text: import warnings
  warnings.filterwarnings("ignore")
  
  import os
  #os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
  os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
  
  import time
  from os import path
  import numpy as np
  import pandas as pd
  from tqdm import tqdm
  import pickle
  
  from deepface.basemodels import VGGFace, OpenFace, Facenet, Facenet512, FbDeepFace, DeepID, DlibWrapper, ArcFace, Boosting
  from deepface.extendedmodels import Age, Gender, Race, Emotion
  from deepface.commons import functions, realtime, distance as dst
  
  import tensorflow as tf
  tf_version = int(tf.__version__.split(".")[0])
  if tf_version == 2:
  	import logging
  	tf.get_logger().setLevel(logging.ERROR)
  
  def build_model(model_name):
  
  	"""
  	This function builds a deepface model
  	Parameters:
  		model_name (string): face recognition or facial attribute model
  			VGG-Face, Facenet, OpenFace, DeepFace, DeepID for face recognition
  			Age, Gender, Emotion, Race for facial attributes
  
  	Returns:
  		built deepface model
  	"""
  
  	global model_obj #singleton design pattern
  
  	models = {
  		'VGG-Face': VGGFace.loadModel,
  		'OpenFace': OpenFace.loadModel,
  		'Facenet': Facenet.loadModel,
  		'Facenet512': Facenet512.loadModel,
  		'DeepFace': FbDeepFace.loadModel,
  		'DeepID': DeepID.loadModel,
  		'Dlib': DlibWrapper.loadModel,
  		'ArcFace': ArcFace.loadModel,
  		'Emotion': Emotion.loadModel,
  		'Age': Age.loadModel,
  		'Gender': Gender.loadModel,
  		'Race': Race.loadModel
  	}
  
  	if not "model_obj" in globals():
  		model_obj = {}
  
  	if not model_name in model_obj.keys():
  		model = models.get(model_name)
  		if model:
  			model = model()
  			model_obj[model_name] = model
  			#print(model_name," built")
  		else:
  			raise ValueError('Invalid model_name passed - {}'.format(model_name))
  
  	return model_obj[model_name]
  
  def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'opencv', align = True, prog_bar = True, normalization = 'base'):
  
  	"""
  	This function verifies an image pair is same person or different persons.
  
  	Parameters:
  		img1_path, img2_path: exact image path, numpy array or based64 encoded images could be passed. If you are going to call verify function for a list of image pairs, then you should pass an array instead of calling the function in for loops.
  
  		e.g. img1_path = [
  			['img1.jpg', 'img2.jpg'],
  			['img2.jpg', 'img3.jpg']
  		]
  
  		model_name (string): VGG-Face, Facenet, OpenFace, DeepFace, DeepID, Dlib, ArcFace or Ensemble
  
  		distance_metric (string): cosine, euclidean, euclidean_l2
  
  		model: Built deepface model. A face recognition model is built every call of verify function. You can pass pre-built face recognition model optionally if you will call verify function several times.
  
  			model = DeepFace.build_model('VGG-Face')
  
  		enforce_detection (boolean): If any face could not be detected in an image, then verify function will return exception. Set this to False not to have this exception. This might be convenient for low resolution images.
  
  		detector_backend (string): set face detector backend as retinaface, mtcnn, opencv, ssd or dlib
  
  		prog_bar (boolean): enable/disable a progress bar
  
  	Returns:
  		Verify function returns a dictionary. If img1_path is a list of image pairs, then the function will return list of dictionary.
  
  		{
  			"verified": True
  			, "distance": 0.2563
  			, "max_threshold_to_verify": 0.40
  			, "model": "VGG-Face"
  			, "similarity_metric": "cosine"
  		}
  
  	"""
  
  	tic = time.time()
  
  	img_list, bulkProcess = functions.initialize_input(img1_path, img2_path)
  
  	resp_objects = []
  
  	#--------------------------------
  
  	if model_name == 'Ensemble':
  		model_names = ["VGG-Face", "Facenet", "OpenFace", "DeepFace"]
  		metrics = ["cosine", "euclidean", "euclidean_l2"]
  	else:
  		model_names = []; metrics = []
  		model_names.append(model_name)
  		metrics.append(distance_metric)
  
