Merge branch 'master' into text-embedder-python
This commit is contained in:
Kinar R
@@ -85,6 +85,7 @@ cc_test(
|
||||
"@com_google_absl//absl/status:statusor",
|
||||
"@com_google_absl//absl/strings",
|
||||
"@com_google_absl//absl/strings:cord",
|
||||
"@com_google_sentencepiece//src:sentencepiece_processor",
|
||||
"@org_tensorflow//tensorflow/lite/core/shims:cc_shims_test_util",
|
||||
],
|
||||
)
|
||||
|
||||
@@ -82,6 +82,7 @@ cc_test(
|
||||
"@com_google_absl//absl/flags:flag",
|
||||
"@com_google_absl//absl/status",
|
||||
"@com_google_absl//absl/status:statusor",
|
||||
"@com_google_sentencepiece//src:sentencepiece_processor",
|
||||
"@org_tensorflow//tensorflow/lite/core/shims:cc_shims_test_util",
|
||||
],
|
||||
)
|
||||
|
||||
@@ -83,6 +83,7 @@ cc_test(
|
||||
":sentencepiece_tokenizer",
|
||||
"//mediapipe/framework/port:gtest_main",
|
||||
"//mediapipe/tasks/cc/core:utils",
|
||||
"@com_google_sentencepiece//src:sentencepiece_processor",
|
||||
],
|
||||
)
|
||||
|
||||
@@ -132,6 +133,7 @@ cc_test(
|
||||
"@com_google_absl//absl/status:statusor",
|
||||
"@com_google_absl//absl/strings",
|
||||
"@com_google_absl//absl/strings:cord",
|
||||
"@com_google_sentencepiece//src:sentencepiece_processor",
|
||||
],
|
||||
)
|
||||
|
||||
|
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@@ -14,7 +14,7 @@
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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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from typing import 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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@@ -36,29 +36,11 @@ class FloatEmbedding:
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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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def create_from_pb2(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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return FloatEmbedding(values=np.array(pb2_obj.values, dtype=float))
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@dataclasses.dataclass
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@@ -71,30 +53,13 @@ class QuantizedEmbedding:
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|
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values: np.ndarray
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@doc_controls.do_not_generate_docs
|
||||
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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|
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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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|
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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.
|
||||
Returns:
|
||||
True if the objects are equal.
|
||||
"""
|
||||
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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values=np.array(bytearray(pb2_obj.values), dtype=np.uint8))
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@dataclasses.dataclass
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@@ -113,58 +78,31 @@ class Embedding:
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head_index: Optional[int] = None
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head_name: Optional[str] = None
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|
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@doc_controls.do_not_generate_docs
|
||||
def to_pb2(self) -> _EmbeddingProto:
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"""Generates a Embedding protobuf object."""
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|
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if self.embedding.dtype == float:
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return _EmbeddingProto(float_embedding=self.embedding,
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head_index=self.head_index,
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head_name=self.head_name)
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elif self.embedding.dtype == np.uint8:
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return _EmbeddingProto(quantized_embedding=bytes(self.embedding),
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head_index=self.head_index,
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head_name=self.head_name)
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else:
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raise ValueError("Invalid dtype. Only float and np.uint8 are supported.")
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|
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@classmethod
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@doc_controls.do_not_generate_docs
|
||||
def create_from_pb2(
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cls, pb2_obj: _EmbeddingProto) -> 'Embedding':
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def create_from_pb2(cls, pb2_obj: _EmbeddingProto) -> 'Embedding':
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"""Creates a `Embedding` object from the given protobuf object."""
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quantized_embedding = np.array(
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bytearray(pb2_obj.quantized_embedding.values))
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float_embedding = np.array(pb2_obj.float_embedding.values, dtype=float)
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if len(quantized_embedding) == 0:
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return Embedding(embedding=float_embedding,
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head_index=pb2_obj.head_index,
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head_name=pb2_obj.head_name)
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if not quantized_embedding:
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return Embedding(
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embedding=float_embedding,
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head_index=pb2_obj.head_index,
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head_name=pb2_obj.head_name)
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else:
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return Embedding(embedding=quantized_embedding,
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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, Embedding):
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return False
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return self.to_pb2().__eq__(other.to_pb2())
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return Embedding(
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embedding=quantized_embedding,
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head_index=pb2_obj.head_index,
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head_name=pb2_obj.head_name)
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||||
@dataclasses.dataclass
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class EmbeddingResult:
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"""Embedding results for a given embedder model.
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||||
Attributes:
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embeddings: A list of `Embedding` objects.
