Merge pull request #4302 from kinaryml:segmenter-python-add-labels
PiperOrigin-RevId: 525571089
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44aa607e06
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@ -45,6 +45,29 @@ _CAT_MASK = 'cat_mask.jpg'
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_MASK_MAGNIFICATION_FACTOR = 10
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_MASK_SIMILARITY_THRESHOLD = 0.98
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_TEST_DATA_DIR = 'mediapipe/tasks/testdata/vision'
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_EXPECTED_LABELS = [
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'background',
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'aeroplane',
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'bicycle',
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'bird',
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'boat',
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'bottle',
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'bus',
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'car',
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'cat',
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'chair',
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'cow',
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'dining table',
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'dog',
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'horse',
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'motorbike',
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'person',
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'potted plant',
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'sheep',
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'sofa',
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'train',
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'tv',
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]
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def _calculate_soft_iou(m1, m2):
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@ -224,6 +247,20 @@ class ImageSegmenterTest(parameterized.TestCase):
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)
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)
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@parameterized.parameters((True, False), (False, True))
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def test_labels_succeeds(self, output_category_mask, output_confidence_masks):
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expected_labels = _EXPECTED_LABELS
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base_options = _BaseOptions(model_asset_path=self.model_path)
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options = _ImageSegmenterOptions(
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base_options=base_options,
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output_category_mask=output_category_mask,
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output_confidence_masks=output_confidence_masks,
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)
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with _ImageSegmenter.create_from_options(options) as segmenter:
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# Performs image segmentation on the input.
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actual_labels = segmenter.labels
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self.assertListEqual(actual_labels, expected_labels)
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def test_missing_result_callback(self):
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options = _ImageSegmenterOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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@ -71,6 +71,7 @@ py_library(
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"//mediapipe/python:_framework_bindings",
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"//mediapipe/python:packet_creator",
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"//mediapipe/python:packet_getter",
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"//mediapipe/tasks/cc/vision/image_segmenter/calculators:tensors_to_segmentation_calculator_py_pb2",
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"//mediapipe/tasks/cc/vision/image_segmenter/proto:image_segmenter_graph_options_py_pb2",
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"//mediapipe/tasks/cc/vision/image_segmenter/proto:segmenter_options_py_pb2",
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"//mediapipe/tasks/python/components/containers:rect",
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@ -20,6 +20,7 @@ from mediapipe.python import packet_creator
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from mediapipe.python import packet_getter
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from mediapipe.python._framework_bindings import image as image_module
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from mediapipe.python._framework_bindings import packet
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from mediapipe.tasks.cc.vision.image_segmenter.calculators import tensors_to_segmentation_calculator_pb2
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from mediapipe.tasks.cc.vision.image_segmenter.proto import image_segmenter_graph_options_pb2
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from mediapipe.tasks.cc.vision.image_segmenter.proto import segmenter_options_pb2
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from mediapipe.tasks.python.components.containers import rect
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@ -36,6 +37,9 @@ _SegmenterOptionsProto = segmenter_options_pb2.SegmenterOptions
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_ImageSegmenterGraphOptionsProto = (
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image_segmenter_graph_options_pb2.ImageSegmenterGraphOptions
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)
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TensorsToSegmentationCalculatorOptionsProto = (
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tensors_to_segmentation_calculator_pb2.TensorsToSegmentationCalculatorOptions
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)
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_RunningMode = vision_task_running_mode.VisionTaskRunningMode
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_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
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_TaskInfo = task_info_module.TaskInfo
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@ -49,6 +53,9 @@ _IMAGE_OUT_STREAM_NAME = 'image_out'
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_IMAGE_TAG = 'IMAGE'
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_NORM_RECT_STREAM_NAME = 'norm_rect_in'
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_NORM_RECT_TAG = 'NORM_RECT'
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_TENSORS_TO_SEGMENTATION_CALCULATOR_NAME = (
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'mediapipe.tasks.TensorsToSegmentationCalculator'
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)
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_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.image_segmenter.ImageSegmenterGraph'
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_MICRO_SECONDS_PER_MILLISECOND = 1000
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@ -124,8 +131,8 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
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Output tensors:
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(kTfLiteUInt8/kTfLiteFloat32)
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- list of segmented masks.
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- if `output_type` is CATEGORY_MASK, uint8 Image, Image vector of size 1.
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- if `output_type` is CONFIDENCE_MASK, float32 Image list of size
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- if `output_category_mask` is True, uint8 Image, Image vector of size 1.
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- if `output_confidence_masks` is True, float32 Image list of size
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`channels`.
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- batch is always 1
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@ -133,6 +140,41 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
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https://tfhub.dev/tensorflow/lite-model/deeplabv3/1/metadata/2
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"""
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def __init__(self, graph_config, running_mode, packet_callback) -> None:
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"""Initializes the `ImageSegmenter` object."""
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super(ImageSegmenter, self).__init__(
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graph_config, running_mode, packet_callback
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)
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self._populate_labels()
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def _populate_labels(self) -> None:
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"""Populate the labelmap in TensorsToSegmentationCalculator to labels field.
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Raises:
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ValueError if there is an error during finding
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TensorsToSegmentationCalculator.
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"""
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self._labels = []
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graph_config = self._runner.get_graph_config()
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found_tensors_to_segmentation = False
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for node in graph_config.node:
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if _TENSORS_TO_SEGMENTATION_CALCULATOR_NAME in node.name:
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if found_tensors_to_segmentation:
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raise ValueError(
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'The graph has more than one '
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f'{_TENSORS_TO_SEGMENTATION_CALCULATOR_NAME}.'
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)
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found_tensors_to_segmentation = True
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options = node.options.Extensions[
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TensorsToSegmentationCalculatorOptionsProto.ext
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]
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if options.label_items:
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for i in range(len(options.label_items)):
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if i not in options.label_items:
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raise ValueError(f'The labelmap has no expected key: {i}.')
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self._labels.append(options.label_items[i].name)
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@classmethod
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def create_from_model_path(cls, model_path: str) -> 'ImageSegmenter':
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"""Creates an `ImageSegmenter` object from a TensorFlow Lite model and the default `ImageSegmenterOptions`.
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@ -375,3 +417,17 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
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normalized_rect.to_pb2()
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).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
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})
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@property
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def labels(self) -> List[str]:
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"""Get the category label list the ImageSegmenter can recognize.
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For CATEGORY_MASK type, the index in the category mask corresponds to the
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category in the label list.
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For CONFIDENCE_MASK type, the output mask list at index corresponds to the
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category in the label list.
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If there is no label map provided in the model file, empty label list is
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returned.
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"""
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return self._labels
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