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

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[instance_segmentation_prediction, object_detection_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
}