Added files for the image embedder implementation and a simple test

mediapipe/python/BUILD

@@ -88,6 +88,7 @@ cc_library(
     name = "builtin_task_graphs",
     deps = [
         "//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",
+        "//mediapipe/tasks/cc/vision/image_embedder:image_embedder_graph",
     ],
 )
 

mediapipe/tasks/python/components/containers/BUILD

@@ -27,6 +27,15 @@ py_library(
     ],
 )
 
+py_library(
+    name = "rect",
+    srcs = ["rect.py"],
+    deps = [
+        "//mediapipe/framework/formats:rect_py_pb2",
+        "//mediapipe/tasks/python/core:optional_dependencies",
+    ],
+)
+
 py_library(
     name = "category",
     srcs = ["category.py"],
@@ -47,3 +56,12 @@ py_library(
         "//mediapipe/tasks/python/core:optional_dependencies",
     ],
 )
+
+py_library(
+    name = "embeddings",
+    srcs = ["embeddings.py"],
+    deps = [
+        "//mediapipe/tasks/cc/components/containers/proto:embeddings_py_pb2",
+        "//mediapipe/tasks/python/core:optional_dependencies",
+    ],
+)

mediapipe/tasks/python/components/containers/embeddings.py

@@ -0,0 +1,246 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Embeddings data class."""
+
+import dataclasses
+from typing import Any, Optional, List
+
+import numpy as np
+from mediapipe.tasks.cc.components.containers.proto import embeddings_pb2
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+
+_FloatEmbeddingProto = embeddings_pb2.FloatEmbedding
+_QuantizedEmbeddingProto = embeddings_pb2.QuantizedEmbedding
+_EmbeddingEntryProto = embeddings_pb2.EmbeddingEntry
+_EmbeddingsProto = embeddings_pb2.Embeddings
+_EmbeddingResultProto = embeddings_pb2.EmbeddingResult
+
+
+@dataclasses.dataclass
+class FloatEmbedding:
+  """Defines a dense floating-point embedding.
+
+  Attributes:
+    values: A NumPy array indicating the raw output of the embedding layer.
+  """
+
+  values: np.ndarray
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _FloatEmbeddingProto:
+    """Generates a FloatEmbedding protobuf object."""
+    return _FloatEmbeddingProto(values=self.values)
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(
+      cls, pb2_obj: _FloatEmbeddingProto) -> 'FloatEmbedding':
+    """Creates a `FloatEmbedding` object from the given protobuf object."""
+    return FloatEmbedding(values=np.array(pb2_obj.value_float, dtype=float))
+
+  def __eq__(self, other: Any) -> bool:
+    """Checks if this object is equal to the given object.
+    Args:
+      other: The object to be compared with.
+    Returns:
+      True if the objects are equal.
+    """
+    if not isinstance(other, FloatEmbedding):
+      return False
+
+    return self.to_pb2().__eq__(other.to_pb2())
+
+
+@dataclasses.dataclass
+class QuantizedEmbedding:
+  """Defines a dense scalar-quantized embedding.
+
+  Attributes:
+    values: A NumPy array indicating the raw output of the embedding layer.
+  """
+
+  values: np.ndarray
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _QuantizedEmbeddingProto:
+    """Generates a QuantizedEmbedding protobuf object."""
+    return _QuantizedEmbeddingProto(values=self.values)
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(
+      cls, pb2_obj: _QuantizedEmbeddingProto) -> 'QuantizedEmbedding':
+    """Creates a `QuantizedEmbedding` object from the given protobuf object."""
+    return QuantizedEmbedding(
+        values=np.array(bytearray(pb2_obj.value_string), dtype=np.uint8))
+
+  def __eq__(self, other: Any) -> bool:
+    """Checks if this object is equal to the given object.
+    Args:
+      other: The object to be compared with.
+    Returns:
+      True if the objects are equal.
+    """
+    if not isinstance(other, QuantizedEmbedding):
+      return False
+
+    return self.to_pb2().__eq__(other.to_pb2())
+
+
+@dataclasses.dataclass
+class EmbeddingEntry:
+  """Floating-point or scalar-quantized embedding with an optional timestamp.
+
+  Attributes:
+    embedding: The actual embedding, either floating-point or scalar-quantized.
+    timestamp_ms: The optional timestamp (in milliseconds) associated to the
+      embedding entry. This is useful for time series use cases, e.g. audio
+      embedding.
+  """
+
+  embedding: np.ndarray
