Roboflow Sam API

openapi: 3.2.0
info:
  title: Roboflow Sam API
  version: 1.3.8
  contact:
    name: Roboflow Inc.
    url: https://roboflow.com/contact
    email: help@roboflow.com
  license:
    name: Apache 2.0
    url: https://www.apache.org/licenses/LICENSE-2.0.html
  termsOfService: https://roboflow.com/terms
  description: 'Operations tagged Sam across 2 of this provider''s published API definitions: roboflow-inference-openapi.json, roboflow-sam-api-openapi.yml. Each path carries the servers of the definition it was published in.'
servers:
- url: https://serverless.roboflow.com
  description: Base URL declared by the provider in apis.yml (roadmap#122).
tags:
- name: Sam
paths:
  /sam/embed_image:
    post:
      summary: SAM Image Embeddings
      description: Run the Meta AI Segmant Anything Model to embed image data.
      operationId: sam_embed_image_sam_embed_image_post
      parameters:
      - name: api_key
        in: query
        required: false
        schema:
          anyOf:
          - type: string
          - type: 'null'
          description: Roboflow API Key that will be passed to the model during initialization for artifact retrieval
          title: Api Key
        description: Roboflow API Key that will be passed to the model during initialization for artifact retrieval
      - name: countinference
        in: query
        required: false
        schema:
          anyOf:
          - type: boolean
          - type: 'null'
          title: Countinference
      - name: service_secret
        in: query
        required: false
        schema:
          anyOf:
          - type: string
          - type: 'null'
          title: Service Secret
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/SamEmbeddingRequest'
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/SamEmbeddingResponse'
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
      tags:
      - Sam
  /sam/segment_image:
    post:
      summary: SAM Image Segmentation
      description: Run the Meta AI Segmant Anything Model to generate segmenations for image data.
      operationId: sam_segment_image_sam_segment_image_post
      parameters:
      - name: api_key
        in: query
        required: false
        schema:
          anyOf:
          - type: string
          - type: 'null'
          description: Roboflow API Key that will be passed to the model during initialization for artifact retrieval
          title: Api Key
        description: Roboflow API Key that will be passed to the model during initialization for artifact retrieval
      - name: countinference
        in: query
        required: false
        schema:
          anyOf:
          - type: boolean
          - type: 'null'
          title: Countinference
      - name: service_secret
        in: query
        required: false
        schema:
          anyOf:
          - type: string
          - type: 'null'
          title: Service Secret
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/SamSegmentationRequest'
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/SamSegmentationResponse'
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
      tags:
      - Sam
components:
  schemas:
    ValidationError:
      properties:
        loc:
          items:
            anyOf:
            - type: string
            - type: integer
          type: array
          title: Location
        msg:
          type: string
          title: Message
        type:
          type: string
          title: Error Type
      type: object
      required:
      - loc
      - msg
      - type
      title: ValidationError
    SamEmbeddingRequest:
      properties:
        id:
          type: string
          title: Id
        api_key:
          anyOf:
          - type: string
          - type: 'null'
          title: Api Key
          description: Roboflow API Key that will be passed to the model during initialization for artifact retrieval
        usage_billable:
          type: boolean
          title: Usage Billable
          default: true
        start:
          anyOf:
          - type: number
          - type: 'null'
          title: Start
        source:
          anyOf:
          - type: string
          - type: 'null'
          title: Source
        source_info:
          anyOf:
          - type: string
          - type: 'null'
          title: Source Info
        stream_pipeline_context_id:
          anyOf:
          - type: string
          - type: 'null'
          title: Stream Pipeline Context Id
          description: Internal stream-pipeline frame pairing id. Not part of the public API.
        disable_model_monitoring:
          anyOf:
          - type: boolean
          - type: 'null'
          title: Disable Model Monitoring
          description: If true, disables model monitoring for this request
          default: false
        sam_version_id:
          anyOf:
          - type: string
          - type: 'null'
          title: Sam Version Id
          description: The version ID of SAM to be used for this request. Must be one of vit_h, vit_l, or vit_b.
          default: vit_h
          examples:
          - vit_h
        model_id:
          anyOf:
          - type: string
          - type: 'null'
          title: Model Id
        image:
          anyOf:
          - $ref: '#/components/schemas/InferenceRequestImage'
          - type: 'null'
          description: The image to be embedded
        image_id:
          anyOf:
          - type: string
          - type: 'null'
          title: Image Id
          description: The ID of the image to be embedded used to cache the embedding.
          examples:
          - image_id
        format:
          anyOf:
          - type: string
          - type: 'null'
          title: Format
          description: The format of the response. Must be one of json or binary. If binary, embedding is returned as a binary numpy array.
          default: json
          examples:
          - json
      type: object
      required:
      - id
      title: SamEmbeddingRequest
      description: "SAM embedding request.\n\nAttributes:\n    image (Optional[inference.core.entities.requests.inference.InferenceRequestImage]): The image to be embedded.\n    image_id (Optional[str]): The ID of the image to be embedded used to cache the embedding.\n    format (Optional[str]): The format of the response. Must be one of json or binary."
