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adding some docstrings

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Sefik Ilkin Serengil 2024-02-10 18:17:11 +00:00
parent 92f0fb6c3b
commit 626aa2e385
1 changed files with 33 additions and 0 deletions
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33
deepface/modules/verification.py
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@ -175,6 +175,14 @@ def verify(
def find_cosine_distance( def find_cosine_distance(
source_representation: Union[np.ndarray, list], test_representation: Union[np.ndarray, list] source_representation: Union[np.ndarray, list], test_representation: Union[np.ndarray, list]
) -> np.float64: ) -> np.float64:
"""
Find cosine distance between two given vectors
Args:
source_representation (np.ndarray or list): 1st vector
test_representation (np.ndarray or list): 2nd vector
Returns
distance (np.float64): calculated cosine distance
"""
if isinstance(source_representation, list): if isinstance(source_representation, list):
source_representation = np.array(source_representation) source_representation = np.array(source_representation)
@ -190,6 +198,14 @@ def find_cosine_distance(
def find_euclidean_distance( def find_euclidean_distance(
source_representation: Union[np.ndarray, list], test_representation: Union[np.ndarray, list] source_representation: Union[np.ndarray, list], test_representation: Union[np.ndarray, list]
) -> np.float64: ) -> np.float64:
"""
Find euclidean distance between two given vectors
Args:
source_representation (np.ndarray or list): 1st vector
test_representation (np.ndarray or list): 2nd vector
Returns
distance (np.float64): calculated euclidean distance
"""
if isinstance(source_representation, list): if isinstance(source_representation, list):
source_representation = np.array(source_representation) source_representation = np.array(source_representation)
@ -203,12 +219,29 @@ def find_euclidean_distance(
def l2_normalize(x: Union[np.ndarray, list]) -> np.ndarray: def l2_normalize(x: Union[np.ndarray, list]) -> np.ndarray:
"""
Normalize input vector with l2
Args:
x (np.ndarray or list): given vector
Returns:
y (np.ndarray): l2 normalized vector
"""
if isinstance(x, list): if isinstance(x, list):
x = np.array(x) x = np.array(x)
return x / np.sqrt(np.sum(np.multiply(x, x))) return x / np.sqrt(np.sum(np.multiply(x, x)))
def find_threshold(model_name: str, distance_metric: str) -> float: def find_threshold(model_name: str, distance_metric: str) -> float:
"""
Retrieve pre-tuned threshold values for a model and distance metric pair
Args:
model_name (str): facial recognition model name
distance_metric (str): distance metric name. Options are cosine, euclidean
and euclidean_l2.
Returns:
threshold (float): threshold value for that model name and distance metric
pair. Distances less than this threshold will be classified same person.
"""
base_threshold = {"cosine": 0.40, "euclidean": 0.55, "euclidean_l2": 0.75} base_threshold = {"cosine": 0.40, "euclidean": 0.55, "euclidean_l2": 0.75}