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timestamp_ms: The optional timestamp (in milliseconds) of the start of the
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@@ -178,33 +116,10 @@ class EmbeddingResult:
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embeddings: List[Embedding]
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timestamp_ms: Optional[int] = None
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||||
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||||
@doc_controls.do_not_generate_docs
|
||||
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
|
||||
def create_from_pb2(
|
||||
cls, pb2_obj: _EmbeddingResultProto) -> 'EmbeddingResult':
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||||
def create_from_pb2(cls, pb2_obj: _EmbeddingResultProto) -> 'EmbeddingResult':
|
||||
"""Creates a `EmbeddingResult` object from the given protobuf object."""
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return EmbeddingResult(
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embeddings=[
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Embedding.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.
|
||||
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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return EmbeddingResult(embeddings=[
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Embedding.create_from_pb2(embedding) for embedding in pb2_obj.embeddings
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])
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@@ -44,16 +44,14 @@ class EmbedderOptions:
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def to_pb2(self) -> _EmbedderOptionsProto:
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"""Generates a EmbedderOptions protobuf object."""
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return _EmbedderOptionsProto(
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l2_normalize=self.l2_normalize,
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quantize=self.quantize)
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l2_normalize=self.l2_normalize, quantize=self.quantize)
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@classmethod
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@doc_controls.do_not_generate_docs
|
||||
def create_from_pb2(cls, pb2_obj: _EmbedderOptionsProto) -> 'EmbedderOptions':
|
||||
"""Creates a `EmbedderOptions` object from the given protobuf object."""
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||||
return EmbedderOptions(
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l2_normalize=pb2_obj.l2_normalize,
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quantize=pb2_obj.quantize)
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l2_normalize=pb2_obj.l2_normalize, quantize=pb2_obj.quantize)
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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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||||
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@@ -14,6 +14,8 @@
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||||
|
||||
# Placeholder for internal Python strict library compatibility macro.
|
||||
|
||||
# Placeholder for internal Python strict library and test compatibility macro.
|
||||
|
||||
package(default_visibility = ["//mediapipe/tasks:internal"])
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||||
|
||||
licenses(["notice"])
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||||
|
||||
@@ -23,6 +23,8 @@ _EmbedderOptions = embedder_options.EmbedderOptions
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||||
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def _compute_cosine_similarity(u, v):
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"""Computes cosine similarity between two embeddings."""
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||||
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||||
if len(u.embedding) <= 0:
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raise ValueError("Cannot compute cosing similarity on empty embeddings.")
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||||
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||||
@@ -38,6 +40,7 @@ def _compute_cosine_similarity(u, v):
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||||
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||||
def cosine_similarity(u: _Embedding, v: _Embedding) -> float:
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||||
"""Utility function to compute cosine similarity between two embedding.
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||||
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||||
May return an InvalidArgumentError if e.g. the feature vectors are of
|
||||
different types (quantized vs. float), have different sizes, or have an
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||||
L2-norm of 0.
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||||
@@ -45,6 +48,9 @@ def cosine_similarity(u: _Embedding, v: _Embedding) -> float:
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||||
Args:
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||||
u: An embedding.
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||||
v: An embedding.
|
||||
|
||||
Returns:
|
||||
Cosine similarity value.
|
||||
