SIFT¶
Class: SIFTBlockV1
Source: inference.core.workflows.core_steps.classical_cv.sift.v1.SIFTBlockV1
The Scale-Invariant Feature Transform (SIFT) algorithm is a popular method in computer vision for detecting and describing features (interesting parts) in images. SIFT is used to find key points in an image and describe them in a way that allows for recognizing the same objects or features in different images, even if the images are taken from different angles, distances, or lighting conditions.
Read more: https://en.wikipedia.org/wiki/Scale-invariant_feature_transform
Type identifier¶
Use the following identifier in step "type"
field: roboflow_core/sift@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.. | ❌ |
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 SIFT
in version v1
.
- inputs:
Crop Visualization
,SIFT
,Stability AI Image Generation
,Triangle Visualization
,Blur Visualization
,Background Color Visualization
,Relative Static Crop
,Color Visualization
,Image Contours
,Camera Focus
,Corner Visualization
,Line Counter Visualization
,Icon Visualization
,Mask Visualization
,Image Convert Grayscale
,Circle Visualization
,Image Blur
,Pixelate Visualization
,Absolute Static Crop
,Model Comparison Visualization
,Image Threshold
,Reference Path Visualization
,Image Slicer
,Stitch Images
,Depth Estimation
,Trace Visualization
,Image Preprocessing
,Classification Label Visualization
,Polygon Visualization
,Stability AI Outpainting
,Keypoint Visualization
,Dot Visualization
,Grid Visualization
,Bounding Box Visualization
,Camera Calibration
,Polygon Zone Visualization
,Ellipse Visualization
,QR Code Generator
,Halo Visualization
,Perspective Correction
,Stability AI Inpainting
,Image Slicer
,SIFT Comparison
,Label Visualization
,Dynamic Crop
- outputs:
QR Code Detection
,Anthropic Claude
,Crop Visualization
,SIFT
,LMM For Classification
,Blur Visualization
,Line Counter Visualization
,Color Visualization
,Image Contours
,Camera Focus
,Mask Visualization
,Image Convert Grayscale
,Google Gemini
,Circle Visualization
,Absolute Static Crop
,VLM as Classifier
,Object Detection Model
,Multi-Label Classification Model
,Keypoint Detection Model
,Stitch Images
,Trace Visualization
,Image Preprocessing
,Qwen2.5-VL
,OCR Model
,Object Detection Model
,Clip Comparison
,SmolVLM2
,Polygon Zone Visualization
,LMM
,YOLO-World Model
,Halo Visualization
,CLIP Embedding Model
,Florence-2 Model
,Moondream2
,Perspective Correction
,Stability AI Inpainting
,Buffer
,Template Matching
,Label Visualization
,VLM as Detector
,Pixel Color Count
,Segment Anything 2 Model
,Perception Encoder Embedding Model
,Stability AI Image Generation
,Keypoint Detection Model
,Triangle Visualization
,Background Color Visualization
,Relative Static Crop
,Detections Stabilizer
,Corner Visualization
,Multi-Label Classification Model
,Icon Visualization
,SIFT Comparison
,Pixelate Visualization
,Image Blur
,Gaze Detection
,Model Comparison Visualization
,VLM as Detector
,Llama 3.2 Vision
,Time in Zone
,Instance Segmentation Model
,Image Threshold
,VLM as Classifier
,Google Vision OCR
,Reference Path Visualization
,Image Slicer
,Roboflow Dataset Upload
,CogVLM
,Byte Tracker
,Barcode Detection
,Depth Estimation
,Roboflow Dataset Upload
,Single-Label Classification Model
,OpenAI
,Classification Label Visualization
,Polygon Visualization
,Stability AI Outpainting
,Keypoint Visualization
,Dot Visualization
,OpenAI
,Single-Label Classification Model
,Bounding Box Visualization
,Camera Calibration
,Ellipse Visualization
,OpenAI
,Florence-2 Model
,Image Slicer
,Instance Segmentation Model
,SIFT Comparison
,Detections Stitch
,Dominant Color
,Clip Comparison
,Dynamic Crop
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
SIFT
in version v1
has.
Bindings
-
input
image
(image
): The input image for this step..
-
output
image
(image
): Image in workflows.keypoints
(image_keypoints
): Image keypoints detected by classical Computer Vision method.descriptors
(numpy_array
): Numpy array.
Example JSON definition of step SIFT
in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/sift@v1",
"image": "$inputs.image"
}