Mask Visualization
Apply a mask over detected objects in an image.
Fill segmentation masks with semi-transparent color overlays, creating solid color fills that precisely follow the shape of detected objects from instance segmentation predictions.
How This Block Works
This block takes an image and instance segmentation predictions (with masks) and fills the mask regions with colored overlays. The block:
Takes an image and instance segmentation predictions (with masks) as input
Extracts segmentation masks for each detected object from the predictions
Applies color styling to each mask based on the selected color palette, with colors assigned by class, index, or track ID
Fills the mask regions with solid colors using Supervision's MaskAnnotator
Blends the colored mask overlays with the original image using the specified opacity level
Returns an annotated image where mask regions are filled with semi-transparent colors, while non-masked areas remain unchanged
The block fills the exact shape of each object's segmentation mask with colored overlays, creating solid color fills that precisely follow object boundaries. Unlike polygon visualization (which draws outlines) or bounding box visualizations (which use rectangular regions), mask visualization fills the entire mask area with color, providing clear visual indication of the segmented regions. The opacity parameter controls how transparent the mask overlay is, allowing you to see the original image details through the colored mask (lower opacity) or create more opaque fills (higher opacity) that better obscure background details. This block requires instance segmentation predictions with mask data, as it specifically works with segmentation masks to create precise, shape-following color fills.
Common Use Cases
Instance Segmentation Visualization: Visualize instance segmentation results by filling mask regions with colors to clearly show segmented objects, validate segmentation quality, or highlight detected regions in analysis workflows
Precise Shape-Following Overlays: Fill objects with colors that exactly match their segmented shapes, useful for applications requiring accurate region visualization such as medical imaging, quality control, or precise object identification
Mask-Based Object Highlighting: Highlight segmented objects with colored overlays that follow exact object boundaries, providing clear visual distinction between different objects or object classes
Segmentation Model Validation: Visualize segmentation predictions with colored mask fills to verify model performance, identify segmentation errors, or validate mask accuracy in model development and debugging workflows
Medical and Scientific Imaging: Display segmented regions in medical imaging, microscopy, or scientific analysis applications where colored mask overlays help visualize tissue boundaries, cell regions, or measured areas
Mask Quality Inspection: Use colored mask fills to inspect segmentation quality, verify mask boundaries, or identify areas where segmentation may need improvement in training data or model outputs
Connecting to Other Blocks
The annotated image from this block can be connected to:
Other visualization blocks (e.g., Label Visualization, Polygon Visualization, Bounding Box Visualization) to combine mask fills with additional annotations (labels, outlines) for comprehensive visualization
Data storage blocks (e.g., Local File Sink, CSV Formatter, Roboflow Dataset Upload) to save images with mask overlays for documentation, reporting, or analysis
Webhook blocks to send visualized results with mask fills 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 mask overlays as visual evidence in alerts or reports
Video output blocks to create annotated video streams or recordings with mask fills for live monitoring, segmentation visualization, or post-processing analysis
Type identifier
Use the following identifier in step "type" field: roboflow_core/mask_visualization@v1 to add the block as a 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_palette
str
Select a color palette for the visualised elements..
✅
palette_size
int
Specify the number of colors in the palette. This applies when using custom or Matplotlib palettes..
✅
custom_colors
List[str]
Define a list of custom colors for bounding boxes in HEX format..
✅
color_axis
str
Choose how bounding box colors are assigned..
✅
opacity
float
Opacity of the mask overlay, ranging from 0.0 (fully transparent) to 1.0 (fully opaque). Controls the transparency of the colored mask fill. Lower values (e.g., 0.3-0.5) create semi-transparent overlays that allow original image details to show through, while higher values (e.g., 0.7-1.0) create more opaque fills that better obscure background details. Typical values range from 0.4 to 0.7 for balanced visualization where both the mask and underlying image are visible..
✅
The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.
Input and Output Bindings
The available connections depend on its binding kinds. Check what binding kinds Mask Visualization in version v1 has.
Input and output 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,rle_instance_segmentation_prediction,semantic_segmentation_prediction]): Segmentation predictions containing masks for detected objects. The block uses segmentation masks to create colored fills that precisely follow object or class boundaries. Requires segmentation model outputs with mask data, which may be RLE-encoded..color_palette(string): Select a color palette for the visualised elements..palette_size(integer): Specify the number of colors in the palette. This applies when using custom or Matplotlib palettes..custom_colors(list_of_values): Define a list of custom colors for bounding boxes in HEX format..color_axis(string): Choose how bounding box colors are assigned..opacity(float_zero_to_one): Opacity of the mask overlay, ranging from 0.0 (fully transparent) to 1.0 (fully opaque). Controls the transparency of the colored mask fill. Lower values (e.g., 0.3-0.5) create semi-transparent overlays that allow original image details to show through, while higher values (e.g., 0.7-1.0) create more opaque fills that better obscure background details. Typical values range from 0.4 to 0.7 for balanced visualization where both the mask and underlying image are visible..
output
image(image): Image in workflows.
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