deepface/deepface/DeepFace.py

import warnings
warnings.filterwarnings("ignore")
import time
import os
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, FbDeepFace, DeepID, DlibWrapper, ArcFace, Boosting
from deepface.extendedmodels import Age, Gender, Race, Emotion
from deepface.commons import functions, realtime, distance as dst

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
	"""
	
	models = {
		'VGG-Face': VGGFace.loadModel, 
		'OpenFace': OpenFace.loadModel,
		'Facenet': Facenet.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
	}

	model = models.get(model_name)
	
	if model:
		model = model()
		#print('Using {} model backend'.format(model_name))
		return model
	else:
		raise ValueError('Invalid model_name passed - {}'.format(model_name))

def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric = 'cosine', 
		   model = None, enforce_detection = True, detector_backend = 'mtcnn'):	
	
	"""
	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 mtcnn, opencv, ssd or dlib
	
	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)	
	functions.initialize_detector(detector_backend = detector_backend)

	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)
		else:
			models = {}
			models[model_name] = model
	
	#------------------------------
	
	#calling deepface in a for loop causes lots of progress bars. this prevents it.
	disable_option = False if len(img_list) > 1 else True
	
	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]
				
				#decide input shape
				input_shape = functions.find_input_shape(custom_model)	
				input_shape_x = input_shape[0]; input_shape_y = input_shape[1]
				
				#----------------------
				#detect and align faces
				
				img1 = functions.preprocess_face(img=img1_path
					, target_size=(input_shape_y, input_shape_x)
					, enforce_detection = enforce_detection
					, detector_backend = detector_backend)
				
				img2 = functions.preprocess_face(img=img2_path
					, target_size=(input_shape_y, input_shape_x)
					, enforce_detection = enforce_detection
					, detector_backend = detector_backend)
				
				#----------------------
				#find embeddings
				
				img1_representation = custom_model.predict(img1)[0,:]
				img2_representation = custom_model.predict(img2)[0,:]
				
				#----------------------
				#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)

					#----------------------
					#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 = {}, enforce_detection = True
			, detector_backend = 'mtcnn'):
	
	"""
	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 (list): The default is ['age', 'gender', 'emotion', 'race']. You can drop some of those attributes.
		
		models: 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 mtcnn, opencv, ssd or dlib.
	Returns:
		The function returns a dictionary. If img_path is a list, then it will return list of dictionary.
		
		{
			"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
			}
		}
		
	"""

	img_paths, bulkProcess = functions.initialize_input(img_path)
	functions.initialize_detector(detector_backend = detector_backend)
	
	#---------------------------------
	
	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
	
	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

		pbar = tqdm(range(0,len(actions)), desc='Finding actions', disable = disable_option)

		img_224 = None # Set to prevent re-detection
		
		#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 = 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 = 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"] = 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)

				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 = 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 = 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 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 = 'mtcnn'):
	
	"""
	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 mtcnn, opencv, ssd or dlib
		
	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)
	functions.initialize_detector(detector_backend = detector_backend)
	
	#-------------------------------
	
	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')
			
			#for employee in employees:
			for index in pbar:
				employee = employees[index]
									
				instance = []
				instance.append(employee)
				
				for j in model_names:
					custom_model = models[j]
					
					#----------------------------------
					#decide input shape
					
					input_shape = functions.find_input_shape(custom_model)	
					input_shape_x = input_shape[0]; input_shape_y = input_shape[1]
					
					#----------------------------------
					
					img = functions.preprocess_face(img = employee
								, target_size = (input_shape_y, input_shape_x)
								, enforce_detection = enforce_detection
								, detector_backend = detector_backend)
					
					representation = custom_model.predict(img)[0,:]
					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')
		for j in global_pbar:
			img_path = img_paths[j]
		
			#find representation for passed image
			
			for j in model_names:
				custom_model = models[j]
				
				#--------------------------------
				#decide input shape
				input_shape = functions.find_input_shape(custom_model)	
				
				#--------------------------------
				
				img = functions.preprocess_face(img = img_path, target_size = input_shape
					, enforce_detection = enforce_detection
					, detector_backend = detector_backend)
					
				target_representation = custom_model.predict(img)[0,:]
				
				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 stream(db_path = '', model_name ='VGG-Face', 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
		
		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))
		
	functions.initialize_detector(detector_backend = 'opencv')
	
	realtime.analysis(db_path, model_name, distance_metric, enable_face_analysis
						, source = source, time_threshold = time_threshold, frame_threshold = frame_threshold)

def detectFace(img_path, detector_backend = 'mtcnn'):
	
	"""
	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 mtcnn, opencv, ssd or dlib
	
	Returns:
		deteced and aligned face in numpy format
	"""
	
	functions.initialize_detector(detector_backend = detector_backend)
	
	img = functions.preprocess_face(img = img_path, detector_backend = detector_backend)[0] #preprocess_face returns (1, 224, 224, 3)
	return img[:, :, ::-1] #bgr to rgb
	
#---------------------------
#main

functions.initializeFolder()