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Deprecated output_type for the ImageSegmenter and InteractiveSegmenter APIs

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This commit is contained in:
kinaryml 2023-04-12 14:37:16 -07:00
parent c7aecb42ff
commit 3f68f90238
6 changed files with 272 additions and 132 deletions
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19
mediapipe/tasks/python/test/vision/BUILD
View File

@ -93,6 +93,25 @@ py_test(
],
)
py_test(
name = "interactive_segmenter_test",
srcs = ["interactive_segmenter_test.py"],
data = [
"//mediapipe/tasks/testdata/vision:test_images",
"//mediapipe/tasks/testdata/vision:test_models",
],
deps = [
"//mediapipe/python:_framework_bindings",
"//mediapipe/tasks/python/components/containers:keypoint",
"//mediapipe/tasks/python/components/containers:rect",
"//mediapipe/tasks/python/core:base_options",
"//mediapipe/tasks/python/test:test_utils",
"//mediapipe/tasks/python/vision:interactive_segmenter",
"//mediapipe/tasks/python/vision/core:image_processing_options",
"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
],
)
py_test(
name = "face_detector_test",
srcs = ["face_detector_test.py"],

186
mediapipe/tasks/python/test/vision/image_segmenter_test.py
View File

@ -30,10 +30,10 @@ from mediapipe.tasks.python.test import test_utils
from mediapipe.tasks.python.vision import image_segmenter
from mediapipe.tasks.python.vision.core import vision_task_running_mode
ImageSegmenterResult = image_segmenter.ImageSegmenterResult
_BaseOptions = base_options_module.BaseOptions
_Image = image_module.Image
_ImageFormat = image_frame.ImageFormat
_OutputType = image_segmenter.ImageSegmenterOptions.OutputType
_Activation = image_segmenter.ImageSegmenterOptions.Activation
_ImageSegmenter = image_segmenter.ImageSegmenter
_ImageSegmenterOptions = image_segmenter.ImageSegmenterOptions
@ -42,11 +42,33 @@ _RUNNING_MODE = vision_task_running_mode.VisionTaskRunningMode
_MODEL_FILE = 'deeplabv3.tflite'
_IMAGE_FILE = 'segmentation_input_rotation0.jpg'
_SEGMENTATION_FILE = 'segmentation_golden_rotation0.png'
_CAT_IMAGE = 'cat.jpg'
_CAT_MASK = 'cat_mask.jpg'
_MASK_MAGNIFICATION_FACTOR = 10
_MASK_SIMILARITY_THRESHOLD = 0.98
_TEST_DATA_DIR = 'mediapipe/tasks/testdata/vision'
def _calculate_soft_iou(m1, m2):
intersection_sum = np.sum(m1 * m2)
union_sum = np.sum(m1 * m1) + np.sum(m2 * m2) - intersection_sum
if union_sum > 0:
return intersection_sum / union_sum
else:
return 0
def _similar_to_float_mask(actual_mask, expected_mask, similarity_threshold):
actual_mask = actual_mask.numpy_view()
expected_mask = expected_mask.numpy_view() / 255.0
return (
actual_mask.shape == expected_mask.shape
and _calculate_soft_iou(actual_mask, expected_mask) > similarity_threshold
)
def _similar_to_uint8_mask(actual_mask, expected_mask):
actual_mask_pixels = actual_mask.numpy_view().flatten()
expected_mask_pixels = expected_mask.numpy_view().flatten()
@ -84,6 +106,14 @@ class ImageSegmenterTest(parameterized.TestCase):
self.model_path = test_utils.get_test_data_path(
os.path.join(_TEST_DATA_DIR, _MODEL_FILE))
def _load_segmentation_mask(self, file_path: str):
# Loads ground truth segmentation file.
gt_segmentation_data = cv2.imread(
test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, file_path)),
cv2.IMREAD_GRAYSCALE,
)
return _Image(_ImageFormat.GRAY8, gt_segmentation_data)
def test_create_from_file_succeeds_with_valid_model_path(self):
# Creates with default option and valid model file successfully.
with _ImageSegmenter.create_from_model_path(self.model_path) as segmenter:
@ -127,20 +157,19 @@ class ImageSegmenterTest(parameterized.TestCase):
raise ValueError('model_file_type is invalid.')
