SIFT¶
Version v1¶
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 |
The unique name of 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¶
Check what blocks you can connect to SIFT in version v1.
- inputs:
Triangle Visualization,Image Blur,Image Preprocessing,Line Counter Visualization,Keypoint Visualization,Blur Visualization,Mask Visualization,Background Color Visualization,Model Comparison Visualization,Ellipse Visualization,Pixelate Visualization,Corner Visualization,Camera Focus,Reference Path Visualization,Relative Static Crop,Image Contours,Image Convert Grayscale,Crop Visualization,Stitch Images,Label Visualization,Polygon Visualization,Stability AI Inpainting,Dynamic Crop,Trace Visualization,Polygon Zone Visualization,SIFT,Image Threshold,Color Visualization,Image Slicer,Perspective Correction,Absolute Static Crop,Bounding Box Visualization,Halo Visualization,SIFT Comparison,Circle Visualization,Dot Visualization - outputs:
OpenAI,Clip Comparison,Keypoint Detection Model,Florence-2 Model,Line Counter Visualization,Barcode Detection,Model Comparison Visualization,Background Color Visualization,Ellipse Visualization,Corner Visualization,QR Code Detection,Anthropic Claude,Object Detection Model,Time in zone,Template Matching,YOLO-World Model,Google Vision OCR,Pixel Color Count,Stability AI Inpainting,Single-Label Classification Model,SIFT,Dominant Color,Color Visualization,Image Slicer,Bounding Box Visualization,Perspective Correction,Halo Visualization,LMM For Classification,Roboflow Dataset Upload,OCR Model,Mask Visualization,Dot Visualization,Triangle Visualization,Image Blur,Image Preprocessing,Keypoint Visualization,Blur Visualization,Pixelate Visualization,Camera Focus,VLM as Detector,Multi-Label Classification Model,Detections Stitch,Reference Path Visualization,VLM as Classifier,Relative Static Crop,Image Contours,OpenAI,SIFT Comparison,Crop Visualization,Stitch Images,Image Convert Grayscale,Google Gemini,Clip Comparison,Label Visualization,Segment Anything 2 Model,Polygon Visualization,Dynamic Crop,Trace Visualization,Polygon Zone Visualization,CogVLM,Image Threshold,LMM,Absolute Static Crop,SIFT Comparison,Roboflow Dataset Upload,Circle Visualization,Instance Segmentation Model
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"
}