+  timestamp_ms: Optional[int] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _EmbeddingEntryProto:
+    """Generates a EmbeddingEntry protobuf object."""
+
+    if self.embedding.dtype == float:
+      return _EmbeddingEntryProto(float_embedding=self.embedding)
+
+    elif self.embedding.dtype == np.uint8:
+      return _EmbeddingEntryProto(quantized_embedding=bytes(self.embedding))
+
+    else:
+      raise ValueError("Invalid dtype. Only float and np.uint8 are supported.")
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(
+      cls, pb2_obj: _EmbeddingEntryProto) -> 'EmbeddingEntry':
+    """Creates a `EmbeddingEntry` object from the given protobuf object."""
+
+    if pb2_obj.float_embedding:
+      return EmbeddingEntry(
+          embedding=np.array(pb2_obj.float_embedding.values, dtype=float))
+
+    elif pb2_obj.quantized_embedding:
+      return EmbeddingEntry(
+          embedding=np.array(bytearray(pb2_obj.quantized_embedding.values),
+                             dtype=np.uint8))
+
+    else:
+      raise ValueError("Either float_embedding or quantized_embedding must "
+                       "exist.")
+
+  def __eq__(self, other: Any) -> bool:
+    """Checks if this object is equal to the given object.
+    Args:
+      other: The object to be compared with.
+    Returns:
+      True if the objects are equal.
+    """
+    if not isinstance(other, EmbeddingEntry):
+      return False
+
+    return self.to_pb2().__eq__(other.to_pb2())
+
+
+@dataclasses.dataclass
+class Embeddings:
+  """Embeddings for a given embedder head.
+  Attributes:
+    entries: A list of `ClassificationEntry` objects.
+    head_index: The index of the embedder head that produced this embedding.
+      This is useful for multi-head models.
+    head_name: The name of the embedder head, which is the corresponding tensor
+      metadata name (if any). This is useful for multi-head models.
+  """
+
+  entries: List[EmbeddingEntry]
+  head_index: int
+  head_name: str
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _EmbeddingsProto:
+    """Generates a Embeddings protobuf object."""
+    return _EmbeddingsProto(
+        entries=[entry.to_pb2() for entry in self.entries],
+        head_index=self.head_index,
+        head_name=self.head_name)
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(cls, pb2_obj: _EmbeddingsProto) -> 'Embeddings':
+    """Creates a `Embeddings` object from the given protobuf object."""
+    return Embeddings(
+        entries=[
+            EmbeddingEntry.create_from_pb2(entry)
+            for entry in pb2_obj.entries
+        ],
+        head_index=pb2_obj.head_index,
+        head_name=pb2_obj.head_name)
+
+  def __eq__(self, other: Any) -> bool:
+    """Checks if this object is equal to the given object.
+    Args:
+      other: The object to be compared with.
+    Returns:
+      True if the objects are equal.
+    """
+    if not isinstance(other, Embeddings):
+      return False
+
+    return self.to_pb2().__eq__(other.to_pb2())
+
+
+@dataclasses.dataclass
+class EmbeddingResult:
+  """Contains one set of results per embedder head.
+  Attributes:
+    embeddings: A list of `Embeddings` objects.
+  """
+
+  embeddings: List[Embeddings]
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _EmbeddingResultProto:
+    """Generates a EmbeddingResult protobuf object."""
+    return _EmbeddingResultProto(
+        embeddings=[
+            embedding.to_pb2() for embedding in self.embeddings
+        ])
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(
+      cls, pb2_obj: _EmbeddingResultProto) -> 'EmbeddingResult':
+    """Creates a `EmbeddingResult` object from the given protobuf object."""
+    return EmbeddingResult(
+        embeddings=[
+            Embeddings.create_from_pb2(embedding)
+            for embedding in pb2_obj.embeddings
+        ])
+
+  def __eq__(self, other: Any) -> bool:
+    """Checks if this object is equal to the given object.
+    Args:
+      other: The object to be compared with.
+    Returns:
+      True if the objects are equal.
+    """
+    if not isinstance(other, EmbeddingResult):
+      return False
+
+    return self.to_pb2().__eq__(other.to_pb2())