options = _ImageSegmenterOptions(
base_options=base_options, output_type=_OutputType.CATEGORY_MASK)
base_options=base_options, output_category_mask=True)
segmenter = _ImageSegmenter.create_from_options(options)
# Performs image segmentation on the input.
category_masks = segmenter.segment(self.test_image)
self.assertLen(category_masks, 1)
category_mask = category_masks[0]
segmentation_result = segmenter.segment(self.test_image)
category_mask = segmentation_result.category_mask
result_pixels = category_mask.numpy_view().flatten()
# Check if data type of `category_mask` is correct.
self.assertEqual(result_pixels.dtype, np.uint8)
self.assertTrue(
_similar_to_uint8_mask(category_masks[0], self.test_seg_image),
_similar_to_uint8_mask(category_mask, self.test_seg_image),
f'Number of pixels in the candidate mask differing from that of the '
f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
@ -152,67 +181,33 @@ class ImageSegmenterTest(parameterized.TestCase):
# Creates segmenter.
base_options = _BaseOptions(model_asset_path=self.model_path)
# Run segmentation on the model in CATEGORY_MASK mode.
options = _ImageSegmenterOptions(
base_options=base_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()
# Load the cat image.
test_image = _Image.create_from_file(
test_utils.get_test_data_path(
os.path.join(_TEST_DATA_DIR, _CAT_IMAGE)))
# Run segmentation on the model in CONFIDENCE_MASK mode.
options = _ImageSegmenterOptions(
base_options=base_options,
output_type=_OutputType.CONFIDENCE_MASK,
activation=_Activation.SOFTMAX)
segmenter = _ImageSegmenter.create_from_options(options)
confidence_masks = segmenter.segment(self.test_image)
with _ImageSegmenter.create_from_options(options) as segmenter:
segmentation_result = segmenter.segment(test_image)
confidence_masks = segmentation_result.confidence_masks
# Check if confidence mask shape is correct.
self.assertLen(
confidence_masks, 21,
'Number of confidence masks must match with number of categories.')
# Gather the confidence masks in a single array `confidence_mask_array`.
confidence_mask_array = np.array(
[confidence_mask.numpy_view() for confidence_mask in confidence_masks])
# Check if data type of `confidence_masks` are correct.
self.assertEqual(confidence_mask_array.dtype, np.float32)
# Compute the category mask from the created confidence mask.
calculated_category_mask = np.argmax(confidence_mask_array, axis=0)
self.assertListEqual(
calculated_category_mask.tolist(), category_mask.tolist(),
'Confidence mask does not match with the category mask.')
# Closes the segmenter explicitly when the segmenter is not used in
# a context.
segmenter.close()
@parameterized.parameters((ModelFileType.FILE_NAME),
(ModelFileType.FILE_CONTENT))
def test_segment_in_context(self, model_file_type):
if model_file_type is ModelFileType.FILE_NAME:
base_options = _BaseOptions(model_asset_path=self.model_path)
elif model_file_type is ModelFileType.FILE_CONTENT:
with open(self.model_path, 'rb') as f:
model_contents = f.read()
base_options = _BaseOptions(model_asset_buffer=model_contents)
else:
# Should never happen
raise ValueError('model_file_type is invalid.')
options = _ImageSegmenterOptions(
base_options=base_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)
self.assertLen(category_masks, 1)
