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Removed SegmenterOptions dataclasses to enumerate options within ImageSegmenterOptions instead

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This commit is contained in:
kinaryml 2022-10-21 13:34:30 -07:00
parent 91b60da1dc
commit 5231a0ad9f
5 changed files with 38 additions and 85 deletions
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6
mediapipe/tasks/python/components/proto/BUILD
View File

@ -20,9 +20,5 @@ licenses(["notice"])
py_library(
name = "segmenter_options",
srcs = ["segmenter_options.py"],
deps = [
"//mediapipe/tasks/cc/components/proto:segmenter_options_py_pb2",
"//mediapipe/tasks/python/core:optional_dependencies",
],
srcs = ["segmenter_options.py"]
)

50
mediapipe/tasks/python/components/proto/segmenter_options.py
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@ -13,14 +13,7 @@
# limitations under the License.
"""Segmenter options data class."""
import dataclasses
import enum
from typing import Any, Optional
from mediapipe.tasks.cc.components.proto import segmenter_options_pb2
from mediapipe.tasks.python.core.optional_dependencies import doc_controls
_SegmenterOptionsProto = segmenter_options_pb2.SegmenterOptions
class OutputType(enum.Enum):
@ -33,46 +26,3 @@ class Activation(enum.Enum):
NONE = 0
SIGMOID = 1
SOFTMAX = 2
@dataclasses.dataclass
class SegmenterOptions:
"""Options for segmentation processor.
Attributes:
output_type: The output mask type allows specifying the type of
post-processing to perform on the raw model results.
activation: Activation function to apply to input tensor.
"""
output_type: Optional[OutputType] = OutputType.CATEGORY_MASK
activation: Optional[Activation] = Activation.NONE
@doc_controls.do_not_generate_docs
def to_pb2(self) -> _SegmenterOptionsProto:
"""Generates a protobuf object to pass to the C++ layer."""
return _SegmenterOptionsProto(
output_type=self.output_type.value,
activation=self.activation.value
)
@classmethod
@doc_controls.do_not_generate_docs
def create_from_pb2(
cls, pb2_obj: _SegmenterOptionsProto) -> "SegmenterOptions":
"""Creates a `SegmenterOptions` object from the given protobuf object."""
return SegmenterOptions(
output_type=OutputType(pb2_obj.output_type),
activation=Activation(pb2_obj.output_type)
)
def __eq__(self, other: Any) -> bool:
"""Checks if this object is equal to the given object.
Args:
other: The object to be compared with.
Returns:
True if the objects are equal.
"""
if not isinstance(other, SegmenterOptions):
return False
return self.to_pb2().__eq__(other.to_pb2())

