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

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
}