Relative Static Crop

Class: RelativeStaticCropBlockV1

Source: inference.core.workflows.core_steps.transformations.relative_static_crop.v1.RelativeStaticCropBlockV1

Crop a Region of Interest (RoI) from an image, using relative coordinates.

This is useful when placed after an ObjectDetection block as part of a multi-stage workflow. For example, you could use an ObjectDetection block to detect objects, then the RelativeStaticCrop block to crop objects, then an OCR block to run character recognition on each of the individual cropped regions.

Type identifier

Use the following identifier in step "type" field: roboflow_core/relative_statoic_crop@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
x_center	float	Center X of static crop (relative coordinate 0.0-1.0).	✅
y_center	float	Center Y of static crop (relative coordinate 0.0-1.0).	✅
width	float	Width of static crop (relative value 0.0-1.0).	✅
height	float	Height of static crop (relative value 0.0-1.0).	✅

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 Relative Static Crop in version v1.

• inputs: Image Preprocessing, Identify Changes, Relative Static Crop, Background Color Visualization, Line Counter Visualization, SIFT, Reference Path Visualization, Grid Visualization, Blur Visualization, Image Contours, Pixelate Visualization, Camera Focus, Color Visualization, Ellipse Visualization, Label Visualization, Image Slicer, Crop Visualization, Stitch Images, Polygon Zone Visualization, Dot Visualization, Bounding Box Visualization, Image Slicer, Detections Consensus, Perspective Correction, Image Blur, Stability AI Image Generation, Dynamic Crop, Image Convert Grayscale, Stability AI Inpainting, Clip Comparison, Identify Outliers, Model Comparison Visualization, Triangle Visualization, Classification Label Visualization, Trace Visualization, Depth Estimation, Corner Visualization, SIFT Comparison, Camera Calibration, Absolute Static Crop, Keypoint Visualization, Mask Visualization, Image Threshold, Halo Visualization, Circle Visualization, Polygon Visualization
• outputs: Relative Static Crop, Background Color Visualization, Line Counter Visualization, OpenAI, Roboflow Dataset Upload, Gaze Detection, Barcode Detection, Detections Stabilizer, LMM For Classification, Object Detection Model, Llama 3.2 Vision, Camera Focus, Color Visualization, Roboflow Dataset Upload, Instance Segmentation Model, Pixel Color Count, Label Visualization, YOLO-World Model, Moondream2, SmolVLM2, Dot Visualization, Object Detection Model, Bounding Box Visualization, Template Matching, Single-Label Classification Model, OCR Model, Google Vision OCR, Dynamic Crop, Clip Comparison, Model Comparison Visualization, Anthropic Claude, Depth Estimation, Corner Visualization, Byte Tracker, Qwen2.5-VL, Multi-Label Classification Model, SIFT Comparison, Camera Calibration, QR Code Detection, Keypoint Visualization, VLM as Detector, Image Threshold, Florence-2 Model, Mask Visualization, Halo Visualization, Polygon Zone Visualization, Google Gemini, Clip Comparison, Polygon Visualization, Image Preprocessing, Instance Segmentation Model, SIFT, Reference Path Visualization, CLIP Embedding Model, Blur Visualization, Image Contours, VLM as Detector, Pixelate Visualization, Dominant Color, Keypoint Detection Model, Detections Stitch, OpenAI, Ellipse Visualization, Crop Visualization, Stitch Images, Florence-2 Model, CogVLM, LMM, Image Slicer, Perspective Correction, Multi-Label Classification Model, Image Blur, Stability AI Image Generation, Time in Zone, Image Convert Grayscale, Stability AI Inpainting, Triangle Visualization, Classification Label Visualization, Keypoint Detection Model, Trace Visualization, Single-Label Classification Model, Buffer, VLM as Classifier, Segment Anything 2 Model, Absolute Static Crop, VLM as Classifier, Circle Visualization, Image Slicer

Input and Output Bindings

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

Bindings

• input

  • images (image): The image to infer on..
  • x_center (float_zero_to_one): Center X of static crop (relative coordinate 0.0-1.0).
  • y_center (float_zero_to_one): Center Y of static crop (relative coordinate 0.0-1.0).
  • width (float_zero_to_one): Width of static crop (relative value 0.0-1.0).
  • height (float_zero_to_one): Height of static crop (relative value 0.0-1.0).

• output

  • crops (image): Image in workflows.

Example JSON definition of step Relative Static Crop in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/relative_statoic_crop@v1",
    "images": "$inputs.image",
    "x_center": 0.3,
    "y_center": 0.3,
    "width": 0.3,
    "height": 0.3
}