOTE:\n If building this release on a machine used to build a previous version of OpenMPF, then please run \nsudo pip install --upgrade pip\n to update to at least pip 10.0.1. If not, the OpenMPF build script will fail to properly download .whl files for Python modules.\n\n\n\n\nDocumentation\n\n\n\n\n\nAdded the \nPython Batch Component API\n.\n\n\nAdded the \nNode Guide\n.\n\n\nAdded the \nGPU Support Guide\n.\n\n\nUpdated the \nInstall Guide\n with an \"(Optional) Install the NVIDIA CUDA Toolkit\" section.\n\n\nRenamed Admin Manual to Admin Guide for consistency.\n\n\n\n\nPython Batch Component API\n\n\n\n\n\nDevelopers can now write batch components in Python using the mpf_component_api module.\n\n\nDependencies can be specified in a setup.py file. OpenMPF will automatically download the .whl files using pip at\n build time.\n\n\nWhen deployed, a virtualenv is created for the Python component so that it runs in a sandbox isolated from the rest of\n the system.\n\n\nOpenMPF ImageReader and VideoCapture tools are provided in the mpf_component_util module.\n\n\nExample Python components are provided for reference.\n\n\n\n\nSpare Nodes\n\n\n\n\n\nSpare nodes can join and leave an OpenMPF cluster while the Workflow Manager is running. You can create a spare node\n by cloning an existing OpenMPF child node. Refer to the \nNode Guide\n.\n\n\nNote that changes made using the Component Registration web page only affect core nodes, not spare nodes. Core nodes\n are those configured during the OpenMPF installation process.\n\n\nAdded \nmpf list-nodes\n command to list the core nodes and available spare nodes.\n\n\nOpenMPF now uses the JGroups FILE_PING protocol for peer discovery instead of TCPPING. This means that the list of\n OpenMPF nodes no longer needs to be fully specified when the Workflow Manager starts. Instead, the Workflow Manager,\n and Node Manager process on each node, use the files in \n$MPF_HOME/share/nodes\n to determine which nodes are currently\n available.\n\n\nUpdated JGroups from 3.6.4. to 4.0.11.\n\n\nThe environment variables specified in \n/etc/profile.d/mpf.sh\n have been simplified. Of note, \nALL_MPF_NODES\n has been\n replaced by \nCORE_MPF_NODES\n.\n\n\n\n\nDefault Detection System Properties\n\n\n\n\n\nThe detection properties that specify the default values when creating new jobs can now be updated at runtime without\n restarting the Workflow Manager. Changing these properties will only have an effect on new jobs, not jobs that are\n currently running.\n\n\nThese default detection system properties are separated from the general system properties in the Properties web page.\n The latter still require the Workflow Manager to be restarted for changes to take effect.\n\n\nThe Apache Commons Configuration library is now used to read and write properties files. When defining a property\n value using an environment variable in the Properties web page, or \n$MPF_HOME/config/mpf-custom.properties\n, be sure\n to prepend the variable name with \nenv:\n. For example:\n\n\n\n\ndetection.models.dir.path=${env:MPF_HOME}/models/\n\n\n\n\n\nAlternatively, you can define system properties using other system properties:\n\n\n\n\ndetection.models.dir.path=${mpf.share.path}/models/\n\n\n\nAdaptive Frame Interval\n\n\n\n\n\nThe \nFRAME_RATE_CAP\n property can be used to set a threshold on the maximum number of frames to process within one\n second of the native video time. This property takes precedence over the user-provided / pipeline-provided value\n for \nFRAME_INTERVAL\n. When the \nFRAME_RATE_CAP\n property is specified, an internal frame interval value is calculated\n as follows:\n\n\n\n\ncalcFrameInterval = max(1, floor(mediaNativeFPS / frameRateCapProp));\n\n\n\n\n\nFRAME_RATE_CAP\n may be disabled by setting it <= 0. \nFRAME_INTERVAL\n can be disabled in the same way.\n\n\nIf \nFRAME_RATE_CAP\n is disabled, then \nFRAME_INTERVAL\n will be used instead.\n\n\nIf both \nFRAME_RATE_CAP\n and \nFRAME_INTERVAL\n are disabled, then a value of 1 will be used for \nFRAME_INTERVAL\n.\n\n\n\n\nDarknet Component\n\n\n\n\n\nThis release includes a component that uses the \nDarknet neural network framework\n to\n perform detection and classification of objects using trained models.\n\n\nPipelines for the Tiny YOLO and YOLOv2 models are provided. Due to its large size, the YOLOv2 weights file must be\n downloaded separately and placed in \n$MPF_HOME/share/models/DarknetDetection\n in order to use the YOLOv2 pipelines.\n Refer to \nDarknetDetection/plugin-files/models/models.ini\n for more information.\n\n\nThis component supports a preprocessor mode and default mode of operation. If preprocessor mode is enabled, and\n multiple Darknet detections in a frame share the same classification, then those are merged into a single detection\n where the region corresponds to the superset region that encapsulates all of the original detections, and the\n confidence value is the probability that at least one of the original detections is a true positive. If disabled,\n multiple Darknet detections in a frame are not merged together.\n\n\nDetections are not tracked across frames. One track is generated per detection.\n\n\nThis component supports an optional \nCLASS_WHITELIST_FILE\n property. When provided, only detections with class names\n listed in the file will be generated.\n\n\nThis component can be compiled with GPU support if the NVIDIA CUDA Toolkit is installed on the build machine. Refer to\n the \nGPU Support Guide\n. If the toolkit is not found, then the component will compile with CPU\n support only.\n\n\nTo run on a GPU, set the \nCUDA_DEVICE_ID\n job property, or set the detection.cuda.device.id system property, >= 0.\n\n\nWhen \nCUDA_DEVICE_ID\n >= 0, you can set the \nFALLBACK_TO_CPU_WHEN_GPU_PROBLEM\n job property, or the\n detection.use.cpu.when.gpu.problem system property, to \nTRUE\n if you want to run the component logic on the CPU\n instead of the GPU when a GPU problem is detected.\n\n\n\n\nModels Directory\n\n\n\n\n\nThe\n$MPF_HOME/share/models\n directory is now used by the Darknet and Caffe components to store model files and\n associated files, such as classification names files, weights files, etc. This allows users to more easily add model\n files post-deployment. Instead of copying the model files to \n$MPF_HOME/plugins//models\n directory on\n each node in the OpenMPF cluster, they only need to copy them to the shared directory once.\n\n\nTo add new models to the Darknet and Caffe component, add an entry to the\n respective \n/plugin-files/models/models.ini\n file.\n\n\n\n\nPackaging and Deployment\n\n\n\n\n\nPython components are packaged with their respective dependencies as .whl files. This can be automated by providing a\n setup.py file. An example OpenCV Python component is provided that demonstrates how the component is packaged and\n deployed with the opencv-python module. When deployed, a virtualenv is created for the component with the .whl files\n installed in it.\n\n\nWhen deploying OpenMPF, \nLD_LIBRARY_PATH\n is no longer set system-wide. Refer to Known Issues.\n\n\n\n\nWeb User Interface\n\n\n\n\n\nUpdated the Nodes page to distinguish between core nodes and spare nodes, and to show when a node is online or\n offline.\n\n\nUpdated the Component Registration page to list the core nodes as a reminder that changes will not affect spare nodes.\n\n\nUpdated the Properties page to separate the default detection properties from the general system properties.\n\n\n\n\nBug Fixes\n\n\n\n\n\nCustom Action, task, and pipeline names can now contain \"(\" and \")\" characters again.\n\n\nDetection location elements for audio tracks and generic tracks in a JSON output object will now have a y value of \n0\n\n instead of \n1\n.\n\n\nStreaming health report and summary report timestamps have been corrected to represent hours in the 0-23 range instead\n of 1-24.\n\n\nSingle-frame .gif files are now segmented properly and no longer result in a NullPointerException.\n\n\nLD_LIBRARY_PATH\n is now set at the process level for Tomcat, the Node Manager, and component services, instead of at\n the system level in \n/etc/profile.d/mpf.sh\n. Also, deployments no longer create \n/etc/ld.so.conf.d/mpf.conf\n. This\n better isolates OpenMPF from the rest of the system and prevents issues, such as being unable to use SSH, when system\n libraries are not compatible with OpenMPF libraries. The latter situation may occur when running \nyum update\n on the\n system, which can make OpenMPF unusable until a new deployment package with compatible libraries is installed.\n\n\nThe Workflow Manager will no longer generate an \"Error retrieving the SingleJobInfo model\" line in the log if someone\n is viewing the Job Status page when a job submitted through the REST API is in progress.\n\n\n\n\nKnown Issues\n\n\n\n\n\nWhen multiple component services of the same type on the same node log to the same file at the same time, sometimes\n log lines will not be captured in the log file. The logging frameworks (log4j and log4cxx) do not support that usage.\n This problem happens more frequently on systems running many component services at the same time.\n\n\nThe following exception was observed:\n\n\n\n\ncom.google.protobuf.InvalidProtocolBufferException: Message missing required fields: data_uri\n\n\n\n\n\n\nFurther debugging is necessary to determine the reason why that message was missing that field. The situation is not easily reproducible. It may occur when ActiveMQ and / or the system is under heavy load and sends duplicate messages in attempt to ensure message delivery. Some of those messages seem to end up in the dead letter queue (DLQ). For now, we've improved the way we handle messages in the DLQ. If OpenMPF can process a message successfully, the job is marked as \nCOMPLETED_WITH_ERRORS\n, and the message is moved from \nActiveMQ.DLQ\n to \nMPF.DLQ_PROCESSED_MESSAGES\n. If OpenMPF cannot process a message successfully, it is moved from \nActiveMQ.DLQ to MPF.DLQ_INVALID_MESSAGES\n.\n\n\n\n\n\n\nThe \nmpf stop\n command will stop the Workflow Manager, which will in turn send commands to all of the available nodes\n to stop all running component services. If a service is processing a sub-job when the quit command is received, that\n service process will not terminate until that sub-job is completely processed. Thus, the service may put a sub-job\n response on the ActiveMQ response queue after the Workflow Manager has terminated. That will not cause a problem\n because the queues are flushed the next time the Workflow Manager starts; however, there will be a problem if the\n service finishes processing the sub-job after the Workflow Manager is restarted. At that time, the Workflow Manager\n will have no knowledge of the old job and will in turn generate warnings in the log about how the job id is \"not known\n to the system\" and/or \"not found as a batch or a streaming job\". These can be safely ignored. Often, if these messages\n appear in the log, then C++ services were running after stopping the Workflow Manager. To address this, you may wish\n to run \nsudo killall amq_detection_component\n after running \nmpf stop\n.\n\n\n\n\nOpenMPF 2.0.x\n\n\n2.0.0: February 2018\n\n\n\n\n\nNOTE:\n Components built for previous releases of OpenMPF are not compatible with OpenMPF 2.0.0 due to Batch Component API changes to support generic detections, and changes made to the format of the \ndescriptor.json\n file to support stream processing.\n\n\nNOTE:\n This release contains basic support for processing video streams. Currently, the only way to make use of that functionality is through the REST API. Streaming jobs and services cannot be created or monitored through the web UI. Only the SuBSENSE component has been updated to support streaming. Only single-stage pipelines are supported at this time.\n\n\n\n\nDocumentation\n\n\n\n\n\nUpdated documents to distinguish the batch component APIs from the streaming component API.\n\n\nAdded the \nC++ Streaming Component API\n.\n\n\nUpdated the \nC++ Batch Component API\n to describe support for generic detections.\n\n\nUpdated the \nREST API\n with endpoints for streaming jobs.\n\n\n\n\nSupport for Generic Detections\n\n\n\n\n\nC++ and Java components can now declare support for the \nUNKNOWN\n data type. The respective batch APIs have been\n updated with a function that will enable a component to process an \nMPFGenericJob\n, which represents a piece of media\n that is not a video, image, or audio file.\n\n\nNote that these API changes make OpenMPF R2.0.0 incompatible with components built for previous releases of OpenMPF.\n Specifically, the new component executor will not be able to load the component logic library.\n\n\n\n\nC++ Batch Component API\n\n\n\n\n\nAdded the following function to support generic detections:\n\n\nMPFDetectionError GetDetections(const MPFGenericJob &job, vector &tracks)\n\n\n\n\n\n\n\n\nJava Batch Component API\n\n\n\n\n\nAdded the following method to support generic detections:\n\n\nList getDetections(MPFGenericJob job)\n\n\n\n\n\n\n\n\nStreaming REST API\n\n\n\n\n\nAdded the following REST endpoints for streaming jobs:\n\n\n[GET] /rest/streaming/jobs\n: Returns a list of streaming job ids.\n\n\n[POST] /rest/streaming/jobs\n: Creates and submits a streaming job. Users can register for health report and\n summary report callbacks.\n\n\n[GET] /rest/streaming/jobs/{id}\n: Gets information about a streaming job.\n\n\n[POST] /rest/streaming/jobs/{id}/cancel\n: Cancels a streaming job.\n\n\n\n\n\n\n\n\nWorkflow Manager\n\n\n\n\n\nUpdated to support generic detections.\n\n\nUpdated Redis to store information about streaming jobs.\n\n\nAdded controllers for streaming job REST endpoints.\n\n\nAdded ability to generate health reports and segment summary reports for streaming jobs.\n\n\nImproved code flow between the Workflow Manager and master Node Manager to support streaming jobs.\n\n\nAdded ActiveMQ queues to enable the C++ Streaming Component Executor to send reports and job status to the Workflow\n Manager.\n\n\n\n\nNode Manager\n\n\n\n\n\nUpdated the master Node Manager and child Node Managers to spawn component services on demand to handle streaming\n jobs, cancel those jobs, and to monitor the status of those processes.\n\n\nUsing .ini files to represent streaming job properties and enable better communication between a child Node Manager\n and C++ Streaming Component Executor.\n\n\n\n\nC++ Streaming Component API\n\n\n\n\n\nDeveloped the C++ Streaming Component API with the following functions:\n\n\nMPFStreamingDetectionComponent(const MPFStreamingVideoJob &job)\n: Constructor that takes a streaming video job.\n\n\nstring GetDetectionType()\n: Returns the type of detection (i.e. \"FACE\").\n\n\nvoid BeginSegment(const VideoSegmentInfo &segment_info)\n: Indicates the beginning of a new video segment.\n\n\nbool ProcessFrame(const cv::Mat &frame, int frame_number)\n: Processes a single frame for the current video\n segment.\n\n\nvector EndSegment()\n: Indicates the end of the current video segment.\n\n\n\n\n\n\nUpdated the C++ Hello World component to support streaming jobs.\n\n\n\n\nC++ Streaming Component Executor\n\n\n\n\n\nDeveloped the C++ Streaming Component Executor to load a streaming component logic library, read frames from a video\n stream, and exercise the component logic through the C++ Streaming Component API.\n\n\nWhen the C++ Streaming Component Executor cannot read a frame from the stream, it will sleep for at least 1\n millisecond, doubling the amount of sleep time per attempt until it reaches the \nstallTimeout\n value specified when\n the job was created. While stalled, the job status will be \nSTALLED\n. After the timeout is exceeded, the job will\n be \nTERMINATED\n.\n\n\nThe C++ Streaming Component Executor supports \nFRAME_INTERVAL\n, as well as rotation, horizontal flipping, and\n cropping (region of interest) properties. Does not support \nUSE_KEY_FRAMES\n.\n\n\n\n\nInteroperability Package\n\n\n\n\n\nAdded the following Java classes to the interoperability package to simplify third party integration:\n\n\nJsonHealthReportCollection\n: Represents the JSON content of a health report callback. Contains one or\n more \nJsonHealthReport\n objects.\n\n\nJsonSegmentSummaryReport\n: Represents the JSON content of a summary report callback. Content is similar to the\n JSON output object used for batch processing.\n\n\n\n\n\n\n\n\nSuBSENSE Component\n\n\n\n\n\nThe SuBSENSE component now supports both batch processing and stream processing.\n\n\nEach video segment will be processed independently of the rest. In other words, tracks will be generated on a\n segment-by-segment basis and tracks will not carry over between segments.\n\n\nNote that the last frame in the previous segment will be used to determine if there is motion in the first frame of\n the next segment.\n\n\n\n\nPackaging and Deployment\n\n\n\n\n\nUpdated \ndescriptor.json\n fields to allow components to support batch and/or streaming jobs. Components that use the\n old \ndescriptor.json\n file format cannot be registered through the web UI.\n\n\nBatch component logic and streaming component logic are compiled into separate libraries.\n\n\nThe mySQL \nstreaming_job_request\n table has been updated with the following fields, which are used to populate the\n JSON health reports:\n\n\nstatus_detail\n: (Optional) A user-friendly description of the current job status.\n\n\nactivity_frame_id\n: The frame id associated with the last job activity. Activity is defined as the start of a new\n track for the current segment.\n\n\nactivity_timestamp\n: The timestamp associated with the last job activity.\n\n\n\n\n\n\n\n\nWeb User Interface\n\n\n\n\n\nAdded column names to the table that appears when the user clicks in the Media button associated with a job on the Job\n Status page. Now descriptive comments are provided when table cells are empty.\n\n\n\n\nBug Fixes\n\n\n\n\n\nUpgraded Tika to 1.17 to resolve an issue with improper indentation in a Python file (rotation.py) that resulted in\n generating at least one error message per image processed. When processing a large number of images, this would\n generate may error messages, causing the Automatic Bug Reporting Tool daemon (abrtd) process to run at 100% CPU. Once\n in that state, that process would stay there, essentially wasting on CPU core. This resulted in some of the Jenkins\n virtual machines we used for testing to become unresponsive.\n\n\n\n\nKnown Issues\n\n\n\n\n\n\n\nOpenCV 3.3.0 \ncv::imread()\n does not properly decode some TIFF images that have EXIF orientation metadata. It can\n handle images that are flipped horizontally, but not vertically. It also has issues with rotated images. Since most\n components rely on that function to read image data, those components may silently fail to generate detections for\n those kinds of images.\n\n\n\n\n\n\nUsing single quotes, apsotrophes, or double quotes in the name of an algorithm, action, task, or pipeline configured\n on an existing OpenMPF system will result in a failure to perform an OpenMPF upgrade on that system. Specifically, the\n step where pre-existing custom actions, tasks, and pipelines are carried over to the upgraded version of OpenMPF will\n fail. Please do not use those special characters while naming those elements. If this has been done already, then\n those elements should be manually renamed in the XML files prior to an upgrade attempt.\n\n\n\n\n\n\nOpenMPF uses OpenCV, which uses FFmpeg, to connect to video streams. If a proxy and/or firewall prevents the network\n connection from succeeding, then OpenCV, or the underlying FFmpeg library, will segfault. This causes the C++\n Streaming Component Executor process to fail. In turn, the job status will be set to \nERROR\n with a status detail\n message of \"Unexpected error. See logs for details\". In this case, the logs will not contain any useful information.\n You can identify a segfault by the following line in the node-manager log:\n\n\n\n\n\n\n2018-02-15 16:01:21,814 INFO [pool-3-thread-4] o.m.m.nms.streaming.StreamingProcess - Process: Component exited with exit code 139\u00a0\n\n\n\n\n\nTo determine if FFmpeg can connect to the stream or not, run \nffmpeg -i \n in a terminal window. Here's an example when it's successful:\n\n\n\n\n[mpf@localhost bin]$ ffmpeg -i rtsp://184.72.239.149/vod/mp4:BigBuckBunny_115k.mov\nffmpeg version n3.3.3-1-ge51e07c Copyright (c) 2000-2017 the FFmpeg developers\n built with gcc 4.8.5 (GCC) 20150623 (Red Hat 4.8.5-4)\n configuration: --prefix=/apps/install --extra-cflags=-I/apps/install/include --extra-ldflags=-L/apps/install/lib --bindir=/apps/install/bin --enable-gpl --enable-nonfree --enable-libtheora --enable-libfreetype --enable-libmp3lame --enable-libvorbis --enable-libx264 --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-version3 --enable-shared --disable-libsoxr --enable-avresample\n libavutil 55. 58.100 / 55. 58.100\n libavcodec 57. 89.100 / 57. 89.100\n libavformat 57. 71.100 / 57. 71.100\n libavdevice 57. 6.100 / 57. 6.100\n libavfilter 6. 82.100 / 6. 82.100\n libavresample 3. 5. 0 / 3. 5. 0\n libswscale 4. 6.100 / 4. 6.100\n libswresample 2. 7.100 / 2. 7.100\n libpostproc 54. 5.100 / 54. 5.100\n[rtsp @ 0x1924240] UDP timeout, retrying with TCP\nInput #0, rtsp, from 'rtsp://184.72.239.149/vod/mp4:BigBuckBunny_115k.mov':\n Metadata:\n title : BigBuckBunny_115k.mov\n Duration: 00:09:56.48, start: 0.000000, bitrate: N/A\n Stream #0:0: Audio: aac (LC), 12000 Hz, stereo, fltp\n Stream #0:1: Video: h264 (Constrained Baseline), yuv420p(progressive), 240x160, 24 fps, 24 tbr, 90k tbn, 48 tbc\nAt least one output file must be specified\n\n\n\n\n\nHere's an example when it's not successful, so there may be network issues:\n\n\n\n\n[mpf@localhost bin]$ ffmpeg -i rtsp://184.72.239.149/vod/mp4:BigBuckBunny_115k.mov\nffmpeg version n3.3.3-1-ge51e07c Copyright (c) 2000-2017 the FFmpeg developers\n built with gcc 4.8.5 (GCC) 20150623 (Red Hat 4.8.5-4)\n configuration: --prefix=/apps/install --extra-cflags=-I/apps/install/include --extra-ldflags=-L/apps/install/lib --bindir=/apps/install/bin --enable-gpl --enable-nonfree --enable-libtheora --enable-libfreetype --enable-libmp3lame --enable-libvorbis --enable-libx264 --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-version3 --enable-shared --disable-libsoxr --enable-avresample\n libavutil 55. 58.100 / 55. 58.100\n libavcodec 57. 89.100 / 57. 89.100\n libavformat 57. 71.100 / 57. 71.100\n libavdevice 57. 6.100 / 57. 6.100\n libavfilter 6. 82.100 / 6. 82.100\n libavresample 3. 5. 0 / 3. 5. 0\n libswscale 4. 6.100 / 4. 6.100\n libswresample 2. 7.100 / 2. 7.100\n libpostproc 54. 5.100 / 54. 5.100\n[tcp @ 0x171c300] Connection to tcp://184.72.239.149:554?timeout=0 failed: Invalid argument\nrtsp://184.72.239.149/vod/mp4:BigBuckBunny_115k.mov: Invalid argument\n\n\n\n\n\nTika 1.17 does not come pre-packaged with support for some embedded image formats in PDF files, possibly to avoid\n patent issues. OpenMPF does not handle embedded images in PDFs, so that's not a problem. Tika will print out the\n following warnings, which can be safely ignored:\n\n\n\n\nJan 22, 2018 11:02:15 AM org.apache.tika.config.InitializableProblemHandler$3 handleInitializableProblem\nWARNING: JBIG2ImageReader not loaded. jbig2 files will be ignored\nSee https://pdfbox.apache.org/2.0/dependencies.html#jai-image-io\nfor optional dependencies.\nTIFFImageWriter not loaded. tiff files will not be processed\nSee https://pdfbox.apache.org/2.0/dependencies.html#jai-image-io\nfor optional dependencies.\nJ2KImageReader not loaded. JPEG2000 files will not be processed.\nSee https://pdfbox.apache.org/2.0/dependencies.html#jai-image-io\nfor optional dependencies.\n\n\n\n\nOpenMPF 1.0.x\n\n\n1.0.0: October 2017\n\n\n\nDocumentation\n\n\n\n\n\nUpdated the \nBuild Guide\n with instructions for installing the latest JDK,\n latest JRE, FFmpeg 3.3.3, new codecs, and OpenCV 3.3.\n\n\nAdded an \nAcknowledgements\n section that provides information on third party dependencies\n leveraged by the OpenMPF.\n\n\nAdded a \nFeed Forward Guide\n that explains feed forward processing and how to use it.\n\n\nAdded missing requirements checklist content to\n the \nInstall Guide\n.\n\n\nUpdated the README at the top level of each of the primary repositories to help with user navigation and provide\n general information.\n\n\n\n\nUpgrade to FFmpeg 3.3.3 and OpenCV 3.3\n\n\n\n\n\nUpdated core framework from FFmpeg 2.6.3 to FFmpeg 3.3.3.\n\n\nAdded the following FFmpeg codecs: x256, VP9, AAC, Opus, Speex.\n\n\nUpdated core framework and components from OpenCV 3.2 to OpenCV 3.3. No longer building with opencv_contrib.\n\n\n\n\nFeed Forward Behavior\n\n\n\n\n\nUpdated the Workflow Manager (WFM) and all video components to optionally perform feed forward processing for batch\n jobs. This allows tracks to be passed forward from one pipeline stage to the next. Components in the next stage will\n only process the frames associated with the detections in those tracks. This differs from the default segmenting\n behavior, which does not preserve detection regions or track information between stages.\n\n\nTo enable this behavior, the optional \nFEED_FORWARD_TYPE\n property must be set to \nFRAME\n, \nSUPERSET_REGION\n,\n or \nREGION\n. If set to \nFRAME\n then the components in the next stage will process the whole frame region associated\n with each detection in the track passed forward. If set to \nSUPERSET_REGION\n then the components in the next stage\n will determine the bounding box that encapsulates all of the detection regions in the track, and only process the\n pixel data within that superset region. If set to \nREGION\n then the components in the next stage will process the\n region associated with each detection in the track passed forward, which may vary in size and position from frame to\n frame.\n\n\nThe optional \nFEED_FORWARD_TOP_CONFIDENCE_COUNT\n property can be set to a number to limit the number of detections\n passed forward in a track. For example, if set to \"5\", then only the top 5 detections in the track will be passed\n forward and processed by the next stage. The top detections are defined as those with the highest confidence values,\n or if the confidence values are the same, those with the lowest frame index.\n\n\nNote that setting the feed forward properties has no effect on the first pipeline stage because there is no prior\n stage that can pass tracks to it.\n\n\n\n\nCaffe Component\n\n\n\n\n\nUpdated the Caffe component to process images in the BGR color space instead of the RGB color space. This addresses a\n bug found in OpenCV. Refer to the Bug Fixes section below.\n\n\nAdded support for processing videos.\n\n\nAdded support for an optional \nACTIVATION_LAYER_LIST\n property. For each network layer specified in the list,\n the \ndetectionProperties\n map in the JSON output object will contain one entry. The value is an encoded string of the\n JSON representation of an OpenCV matrix of the activation values for that layer. The activation values are obtained\n after the Caffe network has processed the frame data.\n\n\nAdded support for an optional \nSPECTRAL_HASH_FILE_LIST\n property. For each JSON file specified in the list,\n the \ndetectionProperties\n map in the JSON output object will contain one entry. The value is a string of 0's and 1's\n representing the spectral hash calculated using the information in the spectral hash JSON file. The spectral hash is\n calculated using activation values after the Caffe network has processed the frame data.\n\n\nAdded a pipeline to showcase the above two features for the GoogLeNet Caffe model.\n\n\nRemoved the \nTRANSPOSE\n property from the Caffe component since it was not necessary.\n\n\nAdded red, green, and blue mean subtraction values to the GoogLeNet pipeline.\n\n\n\n\nUse Key Frames\n\n\n\n\n\nAdded support for an optional \nUSE_KEY_FRAMES\n property to each video component. When true the component will only\n look at key frames (I-frames) from the input video. Can be used in conjunction with \nFRAME_INTERVAL\n. For example,\n when \nUSE_KEY_FRAMES\n is true, and \nFRAME_INTERVAL\n is set to \"2\", then every other key frame will be processed.\n\n\n\n\nMPFVideoCapture and MPFImageReader Tools\n\n\n\n\n\nUpdated the MPFVideoCapture and MPFImageReader tools to handle feed forward properties.\n\n\nUpdated the MPFVideoCapture tool to handle \nFRAME_INTERVAL\n and \nUSE_KEY_FRAMES\n properties.\n\n\nUpdated all existing components to leverage these tools as much as possible.\n\n\nWe encourage component developers to use these tools to automatically take care of common frame grabbing and frame\n manipulation behaviors, and not to reinvent the wheel.\n\n\n\n\nDead Letter Queue\n\n\n\n\n\nIf for some reason a sub-job request that should have gone to a component ends up on the ActiveMQ Dead Letter Queue (\n DLQ), then the WFM will now process that failed request so that the job can complete. The ActiveMQ management page\n will now show that \nActiveMQ.DLQ\n has 1 consumer. It will also show unconsumed messages\n in \nMPF.PROCESSED_DLQ_MESSAGES\n. Those are left for auditing purposes. The \"Message Detail\" for these shows the string\n representation of the original job request protobuf message.\n\n\n\n\nUpgrade Path\n\n\n\n\n\nRemoved the Release 0.8 to Release 0.9 upgrade path in the deployment scripts.\n\n\nAdded support for a Release 0.9 to Release 1.0.0 upgrade path, and a Release 0.10.0 to Release 1.0.0 upgrade path.\n\n\n\n\nMarkup\n\n\n\n\n\nBounding boxes are now drawn along the interpolated path between detection regions whenever there are one or more\n frames in a track which do not have detections associated with them.\n\n\nFor each track, the color of the bounding box is now a randomly selected hue in the HSV color space. The colors are\n evenly distributed using the golden ratio.\n\n\n\n\nBug Fixes\n\n\n\n\n\nFixed a \nbug in OpenCV\n where the Caffe example code was processing\n images in the RGB color space instead of the BGR color space. Updated the OpenMPF Caffe component accordingly.\n\n\nFixed a bug in the OpenCV person detection component that caused bounding boxes to be too large for detections near\n the edge of a frame.\n\n\nResubmitting jobs now properly carries over configured job properties.\n\n\nFixed a bug in the build order of the OpenMPF project so that test modules that the WFM depends on are built before\n the WFM itself.\n\n\nThe Markup component draws bounding boxes between detections when a \nFRAME_INTERVAL\n is specified. This is so that the\n bounding box in the marked-up video appears in every frame. Fixed a bug where the bounding boxes drawn on\n non-detection frames appeared to stand still rather than move along the interpolated path between detection regions.\n\n\nFixed a bug on the OALPR license plate detection component where it was not properly handling the \nSEARCH_REGION_*\n\n properties.\n\n\nSupport for the \nMIN_GAP_BETWEEN_SEGMENTS\n property was not implemented properly. When the gap between two segments is\n less than this property value then the segments should be merged; otherwise, the segments should remain separate. In\n some cases, the exact opposite was happening. This bug has been fixed.