`viam.services.vision`

Submodules

Package Contents

Classes

`Classification`	the general form of the output from a classifier
`Detection`	Abstract base class for protocol messages.
`VisionClient`	Connect to the Vision service, which allows you to access various computer vision algorithms
`Vision`	Vision represents a Vision service.

class viam.services.vision.Classification(*, class_name: str = ..., confidence: float = ...)

Bases: google.protobuf.message.Message

the general form of the output from a classifier

class_name: str: the class name

confidence: float: the confidence score of the classification

class viam.services.vision.Detection(*, x_min: int | None = ..., y_min: int | None = ..., x_max: int | None = ..., y_max: int | None = ..., confidence: float = ..., class_name: str = ...)

Bases: google.protobuf.message.Message

Abstract base class for protocol messages.

Protocol message classes are almost always generated by the protocol compiler. These generated types subclass Message and implement the methods shown below.

x_min: int: the four corners of the box

y_min: int

x_max: int

y_max: int

confidence: float: the confidence of the detection

class_name: str: label associated with the detected object

HasField(field_name: Literal[_x_max, b'_x_max', _x_min, b'_x_min', _y_max, b'_y_max', _y_min, b'_y_min', x_max, b'x_max', x_min, b'x_min', y_max, b'y_max', y_min, b'y_min']) → bool

Checks if a certain field is set for the message.

For a oneof group, checks if any field inside is set. Note that if the field_name is not defined in the message descriptor, ValueError will be raised.

Parameters:: field_name (str) – The name of the field to check for presence.
Returns:: Whether a value has been set for the named field.
Return type:: bool
Raises:: ValueError – if the field_name is not a member of this message.

WhichOneof(oneof_group: Literal[_x_max, b'_x_max']) → Literal[x_max] | None

WhichOneof(oneof_group: Literal[_x_min, b'_x_min']) → Literal[x_min] | None

WhichOneof(oneof_group: Literal[_y_max, b'_y_max']) → Literal[y_max] | None

WhichOneof(oneof_group: Literal[_y_min, b'_y_min']) → Literal[y_min] | None

Returns the name of the field that is set inside a oneof group.

If no field is set, returns None.

Parameters:: oneof_group (str) – the name of the oneof group to check.
Returns:: The name of the group that is set, or None.
Return type:: str or None
Raises:: ValueError – no group with the given name exists

class viam.services.vision.VisionClient(name: str, channel: grpclib.client.Channel)[source]

Bases: viam.services.vision.vision.Vision, viam.resource.rpc_client_base.ReconfigurableResourceRPCClientBase

Connect to the Vision service, which allows you to access various computer vision algorithms (like detection, segmentation, tracking, etc) that usually only require a camera or image input.

client: viam.proto.service.vision.VisionServiceStub

async get_detections_from_camera(camera_name: str, *, extra: Mapping[str, Any] | None = None, timeout: float | None = None) → List[viam.proto.service.vision.Detection][source]

Get a list of detections in the next image given a camera and a detector

camera_name = "cam1"

# Grab the detector you configured on your machine
my_detector = VisionClient.from_robot(robot, "my_detector")

# Get detections from the next image from the camera
detections = await my_detector.get_detections_from_camera(camera_name)

Parameters:: camera_name (str) – The name of the camera to use for detection
Returns:: A list of 2D bounding boxes, their labels, and the confidence score of the labels, around the found objects in the next 2D image from the given camera, with the given detector applied to it.
Return type:: List[viam.proto.service.vision.Detection]

async get_detections(image: viam.media.viam_rgba_plugin.Image.Image | viam.media.video.RawImage, *, extra: Mapping[str, Any] | None = None, timeout: float | None = None) → List[viam.proto.service.vision.Detection][source]

