> ## Documentation Index
> Fetch the complete documentation index at: https://api-reference.scale.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Image & Video Tasks

> Create Semantic Segmentation Annotation Task Create General Video Annotation Task Create Video Playback Annotation Task

# Create Semantic Segmentation Annotation Task

This endpoint creates a `**segmentannotation**` task. In this task, one of our labelers will view the given image and classify pixels in the image according to the labels provided. You will receive a semantic, pixel-wise, dense segmentation of the image. We also support **instance-aware** semantic segmentations, also called **panoptic segmentation**, via **[LabelDescription](/docs/api-reference/labels)** objects. The required parameters for this task are `**attachment**` and `**labels**`. The `**attachment**` is a URL to an image you'd like to be segmented. `**labels**` is an array of strings or **[LabelDescription](/docs/api-reference/labels)** objects describing the different types of objects you'd like to segment the image with. You can optionally provide additional markdown-enabled or Google Doc-based **[instructions](/docs/write-your-instructions)** via the `**instruction**` parameter. You can also optionally set `**allow_unlabeled**` to true, which will allow the existence of unlabeled pixels in the task response - otherwise, all pixels in the image will be classified (in which case it's important that there are labels for everything in the image, to avoid misclassification). The response you will receive will be a series of images where each pixel's value corresponds to the label, either via a numerical index or a color mapping. You will also get separate masks for each label for convenience.If the request successful, Scale will return the generated task object, at which point you should store the `**task_id**` to have a permanent reference to the task.<br />

<ParamField path="Body Params" type="object">
  <ParamField path="project" type="string">
    The name of the **[project](/docs/api-reference/projects)** to associate this task with.

    ***
  </ParamField>

  <ParamField path="batch" type="string">
    A markdown-enabled string or iframe embed google doc explaining how to do the segmentation. You can use markdown to show example images, give structure to your instructions, and more. See our instruction best practices for more details. For Scale Rapid projects, DO NOT set this field unless you specifically want to override the project level instructions.

    ***
  </ParamField>

  <ParamField path="callback_url" type="string">
    The full url (including the scheme `**http://**` or `**https://**`) or email address of the [**callback**](/docs/api-reference/callbacks) that will be used when the task is completed.

    ***
  </ParamField>

  <ParamField path="attachment" type="string" required>
    A URL to the image you'd like to be segmented.

    ***
  </ParamField>

  <ParamField path="attachment_type" type="string">
    Describes what type of file the attachment is. We currently only support image for the segmentannotation.

    ***
  </ParamField>

  <ParamField path="labels" type="array of strings" required>
    An array of strings or LabelDescription objects describing the different types of objects you'd like to be used to segment the image. You may include at most 50 labels.

    ***
  </ParamField>

  <ParamField path="annotation_attributes" type="object">
    This field is used to add additional attributes that you would like to capture per annotation. This only applies to instance annotations. See Annotation Attributes for more details about annotation attributes.

    ***
  </ParamField>

  <ParamField path="allow_unlabeled" type="boolean">
    Whether or not this image can be completed without every pixel being labeled.

    ***
  </ParamField>

  <ParamField path="hypothesis" type="object">
    Editable annotations that a task should be initialized with. This is useful when you've run a model to prelabel the task and want annotators to refine those prelabels. Review the Segmentation Hypothesis Format for more details.

    ***
  </ParamField>

  <ParamField path="metadata" type="object">
    A set of key/value pairs that you can attach to a task object. It can be useful for storing additional information about the task in a structured format. Max 10KB. See the Metadata section for more detail.

    ***
  </ParamField>

  <ParamField path="context_attachments" type="array of objects">
    An array of objects in the form of \{"attachment": "\<link to actual attachment>"} to show to taskers as a reference. Context images themselves can not be labeled. Context images will appear like this in the UI. You cannot use the task's attachment url as a context attachment's url.

    ***
  </ParamField>

  <ParamField path="unique_id" type="string">
    A arbitrary ID that you can assign to a task and then query for later. This ID must be unique across all projects under your account, otherwise the task submission will be rejected. See **[Avoiding Duplicate Tasks](/docs/api-reference/data-engine-reference#avoiding-duplicate-tasks)** for more details.

