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@v1
to 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:
Identify Changes
,Blur Visualization
,Camera Focus
,Image Threshold
,Polygon Zone Visualization
,Stability AI Inpainting
,Relative Static Crop
,Image Preprocessing
,Keypoint Visualization
,Background Color Visualization
,Grid Visualization
,Image Convert Grayscale
,Trace Visualization
,Absolute Static Crop
,Color Visualization
,Perspective Correction
,Classification Label Visualization
,Circle Visualization
,Camera Calibration
,Pixelate Visualization
,Image Slicer
,Cosine Similarity
,Label Visualization
,Halo Visualization
,Triangle Visualization
,Reference Path Visualization
,Image Slicer
,Line Counter Visualization
,Gaze Detection
,Image Blur
,Corner Visualization
,SIFT Comparison
,Image Contours
,Dynamic Crop
,Polygon Visualization
,Depth Estimation
,SIFT
,Ellipse Visualization
,Mask Visualization
,Stitch Images
,Bounding Box Visualization
,Dot Visualization
,Stability AI Image Generation
,Model Comparison Visualization
,Crop Visualization
- outputs:
Camera Focus
,Single-Label Classification Model
,Polygon Zone Visualization
,YOLO-World Model
,Image Convert Grayscale
,Instance Segmentation Model
,Trace Visualization
,Absolute Static Crop
,Perspective Correction
,OpenAI
,Circle Visualization
,Clip Comparison
,Image Slicer
,OpenAI
,Triangle Visualization
,Halo Visualization
,QR Code Detection
,Gaze Detection
,Corner Visualization
,Object Detection Model
,Template Matching
,LMM
,Roboflow Dataset Upload
,VLM as Classifier
,Dynamic Crop
,Depth Estimation
,Buffer
,Stitch Images
,Segment Anything 2 Model
,Object Detection Model
,Llama 3.2 Vision
,Anthropic Claude
,Model Comparison Visualization
,Keypoint Detection Model
,Crop Visualization
,Blur Visualization
,CLIP Embedding Model
,CogVLM
,Dominant Color
,Image Threshold
,Stability AI Inpainting
,SmolVLM2
,VLM as Detector
,Relative Static Crop
,Image Preprocessing
,Pixel Color Count
,Background Color Visualization
,Barcode Detection
,Keypoint Visualization
,Clip Comparison
,Color Visualization
,Moondream2
,OCR Model
,Multi-Label Classification Model
,Classification Label Visualization
,Google Vision OCR
,Pixelate Visualization
,Camera Calibration
,Image Slicer
,Label Visualization
,Time in Zone
,Reference Path Visualization
,Single-Label Classification Model
,Google Gemini
,Roboflow Dataset Upload
,Line Counter Visualization
,Qwen2.5-VL
,Byte Tracker
,Multi-Label Classification Model
,Image Blur
,Florence-2 Model
,SIFT Comparison
,Detections Stabilizer
,LMM For Classification
,Image Contours
,Instance Segmentation Model
,Polygon Visualization
,SIFT
,Florence-2 Model
,Ellipse Visualization
,Mask Visualization
,Keypoint Detection Model
,Detections Stitch
,Bounding Box Visualization
,Dot Visualization
,Stability AI Image Generation
,VLM as Detector
,VLM as Classifier
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
}