# Loads ground truth segmentation file.
expected_mask = self._load_segmentation_mask(_CAT_MASK)
self.assertTrue(
_similar_to_uint8_mask(category_masks[0], self.test_seg_image),
f'Number of pixels in the candidate mask differing from that of the '
f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
_similar_to_float_mask(
confidence_masks[8], expected_mask, _MASK_SIMILARITY_THRESHOLD
)
)
def test_missing_result_callback(self):
options = _ImageSegmenterOptions(
@ -280,20 +275,49 @@ class ImageSegmenterTest(parameterized.TestCase):
ValueError, r'Input timestamp must be monotonically increasing'):
segmenter.segment_for_video(self.test_image, 0)
def test_segment_for_video(self):
def test_segment_for_video_in_category_mask_mode(self):
options = _ImageSegmenterOptions(
base_options=_BaseOptions(model_asset_path=self.model_path),
output_type=_OutputType.CATEGORY_MASK,
output_category_mask=True,
running_mode=_RUNNING_MODE.VIDEO)
with _ImageSegmenter.create_from_options(options) as segmenter:
for timestamp in range(0, 300, 30):
category_masks = segmenter.segment_for_video(self.test_image, timestamp)
self.assertLen(category_masks, 1)
segmentation_result = segmenter.segment_for_video(
self.test_image, timestamp)
category_mask = segmentation_result.category_mask
self.assertTrue(
_similar_to_uint8_mask(category_masks[0], self.test_seg_image),
_similar_to_uint8_mask(category_mask, self.test_seg_image),
f'Number of pixels in the candidate mask differing from that of the '
f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
def test_segment_for_video_in_confidence_mask_mode(self):
# Load the cat image.
test_image = _Image.create_from_file(
test_utils.get_test_data_path(
os.path.join(_TEST_DATA_DIR, _CAT_IMAGE)))
options = _ImageSegmenterOptions(
base_options=_BaseOptions(model_asset_path=self.model_path),
running_mode=_RUNNING_MODE.VIDEO)
with _ImageSegmenter.create_from_options(options) as segmenter:
for timestamp in range(0, 300, 30):
segmentation_result = segmenter.segment_for_video(
test_image, timestamp)
confidence_masks = segmentation_result.confidence_masks
# Check if confidence mask shape is correct.
self.assertLen(
confidence_masks, 21,
'Number of confidence masks must match with number of categories.')
# Loads ground truth segmentation file.
expected_mask = self._load_segmentation_mask(_CAT_MASK)
self.assertTrue(
_similar_to_float_mask(
confidence_masks[8], expected_mask, _MASK_SIMILARITY_THRESHOLD
)
)
def test_calling_segment_in_live_stream_mode(self):
options = _ImageSegmenterOptions(
base_options=_BaseOptions(model_asset_path=self.model_path),
@ -325,13 +349,13 @@ class ImageSegmenterTest(parameterized.TestCase):
ValueError, r'Input timestamp must be monotonically increasing'):
segmenter.segment_async(self.test_image, 0)
def test_segment_async_calls(self):
def test_segment_async_calls_in_category_mask_mode(self):
observed_timestamp_ms = -1
def check_result(result: List[image_module.Image], output_image: _Image,
def check_result(result: ImageSegmenterResult, output_image: _Image,
timestamp_ms: int):
# Get the output category mask.
category_mask = result[0]
category_mask = result.category_mask
self.assertEqual(output_image.width, self.test_image.width)
self.assertEqual(output_image.height, self.test_image.height)
self.assertEqual(output_image.width, self.test_seg_image.width)
@ -345,13 +369,49 @@ class ImageSegmenterTest(parameterized.TestCase):
options = _ImageSegmenterOptions(
base_options=_BaseOptions(model_asset_path=self.model_path),
output_type=_OutputType.CATEGORY_MASK,
output_category_mask=True,
running_mode=_RUNNING_MODE.LIVE_STREAM,
result_callback=check_result)
with _ImageSegmenter.create_from_options(options) as segmenter:
for timestamp in range(0, 300, 30):
segmenter.segment_async(self.test_image, timestamp)
def test_segment_async_calls_in_confidence_mask_mode(self):
# Load the cat image.
test_image = _Image.create_from_file(
test_utils.get_test_data_path(
os.path.join(_TEST_DATA_DIR, _CAT_IMAGE)))
# Loads ground truth segmentation file.
expected_mask = self._load_segmentation_mask(_CAT_MASK)
observed_timestamp_ms = -1
def check_result(result: ImageSegmenterResult, output_image: _Image,
timestamp_ms: int):
# Get the output category mask.
confidence_masks = result.confidence_masks
# Check if confidence mask shape is correct.
self.assertLen(
confidence_masks, 21,
'Number of confidence masks must match with number of categories.')
