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Revised face landmarker implementation and tests

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This commit is contained in:
kinaryml 2023-03-14 00:37:32 -07:00
parent 4a7489cd3a
commit 23681cde0d
5 changed files with 109 additions and 20 deletions
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15
mediapipe/tasks/python/components/containers/matrix_data.py
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@ -17,6 +17,7 @@ import dataclasses
import enum
from typing import Any, Optional
import numpy as np
from mediapipe.framework.formats import matrix_data_pb2
from mediapipe.tasks.python.core.optional_dependencies import doc_controls
@ -32,7 +33,7 @@ class MatrixData:
Attributes:
rows: The number of rows in the matrix.
cols: The number of columns in the matrix.
data: The data stored in the matrix.
data: The data stored in the matrix as a NumPy array.
layout: The order in which the data are stored. Defaults to COLUMN_MAJOR.
"""
@ -40,10 +41,10 @@ class MatrixData:
COLUMN_MAJOR = 0
ROW_MAJOR = 1
rows: Optional[int] = None
cols: Optional[int] = None
data: Optional[float] = None
layout: Optional[Layout] = None
rows: int = None
cols: int = None
data: np.ndarray = None
layout: Optional[Layout] = Layout.COLUMN_MAJOR
@doc_controls.do_not_generate_docs
def to_pb2(self) -> _MatrixDataProto:
@ -51,7 +52,7 @@ class MatrixData:
return _MatrixDataProto(
rows=self.rows,
cols=self.cols,
data=self.data,
data=self.data.tolist(),
layout=self.layout)
@classmethod
@ -61,7 +62,7 @@ class MatrixData:
return MatrixData(
rows=pb2_obj.rows,
cols=pb2_obj.cols,
data=pb2_obj.data,
data=np.array(pb2_obj.data),
layout=pb2_obj.layout)
def __eq__(self, other: Any) -> bool:

2
mediapipe/tasks/python/test/vision/BUILD
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@ -126,10 +126,10 @@ py_test(
deps = [
"//mediapipe/python:_framework_bindings",
"//mediapipe/framework/formats:landmark_py_pb2",
"//mediapipe/framework/formats:classification_py_pb2",
"//mediapipe/tasks/python/components/containers:category",
"//mediapipe/tasks/python/components/containers:landmark",
"//mediapipe/tasks/python/components/containers:rect",
"//mediapipe/tasks/python/components/containers:classification_result",
"//mediapipe/tasks/python/components/containers:matrix_data",
"//mediapipe/tasks/python/core:base_options",
"//mediapipe/tasks/python/test:test_utils",