    ***
  </ParamField>

  <ParamField path="clear_unique_id_on_error" type="boolean">
    If set to be true, if a task errors out after being submitted, the unique id on the task will be unset. This param allows workflows where you can re-submit the same unique id to recover from errors automatically

    ***
  </ParamField>

  <ParamField path="tags" type="array of strings">
    Arbitrary labels that you can assign to a task. At most 5 tags are allowed per task. You can query tasks with specific tags through the task retrieval API.

    ***
  </ParamField>
</ParamField>

<CodeGroup>
  ```python Python theme={null}
  import requests

  # Replace with your actual API key
  API_KEY = 'your_api_key_here'

  # Define the URL for the API endpoint
  url = "https://api.scale.com/v1/task/segmentannotation"

  # Define the payload for the segment annotation task
  payload = {
      "instruction": "**Instructions:** Please label all the things",
      "attachment": "https://i.imgur.com/iDZcXfS.png",
      "attachment_type": "image",
      "annotation_attributes": {
          "newKey": {
              "type": "type",
              "description": "description",
              "choices": "choices",
              "conditions": {
                  "label_condition": ["car", "car2"],
                  "attribute_conditions": {
                      "newKey": "New Value",
                      "newKey-1": "New Value"
                  }
              }
          }
      },
      "allow_unlabeled": False,
      "metadata": {
          "newKey": "New Value",
          "newKey-1": "New Value"
      },
      "project": "Project Name",
      "batch": "Batch Name",
      "callback_url": "http://www.example.com/callback",
      "labels": [["vehicle"], "vehicle 2", "vehicle 3"],
      "context_attachments": [{"attachment": "attachment"}, {"attachment": "attachment2"}],
      "unique_id": "unique_id",
      "clear_unique_id_on_error": True,
      "tags": ["tag", "tag2"]
  }

  # Set up the headers for the request
  headers = {
      "accept": "application/json",       # Specify that we want the response in JSON format
      "content-type": "application/json"  # Specify the content type of the request
  }

  # Adding authentication to the POST request
  # The auth parameter requires a tuple with the API key and an empty string
  response = requests.post(url, json=payload, headers=headers, auth=(API_KEY, ''))

  # Print the response text to see the result
  print(response.text)

  ```

  ```python Python SDK theme={null}
  from scaleapi.tasks import TaskType
  from scaleapi.exceptions import ScaleDuplicateResource

  payload = dict(
      "instruction": "**Instructions:** Please label all the things",
      "attachment": "https://i.imgur.com/iDZcXfS.png",
      "attachment_type": "image",
      "annotation_attributes": { "newKey": {
              "type": "type",
              "description": "description",
              "choices": "choices",
              "conditions": {
                  "label_condition": ["car", "car2"],
                  "attribute_conditions": {
                      "newKey": "New Value",
                      "newKey-1": "New Value"
                  }
              }
          } },
      "allow_unlabeled": False,
      "metadata": {
          "newKey": "New Value",
          "newKey-1": "New Value"
      },
      "project": "Project Name",
      "batch": "Batch Name",
      "callback_url": "http://www.example.com/callback",
      "labels": [["vehicle"], "vehicle 2", "vehicle 3"],
      "context_attachments": [{ "attachment": "attachment" }, { "attachment": "attachment2" }],
      "unique_id": "unique_id",
      "clear_unique_id_on_error": True,
      "tags": ["tag", "tag2"]
    }
  )

  try:
      client.create_task(TaskType.Segmentannotation, **payload)
  except ScaleDuplicateResource as err:
      print(err.message)  # If unique_id is already used for a different task
  ```
</CodeGroup>

```json theme={null}
{
  "task_id": "string",
  "created_at": "string",
  "type": "segmentannotation",
  "status": "pending",
  "instruction": "string",
  "is_test": false,
  "urgency": "standard",
  "metadata": {},
  "project": "string",
  "callback_url": "string",
  "updated_at": "string",
  "work_started": false,
  "params": {
    "allow_unlabeled": false,
    "labels": [
      null
    ],
    "instance_labels": [
      null
    ],
    "attachment_type": "image",
    "attachment": "https://i.imgur.com/SudOKhq.jpg"
  }
}
```

# Create General Video Annotation Task

This endpoint creates a `**videoannotation**` task. Given a series of images sampled from a video (which we will refer to as "frames"), Scale will annotate each frame with the Geometries (box, polygon, line, point, cuboid, and ellipse) you specify.