22
mediapipe/tasks/python/test/vision/image_segmenter_test.py
View File

@ -35,7 +35,6 @@ _Image = image_module.Image
_ImageFormat = image_frame_module.ImageFormat
_OutputType = segmenter_options.OutputType
_Activation = segmenter_options.Activation
_SegmenterOptions = segmenter_options.SegmenterOptions
_ImageSegmenter = image_segmenter.ImageSegmenter
_ImageSegmenterOptions = image_segmenter.ImageSegmenterOptions
_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
@ -125,9 +124,8 @@ class ImageSegmenterTest(parameterized.TestCase):
# Should never happen
raise ValueError('model_file_type is invalid.')
segmenter_options = _SegmenterOptions(output_type=_OutputType.CATEGORY_MASK)
options = _ImageSegmenterOptions(base_options=base_options,
segmenter_options=segmenter_options)
output_type=_OutputType.CATEGORY_MASK)
segmenter = _ImageSegmenter.create_from_options(options)
# Performs image segmentation on the input.
@ -153,19 +151,16 @@ class ImageSegmenterTest(parameterized.TestCase):
base_options = _BaseOptions(model_asset_path=self.model_path)
# Run segmentation on the model in CATEGORY_MASK mode.
segmenter_options = _SegmenterOptions(output_type=_OutputType.CATEGORY_MASK)
options = _ImageSegmenterOptions(base_options=base_options,
segmenter_options=segmenter_options)
output_type=_OutputType.CATEGORY_MASK)
segmenter = _ImageSegmenter.create_from_options(options)
category_masks = segmenter.segment(self.test_image)
category_mask = category_masks[0].numpy_view()
# Run segmentation on the model in CONFIDENCE_MASK mode.
segmenter_options = _SegmenterOptions(
output_type=_OutputType.CONFIDENCE_MASK,
activation=_Activation.SOFTMAX)
options = _ImageSegmenterOptions(base_options=base_options,
segmenter_options=segmenter_options)
output_type=_OutputType.CONFIDENCE_MASK,
activation=_Activation.SOFTMAX)
segmenter = _ImageSegmenter.create_from_options(options)
confidence_masks = segmenter.segment(self.test_image)
@ -204,9 +199,8 @@ class ImageSegmenterTest(parameterized.TestCase):
# Should never happen
raise ValueError('model_file_type is invalid.')
segmenter_options = _SegmenterOptions(output_type=_OutputType.CATEGORY_MASK)
options = _ImageSegmenterOptions(base_options=base_options,
segmenter_options=segmenter_options)
output_type=_OutputType.CATEGORY_MASK)
with _ImageSegmenter.create_from_options(options) as segmenter:
# Performs image segmentation on the input.
category_masks = segmenter.segment(self.test_image)
@ -284,10 +278,9 @@ class ImageSegmenterTest(parameterized.TestCase):
segmenter.segment_for_video(self.test_image, 0)
def test_segment_for_video(self):
segmenter_options = _SegmenterOptions(output_type=_OutputType.CATEGORY_MASK)
options = _ImageSegmenterOptions(
base_options=_BaseOptions(model_asset_path=self.model_path),
segmenter_options=segmenter_options,
output_type=_OutputType.CATEGORY_MASK,
running_mode=_RUNNING_MODE.VIDEO)
with _ImageSegmenter.create_from_options(options) as segmenter:
for timestamp in range(0, 300, 30):
@ -348,10 +341,9 @@ class ImageSegmenterTest(parameterized.TestCase):
self.assertLess(observed_timestamp_ms, timestamp_ms)
self.observed_timestamp_ms = timestamp_ms
segmenter_options = _SegmenterOptions(output_type=_OutputType.CATEGORY_MASK)
options = _ImageSegmenterOptions(
base_options=_BaseOptions(model_asset_path=self.model_path),
segmenter_options=segmenter_options,
output_type=_OutputType.CATEGORY_MASK,
running_mode=_RUNNING_MODE.LIVE_STREAM,
result_callback=check_result)
with _ImageSegmenter.create_from_options(options) as segmenter:

1
mediapipe/tasks/python/vision/BUILD
View File

@ -46,6 +46,7 @@ py_library(
"//mediapipe/python:_framework_bindings",
"//mediapipe/python:packet_creator",
"//mediapipe/python:packet_getter",
"//mediapipe/tasks/cc/components/proto:segmenter_options_py_pb2",
"//mediapipe/tasks/cc/vision/image_segmenter/proto:image_segmenter_options_py_pb2",
"//mediapipe/tasks/python/components/proto:segmenter_options",
"//mediapipe/tasks/python/core:base_options",

