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:
- Takes an image and predictions as input
- Identifies bounding box coordinates for each detected object
- Calculates the four corner positions (top-left, top-right, bottom-left, bottom-right) of each bounding box
- Applies color styling based on the selected color palette, with colors assigned by class, index, or track ID
- Draws corner markers (typically L-shaped lines or corner indicators) at each corner position using Supervision's BoxCornerAnnotator
- Applies the specified thickness and corner length to control the appearance of the corner markers
- 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:
Dynamic Crop,OCR Model,Image Blur,Background Subtraction,Google Vision OCR,Google Gemini,Image Preprocessing,Local File Sink,Object Detection Model,Single-Label Classification Model,Bounding Box Visualization,Model Monitoring Inference Aggregator,Keypoint Detection Model,Camera Focus,Identify Outliers,Dot Visualization,Gaze Detection,Florence-2 Model,Roboflow Dataset Upload,CSV Formatter,Depth Estimation,Polygon Visualization,OpenAI,Line Counter,Image Slicer,Detections List Roll-Up,Line Counter Visualization,Heatmap Visualization,Morphological Transformation,Stability AI Image Generation,Google Gemini,Distance Measurement,Keypoint Visualization,Keypoint Detection Model,Background Color Visualization,Label Visualization,Polygon Visualization,LMM,CogVLM,Time in Zone,Triangle Visualization,Stability AI Outpainting,Mask Visualization,Color Visualization,Detections Combine,Text Display,Bounding Rectangle,Reference Path Visualization,Llama 3.2 Vision,OpenAI,Image Threshold,Clip Comparison,Classification Label Visualization,Clip Comparison,Polygon Zone Visualization,Image Contours,VLM As Classifier,Roboflow Custom Metadata,Dynamic Zone,LMM For Classification,Velocity,Halo Visualization,Blur Visualization,Path Deviation,Absolute Static Crop,Anthropic Claude,SAM 3,Detections Transformation,Ellipse Visualization,Identify Changes,Crop Visualization,SIFT Comparison,Path Deviation,Trace Visualization,Twilio SMS Notification,Stitch Images,Detections Stabilizer,Size Measurement,Detections Merge,Time in Zone,Motion Detection,Email Notification,SIFT Comparison,OpenAI,Seg Preview,Time in Zone,Instance Segmentation Model,Anthropic Claude,Multi-Label Classification Model,Email Notification,Slack Notification,Twilio SMS/MMS Notification,Detections Stitch,VLM As Detector,Camera Focus,SAM 3,Stitch OCR Detections,Perspective Correction,PTZ Tracking (ONVIF),Moondream2,Camera Calibration,Corner Visualization,Icon Visualization,Overlap Filter,Qwen3.5-VL,Byte Tracker,VLM As Detector,Halo Visualization,JSON Parser,Detection Event Log,Pixelate Visualization,Contrast Equalization,Dimension Collapse,VLM As Classifier,Instance Segmentation Model,Detections Classes Replacement,Relative Static Crop,Line Counter,Stitch OCR Detections,Webhook Sink,Circle Visualization,Image Convert Grayscale,Grid Visualization,Mask Area Measurement,Byte Tracker,Florence-2 Model,Buffer,SAM 3,SIFT,YOLO-World Model,Object Detection Model,Byte Tracker,Detections Consensus,Template Matching,Anthropic Claude,Google Gemini,Model Comparison Visualization,Detection Offset,QR Code Generator,EasyOCR,Image Slicer,S3 Sink,Stability AI Inpainting,Segment Anything 2 Model,Detections Filter,OpenAI,Pixel Color Count,Roboflow Dataset Upload - outputs:
Dynamic Crop,OCR Model,Barcode Detection,Motion Detection,Email Notification,Image Blur,Background Subtraction,Google Vision OCR,SIFT Comparison,Google Gemini,OpenAI,Image Preprocessing,Qwen2.5-VL,Seg Preview,Object Detection Model,Instance Segmentation Model,Single-Label Classification Model,Bounding Box Visualization,Multi-Label Classification Model,Anthropic Claude,Multi-Label Classification Model,Keypoint Detection Model,Detections Stitch,Twilio SMS/MMS Notification,Camera Focus,VLM As Detector,Gaze Detection,Florence-2 Model,Dot Visualization,Roboflow Dataset Upload,Camera Focus,SAM 3,Depth Estimation,Polygon Visualization,Moondream2,OpenAI,Perspective Correction,Image Slicer,Icon Visualization,Corner Visualization,Camera Calibration,Qwen3.5-VL,Line Counter Visualization,Heatmap Visualization,Google Gemini,Morphological Transformation,Stability AI Image Generation,Keypoint Visualization,VLM As Detector,Keypoint Detection Model,Halo Visualization,Background Color Visualization,Label Visualization,QR Code Detection,Polygon Visualization,Pixelate Visualization,LMM,CogVLM,Time in Zone,Single-Label Classification Model,Qwen3-VL,Contrast Equalization,Triangle Visualization,Stability AI Outpainting,Mask Visualization,VLM As Classifier,Color Visualization,Instance Segmentation Model,Dominant Color,Text Display,Relative Static Crop,Reference Path Visualization,OpenAI,Llama 3.2 Vision,Clip Comparison,Clip Comparison,Classification Label Visualization,Image Threshold,Circle Visualization,Polygon Zone Visualization,Image Contours,Image Convert Grayscale,VLM As Classifier,Byte Tracker,Buffer,Florence-2 Model,SmolVLM2,SAM 3,Perception Encoder Embedding Model,LMM For Classification,SIFT,YOLO-World Model,Halo Visualization,Template Matching,Object Detection Model,Semantic Segmentation Model,Anthropic Claude,Google Gemini,Model Comparison Visualization,Blur Visualization,EasyOCR,Absolute Static Crop,Image Slicer,Anthropic Claude,SAM 3,CLIP Embedding Model,Stability AI Inpainting,Ellipse Visualization,Crop Visualization,Trace Visualization,Segment Anything 2 Model,Stitch Images,Detections Stabilizer,OpenAI,Pixel Color Count,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[object_detection_prediction,instance_segmentation_prediction,keypoint_detection_prediction,rle_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
}