SIFT Comparison¶
v2¶
Class: SIFTComparisonBlockV2 (there are multiple versions of this block)
Source: inference.core.workflows.core_steps.classical_cv.sift_comparison.v2.SIFTComparisonBlockV2
Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning
Compare SIFT descriptors from multiple images using FLANN-based matcher.
This block is useful for determining if multiple images match based on their SIFT descriptors.
Type identifier¶
Use the following identifier in step "type" field: roboflow_core/sift_comparison@v2to add the block as
as step in your workflow.
Properties¶
| Name | Type | Description | Refs |
|---|---|---|---|
name |
str |
Enter a unique identifier for this step.. | ❌ |
good_matches_threshold |
int |
Threshold for the number of good matches to consider the images as matching. | ✅ |
ratio_threshold |
float |
Ratio threshold for the ratio test, which is used to filter out poor matches by comparing the distance of the closest match to the distance of the second closest match. A lower ratio indicates stricter filtering.. | ✅ |
matcher |
str |
Matcher to use for comparing the SIFT descriptors. | ✅ |
visualize |
bool |
Whether to visualize the keypoints and matches between the two images. | ✅ |
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 Comparison in version v2.
- inputs:
Circle Visualization,Background Color Visualization,Corner Visualization,Twilio SMS Notification,Slack Notification,VLM as Detector,VLM as Classifier,LMM,Polygon Zone Visualization,Camera Focus,Image Slicer,Line Counter,Image Blur,Dot Visualization,Google Gemini,Roboflow Dataset Upload,Stability AI Inpainting,Pixelate Visualization,Line Counter,OpenAI,Detections Consensus,Distance Measurement,Image Convert Grayscale,Absolute Static Crop,Stability AI Image Generation,Webhook Sink,Color Visualization,Image Threshold,Halo Visualization,Polygon Visualization,VLM as Classifier,CogVLM,Camera Calibration,Email Notification,Classification Label Visualization,Single-Label Classification Model,Llama 3.2 Vision,Google Vision OCR,Roboflow Dataset Upload,Ellipse Visualization,Pixel Color Count,Bounding Box Visualization,JSON Parser,Object Detection Model,Line Counter Visualization,Image Preprocessing,Trace Visualization,Label Visualization,Local File Sink,Image Slicer,Anthropic Claude,Crop Visualization,Identify Outliers,OCR Model,Relative Static Crop,Model Comparison Visualization,Stitch OCR Detections,Perspective Correction,OpenAI,Mask Visualization,Clip Comparison,Dynamic Crop,Template Matching,CSV Formatter,Florence-2 Model,Instance Segmentation Model,Keypoint Detection Model,Image Contours,SIFT,SIFT Comparison,Reference Path Visualization,Florence-2 Model,Triangle Visualization,Model Monitoring Inference Aggregator,SIFT Comparison,VLM as Detector,Keypoint Visualization,Identify Changes,Multi-Label Classification Model,LMM For Classification,Roboflow Custom Metadata,Grid Visualization,Stitch Images,Blur Visualization - outputs:
Corner Visualization,Slack Notification,VLM as Detector,VLM as Classifier,Polygon Zone Visualization,Image Slicer,Dot Visualization,Roboflow Dataset Upload,Single-Label Classification Model,Dominant Color,Stability AI Image Generation,Image Convert Grayscale,Halo Visualization,Dynamic Zone,CogVLM,Email Notification,Camera Calibration,Object Detection Model,Single-Label Classification Model,Llama 3.2 Vision,Byte Tracker,Ellipse Visualization,Pixel Color Count,Bounding Box Visualization,Line Counter Visualization,Image Preprocessing,Label Visualization,Image Slicer,Anthropic Claude,Crop Visualization,Detections Stitch,OCR Model,Model Comparison Visualization,Relative Static Crop,QR Code Detection,Clip Comparison,Time in Zone,Florence-2 Model,Keypoint Detection Model,Buffer,SIFT Comparison,Multi-Label Classification Model,Florence-2 Model,Keypoint Visualization,Identify Changes,Multi-Label Classification Model,Roboflow Custom Metadata,Stitch Images,Circle Visualization,Background Color Visualization,Twilio SMS Notification,LMM,Camera Focus,Image Blur,Google Gemini,Detection Offset,Stability AI Inpainting,Pixelate Visualization,OpenAI,Detections Consensus,Gaze Detection,Absolute Static Crop,Webhook Sink,Color Visualization,Image Threshold,Polygon Visualization,VLM as Classifier,Instance Segmentation Model,Classification Label Visualization,Google Vision OCR,Roboflow Dataset Upload,Object Detection Model,Keypoint Detection Model,Trace Visualization,Clip Comparison,Identify Outliers,YOLO-World Model,Stitch OCR Detections,Perspective Correction,Byte Tracker,OpenAI,Qwen2.5-VL,Mask Visualization,Time in Zone,Dynamic Crop,Template Matching,Byte Tracker,Barcode Detection,Instance Segmentation Model,Image Contours,SIFT,Reference Path Visualization,CLIP Embedding Model,Triangle Visualization,Model Monitoring Inference Aggregator,SIFT Comparison,VLM as Detector,LMM For Classification,Grid Visualization,Segment Anything 2 Model,Detections Stabilizer,Blur Visualization
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
SIFT Comparison in version v2 has.