  	#--------------------------------
  
  	if model == None:
  		if model_name == 'Ensemble':
  			models = Boosting.loadModel()
  		else:
  			model = build_model(model_name)
  			models = {}
  			models[model_name] = model
  	else:
  		if model_name == 'Ensemble':
  			Boosting.validate_model(model)
  			models = model.copy()
  		else:
  			models = {}
  			models[model_name] = model
  
  	#------------------------------
  
  	disable_option = (False if len(img_list) > 1 else True) or not prog_bar
  
  	pbar = tqdm(range(0,len(img_list)), desc='Verification', disable = disable_option)
  
  	for index in pbar:
  
  		instance = img_list[index]
  
  		if type(instance) == list and len(instance) >= 2:
  			img1_path = instance[0]; img2_path = instance[1]
  
  			ensemble_features = []
  
  			for i in  model_names:
  				custom_model = models[i]
  
  				#img_path, model_name = 'VGG-Face', model = None, enforce_detection = True, detector_backend = 'mtcnn'
  				img1_representation = represent(img_path = img1_path
  						, model_name = model_name, model = custom_model
  						, enforce_detection = enforce_detection, detector_backend = detector_backend
  						, align = align
  						, normalization = normalization
  						)
  
  				img2_representation = represent(img_path = img2_path
  						, model_name = model_name, model = custom_model
  						, enforce_detection = enforce_detection, detector_backend = detector_backend
  						, align = align
  						, normalization = normalization
  						)
  
  				#----------------------
  				#find distances between embeddings
  
  				for j in metrics:
  
  					if j == 'cosine':
  						distance = dst.findCosineDistance(img1_representation, img2_representation)
  					elif j == 'euclidean':
  						distance = dst.findEuclideanDistance(img1_representation, img2_representation)
  					elif j == 'euclidean_l2':
  						distance = dst.findEuclideanDistance(dst.l2_normalize(img1_representation), dst.l2_normalize(img2_representation))
  					else:
  						raise ValueError("Invalid distance_metric passed - ", distance_metric)
  
  					distance = np.float64(distance) #causes trobule for euclideans in api calls if this is not set (issue #175)
  					#----------------------
  					#decision
  
  					if model_name != 'Ensemble':
  
  						threshold = dst.findThreshold(i, j)
  
  						if distance <= threshold:
  							identified = True
  						else:
  							identified = False
  
  						resp_obj = {
  							"verified": identified
  							, "distance": distance
  							, "max_threshold_to_verify": threshold
  							, "model": model_name
  							, "similarity_metric": distance_metric
  
  						}
  
  						if bulkProcess == True:
  							resp_objects.append(resp_obj)
  						else:
  							return resp_obj
  
  					else: #Ensemble
  
  						#this returns same with OpenFace - euclidean_l2
  						if i == 'OpenFace' and j == 'euclidean':
  							continue
  						else:
  							ensemble_features.append(distance)
  
  			#----------------------
  
  			if model_name == 'Ensemble':
  
  				boosted_tree = Boosting.build_gbm()
  
  				prediction = boosted_tree.predict(np.expand_dims(np.array(ensemble_features), axis=0))[0]
  
  				verified = np.argmax(prediction) == 1
  				score = prediction[np.argmax(prediction)]
  
  				resp_obj = {
  					"verified": verified
  					, "score": score
  					, "distance": ensemble_features
  					, "model": ["VGG-Face", "Facenet", "OpenFace", "DeepFace"]
  					, "similarity_metric": ["cosine", "euclidean", "euclidean_l2"]
  				}
  
  				if bulkProcess == True:
  					resp_objects.append(resp_obj)
  				else:
  					return resp_obj
  
  			#----------------------
  
  		else:
  			raise ValueError("Invalid arguments passed to verify function: ", instance)
  
  	#-------------------------
  
  	toc = time.time()
  
  	if bulkProcess == True:
  
  		resp_obj = {}
  
  		for i in range(0, len(resp_objects)):
  			resp_item = resp_objects[i]
  			resp_obj["pair_%d" % (i+1)] = resp_item
  
  		return resp_obj
  
  def analyze(img_path, actions = ('emotion', 'age', 'gender', 'race') , models = None, enforce_detection = True, detector_backend = 'opencv', prog_bar = True):
  
  	"""
  	This function analyzes facial attributes including age, gender, emotion and race
  
