Triangle Visualization

Class: TriangleVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.triangle.v1.TriangleVisualizationBlockV1

Draw triangular markers on an image to mark specific points on detected objects, with customizable position, size, color, and outline styling, providing directional indicators and geometric markers for visual annotations.

How This Block Works

This block takes an image and detection predictions and draws triangular markers at specified anchor positions on each detected object. The block:

1. Takes an image and predictions as input
2. Determines the triangle position for each detection based on the selected anchor point (center, corners, edges, or center of mass)
3. Applies color styling based on the selected color palette, with colors assigned by class, index, or track ID
4. Draws triangular markers with the specified base width and height dimensions, with optional outline thickness using Supervision's TriangleAnnotator
5. Returns an annotated image with triangular markers overlaid on the original image

The block supports various position options including the center of the bounding box, any of the four corners, edge midpoints, or the center of mass (useful for objects with irregular shapes). Triangles can be customized with different sizes (base width and height), optional outlines for better visibility, and various color palettes. Triangular markers provide a distinctive geometric shape that can serve as directional indicators (pointing in a specific direction) or geometric markers, offering an alternative to circular dots or square markers. This provides a clean visualization style that marks detection locations with directional or geometric emphasis, making it ideal for applications requiring directional indicators, geometric markers, or distinctive point markers.

Common Use Cases

• Directional Object Marking: Mark detected objects with triangular markers that can indicate direction or orientation, useful for tracking applications, motion analysis, or directional workflows where the triangle's pointed shape provides directional information
• Geometric Marker Visualization: Use triangular markers as distinctive geometric shapes to mark detection locations, providing visual variety compared to circular dots or rectangular bounding boxes for design purposes or geometric emphasis
• Minimal Object Marking: Mark detected objects with small triangular markers instead of bounding boxes for cleaner, less cluttered visualizations when working with dense scenes or many detections, with the triangular shape providing visual distinction
• Tracking Visualization: Use triangular markers to visualize object trajectories or tracking IDs over time, creating a cleaner alternative to bounding boxes for tracking workflows, with triangles potentially indicating movement direction
• Point of Interest Highlighting: Mark specific anchor points (corners, center, center of mass) on detected objects with triangular markers for applications like object tracking, spatial analysis, or geometric annotation workflows
• Design and Aesthetic Applications: Create triangular markers for design purposes, user interfaces, dashboards, or artistic visualizations where geometric shapes provide distinctive visual style or aesthetic appeal

Connecting to Other Blocks

The annotated image from this block can be connected to:

• Other visualization blocks (e.g., Bounding Box Visualization, Label Visualization, Trace Visualization) to combine triangular markers with additional annotations for comprehensive visualization
• Data storage blocks (e.g., Local File Sink, CSV Formatter, Roboflow Dataset Upload) to save annotated images with triangular markers for documentation, reporting, or analysis
• Webhook blocks to send visualized results with triangular markers to external systems, APIs, or web applications for display in dashboards or monitoring tools
• Notification blocks (e.g., Email Notification, Slack Notification) to send annotated images with triangular markers as visual evidence in alerts or reports
• Video output blocks to create annotated video streams or recordings with triangular markers for live monitoring, tracking visualization, or post-processing analysis

Type identifier

Use the following identifier in step "type" field: roboflow_core/triangle_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..	✅
position	str	Anchor position for placing the triangle marker relative to the detection's bounding box. Options include: CENTER (center of box), corners (TOP_LEFT, TOP_RIGHT, BOTTOM_LEFT, BOTTOM_RIGHT), edge midpoints (TOP_CENTER, CENTER_LEFT, CENTER_RIGHT, BOTTOM_CENTER), or CENTER_OF_MASS (center of mass of the object, useful for irregular shapes)..	✅
base	int	Base width of the triangle in pixels. Controls the horizontal width of the triangular marker at its base. Larger values create wider triangles, while smaller values create narrower triangles. Works together with height to control the overall triangle size and shape..	✅
height	int	Height of the triangle in pixels. Controls the vertical height of the triangular marker from base to tip. Larger values create taller triangles, while smaller values create shorter triangles. Works together with base to control the overall triangle size and shape..	✅
outline_thickness	int	Thickness of the triangle outline in pixels. A value of 0 creates a filled triangle with no outline. Higher values create thicker outlines around the triangle border, improving visibility and contrast. Useful for making triangular markers more visible against complex backgrounds..	✅

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 Triangle Visualization in version v1.

