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:
VLM As Classifier,Line Counter,MoonshotAI Kimi,Stability AI Image Generation,Trace Visualization,Path Deviation,Image Stack,Anthropic Claude,Per-Class Confidence Filter,Icon Visualization,SIFT Comparison,Morphological Transformation,Color Visualization,LMM For Classification,Perspective Correction,Corner Visualization,Clip Comparison,Roboflow Custom Metadata,Detections Merge,Halo Visualization,Dynamic Zone,Keypoint Detection Model,Qwen-VL,JSON Parser,Email Notification,Halo Visualization,Object Detection Model,Google Gemma,Background Color Visualization,Ellipse Visualization,Email Notification,Twilio SMS/MMS Notification,Text Display,Polygon Visualization,Crop Visualization,Absolute Static Crop,Image Preprocessing,Template Matching,Model Monitoring Inference Aggregator,Relative Static Crop,OpenRouter,OpenAI,PLC ModbusTCP,VLM As Detector,Florence-2 Model,Motion Detection,Heatmap Visualization,OCR Model,OpenAI,Detections Filter,Blur Visualization,Dimension Collapse,Depth Estimation,Instance Segmentation Model,Stability AI Outpainting,Anthropic Claude,YOLO-World Model,Google Gemini,Clip Comparison,Google Gemini,PLC EthernetIP,Background Subtraction,Keypoint Visualization,CSV Formatter,Buffer,Webhook Sink,Byte Tracker,Stitch Images,Florence-2 Model,Current Time,Detections List Roll-Up,Contrast Equalization,Mask Edge Snap,OpenAI,Moondream2,VLM As Detector,Line Counter,Google Gemini,Triangle Visualization,Slack Notification,Overlap Filter,Time in Zone,Detections Stabilizer,SIFT,Local File Sink,Image Contours,VLM As Classifier,Keypoint Detection Model,Pixel Color Count,GLM-OCR,Roboflow Asset Library Attributes,Image Slicer,Polygon Zone Visualization,Contrast Enhancement,Time in Zone,Google Gemma API,Stitch OCR Detections,Image Threshold,Line Counter Visualization,Distance Measurement,Camera Calibration,QR Code Generator,Detection Offset,ByteTrack Tracker,Detection Event Log,Detections Transformation,S3 Sink,Microsoft SQL Server Sink,Mask Area Measurement,Twilio SMS Notification,Google Vision OCR,Image Blur,Detections Combine,Morphological Transformation,Camera Focus,Roboflow Vision Events,Size Measurement,Stability AI Inpainting,PTZ Tracking (ONVIF),Classification Label Visualization,Bounding Rectangle,SAM2 Video Tracker,Stitch OCR Detections,Event Writer,Grid Visualization,Qwen3.5-VL,Mask Visualization,Byte Tracker,Llama 3.2 Vision,Reference Path Visualization,Image Slicer,Label Visualization,Identify Outliers,Velocity,Byte Tracker,SIFT Comparison,OPC UA Writer Sink,Dot Visualization,Identify Changes,Dynamic Crop,Detections Stitch,Circle Visualization,Path Deviation,BoT-SORT Tracker,SAM3 Video Tracker,Camera Focus,Llama 3.2 Vision,Gaze Detection,Segment Anything 2 Model,OpenAI-Compatible LLM,MoonshotAI Kimi,Single-Label Classification Model,CogVLM,Object Detection Model,SAM 3 Interactive,Qwen 3.6 API,Detections Consensus,Bounding Box Visualization,Multi-Label Classification Model,LMM,SAM 3,OpenAI,PLC Reader,Image Convert Grayscale,Instance Segmentation Model,Roboflow Visual Search,EasyOCR,Roboflow Dataset Upload,SAM 3,Detections Classes Replacement,Instance Segmentation Model,Pixelate Visualization,Keypoint Detection Model,Roboflow Dataset Upload,PLC Writer,SORT Tracker,Instance Segmentation Model,Track Class Lock,Qwen 3.5 API,Object Detection Model,Anthropic Claude,Time in Zone,MQTT Writer,Polygon Visualization,OC-SORT Tracker,SAM 3,Model Comparison Visualization,Seg Preview - outputs:
VLM As Classifier,MoonshotAI Kimi,Stability AI Image Generation,Trace Visualization,Qwen2.5-VL,Image Stack,Anthropic Claude,Icon Visualization,SIFT Comparison,Morphological Transformation,Color Visualization,SmolVLM2,LMM For Classification,Single-Label Classification Model,Perspective Correction,Corner Visualization,Clip Comparison,Halo Visualization,Qwen-VL,Keypoint Detection Model,Halo Visualization,Object Detection Model,Google Gemma,Background Color Visualization,Ellipse Visualization,Email Notification,Twilio SMS/MMS Notification,Text Display,Polygon Visualization,Crop Visualization,Absolute Static Crop,Image Preprocessing,Template Matching,Relative Static Crop,OpenRouter,OpenAI,Florence-2 Model,VLM As Detector,OpenAI,Motion Detection,Heatmap Visualization,OCR Model,Perception Encoder Embedding Model,Blur Visualization,Barcode Detection,Depth Estimation,Instance Segmentation Model,Stability AI Outpainting,Anthropic Claude,YOLO-World Model,Google Gemini,Clip Comparison,Google Gemini,Background Subtraction,Keypoint Visualization,Buffer,Stitch Images,Florence-2 Model,Contrast Equalization,Mask Edge Snap,OpenAI,Qwen3-VL,Moondream2,VLM As Detector,Google Gemini,Triangle Visualization,CLIP Embedding Model,Detections Stabilizer,SIFT,Multi-Label Classification Model,Image Contours,Keypoint Detection Model,VLM As Classifier,Pixel Color Count,GLM-OCR,Image Slicer,Polygon Zone Visualization,Contrast Enhancement,Google Gemma API,Time in Zone,Semantic Segmentation Model,Image Threshold,Line Counter Visualization,Semantic Segmentation Model,Multi-Label Classification Model,Camera Calibration,ByteTrack Tracker,Google Vision OCR,Image Blur,Morphological Transformation,Camera Focus,Roboflow Vision Events,Stability AI Inpainting,Classification Label Visualization,SAM2 Video Tracker,Event Writer,Qwen3.5-VL,Mask Visualization,Llama 3.2 Vision,Dominant Color,Reference Path Visualization,Image Slicer,Label Visualization,Byte Tracker,Dot Visualization,Dynamic Crop,Detections Stitch,Circle Visualization,Llama 3.2 Vision,BoT-SORT Tracker,SAM3 Video Tracker,Camera Focus,Gaze Detection,Segment Anything 2 Model,MoonshotAI Kimi,Single-Label Classification Model,QR Code Detection,Qwen3.5,CogVLM,Object Detection Model,SAM 3 Interactive,Qwen 3.6 API,Bounding Box Visualization,Multi-Label Classification Model,LMM,OpenAI,SAM 3,Image Convert Grayscale,Instance Segmentation Model,EasyOCR,Roboflow Visual Search,Roboflow Dataset Upload,SAM 3,Instance Segmentation Model,Keypoint Detection Model,Pixelate Visualization,Roboflow Dataset Upload,SORT Tracker,Instance Segmentation Model,Track Class Lock,Qwen 3.5 API,Object Detection Model,Anthropic Claude,Polygon Visualization,OC-SORT Tracker,SAM 3,Model Comparison Visualization,Single-Label Classification Model,Seg Preview
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[keypoint_detection_prediction,object_detection_prediction,rle_instance_segmentation_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
}