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

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
}