    InferenceRequestImage:
      properties:
        type:
          type: string
          title: Type
          description: The type of image data provided, one of 'url', 'base64', or 'numpy'
          examples:
          - url
        value:
          anyOf:
          - {}
          - type: 'null'
          title: Value
          description: Image data corresponding to the image type, if type = 'url' then value is a string containing the url of an image, else if type = 'base64' then value is a string containing base64 encoded image data, else if type = 'numpy' then value is binary numpy data serialized using pickle.dumps(); array should 3 dimensions, channels last, with values in the range [0,255].
          examples:
          - http://www.example-image-url.com
      type: object
      required:
      - type
      title: InferenceRequestImage
      description: "Image data for inference request.\n\nAttributes:\n    type (str): The type of image data provided, one of 'url', 'base64', or 'numpy'.\n    value (Optional[Any]): Image data corresponding to the image type."
    SamSegmentationResponse:
      properties:
        masks:
          anyOf:
          - items:
              items:
                items:
                  type: integer
                type: array
              type: array
            type: array
          - {}
          title: Masks
          description: The set of output masks. If request format is json, masks is a list of polygons, where each polygon is a list of points, where each point is a tuple containing the x,y pixel coordinates of the point. If request format is binary, masks is a list of binary numpy arrays. The dimensions of each mask are the same as the dimensions of the input image.
        low_res_masks:
          anyOf:
          - items:
              items:
                items:
                  type: integer
                type: array
              type: array
            type: array
          - {}
          title: Low Res Masks
          description: The set of output masks. If request format is json, masks is a list of polygons, where each polygon is a list of points, where each point is a tuple containing the x,y pixel coordinates of the point. If request format is binary, masks is a list of binary numpy arrays. The dimensions of each mask are 256 x 256
        time:
          type: number
          title: Time
          description: The time in seconds it took to produce the segmentation including preprocessing
      type: object
      required:
      - masks
      - low_res_masks
      - time
      title: SamSegmentationResponse
      description: "SAM segmentation response.\n\nAttributes:\n    masks (Union[List[List[List[int]]], Any]): The set of output masks.\n    low_res_masks (Union[List[List[List[int]]], Any]): The set of output low-resolution masks.\n    time (float): The time in seconds it took to produce the segmentation including preprocessing."
    SamSegmentationRequest:
      properties:
        id:
          type: string
          title: Id
        api_key:
          anyOf:
          - type: string
          - type: 'null'
          title: Api Key
          description: Roboflow API Key that will be passed to the model during initialization for artifact retrieval
        usage_billable:
          type: boolean
          title: Usage Billable
          default: true
        start:
          anyOf:
          - type: number
          - type: 'null'
          title: Start
        source:
          anyOf:
          - type: string
          - type: 'null'
          title: Source
        source_info:
          anyOf:
          - type: string
          - type: 'null'
          title: Source Info
        stream_pipeline_context_id:
          anyOf:
          - type: string
          - type: 'null'
          title: Stream Pipeline Context Id
          description: Internal stream-pipeline frame pairing id. Not part of the public API.
        disable_model_monitoring:
          anyOf:
          - type: boolean
          - type: 'null'
          title: Disable Model Monitoring
          description: If true, disables model monitoring for this request
          default: false
        sam_version_id:
          anyOf:
          - type: string
          - type: 'null'
          title: Sam Version Id
          description: The version ID of SAM to be used for this request. Must be one of vit_h, vit_l, or vit_b.
          default: vit_h
          examples:
          - vit_h
        model_id:
          anyOf:
          - type: string
          - type: 'null'
          title: Model Id
        embeddings:
          anyOf:
          - items:
              items:
                items:
                  items:
                    type: number
                  type: array
                type: array
              type: array
            type: array
          - {}
          - type: 'null'
          title: Embeddings
          description: The embeddings to be decoded. The dimensions of the embeddings are 1 x 256 x 64 x 64. If embeddings is not provided, image must be provided.
          examples:
          - '[[[[0.1, 0.2, 0.3, ...] ...] ...]]'
        embeddings_format:
          anyOf:
          - type: string
          - type: 'null'
          title: Embeddings Format
          description: The format of the embeddings. Must be one of json or binary. If binary, embeddings are expected to be a binary numpy array.
          default: json
          examples:
          - json
        format:
          anyOf:
          - type: string
          - type: 'null'
          title: Format
          description: The format of the response. Must be one of json or binary. If binary, masks are returned as binary numpy arrays. If json, masks are converted to polygons, then returned as json.
          default: json
          examples:
          - json
        image:
          anyOf:
          - $ref: '#/components/schemas/InferenceRequestImage'
          - type: 'null'
          description: The image to be segmented. Only required if embeddings are not provided.