\n\n\n\n\nKnown Issues\n\n\n\n\n\nBecause of the number of additional ActiveMQ messages involved, enabling feed forward for low resolution video may\n take longer than the non-feed-forward behavior.\n\n\n\n\nOpenMPF 0.x.x\n\n\n0.10.0: July 2017\n\n\n\n\n\nWARNING:\n There is no longer a \nDEFAULT CAFFE ACTION\n, \nDEFAULT CAFFE TASK\n, or \nDEFAULT CAFFE PIPELINE\n. There is now a \nCAFFE GOOGLENET DETECTION PIPELINE\n and \nCAFFE YAHOO NSFW DETECTION PIPELINE\n, which each have a respective action and task.\n\n\nNOTE:\n MPFImageReader has been re-enabled in this version of OpenMPF since we upgraded to OpenCV 3.2, which addressed the known issues with \nimread()\n, auto-orientation, and jpeg files in OpenCV 3.1.\n\n\n\n\nDocumentation\n\n\n\n\n\nAdded a \nContributor Guide\n that provides guidelines for contributing to the OpenMPF\n codebase.\n\n\nUpdated the \nJava Batch Component API\n with links to the example Java components.\n\n\nUpdated the \nBuild Guide\n with instructions for OpenCV 3.2.\n\n\n\n\nUpgrade to OpenCV 3.2\n\n\n\n\n\nUpdated core framework and components from OpenCV 3.1 to OpenCV 3.2.\n\n\n\n\nSupport for Animated gifs\n\n\n\n\n\nAll gifs are now treated as videos. Each gif will be handled as an MPFVideoJob.\n\n\nUnanimated gifs are treated as 1-frame videos.\n\n\nThe WFM Media Inspector now populates the \nmedia_properties\n map with a \nFRAME_COUNT\n entry (in addition to\n the \nDURATION\n and \nFPS\n entries).\n\n\n\n\nCaffe Component\n\n\n\n\n\nAdded support for the Yahoo Not Suitable for Work (NSFW) Caffe model for explicit material detection.\n\n\nUpdated the Caffe component to support the OpenCV 3.2 Deep Neural Network (DNN) module.\n\n\n\n\nFuture Support for Streaming Video\n\n\n\n\n\nNOTE:\n At this time, OpenMPF does not support streaming video. This section details what's being / has been done so far to prepare for that feature.\n\n\n\n\n\n\nThe codebase is being updated / refactored to support both the current \"batch\" job functionality and new \"streaming\"\n job functionality.\n\n\nbatch job: complete video files are written to disk before they are processed\n\n\nstreaming job: video frames are read from a streaming endpoint (such as RTSP) and processed in near real time\n\n\n\n\n\n\nThe REST API is being updated with endpoints for streaming jobs:\n\n\n[POST] /rest/streaming/jobs\n: Creates and submits a streaming job\n\n\n[POST] /rest/streaming/jobs/{id}/cancel\n: Cancels a streaming job\n\n\n[GET] /rest/streaming/jobs/{id}\n: Gets information about a streaming job\n\n\n\n\n\n\nThe Redis and mySQL databases are being updated to support streaming video jobs.\n\n\nA batch job will never have the same id as a streaming job. The integer ids will always be unique.\n\n\n\n\n\n\n\n\nBug Fixes\n\n\n\n\n\nThe MOG and SuBSENSE component services could segfault and terminate if the \nUSE_MOTION_TRACKING\n property was set to\n \u201c1\u201d and a detection was found close to the edge of the frame. Specifically, this would only happen if the video had a\n width and/or height dimension that was not an exact power of two.\n\n\nThe reason was because the code downsamples each frame by a power of two and rounds the value of the width and\n height up to the nearest integer. Later on when upscaling detection rectangles back to a size that\u2019s relative to\n the original image, the resized rectangle sometimes extended beyond the bounds of the original frame.\n\n\n\n\n\n\n\n\nKnown Issues\n\n\n\n\n\nIf a job is submitted through the REST API, and a user to logged into the web UI and looking at the job status page,\n the WFM may generate \"Error retrieving the SingleJobInfo model for the job with id\" messages.\n\n\nThis is because the job status is only added to the HTTP session object if the job is submitted through the web\n UI. When the UI queries the job status it inspects this object.\n\n\nThis message does not appear if job status is obtained using the \n[GET] /rest/jobs/{id}\n endpoint.\n\n\n\n\n\n\nThe \n[GET] /rest/jobs/stats\n endpoint aggregates information about all of the jobs ever run on the system. If\n thousands of jobs have been run, this call could take minutes to complete. The code should be improved to execute a\n direct mySQL query.\n\n\n\n\n0.9.0: April 2017\n\n\n\n\n\nWARNING:\n MPFImageReader has been disabled in this version of OpenMPF. Component developers should use MPFVideoCapture instead. This affects components developed against previous versions of OpenMPF and components developed against this version of OpenMPF. Please refer to the Known Issues section for more information.\n\n\nWARNING:\n The OALPR Text Detection Component has been renamed to OALPR \nLicense Plate\n Text Detection Component. This affects the name of the component package and the name of the actions, tasks, and pipelines. When upgrading from R0.8 to R0.9, if the old OALPR Text Detection Component is installed in R0.8 then you will be prompted to install it again at the end of the upgrade path script. We recommend declining this prompt because the old component will conflict with the new component.\n\n\nWARNING:\n Action, task, and pipeline names that started with \nMOTION DETECTION PREPROCESSOR\n have been renamed \nMOG MOTION DETECTION PREPROCESSOR\n. Similarly, \nWITH MOTION PREPROCESSOR\n has changed to \nWITH MOG MOTION PREPROCESSOR\n.\n\n\n\n\nDocumentation\n\n\n\n\n\nUpdated the \nREST API\n to reflect job properties, algorithm-specific properties, and\n media-specific properties.\n\n\nStreamlined the \nC++ Batch Component API\n document for clarity and simplicity.\n\n\nCompleted the \nJava Batch Component API\n document.\n\n\nUpdated the \nAdmin Guide\n and \nUser Guide\n to reflect web UI changes.\n\n\nUpdated the \nBuild Guide\n with instructions for GitHub repositories.\n\n\n\n\nWorkflow Manager\n\n\n\n\n\nAdded support for job properties, which will override pre-defined pipeline properties.\n\n\nAdded support for algorithm-specific properties, which will apply to a single stage of the pipeline and will override\n job properties and pre-defined pipeline properties.\n\n\nAdded support for media-specific properties, which will apply to a single piece and media and will override job\n properties, algorithm-specific properties, and pre-defined pipeline properties.\n\n\nComponents can now be automatically registered and installed when the web application starts in Tomcat.\n\n\n\n\nWeb User Interface\n\n\n\n\n\nThe \"Close All\" button on pop-up notifications now dismisses all notifications from the queue, not just the visible\n ones.\n\n\nJob completion notifications now only appear for jobs created during the current login session instead of all jobs.\n\n\nThe \nROTATION\n, \nHORIZONTAL_FLIP\n, and \nSEARCH_REGION_*\n properties can be set using the web interface when creating a\n job. Once files are selected for a job, these properties can be set individually or by groups of files.\n\n\nThe Node and Process Status page has been merged into the Node Configuration page for simplicity and ease of use.\n\n\nThe Media Markup results page has been merged into the Job Status page for simplicity and ease of use.\n\n\nThe File Manager UI has been improved to handle large numbers of files and symbolic links.\n\n\nThe side navigation menu is now replaced by a top navigation bar.\n\n\n\n\nREST API\n\n\n\n\n\nAdded an optional jobProperties object to the \n/rest/jobs/\n request which contains String key-value pairs which\n override the pipeline's pre-configured job properties.\n\n\nAdded an optional algorithmProperties object to the \n/rest/jobs/\n request which can be used to configure properties\n for specific algorithms in the pipeline. These properties override the pipeline's pre-configured job properties. They\n also override the values in the jobProperties object.\n\n\nUpdated the \n/rest/jobs/\n request to add more detail to media, replacing a list of mediaUri Strings with a list of\n media objects, each of which contains a mediaUri and an optional mediaProperties map. The mediaProperties map can be\n used to configure properties for the specific piece of media. These properties override the pipeline's pre-configured\n job properties, values in the jobProperties object, and values in the algorithmProperties object.\n\n\nStreamlined the actions, tasks, and pipelines endpoints that are used by the web UI.\n\n\n\n\nFlipping, Rotation, and Region of Interest\n\n\n\n\n\nThe \nROTATION\n, \nHORIZONTAL_FLIP\n, and \nSEARCH_REGION_*\n properties will no longer appear in the detectionProperties\n map in the JSON detection output object. When applied to an algorithm these properties now appear in the\n pipeline.stages.actions.properties element. When applied to a piece of media these properties will now appear in the\n the media.mediaProperties element.\n\n\nThe OpenMPF now supports multiple regions of interest in a single media file. Each region will produce tracks\n separately, and the tracks for each region will be listed in the JSON output as if from a separate media file.\n\n\n\n\nComponent API\n\n\n\n\n\nJava Batch Component API is functionally complete for third-party development, with the exception of Component Adapter\n and frame transformation utilities classes.\n\n\nRe-architected the Java Batch Component API to use a more traditional Java method structure of returning track lists\n and throwing exceptions (rather than modifying input track lists and returning statuses), and encapsulating job\n properties into MPFJob objects:\n\n\nList getDetections(MPFVideoJob job) throws MPFComponentDetectionError\n\n\nList getDetections(MPFAudioJob job) throws MPFComponentDetectionError\n\n\nList getDetections(MPFImageJob job) throws MPFComponentDetectionError\n\n\n\n\n\n\nCreated examples for the Java Batch Component API.\n\n\nReorganized the Java and C++ component source code to enable component development without the OpenMPF core, which\n will simplify component development and streamline the code base.\n\n\n\n\nJSON Output Objects\n\n\n\n\n\nThe JSON output object for the job now contains a jobProperties map which contains all properties defined for the job\n in the job request. For example, if the job request specifies a \nCONFIDENCE_THRESHOLD\n of then the jobProperties map\n in the output will also list a \nCONFIDENCE_THRESHOLD\n of 5.\n\n\nThe JSON output object for the job now contains a algorithmProperties element which contains all algorithm-specific\n properties defined for the job in the job request. For example, if the job request specifies a \nFRAME_INTERVAL\n of 2\n for FACECV then the algorithmProperties element in the output will contain an entry for \"FACECV\" and that entry will\n list a \nFRAME_INTERVAL\n of 2.\n\n\nEach JSON media output object now contains a mediaProperties map which contains all media-specific properties defined\n by the job request. For example, if the job request specifies a \nROTATION\n of 90 degrees for a single piece of media\n then the mediaProperties map for that piece of piece will list a \nROTATION\n of 90.\n\n\nThe content of JSON output objects are now organized by detection type (e.g. MOTION, FACE, PERSON, TEXT, etc.) rather\n than action type.\n\n\n\n\nCaffe Component\n\n\n\n\n\nAdded support for flip, rotation, and cropping to regions of interest.\n\n\nAdded support for returning multiple classifications per detection based on user-defined settings. The classification\n list is in order of decreasing confidence value.\n\n\n\n\nNew Pipelines\n\n\n\n\n\nNew SuBSENSE motion preprocessor pipelines have been added to components that perform detection on video.\n\n\n\n\nPackaging and Deployment\n\n\n\n\n\nActions.xml\n, \nAlgorithms.xml\n, \nnodeManagerConfig.xml\n, \nnodeServicesPalette.json\n, \nPipelines.xml\n, and \nTasks.xml\n\n are no longer stored within the Workflow Manager WAR file. They are now stored under \n$MPF_HOME/data\n. This makes it\n easier to upgrade the Workflow Manager and makes it easier for users to access these files.\n\n\nEach component can now be optionally installed and registered during deployment. Components not registered are set to\n the \nUPLOADED\n state. They can then be removed or registered through the Component Registration page.\n\n\nJava components are now packaged as tar.gz files instead of RPMs, bringing them into alignment with C++ components.\n\n\nOpenMPF R0.9 can be installed over OpenMPF R0.8. The deployment scripts will determine that an upgrade should take\n place.\n\n\nAfter the upgrade, user-defined actions, tasks, and pipelines will have \"CUSTOM\" prepended to their name.\n\n\nThe job_request table in the mySQL database will have a new \"output_object_version\" column. This column will\n have \"1.0\" for jobs created using OpenMPF R0.8 and \"2.0\" for jobs created using OpenMPF R0.9. The JSON output\n object schema has changed between these versions.\n\n\n\n\n\n\nReorganized source code repositories so that component SDKs can be downloaded separately from the OpenMPF core and so\n that components are grouped by license and maturity. Build scripts have been created to streamline and simplify the\n build process across the various repositories.\n\n\n\n\nUpgrade to OpenCV 3.1\n\n\n\n\n\nThe OpenMPF software has been ported to use OpenCV 3.1, including all of the C++ detection components and the markup\n component. For the OpenALPR license plate detection component, the versions of the openalpr, tesseract, and leptonica\n libraries were also upgraded to openalpr-2.3.0, tesseract-3.0.4, and leptonica-1.7.2. For the SuBSENSE motion\n component, the version of the SuBSENSE library was upgraded to use the code found at this\n location: \nhttps://bitbucket.org/pierre_luc_st_charles/subsense/src\n.\n\n\n\n\nBug Fixes\n\n\n\n\n\nMOG motion detection always detected motion in frame 0 of a video. Because motion can only be detected between two\n adjacent frames, frame 1 is now the first frame in which motion can be detected.\n\n\nMOG motion detection never detected motion in the first frame of a video segment (other than the first video segment\n because of the frame 0 bug described above). Now, motion is detected using the first frame before the start of a\n segment, rather than the first frame of the segment.\n\n\nThe above bugs were also present in SuBSENSE motion detection and have been fixed.\n\n\nSuBSENSE motion detection generated tracks where the frame numbers were off by one. Corrected the frame index logic.\n\n\nVery large video files caused an out of memory error in the system during Workflow Manager media inspection.\n\n\nA job would fail when processing images with an invalid metadata tag for the camera flash setting.\n\n\nUsers were permitted to select invalid file types using the File Manager UI.\n\n\n\n\nKnown Issues\n\n\n\n\n\nMPFImageReader does not work reliably with the current release version of OpenCV 3.1\n: In OpenCV 3.1, new\n functionality was introduced to interpret EXIF information when reading jpeg files.\n\n\nThere are two issues with this new functionality that impact our ability to use the OpenCV \nimread()\n function with\n MPFImageReader:\n\n\nFirst, because of a bug in the OpenCV code, reading a jpeg file that contains exif information could cause it to\n hang. (See \nhttps://github.com/opencv/opencv/issues/6665\n.)\n\n\nSecond, it is not possible to tell the \nimread()\nfunction to ignore the EXIF data, so the image it returns is\n automatically rotated. (See \nhttps://github.com/opencv/opencv/issues/6348\n.) This results in the MPFImageReader\n applying a second rotation to the image due to the EXIF information.\n\n\n\n\n\n\nTo address these issues, we developed the following workarounds:\n\n\nCreated a version of the MPFVideoCapture that works with an MPFImageJob. The new MPFVideoCapture can pull frames\n from both video files and images. MPFVideoCapture leverages cv::VideoCapture, which does not have the two issues\n described above.\n\n\nDisabled the use of MPFImageReader to prevent new users from trying to develop code leveraging this previous\n functionality.",
+ "text": "NOTICE:\n This software (or technical data) was produced for the U.S. Government under contract, and is subject to the\nRights in Data-General Clause 52.227-14, Alt. IV (DEC 2007). Copyright 2026 The MITRE Corporation. All Rights Reserved.\n\n\nOpenMPF 10.0.x\n\n\n10.0.0: January 2026\n\n\n\nDocumentation\n\n\n\n\n\nCreated a new \nArtifact Extraction Guide\n.\n\n\n\n\nNo Language Left Behind (NLLB) Translation Component\n\n\n\nThe No Language Left Behind component is based on \nMeta's No Language Left Behind Project\n. The component translates input text from a given source language to English. The source language can be provided as a job property, or be indicated in the detection properties from a feed-forward track. By default, this component is configured to use the \nfacebook/nllb-200-3.3B\n model\n \nNo Language Left Behind (NLLB)\n. This provides the high translation quality, but also requires significant hardware resources. To accommodate smaller deployment enviroments, this component can use smaller NLLB models, such as \nnllb-200-distilled-1.3B\n or \nnllb-200-distilled-600M\n.\n\n\nRefer to the \nREADME\n for details.\n\n\n FastText Language Identification Component\n\n\n\nThe FastText Language Detection Component utilizes the \nGlotLID\n language identification model\nand the \nfastText\n library to perform language identification on text.\n\n\nRefer to the \nREADME\n for details.\n\n\nSubject Tracking API and OR-Tools Subject Component [Experimental]\n\n\n\n\n\n\n\nThe OR-Tools Subject Tracking component associates detection tracks of different types (e.g., face, person, vehicle) to create a subject and track it in video.\n\n\n\n\n\n\nThe component source can be found \nhere\n.\n\n\n\n\n\n\nAudit Logging\n\n\n\nWe have added internal audit logging that may be used to audit events that occur during OpenMPF operation. Logging has been added to the Workflow Manager to record these types of events:\n\n\n\n\nlogin/logout user information\n\n\ncreate/read/update/download of data, configuration information, or action/pipeline information\n\n\nrest API access\n\n\nload UI content\n\n\nTiesDB and S3 storage operations\n\n\nAccess to Hawtio\n\n\n\n\nThe information is logged as a JSON string. The fields of the logging statement consist of:\n\n\n\n\neid\n : an event id signifying the type of event\n\n\ntime\n : a timestamp for the event in UTC with format YYYY-MM-DDThh:mm:ss.mmmZ\n\n\ntag\n : a tag value, which could be used to categorize events. For this release, all event logging uses a single constant tag value.\n\n\napp\n : the name of the application where the event occurred, which is a constant set to \"openmpf\"\n\n\nuser\n : the username of the user that initiated the activity being logged.\n\n\nop\n : operation identifier, one of c(create), r(read/view), m(modify), d(delete), l(login)\n\n\nres\n : operation result, one of a(allowed), d(denied), e(error)\n\n\nuri\n : [optional] the full web or file path.\n\n\nbucket\n : [optional] The string identifying the S3 bucket for object storage\n\n\nobjectKey\n : [optional] The name that identifies the object in the S3 bucket\n\n\n\n\nmsg\n : a message string\n\n\n\n\n\n\nHere is example audit logging output when creating a job through the REST API:\n\n\n\n\n\n\n{\"eid\":200,\"time\":\"2026-01-14T14:30:40.864Z\",\"tag\":\"&B1E7-FFFF&\",\"app\":\"openmpf\",\"user\":\"admin\",\"op\":\"c\",\"res\":\"a\",\"uri\":\"/rest/jobs\",\"msg\":\"create job succeeded for Pipeline: OCV TINY YOLO VEHICLE DETECTION (WITH MARKUP) PIPELINE, Media URIs: [file:///opt/mpf/share/remote-media/car_video.MOV]\"}\n\n\n\nAudit logging is enabled by default, but can be disabled by setting the \naudit.logging.enabled\n system property to \nfalse\n.\n\n\nAdditional Quality Selection Properties\n\n\n\n\n\nWe have added the ability to perform feed-forward processing and artifact extraction based on component-defined properties, in addition to the standard \nQUALITY_SELECTION_PROPERTY\n. \n\n\nTwo new job properties have been added: \nARTIFACT_EXTRACTION_POLICY_BEST_DETECTION_PROP_NAMES_LIST\n and \nFEED_FORWARD_BEST_DETECTION_PROP_NAMES_LIST\n. A detection component may choose to mark certain detections in a track as \"best\" by adding a custom property to those detections. For example, you could mark the detection whose bounding box has the largest area by adding a detection property named \nBEST_SIZE\n to that detection. If you want to extract the artifact for that detection, then you would add the string \nBEST_SIZE\n to the \nARTIFACT_EXTRACTION_POLICY_BEST_DETECTION_PROP_NAMES_LIST\n. This would result in that detection artifact being extracted in addition to the others that would be extracted based on the \nQUALITY_SELECTION_PROPERTY\n.\n\n\nRefer to the \nArtifact Extraction Guide\n and the \nFeed-Forward Guide\n for more details.\n\n\n\n\nPython 3.12\n\n\n\n\n\nThe version of python that is used throughout OpenMPF has been upgraded from 3.8 to 3.12.\n\n\n\n\nFeed-forward All Tracks [Experimental]\n\n\n\n\n\n\n\nPreviously, the Workflow Manager would feed forward each track from a previous pipeline stage to the next stage one track at a time. To support use cases where the next stage needs all of the tracks in one sub-job, we added a new job property \nFEED_FORWARD_ALL_TRACKS\n. When set to \ntrue\n, the workflow manager will pass all tracks generated in the current stage of the pipeline to the next.\n\n\n\n\n\n\nA new class was added to the python component API:\n\n\n\n\n\n\nclass AllVideoTracksJob(NamedTuple):\n job_name: str\n data_uri: str\n start_frame: int\n stop_frame: int\n job_properties: Mapping[str, str]\n media_properties: Mapping[str, str]\n feed_forward_tracks: List[VideoTrack]\n\n\n\nand a new method: \nget_detections_from_all_video_tracks()\n.\n\n\n\n\n\n\nThis functionality is experimental and currently supported only with the python component API.\n\n\n\n\n\n\nRefer to the \npython component SDK\n for details.\n\n\n\n\n\n\nAn example illustrating the use of this feature can be found in the python SDK test component \ntest_component.py\n.\n\n\n\n\n\n\nSupport for Supplying a Data URI for Media When Creating a Job\n\n\n\n\n\n\n\nUsers may now supply the media for an OpenMPF job using a data URI when creating a job through the REST API. The mediaURI field may contain a valid (properly encoded) URI to a single media source using one of the following URI schemes: \nfile:\n, \nhttp:\n, \nhttps:\n, \ndata:\n.\n\n\n\n\n\n\nThe data URI format is explained \nhere\n.\n\n\n\n\n\n\nHere is an example of a jobCreationRequest using a data URI for an image:\n\n\n{\n \"buildOutput\": true,\n \"callbackMethod\": \"\",\n \"callbackURL\": \"\",\n \"externalId\": \"my-id\",\n \"media\": [\n {\n \"mediaUri\": \"data:image/jpeg;base64,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\"\n }\n ],\n \"pipelineName\": \"OCV YOLO OBJECT DETECTION PIPELINE\",\n \"priority\": 1\n}\n\n\n\nFeatures\n\n\n\n\n\n[\n#1719\n] Remove \nOUTPUT_LAST_TASK_ONLY\n and support \nIS_ANNOTATOR\n and \nSUPPRESS_TRACKS\n WFM properties\n\n\n[\n#1796\n] Translate specific CSV columns\n\n\n[\n#1831\n] Create a No Language Left Behind (NLLB) translation component\n\n\n[\n#1832\n] Add initial subject tracking implementation\n\n\n[\n#1841\n] Create new feature to allow feed-forward and artifact extraction selection based on a set of detection properties\n\n\n[\n#1851\n] Allow users to use JSON Path to specify which parts of a JSON document to process\n\n\n[\n#1862\n] Create a language identification component using FastText\n\n\n[\n#1869\n] Add support for using media selectors with multi-stage pipelines\n\n\n[\n#1875\n] Allow users to provide a data URI for media.\n\n\n[\n#1889\n] Update detection components to register using ActiveMQ.\n\n\n[\n#1907\n] Support feeding forward all tracks to one sub-job (for Python video jobs only)\n\n\n[\n#1920\n] Add audit logging of callbacks\n\n\n[\n#1921\n] Add audit logging for TiesDb\n\n\n[\n#1979\n] Add event ids, bucket keys, and URIs to audit logging\n\n\n[\n#1933\n] Add Custom SSO support\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#1085\n] Some detection processing errors should result in a final \nERROR\n status instead of \nCOMPLETE_WITH_ERRORS\n\n\n[\n#1269\n] Improve \nUNSUPPORTED_DATA_TYPE\n message in JSON output object\n\n\n[\n#1679\n] Retire AzureOcrTextDetection component\n\n\n[\n#1847\n] Suppress the warning when a track does not contain the requested quality selection property\n\n\n[\n#1854\n] Update Jenkins build to generate SBOM\n\n\n[\n#1857\n] Optionally skip TiesDb check through Create Job page\n\n\n[\n#1858\n] Optionally give transient pipeline a name\n\n\n[\n#1879\n] Merge \nCOMPLETE_WITH_ERRORS\n status into \nERROR\n\n\n[\n#1884\n] Upgrade to Python 3.12\n\n\n[\n#1956\n] Initial TiesDB check failure should result in a warning, not an error\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1916\n] JSON button remains disabled on Job Status page when job ends in \nERROR\n\n\n[\n#1926\n] Bad TiesDB URL results in multiple UI notifications and TiesDB status staying \nIN PROGRESS\n\n\n[\n#1984\n] Update CharsetDetectingReader to always use UTF-8 when the input only contains valid UTF-8 bytes\n\n\n\n\nOpenMPF 9.0.x\n\n\n9.0.11: October 2025\n\n\n\nUpdates\n\n\n\n\n\n[\n#1911\n] Update LlamaVideoSummarization to use TIMELINE_CHECK_ACCEPTABLE_THRESHOLD\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1910\n] Check LlamaVideoSummarization timestamps\n\n\n[\n#1970\n] Use postgres:17-alpine\n\n\n\n\n9.0.10: August 2025\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1950\n] HeifConverter fails for some avif files\n\n\n\n\n9.0.9: August 2025\n\n\n\nFeatures\n\n\n\n\n\n[\n#1940\n] Record stats for the ffprobe process that determines PTS value\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1930\n] Reading a file that uses the libaom-av1 decoder causes a crash during artifact extraction\n\n\n[\n#1943\n] Memory leak in HeifConverter.cpp\n\n\n\n\n9.0.8: June 2025\n\n\n\nLLaMA Video Summarization Component\n\n\n\n\n\n\n\nThis component uses the VideoLLaMA3 model to generate a description of the activity in a video. It can describe a segment of the video, or create a summary of the entire video.\n\n\n\n\n\n\nRefer to the \nREADME\n\n for details.\n\n\n\n\n\n\nFeatures\n\n\n\n\n\n[\n#1883\n] Create LLaMA video summarization component\n\n\n[\n#1888\n] Enable segments to be specified by seconds\n\n\n\n\n9.0.7: May 2025\n\n\n\nUpdates\n\n\n\n\n\n[\n#1903\n] Support AVIF images\n\n\n\n\n9.0.6: December 2024\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1865\n] WTP models are not installed in AzureTranslation\n\n\n\n\n9.0.3: July 2024\n\n\n\nNew Keyword Tagging Formats\n\n\n\n\n\n\n\nThe Keyword Tagging component was updated to use the Perl regex syntax, thereby enabling more regex features including look-behind/ahead.\n\n\n\n\n\n\nAdditional patterns were added to the tags file to enhance the base matching capability of the component:\n\n\n\n\n\n\n phone numbers\n emails\n date formats\n time\n P.O Box\n SSN\n currency symbols\n\n\n\n\n\nRefer to the Keyword Tagging Component configuration file \ntext-tags.json\n for a complete list of the regex expressions currently supported.\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#1834\n] Make delay in AzureRead configurable and delay before first poll attempt\n\n\n[\n#1770\n] Update Keyword Tagging to support emails, dates, phone numbers, and other formats\n\n\n\n\n9.0.2: July 2024\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1828\n] Track object provides incorrect parameters to the \nExemplarPolicyUtil.getExemplar()\n function\n\n\n[\n#1827\n] \nnlp_text_splitter\n installation fails because of an issue with the thinc library\n\n\n\n\n9.0.1: June 2024\n\n\n\nUpdates\n\n\n\n\n\n[\n#1822\n] Capitalize all tags produced by the KeywordTagging and TransformerTagging components\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1819\n] Workflow Manager splitterThreadPoolProfile does not properly re-use threads\n\n\n[\n#1823\n] TransformerTagging does not handle multiple feed-forward properties to process\n\n\n\n\n9.0.0: May 2024\n\n\n\nDocumentation\n\n\n\n\n\nCreated a new \nQuality Selection Guide\n.\n\n\n\n\nQuality Selection\n\n\n\n\n\nCan now specify a \nQUALITY_SELECTION_PROPERTY\n and \nQUALITY_SELECTION_THRESHOLD\n for choosing exemplars, artifacts,\n and controlling feed-forward behavior.\n\n\nThe following old job properties and old system properties are no longer supported. The tables show the new properties\n that should be used instead:\n\n\n\n\n\n\n\n\n\n\nOld Job Property\n\n\nNew Job Properties\n\n\n\n\n\n\n\n\n\n\nCONFIDENCE_THRESHOLD\n\n\nQUALITY_SELECTION_PROPERTY\nQUALITY_SELECTION_THRESHOLD\n\n\n\n\n\n\nARTIFACT_EXTRACTION_POLICY_TOP_CONFIDENCE_COUNT\n\n\nARTIFACT_EXTRACTION_POLICY_TOP_QUALITY_COUNT\n\n\n\n\n\n\nFEED_FORWARD_TOP_CONFIDENCE_COUNT\n\n\nFEED_FORWARD_TOP_QUALITY_COUNT\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nOld System Property\n\n\nNew System Properties\n\n\n\n\n\n\n\n\n\n\ndetection.confidence.threshold\n\n\ndetection.quality.selection.prop\ndetection.quality.selection.threshold\n\n\n\n\n\n\ndetection.artifact.extraction.policy.top.confidence.count\n\n\ndetection.artifact.extraction.policy.top.quality.count\n\n\n\n\n\n\n\n\n\n\nBy default, \nQUALITY_SELECTION_PROPERTY\n is set to the value of \ndetection.quality.selection.prop\n system property,\n which, by default, is \nCONFIDENCE\n. In most cases this preserves the previous behavior.\n\n\nBy default, \nQUALITY_SELECTION_THRESHOLD\n is set to the value of \ndetection.quality.selection.threshold\n system\n property, which, by default, is \n-Infinity\n. This setting disables the threshold. Previously, the default value of\n \ndetection.confidence.threshold\n was -1, which disabled the threshold for most components.\n\n\nComponents that previously used \nCONFIDENCE_THRESHOLD\n now have \nQUALITY_SELECTION_PROPERTY=CONFIDENCE\n. Also,\n \nQUALITY_SELECTION_THRESHOLD\n is set to the previous value of \nCONFIDENCE_THRESHOLD\n. For example, see \nthis\n commit\n\n for changes made to the OcvYoloDetection component.\n\n\nEXEMPLAR_POLICY\n is now set to \nQUALITY\n by default. This setting results in choosing the detection within each track\n with the maximum quality according to the \nQUALITY_SELECTION_PROPERTY\n. Previously, the selection was always made\n based on highest detection confidence.\n\n\nSimilarly, the new \nFEED_FORWARD_TOP_QUALITY_COUNT\n and \nARTIFACT_EXTRACTION_POLICY_TOP_QUALITY_COUNT\n properties use\n \nQUALITY_SELECTION_PROPERTY\n and \nQUALITY_SELECTION_THRESHOLD\n.\n\n\nRefer to the \nQuality Selection Guide\n for details.\n\n\n\n\nTransformer Tagging Component\n\n\n\n\n\nThis component uses a user-specified corpus JSON file to match known phrases against each sentence in the input text\n data.\n\n\nThe input text sentences that generate match scores above the threshold are called \"trigger sentences\". These\n sentences are grouped by \"tag\" based on which entry in the corpus they matched against.\n\n\nThe underlying \nall-mpnet-base-v2 model\n was trained\n on a variety of text data in order to understand the commonalities in phrasing, subject, and context.\n\n\nRefer to the \nREADME\n\n for details.