Revised face landmarker implementation and tests
This commit is contained in:
parent
4a7489cd3a
commit
23681cde0d
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@ -17,6 +17,7 @@ import dataclasses
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import enum
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import enum
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from typing import Any, Optional
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from typing import Any, Optional
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import numpy as np
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from mediapipe.framework.formats import matrix_data_pb2
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from mediapipe.framework.formats import matrix_data_pb2
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from mediapipe.tasks.python.core.optional_dependencies import doc_controls
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from mediapipe.tasks.python.core.optional_dependencies import doc_controls
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@ -32,7 +33,7 @@ class MatrixData:
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Attributes:
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Attributes:
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rows: The number of rows in the matrix.
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rows: The number of rows in the matrix.
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cols: The number of columns in the matrix.
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cols: The number of columns in the matrix.
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data: The data stored in the matrix.
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data: The data stored in the matrix as a NumPy array.
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layout: The order in which the data are stored. Defaults to COLUMN_MAJOR.
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layout: The order in which the data are stored. Defaults to COLUMN_MAJOR.
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"""
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"""
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@ -40,10 +41,10 @@ class MatrixData:
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COLUMN_MAJOR = 0
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COLUMN_MAJOR = 0
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ROW_MAJOR = 1
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ROW_MAJOR = 1
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rows: Optional[int] = None
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rows: int = None
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cols: Optional[int] = None
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cols: int = None
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data: Optional[float] = None
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data: np.ndarray = None
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layout: Optional[Layout] = None
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layout: Optional[Layout] = Layout.COLUMN_MAJOR
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@doc_controls.do_not_generate_docs
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _MatrixDataProto:
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def to_pb2(self) -> _MatrixDataProto:
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@ -51,7 +52,7 @@ class MatrixData:
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return _MatrixDataProto(
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return _MatrixDataProto(
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rows=self.rows,
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rows=self.rows,
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cols=self.cols,
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cols=self.cols,
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data=self.data,
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data=self.data.tolist(),
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layout=self.layout)
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layout=self.layout)
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@classmethod
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@classmethod
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@ -61,7 +62,7 @@ class MatrixData:
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return MatrixData(
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return MatrixData(
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rows=pb2_obj.rows,
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rows=pb2_obj.rows,
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cols=pb2_obj.cols,
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cols=pb2_obj.cols,
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data=pb2_obj.data,
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data=np.array(pb2_obj.data),
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layout=pb2_obj.layout)
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layout=pb2_obj.layout)
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def __eq__(self, other: Any) -> bool:
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def __eq__(self, other: Any) -> bool:
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@ -126,10 +126,10 @@ py_test(
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deps = [
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deps = [
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"//mediapipe/python:_framework_bindings",
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"//mediapipe/python:_framework_bindings",
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"//mediapipe/framework/formats:landmark_py_pb2",
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"//mediapipe/framework/formats:landmark_py_pb2",
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"//mediapipe/framework/formats:classification_py_pb2",
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"//mediapipe/tasks/python/components/containers:category",
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"//mediapipe/tasks/python/components/containers:category",
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"//mediapipe/tasks/python/components/containers:landmark",
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"//mediapipe/tasks/python/components/containers:landmark",
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"//mediapipe/tasks/python/components/containers:rect",
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"//mediapipe/tasks/python/components/containers:rect",
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"//mediapipe/tasks/python/components/containers:classification_result",
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"//mediapipe/tasks/python/components/containers:matrix_data",
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"//mediapipe/tasks/python/components/containers:matrix_data",
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"//mediapipe/tasks/python/core:base_options",
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"//mediapipe/tasks/python/core:base_options",
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"//mediapipe/tasks/python/test:test_utils",
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"//mediapipe/tasks/python/test:test_utils",
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@ -22,11 +22,12 @@ import numpy as np
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from google.protobuf import text_format
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from google.protobuf import text_format
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from mediapipe.framework.formats import landmark_pb2
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from mediapipe.framework.formats import landmark_pb2
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from mediapipe.framework.formats import classification_pb2
