Absolute Static Crop

Class: AbsoluteStaticCropBlockV1

Source: inference.core.workflows.core_steps.transformations.absolute_static_crop.v1.AbsoluteStaticCropBlockV1

Crop a Region of Interest (RoI) from an image, using absolute 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 AbsoluteStaticCrop 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/absolute_static_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	int	Center X of static crop (absolute coordinate).	✅
y_center	int	Center Y of static crop (absolute coordinate).	✅
width	int	Width of static crop (absolute value).	✅
height	int	Height of static crop (absolute value).	✅

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 Absolute Static Crop in version v1.

• inputs: Blur Visualization, Classification Label Visualization, Circle Visualization, SIFT Comparison, Crop Visualization, Image Contours, Relative Static Crop, Grid Visualization, Image Preprocessing, Perspective Correction, Ellipse Visualization, Absolute Static Crop, Stitch Images, Triangle Visualization, Contrast Equalization, Stability AI Inpainting, QR Code Generator, Image Slicer, Background Color Visualization, Polygon Zone Visualization, Stability AI Image Generation, Template Matching, Depth Estimation, Distance Measurement, Dot Visualization, Bounding Box Visualization, Camera Focus, Line Counter Visualization, Morphological Transformation, SIFT, Reference Path Visualization, Halo Visualization, SIFT Comparison, Icon Visualization, Image Blur, Image Slicer, Polygon Visualization, Pixelate Visualization, Image Threshold, Image Convert Grayscale, Color Visualization, Line Counter, Label Visualization, Trace Visualization, Pixel Color Count, Dynamic Crop, Line Counter, Model Comparison Visualization, Corner Visualization, Camera Calibration, Mask Visualization, Keypoint Visualization, Stability AI Outpainting
• outputs: VLM as Detector, Google Vision OCR, SAM 3, Classification Label Visualization, Detections Stabilizer, Circle Visualization, Image Contours, Relative Static Crop, Image Preprocessing, LMM For Classification, VLM as Classifier, Ellipse Visualization, Stitch Images, Triangle Visualization, Stability AI Inpainting, Image Slicer, VLM as Classifier, Background Color Visualization, Segment Anything 2 Model, Template Matching, Moondream2, OCR Model, Dot Visualization, Florence-2 Model, SIFT, Morphological Transformation, EasyOCR, Gaze Detection, Halo Visualization, Reference Path Visualization, SIFT Comparison, Buffer, Polygon Visualization, Image Slicer, Florence-2 Model, Clip Comparison, Perception Encoder Embedding Model, Instance Segmentation Model, OpenAI, Byte Tracker, Color Visualization, Image Convert Grayscale, Object Detection Model, Keypoint Detection Model, Google Gemini, Label Visualization, Email Notification, Llama 3.2 Vision, Trace Visualization, QR Code Detection, YOLO-World Model, Corner Visualization, Mask Visualization, Time in Zone, CogVLM, Stability AI Outpainting, OpenAI, Detections Stitch, Barcode Detection, Blur Visualization, Dominant Color, Crop Visualization, VLM as Detector, Single-Label Classification Model, OpenAI, Perspective Correction, Clip Comparison, Single-Label Classification Model, Absolute Static Crop, Seg Preview, Contrast Equalization, Roboflow Dataset Upload, Roboflow Dataset Upload, Polygon Zone Visualization, CLIP Embedding Model, Stability AI Image Generation, Depth Estimation, Bounding Box Visualization, Camera Focus, Line Counter Visualization, Instance Segmentation Model, Multi-Label Classification Model, Icon Visualization, Image Blur, Pixelate Visualization, Image Threshold, Keypoint Detection Model, Anthropic Claude, LMM, Google Gemini, Multi-Label Classification Model, Pixel Color Count, SmolVLM2, Dynamic Crop, Qwen2.5-VL, Model Comparison Visualization, Camera Calibration, Keypoint Visualization, Object Detection Model

Input and Output Bindings

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

Bindings

• input

  • images (image): The image to infer on..
  • x_center (integer): Center X of static crop (absolute coordinate).
  • y_center (integer): Center Y of static crop (absolute coordinate).
  • width (integer): Width of static crop (absolute value).
  • height (integer): Height of static crop (absolute value).

• output

  • crops (image): Image in workflows.

Example JSON definition of step Absolute Static Crop in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/absolute_static_crop@v1",
    "images": "$inputs.image",
    "x_center": 40,
    "y_center": 40,
    "width": 40,
    "height": 40
}