\n\n\n\n\nKeyword Tagging Component Output\n\n\n\n\n\nUpdated the Keyword Tagging Component to generate output in the same format as the Transformer Tagging Component. For\n example, the output properties used to take the form \n TRIGGER WORDS\n and \n TRIGGER WORDS OFFSET\n:\n\n\n\n\nTEXT TRIGGER WORDS\nTEXT TRIGGER WORDS OFFSET\nTRANSLATION TRIGGER WORDS\nTRANSLATION TRIGGER WORDS OFFSET\n\n\n\n\n\nNow the output properties take the form \n TRIGGER WORDS\n and \n TRIGGER WORDS OFFSET\n:\n\n\n\n\nTEXT TRAVEL TRIGGER WORDS\nTEXT TRAVEL TRIGGER WORDS OFFSET\nTRANSLATION TRAVEL TRIGGER WORDS\nTRANSLATION TRAVEL TRIGGER WORDS OFFSET\n\n\n\n\n\nNotice that in the above example the new output properties include the word \nTRAVEL\n. If trigger words are detected\n for other tags, such as \nFINANCIAL\n and \nVEHICLE\n, those words will be used in separate \nTRIGGER WORDS\n and\n \nTRIGGER WORDS OFFSET\n output properties.\n\n\nThis change enables the job consumer to determine which trigger words are associated with each entry in the \nTAGS\n\n output property.\n\n\nRefer to the \"Outputs\" section of the\n \nREADME\n for details.\n\n\n\n\nReporting Component Processing Time\n\n\n\n\n\nThe JSON output object contains a new section for reporting component processing time in milliseconds. For example:\n\n\n\n\n\"timing\": {\n \"processingTime\": 1514,\n \"actions\": [\n {\n \"name\": \"OCV YOLO VEHICLE DETECTION ACTION\",\n \"processingTime\": 1431\n },\n {\n \"name\": \"TENSORFLOW VEHICLE COLOR DETECTION (WITH FF REGION) ACTION\",\n \"processingTime\": 83\n }\n ]\n},\n\n\n\n\n\nThis does not include the time sub-jobs spent waiting in queues, or processing time by the Workflow Manager, such as\n the time to perform media inspection.\n\n\nAlso, the above JSON is reported in the TiesDB job record within the \ndataObject\n field.\n\n\n\n\nNLP Text Splitter Utility\n\n\n\n\n\nThe new NLP Text Splitter utility uses spaCy or \nWhere's the Point (WtP)\n\n models for determining how to break up text into sentences.\n\n\nSupports both CPU processing and optional GPU processing.\n\n\nUpdated the Azure Translation Component to use this utility to ensure that translation requests are within the 50,000\n character limit.\n\n\nRefer to the\n \nREADME\n\n for details.\n\n\n\n\nCLIP Component Video Support\n\n\n\n\n\nThe CLIP Component now supports processing videos in addition to the previous ability to process images. Specify the\n batch size using \nDETECTION_FRAME_BATCH_SIZE\n.\n\n\nThe component also supports a new, larger, and more accurate \nViT-L/14\n model in addition to the previous \nViT-B/32\n\n model. Both models are supported via the optional Triton server as well as within the component itself for non-Triton\n deployments.\n\n\nRefer to the\n \nREADME\n\n for performance metrics.\n\n\nThe \nNUMBER_OF_TEMPLATES\n property has been renamed to \nTEMPLATE_TYPE\n and now accepts one of the following values:\n \nopenai_1\n, \nopenai_7\n, \nopenai_80\n.\n\n\n\n\nImport Root Certificates for Components\n\n\n\n\n\nCan now specify a \nMPF_CA_CERTS\n environment variable for component Docker services to import root certificates.\n\n\nMay be useful when components need to communicate with external web services.\n\n\nRefer to the \nREADME\n for details.\n\n\n\n\nDocker Secrets for Environment Variables\n\n\n\n\n\nCan now use Docker secrets for environment variables in the Docker compose file.\n\n\nThis prevents exposing information as plain text in \ndocker-compose.yml\n.\n\n\nMay be useful for environment variables like:\n\n\nWorkflow Manager username and password: \nWFM_USER\n and \nWFM_PASSWORD\n\n\nKeystore password when enabling Workflow Manager HTTPS: \nKEYSTORE_PASSWORD\n\n\nAzure credentials: \nMPF_PROP_ACS_URL\n and \nMPF_PROP_ACS_SUBSCRIPTION_KEY\n\n\n\n\n\n\nRefer to the\n \nREADME\n\n for details.\n\n\n\n\nFeatures\n\n\n\n\n\n[\n#1692\n] Create a TransformerTagging component\n\n\n[\n#1718\n] Support a \nQUALITY_SELECTION_PROP\n to specify how the WFM should choose an exemplar\n\n\n[\n#1754\n] Report amount of time components spent executing a job\n\n\n[\n#1756\n] Support \nMPF_CA_CERTS\n for components\n\n\n[\n#1771\n] Azure Translation: Identify character limits. Split text using NLP Text Splitter.\n\n\n[\n#1798\n] Add NLP Text Splitter to Python Component SDK\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#1694\n] Update CLIP component to support videos\n\n\n[\n#1706\n] Update KeywordTagging to work with TransformerTagging\n\n\n[\n#1745\n] Support using docker secrets for environment variables in \ndocker-compose.yml\n\n\n[\n#1769\n] Upgrade to proto3 and clean up \n.proto\n files\n\n\n[\n#1774\n] Update how TransformerTagging tokenizes sentences\n\n\n[\n#1785\n] Upgrade to OpenCV 4.9\n\n\n[\n#1786\n] Modify the behavior of Markup when \nCONFIDENCE\n is the bounding box label to be displayed\n\n\n[\n#1797\n] Further update Azure Translation and STT language maps\n\n\n[\n#1803\n] Upgrade Postgres client used by Workflow Manager\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1781\n] Markup boxes are not drawn when animation is disabled and there are gaps in a track\n\n\n[\n#1799\n] Keyword Tagging removes newlines so character offsets don't line up with original text\n\n\n\n\nOpenMPF 8.0.x\n\n\n8.0.4: May 2024\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1805\n] Workflow Manager incorrectly detects whether certain videos\n are constant or variable frame rate\n\n\n\n\n8.0.3: April 2024\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1788\n] Azure Speech and Translation: Update supported language\n mappings\n\n\n\n\n8.0.2: March 2024\n\n\n\nDocumentation\n\n\n\n\n\nUpdated the \nREST API\n with new \n[GET] /rest/queues\n and \n[GET] /rest/queues/{name}\n endpoints.\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#1776\n] Add REST endpoint for retrieving the ActiveMQ message counts\n for each queue\n\n\n\n\n8.0.1: March 2024\n\n\n\nUpdates\n\n\n\n\n\n[\n#1768\n] Add Option to Merge Text Sections in TikaTextDetection\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1763\n] Media inspections fails when ffprobe does not specify a\n stream \"codec_type\"\n\n\n\n\n8.0.0: December 2023\n\n\n\nDocumentation\n\n\n\n\n\nCreated a new \nOpenID Connect Guide\n.\n\n\nUpdated the \nAdmin Guide\n and \nUser Guide\n to remove\n \n/workflow-manager\n from the Workflow Manager base URL. The Admin Guide includes a section for the new Hawtio web\n console.\n\n\nUpdated the \nREST API\n to use path parameters for pipelines, tasks, actions, and algorithms\n endpoints.\n\n\nUpdated the \nComponent Descriptor Reference\n with \nalgorithm.trackType\n.\n\n\nUpdated the \nC++ Batch Component API\n, \nPython Batch Component\n API\n, and \nJava Batch Component API\n to\n remove the ability to get the detection type since track type is now specified in \ndescriptor.json\n.\n\n\nCreated a new \nTrigger Guide\n.\n\n\nCreated a new \nRoll Up Guide\n.\n\n\n\n\nOpenID-Connect (OIDC) Authentication\n\n\n\n\n\nThe Workflow Manager can now optionally use an OpenID Connect (OIDC) provider to handle authentication for users of\n the web UI and clients of the REST API. The URI for the OIDC provider is specified using the \nOIDC_ISSUER_URI\n\n environment variable.\n\n\nWhen enabled, OIDC is used to authenticate components when they register with the Workflow Manager.\n\n\nWhen \nCALLBACK_USE_OIDC\n is set to \ntrue\n, the Workflow Manager will send a token in job request callbacks.\n\n\nWhen \nTIES_DB_USE_OIDC\n is set to \ntrue\n, the Workflow Manager will send a token when posting to a TiesDb server.\n\n\nWhen OIDC is not enabled, the Workflow Manager uses basic authentication with usernames and passwords, as in previous\n versions of OpenMPF.\n\n\nRefer to the \nOpenID Connect Guide\n for more information on the various OIDC\n environment variables and a Keycloak example.\n\n\n\n\nEmbedded ActiveMQ Broker and Hawtio\n\n\n\n\n\nActiveMQ is now part of the Workflow Manager Spring Boot web application and is no longer run as a separate Docker\n service. This enables ActiveMQ to integrate with Spring Security so it can be protected by the Workflow Manager's OIDC\n support.\n\n\nThe Workflow Manager is the sender or recipient of all ActiveMQ messages, so embedding ActiveMQ in the Workflow\n Manager prevents a network hop on all messages.\n\n\nThe ActiveMQ management page has been replaced by \nHawtio\n, which is more feature rich and can be\n used to monitor the state of the ActiveMQ queues used for communication between the Workflow Manager and the\n components. The Hawtio web console can be accessed by selecting \"Hawtio\" from the \"Configuration\" dropdown menu in the\n top menu bar of the web UI.\n\n\nImportantly, the base URL for the Workflow Manager is now http://localhost:8080 instead of\n http://localhost:8080/workflow-manager. \n/workflow-manager\n is no longer part of the path. This change was made to\n enable Hawtio integration.\n\n\n\n\nREST API Updates\n\n\n\n\n\nThe following changes have been made to the REST endpoints to address a limitation with Swagger (OpenAPI). These\n changes enable the REST endpoints to properly show up in the Swagger page, which is accessed by selecting \"REST API\"\n from the \"Configuration\" dropdown menu in the top menu bar of the web UI.\n\n\n\n\n\n\n\n\n\n\nOld REST Endpoint\n\n\nNew REST Endpoint\n\n\n\n\n\n\n\n\n\n\n[GET] /rest/pipelines?name={name}\n\n\n[GET] /rest/pipelines/{name}\n\n\n\n\n\n\n[GET] /rest/tasks?name={name}\n\n\n[GET] /rest/tasks/{name}\n\n\n\n\n\n\n[GET] /rest/actions?name={name}\n\n\n[GET] /rest/actions/{name}\n\n\n\n\n\n\n[GET] /rest/algorithms?name={name}\n\n\n[GET] /rest/algorithms/{name}\n\n\n\n\n\n\n[DELETE] /rest/pipelines?name={name}\n\n\n[DELETE] /rest/pipelines/{name}\n\n\n\n\n\n\n[DELETE] /rest/tasks?name={name}\n\n\n[DELETE] /rest/tasks/{name}\n\n\n\n\n\n\n[DELETE] /rest/actions?name={name}\n\n\n[DELETE] /rest/actions/{name}\n\n\n\n\n\n\n\n\n\n\nIn general, the name is now specified as part of the URL path instead of as a URL parameter.\n\n\n/\n and \n;\n characters are no longer allowed in these names.\n\n\n\n\nPackaging and Deployment\n\n\n\n\n\nEach component's \ndescriptor.json\n now requires an \nalgorithm.trackType\n field. This is used by the Workflow Manager\n to determine the kind of tracks that may be generated by the component (e.g. \nFACE\n, \nTEXT\n, \nCLASS\n, etc.). This is\n now used in place of the component API calls that were used to get the detection type. \n\n\n\n\nComponent API Updates\n\n\n\n\n\nThe following changes were made since the track type is now part of each component's \ndescriptor.json\n:\n\n\nRemoved \nGetDetectionType()\n from the CPP Component API.\n\n\nRemoved \ndetection_type\n from the Python Component API.\n\n\nRemoved \ngetDetectionType()\n from the Java Component API.\n\n\n\n\n\n\n\n\nChanges to JSON Output Object\n\n\n\n\n\nNew JSON output objects use \naction\n instead of \nsource\n in the track type group. Also, \nsource\n is removed from each track.\n\n\nConsider this example of the old JSON output:\n\n\n\n\n\"output\": {\n \"FACE\": [\n {\n \"source\": \"+#MOG MOTION DETECTION (WITH AUTO-ORIENTATION) PREPROCESSOR ACTION#OCV FACE DETECTION (WITH AUTO-ORIENTATION) ACTION\",\n \"algorithm\": \"FACECV\",\n \"tracks\": [\n {\n \"id\": \"4bcba9b95b92a5115b7da1097fcffa962480d0b4424a656772bef12161d775c1\",\n \"startOffsetFrame\": 0,\n \"stopOffsetFrame\": 0,\n \"startOffsetTime\": 0,\n \"stopOffsetTime\": 0,\n \"type\": \"FACE\",\n \"source\": \"+#MOG MOTION DETECTION (WITH AUTO-ORIENTATION) PREPROCESSOR ACTION#OCV FACE DETECTION (WITH AUTO-ORIENTATION) ACTION\",\n \"confidence\": 8.799637,\n ...\n\n\n\n\n\nThe corresponding new JSON output is:\n\n\n\n\n\"output\": {\n \"FACE\": [\n {\n \"action\": \"OCV FACE DETECTION (WITH AUTO-ORIENTATION) ACTION\",\n \"algorithm\": \"FACECV\",\n \"tracks\": [\n {\n \"id\": \"4bcba9b95b92a5115b7da1097fcffa962480d0b4424a656772bef12161d775c1\",\n \"startOffsetFrame\": 0,\n \"stopOffsetFrame\": 0,\n \"startOffsetTime\": 0,\n \"stopOffsetTime\": 0,\n \"type\": \"FACE\",\n \"confidence\": 8.799637,\n ...\n\n\n\nTrigger Support\n\n\n\n\n\nA \nTRIGGER\n property can now be added to any action in a pipeline. It will only be used if \nFEED_FORWARD_TYPE\n is\n provided and set to something other than \nNONE\n. The \nTRIGGER\n property is used to conditionally control if the\n Workflow Manager executes that action. Each feed-forward track that is not executed is passed to the next stage of the\n pipeline. This results in skipping untriggered actions.\n\n\nThe value of \nTRIGGER\n takes the form \n=[;...]\n. For example, if the value is\n \nCLASSIFICATION=car\n then the Workflow Manager would only execute the associated action using feed-forward tracks from\n the previous stage in the pipeline if those tracks have the \nCLASSIFICATION\n track property with a value of \ncar\n.\n This could be useful to skip a license plate detection action. To enable the action to trigger on more than just \ncar\n\n tracks you can provide a list of valid values. For example, \nCLASSIFICATION=car;truck;bus\n.\n\n\nThe \nTrigger Guide\n goes into more detail and provides an example of a pipeline with\n multiple speech-to-text stages. \nTRIGGER\n is used to select which speech-to-text algorithm is executed based on the\n detected language in the media.\n\n\n\n\nRoll Up Support\n\n\n\n\n\nThe Workflow Manager can be configured to replace the values of track and detection properties\n after receiving tracks and detections from a component. For example, the \nCLASSIFICATION\n property\n may be set to \"car\", \"bus\", and \"truck\". Those can be rolled up into \"vehicle\".\n\n\nTo use this feature, set the \nROLL_UP_FILE\n property to the path of a JSON file that matches\n the format of this example:\n\n\n\n\n[\n {\n \"propertyToProcess\": \"CLASSIFICATION\",\n \"originalPropertyCopy\": \"ORIGINAL CLASSIFICATION\",\n \"groups\": [\n {\n \"rollUp\": \"vehicle\",\n \"members\": [\n \"truck\",\n \"car\",\n \"bus\"\n ]\n }\n ]\n }\n]\n\n\n\n\n\nRefer to the \nRoll Up Guide\n for an explanation and more details.\n\n\n\n\nChanged All \"whitelist\" References to \"allow list\"\n\n\n\n\n\nIn an effort to be more culturally sensitive, all references to \"whitelist\" have been removed or renamed to \"allow\n list\".\n\n\nThe \nwhitelist.\n prefix has been removed from the entries in the \nmediaType.properties\n file. For example,\n \nwhitelist.image/gif=VIDEO\n is now \nimage/gif=VIDEO\n.\n\n\nThe OcvDnnDetection component \nFEED_FORWARD_WHITELIST_FILE\n property has been renamed to\n \nFEED_FORWARD_ALLOW_LIST_FILE\n.\n\n\nThe OcvYoloDetection component \nCLASS_WHITELIST_FILE\n property has been renamed to \nCLASS_ALLOW_LIST_FILE\n.\n\n\n\n\nArgos Translation Component\n\n\n\n\n\nThis new component utilizes \nArgos Translate\n to translate input\n text from a given source language to English. It can be used in a feed-forward pipeline to process tracks with\n language and/or script identifiers from an upstream stage.\n\n\nRefer to the \nREADME\n for\n details.\n\n\n\n\nWhisper Speech-to-Text and Translation Component\n\n\n\n\n\nThis new component utilizes \nOpenAI Whisper\n to perform language detection,\n speech-to-text transcription, or speech translation.\n\n\nIf multiple languages are spoken in a single piece of media, language detection will detect only one of them.\n\n\nNote that Whisper is not designed to return a transcription in the source language when performing translation, so we\n implemented the component to perform an additional transcribe call when configured to perform translation.\n\n\nRefer to the \nREADME\n\n for details.\n\n\n\n\nContrastive Language\u2013Image Pre-training (CLIP) Component\n\n\n\n\n\nThis new component utilizes \nCLIP\n to classify images using the 80 COCO classes, 1000\n ImageNet classes, or a list of user-provided classes. It can run on a CPU or GPU, and can make calls to an NVIDIA\n Triton inference server.\n\n\nClassification is performed by taking the class names and filling in one or more text prompts. For example, \"a photo\n of {}\", where \"{}\" can be \"dog\" or \"cat\". An embedding is generated using the text prompt(s) for each class and\n compared against the image embedding to get a match score. Optionally, users can provide a list of their own text\n prompts.\n\n\nOpenAI trained the CLIP model using a wide variety of images and their respective captions from the Internet. This may\n make it suitable for a wide variety of classification tasks without further training (known as zero-shot\n classification). For example, a user could make up a list of classes for arbitrary objects like \"walrus\", \"paperclip\",\n \"pizza\", etc., and use the default text prompts.\n\n\nIt is also possible to use CLIP to classify concepts like scenes and sentiment. For example, using a text prompt of \"a\n {} scene\" where the classes are \"safe\", \"violent\", and \"dangerous\".\n\n\nOptionally, the CLIP component can return the image embedding as the track \nFEATURE\n. For example, this can be used\n for search and retrieval tasks by comparing it to other embeddings enrolled in a database.\n\n\nRefer to the \nREADME\n for\n details.\n\n\n\n\nFeatures\n\n\n\n\n\n[\n#1547\n] Create Argos translation component\n\n\n[\n#1574\n] Update the WFM to support an optional \nTRIGGER\n property on any action\n\n\n[\n#1598\n] Create a Whisper component for speech-to-text and and translation\n\n\n[\n#1644\n] Create CLIP component for processing images\n\n\n[\n#1704\n] Update Workflow Manager to authenticate users and REST clients using OIDC\n\n\n[\n#1730\n] Update Workflow Manager to optionally use OIDC when sending callbacks and posting to TiesDb\n\n\n[\n#1733\n] Update Workflow Manager to use an embedded ActiveMQ broker\n\n\n[\n#1793\n] Add Roll Up support to Workflow Manager\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#799\n] Avoid unnecessary serialization between Camel routes\n\n\n[\n#949\n] Change \n/pipelines?name=MYPIPELINE\n REST endpoint to \n/pipelines/MYPIPELINE\n\n\n[\n#1643\n] Remove \nLONG_SPEAKER_ID\n and instead only use \nSPEAKER_ID\n\n\n[\n#1645\n] Refactor camel code\n\n\n[\n#1705\n] Change all references to \"whitelist\" to \"allow list\" and \"blacklist\" to \"block list\"\n\n\n[\n#1759\n] Disable markup animation by default\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1642\n] \nInProgressBatchJobsService.setProcessedAction\n is now called when a previous task produces no tracks\n\n\n[\n#1755\n] The Workflow Manager logs page does not properly handle multi-byte characters\n\n\n\n\nOpenMPF 7.2.x\n\n\n7.2.6: January 2024\n\n\n\nDocumentation\n\n\n\n\n\nCreated a new \nHealth Check Guide\n.\n\n\n\n\nHealth Check Support\n\n\n\n\n\nThe C++ and Python component executors can be configured to run health checks on components prior to running jobs.\n Health checks are configured using environment variables:\n\n\nHEALTH_CHECK\n: When set to \"ENABLED\", the component executor will run health checks.\n\n\nHEALTH_CHECK_TIMEOUT\n: When set to a positive integer, specifies the minimum number of seconds between health\n checks. When absent or set to 0, a health check will run before every job.\n\n\nHEALTH_CHECK_RETRY_MAX_ATTEMPTS\n: When set to a positive integer, specifies the number of consecutive health\n check failures that will cause the component service to exit. When absent or set to 0, the component service will\n never exit because of a failed health check.\n\n\n\n\n\n\nAlso, an INI file must be provided at \n$MPF_HOME/plugins//health/health-check.ini\n. For example:\n\n\n\n\nmedia=$MPF_HOME/plugins/OcvFaceDetection/health/meds_faces_image.png\nmin_num_tracks=2\nmedia_type=IMAGE\n\n[job_properties]\nJOB PROP1=VALUE1\nJOB PROP2=VALUE2\n\n[media_properties]\nMEDIA PROP=MEDIA VALUE\n\n\n\n\n\nRefer to the \nHealth Check Guide\n for an explanation and more details.\n\n\n\n\nFeatures\n\n\n\n\n\n[\n#1731\n] Implement health checks for C++ and Python components\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#1727\n] Update ffmpeg to 6.1\n\n\n\n\n7.2.5: November 2023\n\n\n\nUpdates\n\n\n\n\n\n[\n#1715\n] Upgrade ActiveMQ to 5.17.6\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1711\n] When selecting detections with the highest confidence,\n Workflow Manager should consistently handle detections with equal confidence\n\n\n\n\n7.2.4: September 2023\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1707\n] Fix bug where TiesDB check status reports\n \nNO_TIES_DB_URL_IN_JOB\n instead of \nMEDIA_MIME_TYPES_ABSENT\n\n\n\n\n7.2.3: June 2023\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1697\n] Prevent OcvYoloDetection component from deadlocking on\n strange frame sizes when using Triton\n\n\n\n\n7.2.2: June 2023\n\n\n\nUpdates\n\n\n\n\n\n[\n#1693\n] Add property to enable/disable SAS in AzureSpeech\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1695\n] Fix memory leak in KeywordTagging component\n\n\n\n\n7.2.1: June 2023\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1678\n] Fix bug where ffmpeg hangs when processing some kinds of\n unsupported/corrupted media\n\n\n\n\n7.2.0: May 2023\n\n\n\nDocumentation\n\n\n\n\n\nCreated a new \nTiesDb Guide\n.\n\n\nUpdated the \nComponent Descriptor Reference\n with \noutputChangedCounter\n.\n\n\nUpdated the \nREST API\n with a new \n[POST] /rest/jobs/tiesdbrepost\n endpoint.\n\n\nUpdated the REST API \n[POST] /rest/jobs\n response with \ntiesDbCheckStatus\n and \noutputObjectUri\n.\n\n\n\n\nTiesDb Re-Post\n\n\n\n\n\nAdded a new \n[POST] /rest/jobs/tiesdbrepost\n endpoint that accepts an array of job ids as an input and will attempt to\n re-post the job assertions (records) to TiesDb for each one. \n\n\nAdded a \"TiesDb\" column to the Job Status page. If there is a problem posting a record to the TiesDb server the column\n will contain an \"ERROR\" button. Clicking on it will provide a description of the error and a button that can be used\n to re-post the associated job records.\n\n\n\n\nTiesDb Checking\n\n\n\n\n\nIf the \nTIES_DB_URL\n job property or \nties.db.url\n system property is set when submitting a job creation request, \n then the Workflow Manager will attempt to check TiesDb for existing job results before running the job again.\n\n\nThe Workflow Manager will attempt to use the most-recently-created job results, preferring jobs that completed without\n errors or warnings, and preferring jobs that completed with warnings over completed with errors.\n\n\nTo prevent this check, set \nSKIP_TIES_DB_CHECK=true\n. That will force the job to run and attempt to post the new\n job results to TiesDb.\n\n\nWhen using TiesDb, we strongly recommend providing both the \nMEDIA_HASH\n and \nMIME_TYPE\n in the \nmedia.metadata\n map\n in the job request. This will enable the Workflow Manager to skip media inspection. When using S3 object storage, this\n means that the Workflow Manager will not need to download the media before checking TiesDb for existing job records.\n\n\nThe \n[POST] /rest/jobs\n response now contains a \ntiesDbCheckStatus\n and \noutputObjectUri\n field. \ntiesDbCheckStatus\n\n will be set to one of the following values:\n\n\nNOT_REQUESTED\n\n\nNO_TIES_DB_URL_IN_JOB\n\n\nMEDIA_HASHES_ABSENT\n\n\nMEDIA_MIME_TYPES_ABSENT\n\n\nNO_MATCH\n\n\nFOUND_MATCH\n\n\n\n\n\n\nWhen there is a \nFOUND_MATCH\n, the \noutputObjectUri\n will be set to the URI of the old TiesDb record if S3 copy is\n not enabled.\n\n\nBy default, the \nties.db.s3.copy.enabled\n system property is set to \ntrue\n. This means that the Workflow Manager will\n attempt to copy all of the artifacts, markup, and derivative media associated with the job in TiesDb from the S3\n locations associated with the old job to the new S3 location specified in the new job. A new JSON output object will\n be generated. To disable this behavior set the system property, or \nTIES_DB_S3_COPY_ENABLED\n, to \nfalse\n. Then the\n Workflow Manager will simply provide a link to the old JSON as the result of the new job.\n\n\nIf there is a problem copying between S3 locations, the \"TiesDb\" column to the Job Status page will show a\n \"COPY ERROR\" button. Clicking on it will provide a description of the error.\n\n\n\n\nTiesDb Linked Media\n\n\n\n\n\nAdded support for \nLINKED_MEDIA_HASH\n in the \nmedia.properties\n section of the job creation request. When specified,\n the value of \nLINKED_MEDIA_HASH\n will be used instead of the actual media hash when creating a record in TiesDb,\n and also when looking for existing records in TiesDb.\n\n\nThis feature can be used to submit a transcoded (or thumbnail) version of an image to process instead of the source\n image. For example, the source image may be in a format not supported by OpenMPF. In this case, the value of\n \nLINKED_MEDIA_HASH\n can be set to the source image, but the rest of the job creation request would specify\n the \nmedia.mediaUri\n and \nmedia.metadata\n for the transcoded version of that image.\n\n\n\n\nOutput Changed Counter\n\n\n\n\n\nAdded the \noutput.changed.counter\n system property to the Workflow Manager and \noutputChangedCounter\n field to each\n component's \ndescriptor.json\n. These values are used when calculating the hash for a job when its record is posted to\n TiesDb, and also when checking TiesDb for existing records when a new job is submitted.\n\n\nIf the Workflow Manager is updated for any reason that should invalidate pre-existing job results, such as a\n change to the fields in the JSON output object, or significant improvements to track merging, for example, then the\n value of \noutput.changed.counter\n should be incremented by one. This will ensure that records in TiesDb will not be\n used so that all future jobs will need to be (re)run at least once until the counter is incremented again.\n\n\nThe same is true for each component. If a component is updated for any reason that should invalidate\n pre-existing job results, such as changes to input or output properties, or substantial improvements to the algorithm,\n then the value of \noutputChangedCounter\n should be incremented by one.\n\n\n\n\nChanges to JSON Output Object\n\n\n\n\n\nNew JSON output objects will include \ntiesDbSourceJobId\n and \ntiesDbSourceMediaPath\n when the Workflow Manager can use\n previous job results stored in TiesDB. Note that the Workflow Manager will not generate a new JSON output object\n unless \nS3_RESULTS_BUCKET\n is set to a valid value, S3 access and secret keys are provided, and\n \nTIES_DB_S3_COPY_ENABLED=true\n.\n\n\n\n\nffprobe for Media Inspection\n\n\n\n\n\nThe Workflow Manager media inspection behavior now uses \nffprobe\n with \n-print_format json\n to return more precise\n \nFPS\n values for the \nmedia.mediaMetadata\n in the JSON output object. For example, the previous version of the\n Workflow Manager would return \n29.97\n, where the new version will return \n29.97002997002997\n. In multi-hour-long\n vidoes this can prevent cases where the last few frames were being ignored.\n\n\nThe previous version of the Workflow Manager was using both \nffmpeg\n and OpenCV to determine the number of frames in\n a video. We removed the OpenCV frame counter in this version because the \nffprobe\n approach is more accurate.\n The \nffprobe\n command replaces the old \nffmpeg\n command. \n\n\n\n\nWeb User Interface\n\n\n\n\n\nUpdated the Job Status page to be more efficient. Searching a database of hundreds of thousands of jobs takes a long\n time. By limiting the search to one page of results at a time the UI is more responsive.\n\n\nRemoved timeout and bootout. The user session will no longer automatically end due to time out, or due to the same\n user logging in from a different host or browser. These behaviors were deemed too disruptive by end users.\n\n\nUpdated the Job Status page to include a \"TiesDb\" column that reports TiesDb status, such as when posting records\n to TiesDb and when retrieving existing records.\n\n\n\n\nFeatures\n\n\n\n\n\n[\n#1438\n] Create a REST endpoint that will attempt to re-post to TiesDb\n\n\n[\n#1613\n] Check TiesDb before running a job\n\n\n[\n#1650\n] Create TiesDb records for thumbnail jobs under the parent media\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#1342\n] Use ffprobe to get FPS during media inspection\n\n\n[\n#1564\n] Use ffprobe's JSON output instead of regexes during media inspection\n\n\n[\n#1601\n] Update the Workflow Manager jobs table to be more efficient\n\n\n[\n#1611\n] Remove Workflow Manager timeout and bootout behavior\n\n\n\n\nOpenMPF 7.1.x\n\n\n7.1.12: March 2023\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1667\n] Handle Webp files with extra data at the end that cause components to crash\n\n\n\n\n7.1.10: March 2023\n\n\n\nUpdates\n\n\n\n\n\n[\n#1662\n] Monitor StorageBackend\n\n\n\n\n7.1.9: February 2023\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1675\n] Prevent upgrade of cudnn in yolo server dockerfile\n\n\n\n\n7.1.8: February 2023\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1649\n] Install specific version of libcudnn8 in Docker build\n\n\n\n\n7.1.7: February 2023\n\n\n\nUpdates\n\n\n\n\n\n[\n#1674\n] Update \nSPEAKER_ID\n logic, set \nLONG_SPEAKER_ID=0\n\n\n\n\n7.1.5: January 2023\n\n\n\nFeatures\n\n\n\n\n\n[\n#1542\n] Update Azure Speech Detection component to select transcription language based on feed-forward track\n\n\n[\n#1543\n] Update audio transcoder to accept subsegments\n\n\n[\n#1605\n] Update Azure Translation to use detected language from upstream\n\n\n\n\n7.1.1: December 2022\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1634\n] Update version numbers to 7.1\n\n\n\n\n7.1.0: December 2022\n\n\n\nDocumentation\n\n\n\n\n\nUpdated the Object Storage Guide with \nS3_UPLOAD_OBJECT_KEY_PREFIX\n.\n\n\nUpdated the Markup Guide with \nMARKUP_TEXT_LABEL_MAX_LENGTH\n.\n\n\n\n\nExemplar Selection Policy\n\n\n\n\n\nThe policy for selecting the exemplar detection for each track can now be set using the \nEXEMPLAR_POLICY\n job property\n with following values:\n\n\nCONFIDENCE\n: Select the detection with the maximum confidence. If some confidences are the same, select the\n detection with the lower frame number. This is the default setting.\n\n\nFIRST\n: Select the detection with the lowest frame number\n\n\nLAST\n: Select the detection with the highest frame number\n\n\nMIDDLE\n: Select the detection with the frame number closest to the middle frame of the track, preferring the\n detection with the lower frame number if there is an even number of frames\n\n\n\n\n\n\n\n\nAutomatic Rotation and Horizontal Flip Enabled by Default\n\n\n\n\n\nIt is no longer necessary to explicitly set \nAUTO_ROTATE\n and \nAUTO_FLIP\n to true since that is now the default value.\n\n\nThese properties affect all video and image components that use the MPFImageReader and MPFVideoCapture tools. When\n true, if the image has EXIF data, or there is metadata associated with a video that ffmpeg understands, the tools will\n use that information to properly orient the frames before returning the frames to the component for processing.\n\n\n\n\nSupport S3 Object Storage Key Prefix\n\n\n\n\n\nSet the \nS3_UPLOAD_OBJECT_KEY_PREFIX\n job property or \ns3.upload.object.key.prefix\n system property to add a prefix to\n object keys when the Workflow Manager uploads objects to the S3 object store. This affects the JSON output object,\n artifacts, markup files, and derivative media.