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refactor: use const for model feature labels

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This commit moves the feature labels for the Emotion, Gender and Race model to their
respective modules.
This commit is contained in:
Nelson Dominguez 2023-01-29 17:30:24 +01:00
parent 4f47dc55c1
commit 96025c1118
4 changed files with 21 additions and 24 deletions
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33
deepface/DeepFace.py
View File

@ -26,6 +26,9 @@ from deepface.basemodels import (
SFace,
)
from deepface.extendedmodels import Age, Gender, Race, Emotion
from deepface.extendedmodels.Emotion import EMOTION_LABELS
from deepface.extendedmodels.Gender import GENDER_LABELS
from deepface.extendedmodels.Race import RACE_LABELS
from deepface.commons import functions, realtime, distance as dst
# -----------------------------------
@ -338,21 +341,12 @@ def analyze(
sum_of_predictions = emotion_predictions.sum()
obj["emotion"] = {}
emotion_labels = [
"angry",
"disgust",
"fear",
"happy",
"sad",
"surprise",
"neutral",
]
for i, emotion_label in enumerate(emotion_labels):
for i, emotion_label in enumerate(EMOTION_LABELS):
emotion_prediction = 100 * emotion_predictions[i] / sum_of_predictions
obj["emotion"][emotion_label] = emotion_prediction
obj["dominant_emotion"] = emotion_labels[np.argmax(emotion_predictions)]
obj["dominant_emotion"] = EMOTION_LABELS[np.argmax(emotion_predictions)]
elif action == "age":
age_predictions = models["age"].predict(img_content, verbose=0)[0, :]
@ -362,32 +356,23 @@ def analyze(
elif action == "gender":
gender_predictions = models["gender"].predict(img_content, verbose=0)[0, :]
gender_labels = ["Woman", "Man"]
obj["gender"] = {}
for i, gender_label in enumerate(gender_labels):
for i, gender_label in enumerate(GENDER_LABELS):
gender_prediction = 100 * gender_predictions[i]
obj["gender"][gender_label] = gender_prediction
obj["dominant_gender"] = gender_labels[np.argmax(gender_predictions)]
obj["dominant_gender"] = GENDER_LABELS[np.argmax(gender_predictions)]
elif action == "race":
race_predictions = models["race"].predict(img_content, verbose=0)[0, :]
sum_of_predictions = race_predictions.sum()
obj["race"] = {}
race_labels = [
"asian",
"indian",
"black",
"white",
"middle eastern",
"latino hispanic",
]
for i, race_label in enumerate(race_labels):
for i, race_label in enumerate(RACE_LABELS):
race_prediction = 100 * race_predictions[i] / sum_of_predictions
obj["race"][race_label] = race_prediction
obj["dominant_race"] = race_labels[np.argmax(race_predictions)]
obj["dominant_race"] = RACE_LABELS[np.argmax(race_predictions)]
# -----------------------------
# mention facial areas

4
deepface/extendedmodels/Emotion.py
View File

@ -70,3 +70,7 @@ def loadModel(
model.load_weights(home + "/.deepface/weights/facial_expression_model_weights.h5")
return model
EMOTION_LABELS = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
"""Labels for the emotions that can be detected by the model."""

4
deepface/extendedmodels/Gender.py
View File

@ -53,3 +53,7 @@ def loadModel(
return gender_model
# --------------------------
GENDER_LABELS = ["Woman", "Man"]
"""Labels for the genders that can be detected by the model."""

4
deepface/extendedmodels/Race.py
View File

@ -50,3 +50,7 @@ def loadModel(
race_model.load_weights(home + "/.deepface/weights/race_model_single_batch.h5")
return race_model
RACE_LABELS = ['asian', 'indian', 'black', 'white', 'middle eastern', 'latino hispanic']
"""Labels for the ethnic phenotypes that can be detected by the model."""