Added files for the image embedder implementation and a simple test
This commit is contained in:
parent
467cd34feb
commit
71d5b69544
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@ -88,6 +88,7 @@ cc_library(
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name = "builtin_task_graphs",
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name = "builtin_task_graphs",
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deps = [
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deps = [
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"//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",
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"//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",
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"//mediapipe/tasks/cc/vision/image_embedder:image_embedder_graph",
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],
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],
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)
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)
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@ -27,6 +27,15 @@ py_library(
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],
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],
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)
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)
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py_library(
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name = "rect",
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srcs = ["rect.py"],
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deps = [
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"//mediapipe/framework/formats:rect_py_pb2",
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"//mediapipe/tasks/python/core:optional_dependencies",
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],
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)
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py_library(
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py_library(
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name = "category",
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name = "category",
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srcs = ["category.py"],
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srcs = ["category.py"],
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@ -47,3 +56,12 @@ py_library(
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"//mediapipe/tasks/python/core:optional_dependencies",
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"//mediapipe/tasks/python/core:optional_dependencies",
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],
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],
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)
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)
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py_library(
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name = "embeddings",
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srcs = ["embeddings.py"],
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deps = [
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"//mediapipe/tasks/cc/components/containers/proto:embeddings_py_pb2",
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"//mediapipe/tasks/python/core:optional_dependencies",
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],
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)
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246
mediapipe/tasks/python/components/containers/embeddings.py
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246
mediapipe/tasks/python/components/containers/embeddings.py
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@ -0,0 +1,246 @@
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# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Embeddings data class."""
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import dataclasses
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from typing import Any, Optional, List
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import numpy as np
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from mediapipe.tasks.cc.components.containers.proto import embeddings_pb2
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from mediapipe.tasks.python.core.optional_dependencies import doc_controls
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_FloatEmbeddingProto = embeddings_pb2.FloatEmbedding
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_QuantizedEmbeddingProto = embeddings_pb2.QuantizedEmbedding
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_EmbeddingEntryProto = embeddings_pb2.EmbeddingEntry
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_EmbeddingsProto = embeddings_pb2.Embeddings
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_EmbeddingResultProto = embeddings_pb2.EmbeddingResult
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@dataclasses.dataclass
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class FloatEmbedding:
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"""Defines a dense floating-point embedding.
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Attributes:
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values: A NumPy array indicating the raw output of the embedding layer.
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"""
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values: np.ndarray
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _FloatEmbeddingProto:
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"""Generates a FloatEmbedding protobuf object."""
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return _FloatEmbeddingProto(values=self.values)
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@classmethod
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@doc_controls.do_not_generate_docs
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def create_from_pb2(
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cls, pb2_obj: _FloatEmbeddingProto) -> 'FloatEmbedding':
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"""Creates a `FloatEmbedding` object from the given protobuf object."""
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return FloatEmbedding(values=np.array(pb2_obj.value_float, dtype=float))
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def __eq__(self, other: Any) -> bool:
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"""Checks if this object is equal to the given object.
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Args:
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other: The object to be compared with.
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Returns:
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True if the objects are equal.
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"""
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if not isinstance(other, FloatEmbedding):
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return False
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return self.to_pb2().__eq__(other.to_pb2())
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@dataclasses.dataclass
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class QuantizedEmbedding:
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"""Defines a dense scalar-quantized embedding.
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Attributes:
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values: A NumPy array indicating the raw output of the embedding layer.
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"""
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values: np.ndarray
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _QuantizedEmbeddingProto:
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"""Generates a QuantizedEmbedding protobuf object."""
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return _QuantizedEmbeddingProto(values=self.values)
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@classmethod
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@doc_controls.do_not_generate_docs
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def create_from_pb2(
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cls, pb2_obj: _QuantizedEmbeddingProto) -> 'QuantizedEmbedding':
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"""Creates a `QuantizedEmbedding` object from the given protobuf object."""
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return QuantizedEmbedding(
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values=np.array(bytearray(pb2_obj.value_string), dtype=np.uint8))
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def __eq__(self, other: Any) -> bool:
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"""Checks if this object is equal to the given object.
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Args:
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other: The object to be compared with.
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Returns:
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True if the objects are equal.
