From 93c94621611ca870a114579e09d4b4b95bee4132 Mon Sep 17 00:00:00 2001 From: Uria Franko Date: Thu, 16 Apr 2020 10:37:23 +0300 Subject: [PATCH] Load models to memory 2 new functions: verify_init(model_name) analyze_init(models) The functions created in order to pre load models to memory for shorter analzye / verify first time Ex: Real time analyze waiting for first face to show without analyze_init() first analyze takes 1.5 sec with analyze_init() first analyze takes 0.3 sec --- deepface/DeepFace.py | 188 ++++++++++++++++++++++++++----------------- 1 file changed, 116 insertions(+), 72 deletions(-) diff --git a/deepface/DeepFace.py b/deepface/DeepFace.py index 16c9b27..e5dc176 100644 --- a/deepface/DeepFace.py +++ b/deepface/DeepFace.py @@ -21,73 +21,117 @@ from deepface.extendedmodels import Age, Gender, Race, Emotion from deepface.commons import functions, realtime, distance as dst +def analyze_init(models = []): + #--------------------------------- + + #if a specific target is not passed, then find them all + if len(models) == 0: + models = ['emotion', 'age', 'gender', 'race'] + + print("Models to initialize: ", models) + + #--------------------------------- + + if 'emotion' in models: + emotion_model = Emotion.loadModel() + + if 'age' in models: + age_model = Age.loadModel() + + if 'gender' in models: + gender_model = Gender.loadModel() + + if 'race' in models: + race_model = Race.loadModel() + + +def verify_init(model_name = 'VGG-Face'): + if model_name == 'VGG-Face': + print("Loading %s model" % model_name) + model = VGGFace.loadModel() + + elif model_name == 'OpenFace': + print("Loading %s model" % model_name) + model = OpenFace.loadModel() + + elif model_name == 'Facenet': + print("Loading %s model" % model_name) + model = Facenet.loadModel() + + elif model_name == 'DeepFace': + print("Loading %s model" % model_name) + model = FbDeepFace.loadModel() + else: + raise ValueError("Invalid model_name passed - ", model_name) + + def verify(img1_path, img2_path='' , model_name ='VGG-Face', distance_metric = 'cosine', model = None): - + tic = time.time() - + if type(img1_path) == list: bulkProcess = True img_list = img1_path.copy() else: bulkProcess = False img_list = [[img1_path, img2_path]] - + #------------------------------ - + if model == None: if model_name == 'VGG-Face': print("Using VGG-Face model backend and", distance_metric,"distance.") model = VGGFace.loadModel() - + elif model_name == 'OpenFace': print("Using OpenFace model backend", distance_metric,"distance.") model = OpenFace.loadModel() - + elif model_name == 'Facenet': print("Using Facenet model backend", distance_metric,"distance.") model = Facenet.loadModel() - + elif model_name == 'DeepFace': print("Using FB DeepFace model backend", distance_metric,"distance.") model = FbDeepFace.loadModel() - + else: raise ValueError("Invalid model_name passed - ", model_name) else: #model != None print("Already built model is passed") - + #------------------------------ #face recognition models have different size of inputs input_shape = model.layers[0].input_shape[1:3] #------------------------------ - + #tuned thresholds for model and metric pair threshold = functions.findThreshold(model_name, distance_metric) - + #------------------------------ resp_objects = [] for instance in img_list: if type(instance) == list and len(instance) >= 2: img1_path = instance[0] img2_path = instance[1] - + #---------------------- #crop and align faces - + img1 = functions.detectFace(img1_path, input_shape) img2 = functions.detectFace(img2_path, input_shape) - + #---------------------- #find embeddings img1_representation = model.predict(img1)[0,:] img2_representation = model.predict(img2)[0,:] - + #---------------------- #find distances between embeddings - + if distance_metric == 'cosine': distance = dst.findCosineDistance(img1_representation, img2_representation) elif distance_metric == 'euclidean': @@ -96,18 +140,18 @@ def verify(img1_path, img2_path='' distance = dst.findEuclideanDistance(dst.l2_normalize(img1_representation), dst.l2_normalize(img2_representation)) else: raise ValueError("Invalid distance_metric passed - ", distance_metric) - + #---------------------- #decision - + if distance <= threshold: identified = "true" else: identified = "false" - + #---------------------- #response object - + resp_obj = "{" resp_obj += "\"verified\": "+identified resp_obj += ", \"distance\": "+str(distance) @@ -115,30 +159,30 @@ def verify(img1_path, img2_path='' resp_obj += ", \"model\": \""+model_name+"\"" resp_obj += ", \"similarity_metric\": \""+distance_metric+"\"" resp_obj += "}" - + resp_obj = json.loads(resp_obj) #string to json - + if bulkProcess == True: resp_objects.append(resp_obj) else: K.clear_session() return resp_obj #---------------------- - + else: raise ValueError("Invalid arguments passed to verify function: ", instance) - + #------------------------- - + toc = time.time() - + #print("identification lasts ",toc-tic," seconds") - + if bulkProcess == True: resp_obj = "{" - + for i in range(0, len(resp_objects)): - resp_item = json.dumps(resp_objects[i]) + resp_item = json.dumps(resp_objects[i]) if i > 0: resp_obj += ", " @@ -157,129 +201,129 @@ def analyze(img_path, actions= []): else: img_paths = [img_path] bulkProcess = False - + #--------------------------------- - + #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) - + #--------------------------------- - + if 'emotion' in actions: emotion_model = Emotion.loadModel() - + if 'age' in actions: age_model = Age.loadModel() - + if 'gender' in actions: gender_model = Gender.loadModel() - + if 'race' in actions: race_model = Race.loadModel() #--------------------------------- - + resp_objects = [] for img_path in img_paths: - + resp_obj = "{" - + #TO-DO: do this in parallel - + pbar = tqdm(range(0,len(actions)), desc='Finding actions') - + action_idx = 0 #for action in actions: for index in pbar: action = actions[index] pbar.set_description("Action: %s" % (action)) - + if action_idx > 0: resp_obj += ", " - + if action == 'emotion': emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'] img = functions.detectFace(img_path, (48, 48), True) - + emotion_predictions = emotion_model.predict(img)[0,:] - + sum_of_predictions = emotion_predictions.sum() - + emotion_obj = "\"emotion\": {" for i in range(0, len(emotion_labels)): emotion_label = emotion_labels[i] emotion_prediction = 100 * emotion_predictions[i] / sum_of_predictions - + if i > 0: emotion_obj += ", " - + emotion_obj += "\"%s\": %s" % (emotion_label, emotion_prediction) - + emotion_obj += "}" - + emotion_obj += ", \"dominant_emotion\": \"%s\"" % (emotion_labels[np.argmax(emotion_predictions)]) - + resp_obj += emotion_obj - + elif action == 'age': img = functions.detectFace(img_path, (224, 224), False) #just emotion model expects grayscale images #print("age prediction") age_predictions = age_model.predict(img)[0,:] apparent_age = Age.findApparentAge(age_predictions) - + resp_obj += "\"age\": %s" % (apparent_age) - + elif action == 'gender': img = functions.detectFace(img_path, (224, 224), False) #just emotion model expects grayscale images #print("gender prediction") - + gender_prediction = gender_model.predict(img)[0,:] - + if np.argmax(gender_prediction) == 0: gender = "Woman" elif np.argmax(gender_prediction) == 1: gender = "Man" - + resp_obj += "\"gender\": \"%s\"" % (gender) - + elif action == 'race': img = functions.detectFace(img_path, (224, 224), False) #just emotion model expects grayscale images race_predictions = race_model.predict(img)[0,:] race_labels = ['asian', 'indian', 'black', 'white', 'middle eastern', 'latino hispanic'] - + sum_of_predictions = race_predictions.sum() - + race_obj = "\"race\": {" for i in range(0, len(race_labels)): race_label = race_labels[i] race_prediction = 100 * race_predictions[i] / sum_of_predictions - + if i > 0: race_obj += ", " - + race_obj += "\"%s\": %s" % (race_label, race_prediction) - + race_obj += "}" race_obj += ", \"dominant_race\": \"%s\"" % (race_labels[np.argmax(race_predictions)]) - + resp_obj += race_obj - + action_idx = action_idx + 1 - + resp_obj += "}" - + resp_obj = json.loads(resp_obj) - + if bulkProcess == True: resp_objects.append(resp_obj) else: return resp_obj - + if bulkProcess == True: resp_obj = "{" - + for i in range(0, len(resp_objects)): - resp_item = json.dumps(resp_objects[i]) + resp_item = json.dumps(resp_objects[i]) if i > 0: resp_obj += ", "