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: SIFT, Image Blur, Relative Static Crop, Triangle Visualization, Detections Classes Replacement, OpenAI, Keypoint Detection Model, Polygon Zone Visualization, Keypoint Visualization, JSON Parser, Detections Consensus, Distance Measurement, Dimension Collapse, CSV Formatter, Line Counter, Image Threshold, Model Comparison Visualization, Ellipse Visualization, Overlap Filter, Instance Segmentation Model, Anthropic Claude, Multi-Label Classification Model, YOLO-World Model, Buffer, Roboflow Dataset Upload, Detections Stabilizer, Dot Visualization, SIFT Comparison, Stitch OCR Detections, Roboflow Dataset Upload, LMM, Grid Visualization, Path Deviation, Clip Comparison, Depth Estimation, Color Visualization, Line Counter, Email Notification, Google Gemini, VLM as Detector, Detections Stitch, Image Slicer, Bounding Box Visualization, SIFT Comparison, Time in Zone, Classification Label Visualization, Halo Visualization, Pixel Color Count, Google Vision OCR, Stitch Images, Detections Merge, Velocity, Clip Comparison, LMM For Classification, Line Counter Visualization, Segment Anything 2 Model, OpenAI, Roboflow Custom Metadata, Blur Visualization, Identify Outliers, Byte Tracker, Perspective Correction, Model Monitoring Inference Aggregator, Slack Notification, Corner Visualization, Image Contours, Label Visualization, Llama 3.2 Vision, Dynamic Crop, Circle Visualization, Instance Segmentation Model, Camera Calibration, Stability AI Image Generation, Single-Label Classification Model, VLM as Classifier, Florence-2 Model, Webhook Sink, Detections Filter, VLM as Detector, OCR Model, Local File Sink, Image Slicer, Camera Focus, Stability AI Inpainting, Detection Offset, Identify Changes, Florence-2 Model, Time in Zone, Object Detection Model, Background Color Visualization, Detections Transformation, Bounding Rectangle, CogVLM, Size Measurement, Polygon Visualization, VLM as Classifier, Dynamic Zone, Byte Tracker, Gaze Detection, Path Deviation, Trace Visualization, Keypoint Detection Model, Reference Path Visualization, Template Matching, Image Preprocessing, Byte Tracker, Image Convert Grayscale, Object Detection Model, Absolute Static Crop, Crop Visualization, Moondream2, Mask Visualization, Twilio SMS Notification, Pixelate Visualization
• outputs: Multi-Label Classification Model, SIFT, Image Blur, Relative Static Crop, Triangle Visualization, OpenAI, Keypoint Detection Model, Polygon Zone Visualization, Qwen2.5-VL, Keypoint Visualization, Model Comparison Visualization, Image Threshold, Instance Segmentation Model, QR Code Detection, Anthropic Claude, Multi-Label Classification Model, Dominant Color, Buffer, YOLO-World Model, Roboflow Dataset Upload, Detections Stabilizer, Dot Visualization, SIFT Comparison, Roboflow Dataset Upload, LMM, Clip Comparison, Depth Estimation, Color Visualization, Barcode Detection, Google Gemini, VLM as Detector, Mask Visualization, Detections Stitch, Image Slicer, Bounding Box Visualization, Time in Zone, Pixel Color Count, Halo Visualization, Classification Label Visualization, Google Vision OCR, Stitch Images, Clip Comparison, LMM For Classification, OpenAI, Line Counter Visualization, Segment Anything 2 Model, Blur Visualization, Byte Tracker, Perspective Correction, Corner Visualization, Image Contours, Label Visualization, SmolVLM2, Llama 3.2 Vision, Dynamic Crop, Circle Visualization, Instance Segmentation Model, Camera Calibration, Single-Label Classification Model, Stability AI Image Generation, VLM as Classifier, Florence-2 Model, OCR Model, VLM as Detector, Image Slicer, Camera Focus, Stability AI Inpainting, Florence-2 Model, Object Detection Model, CogVLM, Background Color Visualization, Polygon Visualization, Gaze Detection, VLM as Classifier, Single-Label Classification Model, Trace Visualization, Keypoint Detection Model, Reference Path Visualization, Template Matching, Image Preprocessing, CLIP Embedding Model, Image Convert Grayscale, Object Detection Model, Absolute Static Crop, Crop Visualization, Moondream2, Ellipse Visualization, Pixelate 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[object_detection_prediction, instance_segmentation_prediction, keypoint_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 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
}