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issue 89 solved
This commit is contained in:
Şefik Serangil
parent
414d58b508
commit
4f7c5273c9
@ -105,8 +105,8 @@ def verify(img1_path, img2_path=''
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input_shape = input_shape[1:3]
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img1 = functions.detectFace(img1_path, input_shape, enforce_detection = enforce_detection)
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img2 = functions.detectFace(img2_path, input_shape, enforce_detection = enforce_detection)
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img1 = functions.preprocess_face(img1_path, input_shape, enforce_detection = enforce_detection)
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img2 = functions.preprocess_face(img2_path, input_shape, enforce_detection = enforce_detection)
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img1_representation = custom_model.predict(img1)[0,:]
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img2_representation = custom_model.predict(img2)[0,:]
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@ -274,8 +274,8 @@ def verify(img1_path, img2_path=''
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#----------------------
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#crop and align faces
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img1 = functions.detectFace(img1_path, (input_shape_y, input_shape_x), enforce_detection = enforce_detection)
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img2 = functions.detectFace(img2_path, (input_shape_y, input_shape_x), enforce_detection = enforce_detection)
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img1 = functions.preprocess_face(img1_path, (input_shape_y, input_shape_x), enforce_detection = enforce_detection)
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img2 = functions.preprocess_face(img2_path, (input_shape_y, input_shape_x), enforce_detection = enforce_detection)
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#----------------------
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#find embeddings
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@ -422,7 +422,7 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True):
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if action == 'emotion':
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emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
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img = functions.detectFace(img_path, target_size = (48, 48), grayscale = True, enforce_detection = enforce_detection)
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img = functions.preprocess_face(img_path, target_size = (48, 48), grayscale = True, enforce_detection = enforce_detection)
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emotion_predictions = emotion_model.predict(img)[0,:]
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@ -445,7 +445,7 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True):
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elif action == 'age':
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if img_224 is None:
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img_224 = functions.detectFace(img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection) #just emotion model expects grayscale images
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img_224 = functions.preprocess_face(img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection) #just emotion model expects grayscale images
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#print("age prediction")
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age_predictions = age_model.predict(img_224)[0,:]
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apparent_age = Age.findApparentAge(age_predictions)
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@ -454,7 +454,7 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True):
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elif action == 'gender':
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if img_224 is None:
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img_224 = functions.detectFace(img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection) #just emotion model expects grayscale images
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img_224 = functions.preprocess_face(img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection) #just emotion model expects grayscale images
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#print("gender prediction")
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gender_prediction = gender_model.predict(img_224)[0,:]
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@ -468,7 +468,7 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True):
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elif action == 'race':
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if img_224 is None:
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img_224 = functions.detectFace(img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection) #just emotion model expects grayscale images
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img_224 = functions.preprocess_face(img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection) #just emotion model expects grayscale images
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race_predictions = race_model.predict(img_224)[0,:]
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race_labels = ['asian', 'indian', 'black', 'white', 'middle eastern', 'latino hispanic']
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@ -516,12 +516,15 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True):
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def detectFace(img_path):
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img = functions.detectFace(img_path)[0] #detectFace returns (1, 224, 224, 3)
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img = functions.preprocess_face(img_path)[0] #preprocess_face returns (1, 224, 224, 3)
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return img[:, :, ::-1] #bgr to rgb
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def find(img_path, db_path
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, model_name ='VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True):
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model_names = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace']
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metric_names = ['cosine', 'euclidean', 'euclidean_l2']
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tic = time.time()
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if type(img_path) == list:
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@ -560,8 +563,6 @@ def find(img_path, db_path
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import lightgbm as lgb #lightgbm==2.3.1
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model_names = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace']
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metric_names = ['cosine', 'euclidean', 'euclidean_l2']
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models = {}
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pbar = tqdm(range(0, len(model_names)), desc='Face recognition models')
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@ -586,6 +587,8 @@ def find(img_path, db_path
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print("Already built model is passed")
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if model_name == 'Ensemble':
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import lightgbm as lgb #lightgbm==2.3.1
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#validate model dictionary because it might be passed from input as pre-trained
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@ -597,6 +600,8 @@ def find(img_path, db_path
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print("Ensemble learning will be applied for ", found_models," models")
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else:
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raise ValueError("You would like to apply ensemble learning and pass pre-built models but models must contain [VGG-Face, Facenet, OpenFace, DeepFace] but you passed "+found_models)
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models = model.copy()
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#threshold = functions.findThreshold(model_name, distance_metric)
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@ -655,7 +660,7 @@ def find(img_path, db_path
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input_shape_x = input_shape[0]; input_shape_y = input_shape[1]
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img = functions.detectFace(employee, (input_shape_y, input_shape_x), enforce_detection = enforce_detection)
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img = functions.preprocess_face(employee, (input_shape_y, input_shape_x), enforce_detection = enforce_detection)
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representation = model.predict(img)[0,:]
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instance = []
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@ -668,11 +673,11 @@ def find(img_path, db_path
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instance.append(employee)
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for j in model_names:
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model = models[j]
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ensemble_model = models[j]
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#input_shape = model.layers[0].input_shape[1:3] #my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue.
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input_shape = model.layers[0].input_shape
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input_shape = ensemble_model.layers[0].input_shape
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if type(input_shape) == list:
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input_shape = input_shape[0][1:3]
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@ -681,8 +686,8 @@ def find(img_path, db_path
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input_shape_x = input_shape[0]; input_shape_y = input_shape[1]
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img = functions.detectFace(employee, (input_shape_y, input_shape_x), enforce_detection = enforce_detection)
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representation = model.predict(img)[0,:]
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img = functions.preprocess_face(employee, (input_shape_y, input_shape_x), enforce_detection = enforce_detection)
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representation = ensemble_model.predict(img)[0,:]
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instance.append(representation)
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#-------------------------------
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@ -715,19 +720,19 @@ def find(img_path, db_path
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if model_name == 'Ensemble':
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for j in model_names:
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model = models[j]
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ensemble_model = models[j]
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#input_shape = model.layers[0].input_shape[1:3] #my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue.