self.assertTrue(
_similar_to_float_mask(
confidence_masks[8], expected_mask, _MASK_SIMILARITY_THRESHOLD
)
)
self.assertLess(observed_timestamp_ms, timestamp_ms)
self.observed_timestamp_ms = timestamp_ms
options = _ImageSegmenterOptions(
base_options=_BaseOptions(model_asset_path=self.model_path),
running_mode=_RUNNING_MODE.LIVE_STREAM,
result_callback=check_result)
with _ImageSegmenter.create_from_options(options) as segmenter:
for timestamp in range(0, 300, 30):
segmenter.segment_async(test_image, timestamp)
if __name__ == '__main__':
absltest.main()

29
mediapipe/tasks/python/test/vision/interactive_segmenter_test.py
View File

@ -30,12 +30,12 @@ from mediapipe.tasks.python.test import test_utils
from mediapipe.tasks.python.vision import interactive_segmenter
from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
InteractiveSegmenterResult = interactive_segmenter.InteractiveSegmenterResult
_BaseOptions = base_options_module.BaseOptions
_Image = image_module.Image
_ImageFormat = image_frame.ImageFormat
_NormalizedKeypoint = keypoint_module.NormalizedKeypoint
_Rect = rect.Rect
_OutputType = interactive_segmenter.InteractiveSegmenterOptions.OutputType
_InteractiveSegmenter = interactive_segmenter.InteractiveSegmenter
_InteractiveSegmenterOptions = interactive_segmenter.InteractiveSegmenterOptions
_RegionOfInterest = interactive_segmenter.RegionOfInterest
@ -200,15 +200,14 @@ class InteractiveSegmenterTest(parameterized.TestCase):
raise ValueError('model_file_type is invalid.')
options = _InteractiveSegmenterOptions(
base_options=base_options, output_type=_OutputType.CATEGORY_MASK
base_options=base_options, output_category_mask=True
)
segmenter = _InteractiveSegmenter.create_from_options(options)
# Performs image segmentation on the input.
roi = _RegionOfInterest(format=roi_format, keypoint=keypoint)
category_masks = segmenter.segment(self.test_image, roi)
self.assertLen(category_masks, 1)
category_mask = category_masks[0]
segmentation_result = segmenter.segment(self.test_image, roi)
category_mask = segmentation_result.category_mask
result_pixels = category_mask.numpy_view().flatten()
# Check if data type of `category_mask` is correct.
@ -219,7 +218,7 @@ class InteractiveSegmenterTest(parameterized.TestCase):
self.assertTrue(
_similar_to_uint8_mask(
category_masks[0], test_seg_image, similarity_threshold
category_mask, test_seg_image, similarity_threshold
),
(
'Number of pixels in the candidate mask differing from that of the'
@ -253,13 +252,12 @@ class InteractiveSegmenterTest(parameterized.TestCase):
roi = _RegionOfInterest(format=roi_format, keypoint=keypoint)
# Run segmentation on the model in CONFIDENCE_MASK mode.
options = _InteractiveSegmenterOptions(
base_options=base_options, output_type=_OutputType.CONFIDENCE_MASK
)
options = _InteractiveSegmenterOptions(base_options=base_options)
with _InteractiveSegmenter.create_from_options(options) as segmenter:
# Perform segmentation
confidence_masks = segmenter.segment(self.test_image, roi)
segmentation_result = segmenter.segment(self.test_image, roi)
confidence_masks = segmentation_result.confidence_masks
# Check if confidence mask shape is correct.
self.assertLen(
@ -286,16 +284,15 @@ class InteractiveSegmenterTest(parameterized.TestCase):
)
# Run segmentation on the model in CONFIDENCE_MASK mode.
options = _InteractiveSegmenterOptions(
base_options=base_options, output_type=_OutputType.CONFIDENCE_MASK
)
options = _InteractiveSegmenterOptions(base_options=base_options)
with _InteractiveSegmenter.create_from_options(options) as segmenter:
# Perform segmentation
image_processing_options = _ImageProcessingOptions(rotation_degrees=-90)
confidence_masks = segmenter.segment(
segmentation_result = segmenter.segment(
self.test_image, roi, image_processing_options
)
