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