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

  • 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
}