confidence_masks = segmentation_result.confidence_masks
# Check if confidence mask shape is correct.
self.assertLen(
@ -313,9 +310,7 @@ class InteractiveSegmenterTest(parameterized.TestCase):
)
# Run segmentation on the model in CONFIDENCE_MASK mode.
options = _InteractiveSegmenterOptions(
base_options=base_options, output_type=_OutputType.CONFIDENCE_MASK
)
options = _InteractiveSegmenterOptions(base_options=base_options)
with self.assertRaisesRegex(
ValueError, "This task doesn't support region-of-interest."

90
mediapipe/tasks/python/vision/image_segmenter.py
View File

@ -31,7 +31,6 @@ from mediapipe.tasks.python.vision.core import base_vision_task_api
from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
from mediapipe.tasks.python.vision.core import vision_task_running_mode
ImageSegmenterResult = List[image_module.Image]
_NormalizedRect = rect.NormalizedRect
_BaseOptions = base_options_module.BaseOptions
_SegmenterOptionsProto = segmenter_options_pb2.SegmenterOptions
@ -42,8 +41,10 @@ _RunningMode = vision_task_running_mode.VisionTaskRunningMode
_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
_TaskInfo = task_info_module.TaskInfo
_SEGMENTATION_OUT_STREAM_NAME = 'segmented_mask_out'
_SEGMENTATION_TAG = 'GROUPED_SEGMENTATION'
_CONFIDENCE_MASKS_STREAM_NAME = 'confidence_masks'
_CONFIDENCE_MASKS_TAG = 'CONFIDENCE_MASKS'
_CATEGORY_MASK_STREAM_NAME = 'category_mask'
_CATEGORY_MASK_TAG = 'CATEGORY_MASK'
_IMAGE_IN_STREAM_NAME = 'image_in'
_IMAGE_OUT_STREAM_NAME = 'image_out'
_IMAGE_TAG = 'IMAGE'
@ -53,6 +54,12 @@ _TASK_GRAPH_NAME = 'mediapipe.tasks.vision.image_segmenter.ImageSegmenterGraph'
_MICRO_SECONDS_PER_MILLISECOND = 1000
@dataclasses.dataclass
class ImageSegmenterResult:
confidence_masks: Optional[List[image_module.Image]] = None
category_mask: Optional[image_module.Image] = None
@dataclasses.dataclass
class ImageSegmenterOptions:
"""Options for the image segmenter task.
@ -64,19 +71,13 @@ class ImageSegmenterOptions:
objects on single image inputs. 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.
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_confidence_masks: Whether to output confidence masks.
output_category_mask: Whether to output category mask.
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.
"""
class OutputType(enum.Enum):
UNSPECIFIED = 0
CATEGORY_MASK = 1
CONFIDENCE_MASK = 2
class Activation(enum.Enum):
NONE = 0
SIGMOID = 1
@ -84,7 +85,8 @@ class ImageSegmenterOptions:
base_options: _BaseOptions
running_mode: _RunningMode = _RunningMode.IMAGE
output_type: Optional[OutputType] = OutputType.CATEGORY_MASK
output_confidence_masks: bool = True
output_category_mask: bool = False
activation: Optional[Activation] = Activation.NONE
result_callback: Optional[
Callable[[ImageSegmenterResult, image_module.Image, int], None]
@ -98,7 +100,7 @@ class ImageSegmenterOptions:
False if self.running_mode == _RunningMode.IMAGE else True
)
segmenter_options_proto = _SegmenterOptionsProto(
output_type=self.output_type.value, activation=self.activation.value
activation=self.activation.value
)
return _ImageSegmenterGraphOptionsProto(
base_options=base_options_proto,
@ -177,27 +179,48 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
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(
output_packets[_SEGMENTATION_OUT_STREAM_NAME]
segmentation_result = ImageSegmenterResult()
if options.output_confidence_masks:
segmentation_result.confidence_masks = packet_getter.get_image_list(
output_packets[_CONFIDENCE_MASKS_STREAM_NAME]
)
if options.output_category_mask:
segmentation_result.category_mask = packet_getter.get_image(
output_packets[_CATEGORY_MASK_STREAM_NAME]
)
image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
timestamp = output_packets[_SEGMENTATION_OUT_STREAM_NAME].timestamp
timestamp = output_packets[_IMAGE_OUT_STREAM_NAME].timestamp
options.result_callback(
segmentation_result,
image,
timestamp.value // _MICRO_SECONDS_PER_MILLISECOND,
)
output_streams = [
':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
]
if options.output_confidence_masks:
output_streams.append(
':'.join([_CONFIDENCE_MASKS_TAG, _CONFIDENCE_MASKS_STREAM_NAME])
)
if options.output_category_mask:
output_streams.append(
':'.join([_CATEGORY_MASK_TAG, _CATEGORY_MASK_STREAM_NAME])
)
task_info = _TaskInfo(
task_graph=_TASK_GRAPH_NAME,
input_streams=[
':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
],
output_streams=[
':'.join([_SEGMENTATION_TAG, _SEGMENTATION_OUT_STREAM_NAME]),
':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
],
output_streams=output_streams,
task_options=options,
)
return cls(
@ -240,9 +263,18 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
normalized_rect.to_pb2()
),
})
segmentation_result = packet_getter.get_image_list(
output_packets[_SEGMENTATION_OUT_STREAM_NAME]
segmentation_result = ImageSegmenterResult()
if _CONFIDENCE_MASKS_STREAM_NAME in output_packets:
segmentation_result.confidence_masks = packet_getter.get_image_list(
output_packets[_CONFIDENCE_MASKS_STREAM_NAME]
)
if _CATEGORY_MASK_STREAM_NAME in output_packets:
segmentation_result.category_mask = packet_getter.get_image(
output_packets[_CATEGORY_MASK_STREAM_NAME]
)
return segmentation_result
def segment_for_video(
@ -285,9 +317,19 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
normalized_rect.to_pb2()
).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
})
segmentation_result = packet_getter.get_image_list(
output_packets[_SEGMENTATION_OUT_STREAM_NAME]
segmentation_result = ImageSegmenterResult()
if _CONFIDENCE_MASKS_STREAM_NAME in output_packets:
segmentation_result.confidence_masks = packet_getter.get_image_list(
output_packets[_CONFIDENCE_MASKS_STREAM_NAME]
)
if _CATEGORY_MASK_STREAM_NAME in output_packets:
segmentation_result.category_mask = packet_getter.get_image(
output_packets[_CATEGORY_MASK_STREAM_NAME]
)
return segmentation_result
def segment_async(

62
mediapipe/tasks/python/vision/interactive_segmenter.py
View File

@ -41,8 +41,10 @@ _RunningMode = vision_task_running_mode.VisionTaskRunningMode
_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
_TaskInfo = task_info_module.TaskInfo
_SEGMENTATION_OUT_STREAM_NAME = 'segmented_mask_out'
_SEGMENTATION_TAG = 'GROUPED_SEGMENTATION'
_CONFIDENCE_MASKS_STREAM_NAME = 'confidence_masks'
_CONFIDENCE_MASKS_TAG = 'CONFIDENCE_MASKS'
_CATEGORY_MASK_STREAM_NAME = 'category_mask'
_CATEGORY_MASK_TAG = 'CATEGORY_MASK'
_IMAGE_IN_STREAM_NAME = 'image_in'
_IMAGE_OUT_STREAM_NAME = 'image_out'
_ROI_STREAM_NAME = 'roi_in'
@ -55,32 +57,32 @@ _TASK_GRAPH_NAME = (
)
@dataclasses.dataclass
class InteractiveSegmenterResult:
confidence_masks: Optional[List[image_module.Image]] = None
category_mask: Optional[image_module.Image] = None
@dataclasses.dataclass
class InteractiveSegmenterOptions:
"""Options for the interactive segmenter task.
