FME Transformers: 2024.2

Categories
Web

Web

Related Transformers
AmazonAthenaConnector
AzureComputerVisionConnector
AzureTextAnalyticsConnector
ComprehendConnector
GoogleLanguageConnector
GoogleVisionConnector
RasterObjectDetector
S3Connector
SQSConnector
Categories
Web

Web

Related Transformers
AmazonAthenaConnector
AzureComputerVisionConnector
AzureTextAnalyticsConnector
ComprehendConnector
GoogleLanguageConnector
GoogleVisionConnector
RasterObjectDetector
S3Connector
SQSConnector

RekognitionConnector

Accesses the Amazon Rekognition Service AI computer vision service to detect objects, faces, and text in images and to describe image contents and faces.

Jump to Configuration

Typical Uses

  • Submitting images to the Amazon AWS Rekognition service to
    • detect individual objects
    • describe the general contents
    • extract small amounts of text
    • detect faces and facial descriptions in an image
    • detect explicit or suggestive adult content in an image
    • detect PPE (Personal Protective Equipment) in an image

How does it work?

The RekognitionConnector uses your Amazon AWS account credentials (either via a previously defined FME web connection, or by setting up a new FME web connection right from the transformer) to access the computer vision service.

It will submit images to the service, and return features with attributes that describe the contents of the image. Object detection, text detection, and face detection can be performed.

  • For object detection, if the service is able to identify the exact location of an object in the image, a bounding box geometry will also be returned.
  • Text detection and face detection always return bounding boxes around the detected text or face.
  • Image moderation will return the input feature, with moderation labels added when unsafe content is detected.

Each input image may result in several output features.

Usage Notes

  • For better performance, requests to the Rekognition service are made in parallel, and are returned as soon as they complete. Consequently, detection results will not be returned in the same order as their associated requests.

  • The RekognitionConnector is able to extract up to 50 words per image when performing text detection. This makes it unsuitable for detecting full pages of text, as from a scanned document.

  • While powerful, the use of AI has important legal and ethical implications. Consult your local AI legislation and ethical guidelines before applying the RekognitionConnector in a production environment.
  • For an overview of Rekognition's capabilities and limitations, please see Amazon's FAQ on the topic: https://aws.amazon.com/rekognition/the-facts-on-facial-recognition-with-artificial-intelligence

Configuration

Input Ports

This transformer accepts any feature. Raster geometries may be used as input if "Raster Geometry" is selected as the image source. The raster will be uploaded to the service as JPEG if possible, otherwise PNG will be used. The service does not support any other raster formats directly. Note that the RekognitionConnector does not support coordinate systems.

Output Ports

Output will depend on the analysis chosen. Each input feature may result in multiple output features. For example, a single image is likely to contain multiple detectable objects.

Object Detection

Successfully identified objects will result in output features with attributes describing the objects. A bounding box geometry will also be returned, with a separate confidence value. Each input image may result in several detection features.

When using a local file or raster geometry as input, the bounding box is in pixel units, and will align with the input. When using a file on S3, the size of the image is not known, so the output bounding box will be expressed in terms of a ratio of the original image. For example, if an object takes up a quarter of the image, the bounding box will be 0.5 by 0.5 in size.

Attributes

_detection_label

A word or short phrase describing the content of the image. Labels may be general descriptors for the image, or may refer to identifiable instances in the image.

For example, a label of "Vegetation" with no bounding box indicates that there are plants somewhere in the image. A label of "Abies" with a bounding box might indicate that there is a fir tree in the top left corner.

_confidence

A number between 0 and 1 that indicates the probability that a given prediction is correct. For more information about confidence, see the Amazon Rekognition FAQs: https://aws.amazon.com/rekognition/faqs

_bounding_box_confidence

A number between 0 and 1 that indicates the probability that the specific bounding box for a detected object is correct.

_parent_label{}

A list of higher hierarchical labels. For example, if "Helicopter" is detected, the parent labels might be "Aircraft," "Vehicle," and "Transportation."

Text Detection

Successfully identified text will result in output features with attributes describing the text. Features will be output for lines of text, and for individual words in each line. Each feature will have a bounding box for the line or word.

