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

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, keypoint_detection_prediction, object_detection_prediction, rle_instance_segmentation_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
}