Background Color Visualization

Class: BackgroundColorVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.background_color.v1.BackgroundColorVisualizationBlockV1

Apply a colored overlay to areas outside detected regions, effectively masking the background while preserving detected objects at their original appearance.

How This Block Works

This block takes an image and detection predictions and applies a colored overlay to all areas outside of the detected objects, leaving the detected regions unchanged. The block:

1. Takes an image and predictions as input
2. Creates a colored mask layer with the specified background color
3. Identifies detected regions from bounding boxes or segmentation masks (preserves detected objects)
4. Applies the colored overlay to all areas outside the detected regions with the specified opacity
5. Blends the colored overlay with the original image based on the opacity setting
6. Returns an annotated image where detected objects appear unchanged, while the background is filled with the specified color

The block works with both object detection predictions (using bounding boxes) and instance segmentation predictions (using masks). When masks are available, it preserves the exact shape of detected objects; otherwise, it uses bounding box regions. The opacity parameter controls how transparent or opaque the background overlay is, allowing you to create effects ranging from subtle background dimming (low opacity) to complete background replacement (high opacity). This creates a visual focus effect that highlights the detected objects by de-emphasizing or completely hiding the background.

Common Use Cases

• Object Focus and Highlighting: Highlight detected objects by dimming or replacing the background, making objects stand out for presentations, documentation, or user interfaces
• Background Removal Effects: Create images where backgrounds are replaced with solid colors or semi-transparent overlays for product photography, content creation, or design workflows
• Privacy and Anonymization: Mask backgrounds while preserving detected objects (e.g., people, vehicles) to anonymize images, protect privacy, or comply with data protection requirements
• Visual Debugging and Validation: Dim backgrounds to focus attention on detected regions when validating model performance, checking detection accuracy, or debugging detection results
• Presentation and Documentation: Create clean, professional visualizations for reports, presentations, or documentation where you want to emphasize detected objects without distracting backgrounds
• Content Creation and Editing: Prepare images for further processing, compositing, or editing by isolating detected objects with colored backgrounds for easier manipulation or integration into other workflows

Connecting to Other Blocks

The annotated image from this block can be connected to:

• Other visualization blocks (e.g., Label Visualization, Bounding Box Visualization, Polygon Visualization) to add additional annotations on top of the background-colored image for comprehensive visualization
• Data storage blocks (e.g., Local File Sink, CSV Formatter, Roboflow Dataset Upload) to save images with background coloring for documentation, reporting, or archiving
• Webhook blocks to send visualized results with background coloring 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 background coloring as visual evidence in alerts or reports
• Video output blocks to create annotated video streams or recordings with background coloring for live monitoring, tracking visualization, or post-processing analysis

Type identifier

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

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
copy_image	bool	Enable this option to create a copy of the input image for visualization, preserving the original. Use this when stacking multiple visualizations..	✅
color	str	Color to use for the background overlay. Areas outside detected regions will be filled with this color. Can be a color name (e.g., 'BLACK', 'WHITE') or color code in HEX format (e.g., '#000000') or RGB format (e.g., 'rgb(0, 0, 0)')..	✅
opacity	float	Opacity of the background overlay, ranging from 0.0 (fully transparent, original background visible) to 1.0 (fully opaque, complete background replacement). Values between 0.0 and 1.0 create a blend between the original image and the background color. Lower values create subtle background dimming, while higher values create stronger background replacement effects..	✅

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

Available Connections

Compatible Blocks

Check what blocks you can connect to Background Color Visualization in version v1.