mediapipe/tasks/python/components/containers/rect.py

@@ -0,0 +1,141 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Rect data class."""
+
+import dataclasses
+from typing import Any, Optional
+
+from mediapipe.framework.formats import rect_pb2
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+
+_RectProto = rect_pb2.Rect
+_NormalizedRectProto = rect_pb2.NormalizedRect
+
+
+@dataclasses.dataclass
+class Rect:
+  """A rectangle with rotation in image coordinates.
+
+  Attributes:
+    x_center : The X coordinate of the top-left corner, in pixels.
+    y_center : The Y coordinate of the top-left corner, in pixels.
+    width: The width of the rectangle, in pixels.
+    height: The height of the rectangle, in pixels.
+    rotation: Rotation angle is clockwise in radians.
+    rect_id:  Optional unique id to help associate different rectangles to each
+      other.
+  """
+
+  x_center: int
+  y_center: int
+  width: int
+  height: int
+  rotation: Optional[float] = 0.0
+  rect_id: Optional[int] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _RectProto:
+    """Generates a Rect protobuf object."""
+    return _RectProto(
+        x_center=self.x_center,
+        y_center=self.y_center,
+        width=self.width,
+        height=self.height,
+    )
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(cls, pb2_obj: _RectProto) -> 'Rect':
+    """Creates a `Rect` object from the given protobuf object."""
+    return Rect(
+        x_center=pb2_obj.x_center,
+        y_center=pb2_obj.y_center,
+        width=pb2_obj.width,
+        height=pb2_obj.height)
+
+  def __eq__(self, other: Any) -> bool:
+    """Checks if this object is equal to the given object.
+
+    Args:
+      other: The object to be compared with.
+
+    Returns:
+      True if the objects are equal.
+    """
+    if not isinstance(other, Rect):
+      return False
+
+    return self.to_pb2().__eq__(other.to_pb2())
+
+
+@dataclasses.dataclass
+class NormalizedRect:
+  """A rectangle with rotation in normalized coordinates. The values of box
+    center location and size are within [0, 1].
+
+  Attributes:
+    x_center : The X normalized coordinate of the top-left corner.
+    y_center : The Y normalized coordinate of the top-left corner.
+    width: The width of the rectangle.
+    height: The height of the rectangle.
+    rotation: Rotation angle is clockwise in radians.
+    rect_id:  Optional unique id to help associate different rectangles to each
+      other.
+  """
+
+  x_center: float
+  y_center: float
+  width: float
+  height: float
+  rotation: Optional[float] = 0.0
+  rect_id: Optional[int] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _NormalizedRectProto:
+    """Generates a NormalizedRect protobuf object."""
+    return _NormalizedRectProto(
+        x_center=self.x_center,
+        y_center=self.y_center,
+        width=self.width,
+        height=self.height,
+        rotation=self.rotation,
+        rect_id=self.rect_id
+    )
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(cls, pb2_obj: _NormalizedRectProto) -> 'NormalizedRect':
+    """Creates a `NormalizedRect` object from the given protobuf object."""
+    return NormalizedRect(
+        x_center=pb2_obj.x_center,
+        y_center=pb2_obj.y_center,
+        width=pb2_obj.width,
+        height=pb2_obj.height,
+        rotation=pb2_obj.rotation,
+        rect_id=pb2_obj.rect_id
+    )
+
+  def __eq__(self, other: Any) -> bool:
+    """Checks if this object is equal to the given object.
+
+    Args:
+      other: The object to be compared with.
+
+    Returns:
+      True if the objects are equal.
+    """
+    if not isinstance(other, NormalizedRect):
+      return False
+
+    return self.to_pb2().__eq__(other.to_pb2())

mediapipe/tasks/python/components/proto/BUILD

@@ -0,0 +1,28 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Placeholder for internal Python strict library compatibility macro.
+
+package(default_visibility = ["//mediapipe/tasks:internal"])
+
+licenses(["notice"])
+
+py_library(
+    name = "embedder_options",
+    srcs = ["embedder_options.py"],
+    deps = [
+        "//mediapipe/tasks/cc/components/proto:embedder_options_py_pb2",
+        "//mediapipe/tasks/python/core:optional_dependencies",
+    ],
+)