• inputs: Triangle Visualization, Detections Stitch, Ellipse Visualization, Detections Classes Replacement, Florence-2 Model, Blur Visualization, Anthropic Claude, Google Gemini, Motion Detection, Keypoint Visualization, Pixelate Visualization, Size Measurement, Image Slicer, SIFT Comparison, Line Counter, Keypoint Detection Model, Dynamic Zone, Distance Measurement, Clip Comparison, SAM 3, Image Slicer, EasyOCR, CSV Formatter, Object Detection Model, Anthropic Claude, Detections Combine, Google Gemini, Pixel Color Count, Background Color Visualization, Image Convert Grayscale, Camera Calibration, Time in Zone, Image Preprocessing, Byte Tracker, VLM As Detector, PTZ Tracking (ONVIF).md), Detections Transformation, SIFT, Stability AI Outpainting, Moondream2, Stitch OCR Detections, Detection Offset, Trace Visualization, OpenAI, YOLO-World Model, Icon Visualization, Dot Visualization, Time in Zone, Email Notification, Instance Segmentation Model, Path Deviation, Camera Focus, Contrast Equalization, Segment Anything 2 Model, Text Display, Detections Filter, Reference Path Visualization, Image Threshold, Perspective Correction, Image Contours, Multi-Label Classification Model, Detection Event Log, Local File Sink, Identify Changes, Byte Tracker, Google Vision OCR, Crop Visualization, Detections List Roll-Up, Google Gemini, Webhook Sink, Classification Label Visualization, VLM As Classifier, Seg Preview, OpenAI, Gaze Detection, Stability AI Image Generation, Roboflow Dataset Upload, Morphological Transformation, LMM, Halo Visualization, Camera Focus, Llama 3.2 Vision, Model Comparison Visualization, VLM As Detector, Stitch OCR Detections, Roboflow Dataset Upload, Line Counter Visualization, Label Visualization, SIFT Comparison, QR Code Generator, Email Notification, Buffer, Slack Notification, Detections Stabilizer, Object Detection Model, Path Deviation, Corner Visualization, Florence-2 Model, SAM 3, OpenAI, Bounding Box Visualization, Keypoint Detection Model, Anthropic Claude, Background Subtraction, Polygon Visualization, Image Blur, VLM As Classifier, Relative Static Crop, Clip Comparison, Detections Merge, Heatmap Visualization, CogVLM, Mask Visualization, Twilio SMS Notification, Instance Segmentation Model, OpenAI, OCR Model, Stitch Images, Dynamic Crop, Model Monitoring Inference Aggregator, Circle Visualization, Byte Tracker, Color Visualization, Dimension Collapse, Velocity, Twilio SMS/MMS Notification, Depth Estimation, Roboflow Custom Metadata, LMM For Classification, Bounding Rectangle, Grid Visualization, Polygon Zone Visualization, Polygon Visualization, Halo Visualization, JSON Parser, SAM 3, Stability AI Inpainting, Time in Zone, Identify Outliers, Detections Consensus, Template Matching, Overlap Filter, Absolute Static Crop, Single-Label Classification Model, Line Counter
• outputs: Triangle Visualization, Detections Stitch, Roboflow Dataset Upload, Ellipse Visualization, Morphological Transformation, LMM, Florence-2 Model, Blur Visualization, CLIP Embedding Model, Anthropic Claude, Halo Visualization, Google Gemini, Camera Focus, Llama 3.2 Vision, Motion Detection, Model Comparison Visualization, VLM As Detector, Keypoint Visualization, Qwen3-VL, Pixelate Visualization, Image Slicer, Roboflow Dataset Upload, Line Counter Visualization, Keypoint Detection Model, SmolVLM2, Label Visualization, SIFT Comparison, Clip Comparison, Buffer, SAM 3, Detections Stabilizer, Object Detection Model, EasyOCR, Image Slicer, Corner Visualization, Florence-2 Model, Object Detection Model, SAM 3, QR Code Detection, Anthropic Claude, OpenAI, Google Gemini, Perception Encoder Embedding Model, Bounding Box Visualization, Keypoint Detection Model, Anthropic Claude, Pixel Color Count, Background Subtraction, Background Color Visualization, Image Convert Grayscale, Camera Calibration, Polygon Visualization, Image Blur, VLM As Classifier, Relative Static Crop, Clip Comparison, Heatmap Visualization, CogVLM, Mask Visualization, Image Preprocessing, VLM As Detector, Instance Segmentation Model, OpenAI, OCR Model, SIFT, Stitch Images, Single-Label Classification Model, Moondream2, Stability AI Outpainting, Dynamic Crop, Circle Visualization, Byte Tracker, OpenAI, Trace Visualization, Color Visualization, Qwen2.5-VL, Icon Visualization, YOLO-World Model, Dot Visualization, Time in Zone, Email Notification, Instance Segmentation Model, Camera Focus, Twilio SMS/MMS Notification, Contrast Equalization, Depth Estimation, Segment Anything 2 Model, LMM For Classification, Text Display, Dominant Color, Reference Path Visualization, Image Threshold, Perspective Correction, Multi-Label Classification Model, Image Contours, Polygon Zone Visualization, Polygon Visualization, Halo Visualization, Google Vision OCR, SAM 3, Stability AI Inpainting, Crop Visualization, Template Matching, Google Gemini, Barcode Detection, VLM As Classifier, Classification Label Visualization, Seg Preview, OpenAI, Absolute Static Crop, Multi-Label Classification Model, Single-Label Classification Model, Gaze Detection, Stability AI Image Generation

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Triangle 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, rle_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..
  • position (string): Anchor position for placing the triangle marker relative to the detection's bounding box. Options include: CENTER (center of box), corners (TOP_LEFT, TOP_RIGHT, BOTTOM_LEFT, BOTTOM_RIGHT), edge midpoints (TOP_CENTER, CENTER_LEFT, CENTER_RIGHT, BOTTOM_CENTER), or CENTER_OF_MASS (center of mass of the object, useful for irregular shapes)..
  • base (integer): Base width of the triangle in pixels. Controls the horizontal width of the triangular marker at its base. Larger values create wider triangles, while smaller values create narrower triangles. Works together with height to control the overall triangle size and shape..
  • height (integer): Height of the triangle in pixels. Controls the vertical height of the triangular marker from base to tip. Larger values create taller triangles, while smaller values create shorter triangles. Works together with base to control the overall triangle size and shape..
  • outline_thickness (integer): Thickness of the triangle outline in pixels. A value of 0 creates a filled triangle with no outline. Higher values create thicker outlines around the triangle border, improving visibility and contrast. Useful for making triangular markers more visible against complex backgrounds..

• output

  • image (image): Image in workflows.

Example JSON definition of step Triangle Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/triangle_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",
    "position": "CENTER",
    "base": 10,
    "height": 10,
    "outline_thickness": 2
}