The required parameter for this task is `**geometries**`.

You can optionally provide additional markdown-enabled or Google Doc-based instructions via the `**instruction**` parameter.

You may also optionally specify `**events_to_annotate**`, a list of strings describing events section to annotate in the video.

If the request is successful, Scale will return the generated task object, at which point you should store the `**task_id**` to have a permanent reference to the task.

<ParamField path="Body Params" type="object">
  <ParamField path="project" type="string">
    The name of the **[project](/docs/api-reference/projects)** to associate this task with.

    ***
  </ParamField>

  <ParamField path="batch" type="string">
    The name of the **[batch](/docs/api-reference/batches)** to associate this task with. Note that if a batch is specified, you need not specify the project, as the task will automatically be associated with the batch's project. For Scale Rapid projects specifying a batch is required. See **[Batches section](/docs/api-reference/data-engine-reference#batches-object-overview)** for more details.

    ***
  </ParamField>

  <ParamField path="instruction" type="string">
    A markdown-enabled string or iframe embed google doc explaining how to do the task. You can use **[markdown](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet)** to show example images, give structure to your instructions, and more. See our **[instruction best practices](/docs/write-your-instructions)** for more details. For Scale Rapid projects, DO NOT set this field unless you specifically want to override the project level instructions.

    ***
  </ParamField>

  <ParamField path="callback_url" type="string">
    The full url (including the scheme `**http://**` or `**https://**`) or email address of the **[callback](/docs/api-reference/callbacks)** that will be used when the task is completed.

    ***
  </ParamField>

  <ParamField path="attachments" type="array of strings">
    An array of URLs for the frames you'd like to be annotated. These image frames are stitched together to create a video. This is required if attachment\_type is image and must be omitted if attachment\_type is video.

    ***
  </ParamField>

  <ParamField path="attachment" type="string">
    A URL pointing to the video file attachment. Only the mp4, webm, and ogg formats are supported.

    ***
  </ParamField>

  <ParamField path="attachment_type" type="string">
    Describes what type of file the attachment(s) are. The only options are image and video.

    ***
  </ParamField>

  <ParamField path="geometries" type="object" required>
    An object mapping `**box**`, `**polygon**`, `**line**`, `**point**`, `**cuboid**`, or `**ellipse**` to Geometry objects

    ***
  </ParamField>

  <ParamField path="annotation_attributes" type="object">
    See the **[Annotation Attributes](/docs/api-reference/data-engine-reference#annotation-attributes-overview)** section for more details about annotation attributes.

    ***
  </ParamField>

  <ParamField path="events_to_annotate" type="array of strings">
    The list of events to annotate.

    ***
  </ParamField>

  <ParamField path="links" type="object">
    Use this field to define links between annotations. See **[Links](/docs/api-reference/data-engine-reference#linked-attributes)** for more details about links.

    ***
  </ParamField>

  <ParamField path="frame_rate" type="int32">
    The number of frames per second to annotate.

    ***
  </ParamField>

  <ParamField path="padding" type="int32">
    The amount of padding in pixels added to the top, bottom, left, and right of each video frame. This allows labelers to extend annotations outside of the frames.

    ***
  </ParamField>

  <ParamField path="paddingX" type="int32">
    The amount of padding in pixels added to the left and right of each video frame. Overrides `**padding**` if set.

    ***
  </ParamField>

  <ParamField path="paddingY" type="int32">
    The amount of padding in pixels added to the top and bottom of each video frame. Overrides padding if set.