mediapipe/tasks/python/components/proto/__init__.py

@@ -0,0 +1,13 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

mediapipe/tasks/python/components/proto/embedder_options.py

@@ -0,0 +1,72 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Embedder options data class."""
+
+import dataclasses
+from typing import Any, Optional
+
+from mediapipe.tasks.cc.components.proto import embedder_options_pb2
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+
+_EmbedderOptionsProto = embedder_options_pb2.EmbedderOptions
+
+
+@dataclasses.dataclass
+class EmbedderOptions:
+  """Shared options used by all embedding extraction tasks.
+
+  Attributes:
+    l2_normalize: Whether to normalize the returned feature vector with L2 norm.
+      Use this option only if the model does not already contain a native
+      L2_NORMALIZATION TF Lite Op. In most cases, this is already the case and
+      L2 norm is thus achieved through TF Lite inference.
+    quantize: Whether the returned embedding should be quantized to bytes via
+      scalar quantization. Embeddings are implicitly assumed to be unit-norm and
+      therefore any dimension is guaranteed to have a value in [-1.0, 1.0]. Use
+      the l2_normalize option if this is not the case.
+  """
+
+  l2_normalize: Optional[bool] = None
+  quantize: Optional[bool] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _EmbedderOptionsProto:
+    """Generates a EmbedderOptions protobuf object."""
+    return _EmbedderOptionsProto(
+        l2_normalize=self.l2_normalize,
+        quantize=self.quantize
+    )
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(cls, pb2_obj: _EmbedderOptionsProto) -> 'EmbedderOptions':
+    """Creates a `EmbedderOptions` object from the given protobuf object."""
+    return EmbedderOptions(
+        l2_normalize=pb2_obj.l2_normalize,
+        quantize=pb2_obj.quantize
+    )
+
+  def __eq__(self, other: Any) -> bool:
+    """Checks if this object is equal to the given object.
+
+    Args:
+      other: The object to be compared with.
+
+    Returns:
+      True if the objects are equal.
+    """
+    if not isinstance(other, EmbedderOptions):
+      return False
+
+    return self.to_pb2().__eq__(other.to_pb2())

mediapipe/tasks/python/test/vision/BUILD

@@ -36,3 +36,22 @@ py_test(
         "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
     ],
 )
+
+py_test(
+    name = "image_embedder_test",
+    srcs = ["image_embedder_test.py"],
+    data = [
+        "//mediapipe/tasks/testdata/vision:test_images",
+        "//mediapipe/tasks/testdata/vision:test_models",
+    ],
+    deps = [
+        "//mediapipe/python:_framework_bindings",
+        "//mediapipe/tasks/python/components/proto:embedder_options",
+        "//mediapipe/tasks/python/components/containers:embeddings",
+        "//mediapipe/tasks/python/components/containers:rect",
+        "//mediapipe/tasks/python/core:base_options",
+        "//mediapipe/tasks/python/test:test_utils",
+        "//mediapipe/tasks/python/vision:image_embedder",
+        "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
+    ],
+)

mediapipe/tasks/python/test/vision/image_embedder_test.py

@@ -0,0 +1,98 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tests for image embedder."""
+
+import enum
+from unittest import mock
+
+import numpy as np
+from absl.testing import absltest
+from absl.testing import parameterized
+
+from mediapipe.python._framework_bindings import image as image_module
+from mediapipe.tasks.python.components.proto import embedder_options as embedder_options_module
+from mediapipe.tasks.python.components.containers import embeddings as embeddings_module
+from mediapipe.tasks.python.components.containers import rect as rect_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 image_embedder
+from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
+
+_NormalizedRect = rect_module.NormalizedRect
+_BaseOptions = base_options_module.BaseOptions
+_EmbedderOptions = embedder_options_module.EmbedderOptions
+_FloatEmbedding = embeddings_module.FloatEmbedding
+_QuantizedEmbedding = embeddings_module.QuantizedEmbedding
+_ClassificationEntry = embeddings_module.EmbeddingEntry
+_Classifications = embeddings_module.Embeddings
+_ClassificationResult = embeddings_module.EmbeddingResult
+_Image = image_module.Image
+_ImageEmbedder = image_embedder.ImageEmbedder
+_ImageEmbedderOptions = image_embedder.ImageEmbedderOptions
+_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
+
+_MODEL_FILE = 'mobilenet_v3_small_100_224_embedder.tflite'
+_IMAGE_FILE = 'burger.jpg'
+_ALLOW_LIST = ['cheeseburger', 'guacamole']
+_DENY_LIST = ['cheeseburger']
+_SCORE_THRESHOLD = 0.5
+_MAX_RESULTS = 3
+
+
+class ModelFileType(enum.Enum):
+  FILE_CONTENT = 1
+  FILE_NAME = 2
+
+
+class ImageClassifierTest(parameterized.TestCase):
+
+  def setUp(self):
+    super().setUp()
+    self.test_image = _Image.create_from_file(
+        test_utils.get_test_data_path(_IMAGE_FILE))
+    self.model_path = test_utils.get_test_data_path(_MODEL_FILE)
+
+  @parameterized.parameters(
+      (ModelFileType.FILE_NAME, False, False),
+      (ModelFileType.FILE_CONTENT, False, False))
+  def test_embed(self, model_file_type, l2_normalize, quantize):
+    # Creates embedder.
+    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_content = f.read()
+      base_options = _BaseOptions(model_asset_buffer=model_content)
+    else:
+      # Should never happen
+      raise ValueError('model_file_type is invalid.')
+
+    embedder_options = _EmbedderOptions(l2_normalize=l2_normalize,
+                                        quantize=quantize)
+    options = _ImageEmbedderOptions(
+        base_options=base_options, embedder_options=embedder_options)
+    embedder = _ImageEmbedder.create_from_options(options)
+
+    # Performs image embedding extraction on the input.
+    image_result = embedder.embed(self.test_image)
+
+    # TODO: Verify results.
+
+    # Closes the embedder explicitly when the classifier is not used in
+    # a context.
+    embedder.close()
+
+
+if __name__ == '__main__':
+  absltest.main()