  	Parameters:
  		img_path: exact image path, numpy array or base64 encoded image could be passed. If you are going to analyze lots of images, then set this to list. e.g. img_path = ['img1.jpg', 'img2.jpg']
  
  		actions (tuple): The default is ('age', 'gender', 'emotion', 'race'). You can drop some of those attributes.
  
  		models: (Optional[dict]) facial attribute analysis models are built in every call of analyze function. You can pass pre-built models to speed the function up.
  
  			models = {}
  			models['age'] = DeepFace.build_model('Age')
  			models['gender'] = DeepFace.build_model('Gender')
  			models['emotion'] = DeepFace.build_model('Emotion')
  			models['race'] = DeepFace.build_model('race')
  
  		enforce_detection (boolean): The function throws exception if a face could not be detected. Set this to True if you don't want to get exception. This might be convenient for low resolution images.
  
  		detector_backend (string): set face detector backend as retinaface, mtcnn, opencv, ssd or dlib.
  
  		prog_bar (boolean): enable/disable a progress bar
  	Returns:
  		The function returns a dictionary. If img_path is a list, then it will return list of dictionary.
  
  		{
  			"region": {'x': 230, 'y': 120, 'w': 36, 'h': 45},
  			"age": 28.66,
  			"gender": "woman",
  			"dominant_emotion": "neutral",
  			"emotion": {
  				'sad': 37.65260875225067,
  				'angry': 0.15512987738475204,
  				'surprise': 0.0022171278033056296,
  				'fear': 1.2489334680140018,
  				'happy': 4.609785228967667,
  				'disgust': 9.698561953541684e-07,
  				'neutral': 56.33133053779602
  			}
  			"dominant_race": "white",
  			"race": {
  				'indian': 0.5480832420289516,
  				'asian': 0.7830780930817127,
  				'latino hispanic': 2.0677512511610985,
  				'black': 0.06337375962175429,
  				'middle eastern': 3.088453598320484,
  				'white': 93.44925880432129
  			}
  		}
  
  	"""
  
  	actions = list(actions)
  	if not models:
  		models = {}
  
  	img_paths, bulkProcess = functions.initialize_input(img_path)
  
  	#---------------------------------
  
  	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 and 'emotion' not in built_models:
  		models['emotion'] = build_model('Emotion')
  
  	if 'age' in actions and 'age' not in built_models:
  		models['age'] = build_model('Age')
  
  	if 'gender' in actions and 'gender' not in built_models:
  		models['gender'] = build_model('Gender')
  
  	if 'race' in actions and 'race' not in built_models:
  		models['race'] = build_model('Race')
  
  	#---------------------------------
  
  	resp_objects = []
  
  	disable_option = (False if len(img_paths) > 1 else True) or not prog_bar
  
  	global_pbar = tqdm(range(0,len(img_paths)), desc='Analyzing', disable = disable_option)
  
  	for j in global_pbar:
  		img_path = img_paths[j]
  
  		resp_obj = {}
  
  		disable_option = (False if len(actions) > 1 else True) or not prog_bar
  
  		pbar = tqdm(range(0, len(actions)), desc='Finding actions', disable = disable_option)
  
  		img_224 = None # Set to prevent re-detection
  
  		region = [] # x, y, w, h of the detected face region
  		region_labels = ['x', 'y', 'w', 'h']
  
  		is_region_set = False
  
  		#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, region = functions.preprocess_face(img = img_path, target_size = (48, 48), grayscale = True, enforce_detection = enforce_detection, detector_backend = detector_backend, return_region = True)
  
  				emotion_predictions = models['emotion'].predict(img)[0,:]
  
  				sum_of_predictions = emotion_predictions.sum()
  
  				resp_obj["emotion"] = {}
  
  				for i in range(0, len(emotion_labels)):
  					emotion_label = emotion_labels[i]
  					emotion_prediction = 100 * emotion_predictions[i] / sum_of_predictions
  					resp_obj["emotion"][emotion_label] = emotion_prediction
  
  				resp_obj["dominant_emotion"] = emotion_labels[np.argmax(emotion_predictions)]
  
  			elif action == 'age':
  				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)
  
  				age_predictions = models['age'].predict(img_224)[0,:]
  				apparent_age = Age.findApparentAge(age_predictions)
  