When using a local file or raster geometry as input, the bounding box is in pixel units, and will align with the input. When using a file on S3, the size of the image is not known, so the output bounding box will be expressed in terms of a ratio of the original image. For example, if a line of text takes up 80% of the width of the image, the bounding box will have a width of 0.8.

Attributes

_text

The detected text in the line or word.

_confidence

A number between 0 and 1 that indicates the probability that a given text string is correct. For more information about confidence, see the Amazon Rekognition FAQs: https://aws.amazon.com/rekognition/faqs

_type

The type of detected text. Lines are sections of text that are aligned along the same horizontal axis. Sentences may be split across multiple lines. Words are sections of text separated by whitespace, and are associated with parent lines.

Options: LINE, WORD

_id

The number identifying the feature. If the feature represents a line of text, the identifier is unique within the image. If the feature represents a word, the identifier is unique within the parent line.

_parent_id

The _id value of the row the word is in. This value will be null for rows.

Face Detection

Successfully identified faces will result in output features with attributes describing the face. Each feature will have a bounding box for the face. Optionally, facial "landmarks," such as "nose," "chinBottom," or "midJawlineLeft" will also be added as additional point geometries on the feature.

When using a local file or raster geometry as input, the bounding box is in pixel units, and will align with the input. When using a file on S3, the size of the image is not known, so the output bounding box will be expressed in terms of a ratio of the original image. For example, if a face takes up a quarter of the image, the bounding box will be 0.5 by 0.5 in size.

Attributes

Many of the attributes for face detection have a value and an associated confidence. This is denoted with two attributes, in the form _characteristic.value and _characteristic.confidence. Confidence ranges from 0 to 1. Unless otherwise specified, all detected characteristics take this form.

_age_range

The estimated age of the subject. There is no explicit confidence value for the age range. A smaller range indicates greater confidence in the estimate.

  • low: The low end of the estimated age range
  • high: The high end of the estimated age range

_beard

Whether or not the subject has a beard.

_confidence

The overall confidence that the detected entity is a human face.

_eyeglasses

Whether the subject is wearing glasses.

_eyes_open

Whether the subject's eyes are open.

_gender

Whether the subject is male or female.

_mouth_open

Whether the subject's mouth is open.

_mustache

Whether the subject has a mustache.

_pose

  • pitch: The angle with which the subject's head is tilted up or down
  • roll: The angle with which the subject's head is tilted sideways
  • yaw: The angle with which the subject is facing left or right

There are no confidence values associated with this characteristic.

_quality

  • brightness: The brightness of the image
  • sharpness: The sharpness of the image

There are no confidence values associated with this characteristic.

_smile

Whether the subject is smiling.

_sunglasses

Whether the subject is wearing sunglasses.

Image Moderation

No features will be returned through the Output port when performing image moderation.

PPE Detection

Successfully identified objects will result in output features with attributes describing the objects. A bounding box geometry will also be returned, with a separate confidence value. Each input image may result in several detection features.

Attributes

_confidence

The confidence score between 0 and 1 that the PPE object is detected

_detection_type

The type of PPE detected, such as FACE_COVER.

_covers_body_part

A Boolean value indicating whether the PPE covers a body part

_covers_body_part_confidence

The confidence score between 0 and 1 that the PPE object covers the body part.

_bouding_box_confidence

The confidence score between 0 and 1 that the bounding box of the PPE is accurate

Output will depend on the analysis chosen. Only one output summary feature will be produced per input feature.

Object Detection

A summary feature with the original geometry and attributes preserved will always be output through the Summary port. Attributes will be added to indicate the labels that apply to the image in general, and not to a specific area.

Attributes

_detection_labels{}.confidence

A number between 0 and 1 that indicates the probability that a given label is correct. For more information about confidence, see the Amazon Rekognition FAQs: https://aws.amazon.com/rekognition/faqs

_detection_labels{}.name

A word or phrase describing the content of the image.

_detection_labels{}.parent

For the descriptor in _labels{n}.name, _labels{n}.parent will contain a comma-separated list of parent descriptors.

