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

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, keypoint_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
}