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@v1to 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:
Image Slicer,Stability AI Inpainting,Pixelate Visualization,Perspective Correction,Relative Static Crop,SIFT Comparison,Grid Visualization,Ellipse Visualization,SIFT,Model Comparison Visualization,Halo Visualization,Image Contours,Crop Visualization,Absolute Static Crop,Camera Focus,Image Blur,Trace Visualization,Circle Visualization,Image Preprocessing,Background Color Visualization,Dot Visualization,Polygon Zone Visualization,Classification Label Visualization,Bounding Box Visualization,Corner Visualization,Image Slicer,Dynamic Crop,Reference Path Visualization,Label Visualization,Mask Visualization,Stitch Images,Triangle Visualization,Stability AI Image Generation,Image Threshold,Line Counter Visualization,Keypoint Visualization,Color Visualization,Blur Visualization,Image Convert Grayscale,Polygon Visualization - outputs:
Segment Anything 2 Model,Image Slicer,Stability AI Inpainting,Clip Comparison,Perspective Correction,Object Detection Model,Object Detection Model,SIFT Comparison,CogVLM,Ellipse Visualization,SIFT,VLM as Detector,Image Contours,Multi-Label Classification Model,OpenAI,Absolute Static Crop,Camera Focus,Trace Visualization,CLIP Embedding Model,Multi-Label Classification Model,VLM as Detector,Dot Visualization,Google Vision OCR,Clip Comparison,Polygon Zone Visualization,Gaze Detection,Roboflow Dataset Upload,VLM as Classifier,Classification Label Visualization,Corner Visualization,Llama 3.2 Vision,Dynamic Crop,Reference Path Visualization,Detections Stabilizer,Label Visualization,Mask Visualization,Triangle Visualization,Template Matching,Line Counter Visualization,Dominant Color,Barcode Detection,Blur Visualization,Anthropic Claude,Instance Segmentation Model,SIFT Comparison,Time in Zone,Instance Segmentation Model,Pixelate Visualization,OpenAI,Relative Static Crop,VLM as Classifier,Keypoint Detection Model,Roboflow Dataset Upload,Google Gemini,Model Comparison Visualization,Halo Visualization,Crop Visualization,Qwen2.5-VL,Image Blur,Circle Visualization,Buffer,Keypoint Detection Model,Image Preprocessing,Background Color Visualization,QR Code Detection,Pixel Color Count,Florence-2 Model,Single-Label Classification Model,Bounding Box Visualization,Byte Tracker,Florence-2 Model,Image Slicer,LMM For Classification,Stitch Images,Stability AI Image Generation,Image Threshold,Detections Stitch,OCR Model,LMM,Keypoint Visualization,Color Visualization,Single-Label Classification Model,YOLO-World Model,Image Convert Grayscale,Polygon Visualization
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"
}