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

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
}