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making distance calculation functions camel case

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This commit is contained in:
Sefik Ilkin Serengil 2024-01-31 19:44:51 +00:00
parent 4800aa3e8c
commit cefb8013bb
5 changed files with 13 additions and 13 deletions
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6
deepface/commons/distance.py
View File

@ -2,7 +2,7 @@ from typing import Union
import numpy as np
def findCosineDistance(
def find_cosine_distance(
source_representation: Union[np.ndarray, list], test_representation: Union[np.ndarray, list]
) -> np.float64:
if isinstance(source_representation, list):
@ -17,7 +17,7 @@ def findCosineDistance(
return 1 - (a / (np.sqrt(b) * np.sqrt(c)))
def findEuclideanDistance(
def find_euclidean_distance(
source_representation: Union[np.ndarray, list], test_representation: Union[np.ndarray, list]
) -> np.float64:
if isinstance(source_representation, list):
@ -38,7 +38,7 @@ def l2_normalize(x: Union[np.ndarray, list]) -> np.ndarray:
return x / np.sqrt(np.sum(np.multiply(x, x)))
def findThreshold(model_name: str, distance_metric: str) -> float:
def find_threshold(model_name: str, distance_metric: str) -> float:
base_threshold = {"cosine": 0.40, "euclidean": 0.55, "euclidean_l2": 0.75}

8
deepface/modules/recognition.py
View File

@ -249,11 +249,11 @@ def find(
)
if distance_metric == "cosine":
distance = dst.findCosineDistance(source_representation, target_representation)
distance = dst.find_cosine_distance(source_representation, target_representation)
elif distance_metric == "euclidean":
distance = dst.findEuclideanDistance(source_representation, target_representation)
distance = dst.find_euclidean_distance(source_representation, target_representation)
elif distance_metric == "euclidean_l2":
distance = dst.findEuclideanDistance(
distance = dst.find_euclidean_distance(
dst.l2_normalize(source_representation),
dst.l2_normalize(target_representation),
)
@ -263,7 +263,7 @@ def find(
distances.append(distance)
# ---------------------------
target_threshold = threshold or dst.findThreshold(model_name, distance_metric)
target_threshold = threshold or dst.find_threshold(model_name, distance_metric)
result_df["threshold"] = target_threshold
result_df["distance"] = distances

8
deepface/modules/verification.py
View File

@ -133,11 +133,11 @@ def verify(
img2_representation = img2_embedding_obj[0]["embedding"]
if distance_metric == "cosine":
distance = dst.findCosineDistance(img1_representation, img2_representation)
distance = dst.find_cosine_distance(img1_representation, img2_representation)
elif distance_metric == "euclidean":
distance = dst.findEuclideanDistance(img1_representation, img2_representation)
distance = dst.find_euclidean_distance(img1_representation, img2_representation)
elif distance_metric == "euclidean_l2":
distance = dst.findEuclideanDistance(
distance = dst.find_euclidean_distance(
dst.l2_normalize(img1_representation), dst.l2_normalize(img2_representation)
)
else:
@ -147,7 +147,7 @@ def verify(
regions.append((img1_region, img2_region))
# -------------------------------
threshold = dst.findThreshold(model_name, distance_metric)
threshold = dst.find_threshold(model_name, distance_metric)
distance = min(distances) # best distance
facial_areas = regions[np.argmin(distances)]

2
tests/face-recognition-how.py
View File

@ -38,7 +38,7 @@ distance_vector = np.square(img1_representation - img2_representation)
current_distance = np.sqrt(distance_vector.sum())
logger.info(f"Euclidean distance: {current_distance}")
threshold = distance.findThreshold(model_name=model_name, distance_metric="euclidean")
threshold = distance.find_threshold(model_name=model_name, distance_metric="euclidean")
logger.info(f"Threshold for {model_name}-euclidean pair is {threshold}")
if current_distance < threshold:

2
tests/test_find.py
View File

@ -6,7 +6,7 @@ from deepface.commons.logger import Logger
logger = Logger("tests/test_find.py")
threshold = distance.findThreshold(model_name="VGG-Face", distance_metric="cosine")
threshold = distance.find_threshold(model_name="VGG-Face", distance_metric="cosine")
def test_find_with_exact_path():