• inputs: Mask Visualization, Classification Label Visualization, Detections Consensus, Detections Merge, Instance Segmentation Model, Webhook Sink, Multi-Label Classification Model, Email Notification, QR Code Generator, VLM As Detector, LMM, SAM 3, Detection Offset, Corner Visualization, Image Convert Grayscale, Stability AI Outpainting, Segment Anything 2 Model, Halo Visualization, JSON Parser, Object Detection Model, Trace Visualization, Google Vision OCR, Instance Segmentation Model, CSV Formatter, Text Display, Stitch Images, Google Gemini, Local File Sink, Slack Notification, VLM As Classifier, Roboflow Dataset Upload, PTZ Tracking (ONVIF).md), Color Visualization, Dot Visualization, Polygon Visualization, Object Detection Model, Anthropic Claude, Byte Tracker, Contrast Equalization, Identify Changes, Detections Classes Replacement, Velocity, Moondream2, SIFT Comparison, Halo Visualization, Florence-2 Model, Blur Visualization, Label Visualization, Twilio SMS/MMS Notification, Ellipse Visualization, OpenAI, SIFT, Model Monitoring Inference Aggregator, Single-Label Classification Model, Detections List Roll-Up, OpenAI, Image Threshold, Background Color Visualization, Model Comparison Visualization, OpenAI, Keypoint Detection Model, Gaze Detection, Polygon Visualization, Twilio SMS Notification, SAM 3, Bounding Box Visualization, OCR Model, Overlap Filter, Icon Visualization, Time in Zone, Google Gemini, Florence-2 Model, Roboflow Dataset Upload, Anthropic Claude, Dynamic Zone, Dynamic Crop, VLM As Detector, Google Gemini, Path Deviation, Image Blur, Line Counter, Byte Tracker, Stability AI Inpainting, Template Matching, Image Contours, Path Deviation, Morphological Transformation, Triangle Visualization, Bounding Rectangle, Detections Stitch, Relative Static Crop, Detections Filter, Camera Calibration, Grid Visualization, Detections Stabilizer, Camera Focus, Image Slicer, Detections Combine, LMM For Classification, Line Counter Visualization, Keypoint Detection Model, Llama 3.2 Vision, SIFT Comparison, Camera Focus, Time in Zone, Background Subtraction, Image Slicer, Circle Visualization, Seg Preview, Identify Outliers, Clip Comparison, Email Notification, Byte Tracker, Image Preprocessing, SAM 3, Depth Estimation, Time in Zone, CogVLM, Absolute Static Crop, Roboflow Custom Metadata, EasyOCR, Stitch OCR Detections, Perspective Correction, Anthropic Claude, Pixelate Visualization, Stability AI Image Generation, Reference Path Visualization, Keypoint Visualization, VLM As Classifier, Detection Event Log, Polygon Zone Visualization, YOLO-World Model, Stitch OCR Detections, Crop Visualization, Motion Detection, OpenAI, Detections Transformation
• outputs: Anthropic Claude, Mask Visualization, Classification Label Visualization, Instance Segmentation Model, Multi-Label Classification Model, Email Notification, Dynamic Crop, CLIP Embedding Model, VLM As Detector, VLM As Detector, Google Gemini, Multi-Label Classification Model, LMM, SAM 3, Image Blur, Corner Visualization, Image Convert Grayscale, Byte Tracker, Stability AI Outpainting, SmolVLM2, Segment Anything 2 Model, Halo Visualization, Stability AI Inpainting, Object Detection Model, Template Matching, Single-Label Classification Model, Image Contours, Trace Visualization, Google Vision OCR, Morphological Transformation, Triangle Visualization, Instance Segmentation Model, Clip Comparison, Detections Stitch, Relative Static Crop, Text Display, Stitch Images, Google Gemini, Camera Calibration, Detections Stabilizer, VLM As Classifier, Roboflow Dataset Upload, Camera Focus, Color Visualization, Dot Visualization, Image Slicer, Polygon Visualization, Object Detection Model, Anthropic Claude, LMM For Classification, Line Counter Visualization, Keypoint Detection Model, Buffer, Llama 3.2 Vision, Contrast Equalization, SIFT Comparison, Camera Focus, Perception Encoder Embedding Model, Dominant Color, Time in Zone, Background Subtraction, Image Slicer, Circle Visualization, Moondream2, Seg Preview, Halo Visualization, Florence-2 Model, Blur Visualization, Qwen3-VL, Twilio SMS/MMS Notification, Label Visualization, Barcode Detection, Clip Comparison, Ellipse Visualization, OpenAI, QR Code Detection, SIFT, Image Preprocessing, SAM 3, Single-Label Classification Model, OpenAI, Image Threshold, Background Color Visualization, Model Comparison Visualization, Depth Estimation, OpenAI, Motion Detection, Keypoint Detection Model, CogVLM, Absolute Static Crop, Gaze Detection, EasyOCR, Perspective Correction, Qwen2.5-VL, Anthropic Claude, Pixelate Visualization, Reference Path Visualization, Stability AI Image Generation, Keypoint Visualization, SAM 3, Polygon Visualization, VLM As Classifier, Bounding Box Visualization, Polygon Zone Visualization, OCR Model, YOLO-World Model, Icon Visualization, Crop Visualization, Pixel Color Count, Google Gemini, OpenAI, Florence-2 Model, Roboflow Dataset Upload

Input and Output Bindings

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

Bindings

• input

  • image (image): The image to visualize on..
  • copy_image (boolean): Enable this option to create a copy of the input image for visualization, preserving the original. Use this when stacking multiple visualizations..
  • predictions (Union[instance_segmentation_prediction, object_detection_prediction, rle_instance_segmentation_prediction, keypoint_detection_prediction]): Model predictions to visualize..
  • color (string): Color to use for the background overlay. Areas outside detected regions will be filled with this color. Can be a color name (e.g., 'BLACK', 'WHITE') or color code in HEX format (e.g., '#000000') or RGB format (e.g., 'rgb(0, 0, 0)')..
  • opacity (float_zero_to_one): Opacity of the background overlay, ranging from 0.0 (fully transparent, original background visible) to 1.0 (fully opaque, complete background replacement). Values between 0.0 and 1.0 create a blend between the original image and the background color. Lower values create subtle background dimming, while higher values create stronger background replacement effects..

• output

  • image (image): Image in workflows.

Example JSON definition of step Background Color Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/background_color_visualization@v1",
    "image": "$inputs.image",
    "copy_image": true,
    "predictions": "$steps.object_detection_model.predictions",
    "color": "WHITE",
    "opacity": 0.5
}