Edge Impulse Classify API

openapi: 3.0.0
info:
  title: Edge Impulse Classify API
  version: 1.0.0
servers:
- url: https://studio.edgeimpulse.com/v1
security:
- ApiKeyAuthentication: []
- JWTAuthentication: []
- JWTHttpHeaderAuthentication: []
tags:
- name: Classify
paths:
  /api/{projectId}/classify/{sampleId}:
    get:
      summary: Classify sample (deprecated)
      description: This API is deprecated, use classifySampleV2 instead (`/v1/api/{projectId}/classify/v2/{sampleId}`). Classify a complete file against the current impulse. This will move the sliding window (dependent on the sliding window length and the sliding window increase parameters in the impulse) over the complete file, and classify for every window that is extracted.
      operationId: classifySample
      tags:
      - Classify
      x-middleware:
      - AllowsReadOnly
      parameters:
      - $ref: '#/components/parameters/ProjectIdParameter'
      - $ref: '#/components/parameters/SampleIdParameter'
      - $ref: '#/components/parameters/IncludeDebugInfoParameter'
      - $ref: '#/components/parameters/OptionalImpulseIdParameter'
      responses:
        '200':
          description: OK
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ClassifySampleResponse'
  /api/{projectId}/classify/v2/{sampleId}:
    post:
      summary: Classify sample
      description: 'Classify a complete file against the current impulse. This will move the sliding window (dependent on

        the sliding window length and the sliding window increase parameters in the impulse) over the complete

        file, and classify for every window that is extracted. Depending on the size of your file, whether your

        sample is resampled, and whether the result is cached you''ll get either the result or a job back. If

        you receive a job, then wait for the completion of the job, and then call this function again to receive

        the results. The unoptimized (float32) model is used by default, and classification with an optimized

        (int8) model can be slower.

        '
      operationId: classifySampleV2
      tags:
      - Classify
      x-middleware:
      - AllowsReadOnly
      parameters:
      - $ref: '#/components/parameters/ProjectIdParameter'
      - $ref: '#/components/parameters/SampleIdParameter'
      - $ref: '#/components/parameters/IncludeDebugInfoParameter'
      - $ref: '#/components/parameters/ModelVariantParameter'
      - $ref: '#/components/parameters/OptionalImpulseIdParameter'
      responses:
        '200':
          description: OK
          content:
            application/json:
              schema:
                anyOf:
                - $ref: '#/components/schemas/ClassifySampleResponse'
                - $ref: '#/components/schemas/StartJobResponse'
  /api/{projectId}/classify/v2/{sampleId}/variants:
    post:
      summary: Classify sample for the given set of variants
      description: 'Classify a complete file against the current impulse, for all given variants.

        Depending on the size of your file and whether the sample is resampled, you may get a job ID in

        the response.

        '
      operationId: classifySampleForVariants
      tags:
      - Classify
      x-middleware:
      - AllowsReadOnly
      parameters:
      - $ref: '#/components/parameters/ProjectIdParameter'
      - $ref: '#/components/parameters/SampleIdParameter'
      - $ref: '#/components/parameters/IncludeDebugInfoParameter'
      - $ref: '#/components/parameters/ModelVariantsListParameter'
      - $ref: '#/components/parameters/OptionalImpulseIdParameter'
      responses:
        '200':
          description: OK
          content:
            application/json:
              schema:
                anyOf:
                - $ref: '#/components/schemas/ClassifySampleResponseMultipleVariants'
                - $ref: '#/components/schemas/StartJobResponse'
  /api/{projectId}/classify/v2/{sampleId}/raw-data/{windowIndex}:
    get:
      summary: Get a window of raw sample features from cache, after a live classification job has completed.
      description: 'Get raw sample features for a particular window. This is only available after a live classification job

        has completed and raw features have been cached.