mediapipe/tasks/python/vision/BUILD

@@ -36,3 +36,22 @@ py_library(
         "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
     ],
 )
+
+py_library(
+    name = "image_embedder",
+    srcs = [
+        "image_embedder.py",
+    ],
+    deps = [
+        "//mediapipe/python:_framework_bindings",
+        "//mediapipe/python:packet_creator",
+        "//mediapipe/python:packet_getter",
+        "//mediapipe/tasks/cc/vision/image_embedder/proto:image_embedder_graph_options_py_pb2",
+        "//mediapipe/tasks/python/components/containers:embeddings",
+        "//mediapipe/tasks/python/core:base_options",
+        "//mediapipe/tasks/python/core:optional_dependencies",
+        "//mediapipe/tasks/python/core:task_info",
+        "//mediapipe/tasks/python/vision/core:base_vision_task_api",
+        "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
+    ],
+)

mediapipe/tasks/python/vision/image_embedder.py

@@ -0,0 +1,288 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MediaPipe image embedder task."""
+
+import dataclasses
+from typing import Callable, Mapping, Optional
+
+from mediapipe.python import packet_creator
+from mediapipe.python import packet_getter
+from mediapipe.python._framework_bindings import image as image_module
+from mediapipe.python._framework_bindings import packet as packet_module
+from mediapipe.python._framework_bindings import task_runner as task_runner_module
+from mediapipe.tasks.cc.vision.image_embedder.proto import image_embedder_graph_options_pb2
+from mediapipe.tasks.python.components.proto import embedder_options
+from mediapipe.tasks.python.components.containers import embeddings as embeddings_module
+from mediapipe.tasks.python.components.containers import rect as rect_module
+from mediapipe.tasks.python.core import base_options as base_options_module
+from mediapipe.tasks.python.core import task_info as task_info_module
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+from mediapipe.tasks.python.vision.core import base_vision_task_api
+from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
+
+_NormalizedRect = rect_module.NormalizedRect
+_BaseOptions = base_options_module.BaseOptions
+_ImageEmbedderGraphOptionsProto = image_embedder_graph_options_pb2.ImageEmbedderGraphOptions
+_EmbedderOptions = embedder_options.EmbedderOptions
+_RunningMode = running_mode_module.VisionTaskRunningMode
+_TaskInfo = task_info_module.TaskInfo
+_TaskRunner = task_runner_module.TaskRunner
+
+_EMBEDDING_RESULT_OUT_STREAM_NAME = 'embedding_result_out'
+_EMBEDDING_RESULT_TAG = 'EMBEDDING_RESULT'
+_IMAGE_IN_STREAM_NAME = 'image_in'
+_IMAGE_OUT_STREAM_NAME = 'image_out'
+_IMAGE_TAG = 'IMAGE'
+_NORM_RECT_NAME = 'norm_rect_in'
+_NORM_RECT_TAG = 'NORM_RECT'
+_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.image_embedder.ImageEmbedderGraph'
+_MICRO_SECONDS_PER_MILLISECOND = 1000
+
+
+def _build_full_image_norm_rect() -> _NormalizedRect:
+  # Builds a NormalizedRect covering the entire image.
+  return _NormalizedRect(x_center=0.5, y_center=0.5, width=1, height=1)
+
+
+@dataclasses.dataclass
+class ImageEmbedderOptions:
+  """Options for the image embedder task.
+
+  Attributes:
+    base_options: Base options for the image embedder task.
+    running_mode: The running mode of the task. Default to the image mode.
+      Image embedder task has three running modes:
+      1) The image mode for embedding image on single image inputs.
+      2) The video mode for embedding image on the decoded frames of a
+         video.
+      3) The live stream mode for embedding image on a live stream of input
+         data, such as from camera.
+    embedder_options: Options for the image embedder task.
+    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.
+  """
+  base_options: _BaseOptions
+  running_mode: _RunningMode = _RunningMode.IMAGE
+  embedder_options: _EmbedderOptions = _EmbedderOptions()
+  result_callback: Optional[
+      Callable[[embeddings_module.EmbeddingResult, image_module.Image,
+                int], None]] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _ImageEmbedderGraphOptionsProto:
+    """Generates an ImageEmbedderOptions protobuf object."""
