Camera Calibration

Class: CameraCalibrationBlockV1

Source: inference.core.workflows.core_steps.transformations.camera_calibration.v1.CameraCalibrationBlockV1

This block uses the OpenCV calibrateCamera function to remove lens distortions from an image. Please refer to OpenCV documentation where camera calibration methodology is described: https://docs.opencv.org/4.x/dc/dbb/tutorial_py_calibration.html

This block requires following parameters in order to perform the calibration: Lens focal length along the x-axis and y-axis (fx, fy) Lens optical centers along the x-axis and y-axis (cx, cy) Radial distortion coefficients (k1, k2, k3) Tangential distortion coefficients (p1, p2)

Based on above parameters, camera matrix will be built as follows: [[fx 0 cx][ 0 fy cy] [ 0 0 1 ]]

Distortions coefficient will be passed as 5-tuple (k1, k2, p1, p2, k3)

Type identifier

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

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
fx	float	Focal length along the x-axis.	✅
fy	float	Focal length along the y-axis.	✅
cx	float	Optical center along the x-axis.	✅
cy	float	Optical center along the y-axis.	✅
k1	float	Radial distortion coefficient k1.	✅
k2	float	Radial distortion coefficient k2.	✅
k3	float	Radial distortion coefficient k3.	✅
p1	float	Distortion coefficient p1.	✅
p2	float	Distortion coefficient p2.	✅

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 Camera Calibration in version v1.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Camera Calibration in version v1 has.

Bindings

• input

  • image (image): Image to remove distortions from.
  • fx (float): Focal length along the x-axis.
  • fy (float): Focal length along the y-axis.
  • cx (float): Optical center along the x-axis.
  • cy (float): Optical center along the y-axis.
  • k1 (float): Radial distortion coefficient k1.
  • k2 (float): Radial distortion coefficient k2.
  • k3 (float): Radial distortion coefficient k3.
  • p1 (float): Distortion coefficient p1.
  • p2 (float): Distortion coefficient p2.

• output

  • calibrated_image (image): Image in workflows.

Example JSON definition of step Camera Calibration in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/camera-calibration@v1",
    "image": "$inputs.image",
    "fx": 0.123,
    "fy": 0.123,
    "cx": 0.123,
    "cy": 0.123,
    "k1": 0.123,
    "k2": 0.123,
    "k3": 0.123,
    "p1": 0.123,
    "p2": 0.123
}