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

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
}