Description:

The Color Detection step enables a FlowWright workflow to detect color schemes present in an image using Azure Cognitive Services.

The step belongs to the Azure Cognitive Services category and is exposed in FlowWright with the display name Color detection. Its XML definition describes the capability as “Detect color schemes in images.”

The step accepts an image file path and stores the resulting color-detection information in a workflow variable or global variable. This allows image color analysis to become part of an automated business process and enables subsequent workflow steps to use the result for classification, routing, reporting, or other processing.

Typical uses include:

• Detecting color schemes in uploaded images
• Classifying images based on visual characteristics
• Enriching image-related business records
• Supporting image-processing workflows
• Routing images based on color information
• Organizing image collections
• Automating visual-content analysis
• Using image characteristics as inputs to downstream business rules

This step allows FlowWright workflows to incorporate color-based image analysis without requiring users to manually inspect and classify images.

Usage:

The Color Detection step is typically used after a workflow receives or generates an image that needs to be analyzed for its color characteristics.

During execution:

1. Provide the image file path.
2. Execute the Color Detection step.
3. The image is analyzed for color schemes.
4. The resulting information is stored in the configured workflow variable or global variable.
5. Continue workflow processing through the True or False execution path.

A typical workflow might look like this:

Typical workflow scenarios include:

• Product-image analysis
• Image classification
• Marketing-content processing
• Image metadata enrichment
• Automated image routing
• Visual-content analysis
• Image-library organization
• Business processes that depend on image characteristics

To use this step, you need to set up an Azure ML service in the FlowWright application. Go to the Status > Settings > Configuration page. Select the Azure ML category from the drop-down menu. Click here to learn more about the Azure ML and Cognitive Service subscription. 

A sample Azure ML configuration is provided here for reference. 

Example:

Let’s build and execute the “clsAzureMLGetColorsDef” example. 

• Create a new process definition named “clsAzureMLGetColorsDef” and open it in designer mode. 
• Drag a “clsAzureMLGetColors” step to the canvas.
• Connect the dots between the “Start” and “clsAzureMLGetColors” steps, as shown above. 
• Define a variable or a global to store the result.
• Click the "clsAzureMLGetColors" step to configure its "Required" properties. Provide a name for the step. Provide the image file path on the app server. Provide a variable or global reference to store the result. Then click the Save button. Note: Click the "AI Predict" button for the Copilot to add new process steps that match your process description. 

• The image file path indicates where the image is stored on the server. The example image used is included below for reference. 

• The “Logging” configuration is necessary for documentation and also measures workflow progress and 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.

• Save the process definition, create a new instance, and run it. Render the process instance. Click the process step to view its properties. The step should retrieve the image's foreground and background color details, and store the result in “variable.result” as configured. 

Tips:

• Ensure that the configured Image file path points to an accessible image before executing the step.
• Store the result in a workflow variable when it is needed by subsequent activities in the current workflow.
• Use a global variable when the result needs to be referenced more broadly within the workflow.
• Validate the returned result before using it for business-critical decisions.
• Use the False path to handle image-processing failures or unexpected input conditions.
• Test representative images before deploying color-analysis workflows into production.
• Combine this step with Decision, Business Rule, database, file-management, notification, and reporting steps to build complete image-processing workflows.

Notes:

• The step belongs to the Azure Cognitive Services category. 
• Its display name is Color detection. 
• An image file path is required. 
• Variable/Global to store the result is required. 
• Both configured properties use the string data type. 
• The step provides True and False execution paths. 
• The XML defines two input connections and two output connections. 
• The XML does not specify the detailed structure, format, number of detected colors, or confidence information contained in the stored result. Those details should not be assumed from the XML alone.

Color Detection vs. Object Detection:

Both Color Detection (clsAzureMLGetColors) and Object Detection (clsAzureMLGetObjects) are FlowWright image-analysis steps in the Azure Cognitive Services category. Their configuration is almost identical, but their analysis objectives are different.

When to use each step:

Use Color Detection when the workflow needs information about the visual color characteristics of an image.

Typical examples include:

• Analyzing product-image color schemes
• Classifying images using visual characteristics
• Enriching image metadata with color information
• Supporting color-oriented business rules
• Routing images based on visual characteristics

Use Object Detection when the workflow needs information about objects contained in an image.

Typical examples include:

• Identifying objects in photographs
• Classifying images based on detected objects
• Routing images according to their contents
• Enriching image records with object information
• Supporting object-oriented image-processing workflows

Key distinction:

The simplest way to distinguish the two is:

Color Detection: “What color scheme does this image contain?”

Object Detection: “What common objects are present in this image?”

The XML definitions explicitly describe the two capabilities as “Detect color schemes in images” and “Detect common objects in images.” 

In short: use Color Detection when the workflow needs color-related image information, and use Object Detection when it needs information about objects contained in the image.

Definition Sample:

You may download the sample workflow definition from the link provided and import it into your FlowWright environment.

Note: Verify and complete any missing configuration after importing the sample, including:

• Image file path
• Variable/Global to store the result
• Image availability and accessibility
• Success and failure workflow branches
• Any downstream processing that consumes the color-detection result

After verifying the configuration, save and publish the workflow before execution.

Click here to download the sample workflow definition.

Click here to download the sample file.