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from mediapipe.python._framework_bindings import image as image_module
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from mediapipe.python._framework_bindings import image as image_module
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from mediapipe.tasks.python.components.containers import category as category_module
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from mediapipe.tasks.python.components.containers import category as category_module
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from mediapipe.tasks.python.components.containers import landmark as landmark_module
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from mediapipe.tasks.python.components.containers import landmark as landmark_module
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from mediapipe.tasks.python.components.containers import matrix_data as matrix_data_module
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from mediapipe.tasks.python.components.containers import rect as rect_module
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from mediapipe.tasks.python.components.containers import rect as rect_module
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from mediapipe.tasks.python.components.containers import classification_result as classification_result_module
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from mediapipe.tasks.python.core import base_options as base_options_module
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from mediapipe.tasks.python.core import base_options as base_options_module
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from mediapipe.tasks.python.test import test_utils
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from mediapipe.tasks.python.test import test_utils
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from mediapipe.tasks.python.vision import face_landmarker
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from mediapipe.tasks.python.vision import face_landmarker
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@ -38,6 +39,7 @@ _BaseOptions = base_options_module.BaseOptions
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_Category = category_module.Category
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_Category = category_module.Category
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_Rect = rect_module.Rect
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_Rect = rect_module.Rect
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_Landmark = landmark_module.Landmark
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_Landmark = landmark_module.Landmark
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_MatrixData = matrix_data_module.MatrixData
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_NormalizedLandmark = landmark_module.NormalizedLandmark
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_NormalizedLandmark = landmark_module.NormalizedLandmark
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_Image = image_module.Image
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_Image = image_module.Image
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_FaceLandmarker = face_landmarker.FaceLandmarker
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_FaceLandmarker = face_landmarker.FaceLandmarker
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@ -51,6 +53,7 @@ _PORTRAIT_IMAGE = 'portrait.jpg'
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_PORTRAIT_EXPECTED_FACE_LANDMARKS = 'portrait_expected_face_landmarks.pbtxt'
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_PORTRAIT_EXPECTED_FACE_LANDMARKS = 'portrait_expected_face_landmarks.pbtxt'
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_PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION = 'portrait_expected_face_landmarks_with_attention.pbtxt'
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_PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION = 'portrait_expected_face_landmarks_with_attention.pbtxt'
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_PORTRAIT_EXPECTED_BLENDSHAPES = 'portrait_expected_blendshapes_with_attention.pbtxt'
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_PORTRAIT_EXPECTED_BLENDSHAPES = 'portrait_expected_blendshapes_with_attention.pbtxt'
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_PORTRAIT_EXPECTED_FACE_GEOMETRY = 'portrait_expected_face_geometry_with_attention.pbtxt'
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_LANDMARKS_DIFF_MARGIN = 0.03
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_LANDMARKS_DIFF_MARGIN = 0.03
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_BLENDSHAPES_DIFF_MARGIN = 0.1
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_BLENDSHAPES_DIFF_MARGIN = 0.1
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_FACIAL_TRANSFORMATION_MATRIX_DIFF_MARGIN = 0.02
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_FACIAL_TRANSFORMATION_MATRIX_DIFF_MARGIN = 0.02
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@ -61,10 +64,40 @@ def _get_expected_face_landmarks(file_path: str):
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with open(proto_file_path, 'rb') as f:
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with open(proto_file_path, 'rb') as f:
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proto = landmark_pb2.NormalizedLandmarkList()
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proto = landmark_pb2.NormalizedLandmarkList()
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text_format.Parse(f.read(), proto)
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text_format.Parse(f.read(), proto)
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landmarks = []
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face_landmarks = []
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for landmark in proto.landmark:
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for landmark in proto.landmark:
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landmarks.append(_NormalizedLandmark.create_from_pb2(landmark))
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face_landmarks.append(_NormalizedLandmark.create_from_pb2(landmark))
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return landmarks
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return face_landmarks
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def _get_expected_face_blendshapes(file_path: str):
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proto_file_path = test_utils.get_test_data_path(file_path)
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with open(proto_file_path, 'rb') as f:
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proto = classification_pb2.ClassificationList()
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text_format.Parse(f.read(), proto)
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face_blendshapes_categories = []
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face_blendshapes_classifications = classification_pb2.ClassificationList()
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face_blendshapes_classifications.MergeFrom(proto)
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for face_blendshapes in face_blendshapes_classifications.classification:
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face_blendshapes_categories.append(
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category_module.Category(
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index=face_blendshapes.index,
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score=face_blendshapes.score,
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display_name=face_blendshapes.display_name,
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category_name=face_blendshapes.label))
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return face_blendshapes_categories
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def _make_expected_facial_transformation_matrixes():