    ***
  </ParamField>

  <ParamField path="hypothesis" type="object">
    Editable annotations that a task should be initialized with. This is useful when you've run a model to prelabel the task and want annotators to refine those prelabels. Review the Segmentation Hypothesis Format for more details.

    ***
  </ParamField>

  <ParamField path="base_annotations" type="object">
    Editable annotations, with the option to be "locked", that a task should be initialized with. This is useful when you've run a model to prelabel the task and want annotators to refine those prelabels. Must contain the annotations field, which has the same format as the annotations field in the response.

    ***
  </ParamField>

  <ParamField path="can_add_base_annotations" type="boolean">
    Whether or not new annotations can be added to the task if base\_annotations are used. If set to true, new annotations can be added to the task in addition to base\_annotations. If set to false, new annotations will not be able to be added to the task.

    ***
  </ParamField>

  <ParamField path="can_edit_base_annotations" type="boolean">
    Whether or not base\_annotations can be edited in the task. If set to true, base\_annotations can be edited by the tasker (position of annotation, attributes, etc). If set to false, all aspects of base\_annotations will be locked.

    ***
  </ParamField>

  <ParamField path="can_edit_base_annotation_labels" type="boolean">
    Whether or not base\_annotations labels can be edited in the task. If set to true, the label of base\_annotations can be edited by the tasker. If set to false, the label will be locked.

    ***
  </ParamField>

  <ParamField path="can_delete_base_annotations" type="boolean">
    Whether or not base\_annotations can be removed from the task. If set to true, base\_annotations can be deleted from the task. If set to false, base\_annotations cannot be deleted from the task.

    ***
  </ParamField>

  <ParamField path="metadata" type="object">
    A set of key/value pairs that you can attach to a task object. It can be useful for storing additional information about the task in a structured format. Max 10KB.

    ***
  </ParamField>

  <ParamField path="priority" type="int32">
    A value of 10, 20, or 30 that defines the priority of a task within a project. The higher the number, the higher the priority.

    ***
  </ParamField>

  <ParamField path="unique_id" type="string">
    A arbitrary ID that you can assign to a task and then query for later. This ID must be unique across all projects under your account, otherwise the task submission will be rejected. See **[Avoiding Duplicate Tasks](/docs/api-reference/data-engine-reference#avoiding-duplicate-tasks)** for more details.

    ***
  </ParamField>

  <ParamField path="clear_unique_id_on_error" type="boolean">
    If set to be true, if a task errors out after being submitted, the unique id on the task will be unset. This param allows workflows where you can re-submit the same unique id to recover from errors automatically

    ***
  </ParamField>

  <ParamField path="tags" type="array of strings">
    Arbitrary labels that you can assign to a task. At most 5 tags are allowed per task. You can query tasks with specific tags through the task retrieval API.

