Halo Visualization¶

v2¶

Create a soft, glowing halo effect around detected objects by blurring and overlaying colored masks, providing a distinctive visual style that highlights object boundaries with a smooth, illuminated appearance.

How This Block Works¶

This block takes an image and instance segmentation predictions (with masks) and creates a glowing halo effect around each detected object. The block:

1. Takes an image and instance segmentation predictions (with masks) as input
2. Extracts segmentation masks for each detected object (uses masks from predictions, or creates bounding box masks if masks are not available)
3. Applies color styling to each mask based on the selected color palette, with colors assigned by class, index, or track ID
4. Creates colored mask overlays for each detection, combining masks from largest to smallest area (to handle overlapping objects correctly)
5. Applies a blur filter (average pooling with specified kernel size) to the colored masks, creating a soft, diffused halo effect around object edges
6. Blends the blurred halo overlay with the original image using the specified opacity level, creating a glowing appearance around detected objects
7. Returns an annotated image with soft halo effects overlaid around each detected object

The block creates halos by blurring the colored masks, which produces a soft, glowing effect that extends beyond the object boundaries. Unlike hard-edged visualizations (like bounding boxes or polygons), halos provide a smooth, illuminated appearance that makes objects stand out while maintaining a visually appealing aesthetic. The blur kernel size controls how far the halo extends beyond the object (larger kernel = wider halo), and the opacity controls the intensity of the glow effect. This block requires instance segmentation predictions with masks, as it uses mask shapes to create the halo effect around object perimeters.

Common Use Cases¶

• Artistic and Aesthetic Visualizations: Create visually appealing, glowing effects around detected objects for artistic presentations, design applications, or user interfaces where soft, illuminated halos provide a modern, polished appearance
• Soft Object Highlighting: Highlight detected objects with gentle, diffused halos when hard edges would be too harsh or distracting, useful for presentations, marketing materials, or consumer-facing applications
• Overlapping Object Visualization: Use halos to visualize overlapping or closely-spaced objects where hard boundaries would create visual clutter, allowing multiple objects to be distinguished while maintaining visual clarity
• Brand and Design Applications: Integrate halo effects into brand visuals, promotional materials, or design systems where soft, glowing annotations match design aesthetics better than angular bounding boxes
• Visual Emphasis and Focus: Draw attention to detected objects with glowing halos that create a natural visual focus point, useful in dashboards, monitoring interfaces, or interactive applications
• Mask-Based Object Highlighting: Visualize instance segmentation results with soft halo effects, providing an alternative to solid mask overlays when you want to show object boundaries without obscuring image details

Connecting to Other Blocks¶

The annotated image from this block can be connected to:

• Other visualization blocks (e.g., Label Visualization, Dot Visualization, Bounding Box Visualization) to combine halo effects with additional annotations for comprehensive visualization
• Data storage blocks (e.g., Local File Sink, CSV Formatter, Roboflow Dataset Upload) to save images with halo effects for documentation, reporting, or analysis
• Webhook blocks to send visualized results with halo effects to external systems, APIs, or web applications for display in dashboards or monitoring tools
• Notification blocks (e.g., Email Notification, Slack Notification) to send annotated images with halo effects as visual evidence in alerts or reports
• Video output blocks to create annotated video streams or recordings with halo effects for live monitoring, artistic visualizations, or post-processing analysis

Type identifier¶

Use the following identifier in step "type" field: roboflow_core/halo_visualization@v2to add the block as as step in your workflow.

Properties¶

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections¶

Input and Output Bindings¶

The available connections depend on its binding kinds. Check what binding kinds Halo Visualization in version v2 has.

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/halo_visualization@v2",
    "image": "$inputs.image",
    "copy_image": true,
    "predictions": "$steps.instance_segmentation_model.predictions",
    "color_palette": "DEFAULT",
    "palette_size": 10,
    "custom_colors": [
        "#FF0000",
        "#00FF00",
        "#0000FF"
    ],
    "color_axis": "CLASS",
    "opacity": 0.8,
    "kernel_size": 40
}

v1¶

Create a soft, glowing halo effect around detected objects by blurring and overlaying colored masks, providing a distinctive visual style that highlights object boundaries with a smooth, illuminated appearance.