+    base_options_proto = self.base_options.to_pb2()
+    base_options_proto.use_stream_mode = False if self.running_mode == _RunningMode.IMAGE else True
+    embedder_options_proto = self.embedder_options.to_pb2()
+
+    return _ImageEmbedderGraphOptionsProto(
+        base_options=base_options_proto,
+        embedder_options=embedder_options_proto
+    )
+
+
+class ImageEmbedder(base_vision_task_api.BaseVisionTaskApi):
+  """Class that performs embedding extraction on images."""
+
+  @classmethod
+  def create_from_model_path(cls, model_path: str) -> 'ImageEmbedder':
+    """Creates an `ImageEmbedder` object from a TensorFlow Lite model and the
+      default `ImageEmbedderOptions`.
+
+    Note that the created `ImageEmbedder` instance is in image mode, for
+    embedding image on single image inputs.
+
+    Args:
+      model_path: Path to the model.
+
+    Returns:
+      `ImageEmbedder` object that's created from the model file and the default
+      `ImageEmbedderOptions`.
+
+    Raises:
+      ValueError: If failed to create `ImageClassifier` object from the provided
+        file such as invalid file path.
+      RuntimeError: If other types of error occurred.
+    """
+    base_options = _BaseOptions(model_asset_path=model_path)
+    options = ImageEmbedderOptions(
+        base_options=base_options, running_mode=_RunningMode.IMAGE)
+    return cls.create_from_options(options)
+
+  @classmethod
+  def create_from_options(cls,
+                          options: ImageEmbedderOptions) -> 'ImageEmbedder':
+    """Creates the `ImageEmbedder` object from image embedder options.
+
+    Args:
+      options: Options for the image embedder task.
+
+    Returns:
+      `ImageEmbedder` object that's created from `options`.
+
+    Raises:
+      ValueError: If failed to create `ImageEmbedder` object from
+        `ImageEmbedderOptions` such as missing the model.
+      RuntimeError: If other types of error occurred.
+    """
+
+    def packets_callback(output_packets: Mapping[str, packet_module.Packet]):
+      if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
+        return
+      embedding_result_proto = packet_getter.get_proto(
+          output_packets[_EMBEDDING_RESULT_OUT_STREAM_NAME])
+
+      embedding_result = embeddings_module.EmbeddingResult([
+          embeddings_module.Embeddings.create_from_pb2(embedding)
+          for embedding in embedding_result_proto.embeddings
+      ])
+      image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
+      timestamp = output_packets[_IMAGE_OUT_STREAM_NAME].timestamp
+      options.result_callback(embedding_result, image,
+                              timestamp.value // _MICRO_SECONDS_PER_MILLISECOND)
+
+    task_info = _TaskInfo(
+        task_graph=_TASK_GRAPH_NAME,
+        input_streams=[
+            ':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
+            ':'.join([_NORM_RECT_TAG, _NORM_RECT_NAME]),
+        ],
+        output_streams=[
+            ':'.join([_EMBEDDING_RESULT_TAG,
+                      _EMBEDDING_RESULT_OUT_STREAM_NAME]),
+            ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME])
+        ],
+        task_options=options)
+    return cls(
+        task_info.generate_graph_config(
+            enable_flow_limiting=options.running_mode ==
+            _RunningMode.LIVE_STREAM), options.running_mode,
+        packets_callback if options.result_callback else None)
+
+  def embed(
+      self,
+      image: image_module.Image,
+      roi: Optional[_NormalizedRect] = None
+  ) -> embeddings_module.EmbeddingResult:
+    """Performs image embedding extraction on the provided MediaPipe Image.
+     Extraction is performed on the region of interest specified by the `roi`
+     argument if provided, or on the entire image otherwise.
+
+    Args:
+      image: MediaPipe Image.
+      roi: The region of interest.
+
+    Returns:
+      A embedding result object that contains a list of embeddings.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If image embedder failed to run.
+    """
+    norm_rect = roi if roi is not None else _build_full_image_norm_rect()
+    output_packets = self._process_image_data({
