Accesses the Azure Computer Vision Service to detect objects in images.
Typical Uses
Submitting text to the Azure Computer Vision service to
- detect individual objects
- describe the general contents
How does it work?
The AzureComputerVisionConnector uses your Azure Cognitive Services 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 service.
It will submit images to the service, and return features with attributes that describe the contents of the image. Services supported are object detection, text detection, and face detection.
- 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 will always return bounding boxes around the detected text.
- For face detection, if the service is able to identify the exact location of a face in the image, a bounding box geometry will also be returned. There is also the option to detect and locate facial landmarks.
Usage Notes
- For better performance, requests to the Computer Vision 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.
- While powerful, the use of AI has important legal and ethical implications. Consult your local AI legislation and ethical guidelines before applying the AzureComputerVisionConnector in a production environment. For information about privacy and compliance with respect to Azure Cognitive Services, please see https://azure.microsoft.com/en-ca/support/legal/cognitive-services-compliance-and-privacy.
Configuration
Input Ports
Input
This transformer accepts any feature. Raster geometries may be used as input if Raster Geometry is selected as the image source.
Note: The AzureComputerVisionConnector does not support coordinate systems.
Output Ports
Output
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 may also be returned, with a separate confidence value. Each input image may result in several detection features. Bounding boxes are in pixel units, and will align with the input.
Attributes
| _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 100 that indicates the probability that a given prediction is correct. |
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 or URL as input, the bounding box is in pixel units, and will align with the input.
Attributes
| _text | The detected text in the line or word. |
| _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. A bounding box geometry will also be returned, with a separate confidence value. Each input image may result in several detection features. Bounding boxes are in pixel units, and will align with the input.
Attributes
The following attributes are returned:
_age, _gender, _smile, _emotion_anger, _emotion_contempt, _emotion_disgust, _emotion_fear, _emotion_happiness, _emotion_neutral, _emotion_sadness, _emotion_surprise, _facial_hair_beard, _facial_hair_moustache, _facial_hair_sideburns, _head_pose_pitch, _head_pose_roll, _head_pose_yaw, _glasses, _hair_bald, _makeup_eye, _makeup_lip, _accessories{}.type, _accessories{}.confidence, _blur_level, _blur_value, _exposure_level, _exposure_value, _noise_level, _noise_value, _hair_color{}.value, _hair_color{}.confidence
Summary
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
| _labels{}.confidence | A number between 0 and 100 that indicates the probability that a given label is correct. |
| _labels{}.name | A word or phrase describing the content of 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 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 that contains details on the total amount of faces that were detected in the image.
Attributes
| _detected_faces | The number of faces that were detected in the image. |
<Initiator>
The incoming feature is output through this port.
<Rejected>
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 AzureComputerVisionConnector 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
Authentication
To use the AzureTextAnalyticsConnector or the AzureComputerVisionConnector you will need a Cognitive Services Account, then generate an endpoint and key to authenticate through our connectors.
| Credential Source |
The AzureComputerVisionConnector 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 Azure Cognitive Services connection, click the 'Account' drop-down box and select 'Add Web Connection...'. The connection can then be managed via Tools -> FME Options... -> Web Connections. |
| Endpoint and Secret Key | Available when the credential source is Embedded. An endpoint and secret key can be specified directly in the transformer instead of in a web connection. |
Request
| Image Source |
Where to get the input image for detection. Options are:
|
| Input Filename | If Local File is selected for the image source, the path to the JPEG or PNG file to use. |
| URL | If URL is selected for the image source, the source URL to use. |
| Detection Type |
The type of operation to perform. Choices are:
|
The remaining parameters available depend on the value of the Request > Detection Type parameter. Parameters for each Detection Type are detailed below.
Parameters - Object Detection
Object Detection Options
Object detection does not require any additional parameters.
Parameters - Text Detection
Text Detection Options
|
Text Analysis Language |
The language to be detected. The language code must be used for the attribute value (for example, “en”). Use “Unknown [unk]” for automatic language detection. |
Parameters - Face Detection
Face Detection Options
|
Output Facial Landmark Points |
Whether to include information about facial landmarks in output features. |
Editing Transformer Parameters
Using a set of menu options, transformer parameters can be assigned by referencing other elements in the workspace. More advanced functions, such as an advanced editor and an arithmetic editor, are also available in some transformers. To access a menu of these options, click 
beside the applicable parameter. For more information, see Transformer Parameter Menu Options.
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. There are a number of tools and shortcuts that can assist in constructing values, generally available from the drop-down context menu adjacent to the value field.
How to Set Parameter Values
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.
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.
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
| These functions manipulate and format strings. | |
|
Special Characters |
A set of control characters is available in the Text Editor. |
| Math functions are available in both editors. | |
| Date/Time Functions | Date and time functions are available in the Text Editor. |
| These operators are available in the Arithmetic Editor. | |
| These return primarily feature-specific values. | |
| FME and workspace-specific parameters may be used. | |
| Creating and Modifying User Parameters | Create your own editable parameters. |
Dialog Options - Tables
Transformers with table-style parameters have additional tools for populating and manipulating values.
Table Tools
|
Row Reordering
|
Enabled once you have clicked on a row item. Choices include:
|
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Cut, Copy, and Paste
|
Enabled once you have clicked on a row item. Choices include:
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. |
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Import
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Import populates the table with a set of new attributes read from a dataset. Specific application varies between transformers. |
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Reset/Refresh
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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.
Reference
|
Processing Behavior |
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|
Feature Holding |
No |
| Dependencies | Azure Cognitive Services Account |
| Aliases | |
| History | Released FME 2019.2 |
FME Community
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Search for all results about the AzureComputerVisionConnector on the FME Community.
Examples may contain information licensed under the Open Government Licence – Vancouver and/or the Open Government Licence – Canada.