FME Transformers: 2024.2

Categories
Rasters

Rasters

Web

Web

Related Transformers
AzureComputerVisionConnector
AzureTextAnalyticsConnector
ComprehendConnector
GoogleBigQueryConnector
GoogleCloudPubSubConnector
GoogleCloudStorageConnector
GoogleDriveConnector
GoogleLanguageConnector
RekognitionConnector
Categories
Rasters

Rasters

Web

Web

Related Transformers
AzureComputerVisionConnector
AzureTextAnalyticsConnector
ComprehendConnector
GoogleBigQueryConnector
GoogleCloudPubSubConnector
GoogleCloudStorageConnector
GoogleDriveConnector
GoogleLanguageConnector
RekognitionConnector

GoogleVisionConnector

Accesses the Google Vision AI API for image recognition.

Jump to Configuration

Typical Uses

Submit an image to the Google Vision Library to

  • detect labels and objects
  • detect faces and emotions
  • detect text

How does it work?

The GoogleVisionConnector uses your Google Cloud account credentials to access the Google Vision client services.

It will submit an image to the service, and return features with attributes about that image. Each input image may result in several output features.

All confidence scores are returned between 0 and 1.

For more information, see Google’s documentation:

https://cloud.google.com/vision/

Configuration

Input Ports

This transformer accepts any feature. Raster geometries may be used as input if Raster Geometry is selected as the image source.

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 objects.

Object Detection

Detects and extract information about objects in an image, across a broad group of categories. Labels can identify general objects, locations, activities, animal species, products, and more. Detected objects will have a bounding box geometry returned. Bounding boxes are in pixel units, and will align with raster and local file inputs. Bounded boxes for URL inputs are returned as normalized values between 0 and 1.

Attributes

_label

Labels that describe detected entities in the image.

_confidence

The confidence score, which ranges from 0 (no confidence) to 1 (very high confidence).

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 LEFT_EYE, NOSE_TIP, or LEFT_EYE_PUPIL will also be added as additional point geometries on the feature.

When using any input image source, the bounding box is in pixel units, and will align with the input.

Likelihood attributes have possible values of:

UNKNOWN, VERY_UNLIKELY, UNLIKELY, POSSIBLE, LIKELY, VERY_LIKELY

Attributes

_confidence

Overall confidence score of the feature, which ranges from 0 (no confidence) to 1 (very high confidence).

_landmark_confidence

Face landmarking confidence score, which ranges from 0 (no confidence) to 1 (very high confidence).

_joy_likelihood

Joy likelihood.

_sorrow_likelihood

Sorrow likelihood.

_anger_likelihood

Anger likelihood.

_surprise_likelihood

Surprise likelihood.

_under_exposed_likelihood

Under-exposed likelihood.

_blurred_likelihood

Blurred likelihood.

_headwear_likelihood

Headwear likelihood.

Text Detection

Detects and extracts text from any image. For example, a photograph might contain a street sign or traffic sign. Results include the entire extracted string, as well as individual words, and their bounding boxes.

When using any input image source, the bounding box is in pixel units, and will align with the input.

Attributes

_text

A detected text in an image.

_type

Type of detected text. Types can be either PAGE, BLOCK, PARAGRAPH, WORD, or SYMBOL. The following is the hierarchy of text structures contained in text detection: PAGE -> BLOCK -> PARAGRAPH -> WORD -> SYMBOL.

_id

The id of the detected text. Determined by the order of detected text.

_confidence

The confidence of the OCR results of the text structure type. This will be a value between 0 and 1.

_break_type

The type of break found. The possible break types are UNKNOWN, SPACE, SURE_SPACE, EOL_SURE_SPACE, HYPHEN, and LINE_BREAK. See the following Break Types table for more information about these types.

_parent_id

The parent that the detected text is contained in. This value can be null with the text having no parents.

Break Types

UNKNOWN

Unknown break label type.

SPACE

Regular space.

SURE_SPACE

Sure space (very wide).

EOL_SURE_SPACE

Line-wrapping break.

HYPHEN

End-line hyphen that is not present in text; does not co-occur with SPACE or LINE_BREAK.

LINE_BREAK

Line break that ends a paragraph.

