Pixelate Visualization

Class: PixelateVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.pixelate.v1.PixelateVisualizationBlockV1

The PixelateVisualization block pixelates detected objects in an image using Supervision's sv.PixelateAnnotator.

Type identifier

Use the following identifier in step "type" field: roboflow_core/pixelate_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..	✅
pixel_size	int	Size of the pixelation..	✅

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 Pixelate Visualization in version v1.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Pixelate 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[keypoint_detection_prediction, instance_segmentation_prediction, object_detection_prediction]): Model predictions to visualize..
  • pixel_size (integer): Size of the pixelation..

• output

  • image (image): Image in workflows.

Example JSON definition of step Pixelate Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/pixelate_visualization@v1",
    "image": "$inputs.image",
    "copy_image": true,
    "predictions": "$steps.object_detection_model.predictions",
    "pixel_size": 20
}