  				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)
  
  				gender_prediction = models['gender'].predict(img_224)[0,:]
  
  				if np.argmax(gender_prediction) == 0:
  					gender = "Woman"
  				elif np.argmax(gender_prediction) == 1:
  					gender = "Man"
  
  				resp_obj["gender"] = gender
  
  			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
  				race_predictions = models['race'].predict(img_224)[0,:]
  				race_labels = ['asian', 'indian', 'black', 'white', 'middle eastern', 'latino hispanic']
  
  				sum_of_predictions = race_predictions.sum()
  
  				resp_obj["race"] = {}
  				for i in range(0, len(race_labels)):
  					race_label = race_labels[i]
  					race_prediction = 100 * race_predictions[i] / sum_of_predictions
  					resp_obj["race"][race_label] = race_prediction
  
  				resp_obj["dominant_race"] = race_labels[np.argmax(race_predictions)]
  
  			#-----------------------------
  
  			if is_region_set != True:
  				resp_obj["region"] = {}
  				is_region_set = True
  				for i, parameter in enumerate(region_labels):
  					resp_obj["region"][parameter] = int(region[i]) #int cast is for the exception - object of type 'float32' is not JSON serializable
  
  		#---------------------------------
  
  		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 = 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'):
  
  	"""
  	This function applies verification several times and find an identity in a database
  
  	Parameters:
  		img_path: exact image path, numpy array or based64 encoded image. If you are going to find several identities, then you should pass img_path as array instead of calling find function in a for loop. e.g. img_path = ["img1.jpg", "img2.jpg"]
  
  		db_path (string): You should store some .jpg files in a folder and pass the exact folder path to this.
  
  		model_name (string): VGG-Face, Facenet, OpenFace, DeepFace, DeepID, Dlib or Ensemble
  
  		distance_metric (string): cosine, euclidean, euclidean_l2
  
  		model: built deepface model. A face recognition models are built in every call of find function. You can pass pre-built models to speed the function up.
  
  			model = DeepFace.build_model('VGG-Face')
  
  		enforce_detection (boolean): The function throws exception if a face could not be detected. Set this to True if you don't want to get exception. This might be convenient for low resolution images.
  
  		detector_backend (string): set face detector backend as retinaface, mtcnn, opencv, ssd or dlib
  
  		prog_bar (boolean): enable/disable a progress bar
  
  	Returns:
  		This function returns pandas data frame. If a list of images is passed to img_path, then it will return list of pandas data frame.
  	"""
  
  	tic = time.time()
  
  	img_paths, bulkProcess = functions.initialize_input(img_path)
  
  	#-------------------------------
  
  	if os.path.isdir(db_path) == True:
  
  		if model == None:
  
  			if model_name == 'Ensemble':
  				print("Ensemble learning enabled")
  				models = Boosting.loadModel()
  
  			else: #model is not ensemble
  				model = build_model(model_name)
  				models = {}
  				models[model_name] = model
  
  		else: #model != None
  			print("Already built model is passed")
  
  			if model_name == 'Ensemble':
  				Boosting.validate_model(model)
  				models = model.copy()
  			else:
  				models = {}
  				models[model_name] = model
  
  		#---------------------------------------
  
  		if model_name == 'Ensemble':
  			model_names = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace']
  			metric_names = ['cosine', 'euclidean', 'euclidean_l2']
  		elif model_name != 'Ensemble':
  			model_names = []; metric_names = []
  			model_names.append(model_name)
  			metric_names.append(distance_metric)
  
  		#---------------------------------------
  
  		file_name = "representations_%s.pkl" % (model_name)
  		file_name = file_name.replace("-", "_").lower()
  
  		if path.exists(db_path+"/"+file_name):
  
  			print("WARNING: Representations for images in ",db_path," folder were previously stored in ", file_name, ". If you added new instances after this file creation, then please delete this file and call find function again. It will create it again.")
  
  			f = open(db_path+'/'+file_name, 'rb')
  			representations = pickle.load(f)
  
  			print("There are ", len(representations)," representations found in ",file_name)
  
  		else: #create representation.pkl from scratch
  			employees = []
  
  			for r, d, f in os.walk(db_path): # r=root, d=directories, f = files
  				for file in f:
  					if ('.jpg' in file.lower()) or ('.png' in file.lower()):
  						exact_path = r + "/" + file
  						employees.append(exact_path)
  
  			if len(employees) == 0:
  				raise ValueError("There is no image in ", db_path," folder! Validate .jpg or .png files exist in this path.")
  
