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