Added files for the image embedder implementation and a simple test

This commit is contained in:
kinaryml 2022-10-20 02:29:14 -07:00
parent 467cd34feb
commit 71d5b69544
11 changed files with 943 additions and 0 deletions

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

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@ -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( py_library(
name = "category", name = "category",
srcs = ["category.py"], srcs = ["category.py"],
@ -47,3 +56,12 @@ py_library(
"//mediapipe/tasks/python/core:optional_dependencies", "//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",
],
)

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@ -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())

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@ -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())

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@ -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",
],
)

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@ -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.

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@ -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())

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@ -36,3 +36,22 @@ py_test(
"//mediapipe/tasks/python/vision/core:vision_task_running_mode", "//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",
],
)

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@ -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()

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@ -36,3 +36,22 @@ py_library(
"//mediapipe/tasks/python/vision/core:vision_task_running_mode", "//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",
],
)

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@ -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)
})