        '
      operationId: getSampleWindowFromCache
      tags:
      - Classify
      x-middleware:
      - AllowsReadOnly
      parameters:
      - $ref: '#/components/parameters/ProjectIdParameter'
      - $ref: '#/components/parameters/SampleIdParameter'
      - $ref: '#/components/parameters/SampleWindowIndexParameter'
      - $ref: '#/components/parameters/OptionalImpulseIdParameter'
      responses:
        '200':
          description: OK
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/GetSampleResponse'
  /api/{projectId}/classify/all/result:
    get:
      summary: Classify job result
      description: Get classify job result, containing the result for the complete testing dataset.
      operationId: getClassifyJobResult
      tags:
      - Classify
      x-middleware:
      - AllowsReadOnly
      parameters:
      - $ref: '#/components/parameters/ProjectIdParameter'
      - $ref: '#/components/parameters/FeatureExplorerOnlyParameter'
      - $ref: '#/components/parameters/ModelVariantParameter'
      - $ref: '#/components/parameters/OptionalImpulseIdParameter'
      responses:
        '200':
          description: OK
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ClassifyJobResponse'
  /api/{projectId}/classify/all/result/page:
    get:
      summary: Single page of a classify job result
      description: Get classify job result, containing the predictions for a given page.
      operationId: getClassifyJobResultPage
      tags:
      - Classify
      x-middleware:
      - AllowsReadOnly
      parameters:
      - $ref: '#/components/parameters/ProjectIdParameter'
      - $ref: '#/components/parameters/LimitResultsParameter'
      - $ref: '#/components/parameters/OffsetResultsParameter'
      - $ref: '#/components/parameters/ModelVariantParameter'
      - $ref: '#/components/parameters/OptionalImpulseIdParameter'
      responses:
        '200':
          description: OK
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ClassifyJobResponsePage'
  /api/{projectId}/classify/all/metrics:
    get:
      summary: Get metrics for all available model variants
      description: Get metrics, calculated during a classify all job, for all available model variants. This is experimental and may change in the future.
      x-internal-api: true
      operationId: getClassifyMetricsAllVariants
      tags:
      - Classify
      x-middleware:
      - AllowsReadOnly
      parameters:
      - $ref: '#/components/parameters/ProjectIdParameter'
      - $ref: '#/components/parameters/OptionalImpulseIdParameter'
      responses:
        '200':
          description: OK
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/MetricsAllVariantsResponse'
  /api/{projectId}/classify/anomaly-gmm/{blockId}/{sampleId}:
    get:
      summary: Classify sample by learn block
      description: This API is deprecated, use classifySampleByLearnBlockV2 (`/v1/api/{projectId}/classify/anomaly-gmm/v2/{blockId}/{sampleId}`) instead. Classify a complete file against the specified learn block. This will move the sliding window (dependent on the sliding window length and the sliding window increase parameters in the impulse) over the complete file, and classify for every window that is extracted.
      operationId: classifySampleByLearnBlock
      tags:
      - Classify
      x-middleware:
      - AllowsReadOnly
      parameters:
      - $ref: '#/components/parameters/ProjectIdParameter'
      - $ref: '#/components/parameters/SampleIdParameter'
      - $ref: '#/components/parameters/BlockIdParameter'
      responses:
        '200':
          description: OK
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ClassifySampleResponse'
  /api/{projectId}/classify/anomaly-gmm/v2/{blockId}/{sampleId}:
    post:
      summary: Classify sample by learn block
      description: 'Classify a complete file against the specified learn block. This will move the sliding window

        (dependent on the sliding window length and the sliding window increase parameters in the impulse)

        over the complete file, and classify for every window that is extracted. Depending on the size of your

        file, whether your sample is resampled, and whether the result is cached you''ll get either the result

        or a job back. If you receive a job, then wait for the completion of the job, and then call this

        function again to receive the results. The unoptimized (float32) model is used by default, and

        classification with an optimized (int8) model can be slower.