        image_id:
          anyOf:
          - type: string
          - type: 'null'
          title: Image Id
          description: The ID of the image to be segmented used to retrieve cached embeddings. If an embedding is cached, it will be used instead of generating a new embedding. If no embedding is cached, a new embedding will be generated and cached.
          examples:
          - image_id
        has_mask_input:
          anyOf:
          - type: boolean
          - type: 'null'
          title: Has Mask Input
          description: Whether or not the request includes a mask input. If true, the mask input must be provided.
          default: false
          examples:
          - true
        mask_input:
          anyOf:
          - items:
              items:
                items:
                  type: number
                type: array
              type: array
            type: array
          - {}
          - type: 'null'
          title: Mask Input
          description: The set of output masks. If request format is json, masks is a list of polygons, where each polygon is a list of points, where each point is a tuple containing the x,y pixel coordinates of the point. If request format is binary, masks is a list of binary numpy arrays. The dimensions of each mask are 256 x 256. This is the same as the output, low resolution mask from the previous inference.
        mask_input_format:
          anyOf:
          - type: string
          - type: 'null'
          title: Mask Input Format
          description: The format of the mask input. Must be one of json or binary. If binary, mask input is expected to be a binary numpy array.
          default: json
          examples:
          - json
        orig_im_size:
          anyOf:
          - items:
              type: integer
            type: array
          - type: 'null'
          title: Orig Im Size
          description: The original size of the image used to generate the embeddings. This is only required if the image is not provided.
          examples:
          - - 640
            - 320
        point_coords:
          anyOf:
          - items:
              items:
                type: number
              type: array
            type: array
          - type: 'null'
          title: Point Coords
          description: The coordinates of the interactive points used during decoding. Each point (x,y pair) corresponds to a label in point_labels.
          default:
          - - 0.0
            - 0.0
          examples:
          - - - 10.0
              - 10.0
        point_labels:
          anyOf:
          - items:
              type: number
            type: array
          - type: 'null'
          title: Point Labels
          description: The labels of the interactive points used during decoding. A 1 represents a positive point (part of the object to be segmented). A -1 represents a negative point (not part of the object to be segmented). Each label corresponds to a point in point_coords.
          default:
          - -1
          examples:
          - - 1
        use_mask_input_cache:
          anyOf:
          - type: boolean
          - type: 'null'
          title: Use Mask Input Cache
          description: Whether or not to use the mask input cache. If true, the mask input cache will be used if it exists. If false, the mask input cache will not be used.
          default: true
          examples:
          - true
      type: object
      required:
      - id
      title: SamSegmentationRequest
      description: "SAM segmentation request.\n\nAttributes:\n    embeddings (Optional[Union[List[List[List[List[float]]]], Any]]): The embeddings to be decoded.\n    embeddings_format (Optional[str]): The format of the embeddings.\n    format (Optional[str]): The format of the response.\n    image (Optional[InferenceRequestImage]): The image to be segmented.\n    image_id (Optional[str]): The ID of the image to be segmented used to retrieve cached embeddings.\n    has_mask_input (Optional[bool]): Whether or not the request includes a mask input.\n    mask_input (Optional[Union[List[List[List[float]]], Any]]): The set of output masks.\n    mask_input_format (Optional[str]): The format of the mask input.\n    orig_im_size (Optional[List[int]]): The original size of the image used to generate the embeddings.\n    point_coords (Optional[List[List[float]]]): The coordinates of the interactive points used during decoding.\n    point_labels (Optional[List[float]]): The labels of the interactive points used during decoding.\n    use_mask_input_cache (Optional[bool]): Whether or not to use the mask input cache."
    HTTPValidationError:
      properties:
        detail:
          items:
            $ref: '#/components/schemas/ValidationError'
          type: array
          title: Detail
      type: object
      title: HTTPValidationError
    SamEmbeddingResponse:
      properties:
        embeddings:
          anyOf:
          - items:
              items:
                items:
                  items:
                    type: number
                  type: array
                type: array
              type: array
            type: array
          - {}
          title: Embeddings
          description: If request format is json, embeddings is a series of nested lists representing the SAM embedding. If request format is binary, embeddings is a binary numpy array. The dimensions of the embedding are 1 x 256 x 64 x 64.
          examples:
          - '[[[[0.1, 0.2, 0.3, ...] ...] ...]]'
        time:
          type: number
          title: Time
          description: The time in seconds it took to produce the embeddings including preprocessing
      type: object
      required:
      - embeddings
      - time
      title: SamEmbeddingResponse
      description: "SAM embedding response.\n\nAttributes:\n    embeddings (Union[List[List[List[List[float]]]], Any]): The SAM embedding.\n    time (float): The time in seconds it took to produce the embeddings including preprocessing."
x-refined-from:
- roboflow-inference-openapi.json
- roboflow-sam-api-openapi.yml