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analyze function is cleaner

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serengil 2020-12-21 23:30:00 +03:00
parent 8980931726
commit 6be5b2d41a
1 changed files with 42 additions and 47 deletions
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89
deepface/DeepFace.py
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@ -254,7 +254,8 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric =
return resp_obj
def analyze(img_path, actions = [], models = {}, enforce_detection = True
def analyze(img_path, actions = ['emotion', 'age', 'gender', 'race']
, models = {}, enforce_detection = True
, detector_backend = 'mtcnn'):
"""
@ -309,42 +310,39 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True
functions.initialize_detector(detector_backend = detector_backend)
#---------------------------------
#if a specific target is not passed, then find them all
if len(actions) == 0:
actions= ['emotion', 'age', 'gender', 'race']
#print("Actions to do: ", actions)
built_models = list(models.keys())
#---------------------------------
#pre-trained models passed but it doesn't exist in actions
if len(built_models) > 0:
if 'emotion' in built_models and 'emotion' not in actions:
actions.append('emotion')
if 'age' in built_models and 'age' not in actions:
actions.append('age')
if 'gender' in built_models and 'gender' not in actions:
actions.append('gender')
if 'race' in built_models and 'race' not in actions:
actions.append('race')
#---------------------------------
if 'emotion' in actions:
if 'emotion' in models:
print("already built emotion model is passed")
emotion_model = models['emotion']
else:
emotion_model = build_model('Emotion')
if 'emotion' in actions and 'emotion' not in built_models:
models['emotion'] = build_model('Emotion')
if 'age' in actions:
if 'age' in models:
#print("already built age model is passed")
age_model = models['age']
else:
age_model = build_model('Age')
if 'age' in actions and 'age' not in built_models:
models['age'] = build_model('Age')
if 'gender' in actions:
if 'gender' in models:
print("already built gender model is passed")
gender_model = models['gender']
else:
gender_model = build_model('Gender')
if 'gender' in actions and 'gender' not in built_models:
models['gender'] = build_model('Gender')
if 'race' in actions:
if 'race' in models:
print("already built race model is passed")
race_model = models['race']
else:
race_model = build_model('Race')
if 'race' in actions and 'race' not in built_models:
models['race'] = build_model('Race')
#---------------------------------
resp_objects = []
@ -353,7 +351,6 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True
global_pbar = tqdm(range(0,len(img_paths)), desc='Analyzing', disable = disable_option)
#for img_path in img_paths:
for j in global_pbar:
img_path = img_paths[j]
@ -363,18 +360,18 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True
pbar = tqdm(range(0,len(actions)), desc='Finding actions', disable = disable_option)
action_idx = 0
img_224 = None # Set to prevent re-detection
#for action in actions:
#facial attribute analysis
for index in pbar:
action = actions[index]
pbar.set_description("Action: %s" % (action))
if action == 'emotion':
emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
img = functions.preprocess_face(img = img_path, target_size = (48, 48), grayscale = True, enforce_detection = enforce_detection, detector_backend = detector_backend)
emotion_predictions = emotion_model.predict(img)[0,:]
emotion_predictions = models['emotion'].predict(img)[0,:]
sum_of_predictions = emotion_predictions.sum()
@ -389,19 +386,17 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True
elif action == 'age':
if img_224 is None:
img_224 = functions.preprocess_face(img = img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection, detector_backend = detector_backend) #just emotion model expects grayscale images
#print("age prediction")
age_predictions = age_model.predict(img_224)[0,:]
img_224 = functions.preprocess_face(img = img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection, detector_backend = detector_backend)
age_predictions = models['age'].predict(img_224)[0,:]
apparent_age = Age.findApparentAge(age_predictions)
resp_obj["age"] = str(int(apparent_age))
resp_obj["age"] = int(apparent_age)
elif action == 'gender':
if img_224 is None:
img_224 = functions.preprocess_face(img = img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection, detector_backend = detector_backend) #just emotion model expects grayscale images
#print("gender prediction")
img_224 = functions.preprocess_face(img = img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection, detector_backend = detector_backend)
gender_prediction = gender_model.predict(img_224)[0,:]
gender_prediction = models['gender'].predict(img_224)[0,:]
if np.argmax(gender_prediction) == 0:
gender = "Woman"
@ -413,7 +408,7 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True
elif action == 'race':
if img_224 is None:
img_224 = functions.preprocess_face(img = img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection, detector_backend = detector_backend) #just emotion model expects grayscale images
race_predictions = race_model.predict(img_224)[0,:]
race_predictions = models['race'].predict(img_224)[0,:]
race_labels = ['asian', 'indian', 'black', 'white', 'middle eastern', 'latino hispanic']
sum_of_predictions = race_predictions.sum()
@ -425,8 +420,8 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True
resp_obj["race"][race_label] = race_prediction
resp_obj["dominant_race"] = race_labels[np.argmax(race_predictions)]
action_idx = action_idx + 1
#---------------------------------
if bulkProcess == True:
resp_objects.append(resp_obj)