"""
|
||||
if len(u.embedding) != len(v.embedding):
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||||
raise ValueError(f"Cannot compute cosine similarity between embeddings "
|
||||
|
||||
@@ -125,7 +125,7 @@ class ModelFileType(enum.Enum):
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||||
FILE_NAME = 2
|
||||
|
||||
|
||||
class ImageClassifierTest(parameterized.TestCase):
|
||||
class TextClassifierTest(parameterized.TestCase):
|
||||
|
||||
def setUp(self):
|
||||
super().setUp()
|
||||
|
||||
@@ -58,6 +58,26 @@ py_test(
|
||||
],
|
||||
)
|
||||
|
||||
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/containers:embedding_result",
|
||||
"//mediapipe/tasks/python/components/containers:rect",
|
||||
"//mediapipe/tasks/python/components/processors:embedder_options",
|
||||
"//mediapipe/tasks/python/core:base_options",
|
||||
"//mediapipe/tasks/python/test:test_utils",
|
||||
"//mediapipe/tasks/python/vision:image_embedder",
|
||||
"//mediapipe/tasks/python/vision/core:image_processing_options",
|
||||
"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
|
||||
],
|
||||
)
|
||||
|
||||
py_test(
|
||||
name = "image_segmenter_test",
|
||||
srcs = ["image_segmenter_test.py"],
|
||||
|
||||
@@ -0,0 +1,402 @@
|
||||
# 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
|
||||
import os
|
||||
from unittest import mock
|
||||
|
||||
from absl.testing import absltest
|
||||
from absl.testing import parameterized
|
||||
import numpy as np
|
||||
|
||||
from mediapipe.python._framework_bindings import image as image_module
|
||||
from mediapipe.tasks.python.components.containers import embedding_result as embedding_result_module
|
||||
from mediapipe.tasks.python.components.containers import rect
|
||||
from mediapipe.tasks.python.components.processors import embedder_options as embedder_options_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 image_processing_options as image_processing_options_module
|
||||
from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
|
||||
|
||||
ImageEmbedderResult = embedding_result_module.EmbeddingResult
|
||||
_Rect = rect.Rect
|
||||
_BaseOptions = base_options_module.BaseOptions
|
||||
_EmbedderOptions = embedder_options_module.EmbedderOptions
|
||||
_FloatEmbedding = embedding_result_module.FloatEmbedding
|
||||
_QuantizedEmbedding = embedding_result_module.QuantizedEmbedding
|
||||
_Embedding = embedding_result_module.Embedding
|
||||
_Image = image_module.Image
|
||||
_ImageEmbedder = image_embedder.ImageEmbedder
|
||||
_ImageEmbedderOptions = image_embedder.ImageEmbedderOptions
|
||||
_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
|
||||
_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
|
||||
|
||||
_MODEL_FILE = 'mobilenet_v3_small_100_224_embedder.tflite'
|
||||
_BURGER_IMAGE_FILE = 'burger.jpg'
|
||||
_BURGER_CROPPED_IMAGE_FILE = 'burger_crop.jpg'
|
||||
_TEST_DATA_DIR = 'mediapipe/tasks/testdata/vision'
|
||||
# Tolerance for embedding vector coordinate values.
|
||||
_EPSILON = 1e-4
|
||||
# Tolerance for cosine similarity evaluation.
|
||||
_SIMILARITY_TOLERANCE = 1e-6
|
||||
|
||||
|
||||
class ModelFileType(enum.Enum):
|
||||
FILE_CONTENT = 1
|
||||
FILE_NAME = 2
|
||||
|
||||
|
||||
class ImageEmbedderTest(parameterized.TestCase):
|
||||
|
||||
def setUp(self):
|
||||
super().setUp()
|
||||
self.test_image = _Image.create_from_file(
|
||||
test_utils.get_test_data_path(
|
||||
os.path.join(_TEST_DATA_DIR, _BURGER_IMAGE_FILE)))
|
||||
self.test_cropped_image = _Image.create_from_file(
|
||||
test_utils.get_test_data_path(
|
||||
os.path.join(_TEST_DATA_DIR, _BURGER_CROPPED_IMAGE_FILE)))
|
||||
self.model_path = test_utils.get_test_data_path(
|
||||
os.path.join(_TEST_DATA_DIR, _MODEL_FILE))
|
||||
|
||||
def test_create_from_file_succeeds_with_valid_model_path(self):
|
||||
# Creates with default option and valid model file successfully.
|
||||
with _ImageEmbedder.create_from_model_path(self.model_path) as embedder:
|
||||
self.assertIsInstance(embedder, _ImageEmbedder)
|
||||
|
||||
def test_create_from_options_succeeds_with_valid_model_path(self):
|
||||
# Creates with options containing model file successfully.
|
||||
base_options = _BaseOptions(model_asset_path=self.model_path)
|
||||
options = _ImageEmbedderOptions(base_options=base_options)
|
||||
with _ImageEmbedder.create_from_options(options) as embedder:
|
||||
self.assertIsInstance(embedder, _ImageEmbedder)
|
||||
|
||||
def test_create_from_options_fails_with_invalid_model_path(self):
|
||||
with self.assertRaisesRegex(
|
||||
RuntimeError, 'Unable to open file at /path/to/invalid/model.tflite'):
|
||||
base_options = _BaseOptions(
|
||||
model_asset_path='/path/to/invalid/model.tflite')
|
||||
options = _ImageEmbedderOptions(base_options=base_options)
|
||||
_ImageEmbedder.create_from_options(options)
|
||||
|
||||
def test_create_from_options_succeeds_with_valid_model_content(self):
|
||||
# Creates with options containing model content successfully.
|
||||
with open(self.model_path, 'rb') as f:
|
||||
base_options = _BaseOptions(model_asset_buffer=f.read())
|
||||
options = _ImageEmbedderOptions(base_options=base_options)
|
||||
embedder = _ImageEmbedder.create_from_options(options)
|
||||
self.assertIsInstance(embedder, _ImageEmbedder)
|
||||
|
||||
def _check_embedding_value(self, result, expected_first_value):
