Anthropic Claude Lookup
Perform a lookup with Anthropic Claude models on objects in an ROI, or on a ROI in the frame.
Overview
The Anthropic Claude Lookup node is designed to perform lookups using the powerful Claude model on objects within a Region of Interest (ROI), or on a ROI within the frame. This functionality is useful for applications requiring advanced recognition and understanding of objects or areas within a video feed.
Inputs & Outputs
- Inputs: 1, Media Format: Raw Video
- Outputs: 1, Media Format: Raw Video
- Output Metadata:
nodes.node_id
,recognized_objs
,recognized_obj_ids
,recognized_obj_count
,recognized_obj_delta
,value_changed_delta
,unrecognized_obj_count
,unrecognized_obj_delta
Properties
Property | Description | Type | Default | Required |
---|---|---|---|---|
roi_labels | Regions of interest labels | hidden | Yes | |
rois | Regions of interest. Type: polygon. Default: null. Conditional on roi_labels . | polygon | null | Yes |
processing_mode | Processing mode. Options: ROIs, at Interval (rois_interval ), ROIs, upon Trigger (rois_trigger ), Objects in an ROI (objects ). | enum | rois_interval | Yes |
interval | Lookup interval. Collect objects or ROIs for lookup at least this many seconds apart. | float | 1 | No |
batch_size | Number of images to process in each Claude request. Set to more than 1 to use prompts that reference multiple images when processing_mode is rois_interval or rois_trigger . | number | 1 | No |
trigger | Queue ROI for lookup when this condition evaluates to true. Conditional on processing_mode being rois_trigger . | trigger-condition | null | No |
enable_prebuffer | If true, samples ROI at interval to fill batch, and performs lookup when trigger is met. Else performs lookup when batch is full. Conditional on processing_mode being rois_trigger . | bool | false | No |
objects_to_process | Object types to process. ex. car ,person ,car.red . Conditional on processing_mode being objects . | model-label | null | No |
min_obj_size_pixels | Min. width and height of an object. Conditional on processing_mode being objects . | number | 64 | No |
obj_lookup_size_change_threshold | Object size ratio change threshold. Slider min: 0.1, max: 2.0, step: 0.2. Conditional on processing_mode being objects . | slider | 0.1 | No |
max_lookups_per_obj | Max. attempts per object. Conditional on processing_mode being objects . | number | 5 | No |
anthropic_api_key | Anthropic API Key. Get it here. | string | null | Yes |
prompt | Claude Prompt | text | null | Yes |
attributes | If provided, model will look for these attributes and add resulting values as object/ROI attributes. Leave empty to output results as text. If present, description is a special attribute which will be added to scene description for search and summarization. See examples below. | json | null | No |
detail_level | Claude Detail Level. Options: Low, High. | enum | low | No |
max_tokens | Maximum number of tokens to return for each request. | number | 500 | No |
display_roi | Display ROI on video? | bool | true | No |
display_objinfo | Display results on video? Options: Disabled, Bottom left, Bottom right, Top left, Top right. | enum | bottom_left | No |
debug | Log debugging information? | bool | false | No |
Prompt Examples
Generate scene description.
Analyze the scene and provide a concise description of any unique, interesting, or noteworthy elements that would be suitable for a push notification alert. Focus on key details that capture the essence of what's happening or what's important in the image.
Attribute Examples
Providing explicit attributes lets the model return structured output that will be added as ROI or object attributes.
Each attribute is a key-value pair. Key is the attribute name, and value is the instruction for the model to extract the attribute.
The model will return the extracted attribute value as a string, which will be added as an attribute to the object/ROI.
Describe the image
{"description": "Describe the image briefly."}
Describe the image and add attributes for vehicle type and numbers
{"description": "Describe the image briefly. Return null if no vehicle is present.", "vehicle_type": "Comma separated list of vehicle types: car\|bus\|van", "vehicle_numbers": "Comma separated list of vehicle numbers"}
Metadata
Metadata Property | Description |
---|---|
nodes.[node_id].rois.[roi_id].label_changed_delta | Indicates if there has been a change in the label of the ROI. |
nodes.[node_id].rois.[roi_id].label_available | Indicates if a label is available for the ROI. |
nodes.[node_id].rois.[roi_id].label | Label contents for the ROI. When attributes property is not specified, this contains results from the model. If attributes property is specified, this will contain results from a description attribute. |
nodes.[node_id].rois.[roi_id].attributes.[attribute_name] | Attribute values for the ROI, if attributes property is specified. Will exclude description attribute. |
nodes.[node_id].recognized_obj_count | The count of recognized objects. |
nodes.[node_id].recognized_obj_delta | The change in the count of recognized objects. |
nodes.[node_id].label_changed_obj_delta | The change in the count of objects with changed labels. |
nodes.[node_id].unrecognized_obj_count | The count of unrecognized objects. |
nodes.[node_id].unrecognized_obj_delta | The change in the count of unrecognized objects. |
Example JSON
{
"nodes": {
"claude1": {
"type": "claude",
"rois": {
"roi2": {
"label_changed_delta": true,
"label_available": true,
"label": {
"door_status": "unblocked"
}
}
},
"recognized_obj_ids": ["2775161862"],
"recognized_obj_count": 1,
"recognized_obj_delta": 1,
"label_changed_obj_delta": 1,
"unrecognized_obj_count": 0,
"unrecognized_obj_delta": 0,
"objects_of_interest_keys": ["recognized_obj_ids"]
}
},
"objects": [{
"id": 2775161862,
"source_node_id": null,
"model_id": null,
"label": "roi2",
"class_id": 10600,
"rect": {
"left": 128,
"top": 72,
"width": 512,
"height": 575
},
"probability": 1.0,
"attributes": [{
"label": "unblocked",
"class_id": 10602,
"probability": 1.0
}, {
"label": "lvm_results",
"class_id": 10601,
"probability": 1.0
}, {
"label": "lvm_roi",
"class_id": 10600,
"probability": 1.0
}],
"corr_id": "75f5141e-020a-4f27-af26-cf17b32c2544"
}]
}
Updated 18 days ago