Crop Visualization¶

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¶

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections¶

Input and Output Bindings¶

The available connections depend on its binding kinds. Check what binding kinds Crop Visualization in version v1 has.

{
    "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
}