diff --git a/deepface/DeepFace.py b/deepface/DeepFace.py index 7bfe12f..43cfb6b 100644 --- a/deepface/DeepFace.py +++ b/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, @@ -414,18 +418,25 @@ 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': - 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) + 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) - 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" + gender_labels = ["Woman", "Man"] + resp_obj["gender"] = {} - resp_obj["gender"] = gender + for i in range(0, len(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 @@ -444,7 +455,7 @@ def analyze(img_path, actions = ('emotion', 'age', 'gender', 'race') , models = #----------------------------- - if is_region_set != True: + if is_region_set != True and region: resp_obj["region"] = {} is_region_set = True for i, parameter in enumerate(region_labels): @@ -458,14 +469,8 @@ def analyze(img_path, actions = ('emotion', 'age', 'gender', 'race') , models = return resp_obj if bulkProcess == True: + return resp_objects - resp_obj = {} - - for i in range(0, len(resp_objects)): - resp_item = resp_objects[i] - resp_obj["instance_%d" % (i+1)] = resp_item - - return resp_obj def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'opencv', align = True, prog_bar = True, normalization = 'base', silent=False): diff --git a/deepface/commons/functions.py b/deepface/commons/functions.py index 0acbf3f..ec034ad 100644 --- a/deepface/commons/functions.py +++ b/deepface/commons/functions.py @@ -83,7 +83,7 @@ def load_image(img): img = loadBase64Img(img) elif url_img: - img = np.array(Image.open(requests.get(img, stream=True).raw)) + img = np.array(Image.open(requests.get(img, stream=True).raw).convert('RGB')) elif exact_image != True: #image path passed as input if os.path.isfile(img) != True: diff --git a/deepface/extendedmodels/Gender.py b/deepface/extendedmodels/Gender.py index e831226..9fc91f1 100644 --- a/deepface/extendedmodels/Gender.py +++ b/deepface/extendedmodels/Gender.py @@ -18,6 +18,7 @@ elif tf_version == 2: #url = 'https://drive.google.com/uc?id=1wUXRVlbsni2FN9-jkS_f4UTUrm1bRLyk' + def loadModel(url = 'https://github.com/serengil/deepface_models/releases/download/v1.0/gender_model_weights.h5'): model = VGGFace.baseModel() diff --git a/tests/test_nonbinary_gender.py b/tests/test_nonbinary_gender.py new file mode 100644 index 0000000..ec36566 --- /dev/null +++ b/tests/test_nonbinary_gender.py @@ -0,0 +1,35 @@ +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() diff --git a/tests/unit_tests.py b/tests/unit_tests.py index 57a71de..aa4739b 100644 --- a/tests/unit_tests.py +++ b/tests/unit_tests.py @@ -3,6 +3,7 @@ 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("-----------------------------------------") @@ -96,7 +97,7 @@ def test_cases(): print(demography) evaluate(demography["age"] > 20 and demography["age"] < 40) - evaluate(demography["gender"] == "Woman") + evaluate(demography["dominant_gender"] == "Woman") print("-----------------------------------------") @@ -108,13 +109,13 @@ 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_race") 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) print("-----------------------------------------") @@ -123,12 +124,12 @@ 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) @@ -151,7 +152,7 @@ def test_cases(): distance = round(resp_obj["distance"], 2) threshold = resp_obj["threshold"] - passed = prediction == result + passed = prediction == result evaluate(passed) @@ -206,7 +207,14 @@ def test_cases(): print("--------------------------") + +def run_gender_prediction_test(): + for detector in detectors: + evaluate(test_gender_prediction_with_detector(detector)) + + test_cases() +run_gender_prediction_test() print("num of test cases run: " + str(num_cases)) print("succeeded test cases: " + str(succeed_cases))