Merge pull request #3738 from kinaryml:image-classification-python-impl

PiperOrigin-RevId: 483818404

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

@@ -0,0 +1,168 @@
+# Copyright 2022 The TensorFlow 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.
+"""Classifications data class."""
+
+import dataclasses
+from typing import Any, List, Optional
+
+from mediapipe.tasks.cc.components.containers.proto import classifications_pb2
+from mediapipe.tasks.python.components.containers import category as category_module
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+
+_ClassificationEntryProto = classifications_pb2.ClassificationEntry
+_ClassificationsProto = classifications_pb2.Classifications
+_ClassificationResultProto = classifications_pb2.ClassificationResult
+
+
+@dataclasses.dataclass
+class ClassificationEntry:
+  """List of predicted classes (aka labels) for a given classifier head.
+
+  Attributes:
+    categories: The array of predicted categories, usually sorted by descending
+      scores (e.g. from high to low probability).
+    timestamp_ms: The optional timestamp (in milliseconds) associated to the
+      classification entry. This is useful for time series use cases, e.g.,
+      audio classification.
+  """
+
+  categories: List[category_module.Category]
+  timestamp_ms: Optional[int] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _ClassificationEntryProto:
+    """Generates a ClassificationEntry protobuf object."""
+    return _ClassificationEntryProto(
+        categories=[category.to_pb2() for category in self.categories],
+        timestamp_ms=self.timestamp_ms)
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(
+      cls, pb2_obj: _ClassificationEntryProto) -> 'ClassificationEntry':
+    """Creates a `ClassificationEntry` object from the given protobuf object."""
+    return ClassificationEntry(
+        categories=[
+            category_module.Category.create_from_pb2(category)
+            for category in pb2_obj.categories
+        ],
+        timestamp_ms=pb2_obj.timestamp_ms)
+
+  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, ClassificationEntry):
+      return False
+
+    return self.to_pb2().__eq__(other.to_pb2())
+
+
+@dataclasses.dataclass
+class Classifications:
+  """Represents the classifications for a given classifier head.
+
+  Attributes:
+    entries: A list of `ClassificationEntry` objects.
+    head_index: The index of the classifier head these categories refer to. This
+      is useful for multi-head models.
+    head_name: The name of the classifier head, which is the corresponding
+      tensor metadata name.
+  """
+
+  entries: List[ClassificationEntry]
+  head_index: int
+  head_name: str
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _ClassificationsProto:
+    """Generates a Classifications protobuf object."""
+    return _ClassificationsProto(
+        entries=[entry.to_pb2() for entry in self.entries],
+        head_index=self.head_index,
+        head_name=self.head_name)
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(cls, pb2_obj: _ClassificationsProto) -> 'Classifications':
+    """Creates a `Classifications` object from the given protobuf object."""
+    return Classifications(
+        entries=[
+            ClassificationEntry.create_from_pb2(entry)
+            for entry in pb2_obj.entries
+        ],
+        head_index=pb2_obj.head_index,
+        head_name=pb2_obj.head_name)
+
+  def __eq__(self, other: Any) -> bool:
+    """Checks if this object is equal to the given object.
+
+    Args:
+      other: The object to be compared with.
+
+    Returns:
+      True if the objects are equal.
+    """
+    if not isinstance(other, Classifications):
+      return False
+
+    return self.to_pb2().__eq__(other.to_pb2())
+
+
+@dataclasses.dataclass
+class ClassificationResult:
+  """Contains one set of results per classifier head.
+
+  Attributes:
+    classifications: A list of `Classifications` objects.
+  """
+
+  classifications: List[Classifications]
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _ClassificationResultProto:
+    """Generates a ClassificationResult protobuf object."""
+    return _ClassificationResultProto(classifications=[
+        classification.to_pb2() for classification in self.classifications
+    ])
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(
+      cls, pb2_obj: _ClassificationResultProto) -> 'ClassificationResult':
+    """Creates a `ClassificationResult` object from the given protobuf object.
+    """
+    return ClassificationResult(classifications=[
+        Classifications.create_from_pb2(classification)
+        for classification in pb2_obj.classifications
+    ])
+
+  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, ClassificationResult):
+      return False
+
+    return self.to_pb2().__eq__(other.to_pb2())