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: Image Contours, Line Counter, Stability AI Inpainting, Detections Consensus, Corner Visualization, CSV Formatter, Google Gemini, Line Counter Visualization, Reference Path Visualization, Keypoint Detection Model, Model Monitoring Inference Aggregator, Florence-2 Model, Circle Visualization, OCR Model, Llama 3.2 Vision, SIFT Comparison, Relative Static Crop, Roboflow Dataset Upload, JSON Parser, PTZ Tracking (ONVIF).md), Dynamic Zone, Image Convert Grayscale, Pixelate Visualization, Model Comparison Visualization, Trace Visualization, LMM, Twilio SMS Notification, Roboflow Dataset Upload, Depth Estimation, Label Visualization, Classification Label Visualization, Blur Visualization, OpenAI, Color Visualization, Bounding Box Visualization, Template Matching, Anthropic Claude, Ellipse Visualization, Pixel Color Count, Instance Segmentation Model, Polygon Zone Visualization, VLM as Classifier, Object Detection Model, Roboflow Custom Metadata, Image Slicer, Image Slicer, VLM as Detector, Crop Visualization, Perspective Correction, Halo Visualization, Dot Visualization, Mask Visualization, Keypoint Visualization, Local File Sink, Absolute Static Crop, Line Counter, Stitch OCR Detections, Image Blur, OpenAI, VLM as Classifier, Clip Comparison, Identify Changes, Distance Measurement, Triangle Visualization, Background Color Visualization, SIFT Comparison, Florence-2 Model, Camera Calibration, Google Vision OCR, Image Threshold, Single-Label Classification Model, Image Preprocessing, OpenAI, CogVLM, Slack Notification, VLM as Detector, Stability AI Image Generation, SIFT, Grid Visualization, Camera Focus, Stitch Images, Stability AI Outpainting, Identify Outliers, Polygon Visualization, Multi-Label Classification Model, Webhook Sink, Dynamic Crop, Email Notification, LMM For Classification
• outputs: Image Contours, Stability AI Inpainting, Corner Visualization, Google Gemini, Model Monitoring Inference Aggregator, Florence-2 Model, YOLO-World Model, SIFT Comparison, PTZ Tracking (ONVIF).md), Image Convert Grayscale, Byte Tracker, Pixelate Visualization, Dominant Color, Model Comparison Visualization, Trace Visualization, Detections Stitch, Twilio SMS Notification, Label Visualization, Classification Label Visualization, Blur Visualization, Moondream2, Bounding Box Visualization, Template Matching, Anthropic Claude, Pixel Color Count, Detection Offset, Polygon Zone Visualization, VLM as Classifier, Gaze Detection, Image Slicer, Image Slicer, Crop Visualization, Perspective Correction, Dot Visualization, QR Code Detection, Mask Visualization, Absolute Static Crop, Image Blur, OpenAI, Triangle Visualization, Background Color Visualization, SIFT Comparison, CLIP Embedding Model, Florence-2 Model, Camera Calibration, Image Threshold, Single-Label Classification Model, Buffer, OpenAI, CogVLM, Keypoint Detection Model, SIFT, Grid Visualization, Stitch Images, Identify Outliers, Stability AI Outpainting, Polygon Visualization, Byte Tracker, Barcode Detection, LMM For Classification, Detections Consensus, Time in Zone, Line Counter Visualization, Reference Path Visualization, Keypoint Detection Model, Circle Visualization, OCR Model, Llama 3.2 Vision, Relative Static Crop, Roboflow Dataset Upload, Dynamic Zone, Detections Classes Replacement, Multi-Label Classification Model, Object Detection Model, LMM, Roboflow Dataset Upload, Depth Estimation, Perception Encoder Embedding Model, OpenAI, Color Visualization, Ellipse Visualization, Instance Segmentation Model, Object Detection Model, Roboflow Custom Metadata, VLM as Detector, Halo Visualization, Keypoint Visualization, Detections Stabilizer, SmolVLM2, Stitch OCR Detections, Clip Comparison, Qwen2.5-VL, VLM as Classifier, Clip Comparison, Byte Tracker, Identify Changes, Instance Segmentation Model, Segment Anything 2 Model, Google Vision OCR, Image Preprocessing, Slack Notification, VLM as Detector, Stability AI Image Generation, Camera Focus, Multi-Label Classification Model, Webhook Sink, Time in Zone, Single-Label Classification Model, Email Notification, Dynamic Crop

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: Image Contours, Line Counter, SIFT Comparison, Depth Estimation, Line Counter, Template Matching, SIFT Comparison, Pixel Color Count, SIFT, Distance Measurement, Perspective Correction
• outputs: Image Contours, Stability AI Inpainting, Detections Consensus, Corner Visualization, Time in Zone, Line Counter Visualization, Reference Path Visualization, Keypoint Detection Model, Model Monitoring Inference Aggregator, Circle Visualization, SIFT Comparison, Roboflow Dataset Upload, PTZ Tracking (ONVIF).md), Dynamic Zone, Detections Classes Replacement, Multi-Label Classification Model, Byte Tracker, Pixelate Visualization, Dominant Color, Object Detection Model, Model Comparison Visualization, Trace Visualization, Twilio SMS Notification, Roboflow Dataset Upload, Label Visualization, Classification Label Visualization, Blur Visualization, Color Visualization, Bounding Box Visualization, Template Matching, Anthropic Claude, Ellipse Visualization, Pixel Color Count, Instance Segmentation Model, Detection Offset, Polygon Zone Visualization, Object Detection Model, Roboflow Custom Metadata, Gaze Detection, Image Slicer, Image Slicer, Crop Visualization, Perspective Correction, Halo Visualization, Dot Visualization, Mask Visualization, Keypoint Visualization, Detections Stabilizer, Absolute Static Crop, Stitch OCR Detections, Image Blur, Byte Tracker, Identify Changes, Instance Segmentation Model, Triangle Visualization, Segment Anything 2 Model, Background Color Visualization, SIFT Comparison, Image Threshold, Single-Label Classification Model, Image Preprocessing, Slack Notification, Keypoint Detection Model, Grid Visualization, Stitch Images, Identify Outliers, Stability AI Outpainting, Polygon Visualization, Multi-Label Classification Model, Webhook Sink, Time in Zone, Byte Tracker, Email Notification, Single-Label Classification Model

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

  • good_matches_count (integer): Integer value.
  • images_match (boolean): Boolean flag.

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
}