Document Text Detection

Detects and extracts text from an image, but is optimized for dense text and documents. For example, an image of a handwritten document may contain blocks, paragraphs, words and symbols. Results include the entire extracted strings for blocks and paragraphs, as well as individual words and symbols.

Attributes

_text

A detected text in an image.

_type

Type of detected text. Types can be either PAGE, BLOCK, PARAGRAPH, WORD, or SYMBOL. The following is the hierarchy of text structures contained in text detection: PAGE -> BLOCK -> PARAGRAPH -> WORD -> SYMBOL.

_id

The id of the detected text. Determined by the order of detected text.

_confidence

The confidence of the OCR results of the text structure type. This will be a value between 0 and 1.

_break_type

The type of break found. The possible break types are UNKNOWN, SPACE, SURE_SPACE, EOL_SURE_SPACE, HYPHEN, and LINE_BREAK. See the following Break Types table for more information about these types.

_parent_id

The parent that the detected text is contained in. This value can be null with the text having no parents.

Break Types

UNKNOWN

Unknown break label type.

SPACE

Regular space.

SURE_SPACE

Sure space (very wide).

EOL_SURE_SPACE

Line-wrapping break.

HYPHEN

End-line hyphen that is not present in text; does not co-occur with SPACE or LINE_BREAK.

LINE_BREAK

Line break that ends a paragraph.

Output will depend on the analysis chosen.

Object Detection

Detects and extracts information about entities in an image. The service may return multiple label guesses for an individual request. For more information, see https://cloud.google.com/vision/docs/labels.

Attributes

_labels{}.name

A list of labels that describe entities in the image.

_labels{}.confidence

A list of the confidence score, which ranges from 0 (no confidence) to 1 (very high confidence).

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

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 text structures found.

Attributes

_detected_pages

The number of pages that were detected in the image

_detected_blocks

The number of blocks that were detected in the image

_detected_paragraphs

The number of paragraphs that were detected in the image

_detected_words

The number of words that were detected in the image

_detected_symbols

The number of symbols that were detected in the image

Document 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 text structures found.

Attributes

_detected_pages

The number of pages that were detected in the image

_detected_blocks

The number of blocks that were detected in the image

_detected_paragraphs

The number of paragraphs that were detected in the image

_detected_words

The number of words that were detected in the image

_detected_symbols

The number of symbols that were detected in the image

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 GoogleVisionConnector 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 GoogleVisionConnector can use credentials from different sources. Using a Service Account File integrates best with FME, but in some cases, you may wish to use a web connection.

Account

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

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

Image Source

The source where the images can come from. Choices are:

  • URL: Specify the URL to an image. Not all URLs are supported. Note that not all links will work. It is recommended to download the image locally or upload it to the Google Cloud Storage.
  • Local File: Specify the path to a local file on disk
  • Raster Geometry: Take in a raster image object.
  • Google Storage Object: Take an image object from Google Cloud Storage. You will need to specify a bucket and path to the object.

Detection Type

The type of operation to perform. Choices are:

  • Document Text Detection: Detects text structures in any image, but optimized for dense text and documents. Bounding boxes will be output for each detected text structure selected.
  • Face Detection: Detects faces and their characteristics in the given image. Bounding boxes will be output for each detected face.
  • Object Detection: Detects entities, then labels and localizes objects in the image.
  • Text Detection: Detects text structures in any image. Bounding boxes will be output for each detected text structure selected.

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

Included Text Detection Features

Pages

Whether page text structures should be detected.

Blocks

Whether block text structures should be detected.

Paragraphs

Whether paragraph text structures should be detected.

Words

Whether word text structures should be detected.

Symbols

Whether symbol text structures should be detected.

Face Detection Options

Face detection does not require any additional parameters.

Object Detection Options

Object detection does not require any additional parameters.

Included Text Detection Features

Pages

Whether page text structures should be detected.

Blocks

Whether block text structures should be detected.

Paragraphs

Whether paragraph text structures should be detected.

Words

Whether word text structures should be detected.

Symbols

Whether symbol text structures should be detected.

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 Google Cloud Account with access to the Cloud Vision API
Aliases  
History Released FME 2019.2

FME Community

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