How This Block Works¶

This block takes an image and instance segmentation predictions (with masks) and creates a glowing halo effect around each detected object. The block:

1. Takes an image and instance segmentation predictions (with masks) as input
2. Extracts segmentation masks for each detected object (uses masks from predictions, or creates bounding box masks if masks are not available)
3. Applies color styling to each mask based on the selected color palette, with colors assigned by class, index, or track ID
4. Creates colored mask overlays for each detection, combining masks from largest to smallest area (to handle overlapping objects correctly)
5. Applies a blur filter (average pooling with specified kernel size) to the colored masks, creating a soft, diffused halo effect around object edges
6. Blends the blurred halo overlay with the original image using the specified opacity level, creating a glowing appearance around detected objects
7. Returns an annotated image with soft halo effects overlaid around each detected object

The block creates halos by blurring the colored masks, which produces a soft, glowing effect that extends beyond the object boundaries. Unlike hard-edged visualizations (like bounding boxes or polygons), halos provide a smooth, illuminated appearance that makes objects stand out while maintaining a visually appealing aesthetic. The blur kernel size controls how far the halo extends beyond the object (larger kernel = wider halo), and the opacity controls the intensity of the glow effect. This block requires instance segmentation predictions with masks, as it uses mask shapes to create the halo effect around object perimeters.

Common Use Cases¶

• Artistic and Aesthetic Visualizations: Create visually appealing, glowing effects around detected objects for artistic presentations, design applications, or user interfaces where soft, illuminated halos provide a modern, polished appearance
• Soft Object Highlighting: Highlight detected objects with gentle, diffused halos when hard edges would be too harsh or distracting, useful for presentations, marketing materials, or consumer-facing applications
• Overlapping Object Visualization: Use halos to visualize overlapping or closely-spaced objects where hard boundaries would create visual clutter, allowing multiple objects to be distinguished while maintaining visual clarity
• Brand and Design Applications: Integrate halo effects into brand visuals, promotional materials, or design systems where soft, glowing annotations match design aesthetics better than angular bounding boxes
• Visual Emphasis and Focus: Draw attention to detected objects with glowing halos that create a natural visual focus point, useful in dashboards, monitoring interfaces, or interactive applications
• Mask-Based Object Highlighting: Visualize instance segmentation results with soft halo effects, providing an alternative to solid mask overlays when you want to show object boundaries without obscuring image details

Connecting to Other Blocks¶

The annotated image from this block can be connected to:

• Other visualization blocks (e.g., Label Visualization, Dot Visualization, Bounding Box Visualization) to combine halo effects with additional annotations for comprehensive visualization
• Data storage blocks (e.g., Local File Sink, CSV Formatter, Roboflow Dataset Upload) to save images with halo effects for documentation, reporting, or analysis
• Webhook blocks to send visualized results with halo effects to external systems, APIs, or web applications for display in dashboards or monitoring tools
• Notification blocks (e.g., Email Notification, Slack Notification) to send annotated images with halo effects as visual evidence in alerts or reports
• Video output blocks to create annotated video streams or recordings with halo effects for live monitoring, artistic visualizations, or post-processing analysis

Type identifier¶

Use the following identifier in step "type" field: roboflow_core/halo_visualization@v1to add the block as as step in your workflow.

Properties¶

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections¶

Input and Output Bindings¶

The available connections depend on its binding kinds. Check what binding kinds Halo Visualization in version v1 has.

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/halo_visualization@v1",
    "image": "$inputs.image",
    "copy_image": true,
    "predictions": "$steps.instance_segmentation_model.predictions",
    "color_palette": "DEFAULT",
    "palette_size": 10,
    "custom_colors": [
        "#FF0000",
        "#00FF00",
        "#0000FF"
    ],
    "color_axis": "CLASS",
    "opacity": 0.8,
    "kernel_size": 40
}