\n\n\nSpecifically, the Workflow Manager will upload objects to\n \n///\n.\n\n\nFor example, if you wish to add \"work/\" to the object key, then set \nS3_UPLOAD_OBJECT_KEY_PREFIX=work/\n.\n\n\n\n\nFeatures\n\n\n\n\n\n[\n#1526\n] Allow markup to display more than 10 characters in the text\n part of the label\n\n\n[\n#1527\n] Enable the Workflow Manager to select the middle detection\n as the exemplar\n\n\n[\n#1566\n] Make \nAUTO_ROTATE\n and \nAUTO_FLIP\n true by default\n\n\n[\n#1569\n] Modify C++ and Python component executor to automatically\n add the job name to log messages\n\n\n[\n#1621\n] Make S3 object keys used for upload configurable\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#1602\n] Update Workflow Manager to use Spring Boot\n\n\n[\n#1631\n] Update byte-buddy, Mockito, and Hibernate versions to\n resolve build issue. Most notably, update Hibernate to 5.6.14.\n\n\n[\n#1632\n] Update ActiveMQ to 5.17.3\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1581\n] Don't change track start and end frame when\n \nFEED_FORWARD_TOP_CONFIDENCE_COUNT\n is disabled\n\n\n[\n#1595\n] Work around how Ubuntu only recognizes certificate files\n that end in .crt\n\n\n[\n#1610\n] Prevent premature pipeline creation when using web UI\n\n\n[\n#1612\n] At startup, prevent Workflow Manager from consuming from\n queues before purging them\n\n\n\n\nOpenMPF 7.0.x\n\n\n7.0.3: September 2022\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1561\n] Fix logging for Python components when running through CLI\n runner\n\n\n[\n#1583\n] Can now properly view media while job is in progress\n\n\n[\n#1587\n] Fix bugs in amq_detection_component's use of select\n\n\n\n\n7.0.2: August 2022\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1562\n] Fix bug where an ffmpeg change prevented detecting video\n rotation\n\n\n\n\n7.0.0: July 2022\n\n\n\nDocumentation\n\n\n\n\n\nUpdated the Development Environment Guide by replacing steps for CentOS 7 with Ubuntu 20.04.\n\n\nAdded the Derivative Media Guide.\n\n\nUpdated the Batch Component APIs with revised error codes.\n\n\nUpdated the Python Batch Component API and Python base Docker image README with instructions for\n using \npyproject.toml\n and \nsetup.cfg\n.\n\n\nUpdated the Admin Guide and User Guide with images that show the new TiesDb and Callback columns in the job status UI.\n\n\nUpdated the REST API with the \npipelineDefinition\n, \nframeRanges\n, and \ntimeRanges\n fields now supported by the\n \n[POST] /rest/jobs\n endpoint.\n\n\nUpdated the OcvYoloDetection component README with information on using the NVIDIA Triton inference server.\n\n\nUpdated the Markup Guide with \nMARKUP_ANIMATION_ENABLED\n and \nMARKUP_LABELS_TRACK_INDEX_ENABLED\n.\n\n\nUpdated the Contributor Guide with new steps for generating documentation.\n\n\n\n\nTransition from CentOS 7 to Ubuntu 20.04\n\n\n\n\n\nAll the Docker images that previously used CentOS 7 as a base now use Ubuntu 20.04.\n\n\nWe decided not to use CentOS 8, which is a version of CentOS Stream, due to concerns about stability.\n\n\nAlso, Ubuntu is a very common OS within the AI and ML space, and has significant community support.\n\n\n\n\nUse Job Id that Enables Load Balancing\n\n\n\n\n\nThe Workflow Manager can now optionally accept job ids of the form \n-\n through\n the REST endpoints, where \n\n is the same as the shorter id used in previous releases. The\n \n-\n prefix enables better tracking and separation of jobs run across multiple\n Workflow Manager instances in a cluster.\n\n\nThe prefix can be set in the \ndocker-compose.yml\n file by assigning \n{{.Node.Hostname}}\n to the \nNODE_HOSTNAME\n\n environment variable for the Workflow Manager service, or hard-coding \nNODE_HOSTNAME\n to the desired hostname.\n\n\nThe shorter version of the id can still be used in REST requests, but the longer id will always be returned by the\n Workflow Manager when responding to those requests.\n\n\nThe shorter id will always be used internally by the Workflow Manager, meaning the job status web UI and log messages\n will all use the shorter job id. \n\n\n\n\nSupport for Derivative Media\n\n\n\n\n\nThe TikaImageDetection component now returns \nMEDIA\n tracks instead of \nIMAGE\n tracks when extracting images from\n documents, such as PDFs, Word documents, and PowerPoint slides. The document is considered the \"source\", or \"parent\",\n media, and the images are considered the \"derivative\", or \"child\", media.\n\n\nActions can now be configured with \nSOURCE_MEDIA_ONLY=true\n or \nDERIVATIVE_MEDIA_ONLY=true\n, which will result in only\n performing the action on that kind of media. Feed forward can still be used to pass track information from one stage\n to another. The tracks will skip the stages (actions) that don't apply.\n\n\nThis enables complex pipelines like one that extracts text from a PDF using TikaTextDetection, OCRs embedded images\n using EastTextDetection and TesseractOCRTextDetection, and runs all of the \nTEXT\n tracks through KeywordTagging.\n\n\nAdded the following pipelines to the TikaImageDetection component:\n\n\nTIKA IMAGE DETECTION WITH DERIVATIVE MEDIA TESSERACT OCR PIPELINE\n\n\nTIKA IMAGE DETECTION WITH DERIVATIVE MEDIA TESSERACT OCR AND KEYWORD TAGGING PIPELINE\n\n\nTIKA IMAGE DETECTION WITH DERIVATIVE MEDIA TESSERACT OCR (WITH EAST REGIONS) AND KEYWORD TAGGING PIPELINE\n\n\nTIKA IMAGE DETECTION WITH DERIVATIVE MEDIA TESSERACT OCR (WITH EAST REGIONS) AND KEYWORD TAGGING AND MARKUP PIPELINE\n\n\nTIKA IMAGE DETECTION WITH DERIVATIVE MEDIA OCV FACE PIPELINE\n\n\nTIKA IMAGE DETECTION WITH DERIVATIVE MEDIA OCV FACE AND MARKUP PIPELINE\n\n\n\n\n\n\n\n\nReport when Job Callbacks and TiesDb POSTs Fail\n\n\n\n\n\nThe job status UI displays two new columns, one that indicates the status of posting to TiesDB, and one that indicates\n the status of posting the job callback to the job producer.\n\n\nAdditionally, the \n[GET] /rest/jobs/{id}\n endpoint now includes a \ntiesDbStatus\n and \ncallbackStatus\n field.\n\n\nNote that, by design, the JSON output itself does not contain these statuses.\n\n\n\n\nAllow Pipelines to be Specified in a Job Request\n\n\n\n\n\nOptionally, the \npipelineDefinition\n field can be provided instead of the \npipelineName\n field when using the\n \n[POST] /rest/jobs\n endpoint in order to specify a pipeline on the fly for that specific job run. It will not be saved\n for later reuse.\n\n\nThe format of the pipeline definition is similar to that in a \ndescriptor.json\n file, with separate sections for\n defining \ntasks\n and \nactions\n. Pre-existing tasks and actions known to the Workflow Manager can be specified in the\n definition. They do not need to be defined again.\n\n\nThis feature is a convenient alternative to creating persistent definitions using the \n[POST] /rest/pipelines\n,\n \n[POST] /rest/tasks\n, and \n[POST] /rest/actions\n endpoints. For example, this feature could be used to quickly add or\n remove a motion preprocessing stage from a pipeline.\n\n\n\n\nAllow User-Specified Segment Boundaries\n\n\n\n\n\nOptionally, multiple \nframeRanges\n and/or \ntimeRanges\n fields can be provided when using the \n[POST] /rest/jobs\n\n endpoint in order to manually specify segment boundaries. These values will override the normal segmenting behavior of\n the Workflow Manager.\n\n\nNote that overlapping ranges will be combined and large ranges may still be split up according to the value of\n \nTARGET_SEGMENT_LENGTH\n and \nVFR_TARGET_SEGMENT_LENGTH\n.\n\n\nNote that \nframeRanges\n is specified using the frame number and \ntimeRanges\n is specified in milliseconds.\n\n\n\n\nAdd Triton Inference Server support to YOLO component\n\n\n\n\n\nThe OcvYoloDetection component now supports the ability to send requests to an NVIDIA Triton Inference Server by\n setting \nENABLE_TRITON=true\n. If set to false, the component will process jobs using OpenCV DNN on the local host\n running the Docker service, as per normal.\n\n\nBy default \nTRITON_SERVER=ocv-yolo-detection-server:8001\n, which\n corresponds to the \nocv-yolo-detection-server\n entry in your \ndocker-compose.yml\n file. Refer to the example entry\n within \ndocker-compose.components.yml\n\n . That entry uses a pre-built and pre-configured version of the Triton server.\n\n\nThe Triton server runs the YOLOv4 model within the TensorRT framework, which performs a warmup operation when the\n server starts up to determine which optimizations to enable for the available GPU hardware. \n*.engine\n files are\n generated within the \nyolo_engine_file\n Docker volume for later reuse.\n\n\nTo further improve inferencing speed, shared memory can be configured between the \nocv-yolo-detection\n client service and the\n \nocv-yolo-detection-server\n service if they are running on the same host. Set \nTRITON_USE_SHM=true\n and configure the\n server with a \n/dev/shm:/dev/shm\n Docker volume.\n\n\nDepending on the available GPU hardware, the Triton server can achieve speeds that are 5x faster than OpenCV DNN with\n tracking enabled, no shared memory, and nearly 9x faster with tracking disabled, with shared memory. Our tests used a\n single RTX 2080 GPU.\n\n\n\n\nRemoved Unused and Redundant Error Codes\n\n\n\n\n\nThe error codes shown on the left were redundant and replaced with the corresponding error codes on the right:\n\n\n\n\n\n\n\n\n\n\nOld Error Code\n\n\nNew Error Code\n\n\n\n\n\n\n\n\n\n\nMPF_IMAGE_READ_ERROR\n\n\nMPF_COULD_NOT_READ_MEDIA\n\n\n\n\n\n\nMPF_BOUNDING_BOX_SIZE_ERROR\n\n\nMPF_BAD_FRAME_SIZE\n\n\n\n\n\n\nMPF_JOB_PROPERTY_IS_NOT_INT\n\n\nMPF_INVALID_PROPERTY\n\n\n\n\n\n\nMPF_JOB_PROPERTY_IS_NOT_FLOAT\n\n\nMPF_INVALID_PROPERTY\n\n\n\n\n\n\nMPF_INVALID_FRAME_INTERVAL\n\n\nMPF_INVALID_PROPERTY\n\n\n\n\n\n\nMPF_DETECTION_TRACKING_FAILED\n\n\nMPF_OTHER_DETECTION_ERROR_TYPE\n\n\n\n\n\n\n\n\nAlso, the following error codes are no longer being used and have been removed:\n\n\n\n\nMPF_UNRECOGNIZED_DATA_TYPE\n\n\nAll media types can now be processed since we support the \nUNKNOWN\n (a.k.a. \"generic\")\n media type\n\n\n\n\n\n\nMPF_INVALID_DATAFILE_URI\n\n\nThe Workflow Manager will reject a job with an invalid media URI before it gets to a\n component\n\n\n\n\n\n\nMPF_INVALID_START_FRAME\n\n\nMPF_INVALID_STOP_FRAME\n\n\nMPF_INVALID_ROTATION\n\n\n\n\nMarkup Improvements\n\n\n\n\n\nBy default, the Markup component draws bounding boxes to fill in the gaps between detections in each track by\n interpolating the box size and position. This can now be disabled by setting the job property\n \nMARKUP_ANIMATION_ENABLED=false\n, or the system property \nmarkup.video.animation.enabled=false\n.\n Disabling this feature can be useful to prevent floating boxes from cluttering the marked-up frames.\n\n\nThe Markup component will now start each bounding box label with a track index like \n[0]\n that can be used to\n correlate the box with the track in the JSON output object. The JSON output now contains an \nindex\n field for every\n track, relative to each piece of media, that is simply an integer that starts at 0 and counts upward. This can be\n disabled by setting the job property \nMARKUP_LABELS_TRACK_INDEX_ENABLED=false\n, or the system property\n \nmarkup.labels.track.index.enabled=false\n.\n\n\n\n\nChanges to JSON Output Object\n\n\n\n\n\nComponents that generate \nMEDIA\n tracks will result in new derivative \nmedia\n entries in the JSON output file. This\n means it's possible to provide a single piece of media as an input and have more than one \nmedia\n entry in the JSON\n output. The output will always include the original media.\n\n\nEach \nmedia\n entry in the JSON output now contains a \nparentMediaId\n in addition to the \nmediaId\n. The \nparentMediaId\n\n for original source media will always be set to -1; otherwise, for derivative media, the \nparentMediaId\n is set the\n \nmediaId\n of the source media from which the child media was derived.\n\n\nEach \nmedia\n entry also contains a new \nframeRanges\n and \ntimeRanges\n collection.\n\n\nThe JSON output file also contains a new \nindex\n field for every track, relative to each piece of media.\n\n\n\n\nFeatures\n\n\n\n\n\n[\n#792\n] Perform detection on images extracted from PDFs\n\n\n[\n#1283\n] Add user-specified segment boundaries\n\n\n[\n#1374\n] Transition from CentOS 7 to Ubuntu 20.04\n\n\n[\n#1396\n] Report when job callbacks and TiesDb POSTs fail\n\n\n[\n#1398\n] Add Triton Inference Server support to YOLO component\n\n\n[\n#1428\n] Allow pipelines to be specified in a job request\n\n\n[\n#1454\n] Transition from Clair scans to Trivy scans\n\n\n[\n#1485\n] Use \npyproject.toml\n and \nsetup.cfg\n instead of \nsetup.py\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#803\n] Update Tika Image Detection to generate one track per piece of extracted media\n\n\n[\n#808\n] Update Tika Text Detection component to not use leading zeros for \nPAGE_NUM\n\n\n[\n#1105\n] Remove dependency on QT from C++ SDK\n\n\n[\n#1282\n] Use job id that enables load balancing\n\n\n[\n#1303\n] Update Tika Image Detection to return \nMEDIA\n tracks\n\n\n[\n#1319\n] Review existing error codes and remove unused or redundant error codes\n\n\n[\n#1384\n] Update Apache Tika to 2.4.1 for TikaImageDetection and TikaTextDetection Components\n\n\n[\n#1436\n] CLI Runner should initialize a component once when handling multiple jobs\n\n\n[\n#1465\n] Remove YoloV3 support from OcvYoloDetection component\n\n\n[\n#1513\n] Update to Spring 5.3.18\n\n\n[\n#1528\n] CLI runner should also sort by startOffsetTime\n\n\n[\n#1540\n] Upgrade to Java 17\n\n\n[\n#1549\n] Allow markup animation to be disabled\n\n\n[\n#1550\n] Add track index to markup\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1372\n] Tika Image Detection no longer misses images in PowerPoint and Word documents\n\n\n[\n#1449\n] Simon data is now refreshed when clicking the Processes tab\n\n\n[\n#1495\n] Fix bug where invalid CSRF token found for \n/workflow-manager/login\n\n\n\n\nOpenMPF 6.3.x\n\n\n6.3.14: May 2022\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1530\n] Fix S3 code memory leak\n\n\n\n\n6.3.12: April 2022\n\n\n\nUpdates\n\n\n\n\n\n[\n#1519\n] Upgrade to OpenCV 4.5.5\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1520\n] S3 code now retries on most 400 errors\n\n\n\n\n6.3.11: April 2022\n\n\n\nDocumentation\n\n\n\n\n\nUpdated the Object Storage Guide with \nS3_SESSION_TOKEN\n, \nS3_USE_VIRTUAL_HOST\n, \nS3_HOST\n, and \nS3_REGION\n.\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#1496\n] Update S3 client code\n\n\n[\n#1514\n] Update Tomcat to 8.5.78\n\n\n\n\n6.3.10: March 2022\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1486\n] Fix bug where \nMOVING\n was being added to immutable map twice\n\n\n[\n#1498\n] Can now provide media metadata when frameTimeInfo is missing\n\n\n[\n#1501\n] MPFVideoCapture now properly reads frames from videos with rotation metadata\n\n\n[\n#1502\n] Detections with \nHORIZONTAL_FLIP\n will no longer result in illformed detections and incorrectly padded regions\n\n\n[\n#1503\n] Videos with rotation metadata will no longer result in corrupt markup\n\n\n\n\n6.3.8: January 2022\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1469\n] \nTENSORFLOW VEHICLE COLOR DETECTION\n pipelines no longer refer to YOLO tasks that no longer exist\n\n\n\n\n6.3.7: January 2022\n\n\n\nUpdates\n\n\n\n\n\n[\n#1466\n] Upgrade log4j to 2.17.1\n\n\n\n\n6.3.6: December 2021\n\n\n\nUpdates\n\n\n\n\n\n[\n#1457\n] Upgrade log4j to 2.16.0\n\n\n\n\n6.3.5: November 2021\n\n\n\nUpdates\n\n\n\n\n\n[\n#1451\n] Make concurrent callbacks configurable\n\n\n\n\n6.3.4: November 2021\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1441\n] Modify AdminStatisticsController so that it doesn't hold all jobs in memory at once\n\n\n\n\n6.3.3: October 2021\n\n\n\nFeatures\n\n\n\n\n\n[\n#1425\n] Make protobuf size limit configurable\n\n\n\n\n6.3.2: October 2021\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1420\n] Sphinx component no longer omits audio at end of video files\n\n\n[\n#1422\n] Media inspection now correctly calculates milliseconds from ffmpeg duration\n\n\n\n\n6.3.1: September 2021\n\n\n\nFeatures\n\n\n\n\n\n[\n#1404\n] Improve OcvDnnDetection vehicle color detection\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#1251\n] Add version to JSON output object\n\n\n[\n#1272\n] Update Keyword Tagging to work on multiple inputs\n\n\n[\n#1350\n] Retire old components to the graveyard: DlibFaceDetection, DarknetDetection, and OcvPersonDetection\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1010\n] \nmpf.output.objects.enabled\n now behaves as expected\n\n\n[\n#1271\n] Azure speech component no longer omits audio at end of video files\n\n\n[\n#1389\n] NLP text correction component now properly reads the value of \nFULL_TEXT_CORRECTION_OUTPUT\n\n\n[\n#1403\n] Corrected README to state that the Azure Speech Component doesn't support v2 of the API\n\n\n[\n#1406\n] Speech detections in videos are no longer dropped if using keyword tagging\n\n\n[\n#1411\n] Exception no longer occurs when adding \nSHRUNK_TO_NOTHING=TRUE\n to an immutable map in multiple pipeline stages\n\n\n[\n#1413\n] Speech detections in videos are no longer dropped if using translation\n\n\n\n\n6.3.0: September 2021\n\n\n\nDocumentation\n\n\n\n\n\nUpdated the API documents, Development Environment Guide, Node Guide, Install Guide, User Guide, Admin Guide, and\n others to clarify the difference between Docker and non-Docker behaviors.\n\n\nTransformed Packaging and Registering a Component document into Component Descriptor Reference.\n\n\nSplit Media Segmentation Guide from User Guide.\n\n\nUpdated and renamed the Workflow Manager document to Workflow Manager Architecture.\n\n\nUpdated the various Docker guides to clarify the difference between building Docker images from scratch versus\n building them using pre-built base images on Docker Hub, emphasizing the latter.\n\n\nUpdated the Contributor Guide to document the hotfix pull request process.\n\n\n\n\nTiesDb Integration\n\n\n\n\n\nTiesDb is a PostgreSQL DB with a RESTful API that stores media metadata. The metadata entries are queried using the\n hash (sha256, md5) of the media file. TIES stands\n for \nTriage Import Export Schema\n. TiesDb is deployed and managed externally to\n OpenMPF. For more information please contact us.\n\n\nWhen a job completes, OpenMPF can post assertions to media entries that exist in TiesDb. In general, one assertion is\n generated for each algorithm run on a piece of media. It contains the job status, algorithm name, detection\n type (\nFACE\n, \nTEXT\n, \nMOTION\n, etc.), and number of tracks generated, as well as a link to the full JSON output\n object.\n\n\nEach assertion serves as a lasting record so that job producers may first check TiesDb to see if an algorithm was run\n on a piece of media before submitting the same job to OpenMPF again.\n\n\nTo enable TiesDb support, set the \nTIES_DB_URL\n job property or \nties.db.url\n system property to\n the \n://:\n part of the URL. The Workflow Manager will append\n the \n/api/db/supplementals?sha256Hash=\n part. Here is an example of a TiesDb POST:\n\n\n\n\n{\n \"dataObject\": {\n \"sha256OutputHash\": \"1f8f2a8b2f5178765dd4a2e952f97f5037c290ee8d011cd7e92fb8f57bc75f17\",\n \"outputType\": \"FACE\",\n \"algorithm\": \"FACECV\",\n \"processDate\": \"2021-09-09T21:37:30.516-04:00\",\n \"pipeline\": \"OCV FACE DETECTION PIPELINE\",\n \"outputUri\": \"file:///home/mpf/git/openmpf-projects/openmpf/trunk/install/share/output-objects/1284/detection.json\",\n \"jobStatus\": \"COMPLETE\",\n \"jobId\": 1284,\n \"systemVersion\": \"6.3\",\n \"trackCount\": 1,\n \"systemHostname\": \"openmpf-master\"\n },\n \"system\": \"OpenMPF\",\n \"securityTag\": \"UNCLASSIFIED\",\n \"informationType\": \"OpenMPF FACE\",\n \"assertionId\": \"4874829f666d79881f7803207c7359dc781b97d2c68b471136bf7235a397c5cd\"\n}\n\n\n\nNatural Language Processing (NLP) Text Correction Component\n\n\n\n\n\nThis component utilizes the \nCyHunspell\n library, which is a Python\n port of the \nHunspell\n spell-checking library, to perform post-processing\n correction of OCR text. In general, it's intended to be used in a pipeline after a component like\n TesseractOCRTextDetection that generates \nTEXT\n tracks. These tracks are then fed-forward into NlpTextCorrection,\n which will add a \nCORRECTED TEXT\n property to the existing tracks.\n The \nTESSERACT OCR TEXT DETECTION WITH NLP TEXT CORRECTION PIPELINE\n performs this behavior. The component can also\n run on its own to process plain text files. Refer to\n the \nREADME\n for details.\n\n\n\n\nAzure Cognitive Services (ACS) Read Component\n\n\n\n\n\nThis component utilizes\n the \nAzure Cognitive Services Read Detection REST endpoint\n\n to extract formatted text from documents (PDFs), images, and videos. Refer to\n the \nREADME\n for\n details.\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#1151\n] Now supports \nIN_PROGRESS_WITH_WARNINGS\n status\n\n\n[\n#1234\n] Now sorts JSON output object media by media id\n\n\n[\n#1341\n] Added job id to all batch-job-specific Workflow Manager log\n messages\n\n\n[\n#1349\n] Improved reporting and recording job status\n\n\n[\n#1353\n] Updated the Workflow Manager to remove and warn about\n zero-size detections\n\n\n[\n#1382\n] Updated Tika version to 1.27 for TikaImageDetection and\n TikaTextDetection components\n\n\n[\n#1387\n] Markup can now be configured in a\n component's \ndescriptor.json\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1080\n] Batch jobs no longer prematurely set to 100% completion\n during artifact extraction\n\n\n[\n#1106\n] When a job ends in \nERROR\n or \nCANCELLED_BY_SHUTDOWN\n the\n job status UI now shows an End Date\n\n\n[\n#1158\n] JSON output object URI no longer changes when callback fails\n\n\n[\n#1317\n] TikaTextDetection no longer generates first PDF track\n at \nPAGE_NUM\n 2\n\n\n[\n#1337\n] Now using \nMPF_BAD_FRAME_SIZE\n instead\n of \nMPF_DETECTION_FAILED\n for OpenCV empty/resize exception\n\n\n[\n#1359\n] Image detection tracks no longer\n have \nendOffsetFrameInclusive\n set to 1\n\n\n[\n#1373\n] When uploading large files through the Workflow Manager web\n UI, now more than the first 865032704 bytes get written\n\n\n[\n#1379\n] TikaImageDetection component now avoids conflicts by no\n longer using the same path when extracting images for jobs with multiple pieces of media\n\n\n[\n#1386\n] FeedForwardFrameCropper in the Python SDK now handles\n negative coordinates properly\n\n\n[\n#1391\n] If a job is configured to upload markup and markup fails,\n the job no longer gets stuck\n\n\n\n\nKnown Issues\n\n\n\n\n\n[\n#1372\n] TikaImageDetection misses images in PowerPoint and Word\n documents\n\n\n[\n#1389\n] NlpTextCorrection does not properly read the value\n of \nFULL_TEXT_CORRECTION_OUTPUT\n\n\n\n\nOpenMPF 6.2.x\n\n\n6.2.5: July 2021\n\n\n\nUpdates\n\n\n\n\n\n[\n#1367\n] Enable cross-origin resource sharing on Workflow Manager\n\n\n\n\n6.2.4: June 2021\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1356\n] AzureSpeech now properly reports when media is missing audio stream\n\n\n[\n#1357\n] AzureSpeech now handles case where speaker id is not present\n\n\n\n\n6.2.2: June 2021\n\n\n\nUpdates\n\n\n\n\n\n[\n#1333\n] Combine media name and job id into one WFM log line\n\n\n[\n#1336\n] Remove duplicate \"Setting status of job to COMPLETE\" Workflow Manager log line and other improvements\n\n\n[\n#1338\n] Update OpenCV DNN Detection component to optionally use feed-forward confidence values\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1237\n] Fixed jQuery DataTables bug: \"int parameter 'draw' is present but cannot be translated into a null value\"\n\n\n[\n#1254\n] Jobs table no longer flickers when polling is enabled and the search box is used\n\n\n[\n#1308\n] Prevent OCV YOLO Tracking from generating zero-sized detections\n\n\n[\n#1313\n] Fix JSON output object timestamps for variable frame rate videos\n\n\n\n\n6.2.1: May 2021\n\n\n\nUpdates\n\n\n\n\n\n[\n#1330\n] Return error codes for \nmodels_ini_parser.py\n exceptions\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1331\n] Decoding certain heic images no longer causes Workflow Manager to segfault\n\n\n\n\n6.2.0: May 2021\n\n\n\nTesseract OCR Text Detection Component Support for Videos\n\n\n\n\n\nThe component can now process videos in addition to images and PDFs. Each video frame is processed sequentially.\n The \nMAX_PARALLEL_SCRIPT_THREADS\n property determines how many threads to use to process each frame, one thread per\n language or script.\n\n\nNote that for videos without much text, it may be faster to disable threading by\n setting \nMAX_PARALLEL_SCRIPT_THREADS=1\n. This will allow the component to reuse TessAPI instances instead of creating\n new ones for every frame. Please refer to the Known Issues section.\n\n\nResolved issues: \n#1285\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#1086\n] Added support for \nCOULD_NOT_OPEN_MEDIA\n\n and \nCOULD_NOT_READ_MEDIA\n error types\n\n\n[\n#1159\n] Split \nIssueCodes.REMOTE_STORAGE\n\n into \nREMOTE_STORAGE_DOWNLOAD\n and \nREMOTE_STORAGE_UPLOAD\n\n\n[\n#1250\n] Modified \n/rest/jobs/{id}\n to include the job's media\n\n\n[\n#1312\n] Created \nNETWORK_ERROR\n error code for when a component\n can't connect to an external server. Updated Python HTTP retry code to return \nNETWORK_ERROR\n. This affects the Azure\n components.\n\n\n\n\nKnown Issues\n\n\n\n\n\n[\n#1008\n] Use global TessAPI instances with parallel processing\n\n\n\n\nOpenMPF 6.1.x\n\n\n6.1.6: May 2021\n\n\n\nHandle Variable Frame Rate Videos\n\n\n\n\n\nThe Workflow Manager will attempt to detect if a video is constant frame rate (CFR) or variable frame rate (VFR)\n during media inspection. If no determination can be made, it will default to VFR behavior. If CFR, the JSON output\n object will have a \nHAS_CONSTANT_FRAME_RATE=true\n property in the \nmediaMetadata\n field.\n\n\nWhen \nMPFVideoCapture\n handles a CFR video it will use OpenCV to set the frame position, unless the position is within\n 16 frames of the current position, in which case it will iteratively use OpenCV \ngrab()\n to advance to the desired\n frame.\n\n\nWhen \nMPFVideoCapture\n handles a VFR video it will always iteratively use OpenCV \ngrab()\n to advance to the desired\n frame because setting the frame position directly has been shown to not work correctly on VFR videos.\n\n\nWhen a video is split into multiple segments, \nMPFVideoCapture\n must iteratively use \ngrab()\n to advance from frame 0\n to the start of the segment. This introduces performance overhead. To mitigate this we recommend using larger video\n segments than those used for CFR videos.\n\n\nIn addition to the existing \nTARGET_SEGMENT_LENGTH\n and \nMIN_SEGMENT_LENGTH\n job\n properties (\ndetection.segment.target.length\n and \ndetection.segment.minimum.length\n system properties) for CFR\n videos, the Workflow Manager now supports the \nVFR_TARGET_SEGMENT_LENGTH\n and \nVFR_MIN_SEGMENT_LENGTH\n job\n properties (\ndetection.vfr.segment.target.length\n and \ndetection.vfr.segment.minimum.length\n system properties) for\n VFR videos.\n\n\nNote that the timestamps associated with tracks and detections in a VFR video may be wrong. Please refer to the Known\n Issues section.\n\n\nResolved issues: \n#1307\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#1287\n] Updated Tika Text Detection Component to break up large\n chunks of text. The component now generates tracks with both a \nPAGE_NUM\n property and \nSECTION_NUM\n property. Please\n refer to\n the \nREADME\n.\n\n\n\n\nKnown Issues\n\n\n\n\n\n[\n#1313\n] Incorrect JSON output object timestamps for variable frame\n rate videos\n\n\n[\n#1317\n] Tika Text Detection component generates first PDF track\n at \nPAGE_NUM\n 2\n\n\n\n\n6.1.5: April 2021\n\n\n\nUpdates\n\n\n\n\n\n[\n#1300\n] Parallelized S3 artifact upload. Use\n the \ndetection.artifact.extraction.parallel.upload.count\n system property to configure the number of parallel uploads.\n\n\n\n\n6.1.4: April 2021\n\n\n\nUpdates\n\n\n\n\n\n[\n#1299\n] Improved artifact extraction performance when there is no\n rotation or flip\n\n\n\n\n6.1.3: April 2021\n\n\n\nUpdates\n\n\n\n\n\n[\n#1295\n] Improved artifact extraction and markup JNI memory\n utilization\n\n\n[\n#1297\n] Limited Workflow Manager IO threads to a reasonable number\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1296\n] Fixed ActiveMQ job priorities\n\n\n\n\n6.1.2: April 2021\n\n\n\nUpdates\n\n\n\n\n\n[\n#1294\n] Limited ffmpeg threads to a reasonable number\n\n\n\n\n6.1.1: April 2021\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1292\n] Don't skip artifact extraction for failed media\n\n\n\n\n6.1.0: April 2021\n\n\n\nOpenMPF Command Line Runner\n\n\n\n\n\nThe Command Line Runner allows users to run jobs with a single component without the Workflow Manager.\n\n\nIt outputs results in a JSON structure that is a subset of the regular OpenMPF output.\n\n\nIt only supports C++ and Python components.\n\n\nSee the\n \nREADME\n\n for more information.\n\n\n\n\nC++ Batch Component API\n\n\n\n\n\nComponent code should no longer configure Log4CXX. The component executor now handles configuring Log4CXX. Component\n code should call \nlog4cxx::Logger::getLogger(\"\")\n\n to get access to the logger. Calls to \nlog4cxx::xml::DOMConfigurator::configure(logconfig_file);\n\n should be removed.\n\n\n\n\nPython Batch Component API \n\n\n\n\n\nComponent code should no longer configure logging. The component executor now handles configuring logging. Calls\n to \nmpf.configure_logging\n should be replaced with\n \nlogging.getLogger('')\n.\n\n\n\n\nDocker Component Base Images\n\n\n\n\n\n\n\nIn order to support running a component through the CLI runner, C++ component developers should set\n the \nLD_LIBRARY_PATH\n environment variable in the final stage of their Dockerfiles. It should generally be set\n like: \nENV LD_LIBRARY_PATH $PLUGINS_DIR//lib\n.\n\n\n\n\n\n\nBecause of the logging changes mentioned above, components no longer need to set the\n \nCOMPONENT_LOG_NAME\n environment variable in their Dockerfiles.\n\n\n\n\n\n\nAdded the\n \nopenmpf_python_executor_ssb\n base image\n\n . It can be used instead of \nopenmpf_python_component_build\n and \nopenmpf_python_executor\n to simplify Dockerfiles for\n Python components that are pure Python and have no build time dependencies.\n\n\n\n\n\n\nLabel Moving vs. Non-Moving Tracks\n\n\n\n\n\nThe Workflow Manager can now identify whether a track is moving or non-moving. This is determined by calculating the\n average bounding box for a track by averaging the size and position of all the detections in the track. Then, for each\n detection in the track, the intersection over union (IoU) is calculated between that detection and the average\n detection. If the IoU for at least \nMOVING_TRACK_MIN_DETECTIONS\n number of detections is less than or equal to\n \nMOVING_TRACK_MAX_IOU\n, then the track is considered a moving track.\n\n\nAdded the following Workflow Manager job properties. These can be set for any video job:\n\n\nMOVING_TRACK_LABELS_ENABLED\n: When set to true, attempt to label tracks as either moving or non-moving objects.\n Each track will have a \nMOVING\n property set to \nTRUE\n or \nFALSE\n.