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"""
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if not isinstance(other, QuantizedEmbedding):
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return False
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return self.to_pb2().__eq__(other.to_pb2())
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@dataclasses.dataclass
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class EmbeddingEntry:
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"""Floating-point or scalar-quantized embedding with an optional timestamp.
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Attributes:
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embedding: The actual embedding, either floating-point or scalar-quantized.
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timestamp_ms: The optional timestamp (in milliseconds) associated to the
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embedding entry. This is useful for time series use cases, e.g. audio
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embedding.
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"""
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embedding: np.ndarray
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timestamp_ms: Optional[int] = None
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _EmbeddingEntryProto:
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"""Generates a EmbeddingEntry protobuf object."""
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if self.embedding.dtype == float:
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return _EmbeddingEntryProto(float_embedding=self.embedding)
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elif self.embedding.dtype == np.uint8:
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return _EmbeddingEntryProto(quantized_embedding=bytes(self.embedding))
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else:
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raise ValueError("Invalid dtype. Only float and np.uint8 are supported.")
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@classmethod
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@doc_controls.do_not_generate_docs
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def create_from_pb2(
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cls, pb2_obj: _EmbeddingEntryProto) -> 'EmbeddingEntry':
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"""Creates a `EmbeddingEntry` object from the given protobuf object."""
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if pb2_obj.float_embedding:
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return EmbeddingEntry(
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embedding=np.array(pb2_obj.float_embedding.values, dtype=float))
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elif pb2_obj.quantized_embedding:
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return EmbeddingEntry(
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embedding=np.array(bytearray(pb2_obj.quantized_embedding.values),
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dtype=np.uint8))
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else:
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raise ValueError("Either float_embedding or quantized_embedding must "
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"exist.")
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def __eq__(self, other: Any) -> bool:
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"""Checks if this object is equal to the given object.
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Args:
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other: The object to be compared with.
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Returns:
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True if the objects are equal.
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"""
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if not isinstance(other, EmbeddingEntry):
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return False
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return self.to_pb2().__eq__(other.to_pb2())
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@dataclasses.dataclass
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class Embeddings:
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"""Embeddings for a given embedder head.
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Attributes:
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entries: A list of `ClassificationEntry` objects.
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head_index: The index of the embedder head that produced this embedding.
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This is useful for multi-head models.
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head_name: The name of the embedder head, which is the corresponding tensor
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metadata name (if any). This is useful for multi-head models.
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"""
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entries: List[EmbeddingEntry]
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head_index: int
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head_name: str
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _EmbeddingsProto:
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"""Generates a Embeddings protobuf object."""
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return _EmbeddingsProto(
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entries=[entry.to_pb2() for entry in self.entries],
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head_index=self.head_index,
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head_name=self.head_name)
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@classmethod
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@doc_controls.do_not_generate_docs
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def create_from_pb2(cls, pb2_obj: _EmbeddingsProto) -> 'Embeddings':
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"""Creates a `Embeddings` object from the given protobuf object."""
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return Embeddings(
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entries=[
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EmbeddingEntry.create_from_pb2(entry)
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for entry in pb2_obj.entries
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],
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head_index=pb2_obj.head_index,
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head_name=pb2_obj.head_name)
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def __eq__(self, other: Any) -> bool:
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"""Checks if this object is equal to the given object.
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Args:
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other: The object to be compared with.
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Returns:
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True if the objects are equal.
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"""
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if not isinstance(other, Embeddings):
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return False
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return self.to_pb2().__eq__(other.to_pb2())
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@dataclasses.dataclass
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class EmbeddingResult:
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"""Contains one set of results per embedder head.
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Attributes:
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embeddings: A list of `Embeddings` objects.
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"""
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embeddings: List[Embeddings]
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _EmbeddingResultProto:
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"""Generates a EmbeddingResult protobuf object."""
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return _EmbeddingResultProto(
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embeddings=[
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embedding.to_pb2() for embedding in self.embeddings
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])
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@classmethod
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@doc_controls.do_not_generate_docs
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def create_from_pb2(
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cls, pb2_obj: _EmbeddingResultProto) -> 'EmbeddingResult':
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"""Creates a `EmbeddingResult` object from the given protobuf object."""
