Added dominant_gender and probabilities for man / woman results in analyze function

deepface/DeepFace.py

@@ -294,7 +294,11 @@ def analyze(img_path, actions = ('emotion', 'age', 'gender', 'race') , models =
 		{
 			"region": {'x': 230, 'y': 120, 'w': 36, 'h': 45},
 			"age": 28.66,
-			"gender": "woman",
+			"dominant_gender": "Woman",
+			"gender": {
+				'Woman': 99.99407529830933,
+				'Man': 0.005928758764639497,
+			}
 			"dominant_emotion": "neutral",
 			"emotion": {
 				'sad': 37.65260875225067,
@@ -417,14 +421,19 @@ def analyze(img_path, actions = ('emotion', 'age', 'gender', 'race') , models =
 				if img_224 is None:
 					img_224, region = functions.preprocess_face(img = img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection, detector_backend = detector_backend, return_region = True)
 
-				gender_prediction = models['gender'].predict(img_224)[0,:]
+				gender_predictions = models['gender'].predict(img_224)[0,:]
 
-				if np.argmax(gender_prediction) == 0:
-					gender = "Woman"
-				elif np.argmax(gender_prediction) == 1:
-					gender = "Man"
+				sum_of_predictions = gender_predictions.sum()
+				gender_labels = ["Woman", "Man"]
+				resp_obj["gender"] = {}
+
+				for i in range(0, len(gender_labels)):
+					gender_label = gender_labels[i]
+					gender_prediction = 100 * gender_predictions[i] / sum_of_predictions
+					resp_obj["gender"][gender_label] = gender_prediction
+
+				resp_obj["dominant_gender"] = gender_labels[np.argmax(gender_predictions)]
 
-				resp_obj["gender"] = gender
 
 			elif action == 'race':
 				if img_224 is None:

deepface/basemodels/ArcFace.py

@@ -23,7 +23,7 @@ def loadModel(url = 'https://github.com/serengil/deepface_models/releases/downlo
 	arcface_model = keras.layers.Flatten()(arcface_model)
 	arcface_model = keras.layers.Dense(512, activation=None, use_bias=True, kernel_initializer="glorot_normal")(arcface_model)
 	embedding = keras.layers.BatchNormalization(momentum=0.9, epsilon=2e-5, name="embedding", scale=True)(arcface_model)
-	model = keras.models.Model(inputs, embedding, name=base_model.name)
+	model = keras.detectors.Model(inputs, embedding, name=base_model.name)
 
 	#---------------------------------------
 	#check the availability of pre-trained weights

tests/test_nonbinary_gender.py

@@ -0,0 +1,32 @@
+from deepface import DeepFace
+
+dataset = [
+	'dataset/img1.jpg',
+	'dataset/img5.jpg',
+	'dataset/img6.jpg',
+	'dataset/img8.jpg',
+    'dataset/img7.jpg',
+    'dataset/img9.jpg',
+    'dataset/img11.jpg',
+    'dataset/img11.jpg',
+]
+
+detectors = ['opencv', 'ssd', 'retinaface', 'mtcnn']  # dlib not tested
+
+
+def test_gender_prediction():
+    for detector in detectors:
+        results = DeepFace.analyze(dataset, actions=('gender',), detector_backend=detector, prog_bar=False)
+        for key in results.keys():
+            result = results[key]
+            assert 'gender' in result.keys()
+            assert 'dominant_gender' in result.keys() and result["dominant_gender"] in ["Man", "Woman"]
+            if result["dominant_gender"] == "Man":
+                assert result["gender"]["Man"] > result["gender"]["Woman"]
+            else:
+                assert result["gender"]["Man"] < result["gender"]["Woman"]
+        print(f'detector {detector} passed')
+
+
+if __name__ == "__main__":
+    test_gender_prediction()

tests/unit_tests.py

@@ -96,7 +96,7 @@ def test_cases():
 	print(demography)
 
 	evaluate(demography["age"] > 20 and demography["age"] < 40)
-	evaluate(demography["gender"] == "Woman")
+	evaluate(demography["dominant_gender"] == "Woman")
 
 	print("-----------------------------------------")
 
@@ -108,12 +108,12 @@ def test_cases():
 
 	#check response is a valid json
 	print("Age: ", demography["age"])
-	print("Gender: ", demography["gender"])
+	print("Gender: ", demography["dominant_gender"])
 	print("Race: ", demography["dominant_race"])
 	print("Emotion: ", demography["dominant_emotion"])
 
 	evaluate(demography.get("age") is not None)
-	evaluate(demography.get("gender") is not None) 
+	evaluate(demography.get("dominant_gender") is not None)
 	evaluate(demography.get("dominant_race") is not None) 
 	evaluate(demography.get("dominant_emotion") is not None)
 
@@ -123,12 +123,12 @@ def test_cases():
 	demography = DeepFace.analyze(img, ['age', 'gender'])
 
 	print("Age: ", demography.get("age"))
-	print("Gender: ", demography.get("gender"))
+	print("Gender: ", demography.get("dominant_gender"))
 	print("Race: ", demography.get("dominant_race"))
 	print("Emotion: ", demography.get("dominant_emotion"))
 
 	evaluate(demography.get("age") is not None)
-	evaluate(demography.get("gender") is not None)
+	evaluate(demography.get("dominant_gender") is not None)
 	evaluate(demography.get("dominant_race") is None)
 	evaluate(demography.get("dominant_emotion") is None)