        '
      operationId: classifySampleByLearnBlockV2
      tags:
      - Classify
      x-middleware:
      - AllowsReadOnly
      parameters:
      - $ref: '#/components/parameters/ProjectIdParameter'
      - $ref: '#/components/parameters/SampleIdParameter'
      - $ref: '#/components/parameters/BlockIdParameter'
      - $ref: '#/components/parameters/ModelVariantParameter'
      responses:
        '200':
          description: OK
          content:
            application/json:
              schema:
                anyOf:
                - $ref: '#/components/schemas/ClassifySampleResponse'
                - $ref: '#/components/schemas/StartJobResponse'
  /api/{projectId}/classify/image:
    post:
      summary: Classify an image
      description: Test out a trained impulse (using a posted image).
      operationId: classifyImage
      tags:
      - Classify
      parameters:
      - $ref: '#/components/parameters/ProjectIdParameter'
      - $ref: '#/components/parameters/OptionalImpulseIdParameter'
      requestBody:
        required: true
        content:
          multipart/form-data:
            schema:
              $ref: '#/components/schemas/UploadImageRequest'
      responses:
        '200':
          description: OK
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/TestPretrainedModelResponse'
components:
  schemas:
    KerasModelVariantEnum:
      type: string
      enum:
      - int8
      - float32
      - akida
    ModelResult:
      type: object
      required:
      - sampleId
      - sample
      - classifications
      properties:
        sampleId:
          type: integer
        sample:
          $ref: '#/components/schemas/Sample'
        classifications:
          type: array
          items:
            $ref: '#/components/schemas/ClassifySampleResponseClassification'
    UploadImageRequest:
      type: object
      required:
      - image
      properties:
        image:
          type: string
          format: binary
    MetricsAllVariantsResponse:
      allOf:
      - $ref: '#/components/schemas/GenericApiResponse'
      - type: object
        properties:
          metrics:
            type: array
            items:
              $ref: '#/components/schemas/MetricsForModelVariant'
    StartJobResponse:
      allOf:
      - $ref: '#/components/schemas/GenericApiResponse'
      - type: object
        required:
        - id
        properties:
          id:
            type: integer
            description: Job identifier. Status updates will include this identifier.
            example: 12873488112
    ClassifySampleResponseClassificationDetails:
      type: object
      properties:
        boxes:
          type: array
          description: Bounding boxes predicted by localization model
          items:
            type: array
            items:
              type: number
        labels:
          type: array
          description: Labels predicted by localization model
          items:
            type: number
        scores:
          type: array
          description: Scores predicted by localization model
          items:
            type: number
        mAP:
          type: number
          description: For object detection, the COCO mAP computed for the predictions on this image
        f1:
          type: number
          description: For FOMO, the F1 score computed for the predictions on this image
    ClassifySampleResponseClassification:
      type: object
      required:
      - learnBlock
      - result
      - expectedLabels
      - minimumConfidenceRating
      properties:
        learnBlock:
          $ref: '#/components/schemas/ImpulseLearnBlock'
        result:
          type: array
          description: Classification result, one item per window.
          example:
          - idle: 0.0002
            wave: 0.9998
            anomaly: -0.42
          items:
            type: object
            description: Classification value per label. For a neural network this will be the confidence, for anomalies the anomaly score.
            additionalProperties:
              type: number
        anomalyResult:
          type: array
          description: Anomaly scores and computed metrics for visual anomaly detection, one item per window.
          items:
            $ref: '#/components/schemas/AnomalyResult'
        structuredResult:
          type: array
          description: Results of inferencing that returns structured data, such as object detection
          items:
            $ref: '#/components/schemas/StructuredClassifyResult'
        minimumConfidenceRating:
          type: number
          description: The minimum confidence rating for this block. For regression, this is the absolute error (which can be larger than 1).
        details:
          type: array
          description: Structured outputs and computed metrics for some model types (e.g. object detection), one item per window.
          items:
            $ref: '#/components/schemas/ClassifySampleResponseClassificationDetails'
        objectDetectionLastLayer:
          $ref: '#/components/schemas/ObjectDetectionLastLayer'
        expectedLabels:
          type: array
          description: An array with an expected label per window.
          items:
            $ref: '#/components/schemas/StructuredLabel'
    MetricsForModelVariant:
      type: object
      required:
      - variant
      properties:
        variant:
          description: The model variant
          $ref: '#/components/schemas/KerasModelVariantEnum'
        accuracy:
          description: The overall accuracy for the given model variant
          type: number
    ClassifyJobResponsePage:
      allOf:
      - $ref: '#/components/schemas/GenericApiResponse'
      - type: object
        required:
        - result
        - predictions
        properties:
          result:
            type: array
            items:
              $ref: '#/components/schemas/ModelResult'
