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

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[rle_instance_segmentation_prediction, keypoint_detection_prediction, object_detection_prediction, instance_segmentation_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
}