Attributes:
base_options: Base options for the interactive segmenter task.
output_type: The output mask type allows specifying the type of
post-processing to perform on the raw model results.
output_confidence_masks: Whether to output confidence masks.
output_category_mask: Whether to output category mask.
"""
class OutputType(enum.Enum):
UNSPECIFIED = 0
CATEGORY_MASK = 1
CONFIDENCE_MASK = 2
base_options: _BaseOptions
output_type: Optional[OutputType] = OutputType.CATEGORY_MASK
output_confidence_masks: bool = True
output_category_mask: bool = False
@doc_controls.do_not_generate_docs
def to_pb2(self) -> _ImageSegmenterGraphOptionsProto:
"""Generates an InteractiveSegmenterOptions protobuf object."""
base_options_proto = self.base_options.to_pb2()
base_options_proto.use_stream_mode = False
segmenter_options_proto = _SegmenterOptionsProto(
output_type=self.output_type.value
)
segmenter_options_proto = _SegmenterOptionsProto()
return _ImageSegmenterGraphOptionsProto(
base_options=base_options_proto,
segmenter_options=segmenter_options_proto,
@ -192,6 +194,20 @@ class InteractiveSegmenter(base_vision_task_api.BaseVisionTaskApi):
RuntimeError: If other types of error occurred.
"""
output_streams = [
':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
]
if options.output_confidence_masks:
output_streams.append(
':'.join([_CONFIDENCE_MASKS_TAG, _CONFIDENCE_MASKS_STREAM_NAME])
)
if options.output_category_mask:
output_streams.append(
':'.join([_CATEGORY_MASK_TAG, _CATEGORY_MASK_STREAM_NAME])
)
task_info = _TaskInfo(
task_graph=_TASK_GRAPH_NAME,
input_streams=[
@ -199,10 +215,7 @@ class InteractiveSegmenter(base_vision_task_api.BaseVisionTaskApi):
':'.join([_ROI_TAG, _ROI_STREAM_NAME]),
':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
],
output_streams=[
':'.join([_SEGMENTATION_TAG, _SEGMENTATION_OUT_STREAM_NAME]),
':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
],
output_streams=output_streams,
task_options=options,
)
return cls(
@ -216,7 +229,7 @@ class InteractiveSegmenter(base_vision_task_api.BaseVisionTaskApi):
image: image_module.Image,
roi: RegionOfInterest,
image_processing_options: Optional[_ImageProcessingOptions] = None,
) -> List[image_module.Image]:
) -> InteractiveSegmenterResult:
"""Performs the actual segmentation task on the provided MediaPipe Image.
The image can be of any size with format RGB.
@ -248,7 +261,16 @@ class InteractiveSegmenter(base_vision_task_api.BaseVisionTaskApi):
normalized_rect.to_pb2()
),
})
segmentation_result = packet_getter.get_image_list(
output_packets[_SEGMENTATION_OUT_STREAM_NAME]
segmentation_result = InteractiveSegmenterResult()
if _CONFIDENCE_MASKS_STREAM_NAME in output_packets:
segmentation_result.confidence_masks = packet_getter.get_image_list(
output_packets[_CONFIDENCE_MASKS_STREAM_NAME]
)
if _CATEGORY_MASK_STREAM_NAME in output_packets:
segmentation_result.category_mask = packet_getter.get_image(
output_packets[_CATEGORY_MASK_STREAM_NAME]
)
return segmentation_result

2
mediapipe/tasks/testdata/vision/BUILD vendored
View File

@ -77,6 +77,7 @@ mediapipe_files(srcs = [
"portrait_selfie_segmentation_landscape_expected_category_mask.jpg",
"pose.jpg",
"pose_detection.tflite",
"ptm_512_hdt_ptm_woid.tflite",
"pose_landmark_lite.tflite",
"pose_landmarker.task",
"right_hands.jpg",
@ -187,6 +188,7 @@ filegroup(
"mobilenet_v3_small_100_224_embedder.tflite",
"palm_detection_full.tflite",
"pose_detection.tflite",
"ptm_512_hdt_ptm_woid.tflite",
"pose_landmark_lite.tflite",
"pose_landmarker.task",
"selfie_segm_128_128_3.tflite",