Crop Visualization

Class: CropVisualizationBlockV1

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

Display scaled-up, zoomed-in views of detected objects overlaid on the original image, allowing detailed inspection of small or distant objects while maintaining context with the full scene.

How This Block Works

This block takes an image and detection predictions and creates scaled-up, zoomed-in crops of each detected object, then displays these enlarged crops on the original image. The block:

1. Takes an image and predictions as input
2. Identifies detected regions from bounding boxes or segmentation masks
3. Extracts the image region for each detected object (crops the object from the original image)
4. Scales up each crop by the specified scale factor (e.g., 2x makes objects twice as large)
5. Applies color styling to the crop border based on the selected color palette, with colors assigned by class, index, or track ID
6. Positions the scaled crop on the image at the specified anchor point relative to the original detection location using Supervision's CropAnnotator
7. Draws a colored border around the scaled crop with the specified thickness
8. Returns an annotated image with scaled-up object crops overlaid on the original image

The block works with both object detection predictions (using bounding boxes) and instance segmentation predictions (using masks). When masks are available, it crops the exact shape of detected objects; otherwise, it crops rectangular bounding box regions. The scale factor allows you to zoom in on objects, making small or distant objects more visible and easier to inspect. The scaled crops are positioned relative to their original detection locations, allowing you to see both the zoomed-in detail and the object's position in the full scene context.

Common Use Cases

• Small Object Inspection: Zoom in on small detected objects (e.g., defects, small products, distant objects) to make them more visible and easier to inspect while maintaining scene context
• Detail Visualization: Display enlarged views of detected objects for detailed analysis, quality control, or inspection workflows where fine details need to be visible
• Multi-Scale Object Display: Show both the full scene and zoomed-in object details simultaneously, useful for applications where context and detail are both important
• Quality Control and Inspection: Inspect detected defects, products, or components at higher magnification while keeping the original detection location visible for reference
• Presentation and Reporting: Create visualizations that highlight detected objects with zoomed-in views for reports, documentation, or presentations where both overview and detail are needed
• User Interface Enhancement: Provide zoomed-in object views in user interfaces, dashboards, or interactive applications where users need to see object details without losing scene context

Connecting to Other Blocks

The annotated image from this block can be connected to:

• Other visualization blocks (e.g., Label Visualization, Bounding Box Visualization, Polygon Visualization) to combine scaled crops with additional annotations for comprehensive visualization
• Data storage blocks (e.g., Local File Sink, CSV Formatter, Roboflow Dataset Upload) to save images with scaled crops for documentation, reporting, or analysis
• Webhook blocks to send visualized results with scaled crops 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 scaled crops as visual evidence in alerts or reports
• Video output blocks to create annotated video streams or recordings with scaled crops for live monitoring, detailed inspection, or post-processing analysis

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	Anchor position for placing the scaled crop relative to the original 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). The scaled crop will be positioned at this anchor point relative to the original detection location..	✅
scale_factor	float	Factor by which to scale (zoom) the cropped object region. A factor of 2.0 doubles the size of the crop, making objects twice as large. A factor of 1.0 shows the crop at original size. Higher values (e.g., 3.0, 4.0) create more zoomed-in views, useful for inspecting small or distant objects. Lower values (e.g., 1.5) provide subtle magnification..	✅
border_thickness	int	Thickness of the border outline around the scaled crop in pixels. Higher values create thicker, more visible borders that help distinguish the scaled crop from the background..	✅

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

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[instance_segmentation_prediction, object_detection_prediction, rle_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): Anchor position for placing the scaled crop relative to the original 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). The scaled crop will be positioned at this anchor point relative to the original detection location..
  • scale_factor (float): Factor by which to scale (zoom) the cropped object region. A factor of 2.0 doubles the size of the crop, making objects twice as large. A factor of 1.0 shows the crop at original size. Higher values (e.g., 3.0, 4.0) create more zoomed-in views, useful for inspecting small or distant objects. Lower values (e.g., 1.5) provide subtle magnification..
  • border_thickness (integer): Thickness of the border outline around the scaled crop in pixels. Higher values create thicker, more visible borders that help distinguish the scaled crop from the background..

• 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
}