\n\n\nMOVING_TRACKS_ONLY\n: When set to true, remove any tracks that were marked as not moving.\n\n\nMOVING_TRACK_MAX_IOU\n: The maximum IoU overlap between detection bounding boxes and the average per-track\n bounding box for objects to be considered moving. Value is expected to be between 0 and 1. Note that the lower\n IoU, the more likely the object is moving.\n\n\nMOVING_TRACK_MIN_DETECTIONS\n: The minimum number of moving detections for a track to be labeled as moving.\n\n\n\n\n\n\n\n\nMarkup Improvements\n\n\n\n\n\nUsers can now watch videos directly in the OpenMPF web UI within the media pop-up dialog for each job. Most modern web\n browsers support videos encoded in VP9 and H.264. If a video cannot be played, users have the option to download it\n and play it using a stand-alone media player.\n\n\nTo set the markup encoder use \nMARKUP_VIDEO_ENCODER\n. The default encoder has changed from \nmjpeg\n to \nvp9\n. As a\n result, it will take longer to generate marked up videos, but they will be higher quality and can be viewed in the web\n UI.\n\n\nEach bounding box in the marked up media is now labeled. By default, the label shows the track-level \nCLASSIFICATION\n\n and associated confidence value. The information shown in the label can be changed by\n setting \nMARKUP_LABELS_TEXT_PROP_TO_SHOW\n and \nMARKUP_LABELS_NUMERIC_PROP_TO_SHOW\n. To show information for each\n individual detection, rather than the entire track, set \nMARKUP_LABELS_FROM_DETECTIONS=TRUE\n.\n\n\nExemplar detections in video tracks include a star icon in their label.\n\n\nOptionally, set \nMARKUP_VIDEO_MOVING_OBJECT_ICONS_ENABLED=TRUE\n to show icons that represent if the track is moving or\n non-moving.\n\n\nOptionally, set \nMARKUP_VIDEO_BOX_SOURCE_ICONS_ENABLED=TRUE\n to show icons that represent the source of the detection.\n For example, if the box is the result of an algorithm detection, tracking performing gap fill, or Workflow Manager\n animation.\n\n\nEach frame of a marked-up video now has a frame number in the upper right corner.\n\n\nPlease refer to the \nMarkup Guide\n for the complete set of markup properties, icon definitions, and\n encoder considerations.\n\n\n\n\nUpdates\n\n\n\n\n\n[\n#1181\n] Updated the Tesseract OCR Text Detection component from\n Tesseract version 4.0.0 to 4.1.1\n\n\n[\n#1232\n] Updated the Azure Speech Detection component from Azure\n Batch Transcription version 2.0 to 3.0\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1187\n] EXIF orientation is now preserved during markup and artifact\n extraction\n\n\n[\n#1257\n] Updated \nOUTPUT_LAST_TASK_ONLY\n to work on all media types\n\n\n\n\nOpenMPF 6.0.x\n\n\n6.0.11: March 2021\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1284\n] Updated the Azure Translation component to count emoji as 2\n characters\n\n\n\n\n6.0.10: March 2021\n\n\n\nUpdates\n\n\n\n\n\n[\n#1270\n] The Azure Cognitive Services components now retry HTTP\n requests\n\n\n\n\n6.0.9: March 2021\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1273\n] Setting \nTRANSLATION\n to the empty string no longer prevents\n Keyword Tagging\n\n\n\n\n6.0.6: March 2021\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1265\n] Updated the Tika Text Detection component to handle\n spreadsheets\n\n\n[\n#1268\n] Updated the Tika Text Detection component to remove metadata\n\n\n\n\n6.0.5: February 2021\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1266\n] The Azure Translation component now handles the final\n segment correctly when guessing sentence breaks\n\n\n\n\n6.0.4: February 2021\n\n\n\nUpdates\n\n\n\n\n\n[\n#1264\n] Updated the Azure Translation component to handle large\n amounts of text\n\n\n[\n#1269\n] AzureTranslation no longer tries to translate text that is\n already in the \nTO_LANGUAGE\n\n\n\n\n6.0.3: February 2021\n\n\n\nOpenCV YOLO Detection Component\n\n\n\n\n\nThis new component utilizes the OpenCV Deep Neural Networks (DNN) framework to detect and classify objects in images\n and videos using Darknet YOLOv4 models trained on the COCO dataset. It supports both CPU and GPU modes of operation.\n Tracking is performed using a combination of intersection over union, pixel difference after Fast Fourier transform (\n FFT) phase correlation, Kalman filtering, and OpenCV MOSSE tracking. Refer to\n the \nREADME\n for details.\n\n\n\n\n6.0.2: January 2021\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1249\n] FFmpeg no longer reports different frame counts for the same\n piece of media\n\n\n\n\n6.0.1: December 2020\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1238\n] The JSON output object is now generated when remote media\n cannot be downloaded.\n\n\n\n\n6.0.0: December 2020\n\n\n\nUpgrade to OpenCV 4.5.0\n\n\n\n\n\nUpdated core framework and components from OpenCV 3.4.7 to OpenCV 4.5.0.\n\n\nOpenCV is now built with CUDA support, including cuDNN (CUDA Deep Neural Network library) and cuBLAS (CUDA Basic\n Linear Algebra Subroutines library). All C++ components that use the base C++ builder and executor Docker images have\n CUDA support built in, giving developers the option to make use of it.\n\n\nAdded GPU support to the OcvDnnDetection component.\n\n\n\n\nAzure Cognitive Services (ACS) Translation Component\n\n\n\n\n\nThis new component utilizes\n the \nAzure Cognitive Services Translator REST endpoint\n\n to translate text from one language (locale) to another. Generally, it's intended to operate on feed-forward tracks\n that contain detections with \nTEXT\n and \nTRANSCRIPT\n properties. It can also operate on plain text file inputs. Refer\n to the \nREADME\n for\n details.\n\n\n\n\nInteroperability Package\n\n\n\n\n\nAdded \nalgorithm\n field to the element that describes a collection of tracks generated by an action in the JSON output\n object. For example:\n\n\n\n\n\"output\": {\n \"FACE\": [{\n \"source\": \"+#MOG MOTION DETECTION PREPROCESSOR ACTION#OCV FACE DETECTION ACTION\",\n \"algorithm\": \"FACECV\",\n \"tracks\": [{ ... }],\n ...\n },\n\n\n\nMerge Tasks in JSON Output Object\n\n\n\n\n\nThe output of two tasks in the JSON output object can be merged by setting the \nOUTPUT_MERGE_WITH_PREVIOUS_TASK\n\n property to true. This is a Workflow Manager property and can be set on any task in any pipeline, although it has no\n effect when set on the first task or the Markup task.\n\n\nWhen the output of two tasks are merged, the tracks for the previous task will not be shown in the JSON output object,\n and no artifacts are generated for it. The task will be listed under \nTRACKS MERGED\n, if it's not already listed\n under \nTRACKS SUPPRESSED\n due to the \nmpf.output.objects.last.task.only\n system property setting,\n or \nOUTPUT_LAST_TASK_ONLY\n property. The tracks associated with the second task will inherit the detection type and\n algorithm of the previous task.\n\n\nFor example, the \nTESSERACT OCR TEXT DETECTION WITH KEYWORD TAGGING PIPELINE\n is defined as\n the \nTESSERACT OCR TEXT DETECTION TASK\n followed by the \nKEYWORD TAGGING (WITH FF REGION) TASK\n. The second task\n sets \nOUTPUT_MERGE_WITH_PREVIOUS_TASK\n to true. The resulting JSON output object contains one set of keyword-tagged\n OCR tracks that have the \nTEXT\n detection type and \nTESSERACTOCR\n algorithm (both inherited from\n the \nTESSERACT OCR TEXT DETECTION TASK\n):\n\n\n\n\n\"output\": {\n \"TRACKS MERGED\": [{\n \"source\": \"+#TESSERACT OCR TEXT DETECTION ACTION\",\n \"algorithm\": \"TESSERACTOCR\"\n }],\n \"TEXT\": [{\n \"source\": \"+#TESSERACT OCR TEXT DETECTION ACTION#KEYWORD TAGGING (WITH FF REGION) ACTION\",\n \"algorithm\": \"TESSERACTOCR\",\n \"tracks\": [{\n \"type\": \"TEXT\",\n \"trackProperties\": {\n \"TAGS\": \"ANIMAL\",\n \"TEXT\": \"The quick brown fox\",\n \"TEXT_LANGUAGE\": \"script/Latin\",\n \"TRIGGER_WORDS\": \"fox\",\n \"TRIGGER_WORDS_OFFSET\": \"16-18\"\n ...\n\n\n\n\n\nNote that you can use the \nOUTPUT_MERGE_WITH_PREVIOUS_TASK\n setting on multiple tasks. For example, if you set it as a\n job property it will be applied to all tasks (with the exception of Markup - in which case the task before Markup is\n used), so you will only get the output of the last task in the pipeline. The last task will inherit the detection type\n and algorithm of the first task in the pipeline.\n\n\n\n\nTesseract Custom Dictionaries\n\n\n\n\n\nThe Tesseract component Docker image now contains an \n/opt/mpf/tessdata_model_updater\n binary that you can use to\n update \n*.traineddata\n models with a custom dictionary, as well as extract files from existing models. Refer to\n the \nDICTIONARIES\n\n guide to learn how to use the tool.\n\n\nIn general, legacy \n*.traineddata\n models are more influenced by words in their dictionary than more modern\n LSTM \n*.traineddata\n models. Also, refer to the known issue below.\n\n\n\n\nKnown Issues\n\n\n\n\n\n[\n#1243\n] Unpacking a \n*.traineddata\n model, for example, in order to\n modify its dictionary, and then repacking it may result in dropping some of the words present in the original\n dictionary file. This may be due to some kind of compression or filtering. It's unknown what effect this has on OCR\n results.\n\n\n\n\nOpenMPF 5.1.x\n\n\n5.1.3: December 2020\n\n\n\nSetting Properties as Docker Environment Variables\n\n\n\n\n\nAny property that can be set as a job property can now be set as a Docker environment variable by prefixing it\n with \nMPF_PROP_\n. For example, setting the \nMPF_PROP_TRTIS_SERVER\n environment variable in the \ntrtis-detection\n\n service in your \ndocker-compose.yml\n file will have the same effect as setting the \nTRTIS_SERVER\n job property.\n\n\nProperties set in this way will take precedence over all other property types (job, algorithm, media, etc). It is not\n possible to change the value of properties set via environment variables at runtime and therefore they should only be\n used to specify properties that will not change throughout the entire lifetime of the service.\n\n\n\n\nUpdates\n\n\n\n\n\nThe \nmpf.output.objects.censored.properties\n system property can be used to prevent properties from being shown in\n JSON output objects. The value for these properties will appear as \n\n.\n\n\nThe Azure Speech Detection component now retries without diarization when diarization is not supported by the selected\n locale.\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1230\n] The Azure Speech Detection component now uses a UUID for the\n recording id associated with a piece of media in order to prevent deleting a piece of media while it's in use.\n\n\n\n\n5.1.1: December 2020\n\n\n\nUpdates\n\n\n\n\n\nOnly generate \nFRAME_COUNT\n warning when the frame difference is > 1. This can be configured using\n the \nwarn.frame.count.diff\n system property.\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1209\n] The Keyword Tagging component now generates video tracks in\n the JSON output object.\n\n\n[\n#1212\n] The Keyword Tagging component now preserves the detection\n bounding box and confidence.\n\n\n\n\n5.1.0: November 2020\n\n\n\nMedia Inspection Improvements\n\n\n\n\n\nThe Workflow Manager will now handle video files that don't have a video stream as an \nAUDIO\n type, and handle video\n files that don't have a video or audio stream as an \nUNKNOWN\n type. The JSON output object contains a\n new \nmedia.mediaType\n field that will be set to \nVIDEO\n, \nAUDIO\n, \nIMAGE\n, or \nUNKNOWN\n.\n\n\nThe Workflow Manager now configures Tika\n with \ncustom MIME type support\n\n . Currently, this enables the detection of \nvideo/vnd.dlna.mpeg-tts\n and \nimage/jxr\n MIME types.\n\n\nIf the Workflow Manager cannot use Tika to determine the media MIME type then it will fall back to using the\n Linux \nfile\n command with\n a \ncustom magicfile\n\n .\n\n\nOpenMPF now supports Apple-optimized PNGs and HEIC images. Refer to the Bug Fixes section below.\n\n\n\n\nEAST Text Region Detection Component Improvements\n\n\n\n\n\nThe \nTEMPORARY_PADDING\n property has been separated into \nTEMPORARY_PADDING_X\n and \nTEMPORARY_PADDING_Y\n so that X and\n Y padding can be configured independently.\n\n\nThe \nMERGE_MIN_OVERLAP\n property has been renamed to \nMERGE_OVERLAP_THRESHOLD\n so that setting it to a value of 0 will\n merge all regions that touch, regardless of how small the amount of overlap.\n\n\nRefer to\n the \nREADME\n\n for details.\n\n\n\n\nMPFVideoCapture and MPFImageReader Tool Improvements\n\n\n\n\n\nThese tools now support a \nROTATION_FILL_COLOR\n property for setting the fill color for pixels near the corners and\n edges of frames when performing non-orthogonal rotations. Previously, the color was hardcoded to \nBLACK\n. That is\n still the default setting for most components. Now the color can be set to \nWHITE\n, which is the default setting for\n the Tesseract component.\n\n\nThese tools now support a \nROTATION_THRESHOLD\n property for adjusting the threshold at which the frame transformer\n performs rotation. Previously, the value was hardcoded to 0.1 degrees. That is still the default value. Rotation is\n not performed on any \nROTATION\n value less than that threshold. The motivation is that some algorithms detect small\n rotations (for example, on structured text) when there is no rotation. In such cases rotating the frame results in\n fewer detections.\n\n\nOpenMPF now uses FFmpeg when counting video frames. Refer to the Bug Fixes section below.\n\n\n\n\nAzure Cognitive Services (ACS) Form Detection Component\n\n\n\n\n\nThis new component utilizes\n the \nAzure Cognitive Services Form Detection REST endpoint\n\n to extract formatted text from documents (PDFs) and images. Refer to\n the \nREADME\n for\n details.\n\n\nThis component is capable of performing detections using a specified ACS endpoint URL. For example, different\n endpoints support receipt detection, business card detection, layout analysis, and support for custom models trained\n with or without labeled data.\n\n\nThis component may output the following detection properties depending on the endpoint, model, and media being\n processed: \nTEXT\n, \nTABLE_CSV_OUTPUT\n, \nKEY_VALUE_PAIRS_JSON\n, and \nDOCUMENT_JSON_FIELDS\n.\n\n\n\n\nKeyword Tagging Component\n\n\n\n\n\nThis new component performs the same keyword tagging behavior that was previously part of the Tesseract component, but\n does so on feed-forward tracks that generate detections with \nTEXT\n and \nTRANSCRIPT\n properties. Refer to\n the \nREADME\n for details.\n\n\nIn addition to the Tesseract component, keyword tagging behavior has been removed from the Tika Text component and ACS\n OCR component.\n\n\nExample pipelines have been added to the following components which make use of a final Keyword Tagging component\n stage:\n\n\nTesseract\n\n\nTika Text\n\n\nACS OCR\n\n\nSphinx\n\n\nACS Speech\n\n\n\n\n\n\n\n\nOptionally Skip Media Inspection\n\n\n\n\n\nThe Workflow Manager will skip media inspection if all of the required media metadata is provided in the job request.\n The \nMEDIA_HASH\n and \nMIME_TYPE\n fields are always required. Depending on the media data type, other fields may be\n required or optional:\n\n\nImages\n\n\nRequired: \nFRAME_WIDTH\n, \nFRAME_HEIGHT\n\n\nOptional: \nHORIZONTAL_FLIP\n, \nROTATION\n\n\n\n\n\n\nVideos\n\n\nRequired: \nFRAME_WIDTH\n, \nFRAME_HEIGHT\n, \nFRAME_COUNT\n, \nFPS\n, \nDURATION\n\n\nOptional: \nHORIZONTAL_FLIP\n, \nROTATION\n\n\n\n\n\n\nAudio files\n\n\nRequired: \nDURATION\n\n\n\n\n\n\n\n\n\n\n\n\nUpdates\n\n\n\n\n\nUpdate OpenMPF Python SDK exception handling for Python 3. Now instead of raising an \nEnvironmentError\n, which has\n been deprecated in Python 3, the SDK will raise an \nmpf.DetectionError\n or allow the underlying exception to be\n thrown.\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1028\n] OpenMPF can now properly handle Apple-optimized PNGs, which\n have a non-standard data chunk named CgBI before the IHDR chunk. The Workflow Manager\n uses \npngdefry\n to convert the image into a standard PNG for processing. Before\n this fix, Tika would throw an error when trying to determine the MIME type of the Apple-optimized PNG.\n\n\n[\n#1130\n] OpenMPF can now properly handle HEIC images. The Workflow\n Manager uses \nlibheif\n to convert the image into a standard PNG for processing.\n Before this fix, the HEIC image was sometimes falsely identified as a video and the Workflow Manager would fail to\n count the number of frames.\n\n\n[\n#1171\n] The MIME type in the JSON output object is no longer null\n when there is a frame counting exception.\n\n\n[\n#1192\n] When processing videos, the frame count is now obtained from\n both OpenCV and FFmpeg. The lower of the two is used. If they don't match, a \nFRAME_COUNT\n warning is generated.\n Before this fix, on some videos OpenCV would return frame counts that were magnitudes higher than the frames that\n could actually be read. This resulted in failing to process many video segments with a \nBAD_FRAME_SIZE\n error.\n\n\n\n\nOpenMPF 5.0.x\n\n\n5.0.9: October 2020\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1200\n] The MPFVideoCapture and MPFImageReader tools now properly\n handle cropping to frame regions when the region coordinates fall outside of the frame boundary. There was a bug that\n would result in an OpenCV error. Note that the bug only occurred when cropping was not performed with rotation or\n flipping.\n\n\n\n\n5.0.8: October 2020\n\n\n\nUpdates\n\n\n\n\n\nThe Tesseract component now supports a \nTESSDATA_MODELS_SUBDIRECTORY\n property. The component will look for tessdata\n files in \n/\n. This allows users to easily switch between \ntessdata\n\n , \ntessdata_best\n, and \ntessdata_fast\n subdirectories.\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1199\n] Added missing synchronized to InProgressBatchJobsService,\n which was resulting in some jobs staying \nIN_PROGRESS\n indefinitely.\n\n\n\n\n5.0.7: September 2020\n\n\n\nTensorRT Inference Server (TRTIS) Object Detection Component\n\n\n\n\n\nThis new component detects objects in images and videos by making use of\n an \nNVIDIA TensorRT Inference Server\n (\n TRTIS), and calculates features that can later be used by other systems to recognize the same object in other media.\n We provide support for running the server as a separate service during a Docker deployment, but an external server\n instance can be used instead.\n\n\nBy default, the ip_irv2_coco model is supported and will optionally classify detected objects\n using \nCOCO labels\n\n . Additionally, features can be generated for whole frames, automatically-detected object regions, and user-specified\n regions. Refer to the \nREADME\n\n .\n\n\n\n\n5.0.6: August 2020\n\n\n\nEnable OcvDnnDetection to Annotate Feed-forward Detections\n\n\n\n\n\nThe OcvDnnDetection component can now by configured to operate only on certain feed-forward detections and annotate\n them with supplementary information. For example, the following pipeline can be configured to generate detections that\n have both \nCLASSIFICATION\n and \nCOLOR\n detection properties:\n\n\n\n\nDarknetDetection (person + vehicle) --> OcvDnnDetection (vehicle color)\n\n\n\n\n\nFor example:\n\n\n\n\n \"detectionProperties\": {\n \"CLASSIFICATION\": \"car\",\n \"CLASSIFICATION CONFIDENCE LIST\": \"0.397336\",\n \"CLASSIFICATION LIST\": \"car\",\n \"COLOR\": \"blue\",\n \"COLOR CONFIDENCE LIST\": \"0.93507; 0.055744\",\n \"COLOR LIST\": \"blue; gray\"\n }\n\n\n\n\n\nThe OcvDnnDetection component now supports the following properties:\n\n\nCLASSIFICATION_TYPE\n: Set this value to change the \nCLASSIFICATION*\n part of each output property name to\n something else. For example, setting it to \nCOLOR\n will generate \nCOLOR\n, \nCOLOR LIST\n,\n and \nCOLOR CONFIDENCE LIST\n. When handling feed-foward detections, the pre-existing \nCLASSIFICATION*\n properties\n will be carried over and the \nCOLOR*\n properties will be added to the detection.\n\n\nFEED_FORWARD_WHITELIST_FILE\n: When \nFEED_FORWARD_TYPE\n is provided and not set to \nNONE\n, only feed-forward\n detections with class names contained in the specified file will be processed. For, example, a file with only \"\n car\" in it will result in performing the exclude behavior (below) for all feed-foward detections that do not have\n a \nCLASSIFICATION\n of \"car\".\n\n\nFEED_FORWARD_EXCLUDE_BEHAVIOR\n: Specifies what to do when excluding detections not specified in\n the \nFEED_FORWARD_WHITELIST_FILE\n. Acceptable values are:\n\n\nPASS_THROUGH\n: Return the excluded detections, without modification, along with any annotated detections.\n\n\nDROP\n: Don't return the excluded detections. Only return annotated detections.\n\n\n\n\n\n\n\n\n\n\n\n\nUpdates\n\n\n\n\n\nMake interop package work with Java 8 to better support exernal job producers and consumers.\n\n\n\n\n5.0.5: August 2020\n\n\n\nUpdates\n\n\n\n\n\nConfigure Camel not to auto-acknowledge messages. Users can now see the number of pending messages in the ActiveMQ\n management console for queues consumed by the Workflow Manager.\n\n\nImprove Tesseract OSD fallback behavior. This prevents selecting the OSD rotation from the fallback pass without the\n OSD script from the fallback pass.\n\n\n\n\n5.0.4: August 2020\n\n\n\nUpdates\n\n\n\n\n\nRetry job callbacks when they fail. The Workflow Manager now supports the \nhttp.callback.timeout.ms\n\n and \nhttp.callback.retries\n system properties.\n\n\nDrop \"duplicate paged in from cursor\" DLQ messages.\n\n\n\n\n5.0.3: July 2020\n\n\n\nUpdates\n\n\n\n\n\nUpdate ActiveMQ to 5.16.0.\n\n\n\n\n5.0.2: July 2020\n\n\n\nUpdates\n\n\n\n\n\nDisable video segmentation for ACS Speech Detection to prevent issues when generating speaker ids.\n\n\n\n\n5.0.1: July 2020\n\n\n\nUpdates\n\n\n\n\n\nUpdated Tessseract component with \nMAX_PIXELS\n setting to prevent processing large images.\n\n\n\n\n5.0.0: June 2020\n\n\n\nDocumentation\n\n\n\n\n\nUpdated the openmpf-docker repo \nREADME\n\n and \nSWARM\n guides to describe the new build process,\n which now includes automatically copying the openmpf repo source code into the openmpf-build image instead of using\n various bind mounts, and building all of the component base builder and executor images.\n\n\nUpdated the openmpf-docker repo \nREADME\n with the\n following sections:\n\n\nHow\n to \nUse Kibana for Log Viewing and Aggregation\n\n\nHow\n to \nRestrict Media Types that a Component Can Process\n\n\nHow\n to \nImport Root Certificates for Additional Certificate Authorities\n\n\n\n\n\n\nUpdated the \nCONTRIBUTING\n guide for Docker\n deployment with information on the new build process and component base builder and executor images.\n\n\nUpdated the \nInstall Guide\n with a pointer to the \"Quick Start\" section on DockerHub.\n\n\nUpdated the \nREST API\n with the new endpoints for getting, deleting, and creating actions, tasks, and\n pipelines, as well as a change to the \n[GET] /rest/info\n endpoint.\n\n\nUpdated the \nC++ Batch Component API\n to describe changes to the \nGetDetection()\n calls,\n which now return a collection of detections or tracks instead of an error code, and to describe improvements to\n exception handling.\n\n\nUpdated the \nC++ Batch Component API\n\n , \nPython Batch Component API\n,\n and \nJava Batch Component API\n with \nMIME_TYPE\n, \nFRAME_WIDTH\n, and \nFRAME_HEIGHT\n media\n properties.\n\n\nUpdated the \nPython Batch Component API\n with information on Python3 and the\n simplification of using a \ndict\n for some of the data members.\n\n\n\n\nJSON Output Object\n\n\n\n\n\nRenamed \nstages\n to \ntasks\n for clarity and consistency with the rest of the code.\n\n\nThe \nmedia\n element no longer contains a \nmessage\n field.\n\n\nEach \ndetectionProcessingError\n element now contains a \ncode\n field.\n\n\nErrors and warnings are now grouped by \nmediaId\n and summarized using a \ndetails\n element that contains a \nsource\n\n , \ncode\n, and \nmessage\n field. Refer\n to \nthis comment\n for an example of the JSON\n structure. Note that errors and warnings generated by the Workflow Manager do not have a \nmediaId\n.\n\n\nWhen an error or warning occurs in multiple frames of a video for a single piece of media it will be represented\n in one \ndetails\n element and the \nmessage\n will list the frame ranges.\n\n\n\n\n\n\n\n\nInteroperability Package\n\n\n\n\n\nRenamed \nJsonStage.java\n to \nJsonTask.java\n.\n\n\nRemoved \nJsonJobRequest.java\n.\n\n\nModified \nJsonDetectionProcessingError.java\n by removing the \nstartOffset\n and \nstopOffset\n fields and adding the\n following new fields: \nstartOffsetFrame\n, \nstopOffsetFrame\n, \nstartOffsetTime\n, \nstopOffsetTime\n, and \ncode\n.\n\n\nUpdated \nJsonMediaOutputObject.java\n by removing \nmessage\n field.\n\n\nAdded \nJsonMediaIssue.java\n and \nJsonIssueDetails.java\n.\n\n\n\n\nPersistent Database\n\n\n\n\n\nThe \ninput_object\n column in the \njob_request\n table has been renamed to \njob\n and the content now contains a\n serialized form of \nBatchJob.java\n instead of \nJsonJobRequest.java\n.\n\n\n\n\nC++ Batch Component API\n\n\n\n\n\nThe \nGetDetection()\n calls now return a collection instead of an error code:\n\n\nstd::vector GetDetections(const MPFImageJob &job)\n\n\nstd::vector GetDetections(const MPFVideoJob &job)\n\n\nstd::vector GetDetections(const MPFAudioJob &job)\n\n\nstd::vector GetDetections(const MPFGenericJob &job)\n\n\n\n\n\n\nMPFDetectionException\n can now be constructed with a \nwhat\n parameter representing a descriptive error message:\n\n\nMPFDetectionException(MPFDetectionError error_code, const std::string &what = \"\")\n\n\nMPFDetectionException(const std::string &what)\n\n\n\n\n\n\n\n\nPython Batch Component API\n\n\n\n\n\nSimplified the \ndetection_properties\n and \nframe_locations\n data members to use a Python \ndict\n instead of a custom\n data type.\n\n\n\n\nFull Docker Conversion\n\n\n\n\n\nEach component is now encapsulated in its own Docker image which self-registers with the Workflow Manager at runtime.\n This deconflicts component dependencies, and allows for greater flexibility when deciding which components to deploy\n at runtime.\n\n\nThe Node Manager image has been removed. For Docker deployments, component services should be managed using Docker\n tools external to OpenMPF.\n\n\nIn Docker deployments, streaming job REST endpoints are disabled, the Nodes web page is no longer available, component\n tar.gz packages cannot be registered through the Component Registration web page, and the \nmpf\n command line script\n can now only be run on the Workflow Manager container to modify user settings. The preexisting features are now\n reserved for non-Docker deployments and development environments.\n\n\nThe OpenMPF Docker stack can optionally be deployed with \nKibana\n (which depends on\n Elasticsearch and Filebeat) for viewing log files. Refer to the\n openmpf-docker \nREADME\n\n .\n\n\n\n\nDocker Component Base Images\n\n\n\n\n\nA base builder image and executor image are provided for\n C++ (\nREADME\n),\n Python (\nREADME\n), and\n Java (\nREADME\n) component\n development. Component developers can also refer to the Dockerfile in the source code for each component as reference\n for how to make use of the base images.\n\n\n\n\nRestrict Media Types that a Component Can Process\n\n\n\n\n\nEach component service now supports an optional \nRESTRICT_MEDIA_TYPES\n Docker environment variable that specifies the\n types of media that service will process. For example, \nRESTRICT_MEDIA_TYPES: VIDEO,IMAGE\n will process both videos\n and images, while \nRESTRICT_MEDIA_TYPES: IMAGE\n will only process images. If not specified, the service will process\n all of the media types it natively supports. For example, this feature can be used to ensure that some services are\n always available to process images while others are processing long videos.\n\n\n\n\nImport Additional Root Certificates into the Workflow Manager\n\n\n\n\n\nAdditional root certificates can be imported into the Workflow Manager at runtime by adding an entry\n for \nMPF_CA_CERTS\n to the workflow-manager service's environment variables in \ndocker-compose.core.yml\n\n . \nMPF_CA_CERTS\n must contain a colon-delimited list of absolute file paths. Of note, a root certificate may be used\n to trust the identity of a remote object storage server.\n\n\n\n\nDockerHub\n\n\n\n\n\nPushed prebuilt OpenMPF Docker images to \nDockerHub\n. Refer to the \"Quick Start\"\n section of the OpenMPF Workflow Manager\n image \ndocumentation\n.\n\n\n\n\nVersion Updates\n\n\n\n\n\nUpdated from Oracle Java 8 to OpenJDK 11, which required updating to Tomcat 8.5.41. We now\n use \nCargo\n to run integration tests.\n\n\nUpdated OpenCV from 3.0.0 to 3.4.7 to update Deep Neural Networks (DNN) support.\n\n\nUpdated Python from 2.7 to 3.8.2.\n\n\n\n\nFFmpeg\n\n\n\n\n\nWe are no longer building separate audio and video encoders and decoders for FFmpeg. Instead, we are using the\n built-in decoders that come with FFmpeg by default. This simplifies the build process and redistribution via Docker\n images.\n\n\n\n\nArtifact Extraction\n\n\n\n\n\nThe \nARTIFACT_EXTRACTION_POLICY\n property can now be assigned a value of \nNONE\n, \nVISUAL_TYPES_ONLY\n, \nALL_TYPES\n,\n or \nALL_DETECTIONS\n.\n\n\nWith the \nVISUAL_TYPES_ONLY\n or \nALL_TYPES\n policy, artifacts will be extracted according to\n the \nARTIFACT_EXTRACTION_POLICY*\n properties. With the \nNONE\n and \nALL_DETECTIONS\n policies, those settings are\n ignored.\n\n\nNote that previously \nNONE\n, \nVISUAL_EXEMPLARS_ONLY\n, \nEXEMPLARS_ONLY\n, \nALL_VISUAL_DETECTIONS\n,\n and \nALL_DETECTIONS\n were supported.\n\n\n\n\n\n\nThe following \nARTIFACT_EXTRACTION_POLICY*\n properties are now supported:\n\n\nARTIFACT_EXTRACTION_POLICY_EXEMPLAR_FRAME_PLUS\n: Extract the exemplar frame from the track, plus this many frames\n before and after the exemplar.\n\n\nARTIFACT_EXTRACTION_POLICY_FIRST_FRAME\n: If true, extract the first frame from the track.\n\n\nARTIFACT_EXTRACTION_POLICY_MIDDLE_FRAME\n: If true, extract the frame with a detection that is closest to the\n middle frame from the track.\n\n\nARTIFACT_EXTRACTION_POLICY_LAST_FRAME\n: If true, extract the last frame from the track.\n\n\nARTIFACT_EXTRACTION_POLICY_TOP_CONFIDENCE_COUNT\n: Sort the detections in a track by confidence and then extract\n this many detections, starting with those which have the highest confidence.\n\n\nARTIFACT_EXTRACTION_POLICY_CROPPING\n: If true, an artifact will be extracted for each detection in each frame\n that is selected according to the other \nARTIFACT_EXTRACTION_POLICY*\n properties. The extracted artifact will be\n cropped to the width and height of the detection bounding box, and the artifact will be rotated according to the\n detection \nROTATION\n property. If false, the artifact extraction behavior is unchanged from the previous release:\n the entire frame will be extracted without any rotation.\n\n\n\n\n\n\nFor clarity, \nOUTPUT_EXEMPLARS_ONLY\n has been renamed to \nOUTPUT_ARTIFACTS_AND_EXEMPLARS_ONLY\n. Extracted artifacts\n will always be reported in the JSON output object.\n\n\nThe \nmpf.output.objects.exemplars.only\n system property has been renamed\n to \nmpf.output.objects.artifacts.and.exemplars.only\n. It works the same as before with the exception that if an\n artifact is extracted for a detection then that detection will always be represented in the JSON output object,\n whether it's an exemplar or not.