code style

deepface/DeepFace.py

@@ -418,7 +418,7 @@ def analyze(img_path, actions = ('emotion', 'age', 'gender', 'race') , models =
 				resp_obj["age"] = int(apparent_age) #int cast is for the exception - object of type 'float32' is not JSON serializable
 
 			elif action == 'gender':
-				try:
+
 				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)
 
@@ -427,16 +427,12 @@ def analyze(img_path, actions = ('emotion', 'age', 'gender', 'race') , models =
 				gender_labels = ["Woman", "Man"]
 				resp_obj["gender"] = {}
 
-					for i in range(0, len(gender_labels)):
+				for i, gender_label in enumerate(gender_labels):
-						gender_label = gender_labels[i]
 					gender_prediction = 100 * gender_predictions[i]
 					resp_obj["gender"][gender_label] = gender_prediction
 
 				resp_obj["dominant_gender"] = gender_labels[np.argmax(gender_predictions)]
-				except Exception as e:
+
-					resp_obj["dominant_gender"] = None
-					resp_obj["gender"] = None
-					resp_obj["error"] = e
 			elif action == 'race':
 				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) #just emotion model expects grayscale images

tests/test_nonbinary_gender.py

@@ -1,35 +0,0 @@
-from deepface import DeepFace
-
-dataset = [
-    'dataset/img1.jpg',
-	'dataset/img5.jpg',
-	'dataset/img6.jpg',
-    'dataset/img7.jpg',
-    'dataset/img9.jpg',
-    'dataset/img11.jpg',
-    'dataset/img11.jpg',
-]
-
-
-def test_gender_prediction():
-    detectors = ['opencv', 'ssd', 'retinaface', 'mtcnn']  # dlib not tested
-    for detector in detectors:
-        test_gender_prediction_with_detector(detector)
-
-
-def test_gender_prediction_with_detector(detector):
-    results = DeepFace.analyze(dataset, actions=('gender',), detector_backend=detector, prog_bar=False,
-                               enforce_detection=False)
-    for result in results:
-        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')
-    return True
-
-
-if __name__ == "__main__":
-    test_gender_prediction()

tests/unit_tests.py

@@ -3,7 +3,6 @@ import os
 import tensorflow as tf
 import cv2
 from deepface import DeepFace
-from tests.test_nonbinary_gender import test_gender_prediction, test_gender_prediction_with_detector
 
 print("-----------------------------------------")
 
@@ -207,14 +206,27 @@ def test_cases():
 
 	print("--------------------------")
 
+	print("non-binary gender tests")
 
-def run_gender_prediction_test():
+	#interface validation - no need to call evaluate here
+
+	for img1_path, img2_path, verified in dataset:
 		for detector in detectors:
-		evaluate(test_gender_prediction_with_detector(detector))
+			result = DeepFace.analyze(img1_path, actions=('gender',), detector_backend=detector, enforce_detection=False)
 
+			print(result)
+
+			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"]
+
+# ---------------------------------------------
 
 test_cases()
-run_gender_prediction_test()
 
 print("num of test cases run: " + str(num_cases))
 print("succeeded test cases: " + str(succeed_cases))