Description:
This step grabs the faces and approximates age and gender from an image using Azure ML.
Inputs
- varGlobalToStoreValue– Variable or global to hold the result
- imageFilePath – file path to the image
Returns
- True – step executed successfully
- False – step failed to execute
Usage:
Example:
Let’s build and execute the “clsAzureMLGetFacesDef” example.
- Create a new process definition
- Drag a get "clsAzureMLgetFaces" step from the toolbox
- Connect the dots between the start and “clsAzureMLgetFaces” step
- Define a variable/global to store the result after execution
- Click the "clsAzureMLgetFaces" step to configure its "Settings" properties. Provide a name to the step. Provide the image file path on the app server. Provide the variable/global to store the result.
- The “Logging” setting configuration is necessary for documentation and also measures the workflow progress and the percent complete. This is achieved by configuring the step state and percent fields individually, as shown in the images below. Configure the “Logging” using the following properties.
- To complete the configuration, navigate to the Status—Settings—Configuration—Azure ML settings menu option. A sample image is included here for reference.
- Save the process definition. Create a new process instance and execute. The step should get the faces age and gender from an image and store the result in variable.result as configured.
Definition Sample:
You may download the sample definition(s) from the link here and later import it (drag-drop) to your FlowWright Process Definition (XML file) or Form Definition (HTML file) page.
NOTE: Please verify and complete the process steps for any missing configurations, such as file path references and database connections after import. Then, save the definition to confirm the changes.
Click here to download the sample file.