109
mediapipe/tasks/python/test/vision/face_landmarker_test.py
View File

@ -22,11 +22,12 @@ import numpy as np
from google.protobuf import text_format
from mediapipe.framework.formats import landmark_pb2
from mediapipe.framework.formats import classification_pb2
from mediapipe.python._framework_bindings import image as image_module
from mediapipe.tasks.python.components.containers import category as category_module
from mediapipe.tasks.python.components.containers import landmark as landmark_module
from mediapipe.tasks.python.components.containers import matrix_data as matrix_data_module
from mediapipe.tasks.python.components.containers import rect as rect_module
from mediapipe.tasks.python.components.containers import classification_result as classification_result_module
from mediapipe.tasks.python.core import base_options as base_options_module
from mediapipe.tasks.python.test import test_utils
from mediapipe.tasks.python.vision import face_landmarker
@ -38,6 +39,7 @@ _BaseOptions = base_options_module.BaseOptions
_Category = category_module.Category
_Rect = rect_module.Rect
_Landmark = landmark_module.Landmark
_MatrixData = matrix_data_module.MatrixData
_NormalizedLandmark = landmark_module.NormalizedLandmark
_Image = image_module.Image
_FaceLandmarker = face_landmarker.FaceLandmarker
@ -51,6 +53,7 @@ _PORTRAIT_IMAGE = 'portrait.jpg'
_PORTRAIT_EXPECTED_FACE_LANDMARKS = 'portrait_expected_face_landmarks.pbtxt'
_PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION = 'portrait_expected_face_landmarks_with_attention.pbtxt'
_PORTRAIT_EXPECTED_BLENDSHAPES = 'portrait_expected_blendshapes_with_attention.pbtxt'
_PORTRAIT_EXPECTED_FACE_GEOMETRY = 'portrait_expected_face_geometry_with_attention.pbtxt'
_LANDMARKS_DIFF_MARGIN = 0.03
_BLENDSHAPES_DIFF_MARGIN = 0.1
_FACIAL_TRANSFORMATION_MATRIX_DIFF_MARGIN = 0.02
@ -61,10 +64,40 @@ def _get_expected_face_landmarks(file_path: str):
with open(proto_file_path, 'rb') as f:
proto = landmark_pb2.NormalizedLandmarkList()
text_format.Parse(f.read(), proto)
landmarks = []
face_landmarks = []
for landmark in proto.landmark:
landmarks.append(_NormalizedLandmark.create_from_pb2(landmark))
return landmarks
face_landmarks.append(_NormalizedLandmark.create_from_pb2(landmark))
return face_landmarks
def _get_expected_face_blendshapes(file_path: str):
proto_file_path = test_utils.get_test_data_path(file_path)
with open(proto_file_path, 'rb') as f:
proto = classification_pb2.ClassificationList()
text_format.Parse(f.read(), proto)
face_blendshapes_categories = []
face_blendshapes_classifications = classification_pb2.ClassificationList()
face_blendshapes_classifications.MergeFrom(proto)
for face_blendshapes in face_blendshapes_classifications.classification:
face_blendshapes_categories.append(
category_module.Category(
index=face_blendshapes.index,
score=face_blendshapes.score,
display_name=face_blendshapes.display_name,
category_name=face_blendshapes.label))
return face_blendshapes_categories
def _make_expected_facial_transformation_matrixes():
data = np.array([[0.9995292, -0.005092691, 0.030254554, -0.37340546],
[0.0072318087, 0.99744856, -0.07102106, 22.212194],
[-0.029815676, 0.07120642, 0.9970159, -64.76358],
[0, 0, 0, 1]])
rows, cols = len(data), len(data[0])
facial_transformation_matrixes_results = []
facial_transformation_matrix = _MatrixData(rows, cols, data)
facial_transformation_matrixes_results.append(facial_transformation_matrix)
return facial_transformation_matrixes_results
class ModelFileType(enum.Enum):
@ -148,30 +181,82 @@ class HandLandmarkerTest(parameterized.TestCase):
self.assertIsInstance(landmarker, _FaceLandmarker)
@parameterized.parameters(
(ModelFileType.FILE_NAME, _FACE_LANDMARKER_BUNDLE_ASSET_FILE,
_get_expected_face_landmarks(
_PORTRAIT_EXPECTED_FACE_LANDMARKS), None, None),
(ModelFileType.FILE_CONTENT, _FACE_LANDMARKER_BUNDLE_ASSET_FILE,
_get_expected_face_landmarks(
_PORTRAIT_EXPECTED_FACE_LANDMARKS), None, None),
(ModelFileType.FILE_NAME,
_get_expected_face_landmarks(_PORTRAIT_EXPECTED_FACE_LANDMARKS)),
_FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE,
_get_expected_face_landmarks(
_PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION), None, None),
(ModelFileType.FILE_CONTENT,
_get_expected_face_landmarks(_PORTRAIT_EXPECTED_FACE_LANDMARKS)))
def test_detect(self, model_file_type, expected_face_landmarks):
_FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE,
_get_expected_face_landmarks(
_PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION), None, None),
(ModelFileType.FILE_NAME,
_FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE,
_get_expected_face_landmarks(
_PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION),
_get_expected_face_blendshapes(
_PORTRAIT_EXPECTED_BLENDSHAPES), None),
(ModelFileType.FILE_CONTENT,
_FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE,
_get_expected_face_landmarks(
_PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION),
_get_expected_face_blendshapes(
_PORTRAIT_EXPECTED_BLENDSHAPES), None),
# (ModelFileType.FILE_NAME,
# _FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE,
# _get_expected_face_landmarks(
# _PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION),
# _get_expected_face_blendshapes(
# _PORTRAIT_EXPECTED_BLENDSHAPES),
# _make_expected_facial_transformation_matrixes()),
# (ModelFileType.FILE_CONTENT,
# _FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE,
# _get_expected_face_landmarks(
# _PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION),
# _get_expected_face_blendshapes(
# _PORTRAIT_EXPECTED_BLENDSHAPES),
# _make_expected_facial_transformation_matrixes())
)
def test_detect(self, model_file_type, model_name, expected_face_landmarks,
expected_face_blendshapes, expected_facial_transformation_matrix):
# Creates face landmarker.
model_path = test_utils.get_test_data_path(model_name)
if model_file_type is ModelFileType.FILE_NAME:
base_options = _BaseOptions(model_asset_path=self.model_path)
base_options = _BaseOptions(model_asset_path=model_path)
elif model_file_type is ModelFileType.FILE_CONTENT:
with open(self.model_path, 'rb') as f:
with open(model_path, 'rb') as f:
model_content = f.read()
base_options = _BaseOptions(model_asset_buffer=model_content)
else:
# Should never happen
raise ValueError('model_file_type is invalid.')
options = _FaceLandmarkerOptions(base_options=base_options)
options = _FaceLandmarkerOptions(
base_options=base_options,
output_face_blendshapes=True if expected_face_blendshapes else False,
output_facial_transformation_matrixes=True
if expected_facial_transformation_matrix else False)
landmarker = _FaceLandmarker.create_from_options(options)
# Performs face landmarks detection on the input.
detection_result = landmarker.detect(self.test_image)
# Comparing results.
self._expect_landmarks_correct(detection_result.face_landmarks[0],
expected_face_landmarks)
if expected_face_landmarks is not None:
self._expect_landmarks_correct(detection_result.face_landmarks[0],
expected_face_landmarks)
if expected_face_blendshapes is not None:
self._expect_blendshapes_correct(detection_result.face_blendshapes[0],
expected_face_blendshapes)
if expected_facial_transformation_matrix is not None:
self._expect_facial_transformation_matrix_correct(
detection_result.facial_transformation_matrixes[0],
expected_facial_transformation_matrix)
# Closes the face landmarker explicitly when the face landmarker is not used
# in a context.
landmarker.close()

