Corner Visualization¶
Class: CornerVisualizationBlockV1
Source: inference.core.workflows.core_steps.visualizations.corner.v1.CornerVisualizationBlockV1
The CornerVisualization block draws the corners of detected
objects in an image using Supervision's sv.BoxCornerAnnotator.
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 lines in pixels.. | ✅ |
corner_length |
int |
Length of the corner lines in pixels.. | ✅ |
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 Zone,Clip Comparison,Triangle Visualization,Mask Visualization,Bounding Rectangle,Instance Segmentation Model,Image Threshold,Anthropic Claude,Single-Label Classification Model,Object Detection Model,Detections Stitch,Dot Visualization,Pixelate Visualization,YOLO-World Model,Detections Consensus,Time in Zone,Keypoint Detection Model,Model Comparison Visualization,CogVLM,Roboflow Dataset Upload,Byte Tracker,Line Counter,Local File Sink,Keypoint Visualization,Circle Visualization,Google Vision OCR,Segment Anything 2 Model,Path Deviation,Blur Visualization,VLM as Classifier,Crop Visualization,SIFT Comparison,Line Counter Visualization,Ellipse Visualization,Pixel Color Count,Distance Measurement,Identify Changes,Twilio SMS Notification,Detections Filter,Google Gemini,Byte Tracker,LMM,OCR Model,Background Color Visualization,Classification Label Visualization,Relative Static Crop,SIFT Comparison,Stitch OCR Detections,Gaze Detection,CSV Formatter,Dimension Collapse,Template Matching,VLM as Classifier,Buffer,Image Convert Grayscale,Image Blur,Halo Visualization,JSON Parser,Path Deviation,Object Detection Model,Absolute Static Crop,SIFT,Stability AI Image Generation,Trace Visualization,Detections Stabilizer,VLM as Detector,Dynamic Crop,Polygon Zone Visualization,Clip Comparison,Roboflow Dataset Upload,Multi-Label Classification Model,Detection Offset,Model Monitoring Inference Aggregator,LMM For Classification,OpenAI,Stitch Images,Time in Zone,Keypoint Detection Model,Florence-2 Model,Webhook Sink,Detections Classes Replacement,Size Measurement,Line Counter,Grid Visualization,Image Slicer,Reference Path Visualization,Image Preprocessing,Camera Calibration,Label Visualization,Perspective Correction,Image Slicer,Identify Outliers,Byte Tracker,Instance Segmentation Model,Stability AI Inpainting,Camera Focus,Velocity,Florence-2 Model,VLM as Detector,Corner Visualization,Color Visualization,OpenAI,Roboflow Custom Metadata,Email Notification,Image Contours,Detections Transformation,Slack Notification,Polygon Visualization,Llama 3.2 Vision,Bounding Box Visualization - outputs:
Clip Comparison,Triangle Visualization,Mask Visualization,Instance Segmentation Model,Image Threshold,Anthropic Claude,Single-Label Classification Model,Object Detection Model,Detections Stitch,Dot Visualization,Barcode Detection,Pixelate Visualization,Multi-Label Classification Model,YOLO-World Model,Time in Zone,Keypoint Detection Model,Model Comparison Visualization,CogVLM,Roboflow Dataset Upload,Keypoint Visualization,Circle Visualization,Google Vision OCR,Segment Anything 2 Model,Blur Visualization,VLM as Classifier,Crop Visualization,Line Counter Visualization,Ellipse Visualization,Pixel Color Count,Dominant Color,Google Gemini,Byte Tracker,OCR Model,LMM,Background Color Visualization,Classification Label Visualization,Relative Static Crop,SIFT Comparison,Gaze Detection,Template Matching,VLM as Classifier,Buffer,Image Convert Grayscale,Image Blur,Halo Visualization,Single-Label Classification Model,Object Detection Model,Absolute Static Crop,Qwen2.5-VL,SIFT,Stability AI Image Generation,Trace Visualization,Detections Stabilizer,VLM as Detector,CLIP Embedding Model,Polygon Zone Visualization,Clip Comparison,Dynamic Crop,Roboflow Dataset Upload,Multi-Label Classification Model,LMM For Classification,OpenAI,Stitch Images,Keypoint Detection Model,Florence-2 Model,QR Code Detection,Image Slicer,Reference Path Visualization,Image Preprocessing,Camera Calibration,Label Visualization,Perspective Correction,Image Slicer,Instance Segmentation Model,Stability AI Inpainting,Camera Focus,Florence-2 Model,VLM as Detector,Corner Visualization,Color Visualization,OpenAI,Image Contours,Polygon Visualization,Llama 3.2 Vision,Bounding Box 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[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 lines in pixels..corner_length(integer): Length of the corner lines in pixels..
-
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
}