    ***
  </ParamField>
</ParamField>

<CodeGroup>
  ```python Python theme={null}
  import requests

  # Replace with your actual API key
  API_KEY = 'your_api_key_here'

  # Define the URL for the API endpoint
  url = "https://api.scale.com/v1/task/videoannotation"

  # Define the payload for the video annotation task
  payload = {
      "instruction": "**Instructions:** Please label all the things",
      "attachments": [
          "https://static.scale.com/scaleapi-lidar-images/2011_09_29_drive_0071_sync/image_02/data/0000000005.png",
          "https://static.scale.com/scaleapi-lidar-images/2011_09_29_drive_0071_sync/image_02/data/0000000008.png"
      ],
      "attachment_type": "image",
      "geometries": {
          "box": {
              "min_height": 10,
              "min_width": 10,
              "can_rotate": True,
              "integer_pixels": False
          },
          "polygon": {
              "min_vertices": 10,
              "max_vertices": 20,
              "objects_to_annotate": ["large vehicle"]
          },
          "line": {
              "min_vertices": 10,
              "max_vertices": 20,
              "objects_to_annotate": ["large vehicle"]
          },
          "point": {
              "objects_to_annotate": ["large vehicle", "large vehicle"]
          },
          "cuboid": {
              "min_height": 10,
              "min_width": 10,
              "camera_intrinsics": {
                  "fx": 10,
                  "fy": 10,
                  "cx": 10,
                  "cy": 10,
                  "skew": 10,
                  "scalefactor": 10
              },
              "camera_rotation_quaternion": {
                  "w": 10,
                  "x": 10,
                  "y": 10,
                  "z": 10
              },
              "camera_height": 10
          },
          "ellipse": {
              "objects_to_annotate": ["large vehicle"]
          }
      },
      "events_to_annotate": ["event_1_name", "event_2_name"],
      "frame_rate": 1,
      "start_time": 10,
      "padding": 10,
      "paddingX": 10,
      "metadata": {
          "newKey": "New Value",
          "newKey-1": "New Value"
      },
      "priority": 30,
      "project": "Project Name",
      "batch": "Batch Name",
      "callback_url": "http://www.example.com/callback",
      "attachment": "attachment_url",
      "duration_time": 10,
      "paddingY": 10,
      "unique_id": "unique_id",
      "clear_unique_id_on_error": True,
      "tags": ["tag1", "tag2"]
  }

  # Set up the headers for the request
  headers = {
      "accept": "application/json",       # Specify that we want the response in JSON format
      "content-type": "application/json"  # Specify the content type of the request
  }

  # Adding authentication to the POST request
  # The auth parameter requires a tuple with the API key and an empty string
  response = requests.post(url, json=payload, headers=headers, auth=(API_KEY, ''))

  # Print the response text to see the result
  print(response.text)

  ```

  ```python Python SDK theme={null}
  from scaleapi.tasks import TaskType
  from scaleapi.exceptions import ScaleDuplicateResource

  payload = dict(
      "instruction": "**Instructions:** Please label all the things",
      "attachments": ["https://static.scale.com/scaleapi-lidar-images/2011_09_29_drive_0071_sync/image_02/data/0000000005.png", "https://static.scale.com/scaleapi-lidar-images/2011_09_29_drive_0071_sync/image_02/data/0000000008.png"],
      "attachment_type": "image",
      "geometries": {
          "box": {
              "min_height": 10,
              "min_width": 10,
              "can_rotate": True,
              "integer_pixels": False
          },
          "polygon": {
              "min_vertices": 10,
              "max_vertices": 20,
              "objects_to_annotate": ["large vehicle"]
          },
          "line": {
              "min_vertices": 10,
              "max_vertices": 20,
              "objects_to_annotate": ["large vehicle"]
          },
          "point": { "objects_to_annotate": ["large vehicle", "large vehicle"] },
          "cuboid": {
              "min_height": 10,
              "min_width": 10,
              "camera_intrinsics": {
                  "fx": 10,
                  "fy": 10,
                  "cx": 10,
                  "cy": 10,
                  "skew": 10,
                  "scalefactor": 10
              },
              "camera_rotation_quaternion": {
                  "w": 10,
                  "x": 10,
                  "y": 10,
                  "z": 10
              },
              "camera_height": 10
          },
          "ellipse": { "objects_to_annotate": ["large vehicle"] }
      },
      "events_to_annotate": ["event_1_name", "event_2_name"],
      "frame_rate": 1,
      "start_time": 10,
      "padding": 10,
      "paddingX": 10,
      "metadata": {
          "newKey": "New Value",
          "newKey-1": "New Value"
      },
      "priority": 30,
      "project": "Project Name",
      "batch": "Batch Name",
      "callback_url": "http://www.example.com/callback",
      "attachment": "attachment_url",
      "duration_time": 10,
      "paddingY": 10,
      "unique_id": "unique_id",
      "clear_unique_id_on_error": True,
      "tags": ["tag1", "tag2"]
  )

  try:
      client.create_task(TaskType.VideoAnnotation, **payload)
  except ScaleDuplicateResource as err:
      print(err.message)  # If unique_id is already used for a different task
  ```
</CodeGroup>

```json theme={null}
{
  "task_id": "string",
  "created_at": "string",
  "type": "videoannotation",
  "status": "pending",
  "instruction": "string",
  "is_test": false,
  "urgency": "standard",
  "metadata": {},
  "project": "string",
  "callback_url": "string",
  "updated_at": "string",
  "work_started": false,
  "params": {
    "attachment_type": "website",
    "attachment": [
      null
    ],
    "geometries": {
      "box": {
        "objects_to_annotate": [
          null
        ],
        "min_height": 10,
        "min_width": 10
      },
      "polygon": {
        "objects_to_annotate": [
          null
        ]
      },
      "point": {
        "objects_to_annotate": [
          null
        ]
      }
    },
    "annotation_attributes": {
      "additionalProp": {
        "description": "string",
        "choice": "string"
      }
    },
    "events_to_annotate": [
      null
    ],
    "with_labels": true
  }
}
```

