Crop Visualization

Class: CropVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.crop.v1.CropVisualizationBlockV1

The CropVisualization block draws scaled up crops of detections on the scene using Supervision's sv.CropAnnotator.

Type identifier

Use the following identifier in step "type" field: roboflow_core/crop_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	The anchor position for placing the crop..	✅
scale_factor	float	The factor by which to scale the cropped image part. A factor of 2, for example, would double the size of the cropped area, allowing for a closer view of the detection..	✅
border_thickness	int	Thickness of the outline 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 Crop Visualization in version v1.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Crop 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]): 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): The anchor position for placing the crop..
  • scale_factor (float): The factor by which to scale the cropped image part. A factor of 2, for example, would double the size of the cropped area, allowing for a closer view of the detection..
  • border_thickness (integer): Thickness of the outline in pixels..

• output

  • image (image): Image in workflows.

Example JSON definition of step Crop Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/crop_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",
    "scale_factor": 2.0,
    "border_thickness": 2
}