|
||||
# Check embedding first value.
|
||||
self.assertAlmostEqual(
|
||||
result.embeddings[0].embedding[0], expected_first_value, delta=_EPSILON)
|
||||
|
||||
def _check_embedding_size(self, result, quantize, expected_embedding_size):
|
||||
# Check embedding size.
|
||||
self.assertLen(result.embeddings, 1)
|
||||
embedding_result = result.embeddings[0]
|
||||
self.assertLen(embedding_result.embedding, expected_embedding_size)
|
||||
if quantize:
|
||||
self.assertEqual(embedding_result.embedding.dtype, np.uint8)
|
||||
else:
|
||||
self.assertEqual(embedding_result.embedding.dtype, float)
|
||||
|
||||
def _check_cosine_similarity(self, result0, result1, expected_similarity):
|
||||
# Checks cosine similarity.
|
||||
similarity = _ImageEmbedder.cosine_similarity(result0.embeddings[0],
|
||||
result1.embeddings[0])
|
||||
self.assertAlmostEqual(
|
||||
similarity, expected_similarity, delta=_SIMILARITY_TOLERANCE)
|
||||
|
||||
@parameterized.parameters(
|
||||
(False, False, False, ModelFileType.FILE_NAME, 0.925519, 1024,
|
||||
(-0.2101883, -0.193027)),
|
||||
(True, False, False, ModelFileType.FILE_NAME, 0.925519, 1024,
|
||||
(-0.0142344, -0.0131606)),
|
||||
# (False, True, False, ModelFileType.FILE_NAME,
|
||||
# 0.926791, 1024, (229, 231)),
|
||||
(False, False, True, ModelFileType.FILE_CONTENT, 0.999931, 1024,
|
||||
(-0.195062, -0.193027)))
|
||||
def test_embed(self, l2_normalize, quantize, with_roi, model_file_type,
|
||||
expected_similarity, expected_size, expected_first_values):
|
||||
# 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)
|
||||
|
||||
image_processing_options = None
|
||||
if with_roi:
|
||||
# Region-of-interest in "burger.jpg" corresponding to "burger_crop.jpg".
|
||||
roi = _Rect(left=0, top=0, right=0.833333, bottom=1)
|
||||
image_processing_options = _ImageProcessingOptions(roi)
|
||||
|
||||
# Extracts both embeddings.
|
||||
image_result = embedder.embed(self.test_image, image_processing_options)
|
||||
crop_result = embedder.embed(self.test_cropped_image)
|
||||
|
||||
# Checks embeddings and cosine similarity.
|
||||
expected_result0_value, expected_result1_value = expected_first_values
|
||||
self._check_embedding_size(image_result, quantize, expected_size)
|
||||
self._check_embedding_size(crop_result, quantize, expected_size)
|
||||
self._check_embedding_value(image_result, expected_result0_value)
|
||||
self._check_embedding_value(crop_result, expected_result1_value)
|
||||
self._check_cosine_similarity(image_result, crop_result,
|
||||
expected_similarity)
|
||||
# Closes the embedder explicitly when the embedder is not used in
|
||||
# a context.
|
||||
embedder.close()
|
||||
|
||||
@parameterized.parameters(
|
||||
(False, False, ModelFileType.FILE_NAME, 0.925519),
|
||||
(False, False, ModelFileType.FILE_CONTENT, 0.925519))
|
||||
def test_embed_in_context(self, l2_normalize, quantize, model_file_type,
|
||||
expected_similarity):
|
||||
# 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)
|
||||
|
||||
with _ImageEmbedder.create_from_options(options) as embedder:
|
||||
# Extracts both embeddings.
|
||||
image_result = embedder.embed(self.test_image)
|
||||
crop_result = embedder.embed(self.test_cropped_image)