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return EmbeddingResult(
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embeddings=[
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Embeddings.create_from_pb2(embedding)
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for embedding in pb2_obj.embeddings
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])
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def __eq__(self, other: Any) -> bool:
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"""Checks if this object is equal to the given object.
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Args:
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other: The object to be compared with.
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Returns:
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True if the objects are equal.
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"""
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if not isinstance(other, EmbeddingResult):
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return False
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return self.to_pb2().__eq__(other.to_pb2())
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141
mediapipe/tasks/python/components/containers/rect.py
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141
mediapipe/tasks/python/components/containers/rect.py
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@ -0,0 +1,141 @@
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# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Rect data class."""
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import dataclasses
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from typing import Any, Optional
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from mediapipe.framework.formats import rect_pb2
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from mediapipe.tasks.python.core.optional_dependencies import doc_controls
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_RectProto = rect_pb2.Rect
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_NormalizedRectProto = rect_pb2.NormalizedRect
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@dataclasses.dataclass
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class Rect:
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"""A rectangle with rotation in image coordinates.
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Attributes:
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x_center : The X coordinate of the top-left corner, in pixels.
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y_center : The Y coordinate of the top-left corner, in pixels.
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width: The width of the rectangle, in pixels.
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height: The height of the rectangle, in pixels.
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rotation: Rotation angle is clockwise in radians.
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rect_id: Optional unique id to help associate different rectangles to each
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other.
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"""
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x_center: int
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y_center: int
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width: int
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height: int
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rotation: Optional[float] = 0.0
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rect_id: Optional[int] = None
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _RectProto:
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"""Generates a Rect protobuf object."""
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return _RectProto(
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x_center=self.x_center,
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y_center=self.y_center,
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width=self.width,
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height=self.height,
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)
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@classmethod
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@doc_controls.do_not_generate_docs
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def create_from_pb2(cls, pb2_obj: _RectProto) -> 'Rect':
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"""Creates a `Rect` object from the given protobuf object."""
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return Rect(
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x_center=pb2_obj.x_center,
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y_center=pb2_obj.y_center,
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width=pb2_obj.width,
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height=pb2_obj.height)
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def __eq__(self, other: Any) -> bool:
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"""Checks if this object is equal to the given object.
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Args:
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other: The object to be compared with.
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Returns:
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True if the objects are equal.
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"""
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if not isinstance(other, Rect):
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return False
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return self.to_pb2().__eq__(other.to_pb2())
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@dataclasses.dataclass
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class NormalizedRect:
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"""A rectangle with rotation in normalized coordinates. The values of box
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center location and size are within [0, 1].
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Attributes:
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x_center : The X normalized coordinate of the top-left corner.
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y_center : The Y normalized coordinate of the top-left corner.
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width: The width of the rectangle.
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height: The height of the rectangle.
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rotation: Rotation angle is clockwise in radians.
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rect_id: Optional unique id to help associate different rectangles to each
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other.
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"""
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x_center: float
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y_center: float
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width: float
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height: float
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rotation: Optional[float] = 0.0
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rect_id: Optional[int] = None
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _NormalizedRectProto:
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||||||
|
"""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())
|
28
mediapipe/tasks/python/components/proto/BUILD
Normal file
28
mediapipe/tasks/python/components/proto/BUILD
Normal file
|
@ -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",
|
||||||
|
],
|
||||||
|
)
|
13
mediapipe/tasks/python/components/proto/__init__.py
Normal file
13
mediapipe/tasks/python/components/proto/__init__.py
Normal file
|
@ -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.
|
72
mediapipe/tasks/python/components/proto/embedder_options.py
Normal file
72
mediapipe/tasks/python/components/proto/embedder_options.py
Normal file
|
@ -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())
|
|
@ -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",
|
||||||
|
],
|
||||||
|
)
|
||||||
|
|
98
mediapipe/tasks/python/test/vision/image_embedder_test.py
Normal file
98
mediapipe/tasks/python/test/vision/image_embedder_test.py
Normal file
|
@ -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()
|
|
@ -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",
|
||||||
|
],
|
||||||
|
)
|
||||||
|
|
288
mediapipe/tasks/python/vision/image_embedder.py
Normal file
288
mediapipe/tasks/python/vision/image_embedder.py
Normal file
|
@ -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)
|
||||||
|
})
|
Loading…
Reference in New Issue
Block a user