\n\n\nThe \nmpf.output.objects.last.stage.only\n system property has been renamed to \nmpf.output.objects.last.task.only\n. It\n works the same as before with the exception that when set to true artifact extraction is skipped for all tasks but the\n last task.\n\n\n\n\nREST Endpoints\n\n\n\n\n\nModified \n[GET] /rest/info\n. Now returns output like \n{\"version\": \"4.1.0\", \"dockerEnabled\": true}\n.\n\n\nAdded the following REST endpoints for getting, removing, and creating actions, tasks, and pipelines. Refer to\n the \nREST API\n for more information:\n\n\n[GET] /rest/actions\n, \n[GET] /rest/tasks\n, \n[GET] /rest/pipelines\n\n\n[DELETE] /rest/actions\n, \n[DELETE] /rest/tasks\n, \n[DELETE] /rest/pipelines\n\n\n[POST] /rest/actions\n , \n[POST] /rest/tasks\n, \n[POST] /rest/pipelines\n\n\n\n\n\n\nAll of the endpoints above are new with the exception of \n[GET] /rest/pipelines\n. The endpoint has changed since the\n last version of OpenMPF. Some fields in the response JSON have been removed and renamed. Also, it now returns a\n collection of tasks for each pipelines. Refer to the REST API.\n\n\n[GET] /rest/algorithms\n can be used to get information about algorithms. Note that algorithms are tied to registered\n components, so to remove an algorithm you must unregister the associated component. To add an algorithm, start the\n associated component's Docker container so it self-registers with the Workflow Manager.\n\n\n\n\nIncomplete Actions, Tasks, and Pipelines\n\n\n\n\n\nThe previous version of OpenMPF would generate an error when attempting to register a component that included actions,\n tasks, or pipelines that depend on algorithms, actions, or tasks that are not yet registered with the Workflow\n Manager. This required components to be registered in a specific order. Also, when unregistering a component, it\n required the components which depend on it to be unregistered. These dependency checks are no longer enforced.\n\n\nIn general, the Workflow Manager now appropriately handles incomplete actions, tasks, and pipelines by checking if all\n of the elements are defined before executing a job, and then preserving that information in memory until the job is\n complete. This allows components to be registered and removed in an arbitrary order without affecting the state of\n other components, actions, tasks, or pipelines. This also allows actions and tasks to be removed using the new REST\n endpoints and then re-added at a later time while still preserving the elements that depend on them.\n\n\nNote that unregistering a component while a job is running will cause it to stall. Please ensure that no jobs are\n using a component before unregistering it.\n\n\n\n\nPython Arbitrary Rotation\n\n\n\n\n\nThe Python MPFVideoCapture and MPFImageReader tools now support \nROTATION\n values other than 0, 90, 180, and 270\n degrees. Users can now specify a clockwise \nROTATION\n job property in the range [0, 360). Values outside that range\n will be normalized to that range. Floating point values are accepted. This is similar to the existing support\n for \nC++ arbitrary rotation\n.\n\n\n\n\nOpenCV Deep Neural Networks (DNN) Detection Component\n\n\n\n\n\nThis new component replaces the old CaffeDetection component. It supports the same GoogLeNet and Yahoo Not Suitable\n For Work (NSFW) models as the old component, but removes support for the Rezafuad vehicle color detection model in\n favor of a custom TensorFlow vehicle color detection model. In our tests, the new model has proven to be more\n generalizable and provide more accurate results on never-before-seen test data. Refer to\n the \nREADME\n.\n\n\n\n\nAzure Cognitive Services (ACS) Speech Detection Component\n\n\n\n\n\nThis new component utilizes\n the \nAzure Cognitive Services Batch Transcription REST endpoint\n\n to transcribe speech from audio and video files. Refer to\n the \nREADME\n.\n\n\n\n\nTesseract OCR Text Detection Component\n\n\n\n\n\nText tagging has been simplified to only support regular expression searches. Whole keyword searches are a subset of\n regular expression searches, and are therefore still supported. Also, the \ntext-tags.json\n file format has been\n updated to allow for specifying case-sensitive regular expression searches.\n\n\nAdditionally, the \nTRIGGER_WORDS\n and \nTRIGGER_WORDS_OFFSET\n detection properties are now supported, which list the\n OCR'd words that resulted in adding a \nTAG\n to the detection, and the character offset of those words within the\n OCR'd \nTEXT\n, respectively.\n\n\nKey changes to tagging output and \ntext-tags.json\n format are outlined below. Refer to\n the \nREADME\n\n for more information:\n\n\nRegex patterns should now be entered in the format \n{\"pattern\": \"regex_pattern\"}\n. Users can add and toggle\n the \n\"caseSensitive\"\n regex flag for each pattern.\n\n\nFor example: \n{\"pattern\": \"(\\\\b)bus(\\\\b)\", \"caseSensitive\": true}\n enables case-sensitive regex pattern\n matching.\n\n\nBy default, each regex pattern, including those in the legacy format, will be case-insensitive.\n\n\n\n\n\n\nAs part of the text tagging update, the \nTAGS\n outputs are now separated by semicolons \n;\n rather than commas \n,\n\n to be consistent with the delimiters for \nTRIGGER_WORDS\n and \nTRIGGER_WORDS_OFFSET\n output patterns.\n\n\nBecause semicolons can be part of the trigger word itself, those semicolons will be encapsulated in brackets.\n\n\nFor example, \ndetected trigger with a ;\n in the OCR'd \nTEXT\n is reported\n as \nTRIGGER_WORDS=detected trigger with a [;]; some other trigger\n.\n\n\n\n\n\n\nCommas are now used to group each set of \nTRIGGER_WORDS_OFFSET\n with its respective \nTRIGGER_WORDS\n output.\n Both \nTAGS\n and \nTRIGGER_WORDS\n are separated by semicolons only.\n\n\nFor example: \nTRIGGER_WORDS=trigger1; trigger2\n, \nTRIGGER_WORDS_OFFSET=0-5, 6-10; 12-15\n, means\n that \ntrigger1\n occurs twice in the text at the index ranges 0-5 and 6-10, and \ntrigger2\n occurs at index\n range 12-15.\n\n\n\n\n\n\n\n\n\n\nRegex tagging now follows the C++ ECMAS format (\n see \nexamples here\n) after resolving JSON string conversion\n for regex tags.\n\n\nAs a result the regex patterns \n\\b\n and \n\\p\n in the text tagging file must now be written as \n\\\\b\n and \n\\\\p\n,\n respectively, to match the format of other regex character patterns (ex. \n\\\\d\n, \n\\\\w\n, \n\\\\s\n, etc.).\n\n\n\n\n\n\nThe \nMAX_PARALLEL_SCRIPT_THREADS\n and \nMAX_PARALLEL_PAGE_THREADS\n properties are now supported. When processing\n images, the first property is used to determine how many threads to run in parallel. Each thread performs OCR using a\n different language or script model. When processing PDFs, the second property is used to determine how many threads to\n run in parallel. Each thread performs OCR on a different page of the PDF.\n\n\nThe \nENABLE_OSD_FALLBACK\n property is now supported. If enabled, an additional round of OSD is performed when the\n first round fails to generate script predictions that are above the OSD score and confidence thresholds. In the second\n pass, the component will run OSD on multiple copies of the input text image to get an improved prediction score\n and \nOSD_FALLBACK_OCCURRED\n detection property will be set to true.\n\n\nIf any OSD-detected models are missing, the new \nMISSING_LANGUAGE_MODELS\n detection property will list the missing\n models.\n\n\n\n\nTika Text Detection Component\n\n\n\n\n\nThe Tika text detection component now supports text tagging in the same way as the Tesseract component. Refer to\n the \nREADME\n.\n\n\n\n\nOther Improvements\n\n\n\n\n\nSimplified component \ndescriptor.json\n files by moving the specification of common properties, such\n as \nCONFIDENCE_THRESHOLD\n, \nFRAME_INTERVAL\n, \nMIN_SEGMENT_LENGTH\n, etc., to a single \nworkflow-properties.json\n file.\n Now when the Workflow Manager is updated to support new features, the component \ndescriptor.json\n file will not need\n to be updated.\n\n\nUpdated the Sphinx component to return \nTRANSCRIPT\n instead of \nTRANSCRIPTION\n, which is grammatically correct.\n\n\nWhitespace is now trimmed from property names when jobs are submitted via the REST API.\n\n\nThe Darknet Docker image now includes the YOLOv3 model weights.\n\n\nThe C++ and Python ModelsIniParser now allows users to specify optional fields.\n\n\nWhen a job completion callback fails, but otherwise the job is successful, the final state of the job will\n be \nCOMPLETE_WITH_WARNINGS\n.\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#772\n] Can now create a custom pipeline with long action names using\n the Pipelines 2 UI.\n\n\n[\n#812\n] Now properly setting the start and stop index for elements in\n the \ndetectionProcessingErrors\n collection in the JSON output object. Errors reported for each job segment will now\n appear in the collection.\n\n\n[\n#941\n] Tesseract component no longer segfaults when handling corrupt\n media.\n\n\n[\n#1005\n] Fixed a bug that caused a NullPointerException when\n attempting to get output object JSON via REST before a job completes.\n\n\n[\n#1035\n] The search bar in the Job Status UI can once again for used\n to search for job id.\n\n\n[\n#1104\n] Fixed C++/Python component executor memory leaks.\n\n\n[\n#1108\n] Fixed a bug when handling frames and detections that are\n horizontally flipped. This affected both markup and feed-forward behaviors.\n\n\n[\n#1119\n] Fixed Tesseract component memory leaks and uninitialized\n read issues.\n\n\n\n\nKnown Issues\n\n\n\n\n\n[\n#1028\n] Media inspection fails to handle Apple-optimized PNGs with\n the CgBI data chunk before the IHDR chunk.\n\n\n[\n#1109\n] We made the search bar in the Job Status UI more efficient\n by shifting it to a database query, but in doing so introduced a bug where the search operates on UTC time instead of\n local system time.\n\n\n[\n#1010\n] \nmpf.output.objects.enabled\n does not behave as expected for\n batch jobs. A user would expect it to control whether the JSON output object is generated, but it's generated\n regardless of that setting.\n\n\n[\n#1032\n] Jobs fail on corrupt QuickTime videos. For these videos, the\n OpenCV-reported frame count is more than twice the actual frame count.\n\n\n[\n#1106\n] When a job ends in ERROR the job status UI does not show an\n End Date.\n\n\n\n\nOpenMPF 4.1.x\n\n\n4.1.14: June 2020\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1120\n] The node-manager Docker image now correctly installs CUDA\n libraries so that GPU-enabled components on that image can run on the GPU.\n\n\n[\n#1064\n] Fixed memory leaks in the Darknet component for various\n network types, and when using GPU resources. This bug covers everything not addressed\n by \n#1062\n.\n\n\n\n\n4.1.13: June 2020\n\n\n\nUpdates\n\n\n\n\n\nUpdated the OpenCV build and media inspection process to properly handle webp images.\n\n\n\n\n4.1.12: May 2020\n\n\n\nUpdates\n\n\n\n\n\nUpdated JDK from \njdk-8u181-linux-x64.rpm\n to \njdk-8u251-linux-x64.rpm\n.\n\n\n\n\n4.1.11: May 2020\n\n\n\nTesseract OCR Text Detection Component\n\n\n\n\n\nAdded \nINVALID_MIN_IMAGE_SIZE\n job property to filter out images with extremely low width or height.\n\n\nUpdated image rescaling behavior to account for image dimension limits.\n\n\nFixed handling of \nnullptr\n returns from Tesseract API OCR calls.\n\n\n\n\n4.1.8: May 2020\n\n\n\nAzure Cognitive Services (ACS) OCR Component\n\n\n\n\n\nThis new component utilizes\n the \nACS OCR REST endpoint\n\n to extract text from images and videos. Refer to\n the \nREADME\n.\n\n\n\n\n4.1.6: April 2020\n\n\n\nUpdates\n\n\n\n\n\nNow silently discarding ActiveMQ DLQ \"Suppressing duplicate delivery on connection\" messages in addition to \"duplicate\n from store\" messages.\n\n\n\n\n4.1.5: March 2020\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1062\n] Fixed a memory leak in the Darknet component that occurred\n when running jobs on CPU resources with the Tiny YOLO model.\n\n\n\n\nKnown Issues\n\n\n\n\n\n[\n#1064\n] The Darknet component has memory leaks for various network\n types, and potentially when using GPU resources. This bug covers everything not addressed\n by \n#1062\n.\n\n\n\n\n4.1.4: March 2020\n\n\n\nUpdates\n\n\n\n\n\nUpdated from Hibernate 5.0.8 to 5.4.12 to support schema-based multitenancy. This allows multiple instances of OpenMPF\n to use the same PostgreSQL database as long as each instance connects to the database as a separate user, and the\n database is configured appropriately. This also required updating Tomcat from 7.0.72 to 7.0.76.\n\n\n\n\nJSON Output Object\n\n\n\n\n\nUpdated the Workflow Manager to include an \noutputobjecturi\n in GET callbacks, and \noutputObjectUri\n in POST\n callbacks, when jobs complete. This URI specifies a file path, or path on the object storage server, depending on\n where the JSON output object is located.\n\n\n\n\nInteroperability Package\n\n\n\n\n\nUpdated \nJsonCallbackBody.java\n to contain an \noutputObjectUri\n field.\n\n\n\n\n4.1.3: February 2020\n\n\n\nFeatures\n\n\n\n\n\nAdded support for \nDETECTION_PADDING_X\n and \nDETECTION_PADDING_Y\n optional job properties. The value can be a\n percentage or whole-number pixel value. When positive, each detection region in each track will be expanded. When\n negative, the region will shrink. If the detection region is shrunk to nothing, the shrunk dimension(s) will be set to\n a value of 1 pixel and the \nSHRUNK_TO_NOTHING\n detection property will be set to true.\n\n\nAdded support for \nDISTANCE_CONFIDENCE_WEIGHT_FACTOR\n and \nSIZE_CONFIDENCE_WEIGHT_FACTOR\n SuBSENSE algorithm\n properties. Increasing the value of the first property will generate detection confidence values that favor being\n closer to the center frame of a track. Increasing the value of the second property will generate detection confidence\n values that favor large detection regions.\n\n\n\n\n4.1.1: January 2020\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#1016\n] Fixed a bug that caused a deadlock situation when the media\n inspection process failed quickly when processing many jobs using a pipeline with more than one stage.\n\n\n\n\n4.1.0: July 2019\n\n\n\nDocumentation\n\n\n\n\n\nUpdated the \nC++ Batch Component API\n to describe the \nROTATION\n\n detection property. See the \nC++ Arbitrary Rotation\n section below.\n\n\nUpdated the \nREST API\n with new component registration REST endpoints. See\n the \nComponent Registration REST Endpoints\n section below.\n\n\nAdded a \nREADME\n for\n the EAST text region detection component. See\n the \nEAST Text Region Detection Component\n section below.\n\n\nUpdated the Tesseract OCR text detection\n component \nREADME\n\n . See the \nTesseract OCR Text Detection Component\n section below.\n\n\nUpdated the openmpf-docker repo \nREADME\n\n and \nSWARM\n guide to describe the new streamlined\n approach to using \ndocker-compose config\n. See the \nDocker Deployment\n section below.\n\n\nFixed the description of \nMIN_SEGMENT_LENGTH\n and associated examples in\n the \nUser Guide\n for\n issue \n#891\n.\n\n\nUpdated the \nJava Batch Component API\n with information on how to use Log4j2.\n Related to resolving issue \n#855\n.\n\n\nUpdated the \nInstall Guide\n to point to the\n Docker \nREADME\n.\n\n\nTransformed the Build Guide into a \nDevelopment Environment Guide\n.\n\n\n\n\n\n\nC++ Arbitrary Rotation\n\n\n\n\n\nThe C++ MPFVideoCapture and MPFImageReader tools now support \nROTATION\n values other than 0, 90, 180, and 270 degrees.\n Users can now specify a clockwise \nROTATION\n job property in the range [0, 360). Values outside that range will be\n normalized to that range. Floating point values are accepted.\n\n\nWhen using those tools to read frame data, they will automatically correct for rotation so that the returned frame is\n horizontally oriented toward the normal 3 o'clock position.\n\n\nWhen \nFEED_FORWARD_TYPE=REGION\n, these tools will look for a \nROTATION\n detection property in the feed-forward\n detections and automatically correct for rotation. For example, a detection property of \nROTATION=90\n represents\n that the region is rotated 90 degrees counter clockwise, and therefore must be rotated 90 degrees clockwise to\n correct for it.\n\n\nWhen \nFEED_FORWARD_TYPE=SUPERSET_REGION\n, these tools will properly account for the \nROTATION\n detection property\n associated with each feed-forward detection when calculating the bounding box that encapsulates all of those\n regions.\n\n\nWhen \nFEED_FORWARD_TYPE=FRAME\n, these tools will rotate the frame according to the \nROTATION\n job property. It's\n important to note that for rotations other than 0, 90, 180, and 270 degrees the rotated frame dimensions will be\n larger than the original frame dimensions. This is because the frame needs to be expanded to encapsulate the\n entirety of the original rotated frame region. Black pixels are used to fill the empty space near the edges of the\n original frame.\n\n\n\n\n\n\nThe Markup component now places a colored dot at the upper-left corner of each detection region so that users can\n determine the rotation of the region relative to the entire frame.\n\n\n\n\n\n\nComponent Registration REST Endpoints\n\n\n\n\n\nAdded a \n[POST] /rest/components/registerUnmanaged\n endpoint so that components running as separate Docker containers\n can self-register with the Workflow Manager.\n\n\nSince these components are not managed by the Node Manager, they are considered unmanaged OpenMPF components.\n These components are not displayed in Nodes web UI and are tagged as unmanaged in the Component Registration web\n UI where they can only be removed.\n\n\nNote that components uploaded to the Component Registration web UI as .tar.gz files are considered managed\n components.\n\n\n\n\n\n\nAdded a \n[DELETE] /rest/components/{componentName}\n endpoint that can be used to remove managed and unmanaged\n components.\n\n\n\n\nPython Component Executor Docker Image\n\n\n\n\n\nComponent developers can now use a Python component executor Docker image to write a Python component for OpenMPF that\n can be encapsulated within a Docker container. This isolates the build and execution environment from the rest of\n OpenMPF. For more information, see\n the \nREADME\n.\n\n\nComponents developed with this image are not managed by the Node Manager; rather, they self-register with the Workflow\n Manager and their lifetime is determined by their own Docker container.\n\n\n\n\n\n\nDocker Deployment\n\n\n\n\n\nStreamlined single-host \ndocker-compose up\n deployments and multi-host \ndocker stack deploy\n swarm deployments. Now\n users are instructed to create a single \ndocker-compose.yml\n file for both types of deployments.\n\n\nRemoved the \ndocker-generate-compose-files.sh\n script in favor of allowing users the flexibility of combining\n multiple \ndocker-compose.*.yml\n files together using \ndocker-compose config\n. See\n the \nGenerate docker-compose.yml\n\n section of the README.\n\n\nComponents based on the Python component executor Docker image can now be defined and configured directly\n in \ndocker-compose.yml\n.\n\n\nOpenMPF Docker images now make use of Docker labels.\n\n\n\n\n\n\nEAST Text Region Detection Component\n\n\n\n\n\nThis new component uses the Efficient and Accurate Scene Text (EAST) detection model to detect text regions in images\n and videos. It reports their location, angle of rotation, and text type (\nSTRUCTURED\n or \nUNSTRUCTURED\n), and supports\n a variety of settings to control the behavior of merging text regions into larger regions. It does not perform OCR on\n the text or track detections across video frames. Thus, each video track is at most one detection long. For more\n information, see\n the \nREADME\n.\n\n\nOptionally, this component can be built as a Docker image using the Python component executor Docker image, allowing\n it to exist apart from the Node Manager image.\n\n\n\n\n\n\nTesseract OCR Text Detection Component\n\n\n\n\n\nUpdated to support reading tessdata \n*.traineddata\n files at a specified \nMODELS_DIR_PATH\n. This allows users to\n install new \n*.traineddata\n files post deployment.\n\n\nUpdated to optionally perform Tesseract Orientation and Script Detection (OSD). When enabled, the component will\n attempt to use the orientation results of OSD to automatically rotate the image, as well as perform OCR using the\n scripts detected by OSD.\n\n\nUpdated to optionally rotate a feed-forward text region 180 degrees to account for upside-down text.\n\n\nNow supports the following preprocessing properties for both structured and unstructured text:\n\n\nText sharpening\n\n\nText rescaling\n\n\nOtsu image thresholding\n\n\nAdaptive thresholding\n\n\nHistogram equalization\n\n\nAdaptive histogram equalization (also known as Contrast Limited Adaptive Histogram Equalization (CLAHE))\n\n\n\n\n\n\nWill use the \nTEXT_TYPE\n detection property in feed-forward regions provided by the EAST component to determine which\n preprocessing steps to perform.\n\n\nFor more information on these new features, see\n the \nREADME\n.\n\n\nRemoved gibberish and string filters since they only worked on English text.\n\n\n\n\nActiveMQ Profiles\n\n\n\n\n\nThe ActiveMQ Docker image now supports custom profiles. The container selects an \nactivemq.xml\n and \nenv\n file to use\n at runtime based on the value of the \nACTIVE_MQ_PROFILE\n environment variable. Among others, these files contain\n configuration settings for Java heap space and component queue memory limits.\n\n\nThis release only supports a \ndefault\n profile setting, as defined by \nactivemq-default.xml\n and \nenv.default\n;\n however, developers are free to add other \nactivemq-.xml\n and \nenv.\n files to the ActiveMQ Docker\n image to suit their needs.\n\n\n\n\nDisabled ActiveMQ Prefetch\n\n\n\n\n\nDisabled ActiveMQ prefetching on all component queues. Previously, a prefetch value of one was resulting in situations\n where one component service could be dispatched two sub-jobs, thereby starving other available component services\n which could process one of those sub-jobs in parallel.\n\n\n\n\nSearch Region Percentages\n\n\n\n\n\nIn addition to using exact pixel values, users can now use percentages for the following properties when specifying\n search regions for C++ and Python components:\n\n\nSEARCH_REGION_TOP_LEFT_X_DETECTION\n\n\nSEARCH_REGION_TOP_LEFT_Y_DETECTION\n\n\nSEARCH_REGION_BOTTOM_RIGHT_X_DETECTION\n\n\nSEARCH_REGION_BOTTOM_RIGHT_Y_DETECTION\n\n\n\n\n\n\nFor example, setting \nSEARCH_REGION_TOP_LEFT_X_DETECTION=50%\n will result in components only processing the right half\n of an image or video.\n\n\nOptionally, users can specify exact pixel values of some of these properties and percentages for others.\n\n\n\n\nOther Improvements\n\n\n\n\n\nIncreased the number of ActiveMQ maxConcurrentConsumers for the \nMPF.COMPLETED_DETECTIONS\n queue from 30 to 60.\n\n\nThe Create Job web UI now only displays the content of the \n$MPF_HOME/share/remote-media\n directory instead of all\n of \n$MPF_HOME/share\n, which prevents the Workflow Manager from indexing generated JSON output files, artifacts, and\n markup. Indexing the latter resulted in Java heap space issues for large scale production systems. This is a\n mitigation for issue \n#897\n.\n\n\nThe Job Status web UI now makes proper use of pagination in SQL/Hibernate through the Workflow Manager to avoid\n retrieving the entire jobs table, which was inefficient.\n\n\nThe Workflow Manager will now silently discard all duplicate messages in the ActiveMQ Dead Letter Queue (DLQ),\n regardless of destination. Previously, only messages destined for component sub-job request queues were discarded.\n\n\n\n\nBug Fixes\n\n\n\n\n\n[\n#891\n] Fixed a bug where the Workflow Manager media segmenter\n generated short segments that were minimally \nMIN_SEGMENT_LENGTH+1\n in size instead of \nMIN_SEGMENT_LENGTH\n.\n\n\n[\n#745\n] In environments where thousands of jobs are processed, users\n have observed that, on occasion, pending sub-job messages in ActiveMQ queues are not processed until a new job is\n created. This seems to have been resolved by disabling ActiveMQ prefetch behavior on component queues.\n\n\n[\n#855\n] A logback circular reference suppressed exception no longer\n throws a StackOverflowError. This was resolved by transitioning the Workflow Manager and Java components from the\n Logback framework to Log4j2.\n\n\n\n\nKnown Issues\n\n\n\n\n\n[\n#897\n] OpenMPF will attempt to index files located\n in \n$MPF_HOME/share\n as soon as the webapp is started by Tomcat. This is so that those files can be listed in a\n directory tree in the Create Job web UI. The main problem is that once a file gets indexed it's never removed from the\n cache, even if the file is manually deleted, resulting in a memory leak.\n\n\n\n\nLate Additions: November 2019\n\n\n\n\n\nUser names, roles, and passwords can now be set by using an optional \nuser.properties\n file. This allows\n administrators to override the default OpenMPF users that come preconfigured, which may be a security risk. Refer to\n the \"Configure Users\" section of the\n openmpf-docker \nREADME\n for\n more information.\n\n\n\n\nLate Additions: December 2019\n\n\n\n\n\nTransitioned from using a mySQL persistent database to PostgreSQL to support users that use an external PostgreSQL\n database in the cloud.\n\n\nUpdated the EAST component to support a \nTEMPORARY_PADDING\n and \nFINAL_PADDING\n property. The first property\n determines how much padding is added to detections during the non-maximum suppression or merging step. This padding is\n effectively removed from the final detections. The second property is used to control the final amount of padding on\n the output regions. Refer to\n the \nREADME\n.\n\n\n\n\nOpenMPF 4.0.x\n\n\n4.0.0: February 2019\n\n\n\nDocumentation\n\n\n\n\n\nAdded an \nObject Storage Guide\n with information on how to configure OpenMPF to work\n with a custom NGINX object storage server, and how to run jobs that use an S3 object storage server. Note that the\n system properties for the custom NGINX object storage server have changed since the last release.\n\n\n\n\nUpgrade to Tesseract 4.0\n\n\n\n\n\nBoth the Tesseract OCR Text Detection Component and OpenALPR License Plate Detection Components have been updated to\n use the new version of Tesseract.\n\n\nAdditionally, Leptonica has been upgraded from 1.72 to 1.75.\n\n\n\n\nDocker Deployment\n\n\n\n\n\nThe Docker images now use the yum package manager to install ImageMagick6 from a public RPM repository instead of\n downloading the RPMs directly from imagemagick.org. This resolves an issue with the OpenMPF Docker build where RPMs\n on \nimagemagick.org\n were no longer available.\n\n\n\n\nTesseract OCR Text Detection Component\n\n\n\n\n\nUpdated to allow the user to set a \nTESSERACT_OEM\n property in order to select an OCR engine mode (OEM).\n\n\n\"script/Latin\" can now be specified as the \nTESSERACT_LANGUAGE\n. When selected, Tesseract will select all Latin\n characters, which can be from different Latin languages.\n\n\n\n\nCeph S3 Object Storage\n\n\n\n\n\nAdded support for downloading files from, and uploading files to, an S3 object storage server. The following job\n properties can be provided: \nS3_ACCESS_KEY\n, \nS3_SECRET_KEY\n, \nS3_RESULTS_BUCKET\n, \nS3_UPLOAD_ONLY\n.\n\n\nAt this time, only support for Ceph object storage has been tested. However, the Workflow Manager uses the AWS SDK for\n Java to communicate with the object store, so it is possible that other S3-compatible storage solutions may work as\n well.\n\n\n\n\nISO-8601 Timestamps\n\n\n\n\n\nAll timestamps in the JSON output object, and streaming video callbacks, are now in the ISO-8601 format (e.g. \"\n 2018-12-19T12:12:59.995-05:00\"). This new format includes the time zone, which makes it possible to compare timestamps\n generated between systems in different time zones.\n\n\nThis change does not affect the track and detection start and stop offset times, which are still reported in\n milliseconds since the start of the video.\n\n\n\n\nReduced Redis Usage\n\n\n\n\n\nThe Workflow Manager has been refactored to reduce usage of the Redis in-memory database. In general, Redis is not\n necessary for storing job information and only resulted in introducing potential delays in accessing that data over\n the network stack.\n\n\nNow, only track and detection data is stored in Redis for batch jobs. This reduces the amount of memory the Workflow\n Manager requires of the Java Virtual Machine. Compared to the other job information, track and detection data can\n potentially be relatively much larger. In the future, we plan to store frame data in Redis for streaming jobs as well.\n\n\n\n\nCaffe Vehicle Color Estimation\n\n\n\n\n\nThe Caffe\n Component \nmodels.ini\n\n file has been updated with a \"vehicle_color\" section with links for downloading\n the \nReza Fuad Rachmadi's Vehicle Color Recognition Using Convolutional Neural Network\n\n model files.\n\n\nThe following pipelines have been added. These require the above model files to be placed\n in \n$MPF_HOME/share/models/CaffeDetection\n:\n\n\nCAFFE REZAFUAD VEHICLE COLOR DETECTION PIPELINE\n\n\nCAFFE REZAFUAD VEHICLE COLOR DETECTION (WITH FF REGION FROM TINY YOLO VEHICLE DETECTOR) PIPELINE\n\n\nCAFFE REZAFUAD VEHICLE COLOR DETECTION (WITH FF REGION FROM YOLO VEHICLE DETECTOR) PIPELINE\n\n\n\n\n\n\n\n\nTrack Merging and Minimum Track Length\n\n\n\n\n\nThe following system properties now have \"video\" in their names:\n\n\ndetection.video.track.merging.enabled\n\n\ndetection.video.track.min.gap\n\n\ndetection.video.track.min.length\n\n\ndetection.video.track.overlap.threshold\n\n\n\n\n\n\nThe above properties can be overridden by the following job properties, respectively. These have not been renamed\n since the last release:\n\n\nMERGE_TRACKS\n\n\nMIN_GAP_BETWEEN_TRACKS\n\n\nMIN_TRACK_LENGTH\n\n\nMIN_OVERLAP\n\n\n\n\n\n\nThese system and job properties now only apply to video media. This resolves an issue where users had\n set \ndetection.track.min.length=5\n, which resulted in dropping all image media tracks. By design, each image track can\n only contain a single detection.\n\n\n\n\nBug Fixes\n\n\n\n\n\nFixed a bug where the Docker entrypoint scripts appended properties to the end\n of \n$MPF_HOME/share/config/mpf-custom.properties\n every time the Docker deployment was restarted, resulting in entries\n like \ndetection.segment.target.length=5000,5000,5000\n.\n\n\nUpgrading to Tesseract 4 fixes a bug where, when specifying \nTESSERACT_LANGUAGE\n, if one of the languages is Arabic,\n then Arabic must be specified last. Arabic can now be specified first, for example: \nara+eng\n.\n\n\nFixed a bug where the minimum track length property was being applied to image tracks. Now it's only applied to video\n tracks.\n\n\nFixed a bug where ImageMagick6 installation failed while building Docker images.\n\n\n\n\nOpenMPF 3.0.x\n\n\n3.0.0: December 2018\n\n\n\n\n\nNOTE:\n The \nBuild Guide\n and \nInstall Guide\n are outdated. The old process for manually configuring a Build VM, using it to build an OpenMPF package, and installing that package, is deprecated in favor of Docker containers. Please refer to the openmpf-docker \nREADME\n.