# Create Video Playback Annotation Task

This endpoint creates a `**videoplaybackannotation**` task. In this task, we will view the given video file and draw annotations around the specified objects.

You are required to provide a URL to the video file as the `**attachment**`. It can be in `**mp4**`, `**webm**`, or `**ogg**` format.

You can optionally provide additional markdown-enabled or Google Doc-based **[instructions](/docs/write-your-instructions)** via the `**instruction**` parameter.

You may optionally specify a `**frame_rate**`, which will determine how many frames per second will be used to annotate the given video. The default value is `**1**`.

You may also optionally specify `**events_to_annotate**`, a list of strings describing **events section** to annotate in the video.

If the request is successful, Scale will return the generated task object, at which point you should store the `**task_id**` to have a permanent reference to the task.<br />

<ParamField path="Body Params" type="object">
  <ParamField path="project" type="string">
    The name of the **[project](/docs/api-reference/projects)** to associate this task with.

    ***
  </ParamField>

  <ParamField path="batch" type="string">
    The name of the **[batch](/docs/api-reference/batches)** to associate this task with. Note that if a batch is specified, you need not specify the project, as the task will automatically be associated with the batch's project. For Scale Rapid projects specifying a batch is required. See **[Batches section](/docs/api-reference/batches)** for more details.

    ***
  </ParamField>

  <ParamField path="instruction" type="string">
    A markdown-enabled string or iframe embed google doc explaining how to do the task. You can use **[markdown](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet)** to show example images, give structure to your instructions, and more. See our **[instruction best practices](/docs/write-your-instructions)** for more details. For Scale Rapid projects, DO NOT set this field unless you specifically want to override the project level instructions.

    ***
  </ParamField>

  <ParamField path="callback_url" type="string">
    The full url (including the scheme `**http://**` or `**https://**`) or email address of the **[callback](/docs/api-reference/callbacks)** that will be used when the task is completed.

    ***
  </ParamField>

  <ParamField path="attachments" type="array of strings">
    An array of URLs for the frames you'd like to be annotated. These image frames are stitched together to create a video. This is required if attachment\_type is image and must be omitted if attachment\_type is video.

    ***
  </ParamField>

  <ParamField path="attachment" type="string">
    A URL pointing to the video file attachment. Only the mp4, webm, and ogg formats are supported.

    ***
  </ParamField>

  <ParamField path="attachment_type" type="string">
    Describes what type of file the attachment(s) are. The only options are image and video.

    ***
  </ParamField>

  <ParamField path="geometries" type="object" required>
    An object mapping box, polygon, line, point, cuboid, or ellipse to Geometry objects

    ***
  </ParamField>

  <ParamField path="annotation_attributes" type="object">
    See the **[Annotation Attributes](/docs/api-reference/data-engine-reference#annotation-attributes-overview)** section for more details about annotation attributes.

    ***
  </ParamField>

  <ParamField path="events_to_annotate" type="int32">
    The list of events to annotate.

    ***
  </ParamField>

  <ParamField path="duration_time" type="array of strings">
    The duration of the video in seconds. This is ignored if attachment\_type is image. Default is full video length.

    ***
  </ParamField>

  <ParamField path="frame_rate" type="object">
    The number of frames to capture in one second. This is ignored if attachment\_type is image.

