Updated face landmarker implementation and tests
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kinaryml
parent
89be4c7b64
commit
efae2830f1
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@ -94,6 +94,7 @@ cc_library(
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"//mediapipe/tasks/cc/vision/image_embedder:image_embedder_graph",
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"//mediapipe/tasks/cc/vision/image_segmenter:image_segmenter_graph",
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"//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",
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"//mediapipe/tasks/cc/vision/face_landmarker:face_landmarker_graph",
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] + select({
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# TODO: Build text_classifier_graph and text_embedder_graph on Windows.
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"//mediapipe:windows": [],
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@ -152,7 +152,7 @@ class HandLandmarkerTest(parameterized.TestCase):
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_get_expected_face_landmarks(_PORTRAIT_EXPECTED_FACE_LANDMARKS)),
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(ModelFileType.FILE_CONTENT,
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_get_expected_face_landmarks(_PORTRAIT_EXPECTED_FACE_LANDMARKS)))
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def test_detect(self, model_file_type, expected_result):
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def test_detect(self, model_file_type, expected_face_landmarks):
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# Creates face landmarker.
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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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@ -164,15 +164,14 @@ class HandLandmarkerTest(parameterized.TestCase):
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# Should never happen
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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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output_face_blendshapes=True)
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options = _FaceLandmarkerOptions(base_options=base_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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detection_result = landmarker.detect(self.test_image)
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# Comparing results.
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self._expect_landmarks_correct(detection_result.face_landmarks,
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expected_result.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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# Closes the face landmarker explicitly when the face landmarker is not used
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# in a context.
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landmarker.close()
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@ -132,10 +132,6 @@ def _build_landmarker_result(
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"""Constructs a `FaceLandmarkerResult` from output packets."""
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face_landmarks_proto_list = packet_getter.get_proto_list(
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output_packets[_NORM_LANDMARKS_STREAM_NAME])
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face_blendshapes_proto_list = packet_getter.get_proto_list(
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output_packets[_BLENDSHAPES_STREAM_NAME])
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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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face_landmarks_results = []
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for proto in face_landmarks_proto_list:
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@ -143,30 +139,36 @@ def _build_landmarker_result(
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face_landmarks.MergeFrom(proto)
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face_landmarks_list = []
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for face_landmark in face_landmarks.landmark:
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face_landmarks.append(
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face_landmarks_list.append(
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landmark_module.NormalizedLandmark.create_from_pb2(face_landmark))
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face_landmarks_results.append(face_landmarks_list)
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face_blendshapes_results = []
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for proto in face_blendshapes_proto_list:
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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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face_blendshapes_results.append(face_blendshapes_categories)
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if _BLENDSHAPES_STREAM_NAME in output_packets:
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face_blendshapes_proto_list = packet_getter.get_proto_list(
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output_packets[_BLENDSHAPES_STREAM_NAME])
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for proto in face_blendshapes_proto_list:
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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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face_blendshapes_results.append(face_blendshapes_categories)
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facial_transformation_matrixes_results = []
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for proto in facial_transformation_matrixes_proto_list:
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matrix_data = matrix_data_pb2.MatrixData()
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matrix_data.MergeFrom(proto)
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matrix = matrix_data_module.MatrixData.create_from_pb2(matrix_data)
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facial_transformation_matrixes_results.append(matrix)
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if _FACE_GEOMETRY_STREAM_NAME in output_packets:
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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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for proto in facial_transformation_matrixes_proto_list:
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matrix_data = matrix_data_pb2.MatrixData()
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matrix_data.MergeFrom(proto)
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matrix = matrix_data_module.MatrixData.create_from_pb2(matrix_data)
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facial_transformation_matrixes_results.append(matrix)
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return FaceLandmarkerResult(face_landmarks_results, face_blendshapes_results,
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facial_transformation_matrixes_results)
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@ -298,19 +300,25 @@ class FaceLandmarker(base_vision_task_api.BaseVisionTaskApi):
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options.result_callback(face_landmarks_result, image,
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timestamp.value // _MICRO_SECONDS_PER_MILLISECOND)
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output_streams = [
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':'.join([_NORM_LANDMARKS_TAG, _NORM_LANDMARKS_STREAM_NAME]),
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':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME])
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]
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if options.output_face_blendshapes:
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output_streams.append(
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':'.join([_BLENDSHAPES_TAG, _BLENDSHAPES_STREAM_NAME]))
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if options.output_facial_transformation_matrixes:
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output_streams.append(
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':'.join([_FACE_GEOMETRY_TAG, _FACE_GEOMETRY_STREAM_NAME]))
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task_info = _TaskInfo(
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task_graph=_TASK_GRAPH_NAME,
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input_streams=[
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':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
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':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
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],
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output_streams=[
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':'.join([_NORM_LANDMARKS_TAG, _NORM_LANDMARKS_STREAM_NAME]),
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':'.join([_BLENDSHAPES_TAG, _BLENDSHAPES_STREAM_NAME]),
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':'.join([
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_FACE_GEOMETRY_TAG, _FACE_GEOMETRY_STREAM_NAME
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]), ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME])
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],
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output_streams=output_streams,
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task_options=options)
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return cls(
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task_info.generate_graph_config(
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