Text Detection

A summary feature with the original geometry and attributes preserved will always be output through the Summary port. Attributes will be added to indicate the number of lines and words detected.

Attributes

_detected_words

The number of words that were detected in the image.

_detected_lines

The number of lines of text that were detected in the image.

Face Detection

A summary feature with the original geometry and attributes preserved will always be output through the Summary port. Attributes will be added to indicate the number of faces detected.

Attributes

_detected_faces

The number of faces that were detected in the image.

Image Moderation

A summary feature with the original geometry and attributes preserved will always be output through the Summary port. If unsafe content is detected, list attributes will be added indicating what type of unsafe content was found.

Attributes

_safe_image

A Boolean value (yes/no) indicating whether the image is deemed to be safe, within the requested confidence.

For example, if the confidence level was set to 0.95, and the image is deemed to be safe, it means that no moderation labels could be detected with a confidence score of at least 0.95.

_moderation_labels{}.confidence

A number between 0 and 1 that indicates the probability that a given moderation label is correct. For more information about confidence, see the Amazon Rekognition FAQs: https://aws.amazon.com/rekognition/faqs

_moderation_labels{}.name

A phrase indicating the type of unsafe content. There are several top-level categories that the service detects. Within each category, there are subcategories which may also appear in this list.

  • Explicit Nudity
    • Nudity
    • Graphic Male Nudity
    • Graphic Female Nudity
    • Sexual Activity
    • Partial Nudity
  • Suggestive
    • Female Swimwear Or Underwear
    • Male Swimwear Or Underwear
    • Revealing Clothes
  • Violence

_moderation_labels{}.parent

If the list item for _labels{n}.name is a subcategory, _labels{n}.parent will contain its parent category.

PPE Detection

A summary feature with the original geometry and attributes preserved will always be output through the Summary port. Attributes will be added to indicate the labels that apply to the image in general, and not to a specific area.

Attributes

_person_count

The total number of people

_ppe_count

The total number of PPE equipment

The incoming feature is output through this port.

Features that cause the operation to fail are output through this port. An fme_rejection_code attribute, having the value ERROR_DURING_PROCESSING, will be added, along with a more descriptive fme_rejection_message attribute which contains more specific details as to the reason for the failure.

Note  If a feature comes in to the RekognitionConnector already having a value for fme_rejection_code, this value will be removed.

Rejected Feature Handling: can be set to either terminate the translation or continue running when it encounters a rejected feature. This setting is available both as a default FME option and as a workspace parameter.

Parameters

Credential Source

The RekognitionConnector can use credentials from different sources. Using a web connection integrates best with FME, but in some cases, you may wish to use one of the other sources.

Account

Available when the credential source is Web Connection. To create a Rekognition connection, click the 'Account' drop-down box and select 'Add Web Connection...'.

The connection can then be managed via Tools -> FME Options... -> Web Connections.

Region

The AWS Region through which to access Rekognition. To optimize latency, it is best practice to specify the correct region.

Access Key and Secret Access Key

Available when the credential source is Embedded. An access key ID and secret access key can be specified directly in the transformer instead of in a web connection.

Image Source

Where to get the input image for detection. Options are:

  • File: specify a path to a JPEG or PNG file on disk
  • File on S3: specify an S3 bucket and key
  • Raster Geometry: use the raster geometry of incoming features

Input Filename

If File is selected for the image source, the path to the JPEG or PNG file to use

Bucket/Key

If File on S3 is selected for the image source, the path on S3 to use

Action

The type of operation to perform. Choices are:

  • Object Detection: Detect labels for the given image. If possible, instances of each label will be identified with output bounding boxes.
  • Text Detection: Detect text (up to 50 words) in the given image. Bounding boxes will be output for each detected line and for each detected word within the line.
  • Face Detection: Detect faces and their characteristics in the given image. Bounding boxes will be output for each detected face.
  • Image Moderation: Detect unsafe content in images.

  • PPE Detection: Detect Personal Protective Equipment such as masks and gloves in an image. Bounding boxes are output for each piece of equipment and body part they apply to, such as a face.