Get a list of detections in the given image using the specified detector

# Grab camera from the machine
cam1 = Camera.from_robot(robot, "cam1")

# Get the detector you configured on your machine
my_detector = VisionClient.from_robot(robot, "my_detector")

# Get an image from the camera
img = await cam1.get_image()

# Get detections from that image
detections = await my_detector.get_detections(img)

Parameters:: image (Image) – The image to get detections from
Returns:: A list of 2D bounding boxes, their labels, and the confidence score of the labels, around the found objects in the next 2D image from the given camera, with the given detector applied to it.
Return type:: List[viam.proto.service.vision.Detection]

async get_classifications_from_camera(camera_name: str, count: int, *, extra: Mapping[str, Any] | None = None, timeout: float | None = None) → List[viam.proto.service.vision.Classification][source]

Get a list of classifications in the next image given a camera and a classifier

camera_name = "cam1"

# Grab the classifier you configured on your machine
my_classifier = VisionClient.from_robot(robot, "my_classifier")

# Get the 2 classifications with the highest confidence scores from the next image from the camera
classifications = await my_classifier.get_classifications_from_camera(
    camera_name, 2)

Parameters:

camera_name (str) – The name of the camera to use for detection
count (int) – The number of classifications desired

Returns:

The list of Classifications

Return type:

List[viam.proto.service.vision.Classification]

async get_classifications(image: viam.media.viam_rgba_plugin.Image.Image | viam.media.video.RawImage, count: int, *, extra: Mapping[str, Any] | None = None, timeout: float | None = None) → List[viam.proto.service.vision.Classification][source]

Get a list of classifications in the given image using the specified classifier

# Grab camera from the machine
cam1 = Camera.from_robot(robot, "cam1")

# Get the classifier you configured on your machine
my_classifier = VisionClient.from_robot(robot, "my_classifier")

# Get an image from the camera
img = await cam1.get_image()

# Get the 2 classifications with the highest confidence scores
classifications = await my_classifier.get_classifications(img, 2)

Parameters:

image (Image) – The image to get detections from
count (int) – The number of classifications desired

Returns:

The list of Classifications

Return type:

List[viam.proto.service.vision.Classification]

async get_object_point_clouds(camera_name: str, *, extra: Mapping[str, Any] | None = None, timeout: float | None = None) → List[viam.proto.common.PointCloudObject][source]

Returns a list of the 3D point cloud objects and associated metadata in the latest picture obtained from the specified 3D camera (using the specified segmenter).

To deserialize the returned information into a numpy array, use the Open3D library.

import numpy as np
import open3d as o3d

# Grab the 3D camera from the machine
cam1 = Camera.from_robot(robot, "cam1")
# Grab the object segmenter you configured on your machine
my_segmenter = VisionClient.from_robot(robot, "my_segmenter")
# Get the objects from the camera output
objects = await my_segmenter.get_object_point_clouds(cam1)
# write the first object point cloud into a temporary file
with open("/tmp/pointcloud_data.pcd", "wb") as f:
    f.write(objects[0].point_cloud)
pcd = o3d.io.read_point_cloud("/tmp/pointcloud_data.pcd")
points = np.asarray(pcd.points)

Parameters:: camera_name (str) – The name of the camera
Returns:: The pointcloud objects with metadata
Return type:: List[viam.proto.common.PointCloudObject]

async do_command(command: Mapping[str, viam.utils.ValueTypes], *, timeout: float | None = None, **__) → Mapping[str, viam.utils.ValueTypes][source]

Send/receive arbitrary commands.

motion = MotionClient.from_robot(robot, "builtin")

my_command = {
  "cmnd": "dosomething",
  "someparameter": 52
}

# Can be used with any resource, using the motion service as an example
await motion.do_command(command=my_command)

Parameters:: command (Dict[str, ValueTypes]) – The command to execute
Returns:: Result of the executed command
Return type:: Dict[str, ValueTypes]

classmethod from_robot(robot: viam.robot.client.RobotClient, name: str) → typing_extensions.Self

Get the service named name from the provided robot.

async def connect() -> ViamClient:
    # Replace "<API-KEY>" (including brackets) with your API key and "<API-KEY-ID>" with your API key ID
    dial_options = DialOptions.with_api_key("<API-KEY>", "<API-KEY-ID>")
    return await ViamClient.create_from_dial_options(dial_options)

async def main():
    robot = await connect()

    # Can be used with any resource, using the motion service as an example
    motion = MotionClient.from_robot(robot=robot, name="builtin")

    robot.close()

Parameters:

robot (RobotClient) – The robot
name (str) – The name of the service

Returns:

The service, if it exists on the robot

Return type:

Self

classmethod get_resource_name(name: str) → viam.proto.common.ResourceName

Get the ResourceName for this Resource with the given name

# Can be used with any resource, using an arm as an example
my_arm_name = my_arm.get_resource_name("my_arm")

Parameters:: name (str) – The name of the Resource

get_operation(kwargs: Mapping[str, Any]) → viam.operations.Operation

Get the Operation associated with the currently running function.

