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

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
}