|
||||
|
||||
# Checks cosine similarity.
|
||||
self._check_cosine_similarity(image_result, crop_result,
|
||||
expected_similarity)
|
||||
|
||||
def test_missing_result_callback(self):
|
||||
options = _ImageEmbedderOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM)
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'result callback must be provided'):
|
||||
with _ImageEmbedder.create_from_options(options) as unused_embedder:
|
||||
pass
|
||||
|
||||
@parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO))
|
||||
def test_illegal_result_callback(self, running_mode):
|
||||
options = _ImageEmbedderOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=running_mode,
|
||||
result_callback=mock.MagicMock())
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'result callback should not be provided'):
|
||||
with _ImageEmbedder.create_from_options(options) as unused_embedder:
|
||||
pass
|
||||
|
||||
def test_calling_embed_for_video_in_image_mode(self):
|
||||
options = _ImageEmbedderOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.IMAGE)
|
||||
with _ImageEmbedder.create_from_options(options) as embedder:
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'not initialized with the video mode'):
|
||||
embedder.embed_for_video(self.test_image, 0)
|
||||
|
||||
def test_calling_embed_async_in_image_mode(self):
|
||||
options = _ImageEmbedderOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.IMAGE)
|
||||
with _ImageEmbedder.create_from_options(options) as embedder:
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'not initialized with the live stream mode'):
|
||||
embedder.embed_async(self.test_image, 0)
|
||||
|
||||
def test_calling_embed_in_video_mode(self):
|
||||
options = _ImageEmbedderOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.VIDEO)
|
||||
with _ImageEmbedder.create_from_options(options) as embedder:
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'not initialized with the image mode'):
|
||||
embedder.embed(self.test_image)
|
||||
|
||||
def test_calling_embed_async_in_video_mode(self):
|
||||
options = _ImageEmbedderOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.VIDEO)
|
||||
with _ImageEmbedder.create_from_options(options) as embedder:
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'not initialized with the live stream mode'):
|
||||
embedder.embed_async(self.test_image, 0)
|
||||
|
||||
def test_embed_for_video_with_out_of_order_timestamp(self):
|
||||
options = _ImageEmbedderOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.VIDEO)
|
||||
with _ImageEmbedder.create_from_options(options) as embedder:
|
||||
unused_result = embedder.embed_for_video(self.test_image, 1)
|
||||
with self.assertRaisesRegex(
|
||||
ValueError, r'Input timestamp must be monotonically increasing'):
|
||||
embedder.embed_for_video(self.test_image, 0)
|
||||
|
||||
def test_embed_for_video(self):
|
||||
options = _ImageEmbedderOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.VIDEO)
|
||||
with _ImageEmbedder.create_from_options(options) as embedder0, \
|
||||
_ImageEmbedder.create_from_options(options) as embedder1:
|
||||
for timestamp in range(0, 300, 30):
|
||||
# Extracts both embeddings.
|
||||
image_result = embedder0.embed_for_video(self.test_image, timestamp)
|
||||
crop_result = embedder1.embed_for_video(self.test_cropped_image,
|
||||
timestamp)
|
||||
# Checks cosine similarity.
|
||||
self._check_cosine_similarity(
|
||||
image_result, crop_result, expected_similarity=0.925519)
|
||||
|
||||
def test_embed_for_video_succeeds_with_region_of_interest(self):
|
||||
options = _ImageEmbedderOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.VIDEO)
|
||||
with _ImageEmbedder.create_from_options(options) as embedder0, \
|
||||
_ImageEmbedder.create_from_options(options) as embedder1:
|
||||
# Region-of-interest in "burger.jpg" corresponding to "burger_crop.jpg".
|
||||
roi = _Rect(left=0, top=0, right=0.833333, bottom=1)
|
||||
image_processing_options = _ImageProcessingOptions(roi)
|
||||
|
||||
for timestamp in range(0, 300, 30):
|
||||
# Extracts both embeddings.
|
||||
image_result = embedder0.embed_for_video(self.test_image, timestamp,
|
||||
image_processing_options)
|
||||
crop_result = embedder1.embed_for_video(self.test_cropped_image,
|
||||
timestamp)
|
||||
|
||||
# Checks cosine similarity.
|
||||
self._check_cosine_similarity(
|
||||
image_result, crop_result, expected_similarity=0.999931)
|
||||
|
||||
def test_calling_embed_in_live_stream_mode(self):
|
||||
options = _ImageEmbedderOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
result_callback=mock.MagicMock())
|
||||
with _ImageEmbedder.create_from_options(options) as embedder:
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'not initialized with the image mode'):
|
||||
embedder.embed(self.test_image)
|
||||
|
||||
def test_calling_embed_for_video_in_live_stream_mode(self):
|
||||
options = _ImageEmbedderOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
result_callback=mock.MagicMock())
|
||||
with _ImageEmbedder.create_from_options(options) as embedder:
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'not initialized with the video mode'):
|
||||
embedder.embed_for_video(self.test_image, 0)
|
||||
|
||||
def test_embed_async_calls_with_illegal_timestamp(self):
|
||||
options = _ImageEmbedderOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
result_callback=mock.MagicMock())
|
||||
with _ImageEmbedder.create_from_options(options) as embedder:
|
||||
embedder.embed_async(self.test_image, 100)
|
||||
with self.assertRaisesRegex(
|
||||
ValueError, r'Input timestamp must be monotonically increasing'):
|
||||
embedder.embed_async(self.test_image, 0)
|
||||
|
||||
def test_embed_async_calls(self):
