Corner Visualization

Class: CornerVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.corner.v1.CornerVisualizationBlockV1

Draw corner markers at the four corners of detected object bounding boxes, providing a minimal, clean visualization style that marks object locations without full bounding box outlines.

How This Block Works

This block takes an image and detection predictions and draws corner markers at the four corners of each detected object's bounding box. The block:

1. Takes an image and predictions as input
2. Identifies bounding box coordinates for each detected object
3. Calculates the four corner positions (top-left, top-right, bottom-left, bottom-right) of each bounding box
4. Applies color styling based on the selected color palette, with colors assigned by class, index, or track ID
5. Draws corner markers (typically L-shaped lines or corner indicators) at each corner position using Supervision's BoxCornerAnnotator
6. Applies the specified thickness and corner length to control the appearance of the corner markers
7. Returns an annotated image with corner markers overlaid on the original image

The block draws minimal corner markers instead of full bounding boxes, creating a clean, unobtrusive visualization style. This approach marks object locations clearly while maintaining a minimal aesthetic that doesn't overwhelm the image with full rectangular outlines. The corner markers can be customized with different thickness and length values, and colors can be assigned based on object class, index, or tracking ID, making it easy to distinguish between different objects or object types.

Common Use Cases

• Minimal Object Marking: Mark detected objects with corner indicators instead of full bounding boxes for a clean, unobtrusive visualization style that preserves image clarity while still indicating object locations
• Aesthetic Visualization Design: Create visually minimal annotations for presentations, dashboards, or user interfaces where full bounding boxes would be too visually intrusive but corner markers provide sufficient location indication
• Dense Scene Visualization: Use corner markers when working with many detected objects in dense scenes where full bounding boxes would overlap excessively and create visual clutter
• Design-Oriented Applications: Apply corner markers in design workflows, artistic visualizations, or creative applications where a minimal, modern aesthetic is preferred over traditional bounding box outlines
• Subtle Object Highlighting: Mark object locations subtly without drawing attention away from the main image content, useful for background annotations or when object location indication is needed without visual prominence
• UI and Dashboard Integration: Integrate corner markers into user interfaces, dashboards, or interactive applications where minimal visual indicators are preferred for better user experience and reduced visual noise

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 corner markers with additional annotations for comprehensive visualization
• Data storage blocks (e.g., Local File Sink, CSV Formatter, Roboflow Dataset Upload) to save annotated images with corner markers for documentation, reporting, or analysis
• Webhook blocks to send visualized results with corner markers 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 corner markers as visual evidence in alerts or reports
• Video output blocks to create annotated video streams or recordings with corner markers for live monitoring, tracking visualization, or post-processing analysis

Type identifier

Use the following identifier in step "type" field: roboflow_core/corner_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_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..	✅
thickness	int	Thickness of the corner marker lines in pixels. Higher values create thicker, more visible corner markers..	✅
corner_length	int	Length of each corner marker line segment in pixels. This controls how long the corner indicators extend from each corner point. Higher values create longer, more prominent corner markers..	✅

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 Corner 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, Clip Comparison, 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, Buffer, Byte Tracker, Contrast Equalization, Identify Changes, Detections Classes Replacement, Dimension Collapse, 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, Size Measurement, 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, Distance Measurement, 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, Line Counter, 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, Pixel Color Count, 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 Corner 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_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..
  • thickness (integer): Thickness of the corner marker lines in pixels. Higher values create thicker, more visible corner markers..
  • corner_length (integer): Length of each corner marker line segment in pixels. This controls how long the corner indicators extend from each corner point. Higher values create longer, more prominent corner markers..

• output

  • image (image): Image in workflows.

Example JSON definition of step Corner Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/corner_visualization@v1",
    "image": "$inputs.image",
    "copy_image": true,
    "predictions": "$steps.object_detection_model.predictions",
    "color_palette": "DEFAULT",
    "palette_size": 10,
    "custom_colors": [
        "#FF0000",
        "#00FF00",
        "#0000FF"
    ],
    "color_axis": "CLASS",
    "thickness": 4,
    "corner_length": 15
}