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Merge pull request #1299 from kremnik/distances

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Distances
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Sefik Ilkin Serengil 2024-08-05 07:47:09 +01:00 committed by GitHub
commit 285c6b7e36
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1 changed files with 10 additions and 11 deletions
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deepface/modules/verification.py
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@ -200,8 +200,9 @@ def verify(
# find the face pair with minimum distance
threshold = threshold or find_threshold(model_name, distance_metric)
distance = float(min(distances)) # best distance
facial_areas = facial_areas[np.argmin(distances)]
min_index = np.argmin(distances)
distance = float(distances[min_index]) # best distance
facial_areas = facial_areas[min_index]
toc = time.time()
@ -285,10 +286,10 @@ def find_cosine_distance(
if isinstance(test_representation, list):
test_representation = np.array(test_representation)
a = np.matmul(np.transpose(source_representation), test_representation)
b = np.sum(np.multiply(source_representation, source_representation))
c = np.sum(np.multiply(test_representation, test_representation))
return 1 - (a / (np.sqrt(b) * np.sqrt(c)))
a = np.dot(source_representation, test_representation)
b = np.linalg.norm(source_representation)
c = np.linalg.norm(test_representation)
return 1 - a / (b * c)
def find_euclidean_distance(
@ -308,10 +309,7 @@ def find_euclidean_distance(
if isinstance(test_representation, list):
test_representation = np.array(test_representation)
euclidean_distance = source_representation - test_representation
euclidean_distance = np.sum(np.multiply(euclidean_distance, euclidean_distance))
euclidean_distance = np.sqrt(euclidean_distance)
return euclidean_distance
return np.linalg.norm(source_representation - test_representation)
def l2_normalize(x: Union[np.ndarray, list]) -> np.ndarray:
@ -324,7 +322,8 @@ def l2_normalize(x: Union[np.ndarray, list]) -> np.ndarray:
"""
if isinstance(x, list):
x = np.array(x)
return x / np.sqrt(np.sum(np.multiply(x, x)))
norm = np.linalg.norm(x)
return x if norm == 0 else x / norm
def find_distance(