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