Bindings
-
input
input_1(Union[numpy_array,image]): Reference to Image or SIFT descriptors from the first image to compare.input_2(Union[numpy_array,image]): Reference to Image or SIFT descriptors from the second image to compare.good_matches_threshold(integer): Threshold for the number of good matches to consider the images as matching.ratio_threshold(float_zero_to_one): Ratio threshold for the ratio test, which is used to filter out poor matches by comparing the distance of the closest match to the distance of the second closest match. A lower ratio indicates stricter filtering..matcher(string): Matcher to use for comparing the SIFT descriptors.visualize(boolean): Whether to visualize the keypoints and matches between the two images.
-
output
images_match(boolean): Boolean flag.good_matches_count(integer): Integer value.keypoints_1(image_keypoints): Image keypoints detected by classical Computer Vision method.descriptors_1(numpy_array): Numpy array.keypoints_2(image_keypoints): Image keypoints detected by classical Computer Vision method.descriptors_2(numpy_array): Numpy array.visualization_1(image): Image in workflows.visualization_2(image): Image in workflows.visualization_matches(image): Image in workflows.
Example JSON definition of step SIFT Comparison in version v2
{
"name": "<your_step_name_here>",
"type": "roboflow_core/sift_comparison@v2",
"input_1": "$inputs.image1",
"input_2": "$inputs.image2",
"good_matches_threshold": 50,
"ratio_threshold": 0.7,
"matcher": "FlannBasedMatcher",
"visualize": true
}
v1¶
Class: SIFTComparisonBlockV1 (there are multiple versions of this block)
Source: inference.core.workflows.core_steps.classical_cv.sift_comparison.v1.SIFTComparisonBlockV1
Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning
Compare SIFT descriptors from multiple images using FLANN-based matcher.
This block is useful for determining if multiple images match based on their SIFT descriptors.
Type identifier¶
Use the following identifier in step "type" field: roboflow_core/sift_comparison@v1to add the block as
as step in your workflow.
Properties¶
| Name | Type | Description | Refs |
|---|---|---|---|
name |
str |
Enter a unique identifier for this step.. | ❌ |
good_matches_threshold |
int |
Threshold for the number of good matches to consider the images as matching. | ✅ |
ratio_threshold |
float |
Ratio threshold for the ratio test, which is used to filter out poor matches by comparing the distance of the closest match to the distance of the second closest match. A lower ratio indicates stricter filtering.. | ✅ |
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 Comparison in version v1.
- inputs:
Distance Measurement,Line Counter,SIFT Comparison,SIFT Comparison,Template Matching,Image Contours,Line Counter,SIFT,Pixel Color Count - outputs:
Circle Visualization,Background Color Visualization,Corner Visualization,Twilio SMS Notification,Slack Notification,Polygon Zone Visualization,Image Slicer,Image Blur,Dot Visualization,Detection Offset,Roboflow Dataset Upload,Single-Label Classification Model,Pixelate Visualization,Detections Consensus,Gaze Detection,Dominant Color,Absolute Static Crop,Webhook Sink,Color Visualization,Image Threshold,Halo Visualization,Polygon Visualization,Dynamic Zone,Instance Segmentation Model,Email Notification,Object Detection Model,Classification Label Visualization,Single-Label Classification Model,Roboflow Dataset Upload,Byte Tracker,Ellipse Visualization,Pixel Color Count,Bounding Box Visualization,Object Detection Model,Line Counter Visualization,Image Preprocessing,Keypoint Detection Model,Trace Visualization,Label Visualization,Image Slicer,Anthropic Claude,Crop Visualization,Identify Outliers,Model Comparison Visualization,Stitch OCR Detections,Byte Tracker,Perspective Correction,Mask Visualization,Time in Zone,Time in Zone,Template Matching,Byte Tracker,Instance Segmentation Model,Keypoint Detection Model,Image Contours,SIFT Comparison,Reference Path Visualization,Multi-Label Classification Model,Triangle Visualization,Model Monitoring Inference Aggregator,SIFT Comparison,Keypoint Visualization,Identify Changes,Multi-Label Classification Model,Roboflow Custom Metadata,Grid Visualization,Segment Anything 2 Model,Detections Stabilizer,Stitch Images,Blur Visualization
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
SIFT Comparison in version v1 has.
Bindings
-
input
descriptor_1(numpy_array): Reference to SIFT descriptors from the first image to compare.descriptor_2(numpy_array): Reference to SIFT descriptors from the second image to compare.good_matches_threshold(integer): Threshold for the number of good matches to consider the images as matching.ratio_threshold(integer): Ratio threshold for the ratio test, which is used to filter out poor matches by comparing the distance of the closest match to the distance of the second closest match. A lower ratio indicates stricter filtering..
-
output
Example JSON definition of step SIFT Comparison in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/sift_comparison@v1",
"descriptor_1": "$steps.sift.descriptors",
"descriptor_2": "$steps.sift.descriptors",
"good_matches_threshold": 50,
"ratio_threshold": 0.7
}