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#input_shape = ensemble_model.layers[0].input_shape[1:3] #my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue.
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input_shape = model.layers[0].input_shape
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input_shape = ensemble_model.layers[0].input_shape
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if type(input_shape) == list:
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input_shape = input_shape[0][1:3]
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else:
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input_shape = input_shape[1:3]
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img = functions.detectFace(img_path, input_shape, enforce_detection = enforce_detection)
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target_representation = model.predict(img)[0,:]
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img = functions.preprocess_face(img_path, input_shape, enforce_detection = enforce_detection)
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target_representation = ensemble_model.predict(img)[0,:]
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for k in metric_names:
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distances = []
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@ -818,7 +823,7 @@ def find(img_path, db_path
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input_shape_x = input_shape[0]; input_shape_y = input_shape[1]
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img = functions.detectFace(img_path, (input_shape_y, input_shape_x), enforce_detection = enforce_detection)
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img = functions.preprocess_face(img_path, (input_shape_y, input_shape_x), enforce_detection = enforce_detection)
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target_representation = model.predict(img)[0,:]
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distances = []
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@ -163,7 +163,7 @@ def get_opencv_path():
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return path+"/data/"
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def detectFace(img, target_size=(224, 224), grayscale = False, enforce_detection = True):
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def preprocess_face(img, target_size=(224, 224), grayscale = False, enforce_detection = True):
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img_path = ""
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@ -112,7 +112,7 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True):
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employee = employees[index]
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pbar.set_description("Finding embedding for %s" % (employee.split("/")[-1]))
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embedding = []
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img = functions.detectFace(employee, (input_shape_y, input_shape_x))
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img = functions.preprocess_face(employee, (input_shape_y, input_shape_x))
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img_representation = model.predict(img)[0,:]
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embedding.append(employee)
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@ -222,7 +222,7 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True):
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if enable_face_analysis == True:
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gray_img = functions.detectFace(custom_face, (48, 48), True)
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gray_img = functions.preprocess_face(custom_face, (48, 48), True)
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emotion_labels = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
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emotion_predictions = emotion_model.predict(gray_img)[0,:]
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sum_of_predictions = emotion_predictions.sum()
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@ -300,7 +300,7 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True):
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#-------------------------------
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face_224 = functions.detectFace(custom_face, (224, 224), False)
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face_224 = functions.preprocess_face(custom_face, (224, 224), False)
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age_predictions = age_model.predict(face_224)[0,:]
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apparent_age = Age.findApparentAge(age_predictions)
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@ -355,9 +355,9 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True):
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#-------------------------------
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#face recognition
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custom_face = functions.detectFace(custom_face, (input_shape_y, input_shape_x))
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custom_face = functions.preprocess_face(custom_face, (input_shape_y, input_shape_x))
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#check detectFace function handled
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#check preprocess_face function handled
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if custom_face.shape[1:3] == input_shape:
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if df.shape[0] > 0: #if there are images to verify, apply face recognition
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img1_representation = model.predict(custom_face)[0,:]
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@ -5,7 +5,10 @@ import os
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
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#-----------------------------------------
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print("Bulk tests")
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dataset = [
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['dataset/img1.jpg', 'dataset/img2.jpg', True],
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['dataset/img5.jpg', 'dataset/img6.jpg', True]
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]
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print("-----------------------------------------")
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@ -24,20 +27,7 @@ print(df.head())
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print("-----------------------------------------")
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print("Bulk face recognition tests")
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dataset = [
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['dataset/img1.jpg', 'dataset/img2.jpg', True],
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['dataset/img5.jpg', 'dataset/img6.jpg', True]
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]
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resp_obj = DeepFace.verify(dataset)
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print(resp_obj["pair_1"]["verified"] == True)
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print(resp_obj["pair_2"]["verified"] == True)
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print("-----------------------------------------")
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print("Ensemble learning bulk")
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print("Ensemble for verify function")
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resp_obj = DeepFace.verify(dataset, model_name = "Ensemble")
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for i in range(0, len(dataset)):
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@ -48,6 +38,14 @@ for i in range(0, len(dataset)):
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print("-----------------------------------------")
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print("Bulk face recognition tests")
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resp_obj = DeepFace.verify(dataset)
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print(resp_obj["pair_1"]["verified"] == True)
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print(resp_obj["pair_2"]["verified"] == True)
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print("-----------------------------------------")
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print("Bulk facial analysis tests")
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dataset = [
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@ -159,12 +157,16 @@ else:
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from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
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#-----------------------------------
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print("--------------------------")
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print("Verify function with passing pre-trained model")
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vggface_model = VGGFace.loadModel()
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resp_obj = DeepFace.verify("dataset/img1.jpg", "dataset/img2.jpg", model_name = "VGG-Face", model = vggface_model)
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print(resp_obj)
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#-----------------------------------
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print("--------------------------")
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print("Analyze function with passing pre-trained model")
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from deepface.extendedmodels import Age, Gender, Race, Emotion
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@ -180,3 +182,8 @@ facial_attribute_models["gender"] = gender_model
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facial_attribute_models["race"] = race_model
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resp_obj = DeepFace.analyze("dataset/img1.jpg", models=facial_attribute_models)
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print(resp_obj)
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#-----------------------------------
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print("--------------------------")
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