\n\n\nNOTE:\n Do not attempt to register or unregister a component through the Nodes UI in a Docker deployment. It may appear to succeed, but the changes will not affect the child Node Manager containers, only the Workflow Manager container. Also, do not attempt to use the \nmpf\n command line tools in a Docker deployment.\n\n\n\n\nDocumentation\n\n\n\n\n\nAdded a \nREADME\n\n , \nSWARM\n guide,\n and \nCONTRIBUTING\n guide for Docker deployment.\n\n\nUpdated the \nUser Guide\n with information on how track\n properties and track confidence are handled when merging tracks.\n\n\nAdded README files for new components. Refer to the component sections below.\n\n\n\n\nDocker Support\n\n\n\n\n\nOpenMPF can now be built and distributed as 5 Docker images: openmpf_workflow_manager, openmpf_node_manager,\n openmpf_active_mq, mysql_database, and redis.\n\n\nThese images can be deployed on a single host using \ndocker-compose up\n.\n\n\nThey can also be deployed across multiple hosts in a Docker swarm cluster using \ndocker stack deploy\n.\n\n\nGPU support is enabled through the NVIDIA Docker runtime.\n\n\nBoth HTTP and HTTPS deployments are supported.\n\n\n\n\n\n\nJSON Output Object\n\n\n\n\n\nAdded a \ntrackProperties\n field at the track level that works in much the same way as the \ndetectionProperties\n field\n at the detection level. Both are maps that contain zero or more key-value pairs. The component APIs have always\n supported the ability to return track-level properties, but they were never represented in the JSON output object,\n until now.\n\n\nSimilarly, added a track \nconfidence\n field. The component APIs always supported setting it, but the value was never\n used in the JSON output object, until now.\n\n\nAdded \njobErrors\n and\njobWarnings\n fields. The \njobErrors\n field will mention that there are items\n in \ndetectionProcessingErrors\n fields.\n\n\nThe \noffset\n, \nstartOffset\n, and \nstopOffset\n fields have been removed in favor of the existing \noffsetFrame\n\n , \nstartOffsetFrame\n, and \nstopOffsetFrame\n fields, respectively. They were redundant and deprecated.\n\n\nAdded a \nmpf.output.objects.exemplars.only\n system property, and \nOUTPUT_EXEMPLARS_ONLY\n job property, that can be set\n to reduce the size of the JSON output object by only recording the track exemplars instead of all of the detections in\n each track.\n\n\nAdded a \nmpf.output.objects.last.stage.only\n system property, and \nOUTPUT_LAST_STAGE_ONLY\n job property, that can be\n set to reduce the size of the JSON output object by only recording the detections for the last non-markup stage of a\n pipeline.\n\n\n\n\nDarknet Component\n\n\n\n\n\nThe Darknet component can now support processing streaming video.\n\n\nIn batch mode, video frames are prefetched, decoded, and stored in a buffer using a separate thread from the one that\n performs the detection. The size of the prefetch buffer can be configured by setting \nFRAME_QUEUE_CAPACITY\n.\n\n\nThe Darknet component can now perform basic tracking and generate video tracks with multiple detections. Both the\n default detection mode and preprocessor detection mode are supported.\n\n\nThe Darknet component has been updated to support the full and tiny YOLOv3 models. The YOLOv2 models are no longer\n supported.\n\n\n\n\nTesseract OCR Text Detection Component\n\n\n\n\n\nThis new component extracts text found in an image and reports it as a single-detection track.\n\n\nPDF documents can also be processed with one track detection per page.\n\n\nUsers may set the language of each track using the \nTESSERACT_LANGUAGE\n property as well as adjust other image\n preprocessing properties for text extraction.\n\n\nRefer to\n the \nREADME\n.\n\n\n\n\nOpenCV Scene Change Detection Component\n\n\n\n\n\nThis new component detects and segments a given video by scenes. Each scene change is detected using histogram\n comparison, edge comparison, brightness (fade outs), and overall hue/saturation/value differences between adjacent\n frames.\n\n\nUsers can toggle each type of of scene change detection technique as well as threshold properties for each detection\n method.\n\n\nRefer to the \nREADME\n.\n\n\n\n\nTika Text Detection Component\n\n\n\n\n\nThis new component extracts text contained in documents and performs language detection. 71 languages and most\n document formats (.txt, .pptx, .docx, .doc, .pdf, etc.) are supported.\n\n\nRefer to the \nREADME\n.\n\n\n\n\nTika Image Detection Component\n\n\n\n\n\nThis new component extracts images embedded in document formats (.pdf, .ppt, .doc) and stores them on disk in a\n specified directory.\n\n\nRefer to the \nREADME\n.\n\n\n\n\nTrack-Level Properties and Confidence\n\n\n\n\n\nRefer to the addition of track-level properties and confidence in the \nJSON Output Object\n\n section.\n\n\nComponents have been updated to return meaningful track-level properties. Caffe and Darknet include \nCLASSIFICATION\n,\n OALPR includes the exemplar \nTEXT\n, and Sphinx includes the \nTRANSCRIPTION\n.\n\n\nThe Workflow Manager will now populate the track-level confidence. It is the same as the exemplar confidence, which is\n the max of all of the track detections.\n\n\n\n\nCustom NGINX HTTP Object Storage\n\n\n\n\n\nAdded \nhttp.object.storage.*\n system properties for configuring an optional custom NGINX object storage server on\n which to store generated detection artifacts, JSON output objects, and markup files.\n\n\nWhen a file cannot be uploaded to the server, the Workflow Manager will fall back to storing it in \n$MPF_HOME/share\n,\n which is the default behavior when an object storage server is not specified.\n\n\nIf and when a failure occurs, the JSON output object will contain a descriptive message in the \njobWarnings\n field,\n and, if appropriate, the \nmarkupResult.message\n field. If the job completes without other issues, the final status\n will be \nCOMPLETE_WITH_WARNINGS\n.\n\n\nThe NGINX storage server runs custom server-side code which we can make available upon request. In the future, we plan\n to support more common storage server solutions, such as Amazon S3.\n\n\n\n\n\n\nActiveMQ\n\n\n\n\n\nThe \nMPF_OUTPUT\n queue is no longer supported and has been removed. Job producers can specify a callback URL when\n creating a job so that they are alerted when the job is complete. Users observed heap space issues with ActiveMQ after\n running thousands of jobs without consuming messages from the \nMPF_OUTPUT\n queue.\n\n\nThe Workflow Manager will now silently discard duplicate sub-job request messages in the ActiveMQ Dead Letter Queue (\n DLQ). This fixes a bug where the Workflow Manager would prematurely terminate jobs corresponding to the duplicate\n messages. It's assumed that ActiveMQ will only place a duplicate message in the DLQ if the original message, or\n another duplicate, can be delivered.\n\n\n\n\nNode Auto-Configuration\n\n\n\n\n\nAdded the \nnode.auto.config.enabled\n, \nnode.auto.unconfig.enabled\n, and \nnode.auto.config.num.services.per.component\n\n system properties for automatically managing the configuration of services when nodes join and leave the OpenMPF\n cluster.\n\n\nDocker will assign a a hostname with a randomly-generated id to containers in a swarm deployment. The above properties\n allow the Workflow Manager to automatically discover and configure services on child Node Manager components, which is\n convenient since the hostname of those containers cannot be known in advance, and new containers with new hostnames\n are created when the swarm is restarted.\n\n\n\n\nJob Status Web UI\n\n\n\n\n\nAdded the \nweb.broadcast.job.status.enabled\n and \nweb.job.polling.interval\n system properties that can be used to\n configure if the Workflow Manager automatically broadcasts updates to the Job Status web UI. By default, the\n broadcasts are enabled.\n\n\nIn a production environment that processes hundreds of jobs or more at the same time, this behavior can result in\n overloading the web UI, causing it to slow down and freeze up. To prevent this, set \nweb.broadcast.job.status.enabled\n\n to \nfalse\n. If \nweb.job.polling.interval\n is set to a non-zero value, the web UI will poll for updates at that\n interval (specified in milliseconds).\n\n\nTo disable broadcasts and polling, set \nweb.broadcast.job.status.enabled\n to \nfalse\n and \nweb.job.polling.interval\n to\n a zero or negative value. Users will then need to manually refresh the Job Status web page using their web browser.\n\n\n\n\nOther Improvements\n\n\n\n\n\nNow using variable-length text fields in the mySQL database for string data that may exceed 255 characters.\n\n\nUpdated the MPFImageReader tool to use OpenCV video capture behind the scenes to support reading data from HTTP URLs.\n\n\nPython components can now include pre-built wheel files in the plugin package.\n\n\nWe now use a \nJenkinsfile\n Groovy script for our\n Jenkins build process. This allows us to use revision control for our continuous integration process and share that\n process with the open source community.\n\n\nAdded \nremote.media.download.retries\n and \nremote.media.download.sleep\n system properties that can be used to\n configure how the Workflow Manager will attempt to retry downloading remote media if it encounters a problem.\n\n\nArtifact extraction now uses MPFVideoCapture, which employs various fallback strategies for extracting frames in cases\n where a video is not well-formed or corrupted. For components that use MPFVideoCapture, this enables better\n consistency between the frames they process and the artifacts that are later extracted.\n\n\n\n\nBug Fixes\n\n\n\n\n\nJobs now properly end in \nERROR\n if an invalid media URL is provided or there is a problem accessing remote media.\n\n\nJobs now end in \nCOMPLETE_WITH_ERRORS\n when a detection splitter error occurs due to missing system properties.\n\n\nComponents can now include their own version of the Google Protobuf library. It will not conflict with the version\n used by the rest of OpenMPF.\n\n\nThe Java component executor now sets the proper job id in the job name instead of using the ActiveMQ message request\n id.\n\n\nThe Java component executor now sets the run directory using \nsetRunDirectory()\n.\n\n\nActions can now be properly added using an \"extras\" component. An extras component only includes a \ndescriptor.json\n\n file and declares Actions, Tasks, and Pipelines using other component algorithms.\n\n\nRefer to the items listed in the \nActiveMQ\n section.\n\n\nRefer to the addition of track-level properties and confidence in the \nJSON Output Object\n\n section.\n\n\n\n\nKnown Issues\n\n\n\n\n\n[\n#745\n] In environments where thousands of jobs are processed, users\n have observed that, on occasion, pending sub-job messages in ActiveMQ queues are not processed until a new job is\n created. The reason is currently unknown.\n\n\n[\n#544\n] Image artifacts retain some permissions from source files\n available on the local host. This can result in some of the image artifacts having executable permissions.\n\n\n[\n#604\n] The Sphinx component cannot be unregistered\n because \n$MPF_HOME/plugins/SphinxSpeechDetection/lib\n is owned by root on a deployment machine.\n\n\n[\n#623\n] The Nodes UI does not work correctly\n when \n[POST] /rest/nodes/config\n is used at the same time. This is because the UI's state is not automatically updated\n to reflect changes made through the REST endpoint.\n\n\n[\n#783\n] The Tesseract OCR Text Detection Component has\n a \nknown issue\n because it uses Tesseract 3. If a combination\n of languages is specified using \nTESSERACT_LANGUAGE\n, and one of the languages is Arabic, then Arabic must be\n specified last. For example, for English and Arabic, \neng+ara\n will work, but \nara+eng\n will not.\n\n\n[\n#784\n] Sometimes services do not start on OpenMPF nodes, and those\n services cannot be started through the Nodes web UI. This is not a Docker-specific problem, but it has been observed\n in a Docker swarm deployment when auto-configuration is enabled. The workaround is to restart the Docker swarm\n deployment, or remove the entire node in the Nodes UI and add it again.\n\n\n\n\nOpenMPF 2.1.x\n\n\n2.1.0: June 2018\n\n\n\n\n\nNOTE:\n If building this release on a machine used to build a previous version of OpenMPF, then please run \nsudo pip install --upgrade pip\n to update to at least pip 10.0.1. If not, the OpenMPF build script will fail to properly download .whl files for Python modules.\n\n\n\n\nDocumentation\n\n\n\n\n\nAdded the \nPython Batch Component API\n.\n\n\nAdded the \nNode Guide\n.\n\n\nAdded the \nGPU Support Guide\n.\n\n\nUpdated the \nInstall Guide\n with an \"(Optional) Install the NVIDIA CUDA Toolkit\" section.\n\n\nRenamed Admin Manual to Admin Guide for consistency.\n\n\n\n\nPython Batch Component API\n\n\n\n\n\nDevelopers can now write batch components in Python using the mpf_component_api module.\n\n\nDependencies can be specified in a setup.py file. OpenMPF will automatically download the .whl files using pip at\n build time.\n\n\nWhen deployed, a virtualenv is created for the Python component so that it runs in a sandbox isolated from the rest of\n the system.\n\n\nOpenMPF ImageReader and VideoCapture tools are provided in the mpf_component_util module.\n\n\nExample Python components are provided for reference.\n\n\n\n\nSpare Nodes\n\n\n\n\n\nSpare nodes can join and leave an OpenMPF cluster while the Workflow Manager is running. You can create a spare node\n by cloning an existing OpenMPF child node. Refer to the \nNode Guide\n.\n\n\nNote that changes made using the Component Registration web page only affect core nodes, not spare nodes. Core nodes\n are those configured during the OpenMPF installation process.\n\n\nAdded \nmpf list-nodes\n command to list the core nodes and available spare nodes.\n\n\nOpenMPF now uses the JGroups FILE_PING protocol for peer discovery instead of TCPPING. This means that the list of\n OpenMPF nodes no longer needs to be fully specified when the Workflow Manager starts. Instead, the Workflow Manager,\n and Node Manager process on each node, use the files in \n$MPF_HOME/share/nodes\n to determine which nodes are currently\n available.\n\n\nUpdated JGroups from 3.6.4. to 4.0.11.\n\n\nThe environment variables specified in \n/etc/profile.d/mpf.sh\n have been simplified. Of note, \nALL_MPF_NODES\n has been\n replaced by \nCORE_MPF_NODES\n.\n\n\n\n\nDefault Detection System Properties\n\n\n\n\n\nThe detection properties that specify the default values when creating new jobs can now be updated at runtime without\n restarting the Workflow Manager. Changing these properties will only have an effect on new jobs, not jobs that are\n currently running.\n\n\nThese default detection system properties are separated from the general system properties in the Properties web page.\n The latter still require the Workflow Manager to be restarted for changes to take effect.\n\n\nThe Apache Commons Configuration library is now used to read and write properties files. When defining a property\n value using an environment variable in the Properties web page, or \n$MPF_HOME/config/mpf-custom.properties\n, be sure\n to prepend the variable name with \nenv:\n. For example:\n\n\n\n\ndetection.models.dir.path=${env:MPF_HOME}/models/\n\n\n\n\n\nAlternatively, you can define system properties using other system properties:\n\n\n\n\ndetection.models.dir.path=${mpf.share.path}/models/\n\n\n\nAdaptive Frame Interval\n\n\n\n\n\nThe \nFRAME_RATE_CAP\n property can be used to set a threshold on the maximum number of frames to process within one\n second of the native video time. This property takes precedence over the user-provided / pipeline-provided value\n for \nFRAME_INTERVAL\n. When the \nFRAME_RATE_CAP\n property is specified, an internal frame interval value is calculated\n as follows:\n\n\n\n\ncalcFrameInterval = max(1, floor(mediaNativeFPS / frameRateCapProp));\n\n\n\n\n\nFRAME_RATE_CAP\n may be disabled by setting it <= 0. \nFRAME_INTERVAL\n can be disabled in the same way.\n\n\nIf \nFRAME_RATE_CAP\n is disabled, then \nFRAME_INTERVAL\n will be used instead.\n\n\nIf both \nFRAME_RATE_CAP\n and \nFRAME_INTERVAL\n are disabled, then a value of 1 will be used for \nFRAME_INTERVAL\n.\n\n\n\n\nDarknet Component\n\n\n\n\n\nThis release includes a component that uses the \nDarknet neural network framework\n to\n perform detection and classification of objects using trained models.\n\n\nPipelines for the Tiny YOLO and YOLOv2 models are provided. Due to its large size, the YOLOv2 weights file must be\n downloaded separately and placed in \n$MPF_HOME/share/models/DarknetDetection\n in order to use the YOLOv2 pipelines.\n Refer to \nDarknetDetection/plugin-files/models/models.ini\n for more information.\n\n\nThis component supports a preprocessor mode and default mode of operation. If preprocessor mode is enabled, and\n multiple Darknet detections in a frame share the same classification, then those are merged into a single detection\n where the region corresponds to the superset region that encapsulates all of the original detections, and the\n confidence value is the probability that at least one of the original detections is a true positive. If disabled,\n multiple Darknet detections in a frame are not merged together.\n\n\nDetections are not tracked across frames. One track is generated per detection.\n\n\nThis component supports an optional \nCLASS_WHITELIST_FILE\n property. When provided, only detections with class names\n listed in the file will be generated.\n\n\nThis component can be compiled with GPU support if the NVIDIA CUDA Toolkit is installed on the build machine. Refer to\n the \nGPU Support Guide\n. If the toolkit is not found, then the component will compile with CPU\n support only.\n\n\nTo run on a GPU, set the \nCUDA_DEVICE_ID\n job property, or set the detection.cuda.device.id system property, >= 0.\n\n\nWhen \nCUDA_DEVICE_ID\n >= 0, you can set the \nFALLBACK_TO_CPU_WHEN_GPU_PROBLEM\n job property, or the\n detection.use.cpu.when.gpu.problem system property, to \nTRUE\n if you want to run the component logic on the CPU\n instead of the GPU when a GPU problem is detected.\n\n\n\n\nModels Directory\n\n\n\n\n\nThe\n$MPF_HOME/share/models\n directory is now used by the Darknet and Caffe components to store model files and\n associated files, such as classification names files, weights files, etc. This allows users to more easily add model\n files post-deployment. Instead of copying the model files to \n$MPF_HOME/plugins//models\n directory on\n each node in the OpenMPF cluster, they only need to copy them to the shared directory once.\n\n\nTo add new models to the Darknet and Caffe component, add an entry to the\n respective \n/plugin-files/models/models.ini\n file.\n\n\n\n\nPackaging and Deployment\n\n\n\n\n\nPython components are packaged with their respective dependencies as .whl files. This can be automated by providing a\n setup.py file. An example OpenCV Python component is provided that demonstrates how the component is packaged and\n deployed with the opencv-python module. When deployed, a virtualenv is created for the component with the .whl files\n installed in it.\n\n\nWhen deploying OpenMPF, \nLD_LIBRARY_PATH\n is no longer set system-wide. Refer to Known Issues.\n\n\n\n\nWeb User Interface\n\n\n\n\n\nUpdated the Nodes page to distinguish between core nodes and spare nodes, and to show when a node is online or\n offline.\n\n\nUpdated the Component Registration page to list the core nodes as a reminder that changes will not affect spare nodes.\n\n\nUpdated the Properties page to separate the default detection properties from the general system properties.\n\n\n\n\nBug Fixes\n\n\n\n\n\nCustom Action, task, and pipeline names can now contain \"(\" and \")\" characters again.\n\n\nDetection location elements for audio tracks and generic tracks in a JSON output object will now have a y value of \n0\n\n instead of \n1\n.\n\n\nStreaming health report and summary report timestamps have been corrected to represent hours in the 0-23 range instead\n of 1-24.\n\n\nSingle-frame .gif files are now segmented properly and no longer result in a NullPointerException.\n\n\nLD_LIBRARY_PATH\n is now set at the process level for Tomcat, the Node Manager, and component services, instead of at\n the system level in \n/etc/profile.d/mpf.sh\n. Also, deployments no longer create \n/etc/ld.so.conf.d/mpf.conf\n. This\n better isolates OpenMPF from the rest of the system and prevents issues, such as being unable to use SSH, when system\n libraries are not compatible with OpenMPF libraries. The latter situation may occur when running \nyum update\n on the\n system, which can make OpenMPF unusable until a new deployment package with compatible libraries is installed.\n\n\nThe Workflow Manager will no longer generate an \"Error retrieving the SingleJobInfo model\" line in the log if someone\n is viewing the Job Status page when a job submitted through the REST API is in progress.\n\n\n\n\nKnown Issues\n\n\n\n\n\nWhen multiple component services of the same type on the same node log to the same file at the same time, sometimes\n log lines will not be captured in the log file. The logging frameworks (log4j and log4cxx) do not support that usage.\n This problem happens more frequently on systems running many component services at the same time.\n\n\nThe following exception was observed:\n\n\n\n\ncom.google.protobuf.InvalidProtocolBufferException: Message missing required fields: data_uri\n\n\n\n\n\n\nFurther debugging is necessary to determine the reason why that message was missing that field. The situation is not easily reproducible. It may occur when ActiveMQ and / or the system is under heavy load and sends duplicate messages in attempt to ensure message delivery. Some of those messages seem to end up in the dead letter queue (DLQ). For now, we've improved the way we handle messages in the DLQ. If OpenMPF can process a message successfully, the job is marked as \nCOMPLETED_WITH_ERRORS\n, and the message is moved from \nActiveMQ.DLQ\n to \nMPF.DLQ_PROCESSED_MESSAGES\n. If OpenMPF cannot process a message successfully, it is moved from \nActiveMQ.DLQ to MPF.DLQ_INVALID_MESSAGES\n.\n\n\n\n\n\n\nThe \nmpf stop\n command will stop the Workflow Manager, which will in turn send commands to all of the available nodes\n to stop all running component services. If a service is processing a sub-job when the quit command is received, that\n service process will not terminate until that sub-job is completely processed. Thus, the service may put a sub-job\n response on the ActiveMQ response queue after the Workflow Manager has terminated. That will not cause a problem\n because the queues are flushed the next time the Workflow Manager starts; however, there will be a problem if the\n service finishes processing the sub-job after the Workflow Manager is restarted. At that time, the Workflow Manager\n will have no knowledge of the old job and will in turn generate warnings in the log about how the job id is \"not known\n to the system\" and/or \"not found as a batch or a streaming job\". These can be safely ignored. Often, if these messages\n appear in the log, then C++ services were running after stopping the Workflow Manager. To address this, you may wish\n to run \nsudo killall amq_detection_component\n after running \nmpf stop\n.\n\n\n\n\nOpenMPF 2.0.x\n\n\n2.0.0: February 2018\n\n\n\n\n\nNOTE:\n Components built for previous releases of OpenMPF are not compatible with OpenMPF 2.0.0 due to Batch Component API changes to support generic detections, and changes made to the format of the \ndescriptor.json\n file to support stream processing.\n\n\nNOTE:\n This release contains basic support for processing video streams. Currently, the only way to make use of that functionality is through the REST API. Streaming jobs and services cannot be created or monitored through the web UI. Only the SuBSENSE component has been updated to support streaming. Only single-stage pipelines are supported at this time.\n\n\n\n\nDocumentation\n\n\n\n\n\nUpdated documents to distinguish the batch component APIs from the streaming component API.\n\n\nAdded the \nC++ Streaming Component API\n.\n\n\nUpdated the \nC++ Batch Component API\n to describe support for generic detections.\n\n\nUpdated the \nREST API\n with endpoints for streaming jobs.\n\n\n\n\nSupport for Generic Detections\n\n\n\n\n\nC++ and Java components can now declare support for the \nUNKNOWN\n data type. The respective batch APIs have been\n updated with a function that will enable a component to process an \nMPFGenericJob\n, which represents a piece of media\n that is not a video, image, or audio file.\n\n\nNote that these API changes make OpenMPF R2.0.0 incompatible with components built for previous releases of OpenMPF.\n Specifically, the new component executor will not be able to load the component logic library.\n\n\n\n\nC++ Batch Component API\n\n\n\n\n\nAdded the following function to support generic detections:\n\n\nMPFDetectionError GetDetections(const MPFGenericJob &job, vector &tracks)\n\n\n\n\n\n\n\n\nJava Batch Component API\n\n\n\n\n\nAdded the following method to support generic detections:\n\n\nList getDetections(MPFGenericJob job)\n\n\n\n\n\n\n\n\nStreaming REST API\n\n\n\n\n\nAdded the following REST endpoints for streaming jobs:\n\n\n[GET] /rest/streaming/jobs\n: Returns a list of streaming job ids.\n\n\n[POST] /rest/streaming/jobs\n: Creates and submits a streaming job. Users can register for health report and\n summary report callbacks.\n\n\n[GET] /rest/streaming/jobs/{id}\n: Gets information about a streaming job.\n\n\n[POST] /rest/streaming/jobs/{id}/cancel\n: Cancels a streaming job.\n\n\n\n\n\n\n\n\nWorkflow Manager\n\n\n\n\n\nUpdated to support generic detections.\n\n\nUpdated Redis to store information about streaming jobs.\n\n\nAdded controllers for streaming job REST endpoints.\n\n\nAdded ability to generate health reports and segment summary reports for streaming jobs.\n\n\nImproved code flow between the Workflow Manager and master Node Manager to support streaming jobs.\n\n\nAdded ActiveMQ queues to enable the C++ Streaming Component Executor to send reports and job status to the Workflow\n Manager.\n\n\n\n\nNode Manager\n\n\n\n\n\nUpdated the master Node Manager and child Node Managers to spawn component services on demand to handle streaming\n jobs, cancel those jobs, and to monitor the status of those processes.\n\n\nUsing .ini files to represent streaming job properties and enable better communication between a child Node Manager\n and C++ Streaming Component Executor.\n\n\n\n\nC++ Streaming Component API\n\n\n\n\n\nDeveloped the C++ Streaming Component API with the following functions:\n\n\nMPFStreamingDetectionComponent(const MPFStreamingVideoJob &job)\n: Constructor that takes a streaming video job.\n\n\nstring GetDetectionType()\n: Returns the type of detection (i.e. \"FACE\").\n\n\nvoid BeginSegment(const VideoSegmentInfo &segment_info)\n: Indicates the beginning of a new video segment.\n\n\nbool ProcessFrame(const cv::Mat &frame, int frame_number)\n: Processes a single frame for the current video\n segment.\n\n\nvector EndSegment()\n: Indicates the end of the current video segment.\n\n\n\n\n\n\nUpdated the C++ Hello World component to support streaming jobs.\n\n\n\n\nC++ Streaming Component Executor\n\n\n\n\n\nDeveloped the C++ Streaming Component Executor to load a streaming component logic library, read frames from a video\n stream, and exercise the component logic through the C++ Streaming Component API.\n\n\nWhen the C++ Streaming Component Executor cannot read a frame from the stream, it will sleep for at least 1\n millisecond, doubling the amount of sleep time per attempt until it reaches the \nstallTimeout\n value specified when\n the job was created. While stalled, the job status will be \nSTALLED\n. After the timeout is exceeded, the job will\n be \nTERMINATED\n.\n\n\nThe C++ Streaming Component Executor supports \nFRAME_INTERVAL\n, as well as rotation, horizontal flipping, and\n cropping (region of interest) properties. Does not support \nUSE_KEY_FRAMES\n.\n\n\n\n\nInteroperability Package\n\n\n\n\n\nAdded the following Java classes to the interoperability package to simplify third party integration:\n\n\nJsonHealthReportCollection\n: Represents the JSON content of a health report callback. Contains one or\n more \nJsonHealthReport\n objects.\n\n\nJsonSegmentSummaryReport\n: Represents the JSON content of a summary report callback. Content is similar to the\n JSON output object used for batch processing.\n\n\n\n\n\n\n\n\nSuBSENSE Component\n\n\n\n\n\nThe SuBSENSE component now supports both batch processing and stream processing.\n\n\nEach video segment will be processed independently of the rest. In other words, tracks will be generated on a\n segment-by-segment basis and tracks will not carry over between segments.\n\n\nNote that the last frame in the previous segment will be used to determine if there is motion in the first frame of\n the next segment.\n\n\n\n\nPackaging and Deployment\n\n\n\n\n\nUpdated \ndescriptor.json\n fields to allow components to support batch and/or streaming jobs. Components that use the\n old \ndescriptor.json\n file format cannot be registered through the web UI.\n\n\nBatch component logic and streaming component logic are compiled into separate libraries.\n\n\nThe mySQL \nstreaming_job_request\n table has been updated with the following fields, which are used to populate the\n JSON health reports:\n\n\nstatus_detail\n: (Optional) A user-friendly description of the current job status.\n\n\nactivity_frame_id\n: The frame id associated with the last job activity. Activity is defined as the start of a new\n track for the current segment.\n\n\nactivity_timestamp\n: The timestamp associated with the last job activity.\n\n\n\n\n\n\n\n\nWeb User Interface\n\n\n\n\n\nAdded column names to the table that appears when the user clicks in the Media button associated with a job on the Job\n Status page. Now descriptive comments are provided when table cells are empty.\n\n\n\n\nBug Fixes\n\n\n\n\n\nUpgraded Tika to 1.17 to resolve an issue with improper indentation in a Python file (rotation.py) that resulted in\n generating at least one error message per image processed. When processing a large number of images, this would\n generate may error messages, causing the Automatic Bug Reporting Tool daemon (abrtd) process to run at 100% CPU. Once\n in that state, that process would stay there, essentially wasting on CPU core. This resulted in some of the Jenkins\n virtual machines we used for testing to become unresponsive.\n\n\n\n\nKnown Issues\n\n\n\n\n\n\n\nOpenCV 3.3.0 \ncv::imread()\n does not properly decode some TIFF images that have EXIF orientation metadata. It can\n handle images that are flipped horizontally, but not vertically. It also has issues with rotated images. Since most\n components rely on that function to read image data, those components may silently fail to generate detections for\n those kinds of images.