|
||||
# Get the embedding result for the cropped image.
|
||||
options = _ImageEmbedderOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.IMAGE)
|
||||
with _ImageEmbedder.create_from_options(options) as embedder:
|
||||
crop_result = embedder.embed(self.test_cropped_image)
|
||||
|
||||
observed_timestamp_ms = -1
|
||||
|
||||
def check_result(result: ImageEmbedderResult, output_image: _Image,
|
||||
timestamp_ms: int):
|
||||
# Checks cosine similarity.
|
||||
self._check_cosine_similarity(
|
||||
result, crop_result, expected_similarity=0.925519)
|
||||
self.assertTrue(
|
||||
np.array_equal(output_image.numpy_view(),
|
||||
self.test_image.numpy_view()))
|
||||
self.assertLess(observed_timestamp_ms, timestamp_ms)
|
||||
self.observed_timestamp_ms = timestamp_ms
|
||||
|
||||
options = _ImageEmbedderOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
result_callback=check_result)
|
||||
with _ImageEmbedder.create_from_options(options) as embedder:
|
||||
for timestamp in range(0, 300, 30):
|
||||
embedder.embed_async(self.test_image, timestamp)
|
||||
|
||||
def test_embed_async_succeeds_with_region_of_interest(self):
|
||||
# Get the embedding result for the cropped image.
|
||||
options = _ImageEmbedderOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.IMAGE)
|
||||
with _ImageEmbedder.create_from_options(options) as embedder:
|
||||
crop_result = embedder.embed(self.test_cropped_image)
|
||||
|
||||
# Region-of-interest in "burger.jpg" corresponding to "burger_crop.jpg".
|
||||
roi = _Rect(left=0, top=0, right=0.833333, bottom=1)
|
||||
image_processing_options = _ImageProcessingOptions(roi)
|
||||
observed_timestamp_ms = -1
|
||||
|
||||
def check_result(result: ImageEmbedderResult, output_image: _Image,
|
||||
timestamp_ms: int):
|
||||
# Checks cosine similarity.
|
||||
self._check_cosine_similarity(
|
||||
result, crop_result, expected_similarity=0.999931)
|
||||
self.assertTrue(
|
||||
np.array_equal(output_image.numpy_view(),
|
||||
self.test_image.numpy_view()))
|
||||
self.assertLess(observed_timestamp_ms, timestamp_ms)
|
||||
self.observed_timestamp_ms = timestamp_ms
|
||||
|
||||
options = _ImageEmbedderOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
result_callback=check_result)
|
||||
with _ImageEmbedder.create_from_options(options) as embedder:
|
||||
for timestamp in range(0, 300, 30):
|
||||
embedder.embed_async(self.test_image, timestamp,
|
||||
image_processing_options)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
absltest.main()
|
||||
@@ -79,6 +79,29 @@ py_library(
|
||||
],
|
||||
)
|
||||
|
||||
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/components/containers/proto:embeddings_py_pb2",
|
||||
"//mediapipe/tasks/cc/vision/image_embedder/proto:image_embedder_graph_options_py_pb2",
|
||||
"//mediapipe/tasks/python/components/containers:embedding_result",
|
||||
"//mediapipe/tasks/python/components/processors:embedder_options",
|
||||
"//mediapipe/tasks/python/components/utils:cosine_similarity",
|
||||
"//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:image_processing_options",
|
||||
"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
|
||||
],
|
||||
)
|
||||
|
||||
py_library(
|
||||
name = "gesture_recognizer",
|
||||
srcs = [
|
||||
|
||||
@@ -18,6 +18,7 @@ import mediapipe.tasks.python.vision.core
|
||||
import mediapipe.tasks.python.vision.gesture_recognizer
|
||||
import mediapipe.tasks.python.vision.hand_landmarker
|
||||
import mediapipe.tasks.python.vision.image_classifier
|
||||
import mediapipe.tasks.python.vision.image_embedder
|
||||
import mediapipe.tasks.python.vision.image_segmenter
|
||||
import mediapipe.tasks.python.vision.object_detector
|
||||
|
||||
@@ -29,6 +30,10 @@ HandLandmarkerOptions = hand_landmarker.HandLandmarkerOptions
|
||||
HandLandmarkerResult = hand_landmarker.HandLandmarkerResult
|
||||
ImageClassifier = image_classifier.ImageClassifier
|
||||
ImageClassifierOptions = image_classifier.ImageClassifierOptions
|
||||
ImageClassifierResult = image_classifier.ImageClassifierResult
|