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data = np.array([[0.9995292, -0.005092691, 0.030254554, -0.37340546],
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[0.0072318087, 0.99744856, -0.07102106, 22.212194],
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[-0.029815676, 0.07120642, 0.9970159, -64.76358],
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[0, 0, 0, 1]])
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rows, cols = len(data), len(data[0])
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facial_transformation_matrixes_results = []
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facial_transformation_matrix = _MatrixData(rows, cols, data)
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facial_transformation_matrixes_results.append(facial_transformation_matrix)
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return facial_transformation_matrixes_results
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class ModelFileType(enum.Enum):
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class ModelFileType(enum.Enum):
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@ -148,30 +181,82 @@ class HandLandmarkerTest(parameterized.TestCase):
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self.assertIsInstance(landmarker, _FaceLandmarker)
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self.assertIsInstance(landmarker, _FaceLandmarker)
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@parameterized.parameters(
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@parameterized.parameters(
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(ModelFileType.FILE_NAME, _FACE_LANDMARKER_BUNDLE_ASSET_FILE,
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_get_expected_face_landmarks(
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_PORTRAIT_EXPECTED_FACE_LANDMARKS), None, None),
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(ModelFileType.FILE_CONTENT, _FACE_LANDMARKER_BUNDLE_ASSET_FILE,
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_get_expected_face_landmarks(
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_PORTRAIT_EXPECTED_FACE_LANDMARKS), None, None),
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(ModelFileType.FILE_NAME,
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(ModelFileType.FILE_NAME,
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_get_expected_face_landmarks(_PORTRAIT_EXPECTED_FACE_LANDMARKS)),
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_FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE,
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_get_expected_face_landmarks(
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_PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION), None, None),
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(ModelFileType.FILE_CONTENT,
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(ModelFileType.FILE_CONTENT,
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_get_expected_face_landmarks(_PORTRAIT_EXPECTED_FACE_LANDMARKS)))
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_FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE,
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def test_detect(self, model_file_type, expected_face_landmarks):
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_get_expected_face_landmarks(
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_PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION), None, None),
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(ModelFileType.FILE_NAME,
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_FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE,
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_get_expected_face_landmarks(
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_PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION),
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_get_expected_face_blendshapes(
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_PORTRAIT_EXPECTED_BLENDSHAPES), None),
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(ModelFileType.FILE_CONTENT,
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_FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE,
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_get_expected_face_landmarks(
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_PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION),
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_get_expected_face_blendshapes(
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_PORTRAIT_EXPECTED_BLENDSHAPES), None),
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# (ModelFileType.FILE_NAME,
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# _FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE,
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# _get_expected_face_landmarks(
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# _PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION),
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# _get_expected_face_blendshapes(
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# _PORTRAIT_EXPECTED_BLENDSHAPES),
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# _make_expected_facial_transformation_matrixes()),
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# (ModelFileType.FILE_CONTENT,
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# _FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE,
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# _get_expected_face_landmarks(
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# _PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION),
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# _get_expected_face_blendshapes(
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# _PORTRAIT_EXPECTED_BLENDSHAPES),
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# _make_expected_facial_transformation_matrixes())
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)
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def test_detect(self, model_file_type, model_name, expected_face_landmarks,
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expected_face_blendshapes, expected_facial_transformation_matrix):
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# Creates face landmarker.
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# Creates face landmarker.
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model_path = test_utils.get_test_data_path(model_name)
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if model_file_type is ModelFileType.FILE_NAME:
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if model_file_type is ModelFileType.FILE_NAME:
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base_options = _BaseOptions(model_asset_path=self.model_path)
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base_options = _BaseOptions(model_asset_path=model_path)
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elif model_file_type is ModelFileType.FILE_CONTENT:
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elif model_file_type is ModelFileType.FILE_CONTENT:
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with open(self.model_path, 'rb') as f:
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with open(model_path, 'rb') as f:
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model_content = f.read()
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model_content = f.read()
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base_options = _BaseOptions(model_asset_buffer=model_content)
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base_options = _BaseOptions(model_asset_buffer=model_content)
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else:
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else:
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# Should never happen
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# Should never happen
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raise ValueError('model_file_type is invalid.')