    ***
  </ParamField>

  <ParamField path="start_time" type="int32">
    The start time in seconds. This is ignored if attachment\_type is image.

    ***
  </ParamField>

  <ParamField path="padding" type="int32">
    The amount of padding in pixels added to the top, bottom, left, and right of each video frame. This allows labelers to extend annotations outside of the frames.

    ***
  </ParamField>

  <ParamField path="paddingX" type="int32">
    The amount of padding in pixels added to the left and right of each video frame. Overrides padding if set.

    ***
  </ParamField>

  <ParamField path="paddingY" type="int32">
    The amount of padding in pixels added to the top and bottom of each video frame. Overrides padding if set.

    ***
  </ParamField>

  <ParamField path="base_annotations" type="object">
    Editable annotations, with the option to be "locked", that a task should be initialized with. This is useful when you've run a model to prelabel the task and want annotators to refine those prelabels. Must contain the annotations field, which has the same format as the annotations field in the response.

    ***
  </ParamField>

  <ParamField path="can_add_base_annotations" type="boolean">
    Whether or not new annotations can be added to the task if base\_annotations are used. If set to true, new annotations can be added to the task in addition to base\_annotations. If set to false, new annotations will not be able to be added to the task.

    ***
  </ParamField>

  <ParamField path="can_edit_base_annotations" type="boolean">
    Whether or not base\_annotations can be edited in the task. If set to true, base\_annotations can be edited by the tasker (position of annotation, attributes, etc). If set to false, all aspects of base\_annotations will be locked.

    ***
  </ParamField>

  <ParamField path="can_edit_base_annotation_labels" type="boolean">
    Whether or not base\_annotations labels can be edited in the task. If set to true, the label of base\_annotations can be edited by the tasker. If set to false, the label will be locked.

    ***
  </ParamField>

  <ParamField path="can_delete_base_annotations" type="boolean">
    Whether or not base\_annotations can be removed from the task. If set to true, base\_annotations can be deleted from the task. If set to false, base\_annotations cannot be deleted from the task.

    ***
  </ParamField>

  <ParamField path="metadata" type="object">
    A set of key/value pairs that you can attach to a task object. It can be useful for storing additional information about the task in a structured format. Max 10KB.

    ***
  </ParamField>

  <ParamField path="priority" type="int32">
    A value of 10, 20, or 30 that defines the priority of a task within a project. The higher the number, the higher the priority.

    ***
  </ParamField>

  <ParamField path="unique_id" type="string">
    A arbitrary ID that you can assign to a task and then query for later. This ID must be unique across all projects under your account, otherwise the task submission will be rejected. See **[Avoiding Duplicate Tasks](/docs/api-reference/data-engine-reference#avoiding-duplicate-tasks)** for more details.

    ***
  </ParamField>

  <ParamField path="clear_unique_id_on_error" type="boolean">
    If set to be true, if a task errors out after being submitted, the unique id on the task will be unset. This param allows workflows where you can re-submit the same unique id to recover from errors automatically

    ***
  </ParamField>

  <ParamField path="tags" type="array of strings">
    Arbitrary labels that you can assign to a task. At most 5 tags are allowed per task. You can query tasks with specific tags through the task retrieval API.