||||
ImageEmbedder = image_embedder.ImageEmbedder
|
||||
ImageEmbedderOptions = image_embedder.ImageEmbedderOptions
|
||||
ImageEmbedderResult = image_embedder.ImageEmbedderResult
|
||||
ImageSegmenter = image_segmenter.ImageSegmenter
|
||||
ImageSegmenterOptions = image_segmenter.ImageSegmenterOptions
|
||||
ObjectDetector = object_detector.ObjectDetector
|
||||
@@ -40,6 +45,7 @@ del core
|
||||
del gesture_recognizer
|
||||
del hand_landmarker
|
||||
del image_classifier
|
||||
del image_embedder
|
||||
del image_segmenter
|
||||
del object_detector
|
||||
del mediapipe
|
||||
|
||||
@@ -0,0 +1,309 @@
|
||||
# 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.tasks.cc.components.containers.proto import embeddings_pb2
|
||||
from mediapipe.tasks.cc.vision.image_embedder.proto import image_embedder_graph_options_pb2
|
||||
from mediapipe.tasks.python.components.containers import embedding_result as embedding_result_module
|
||||
from mediapipe.tasks.python.components.processors import embedder_options
|
||||
from mediapipe.tasks.python.components.utils import cosine_similarity
|
||||
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 image_processing_options as image_processing_options_module
|
||||
from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
|
||||
|
||||
ImageEmbedderResult = embedding_result_module.EmbeddingResult
|
||||
_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
|
||||
_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
|
||||
|
||||
_EMBEDDINGS_OUT_STREAM_NAME = 'embeddings_out'
|
||||
_EMBEDDINGS_TAG = 'EMBEDDINGS'
|
||||
_IMAGE_IN_STREAM_NAME = 'image_in'
|
||||
_IMAGE_OUT_STREAM_NAME = 'image_out'
|
||||
_IMAGE_TAG = 'IMAGE'
|
||||
_NORM_RECT_STREAM_NAME = 'norm_rect_in'
|
||||
_NORM_RECT_TAG = 'NORM_RECT'
|
||||
_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.image_embedder.ImageEmbedderGraph'
|
||||
_MICRO_SECONDS_PER_MILLISECOND = 1000
|
||||
|
||||
|
||||
@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[
|
||||
[ImageEmbedderResult, 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 `ImageEmbedder` 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 = embeddings_pb2.EmbeddingResult()
|
||||
embedding_result_proto.CopyFrom(
|
||||
packet_getter.get_proto(output_packets[_EMBEDDINGS_OUT_STREAM_NAME]))
|
||||
|
||||
image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
|
||||
timestamp = output_packets[_IMAGE_OUT_STREAM_NAME].timestamp
|
||||
options.result_callback(
|
||||
ImageEmbedderResult.create_from_pb2(embedding_result_proto), 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_STREAM_NAME]),
|
||||
],
|
||||
output_streams=[
|
||||
':'.join([_EMBEDDINGS_TAG, _EMBEDDINGS_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,
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None
|
||||
) -> ImageEmbedderResult:
|
||||
"""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.
|
||||
image_processing_options: Options for image processing.
|
||||
|
||||
Returns:
|
||||
An 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.
|
||||
"""
|
||||
normalized_rect = self.convert_to_normalized_rect(image_processing_options)
|
||||
output_packets = self._process_image_data({
|
||||
_IMAGE_IN_STREAM_NAME:
|
||||
packet_creator.create_image(image),
|
||||
_NORM_RECT_STREAM_NAME:
|
||||
packet_creator.create_proto(normalized_rect.to_pb2())
|
||||
})
|
||||
|
||||
embedding_result_proto = embeddings_pb2.EmbeddingResult()
|
||||
embedding_result_proto.CopyFrom(
|
||||
packet_getter.get_proto(output_packets[_EMBEDDINGS_OUT_STREAM_NAME]))
|
||||
|
||||
return ImageEmbedderResult.create_from_pb2(embedding_result_proto)
|
||||
|
||||
def embed_for_video(
|
||||
self,
|
||||
image: image_module.Image,
|
||||
timestamp_ms: int,
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None
|
||||
) -> ImageEmbedderResult:
|
||||
"""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.
|
||||
image_processing_options: Options for image processing.
|
||||
|
||||
Returns:
|
||||
An 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.
