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