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

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
}