  			#------------------------
  			#find representations for db images
  
  			representations = []
  
  			pbar = tqdm(range(0,len(employees)), desc='Finding representations', disable = prog_bar)
  
  			#for employee in employees:
  			for index in pbar:
  				employee = employees[index]
  
  				instance = []
  				instance.append(employee)
  
  				for j in model_names:
  					custom_model = models[j]
  
  					representation = represent(img_path = employee
  						, model_name = model_name, model = custom_model
  						, enforce_detection = enforce_detection, detector_backend = detector_backend
  						, align = align
  						, normalization = normalization
  						)
  
  					instance.append(representation)
  
  				#-------------------------------
  
  				representations.append(instance)
  
  			f = open(db_path+'/'+file_name, "wb")
  			pickle.dump(representations, f)
  			f.close()
  
  			print("Representations stored in ",db_path,"/",file_name," file. Please delete this file when you add new identities in your database.")
  
  		#----------------------------
  		#now, we got representations for facial database
  
  		if model_name != 'Ensemble':
  			df = pd.DataFrame(representations, columns = ["identity", "%s_representation" % (model_name)])
  		else: #ensemble learning
  
  			columns = ['identity']
  			[columns.append('%s_representation' % i) for i in model_names]
  
  			df = pd.DataFrame(representations, columns = columns)
  
  		df_base = df.copy() #df will be filtered in each img. we will restore it for the next item.
  
  		resp_obj = []
  
  		global_pbar = tqdm(range(0, len(img_paths)), desc='Analyzing', disable = prog_bar)
  		for j in global_pbar:
  			img_path = img_paths[j]
  
  			#find representation for passed image
  
  			for j in model_names:
  				custom_model = models[j]
  
  				target_representation = represent(img_path = img_path
  					, model_name = model_name, model = custom_model
  					, enforce_detection = enforce_detection, detector_backend = detector_backend
  					, align = align
  					, normalization = normalization
  					)
  
  				for k in metric_names:
  					distances = []
  					for index, instance in df.iterrows():
  						source_representation = instance["%s_representation" % (j)]
  
  						if k == 'cosine':
  							distance = dst.findCosineDistance(source_representation, target_representation)
  						elif k == 'euclidean':
  							distance = dst.findEuclideanDistance(source_representation, target_representation)
  						elif k == 'euclidean_l2':
  							distance = dst.findEuclideanDistance(dst.l2_normalize(source_representation), dst.l2_normalize(target_representation))
  
  						distances.append(distance)
  
  					#---------------------------
  
  					if model_name == 'Ensemble' and j == 'OpenFace' and k == 'euclidean':
  						continue
  					else:
  						df["%s_%s" % (j, k)] = distances
  
  						if model_name != 'Ensemble':
  							threshold = dst.findThreshold(j, k)
  							df = df.drop(columns = ["%s_representation" % (j)])
  							df = df[df["%s_%s" % (j, k)] <= threshold]
  
  							df = df.sort_values(by = ["%s_%s" % (j, k)], ascending=True).reset_index(drop=True)
  
  							resp_obj.append(df)
  							df = df_base.copy() #restore df for the next iteration
  
  			#----------------------------------
  
  			if model_name == 'Ensemble':
  
  				feature_names = []
  				for j in model_names:
  					for k in metric_names:
  						if model_name == 'Ensemble' and j == 'OpenFace' and k == 'euclidean':
  							continue
  						else:
  							feature = '%s_%s' % (j, k)
  							feature_names.append(feature)
  
  				#print(df.head())
  
  				x = df[feature_names].values
  
  				#--------------------------------------
  
  				boosted_tree = Boosting.build_gbm()
  
  				y = boosted_tree.predict(x)
  
  				verified_labels = []; scores = []
  				for i in y:
  					verified = np.argmax(i) == 1
  					score = i[np.argmax(i)]
  
  					verified_labels.append(verified)
  					scores.append(score)
  
  				df['verified'] = verified_labels
  				df['score'] = scores
  
  				df = df[df.verified == True]
  				#df = df[df.score > 0.99] #confidence score
  				df = df.sort_values(by = ["score"], ascending=False).reset_index(drop=True)
  				df = df[['identity', 'verified', 'score']]
  
  				resp_obj.append(df)
  				df = df_base.copy() #restore df for the next iteration
  
  			#----------------------------------
  
  		toc = time.time()
  
  		print("find function lasts ",toc-tic," seconds")
  
  		if len(resp_obj) == 1:
  			return resp_obj[0]
  
  		return resp_obj
  
  	else:
  		raise ValueError("Passed db_path does not exist!")
  
  	return None
  
  def represent(img_path, model_name = 'VGG-Face', model = None, enforce_detection = True, detector_backend = 'opencv', align = True, normalization = 'base'):
  
  	"""
  	This function represents facial images as vectors.
  