When writing custom resources, you should get the Operation by calling this function and check to see if it’s cancelled. If the Operation is cancelled, then you can perform any necessary (terminating long running tasks, cleaning up connections, etc. ).

Parameters:: kwargs (Mapping[str, Any]) – The kwargs object containing the operation
Returns:: The operation associated with this function
Return type:: viam.operations.Operation

async close()

Safely shut down the resource and prevent further use.

Close must be idempotent. Later configuration may allow a resource to be “open” again. If a resource does not want or need a close function, it is assumed that the resource does not need to return errors when future non-Close methods are called.

await component.close()

class viam.services.vision.Vision(name: str)[source]

Bases: viam.services.service_base.ServiceBase

Vision represents a Vision service.

This acts as an abstract base class for any drivers representing specific arm implementations. This cannot be used on its own. If the __init__() function is overridden, it must call the super().__init__() function.

SUBTYPE: Final

abstract async get_detections_from_camera(camera_name: str, *, extra: Mapping[str, Any] | None = None, timeout: float | None = None) → List[viam.proto.service.vision.Detection][source]

Get a list of detections in the next image given a camera and a detector

camera_name = "cam1"

# Grab the detector you configured on your machine
my_detector = VisionClient.from_robot(robot, "my_detector")

# Get detections from the next image from the camera
detections = await my_detector.get_detections_from_camera(camera_name)

Parameters:: camera_name (str) – The name of the camera to use for detection
Returns:: A list of 2D bounding boxes, their labels, and the confidence score of the labels, around the found objects in the next 2D image from the given camera, with the given detector applied to it.
Return type:: List[viam.proto.service.vision.Detection]

abstract async get_detections(image: PIL.Image.Image | viam.media.video.RawImage, *, extra: Mapping[str, Any] | None = None, timeout: float | None = None) → List[viam.proto.service.vision.Detection][source]

Get a list of detections in the given image using the specified detector

# Grab camera from the machine
cam1 = Camera.from_robot(robot, "cam1")

# Get the detector you configured on your machine
my_detector = VisionClient.from_robot(robot, "my_detector")

# Get an image from the camera
img = await cam1.get_image()

# Get detections from that image
detections = await my_detector.get_detections(img)

Parameters:: image (Image) – The image to get detections from
Returns:: A list of 2D bounding boxes, their labels, and the confidence score of the labels, around the found objects in the next 2D image from the given camera, with the given detector applied to it.
Return type:: List[viam.proto.service.vision.Detection]

abstract async get_classifications_from_camera(camera_name: str, count: int, *, extra: Mapping[str, Any] | None = None, timeout: float | None = None) → List[viam.proto.service.vision.Classification][source]

Get a list of classifications in the next image given a camera and a classifier

camera_name = "cam1"

# Grab the classifier you configured on your machine
my_classifier = VisionClient.from_robot(robot, "my_classifier")

# Get the 2 classifications with the highest confidence scores from the next image from the camera
classifications = await my_classifier.get_classifications_from_camera(
    camera_name, 2)

Parameters:

camera_name (str) – The name of the camera to use for detection
count (int) – The number of classifications desired

Returns:

The list of Classifications

Return type:

List[viam.proto.service.vision.Classification]

abstract async get_classifications(image: PIL.Image.Image | viam.media.video.RawImage, count: int, *, extra: Mapping[str, Any] | None = None, timeout: float | None = None) → List[viam.proto.service.vision.Classification][source]