    ***
  </ParamField>
</ParamField>

<CodeGroup>
  ```python Python theme={null}
  import requests

  # Replace with your actual API key
  API_KEY = 'your_api_key_here'

  # Define the URL for the API endpoint
  url = "https://api.scale.com/v1/task/videoplaybackannotation"

  # Define the payload for the video playback annotation task
  payload = {
      "instruction": "**Instructions:** Please label all the things",
      "attachments": [
          "https://static.scale.com/scaleapi-lidar-images/2011_09_26_drive_0051_sync/image_02/data/0000000000.png",
          "https://static.scale.com/scaleapi-lidar-images/2011_09_26_drive_0051_sync/image_02/data/0000000001.png"
      ],
      "attachment": "https://scale-static-assets.s3-us-west-2.amazonaws.com/demos/multimodal-video.mp4",
      "attachment_type": "image",
      "geometries": {
          "box": {
              "min_height": 10,
              "min_width": 10
          },
          "polygon": {
              "min_vertices": 1,
              "max_vertices": " "
          },
          "line": {
              "min_vertices": 1,
              "max_vertices": " "
          },
          "point": {
              "x": " ",
              "y": " "
          },
          "cuboid": {
              "min_height": 0,
              "min_width": 0,
              "camera_intrinsics": {
                  "fx": " ",
                  "fy": " ",
                  "cx": " ",
                  "cy": " ",
                  "skew": 0,
                  "scalefactor": 1
              },
              "camera_rotation_quaternion": {
                  "w": " ",
                  "x": " ",
                  "y": " ",
                  "z": " "
              },
              "camera_height": " "
          }
      },
      "frame_rate": 1,
      "padding": 0,
      "paddingX": 0,
      "paddingY": 0,
      "priority": 30
  }

  # Set up the headers for the request
  headers = {
      "accept": "application/json",       # Specify that we want the response in JSON format
      "content-type": "application/json"  # Specify the content type of the request
  }

  # Adding authentication to the POST request
  # The auth parameter requires a tuple with the API key and an empty string
  response = requests.post(url, json=payload, headers=headers, auth=(API_KEY, ''))

  # Print the response text to see the result
  print(response.text)

  ```

  ```text Python SDK theme={null}
  from scaleapi.tasks import TaskType
  from scaleapi.exceptions import ScaleDuplicateResource

  payload = dict(
      "instruction": "**Instructions:** Please label all the things",
      "attachments": ["https://static.scale.com/scaleapi-lidar-images/2011_09_26_drive_0051_sync/image_02/data/0000000000.png", "https://static.scale.com/scaleapi-lidar-images/2011_09_26_drive_0051_sync/image_02/data/0000000001.png"],
      "attachment": "https://scale-static-assets.s3-us-west-2.amazonaws.com/demos/multimodal-video.mp4",
      "attachment_type": "image",
      "geometries": {
          "box": {
              "min_height": 10,
              "min_width": 10
          },
          "polygon": {
              "min_vertices": 1,
              "max_vertices": 10
          },
          "line": {
              "min_vertices": 1,
              "max_vertices": 10
          },
          "point": {
              "x": " ",
              "y": " "
          },
          "cuboid": {
              "min_height": 0,
              "min_width": 0,
              "camera_intrinsics": {
                  "fx": 10,
                  "fy": 10,
                  "cx": 10,
                  "cy": 10,
                  "skew": 0,
                  "scalefactor": 1
              },
              "camera_rotation_quaternion": {
                  "w": 10,
                  "x": 10,
                  "y": 10,
                  "z": 10
              },
              "camera_height": 10
          }
      },
      "frame_rate": 1,
      "padding": 0,
      "paddingX": 0,
      "paddingY": 0,
      "priority": 30
  )

  try:
      client.create_task(TaskType.VideoPlaybackAnnotation, **payload)
  except ScaleDuplicateResource as err:
      print(err.message)  # If unique_id is already used for a different task
  ```
</CodeGroup>

```json theme={null}
{
  "task_id": "string",
  "created_at": "string",
  "type": "imageannotation",
  "status": "pending",
  "instruction": "string",
  "is_test": false,
  "urgency": "standard",
  "metadata": {},
  "project": "string",
  "callback_url": "string",
  "updated_at": "string",
  "work_started": false,
  "params": {
    "attachment_type": "image",
    "attachment": "http://i.imgur.com/3Cpje3l.jpg",
    "geometries": {
      "box": {
        "objects_to_annotate": [
          null
        ],
        "min_height": 5,
        "min_width": 5
      },
      "polygon": {
        "objects_to_annotate": [
          null
        ]
      },
      "point": {
        "objects_to_annotate": [
          null
        ]
      }
    },
    "annotation_attributes": {
      "additionalProp": {
        "type": "category",
        "description": "string",
        "choice": "string"
      }
    }
  }
}
```