+        _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image),
+        _NORM_RECT_NAME: packet_creator.create_proto(norm_rect.to_pb2())})
+    embedding_result_proto = packet_getter.get_proto(
+        output_packets[_EMBEDDING_RESULT_OUT_STREAM_NAME])
+
+    return embeddings_module.EmbeddingResult([
+        embeddings_module.Embeddings.create_from_pb2(embedding)
+        for embedding in embedding_result_proto.embeddings
+    ])
+
+  def embed_for_video(
+      self, image: image_module.Image,
+      timestamp_ms: int,
+      roi: Optional[_NormalizedRect] = None
+  ) -> embeddings_module.EmbeddingResult:
+    """Performs image embedding extraction on the provided video frames.
+    Extraction is performed on the region of interested specified by the `roi`
+    argument if provided, or on the entire image otherwise.
+
+    Only use this method when the ImageEmbedder is created with the video
+    running mode. It's required to provide the video frame's timestamp (in
+    milliseconds) along with the video frame. The input timestamps should be
+    monotonically increasing for adjacent calls of this method.
+
+    Args:
+      image: MediaPipe Image.
+      timestamp_ms: The timestamp of the input video frame in milliseconds.
+      roi: The region of interest.
+
+    Returns:
+      A embedding result object that contains a list of embeddings.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If image embedder failed to run.
+    """
+    norm_rect = roi if roi is not None else _build_full_image_norm_rect()
+    output_packets = self._process_video_data({
+        _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).at(
+            timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
+        _NORM_RECT_NAME: packet_creator.create_proto(norm_rect.to_pb2()).at(
+            timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
+    })
+    embedding_result_proto = packet_getter.get_proto(
+      output_packets[_EMBEDDING_RESULT_OUT_STREAM_NAME])
+
+    return embeddings_module.EmbeddingResult([
+        embeddings_module.Embeddings.create_from_pb2(embedding)
+        for embedding in embedding_result_proto.embeddings
+    ])
+
+  def embed_async(
+      self,
+      image: image_module.Image,
+      timestamp_ms: int,
+      roi: Optional[_NormalizedRect] = None
+  ) -> None:
+    """ Sends live image data to embedder, and the results will be available via
+    the "result_callback" provided in the ImageEmbedderOptions. Embedding
+    extraction is performed on the region of interested specified by the `roi`
+    argument if provided, or on the entire image otherwise.
+
+    Only use this method when the ImageEmbedder is created with the live
+    stream running mode. The input timestamps should be monotonically increasing
+    for adjacent calls of this method. This method will return immediately after
+    the input image is accepted. The results will be available via the
+    `result_callback` provided in the `ImageEmbedderOptions`. The
+    `embed_async` method is designed to process live stream data such as
+    camera input. To lower the overall latency, image embedder may drop the
+    input images if needed. In other words, it's not guaranteed to have output
+    per input image.
+
+    The `result_callback` provides:
+      - A embedding result object that contains a list of embeddings.
+      - The input image that the image embedder runs on.
+      - The input timestamp in milliseconds.
+
+    Args:
+      image: MediaPipe Image.
+      timestamp_ms: The timestamp of the input image in milliseconds.
+      roi: The region of interest.
+
+    Raises:
+      ValueError: If the current input timestamp is smaller than what the image
+        embedder has already processed.
+    """
+    norm_rect = roi if roi is not None else _build_full_image_norm_rect()
+    self._send_live_stream_data({
+        _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).at(
+            timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
+        _NORM_RECT_NAME: packet_creator.create_proto(norm_rect.to_pb2()).at(
+            timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
+    })