|
||||
"""
|
||||
normalized_rect = self.convert_to_normalized_rect(image_processing_options)
|
||||
output_packets = self._process_video_data({
|
||||
_IMAGE_IN_STREAM_NAME:
|
||||
packet_creator.create_image(image).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
|
||||
_NORM_RECT_STREAM_NAME:
|
||||
packet_creator.create_proto(normalized_rect.to_pb2()).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
|
||||
})
|
||||
embedding_result_proto = embeddings_pb2.EmbeddingResult()
|
||||
embedding_result_proto.CopyFrom(
|
||||
packet_getter.get_proto(output_packets[_EMBEDDINGS_OUT_STREAM_NAME]))
|
||||
|
||||
return ImageEmbedderResult.create_from_pb2(embedding_result_proto)
|
||||
|
||||
def embed_async(
|
||||
self,
|
||||
image: image_module.Image,
|
||||
timestamp_ms: int,
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None
|
||||
) -> None:
|
||||
"""Sends live image data to embedder.
|
||||
|
||||
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:
|
||||
- An 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.
|
||||
image_processing_options: Options for image processing.
|
||||
|
||||
Raises:
|
||||
ValueError: If the current input timestamp is smaller than what the image
|
||||
embedder has already processed.
|
||||
"""
|
||||
normalized_rect = self.convert_to_normalized_rect(image_processing_options)
|
||||
self._send_live_stream_data({
|
||||
_IMAGE_IN_STREAM_NAME:
|
||||
packet_creator.create_image(image).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
|
||||
_NORM_RECT_STREAM_NAME:
|
||||
packet_creator.create_proto(normalized_rect.to_pb2()).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
|
||||
})
|
||||
|
||||
@classmethod
|
||||
def cosine_similarity(cls, u: embedding_result_module.Embedding,
|
||||
v: embedding_result_module.Embedding) -> float:
|
||||
"""Utility function to compute cosine similarity between two embedding entries.
|
||||
|
||||
May return an InvalidArgumentError if e.g. the feature vectors are of
|
||||
different types (quantized vs. float), have different sizes, or have an
|
||||
L2-norm of 0.
|
||||
|
||||
Args:
|
||||
u: An embedding entry.
|
||||
v: An embedding entry.
|
||||
|
||||
Returns:
|
||||
The cosine similarity for the two embeddings.
|
||||
|
||||
Raises:
|
||||
ValueError: May return an error if e.g. the feature vectors are of
|
||||
different types (quantized vs. float), have different sizes, or have
|
||||
an L2-norm of 0.
|
||||
"""
|
||||
return cosine_similarity.cosine_similarity(u, v)
|
||||
@@ -31,6 +31,7 @@ rollup_bundle(
|
||||
name = "audio_bundle",
|
||||
config_file = "rollup.config.mjs",
|
||||
entry_point = "audio.ts",
|
||||
format = "cjs",
|
||||
output_dir = False,
|
||||
deps = [
|
||||
":audio_lib",
|
||||
@@ -67,6 +68,7 @@ rollup_bundle(
|
||||
name = "text_bundle",
|
||||
config_file = "rollup.config.mjs",
|
||||
entry_point = "text.ts",
|
||||
format = "cjs",
|
||||
output_dir = False,
|
||||
deps = [
|
||||
":text_lib",
|
||||
@@ -103,6 +105,7 @@ rollup_bundle(
|
||||
name = "vision_bundle",
|
||||
config_file = "rollup.config.mjs",
|
||||
entry_point = "vision.ts",
|
||||
format = "cjs",
|
||||
output_dir = False,
|
||||
deps = [
|
||||
":vision_lib",
|
||||
|
||||
@@ -2,16 +2,15 @@
|
||||
"name": "@mediapipe/tasks-__NAME__",
|
||||
"version": "__VERSION__",
|
||||
"description": "__DESCRIPTION__",
|
||||
"main": "__NAME__bundle.js",
|
||||
"module": "__NAME__bundle.js",
|
||||
"main": "__NAME___bundle.js",
|
||||
"module": "__NAME___bundle.js",
|
||||
"exports": {
|
||||
".": "./__NAME__bundle.js",
|
||||
"./loader": "./wasm/__NAME__wasm_internal.js",
|
||||
"./wasm": "./wasm/__NAME__wasm_internal.wasm"
|
||||
".": "./__NAME___bundle.js",
|
||||
"./loader": "./wasm/__NAME___wasm_internal.js",
|
||||
"./wasm": "./wasm/__NAME___wasm_internal.wasm"
|
||||
},
|
||||
"author": "[email protected]",
|
||||
"license": "Apache-2.0",
|
||||
"type": "module",
|
||||
"dependencies": {
|
||||
"google-protobuf": "^3.21.2"
|
||||
},
|
||||
|
||||
Reference in New Issue
Block a user