diff --git a/deepface/DeepFace.py b/deepface/DeepFace.py
index 67abf0d..72d3668 100644
--- a/deepface/DeepFace.py
+++ b/deepface/DeepFace.py
@@ -858,12 +858,11 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
 	return None
 	
 def stream(db_path = '', model_name ='VGG-Face', distance_metric = 'cosine', enable_face_analysis = True):
+	
+	functions.initialize_detector(detector_backend = 'opencv')
+	
 	realtime.analysis(db_path, model_name, distance_metric, enable_face_analysis)
 
-def allocateMemory():
-	print("Analyzing your system...")
-	functions.allocateMemory()
-
 def DlibResNet_():
 	#this is not a regular Keras model.
 	from deepface.basemodels.DlibResNet import DlibResNet
diff --git a/deepface/commons/functions.py b/deepface/commons/functions.py
index 1e2e616..9a0600d 100644
--- a/deepface/commons/functions.py
+++ b/deepface/commons/functions.py
@@ -170,6 +170,12 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
 	
 	home = str(Path.home())
 	
+	#TODO: what if I directly call detect_face? In this case, initialize_detector hadn't been called before...
+	
+	#if functions.preproces_face is called directly, then face_detector global variable might not been initialized.
+	if not "face_detector" in globals():
+		initialize_detector(detector_backend = detector_backend)
+		
 	if detector_backend == 'opencv':
 		
 		faces = []
@@ -443,89 +449,3 @@ def preprocess_face(img, target_size=(224, 224), grayscale = False, enforce_dete
 	img_pixels /= 255 #normalize input in [0, 1]
 	
 	return img_pixels
-	
-def allocateMemory():
-	
-	#find allocated memories
-	gpu_indexes = []
-	memory_usage_percentages = []; available_memories = []; total_memories = []; utilizations = []
-	power_usages = []; power_capacities = []
-	
-	try:
-		result = subprocess.check_output(['nvidia-smi'])
-
-		dashboard = result.decode("utf-8").split("=|")
-
-		dashboard = dashboard[1].split("\n")
-		
-		gpu_idx = 0
-		for line in dashboard:
-			if ("MiB" in line):
-				power_info = line.split("|")[1]
-				power_capacity = int(power_info.split("/")[-1].replace("W", ""))
-				power_usage = int((power_info.split("/")[-2]).strip().split(" ")[-1].replace("W", ""))
-				
-				power_usages.append(power_usage)
-				power_capacities.append(power_capacity)
-				
-				#----------------------------
-				
-				memory_info = line.split("|")[2].replace("MiB","").split("/")
-				utilization_info = int(line.split("|")[3].split("%")[0])
-				
-				allocated = int(memory_info[0])
-				total_memory = int(memory_info[1])
-				available_memory = total_memory - allocated
-				
-				total_memories.append(total_memory)
-				available_memories.append(available_memory)
-				memory_usage_percentages.append(round(100*int(allocated)/int(total_memory), 4))
-				utilizations.append(utilization_info)
-				gpu_indexes.append(gpu_idx)
-				
-				gpu_idx = gpu_idx + 1
-		
-		gpu_count = gpu_idx * 1
-				
-	except Exception as err:
-		gpu_count = 0
-		#print(str(err))
-		
-	#------------------------------
-	
-	df = pd.DataFrame(gpu_indexes, columns = ["gpu_index"])
-	df["total_memories_in_mb"] = total_memories
-	df["available_memories_in_mb"] = available_memories
-	df["memory_usage_percentage"] = memory_usage_percentages
-	df["utilizations"] = utilizations
-	df["power_usages_in_watts"] = power_usages
-	df["power_capacities_in_watts"] = power_capacities
-	
-	df = df.sort_values(by = ["available_memories_in_mb"], ascending = False).reset_index(drop = True)
-	
-	#------------------------------
-	
-	required_memory = 10000 #All deepface models require 9016 MiB
-	
-	if df.shape[0] > 0: #has gpu
-		if df.iloc[0].available_memories_in_mb > required_memory:
-			my_gpu = str(int(df.iloc[0].gpu_index))
-			os.environ["CUDA_VISIBLE_DEVICES"] = my_gpu
-			
-			#------------------------------
-			#tf allocates all memory by default
-			#this block avoids greedy approach
-			
-			config = tf.ConfigProto()
-			config.gpu_options.allow_growth = True
-			session = tf.Session(config=config)
-			keras.backend.set_session(session)
-			
-			print("DeepFace will run on GPU (gpu_", my_gpu,")")
-		else:
-			#this case has gpu but no enough memory to allocate
-			os.environ["CUDA_VISIBLE_DEVICES"] = "" #run it on cpu
-			print("Even though the system has GPUs, there is no enough space in memory to allocate.")
-			print("DeepFace will run on CPU")
-	else:
-		print("DeepFace will run on CPU")