Get a list of classifications in the given image using the specified classifier

# Grab camera from the machine
cam1 = Camera.from_robot(robot, "cam1")

# Get the classifier you configured on your machine
my_classifier = VisionClient.from_robot(robot, "my_classifier")

# Get an image from the camera
img = await cam1.get_image()

# Get the 2 classifications with the highest confidence scores
classifications = await my_classifier.get_classifications(img, 2)

Parameters:

image (Image) – The image to get detections from
count (int) – The number of classifications desired

Returns:

The list of Classifications

Return type:

List[viam.proto.service.vision.Classification]

abstract async get_object_point_clouds(camera_name: str, *, extra: Mapping[str, Any] | None = None, timeout: float | None = None) → List[viam.proto.common.PointCloudObject][source]

Returns a list of the 3D point cloud objects and associated metadata in the latest picture obtained from the specified 3D camera (using the specified segmenter).

To deserialize the returned information into a numpy array, use the Open3D library.

import numpy as np
import open3d as o3d

# Grab the 3D camera from the machine
cam1 = Camera.from_robot(robot, "cam1")
# Grab the object segmenter you configured on your machine
my_segmenter = VisionClient.from_robot(robot, "my_segmenter")
# Get the objects from the camera output
objects = await my_segmenter.get_object_point_clouds(cam1)
# write the first object point cloud into a temporary file
with open("/tmp/pointcloud_data.pcd", "wb") as f:
    f.write(objects[0].point_cloud)
pcd = o3d.io.read_point_cloud("/tmp/pointcloud_data.pcd")
points = np.asarray(pcd.points)

Parameters:: camera_name (str) – The name of the camera
Returns:: The pointcloud objects with metadata
Return type:: List[viam.proto.common.PointCloudObject]

classmethod from_robot(robot: viam.robot.client.RobotClient, name: str) → typing_extensions.Self

Get the service named name from the provided robot.

async def connect() -> ViamClient:
    # Replace "<API-KEY>" (including brackets) with your API key and "<API-KEY-ID>" with your API key ID
    dial_options = DialOptions.with_api_key("<API-KEY>", "<API-KEY-ID>")
    return await ViamClient.create_from_dial_options(dial_options)

async def main():
    robot = await connect()

    # Can be used with any resource, using the motion service as an example
    motion = MotionClient.from_robot(robot=robot, name="builtin")

    robot.close()

Parameters:

robot (RobotClient) – The robot
name (str) – The name of the service

Returns:

The service, if it exists on the robot

Return type:

Self

abstract async do_command(command: Mapping[str, viam.utils.ValueTypes], *, timeout: float | None = None, **kwargs) → Mapping[str, viam.utils.ValueTypes]

Send/receive arbitrary commands.

motion = MotionClient.from_robot(robot, "builtin")

my_command = {
  "cmnd": "dosomething",
  "someparameter": 52
}

# Can be used with any resource, using the motion service as an example
await motion.do_command(command=my_command)

Parameters:: command (Dict[str, ValueTypes]) – The command to execute
Returns:: Result of the executed command
Return type:: Dict[str, ValueTypes]

classmethod get_resource_name(name: str) → viam.proto.common.ResourceName

Get the ResourceName for this Resource with the given name

# Can be used with any resource, using an arm as an example
my_arm_name = my_arm.get_resource_name("my_arm")

Parameters:: name (str) – The name of the Resource

get_operation(kwargs: Mapping[str, Any]) → viam.operations.Operation

Get the Operation associated with the currently running function.

When writing custom resources, you should get the Operation by calling this function and check to see if it’s cancelled. If the Operation is cancelled, then you can perform any necessary (terminating long running tasks, cleaning up connections, etc. ).

Parameters:: kwargs (Mapping[str, Any]) – The kwargs object containing the operation
Returns:: The operation associated with this function
Return type:: viam.operations.Operation

async close()

Safely shut down the resource and prevent further use.

Close must be idempotent. Later configuration may allow a resource to be “open” again. If a resource does not want or need a close function, it is assumed that the resource does not need to return errors when future non-Close methods are called.

await component.close()

viam.services.vision

Submodules

Package Contents

Classes

`viam.services.vision`