1
mediapipe/tasks/python/vision/face_landmarker.py
View File

@ -162,6 +162,7 @@ def _build_landmarker_result(
facial_transformation_matrixes_results = []
if _FACE_GEOMETRY_STREAM_NAME in output_packets:
print(output_packets[_FACE_GEOMETRY_STREAM_NAME])
facial_transformation_matrixes_proto_list = packet_getter.get_proto_list(
output_packets[_FACE_GEOMETRY_STREAM_NAME])
for proto in facial_transformation_matrixes_proto_list:

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

@ -156,6 +156,7 @@ filegroup(
"face_landmark.tflite",
"face_landmark_with_attention.tflite",
"face_landmarker.task",
"face_landmarker_with_blendshapes.task",
"hair_segmentation.tflite",
"hand_landmark_full.tflite",
"hand_landmark_lite.tflite",
@ -191,6 +192,7 @@ filegroup(
"pointing_up_landmarks.pbtxt",
"pointing_up_rotated_landmarks.pbtxt",
"portrait_expected_detection.pbtxt",
"portrait_expected_blendshapes_with_attention.pbtxt",
"portrait_expected_face_geometry_with_attention.pbtxt",
"portrait_expected_face_landmarks.pbtxt",
"portrait_expected_face_landmarks_with_attention.pbtxt",