          predictions:
            type: array
            items:
              $ref: '#/components/schemas/ModelPrediction'
    GetSampleResponse:
      allOf:
      - $ref: '#/components/schemas/GenericApiResponse'
      - $ref: '#/components/schemas/RawSampleData'
    TestPretrainedModelResponse:
      allOf:
      - $ref: '#/components/schemas/GenericApiResponse'
      - type: object
        properties:
          result:
            type: object
            description: Classification value per label. For a neural network this will be the confidence, for anomalies the anomaly score.
            additionalProperties:
              type: number
          boundingBoxes:
            type: array
            items:
              $ref: '#/components/schemas/BoundingBoxWithScore'
    AdditionalMetric:
      type: object
      required:
      - name
      - value
      - fullPrecisionValue
      properties:
        name:
          type: string
        value:
          type: string
        fullPrecisionValue:
          type: number
        tooltipText:
          type: string
        link:
          type: string
    ImpulseLearnBlock:
      type: object
      required:
      - id
      - type
      - name
      - dsp
      - title
      - primaryVersion
      properties:
        id:
          type: integer
          minimum: 1
          description: Identifier for this block. Make sure to up this number when creating a new block, and don't re-use identifiers. If the block hasn't changed, keep the ID as-is. ID must be unique across the project and greather than zero (>0).
        type:
          $ref: '#/components/schemas/LearnBlockType'
        name:
          type: string
          description: Block name, will be used in menus. If a block has a baseBlockId, this field is ignored and the base block's name is used instead.
          example: NN Classifier
        dsp:
          type: array
          description: DSP dependencies, identified by DSP block ID
          items:
            type: integer
            example: 27
        title:
          type: string
          description: Block title, used in the impulse UI
          example: Classification (Keras)
        description:
          type: string
          description: A short description of the block version, displayed in the block versioning UI
          example: Reduced learning rate and more layers
        createdBy:
          type: string
          description: The system component that created the block version (createImpulse | clone | tuner). Cannot be set via API.
          example: createImpulse
        createdAt:
          type: string
          format: date-time
          description: The datetime that the block version was created. Cannot be set via API.
    Sensor:
      type: object
      required:
      - name
      - units
      properties:
        name:
          type: string
          description: Name of the axis
          example: accX
        units:
          type: string
          description: Type of data on this axis. Needs to comply to SenML units (see https://www.iana.org/assignments/senml/senml.xhtml).
    BoundingBoxWithScore:
      type: object
      description: This has the _ratio_ for x/y/w/h (so 0..1)
      required:
      - label
      - x
      - y
      - width
      - height
      - score
      properties:
        label:
          type: string
        x:
          type: number
        y:
          type: number
        width:
          type: number
        height:
          type: number
        score:
          type: number
    GenericApiResponse:
      type: object
      required:
      - success
      properties:
        success:
          type: boolean
          description: Whether the operation succeeded
        error:
          type: string
          description: Optional error description (set if 'success' was false)
    BoundingBox:
      type: object
      description: This has the _absolute values_ for x/y/w/h (so 0..x (where x is the w/h of the image))
      required:
      - label
      - x
      - y
      - width
      - height
      properties:
        label:
          type: string
        x:
          type: integer
        y:
          type: integer
        width:
          type: integer
        height:
          type: integer
    AnomalyResult:
      type: object
      properties:
        boxes:
          type: array
          description: For visual anomaly detection. An array of bounding box objects, (x, y, width, height, score, label), one per detection in the image. Filtered by the minimum confidence rating of the learn block.
          items:
            $ref: '#/components/schemas/BoundingBoxWithScore'
        scores:
          type: array
          description: 2D array of shape (n, n) with raw anomaly scores for visual anomaly detection, where n can be calculated as ((1/8 of image input size)/2 - 1). The scores corresponds to each grid cell in the image's spatial matrix.
          items:
            type: array
            items:
              type: number
        meanScore:
          type: number
          description: Mean value of the scores.
        maxScore:
          type: number
          description: Maximum value of the scores.
    ObjectDetectionLastLayer:
      type: string
      enum:
      - mobilenet-ssd
      - fomo
      - yolov2-akida
      - yolov5
      - yolov5v5-drpai
      - yolox
      - yolov7
      - tao-retinanet
      - tao-ssd
      - tao-yolov3
      - tao-yolov4
    RawSampleData:
      type: object
      required:
      - sample
      - payload
      - totalPayloadLength
      properties:
        sample:
          $ref: '#/components/schemas/Sample'
        payload:
          $ref: '#/components/schemas/RawSamplePayload'
        totalPayloadLength:
          type: integer
          description: Total number of payload values
    StructuredClassifyResult:
      type: object
      required:
      - boxes
      - scores
