Merge pull request #17 from uriafranko/uria-franko

passing already built models to analyze function as well

Contributors.md

@@ -0,0 +1 @@
+uriafranko

deepface/DeepFace.py

@@ -20,73 +20,74 @@ from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
 from deepface.extendedmodels import Age, Gender, Race, Emotion
 from deepface.commons import functions, realtime, distance as dst
 
+
 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':
@@ -95,18 +96,18 @@ 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)
@@ -114,30 +115,30 @@ 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 += ", "
@@ -148,7 +149,8 @@ def verify(img1_path, img2_path=''
 		return resp_obj
 		#return resp_objects
 
-def analyze(img_path, actions= []):
+
+def analyze(img_path, actions= [], models= {}):
 
 	if type(img_path) == list:
 		img_paths = img_path.copy()
@@ -156,129 +158,141 @@ 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 'emotion' in models:
-	
+			emotion_model = models['emotion']
+		else:
+			emotion_model = Emotion.loadModel()
+
 	if 'age' in actions:
-		age_model = Age.loadModel()
+		if 'age' in models:
-	
+			age_model = models['age']
+		else:
+			age_model = Age.loadModel()
+
 	if 'gender' in actions:
-		gender_model = Gender.loadModel()
+		if 'gender' in models:
-	
+			gender_model = models['gender']
+		else:
+			gender_model = Gender.loadModel()
+
 	if 'race' in actions:
-		race_model = Race.loadModel()
+		if 'race' in models:
+			race_model = models['race']
+		else:
+			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 += ", "
@@ -289,13 +303,16 @@ def analyze(img_path, actions= []):
 		return resp_obj
 		#return resp_objects
 
+
 def detectFace(img_path):
 	img = functions.detectFace(img_path)[0] #detectFace returns (1, 224, 224, 3)
 	return img[:, :, ::-1] #bgr to rgb
 
+
 def stream(db_path, model_name ='VGG-Face', distance_metric = 'cosine', enable_face_analysis = True):
 	realtime.analysis(db_path, model_name, distance_metric, enable_face_analysis)
 
+
 #---------------------------
 
 functions.initializeFolder()