\n\n\n\n\n\n\nUsing single quotes, apsotrophes, or double quotes in the name of an algorithm, action, task, or pipeline configured\n on an existing OpenMPF system will result in a failure to perform an OpenMPF upgrade on that system. Specifically, the\n step where pre-existing custom actions, tasks, and pipelines are carried over to the upgraded version of OpenMPF will\n fail. Please do not use those special characters while naming those elements. If this has been done already, then\n those elements should be manually renamed in the XML files prior to an upgrade attempt.\n\n\n\n\n\n\nOpenMPF uses OpenCV, which uses FFmpeg, to connect to video streams. If a proxy and/or firewall prevents the network\n connection from succeeding, then OpenCV, or the underlying FFmpeg library, will segfault. This causes the C++\n Streaming Component Executor process to fail. In turn, the job status will be set to \nERROR\n with a status detail\n message of \"Unexpected error. See logs for details\". In this case, the logs will not contain any useful information.\n You can identify a segfault by the following line in the node-manager log:\n\n\n\n\n\n\n2018-02-15 16:01:21,814 INFO [pool-3-thread-4] o.m.m.nms.streaming.StreamingProcess - Process: Component exited with exit code 139\u00a0\n\n\n\n\n\nTo determine if FFmpeg can connect to the stream or not, run \nffmpeg -i \n in a terminal window. Here's an example when it's successful:\n\n\n\n\n[mpf@localhost bin]$ ffmpeg -i rtsp://184.72.239.149/vod/mp4:BigBuckBunny_115k.mov\nffmpeg version n3.3.3-1-ge51e07c Copyright (c) 2000-2017 the FFmpeg developers\n built with gcc 4.8.5 (GCC) 20150623 (Red Hat 4.8.5-4)\n configuration: --prefix=/apps/install --extra-cflags=-I/apps/install/include --extra-ldflags=-L/apps/install/lib --bindir=/apps/install/bin --enable-gpl --enable-nonfree --enable-libtheora --enable-libfreetype --enable-libmp3lame --enable-libvorbis --enable-libx264 --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-version3 --enable-shared --disable-libsoxr --enable-avresample\n libavutil 55. 58.100 / 55. 58.100\n libavcodec 57. 89.100 / 57. 89.100\n libavformat 57. 71.100 / 57. 71.100\n libavdevice 57. 6.100 / 57. 6.100\n libavfilter 6. 82.100 / 6. 82.100\n libavresample 3. 5. 0 / 3. 5. 0\n libswscale 4. 6.100 / 4. 6.100\n libswresample 2. 7.100 / 2. 7.100\n libpostproc 54. 5.100 / 54. 5.100\n[rtsp @ 0x1924240] UDP timeout, retrying with TCP\nInput #0, rtsp, from 'rtsp://184.72.239.149/vod/mp4:BigBuckBunny_115k.mov':\n Metadata:\n title : BigBuckBunny_115k.mov\n Duration: 00:09:56.48, start: 0.000000, bitrate: N/A\n Stream #0:0: Audio: aac (LC), 12000 Hz, stereo, fltp\n Stream #0:1: Video: h264 (Constrained Baseline), yuv420p(progressive), 240x160, 24 fps, 24 tbr, 90k tbn, 48 tbc\nAt least one output file must be specified\n\n\n\n\n\nHere's an example when it's not successful, so there may be network issues:\n\n\n\n\n[mpf@localhost bin]$ ffmpeg -i rtsp://184.72.239.149/vod/mp4:BigBuckBunny_115k.mov\nffmpeg version n3.3.3-1-ge51e07c Copyright (c) 2000-2017 the FFmpeg developers\n built with gcc 4.8.5 (GCC) 20150623 (Red Hat 4.8.5-4)\n configuration: --prefix=/apps/install --extra-cflags=-I/apps/install/include --extra-ldflags=-L/apps/install/lib --bindir=/apps/install/bin --enable-gpl --enable-nonfree --enable-libtheora --enable-libfreetype --enable-libmp3lame --enable-libvorbis --enable-libx264 --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-version3 --enable-shared --disable-libsoxr --enable-avresample\n libavutil 55. 58.100 / 55. 58.100\n libavcodec 57. 89.100 / 57. 89.100\n libavformat 57. 71.100 / 57. 71.100\n libavdevice 57. 6.100 / 57. 6.100\n libavfilter 6. 82.100 / 6. 82.100\n libavresample 3. 5. 0 / 3. 5. 0\n libswscale 4. 6.100 / 4. 6.100\n libswresample 2. 7.100 / 2. 7.100\n libpostproc 54. 5.100 / 54. 5.100\n[tcp @ 0x171c300] Connection to tcp://184.72.239.149:554?timeout=0 failed: Invalid argument\nrtsp://184.72.239.149/vod/mp4:BigBuckBunny_115k.mov: Invalid argument\n\n\n\n\n\nTika 1.17 does not come pre-packaged with support for some embedded image formats in PDF files, possibly to avoid\n patent issues. OpenMPF does not handle embedded images in PDFs, so that's not a problem. Tika will print out the\n following warnings, which can be safely ignored:\n\n\n\n\nJan 22, 2018 11:02:15 AM org.apache.tika.config.InitializableProblemHandler$3 handleInitializableProblem\nWARNING: JBIG2ImageReader not loaded. jbig2 files will be ignored\nSee https://pdfbox.apache.org/2.0/dependencies.html#jai-image-io\nfor optional dependencies.\nTIFFImageWriter not loaded. tiff files will not be processed\nSee https://pdfbox.apache.org/2.0/dependencies.html#jai-image-io\nfor optional dependencies.\nJ2KImageReader not loaded. JPEG2000 files will not be processed.\nSee https://pdfbox.apache.org/2.0/dependencies.html#jai-image-io\nfor optional dependencies.\n\n\n\n\nOpenMPF 1.0.x\n\n\n1.0.0: October 2017\n\n\n\nDocumentation\n\n\n\n\n\nUpdated the \nBuild Guide\n with instructions for installing the latest JDK,\n latest JRE, FFmpeg 3.3.3, new codecs, and OpenCV 3.3.\n\n\nAdded an \nAcknowledgements\n section that provides information on third party dependencies\n leveraged by the OpenMPF.\n\n\nAdded a \nFeed Forward Guide\n that explains feed forward processing and how to use it.\n\n\nAdded missing requirements checklist content to\n the \nInstall Guide\n.\n\n\nUpdated the README at the top level of each of the primary repositories to help with user navigation and provide\n general information.\n\n\n\n\nUpgrade to FFmpeg 3.3.3 and OpenCV 3.3\n\n\n\n\n\nUpdated core framework from FFmpeg 2.6.3 to FFmpeg 3.3.3.\n\n\nAdded the following FFmpeg codecs: x256, VP9, AAC, Opus, Speex.\n\n\nUpdated core framework and components from OpenCV 3.2 to OpenCV 3.3. No longer building with opencv_contrib.\n\n\n\n\nFeed Forward Behavior\n\n\n\n\n\nUpdated the Workflow Manager (WFM) and all video components to optionally perform feed forward processing for batch\n jobs. This allows tracks to be passed forward from one pipeline stage to the next. Components in the next stage will\n only process the frames associated with the detections in those tracks. This differs from the default segmenting\n behavior, which does not preserve detection regions or track information between stages.\n\n\nTo enable this behavior, the optional \nFEED_FORWARD_TYPE\n property must be set to \nFRAME\n, \nSUPERSET_REGION\n,\n or \nREGION\n. If set to \nFRAME\n then the components in the next stage will process the whole frame region associated\n with each detection in the track passed forward. If set to \nSUPERSET_REGION\n then the components in the next stage\n will determine the bounding box that encapsulates all of the detection regions in the track, and only process the\n pixel data within that superset region. If set to \nREGION\n then the components in the next stage will process the\n region associated with each detection in the track passed forward, which may vary in size and position from frame to\n frame.\n\n\nThe optional \nFEED_FORWARD_TOP_CONFIDENCE_COUNT\n property can be set to a number to limit the number of detections\n passed forward in a track. For example, if set to \"5\", then only the top 5 detections in the track will be passed\n forward and processed by the next stage. The top detections are defined as those with the highest confidence values,\n or if the confidence values are the same, those with the lowest frame index.\n\n\nNote that setting the feed forward properties has no effect on the first pipeline stage because there is no prior\n stage that can pass tracks to it.\n\n\n\n\nCaffe Component\n\n\n\n\n\nUpdated the Caffe component to process images in the BGR color space instead of the RGB color space. This addresses a\n bug found in OpenCV. Refer to the Bug Fixes section below.\n\n\nAdded support for processing videos.\n\n\nAdded support for an optional \nACTIVATION_LAYER_LIST\n property. For each network layer specified in the list,\n the \ndetectionProperties\n map in the JSON output object will contain one entry. The value is an encoded string of the\n JSON representation of an OpenCV matrix of the activation values for that layer. The activation values are obtained\n after the Caffe network has processed the frame data.\n\n\nAdded support for an optional \nSPECTRAL_HASH_FILE_LIST\n property. For each JSON file specified in the list,\n the \ndetectionProperties\n map in the JSON output object will contain one entry. The value is a string of 0's and 1's\n representing the spectral hash calculated using the information in the spectral hash JSON file. The spectral hash is\n calculated using activation values after the Caffe network has processed the frame data.\n\n\nAdded a pipeline to showcase the above two features for the GoogLeNet Caffe model.\n\n\nRemoved the \nTRANSPOSE\n property from the Caffe component since it was not necessary.\n\n\nAdded red, green, and blue mean subtraction values to the GoogLeNet pipeline.\n\n\n\n\nUse Key Frames\n\n\n\n\n\nAdded support for an optional \nUSE_KEY_FRAMES\n property to each video component. When true the component will only\n look at key frames (I-frames) from the input video. Can be used in conjunction with \nFRAME_INTERVAL\n. For example,\n when \nUSE_KEY_FRAMES\n is true, and \nFRAME_INTERVAL\n is set to \"2\", then every other key frame will be processed.\n\n\n\n\nMPFVideoCapture and MPFImageReader Tools\n\n\n\n\n\nUpdated the MPFVideoCapture and MPFImageReader tools to handle feed forward properties.\n\n\nUpdated the MPFVideoCapture tool to handle \nFRAME_INTERVAL\n and \nUSE_KEY_FRAMES\n properties.\n\n\nUpdated all existing components to leverage these tools as much as possible.\n\n\nWe encourage component developers to use these tools to automatically take care of common frame grabbing and frame\n manipulation behaviors, and not to reinvent the wheel.\n\n\n\n\nDead Letter Queue\n\n\n\n\n\nIf for some reason a sub-job request that should have gone to a component ends up on the ActiveMQ Dead Letter Queue (\n DLQ), then the WFM will now process that failed request so that the job can complete. The ActiveMQ management page\n will now show that \nActiveMQ.DLQ\n has 1 consumer. It will also show unconsumed messages\n in \nMPF.PROCESSED_DLQ_MESSAGES\n. Those are left for auditing purposes. The \"Message Detail\" for these shows the string\n representation of the original job request protobuf message.\n\n\n\n\nUpgrade Path\n\n\n\n\n\nRemoved the Release 0.8 to Release 0.9 upgrade path in the deployment scripts.\n\n\nAdded support for a Release 0.9 to Release 1.0.0 upgrade path, and a Release 0.10.0 to Release 1.0.0 upgrade path.\n\n\n\n\nMarkup\n\n\n\n\n\nBounding boxes are now drawn along the interpolated path between detection regions whenever there are one or more\n frames in a track which do not have detections associated with them.\n\n\nFor each track, the color of the bounding box is now a randomly selected hue in the HSV color space. The colors are\n evenly distributed using the golden ratio.\n\n\n\n\nBug Fixes\n\n\n\n\n\nFixed a \nbug in OpenCV\n where the Caffe example code was processing\n images in the RGB color space instead of the BGR color space. Updated the OpenMPF Caffe component accordingly.\n\n\nFixed a bug in the OpenCV person detection component that caused bounding boxes to be too large for detections near\n the edge of a frame.\n\n\nResubmitting jobs now properly carries over configured job properties.\n\n\nFixed a bug in the build order of the OpenMPF project so that test modules that the WFM depends on are built before\n the WFM itself.\n\n\nThe Markup component draws bounding boxes between detections when a \nFRAME_INTERVAL\n is specified. This is so that the\n bounding box in the marked-up video appears in every frame. Fixed a bug where the bounding boxes drawn on\n non-detection frames appeared to stand still rather than move along the interpolated path between detection regions.\n\n\nFixed a bug on the OALPR license plate detection component where it was not properly handling the \nSEARCH_REGION_*\n\n properties.\n\n\nSupport for the \nMIN_GAP_BETWEEN_SEGMENTS\n property was not implemented properly. When the gap between two segments is\n less than this property value then the segments should be merged; otherwise, the segments should remain separate. In\n some cases, the exact opposite was happening. This bug has been fixed.\n\n\n\n\nKnown Issues\n\n\n\n\n\nBecause of the number of additional ActiveMQ messages involved, enabling feed forward for low resolution video may\n take longer than the non-feed-forward behavior.\n\n\n\n\nOpenMPF 0.x.x\n\n\n0.10.0: July 2017\n\n\n\n\n\nWARNING:\n There is no longer a \nDEFAULT CAFFE ACTION\n, \nDEFAULT CAFFE TASK\n, or \nDEFAULT CAFFE PIPELINE\n. There is now a \nCAFFE GOOGLENET DETECTION PIPELINE\n and \nCAFFE YAHOO NSFW DETECTION PIPELINE\n, which each have a respective action and task.\n\n\nNOTE:\n MPFImageReader has been re-enabled in this version of OpenMPF since we upgraded to OpenCV 3.2, which addressed the known issues with \nimread()\n, auto-orientation, and jpeg files in OpenCV 3.1.\n\n\n\n\nDocumentation\n\n\n\n\n\nAdded a \nContributor Guide\n that provides guidelines for contributing to the OpenMPF\n codebase.\n\n\nUpdated the \nJava Batch Component API\n with links to the example Java components.\n\n\nUpdated the \nBuild Guide\n with instructions for OpenCV 3.2.\n\n\n\n\nUpgrade to OpenCV 3.2\n\n\n\n\n\nUpdated core framework and components from OpenCV 3.1 to OpenCV 3.2.\n\n\n\n\nSupport for Animated gifs\n\n\n\n\n\nAll gifs are now treated as videos. Each gif will be handled as an MPFVideoJob.\n\n\nUnanimated gifs are treated as 1-frame videos.\n\n\nThe WFM Media Inspector now populates the \nmedia_properties\n map with a \nFRAME_COUNT\n entry (in addition to\n the \nDURATION\n and \nFPS\n entries).\n\n\n\n\nCaffe Component\n\n\n\n\n\nAdded support for the Yahoo Not Suitable for Work (NSFW) Caffe model for explicit material detection.\n\n\nUpdated the Caffe component to support the OpenCV 3.2 Deep Neural Network (DNN) module.\n\n\n\n\nFuture Support for Streaming Video\n\n\n\n\n\nNOTE:\n At this time, OpenMPF does not support streaming video. This section details what's being / has been done so far to prepare for that feature.\n\n\n\n\n\n\nThe codebase is being updated / refactored to support both the current \"batch\" job functionality and new \"streaming\"\n job functionality.\n\n\nbatch job: complete video files are written to disk before they are processed\n\n\nstreaming job: video frames are read from a streaming endpoint (such as RTSP) and processed in near real time\n\n\n\n\n\n\nThe REST API is being updated with endpoints for streaming jobs:\n\n\n[POST] /rest/streaming/jobs\n: Creates and submits a streaming job\n\n\n[POST] /rest/streaming/jobs/{id}/cancel\n: Cancels a streaming job\n\n\n[GET] /rest/streaming/jobs/{id}\n: Gets information about a streaming job\n\n\n\n\n\n\nThe Redis and mySQL databases are being updated to support streaming video jobs.\n\n\nA batch job will never have the same id as a streaming job. The integer ids will always be unique.\n\n\n\n\n\n\n\n\nBug Fixes\n\n\n\n\n\nThe MOG and SuBSENSE component services could segfault and terminate if the \nUSE_MOTION_TRACKING\n property was set to\n \u201c1\u201d and a detection was found close to the edge of the frame. Specifically, this would only happen if the video had a\n width and/or height dimension that was not an exact power of two.\n\n\nThe reason was because the code downsamples each frame by a power of two and rounds the value of the width and\n height up to the nearest integer. Later on when upscaling detection rectangles back to a size that\u2019s relative to\n the original image, the resized rectangle sometimes extended beyond the bounds of the original frame.\n\n\n\n\n\n\n\n\nKnown Issues\n\n\n\n\n\nIf a job is submitted through the REST API, and a user to logged into the web UI and looking at the job status page,\n the WFM may generate \"Error retrieving the SingleJobInfo model for the job with id\" messages.\n\n\nThis is because the job status is only added to the HTTP session object if the job is submitted through the web\n UI. When the UI queries the job status it inspects this object.\n\n\nThis message does not appear if job status is obtained using the \n[GET] /rest/jobs/{id}\n endpoint.\n\n\n\n\n\n\nThe \n[GET] /rest/jobs/stats\n endpoint aggregates information about all of the jobs ever run on the system. If\n thousands of jobs have been run, this call could take minutes to complete. The code should be improved to execute a\n direct mySQL query.\n\n\n\n\n0.9.0: April 2017\n\n\n\n\n\nWARNING:\n MPFImageReader has been disabled in this version of OpenMPF. Component developers should use MPFVideoCapture instead. This affects components developed against previous versions of OpenMPF and components developed against this version of OpenMPF. Please refer to the Known Issues section for more information.\n\n\nWARNING:\n The OALPR Text Detection Component has been renamed to OALPR \nLicense Plate\n Text Detection Component. This affects the name of the component package and the name of the actions, tasks, and pipelines. When upgrading from R0.8 to R0.9, if the old OALPR Text Detection Component is installed in R0.8 then you will be prompted to install it again at the end of the upgrade path script. We recommend declining this prompt because the old component will conflict with the new component.\n\n\nWARNING:\n Action, task, and pipeline names that started with \nMOTION DETECTION PREPROCESSOR\n have been renamed \nMOG MOTION DETECTION PREPROCESSOR\n. Similarly, \nWITH MOTION PREPROCESSOR\n has changed to \nWITH MOG MOTION PREPROCESSOR\n.\n\n\n\n\nDocumentation\n\n\n\n\n\nUpdated the \nREST API\n to reflect job properties, algorithm-specific properties, and\n media-specific properties.\n\n\nStreamlined the \nC++ Batch Component API\n document for clarity and simplicity.\n\n\nCompleted the \nJava Batch Component API\n document.\n\n\nUpdated the \nAdmin Guide\n and \nUser Guide\n to reflect web UI changes.\n\n\nUpdated the \nBuild Guide\n with instructions for GitHub repositories.\n\n\n\n\nWorkflow Manager\n\n\n\n\n\nAdded support for job properties, which will override pre-defined pipeline properties.\n\n\nAdded support for algorithm-specific properties, which will apply to a single stage of the pipeline and will override\n job properties and pre-defined pipeline properties.\n\n\nAdded support for media-specific properties, which will apply to a single piece and media and will override job\n properties, algorithm-specific properties, and pre-defined pipeline properties.\n\n\nComponents can now be automatically registered and installed when the web application starts in Tomcat.\n\n\n\n\nWeb User Interface\n\n\n\n\n\nThe \"Close All\" button on pop-up notifications now dismisses all notifications from the queue, not just the visible\n ones.\n\n\nJob completion notifications now only appear for jobs created during the current login session instead of all jobs.\n\n\nThe \nROTATION\n, \nHORIZONTAL_FLIP\n, and \nSEARCH_REGION_*\n properties can be set using the web interface when creating a\n job. Once files are selected for a job, these properties can be set individually or by groups of files.\n\n\nThe Node and Process Status page has been merged into the Node Configuration page for simplicity and ease of use.\n\n\nThe Media Markup results page has been merged into the Job Status page for simplicity and ease of use.\n\n\nThe File Manager UI has been improved to handle large numbers of files and symbolic links.\n\n\nThe side navigation menu is now replaced by a top navigation bar.\n\n\n\n\nREST API\n\n\n\n\n\nAdded an optional jobProperties object to the \n/rest/jobs/\n request which contains String key-value pairs which\n override the pipeline's pre-configured job properties.\n\n\nAdded an optional algorithmProperties object to the \n/rest/jobs/\n request which can be used to configure properties\n for specific algorithms in the pipeline. These properties override the pipeline's pre-configured job properties. They\n also override the values in the jobProperties object.\n\n\nUpdated the \n/rest/jobs/\n request to add more detail to media, replacing a list of mediaUri Strings with a list of\n media objects, each of which contains a mediaUri and an optional mediaProperties map. The mediaProperties map can be\n used to configure properties for the specific piece of media. These properties override the pipeline's pre-configured\n job properties, values in the jobProperties object, and values in the algorithmProperties object.\n\n\nStreamlined the actions, tasks, and pipelines endpoints that are used by the web UI.\n\n\n\n\nFlipping, Rotation, and Region of Interest\n\n\n\n\n\nThe \nROTATION\n, \nHORIZONTAL_FLIP\n, and \nSEARCH_REGION_*\n properties will no longer appear in the detectionProperties\n map in the JSON detection output object. When applied to an algorithm these properties now appear in the\n pipeline.stages.actions.properties element. When applied to a piece of media these properties will now appear in the\n the media.mediaProperties element.\n\n\nThe OpenMPF now supports multiple regions of interest in a single media file. Each region will produce tracks\n separately, and the tracks for each region will be listed in the JSON output as if from a separate media file.\n\n\n\n\nComponent API\n\n\n\n\n\nJava Batch Component API is functionally complete for third-party development, with the exception of Component Adapter\n and frame transformation utilities classes.\n\n\nRe-architected the Java Batch Component API to use a more traditional Java method structure of returning track lists\n and throwing exceptions (rather than modifying input track lists and returning statuses), and encapsulating job\n properties into MPFJob objects:\n\n\nList getDetections(MPFVideoJob job) throws MPFComponentDetectionError\n\n\nList getDetections(MPFAudioJob job) throws MPFComponentDetectionError\n\n\nList getDetections(MPFImageJob job) throws MPFComponentDetectionError\n\n\n\n\n\n\nCreated examples for the Java Batch Component API.\n\n\nReorganized the Java and C++ component source code to enable component development without the OpenMPF core, which\n will simplify component development and streamline the code base.\n\n\n\n\nJSON Output Objects\n\n\n\n\n\nThe JSON output object for the job now contains a jobProperties map which contains all properties defined for the job\n in the job request. For example, if the job request specifies a \nCONFIDENCE_THRESHOLD\n of then the jobProperties map\n in the output will also list a \nCONFIDENCE_THRESHOLD\n of 5.\n\n\nThe JSON output object for the job now contains a algorithmProperties element which contains all algorithm-specific\n properties defined for the job in the job request. For example, if the job request specifies a \nFRAME_INTERVAL\n of 2\n for FACECV then the algorithmProperties element in the output will contain an entry for \"FACECV\" and that entry will\n list a \nFRAME_INTERVAL\n of 2.\n\n\nEach JSON media output object now contains a mediaProperties map which contains all media-specific properties defined\n by the job request. For example, if the job request specifies a \nROTATION\n of 90 degrees for a single piece of media\n then the mediaProperties map for that piece of piece will list a \nROTATION\n of 90.\n\n\nThe content of JSON output objects are now organized by detection type (e.g. MOTION, FACE, PERSON, TEXT, etc.) rather\n than action type.\n\n\n\n\nCaffe Component\n\n\n\n\n\nAdded support for flip, rotation, and cropping to regions of interest.\n\n\nAdded support for returning multiple classifications per detection based on user-defined settings. The classification\n list is in order of decreasing confidence value.\n\n\n\n\nNew Pipelines\n\n\n\n\n\nNew SuBSENSE motion preprocessor pipelines have been added to components that perform detection on video.\n\n\n\n\nPackaging and Deployment\n\n\n\n\n\nActions.xml\n, \nAlgorithms.xml\n, \nnodeManagerConfig.xml\n, \nnodeServicesPalette.json\n, \nPipelines.xml\n, and \nTasks.xml\n\n are no longer stored within the Workflow Manager WAR file. They are now stored under \n$MPF_HOME/data\n. This makes it\n easier to upgrade the Workflow Manager and makes it easier for users to access these files.\n\n\nEach component can now be optionally installed and registered during deployment. Components not registered are set to\n the \nUPLOADED\n state. They can then be removed or registered through the Component Registration page.\n\n\nJava components are now packaged as tar.gz files instead of RPMs, bringing them into alignment with C++ components.\n\n\nOpenMPF R0.9 can be installed over OpenMPF R0.8. The deployment scripts will determine that an upgrade should take\n place.\n\n\nAfter the upgrade, user-defined actions, tasks, and pipelines will have \"CUSTOM\" prepended to their name.\n\n\nThe job_request table in the mySQL database will have a new \"output_object_version\" column. This column will\n have \"1.0\" for jobs created using OpenMPF R0.8 and \"2.0\" for jobs created using OpenMPF R0.9. The JSON output\n object schema has changed between these versions.\n\n\n\n\n\n\nReorganized source code repositories so that component SDKs can be downloaded separately from the OpenMPF core and so\n that components are grouped by license and maturity. Build scripts have been created to streamline and simplify the\n build process across the various repositories.\n\n\n\n\nUpgrade to OpenCV 3.1\n\n\n\n\n\nThe OpenMPF software has been ported to use OpenCV 3.1, including all of the C++ detection components and the markup\n component. For the OpenALPR license plate detection component, the versions of the openalpr, tesseract, and leptonica\n libraries were also upgraded to openalpr-2.3.0, tesseract-3.0.4, and leptonica-1.7.2. For the SuBSENSE motion\n component, the version of the SuBSENSE library was upgraded to use the code found at this\n location: \nhttps://bitbucket.org/pierre_luc_st_charles/subsense/src\n.\n\n\n\n\nBug Fixes\n\n\n\n\n\nMOG motion detection always detected motion in frame 0 of a video. Because motion can only be detected between two\n adjacent frames, frame 1 is now the first frame in which motion can be detected.\n\n\nMOG motion detection never detected motion in the first frame of a video segment (other than the first video segment\n because of the frame 0 bug described above). Now, motion is detected using the first frame before the start of a\n segment, rather than the first frame of the segment.\n\n\nThe above bugs were also present in SuBSENSE motion detection and have been fixed.\n\n\nSuBSENSE motion detection generated tracks where the frame numbers were off by one. Corrected the frame index logic.\n\n\nVery large video files caused an out of memory error in the system during Workflow Manager media inspection.\n\n\nA job would fail when processing images with an invalid metadata tag for the camera flash setting.\n\n\nUsers were permitted to select invalid file types using the File Manager UI.\n\n\n\n\nKnown Issues\n\n\n\n\n\nMPFImageReader does not work reliably with the current release version of OpenCV 3.1\n: In OpenCV 3.1, new\n functionality was introduced to interpret EXIF information when reading jpeg files.\n\n\nThere are two issues with this new functionality that impact our ability to use the OpenCV \nimread()\n function with\n MPFImageReader:\n\n\nFirst, because of a bug in the OpenCV code, reading a jpeg file that contains exif information could cause it to\n hang. (See \nhttps://github.com/opencv/opencv/issues/6665\n.)\n\n\nSecond, it is not possible to tell the \nimread()\nfunction to ignore the EXIF data, so the image it returns is\n automatically rotated. (See \nhttps://github.com/opencv/opencv/issues/6348\n.) This results in the MPFImageReader\n applying a second rotation to the image due to the EXIF information.\n\n\n\n\n\n\nTo address these issues, we developed the following workarounds:\n\n\nCreated a version of the MPFVideoCapture that works with an MPFImageJob. The new MPFVideoCapture can pull frames\n from both video files and images. MPFVideoCapture leverages cv::VideoCapture, which does not have the two issues\n described above.\n\n\nDisabled the use of MPFImageReader to prevent new users from trying to develop code leveraging this previous\n functionality.",
"title": "Release Notes"
},
+ {
+ "location": "/Release-Notes/index.html#openmpf-100x",
+ "text": "",
+ "title": "OpenMPF 10.0.x"
+ },
{
"location": "/Release-Notes/index.html#openmpf-90x",
"text": "",
diff --git a/docs/site/sitemap.xml b/docs/site/sitemap.xml
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/index.html
- 2025-12-18
+ 2026-01-14daily/Release-Notes/index.html
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+ 2026-01-14daily/License-And-Distribution/index.html
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+ 2026-01-14daily/Acknowledgements/index.html
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+ 2026-01-14daily/Install-Guide/index.html
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+ 2026-01-14daily/Admin-Guide/index.html
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+ 2026-01-14daily/User-Guide/index.html
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+ 2026-01-14daily/OpenID-Connect-Guide/index.html
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+ 2026-01-14daily/Media-Segmentation-Guide/index.html
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+ 2026-01-14daily/Feed-Forward-Guide/index.html
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+ 2026-01-14daily/Derivative-Media-Guide/index.html
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+ 2026-01-14daily/Object-Storage-Guide/index.html
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+ 2026-01-14daily/Markup-Guide/index.html
- 2025-12-18
+ 2026-01-14daily/TiesDb-Guide/index.html
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+ 2026-01-14daily/Trigger-Guide/index.html
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+ 2026-01-14daily/Roll-Up-Guide/index.html
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+ 2026-01-14daily/Health-Check-Guide/index.html
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+ 2026-01-14daily/Artifact-Extraction-Guide/index.html
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+ 2026-01-14daily/Quality-Selection-Guide/index.html
- 2025-12-18
+ 2026-01-14daily/Media-Selectors-Guide/index.html
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+ 2026-01-14daily/REST-API/index.html
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+ 2026-01-14daily/Component-API-Overview/index.html
- 2025-12-18
+ 2026-01-14daily/Component-Descriptor-Reference/index.html
- 2025-12-18
+ 2026-01-14daily/CPP-Batch-Component-API/index.html
- 2025-12-18
+ 2026-01-14daily/Python-Batch-Component-API/index.html
- 2025-12-18
+ 2026-01-14daily/Java-Batch-Component-API/index.html
- 2025-12-18
+ 2026-01-14daily/GPU-Support-Guide/index.html
- 2025-12-18
+ 2026-01-14daily/Contributor-Guide/index.html
- 2025-12-18
+ 2026-01-14daily/Development-Environment-Guide/index.html
- 2025-12-18
+ 2026-01-14daily/Node-Guide/index.html
- 2025-12-18
+ 2026-01-14daily/Workflow-Manager-Architecture/index.html
- 2025-12-18
+ 2026-01-14daily/CPP-Streaming-Component-API/index.html
- 2025-12-18
+ 2026-01-14daily
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diff --git a/index.html b/index.html
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