The remaining parameters available depend on the value of the Request > Action parameter. Parameters for each Action are detailed below.

Minimum Confidence (0.0-1.0)

The lowest detection confidence level to include in results.

Maximum Number of Objects

The maximum number of objects (labels) to detect per supplied image.

Text detection does not require any additional parameters.

Output Facial Landmark Points

Yes: the output features will have aggregate geometry, consisting of the bounding box polygon and points indicating facial "landmarks." For more information about landmarks, see https://docs.aws.amazon.com/rekognition/latest/dg/faces-detect-images.html for an illustration of the available landmarks. The geometry name for these points will be a descriptor of the landmark, such as "nose" or "chinBottom."

No: the output features will have polygon geometry for the bounding box only.

Minimum Confidence (0.0-1.0)

The lowest detection confidence level to include in results.

PPE detection does not require any additional parameters.

Editing Transformer Parameters

Transformer parameters can be set by directly entering values, using expressions, or referencing other elements in the workspace such as attribute values or user parameters. Various editors and context menus are available to assist. To see what is available, click beside the applicable parameter.

Defining Values

There are several ways to define a value for use in a Transformer. The simplest is to simply type in a value or string, which can include functions of various types such as attribute references, math and string functions, and workspace parameters.

Using the Text Editor

The Text Editor provides a convenient way to construct text strings (including regular expressions) from various data sources, such as attributes, parameters, and constants, where the result is used directly inside a parameter.

Text Editor

Using the Arithmetic Editor

The Arithmetic Editor provides a convenient way to construct math expressions from various data sources, such as attributes, parameters, and feature functions, where the result is used directly inside a parameter.

Arithmetic Editor

Conditional Values

Set values depending on one or more test conditions that either pass or fail.

Parameter Condition Definition Dialog

Content

Expressions and strings can include a number of functions, characters, parameters, and more.

When setting values - whether entered directly in a parameter or constructed using one of the editors - strings and expressions containing String, Math, Date/Time or FME Feature Functions will have those functions evaluated. Therefore, the names of these functions (in the form @<function_name>) should not be used as literal string values.

String Functions

These functions manipulate and format strings.

Special Characters

 A set of control characters is available in the Text Editor.

Math Functions

Math functions are available in both editors.
Date/Time Functions Date and time functions are available in the Text Editor.

Math Operators

These operators are available in the Arithmetic Editor.

FME Feature Functions

These return primarily feature-specific values.

FME Parameters

FME and workspace-specific parameters may be used.
Creating and Modifying User Parameters Create your own editable parameters.

Table Tools

Transformers with table-style parameters have additional tools for populating and manipulating values.

Row Reordering

Enabled once you have clicked on a row item. Choices include:

  • Add a row
  • Remove a row
  • Move current row up one
  • Move current row down one
  • Move current row to top
  • Move current row to bottom

Cut, Copy, and Paste

Enabled once you have clicked on a row item. Choices include:

  • Cut a row - delete and copy to clipboard
  • Copy a row to the clipboard
  • Paste a row from the clipboard

Cut, copy, and paste may be used within a transformer, or between transformers.

Filter

Start typing a string, and the matrix will only display rows matching those characters. Searches all columns. This only affects the display of attributes within the transformer - it does not alter which attributes are output.

Import

Import populates the table with a set of new attributes read from a dataset. Specific application varies between transformers.

Reset/Refresh

Generally resets the table to its initial state, and may provide additional options to remove invalid entries. Behavior varies between transformers.

Note: Not all tools are available in all transformers.

For more information, see Transformer Parameter Menu Options.

Reference

Processing Behavior

Feature-Based

Feature Holding

No
Dependencies Amazon AWS Account with Rekognition access
Aliases AmazonAWSRekognitionConnector
History Released FME 2019.2

FME Community

The FME Community has a wealth of FME knowledge with over 20,000 active members worldwide. Get help with FME, share knowledge, and connect with users globally.

Search for all results about the RekognitionConnector on the FME Community.

 

Examples may contain information licensed under the Open Government Licence – Vancouver, Open Government Licence - British Columbia, and/or Open Government Licence – Canada.