44
mediapipe/tasks/python/vision/image_segmenter.py
View File

@ -19,22 +19,25 @@ from typing import Callable, List, Mapping, Optional
from mediapipe.python import packet_creator
from mediapipe.python import packet_getter
from mediapipe.python._framework_bindings import image as image_module
from mediapipe.python._framework_bindings import packet as packet_module
from mediapipe.python._framework_bindings import task_runner as task_runner_module
from mediapipe.python._framework_bindings import packet
from mediapipe.python._framework_bindings import task_runner
from mediapipe.tasks.cc.components.proto import segmenter_options_pb2
from mediapipe.tasks.cc.vision.image_segmenter.proto import image_segmenter_options_pb2
from mediapipe.tasks.python.components.proto import segmenter_options
from mediapipe.tasks.python.core import base_options as base_options_module
from mediapipe.tasks.python.core import task_info as task_info_module
from mediapipe.tasks.python.core.optional_dependencies import doc_controls
from mediapipe.tasks.python.vision.core import base_vision_task_api
from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
from mediapipe.tasks.python.vision.core import vision_task_running_mode
_BaseOptions = base_options_module.BaseOptions
_SegmenterOptionsProto = segmenter_options_pb2.SegmenterOptions
_ImageSegmenterOptionsProto = image_segmenter_options_pb2.ImageSegmenterOptions
_SegmenterOptions = segmenter_options.SegmenterOptions
_RunningMode = running_mode_module.VisionTaskRunningMode
_OutputType = segmenter_options.OutputType
_Activation = segmenter_options.Activation
_RunningMode = vision_task_running_mode.VisionTaskRunningMode
_TaskInfo = task_info_module.TaskInfo
_TaskRunner = task_runner_module.TaskRunner
_TaskRunner = task_runner.TaskRunner
_SEGMENTATION_OUT_STREAM_NAME = 'segmented_mask_out'
_SEGMENTATION_TAG = 'GROUPED_SEGMENTATION'
@ -57,14 +60,17 @@ class ImageSegmenterOptions:
2) The video mode for segmenting objects on the decoded frames of a video.
3) The live stream mode for segmenting objects on a live stream of input
data, such as from camera.
segmenter_options: Options for the image segmenter task.
output_type: The output mask type allows specifying the type of
post-processing to perform on the raw model results.
activation: Activation function to apply to input tensor.
result_callback: The user-defined result callback for processing live stream
data. The result callback should only be specified when the running mode
is set to the live stream mode.
"""
base_options: _BaseOptions
running_mode: _RunningMode = _RunningMode.IMAGE
segmenter_options: _SegmenterOptions = _SegmenterOptions()
output_type: Optional[_OutputType] = _OutputType.CATEGORY_MASK
activation: Optional[_Activation] = _Activation.NONE
result_callback: Optional[
Callable[[List[image_module.Image], image_module.Image, int],
None]] = None
@ -74,8 +80,10 @@ class ImageSegmenterOptions:
"""Generates an ImageSegmenterOptions protobuf object."""
base_options_proto = self.base_options.to_pb2()
base_options_proto.use_stream_mode = False if self.running_mode == _RunningMode.IMAGE else True
segmenter_options_proto = self.segmenter_options.to_pb2()
segmenter_options_proto = _SegmenterOptionsProto(
output_type=self.output_type.value,
activation=self.activation.value
)
return _ImageSegmenterOptionsProto(
base_options=base_options_proto,
segmenter_options=segmenter_options_proto
@ -127,7 +135,7 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
RuntimeError: If other types of error occurred.
"""
def packets_callback(output_packets: Mapping[str, packet_module.Packet]):
def packets_callback(output_packets: Mapping[str, packet.Packet]):
if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
return
segmentation_result = packet_getter.get_image_list(
@ -159,8 +167,11 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
image: MediaPipe Image.
Returns:
A segmentation result object that contains a list of segmentation masks
as images.
If the output_type is CATEGORY_MASK, the returned vector of images is
per-category segmented image mask.
If the output_type is CONFIDENCE_MASK, the returned vector of images
contains only one confidence image mask. A segmentation result object that
contains a list of segmentation masks as images.
Raises:
ValueError: If any of the input arguments is invalid.
@ -186,8 +197,11 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
timestamp_ms: The timestamp of the input video frame in milliseconds.
Returns:
A segmentation result object that contains a list of segmentation masks
as images.
If the output_type is CATEGORY_MASK, the returned vector of images is
per-category segmented image mask.
If the output_type is CONFIDENCE_MASK, the returned vector of images
contains only one confidence image mask. A segmentation result object that
contains a list of segmentation masks as images.
Raises:
ValueError: If any of the input arguments is invalid.