      - mAP
      - f1
      - precision
      - recall
      properties:
        boxes:
          type: array
          description: For object detection. An array of bounding box arrays, (x, y, width, height), one per detection in the image.
          items:
            type: array
            items:
              type: number
        labels:
          type: array
          description: For object detection. An array of labels, one per detection in the image.
          items:
            type: string
        scores:
          type: array
          description: For object detection. An array of probability scores, one per detection in the image.
          items:
            type: number
        mAP:
          type: number
          description: For object detection. A score that indicates accuracy compared to the ground truth, if available.
        f1:
          type: number
          description: For FOMO. A score that combines the precision and recall of a classifier into a single metric, if available.
        precision:
          type: number
          description: A measure of how many of the positive predictions made are correct (true positives).
        recall:
          type: number
          description: A measure of how many of the positive cases the classifier correctly predicted, over all the positive cases.
        debugInfoJson:
          type: string
          description: Debug info in JSON format
          example: "{\n    \"y_trues\": [\n        {\"x\": 0.854, \"y\": 0.453125, \"label\": 1},\n        {\"x\": 0.197, \"y\": 0.53125, \"label\": 2}\n    ],\n    \"y_preds\": [\n        {\"x\": 0.916, \"y\": 0.875, \"label\": 1},\n        {\"x\": 0.25, \"y\": 0.541, \"label\": 2}\n    ],\n    \"assignments\": [\n        {\"yp\": 1, \"yt\": 1, \"label\": 2, \"distance\": 0.053}\n    ],\n    \"normalised_min_distance\": 0.2,\n    \"all_pairwise_distances\": [\n        [0, 0, 0.426],\n        [1, 1, 0.053]\n    ],\n    \"unassigned_y_true_idxs\": [0],\n    \"unassigned_y_pred_idxs\": [0]\n}\n"
    ClassifySampleResponseMultipleVariants:
      allOf:
      - $ref: '#/components/schemas/GenericApiResponse'
      - type: object
        required:
        - classifications
        - sample
        - windowSizeMs
        - windowIncreaseMs
        - alreadyInDatabase
        properties:
          results:
            type: array
            items:
              $ref: '#/components/schemas/ClassifySampleResponseVariantResults'
          sample:
            $ref: '#/components/schemas/RawSampleData'
          windowSizeMs:
            type: integer
            description: Size of the sliding window (as set by the impulse) in milliseconds.
            example: 2996
          windowIncreaseMs:
            type: integer
            description: Number of milliseconds that the sliding window increased with (as set by the impulse)
            example: 10
          alreadyInDatabase:
            type: boolean
            description: Whether this sample is already in the training database
    ClassifySampleResponse:
      allOf:
      - $ref: '#/components/schemas/GenericApiResponse'
      - type: object
        required:
        - classifications
        - sample
        - windowSizeMs
        - windowIncreaseMs
        - alreadyInDatabase
        properties:
          classifications:
            type: array
            items:
              $ref: '#/components/schemas/ClassifySampleResponseClassification'
          sample:
            $ref: '#/components/schemas/RawSampleData'
          windowSizeMs:
            type: integer
            description: Size of the sliding window (as set by the impulse) in milliseconds.
            example: 2996
          windowIncreaseMs:
            type: integer
            description: Number of milliseconds that the sliding window increased with (as set by the impulse)
            example: 10
          alreadyInDatabase:
            type: boolean
            description: Whether this sample is already in the training database
          warning:
            type: string
    ModelPrediction:
      type: object
      required:
      - sampleId
      - startMs
      - endMs
      - prediction
      properties:
        sampleId:
          type: integer
        startMs:
          type: number
        endMs:
          type: number
        label:
          type: string
        prediction:
          type: string
        predictionCorrect:
          type: boolean
        f1Score:
          type: number
          description: Only set for object detection projects
        anomalyScores:
          type: array
          description: Only set for visual anomaly projects. 2D array of shape (n, n) with raw anomaly scores, where n varies based on the image input size and the specific visual anomaly algorithm used. The scores corresponds to each grid cell in the image's spatial matrix.
          items:
            type: array
            items:
              type: number
    RawSamplePayload:
      type: object
      description: Sensor readings and metadata
      required:
      - device_type
      - sensors
      - values
      properties:
        device_name:
          type: string
          description: Unique identifier for this device. **Only** set this when the device has a globally unique identifier (e.g. MAC address).
          example: ac:87:a3:0a:2d:1b
        device_type:
          type: string
          description: Device type, for example the exact model of the device. Should be the same for all similar devices.
          example: DISCO-L475VG-IOT01A
        sensors:
          type: array
          description: Array with sensor axes
          items:
            $ref: '#/components/schemas/Sensor'
        values:
          type: array
          description: 'Array of sensor values. One array item per interval, and as many items in this array as there are sensor axes. This type is returned if there are multiple axes.