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raise ValueError('model_file_type is invalid.')
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options = _FaceLandmarkerOptions(base_options=base_options)
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options = _FaceLandmarkerOptions(
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base_options=base_options,
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output_face_blendshapes=True if expected_face_blendshapes else False,
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output_facial_transformation_matrixes=True
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if expected_facial_transformation_matrix else False)
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landmarker = _FaceLandmarker.create_from_options(options)
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landmarker = _FaceLandmarker.create_from_options(options)
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# Performs face landmarks detection on the input.
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# Performs face landmarks detection on the input.
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detection_result = landmarker.detect(self.test_image)
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detection_result = landmarker.detect(self.test_image)
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# Comparing results.
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# Comparing results.
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self._expect_landmarks_correct(detection_result.face_landmarks[0],
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if expected_face_landmarks is not None:
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expected_face_landmarks)
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self._expect_landmarks_correct(detection_result.face_landmarks[0],
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expected_face_landmarks)
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if expected_face_blendshapes is not None:
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self._expect_blendshapes_correct(detection_result.face_blendshapes[0],
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expected_face_blendshapes)
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if expected_facial_transformation_matrix is not None:
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self._expect_facial_transformation_matrix_correct(
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detection_result.facial_transformation_matrixes[0],
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expected_facial_transformation_matrix)
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# Closes the face landmarker explicitly when the face landmarker is not used
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# Closes the face landmarker explicitly when the face landmarker is not used
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# in a context.
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# in a context.
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landmarker.close()
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landmarker.close()
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@ -162,6 +162,7 @@ def _build_landmarker_result(
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facial_transformation_matrixes_results = []
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facial_transformation_matrixes_results = []
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if _FACE_GEOMETRY_STREAM_NAME in output_packets:
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if _FACE_GEOMETRY_STREAM_NAME in output_packets:
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print(output_packets[_FACE_GEOMETRY_STREAM_NAME])
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facial_transformation_matrixes_proto_list = packet_getter.get_proto_list(
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facial_transformation_matrixes_proto_list = packet_getter.get_proto_list(
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output_packets[_FACE_GEOMETRY_STREAM_NAME])
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output_packets[_FACE_GEOMETRY_STREAM_NAME])
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for proto in facial_transformation_matrixes_proto_list:
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for proto in facial_transformation_matrixes_proto_list:
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2
mediapipe/tasks/testdata/vision/BUILD
vendored
2
mediapipe/tasks/testdata/vision/BUILD
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@ -156,6 +156,7 @@ filegroup(
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"face_landmark.tflite",
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"face_landmark.tflite",
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"face_landmark_with_attention.tflite",
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"face_landmark_with_attention.tflite",
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"face_landmarker.task",
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"face_landmarker.task",
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"face_landmarker_with_blendshapes.task",
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"hair_segmentation.tflite",
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"hair_segmentation.tflite",
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"hand_landmark_full.tflite",
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"hand_landmark_full.tflite",
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"hand_landmark_lite.tflite",
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"hand_landmark_lite.tflite",
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@ -191,6 +192,7 @@ filegroup(
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"pointing_up_landmarks.pbtxt",
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"pointing_up_landmarks.pbtxt",
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"pointing_up_rotated_landmarks.pbtxt",
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"pointing_up_rotated_landmarks.pbtxt",
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"portrait_expected_detection.pbtxt",
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"portrait_expected_detection.pbtxt",
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"portrait_expected_blendshapes_with_attention.pbtxt",
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"portrait_expected_face_geometry_with_attention.pbtxt",
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"portrait_expected_face_geometry_with_attention.pbtxt",
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"portrait_expected_face_landmarks.pbtxt",
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"portrait_expected_face_landmarks.pbtxt",
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"portrait_expected_face_landmarks_with_attention.pbtxt",
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"portrait_expected_face_landmarks_with_attention.pbtxt",
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