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venv/lib/python3.12/site-packages/mediapipe/tasks/python/components/utils/__init__.py

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+# Copyright 2022 The MediaPipe Authors.
+#
+# 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.

venv/lib/python3.12/site-packages/mediapipe/tasks/python/components/utils/cosine_similarity.py

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+# Copyright 2022 The MediaPipe Authors.
+#
+# 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.
+"""Cosine similarity utilities."""
+
+import numpy as np
+
+from mediapipe.tasks.python.components.containers import embedding_result
+
+_Embedding = embedding_result.Embedding
+
+
+def _compute_cosine_similarity(u: np.ndarray, v: np.ndarray):
+  """Computes cosine similarity between two embeddings."""
+
+  if len(u) <= 0:
+    raise ValueError("Cannot compute cosing similarity on empty embeddings.")
+
+  norm_u = np.linalg.norm(u)
+  norm_v = np.linalg.norm(v)
+
+  if norm_u <= 0 or norm_v <= 0:
+    raise ValueError(
+        "Cannot compute cosine similarity on embedding with 0 norm.")
+
+  return u.dot(v) / (norm_u * norm_v)
+
+
+def cosine_similarity(u: _Embedding, v: _Embedding) -> float:
+  """Utility function to compute cosine similarity between two embedding.
+
+  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.
+    v: An embedding.
+
+  Returns:
+    Cosine similarity value.
+  """
+  if len(u.embedding) != len(v.embedding):
+    raise ValueError(f"Cannot compute cosine similarity between embeddings "
+                     f"of different sizes "
+                     f"({len(u.embedding)} vs. {len(v.embedding)}).")
+
+  if u.embedding.dtype == float and v.embedding.dtype == float:
+    return _compute_cosine_similarity(u.embedding, v.embedding)
+
+  if u.embedding.dtype == np.uint8 and v.embedding.dtype == np.uint8:
+    return _compute_cosine_similarity(
+        u.embedding.view("int8").astype("float"),
+        v.embedding.view("int8").astype("float"),
+    )
+
+  raise ValueError("Cannot compute cosine similarity between quantized and "
+                   "float embeddings.")