            '
          items:
            type: array
            items:
              type: number
        cropStart:
          type: integer
          description: New start index of the cropped sample
          example: 0
        cropEnd:
          type: integer
          description: New end index of the cropped sample
          example: 128
    ClassifySampleResponseVariantResults:
      type: object
      required:
      - variant
      - classifications
      properties:
        variant:
          description: The model variant
          $ref: '#/components/schemas/KerasModelVariantEnum'
        classifications:
          type: array
          items:
            $ref: '#/components/schemas/ClassifySampleResponseClassification'
    StructuredLabel:
      type: object
      description: 'A structured label contains a label, and the range for which this label is valid. `endIndex` is inclusive. E.g. `{ startIndex: 10, endIndex: 13, label: ''running'' }` means that the values at index 10, 11, 12, 13 are labeled ''running''. To get time codes you can multiple by the sample''s `intervalMs` property.'
      required:
      - startIndex
      - endIndex
      - label
      properties:
        startIndex:
          type: integer
          description: Start index of the label (e.g. 0)
        endIndex:
          type: integer
          description: 'End index of the label (e.g. 3). This value is inclusive, so { startIndex: 0, endIndex: 3 } covers 0, 1, 2, 3.'
        label:
          type: string
          description: The label for this section.
    Sample:
      type: object
      required:
      - id
      - filename
      - signatureValidate
      - created
      - lastModified
      - category
      - coldstorageFilename
      - label
      - intervalMs
      - frequency
      - originalIntervalMs
      - originalFrequency
      - deviceType
      - sensors
      - valuesCount
      - added
      - boundingBoxes
      - boundingBoxesType
      - chartType
      - isDisabled
      - isProcessing
      - processingError
      - isCropped
      - projectId
      - sha256Hash
      properties:
        id:
          type: integer
          example: 2
        filename:
          type: string
          example: idle01.d8Ae
        signatureValidate:
          type: boolean
          description: Whether signature validation passed
          example: true
        signatureMethod:
          type: string
          example: HS256
        signatureKey:
          type: string
          description: Either the shared key or the public key that was used to validate the sample
        created:
          type: string
          format: date-time
          description: Timestamp when the sample was created on device, or if no accurate time was known on device, the time that the file was processed by the ingestion service.
        lastModified:
          type: string
          format: date-time
          description: Timestamp when the sample was last modified.
        category:
          type: string
          example: training
        coldstorageFilename:
          type: string
        label:
          type: string
          example: healthy-machine
        intervalMs:
          type: number
          description: Interval between two windows (1000 / frequency). If the data was resampled, then this lists the resampled interval.
          example: 16
        frequency:
          type: number
          description: Frequency of the sample. If the data was resampled, then this lists the resampled frequency.
          example: 62.5
        originalIntervalMs:
          type: number
          description: Interval between two windows (1000 / frequency) in the source data (before resampling).
          example: 16
        originalFrequency:
          type: number
          description: Frequency of the sample in the source data (before resampling).
          example: 62.5
        deviceName:
          type: string
        deviceType:
          type: string
        sensors:
          type: array
          items:
            $ref: '#/components/schemas/Sensor'
        valuesCount:
          type: integer
          description: Number of readings in this file
        totalLengthMs:
          type: number
          description: Total length (in ms.) of this file
        added:
          type: string
          format: date-time
          description: Timestamp when the sample was added to the current acquisition bucket.
        boundingBoxes:
          type: array
          items:
            $ref: '#/components/schemas/BoundingBox'
        boundingBoxesType:
          type: string
          enum:
          - object_detection
          - constrained_object_detection
        chartType:
          type: string
          enum:
          - chart
          - image
          - video
          - table
        thumbnailVideo:
          type: string
        thumbnailVideoFull:
          type: string
        isDisabled:
          type: boolean
          description: True if the current sample is excluded from use
        isProcessing:
          type: boolean
          description: True if the current sample is still processing (e.g. for video)
        processingJobId:
          type: integer
          description: Set when sample is processing and a job has picked up the request
        processingError:
          type: boolean
          description: Set when processing this sample failed
        processingErrorString:
          type: string
          description: Error (only set when processing this sample failed)
        isCropped:
          type: boolean
          description: Whether the 

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