RekognitionConnector

Connects to Amazon Rekognition for object detection in images.

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 to connect and submit images for analysis.

Images may be provided as local files, files on S3, or as raster geometry, and each one may produce multiple output features.

Services supported are:

Detection Type	Analysis
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. Facial landmarks may be included, 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.
Image Moderation	Images submitted for moderation are not output via the Output port. One Summary feature per image is produced, with details of potentially unsafe content.
Object Detection	Successfully identified objects will result in output features with attributes describing the objects. If the service is able to identify the location of an object in the image, a bounding box geometry will also be returned, with a separate confidence value. 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.
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.
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.

Detection Type

Analysis

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. Facial landmarks may be included, 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.

Image Moderation

Images submitted for moderation are not output via the Output port.

One Summary feature per image is produced, with details of potentially unsafe content.

Object Detection

Successfully identified objects will result in output features with attributes describing the objects.

If the service is able to identify the location of an object in the image, a bounding box geometry will also be returned, with a separate confidence value.

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.

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.

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.

Optional Input Port

This transformer has two modes, depending on whether a connector is attached to the Input port or not:

Input-driven: When input features are connected, the transformer runs once for each feature it receives in the Input port.
Run Once: When no input features are connected, the transformer runs one time.

When the Input port is in use, the Initiator output port is also enabled.

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.
For an overview of Rekognition's capabilities and limitations, see Amazon’s The Facts on Facial Recognition with Artificial Intelligence.

Configuration

Input Ports

Output Ports

Output

Features with added attributes, as specified in parameters and according to Detection Type.

Detection Type	Output - Input-Driven	Output - Run Once
Face Detection	Input feature(s), one copy for each face identified, with details about the face.	New feature(s), one for each face identified, with details about the face.
Image Moderation	None.	None.
Object Detection	Input feature(s), one copy for each object identified, with details about the object.	New feature(s), one for each object identified, with details about the object.
PPE Detection	Input feature(s), one copy for each PPE object identified, with details about the object.	New feature(s), one for each PPE object identified, with details about the object.
Text Detection	Input features, one copy for each text element, with details about the text.	New features, one copy for each text element, with details about the text.

Summary

One feature per image is output here, with added attributes describing detection result success.

Detection Type

Summary Feature Attributes

Face Detection

_detected_faces

The number of faces that were detected in the image.

Image Moderation

_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. See Amazon Rekognition > Moderating content.
_moderation_labels{}.parent	If the list item for _labels{n}.name is a subcategory, _labels{n}.parent will contain its parent category.

Object Detection

_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.

PPE Detection

_person_count	The total number of people
_ppe_count	The total number of PPE equipment

Text Detection

_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.

Parameters

Authentication

Credential Source

Select the type of credentials to use:

Web Connection (Recommended): Use an Amazon Web Services web connection.
Embedded: Embed an access key ID and secret access key as parameters in the transformer.
System: Use credentials already configured on the system in the environment variables AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY. For more information about configuring AWS credentials system-wide, see Getting started with the AWS CLI .

Account

When Credential Source is Web Connection, select or create a Web Connection connecting to an Amazon Web ServicesWeb Service.

See Using Web Connections.

Region

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

Embedded Credentials

When Credential Source is Embedded:

Access Key ID	Provide S3 account credentials.
Secret Access Key	Provide S3 account credentials.

Request

Image Source	Select the source of the image: File: A JPEG or PNG file on disk. File on S3: An image stored on S3. Raster Geometry: Raster geometry on a feature. Note that the raster will be uploaded to the service as JPEG if possible, otherwise PNG will be used.
Input Filename	When Image Source is File, provide the path and filename.
Bucket	When Image Source is File on S3, provide the bucket.
Key	When Image Source is File on S3, provide the key.
Action	Select the type of detection to perform: Face Detection Image Moderation Object Detection PPE Detection Text Detection

Action > Face Detection

Face Detection Options

Output Facial Landmark Points

Select an option for including landmarks:

Yes: Output features have aggregate geometry, consisting of the bounding box polygon, points indicating facial landmarks, and descriptor labels.
No: Do not include landmarks.

See Amazon Rekognition > Detecting faces in an image.

Added Attributes

Output features will receive these attributes.

_age_range.high

_age_range.low

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.confidence

_beard.value

Whether or not the subject has a beard.

_confidence

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

_emotions{}.confidence

_emotions{}.value

List attribute of detected emotions.

_eyeglasses.confidence

_eyeglasses.value

Whether the subject is wearing glasses.

_eyes_open.confidence

_eyes_open.value

Whether the subject's eyes are open.

_gender.confidence

_gender.value

Whether the subject is male or female.

_mouth_open.confidence

_mouth_open.value

Whether the subject's mouth is open.

_mustache.confidence

_mustache.value

Whether the subject has a mustache.

_pose.pitch

_pose.roll

_pose.yaw

Pose orientation:

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

_quality.sharpness

Overall image quality:

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

_smile.confidence

_smile.value

Whether the subject is smiling.

_sunglasses.confidence

_sunglasses.value

Whether the subject is wearing sunglasses.

Action > Object Detection

Object Detection Options

Minimum Confidence (0.0-1.0)	Specify the lowest detection confidence level to include in results.
Maximum Number of Objects	Specify the maximum number of objects (labels) to detect per supplied image.

Added Attributes

Output features will receive these 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 at that location.
_confidence	A number between 0 and 1 that indicates the probability that a given prediction is correct.
_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.

Action > PPE Detection

PPE Detection Options

PPE detection has no parameters to configure.

Added Attributes

Output features will receive these 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.

Action > Text Detection

Text Detection Options

Text detection has no parameters to configure.

Added Attributes

Output features will receive these 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.
_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.

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.

How to Set Parameter Values

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.

Content Types

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.

Dialog Options - Tables

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 Online Resources

The FME Community and Support Center Knowledge Base have a wealth of information, including active forums with 35,000+ members and thousands of articles.

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.

RekognitionConnector

Typical Uses

How does it work?

Optional Input Port

Usage Notes

Configuration

Input Ports

Output Ports

Parameters

Face Detection Options

Added Attributes

Image Moderation Options

Added Attributes

Object Detection Options

Added Attributes

PPE Detection Options

Added Attributes

Text Detection Options

Added Attributes

Editing Transformer Parameters

Defining Values

Using the Text Editor

Using the Arithmetic Editor

Conditional Values

Content

Table Tools

Reference

FME Online Resources