clean code refactoring

deepface/DeepFace.py

@@ -26,9 +26,6 @@ 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
 
 # -----------------------------------
@@ -342,11 +339,11 @@ def analyze(
 
                     obj["emotion"] = {}
 
-                    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, :]
@@ -357,22 +354,22 @@ def analyze(
                 elif action == "gender":
                     gender_predictions = models["gender"].predict(img_content, verbose=0)[0, :]
                     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"] = {}
-                    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

deepface/commons/realtime.py

@@ -4,7 +4,7 @@ import numpy as np
 import pandas as pd
 import cv2
 from deepface import DeepFace
-from deepface.commons import functions, distance as dst
+from deepface.commons import functions
 
 # dependency configuration
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
@@ -30,7 +30,6 @@ def analysis(
     enable_age_gender = True
     # ------------------------
     # find custom values for this input set
-    threshold = dst.findThreshold(model_name, distance_metric)
     target_size = functions.find_target_size(model_name=model_name)
     # ------------------------
     # build models once to store them in the memory
@@ -423,8 +422,7 @@ def analysis(
                             if df.shape[0] > 0:
                                 candidate = df.iloc[0]
                                 label = candidate["identity"]
-                                best_distance = candidate[f"{model_name}_{distance_metric}"]
+
-                                if best_distance <= threshold:
                                 # to use this source image as is
                                 display_img = cv2.imread(label)
                                 # to use extracted face

deepface/extendedmodels/Emotion.py

@@ -24,6 +24,9 @@ elif tf_version == 2:
     )
 # -------------------------------------------
 
+# Labels for the emotions that can be detected by the model.
+labels = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"]
+
 
 def loadModel(
     url="https://github.com/serengil/deepface_models/releases/download/v1.0/facial_expression_model_weights.h5",
@@ -70,7 +73,3 @@ 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."""

deepface/extendedmodels/Gender.py

@@ -18,6 +18,11 @@ elif tf_version == 2:
     from tensorflow.keras.models import Model, Sequential
     from tensorflow.keras.layers import Convolution2D, Flatten, Activation
 # -------------------------------------
+
+# Labels for the genders that can be detected by the model.
+labels = ["Woman", "Man"]
+
+
 def loadModel(
     url="https://github.com/serengil/deepface_models/releases/download/v1.0/gender_model_weights.h5",
 ):
@@ -51,9 +56,3 @@ def loadModel(
     gender_model.load_weights(home + "/.deepface/weights/gender_model_weights.h5")
 
     return gender_model
-
-    # --------------------------
-
-
-GENDER_LABELS = ["Woman", "Man"]
-"""Labels for the genders that can be detected by the model."""

deepface/extendedmodels/Race.py

@@ -17,6 +17,10 @@ elif tf_version == 2:
     from tensorflow.keras.models import Model, Sequential
     from tensorflow.keras.layers import Convolution2D, Flatten, Activation
 # --------------------------
+# Labels for the ethnic phenotypes that can be detected by the model.
+labels = ["asian", "indian", "black", "white", "middle eastern", "latino hispanic"]
+
+
 def loadModel(
     url="https://github.com/serengil/deepface_models/releases/download/v1.0/race_model_single_batch.h5",
 ):
@@ -50,7 +54,3 @@ 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."""