  	Parameters:
  		img_path: exact image path, numpy array or based64 encoded images could be passed.
  
  		model_name (string): VGG-Face, Facenet, OpenFace, DeepFace, DeepID, Dlib, ArcFace.
  
  		model: Built deepface model. A face recognition model is built every call of verify function. You can pass pre-built face recognition model optionally if you will call verify function several times. Consider to pass model if you are going to call represent function in a for loop.
  
  			model = DeepFace.build_model('VGG-Face')
  
  		enforce_detection (boolean): If any face could not be detected in an image, then verify function will return exception. Set this to False not to have this exception. This might be convenient for low resolution images.
  
  		detector_backend (string): set face detector backend as retinaface, mtcnn, opencv, ssd or dlib
  
  		normalization (string): normalize the input image before feeding to model
  
  	Returns:
  		Represent function returns a multidimensional vector. The number of dimensions is changing based on the reference model. E.g. FaceNet returns 128 dimensional vector; VGG-Face returns 2622 dimensional vector.
  	"""
  
  	if model is None:
  		model = build_model(model_name)
  
  	#---------------------------------
  
  	#decide input shape
  	input_shape_x, input_shape_y = functions.find_input_shape(model)
  
  	#detect and align
  	img = functions.preprocess_face(img = img_path
  		, target_size=(input_shape_y, input_shape_x)
  		, enforce_detection = enforce_detection
  		, detector_backend = detector_backend
  		, align = align)
  
  	#---------------------------------
  	#custom normalization
  
  	img = functions.normalize_input(img = img, normalization = normalization)
  
  	#---------------------------------
  
  	#represent
  	embedding = model.predict(img)[0].tolist()
  
  	return embedding
  
  def stream(db_path = '', model_name ='VGG-Face', detector_backend = 'opencv', distance_metric = 'cosine', enable_face_analysis = True, source = 0, time_threshold = 5, frame_threshold = 5):
  
  	"""
  	This function applies real time face recognition and facial attribute analysis
  
  	Parameters:
  		db_path (string): facial database path. You should store some .jpg files in this folder.
  
  		model_name (string): VGG-Face, Facenet, OpenFace, DeepFace, DeepID, Dlib or Ensemble
  
  		detector_backend (string): opencv, ssd, mtcnn, dlib, retinaface
  
  		distance_metric (string): cosine, euclidean, euclidean_l2
  
  		enable_facial_analysis (boolean): Set this to False to just run face recognition
  
  		source: Set this to 0 for access web cam. Otherwise, pass exact video path.
  
  		time_threshold (int): how many second analyzed image will be displayed
  
  		frame_threshold (int): how many frames required to focus on face
  
  	"""
  
  	if time_threshold < 1:
  		raise ValueError("time_threshold must be greater than the value 1 but you passed "+str(time_threshold))
  
  	if frame_threshold < 1:
  		raise ValueError("frame_threshold must be greater than the value 1 but you passed "+str(frame_threshold))
  
  	realtime.analysis(db_path, model_name, detector_backend, distance_metric, enable_face_analysis
  						, source = source, time_threshold = time_threshold, frame_threshold = frame_threshold)
  
  def detectFace(img_path, detector_backend = 'opencv', enforce_detection = True, align = True):
  
  	"""
  	This function applies pre-processing stages of a face recognition pipeline including detection and alignment
  
  	Parameters:
  		img_path: exact image path, numpy array or base64 encoded image
  
  		detector_backend (string): face detection backends are retinaface, mtcnn, opencv, ssd or dlib
  
  	Returns:
  		deteced and aligned face in numpy format
  	"""
  
  	img = functions.preprocess_face(img = img_path, detector_backend = detector_backend
  		, enforce_detection = enforce_detection, align = align)[0] #preprocess_face returns (1, 224, 224, 3)
  	return img[:, :, ::-1] #bgr to rgb
  
  #---------------------------
  #main
  
  functions.initialize_folder()
  