list operations supported

README.md

@@ -34,6 +34,16 @@ print("Is verified: ", result["verified"])
 print("Distance: ", result["distance"])
 ```
 
+Each call of verification function builds a face recognition model scratch and this is a costly operation. If you are going to verify multiple faces sequentially, then you should pass an array to verify function to speed up.
+
+```python
+dataset = [
+	['dataset/img1.jpg', 'dataset/img2.jpg'],
+	['dataset/img5.jpg', 'dataset/img6.jpg']
+]
+result = DeepFace.verify(dataset)
+```
+
 ## Face recognition models
 
 Face recognition can be handled by different models. Currently, [`VGG-Face`](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) , [`Google Facenet`](https://sefiks.com/2018/09/03/face-recognition-with-facenet-in-keras/), [`OpenFace`](https://sefiks.com/2019/07/21/face-recognition-with-openface-in-keras/) and [`Facebook DeepFace`](https://sefiks.com/2020/02/17/face-recognition-with-facebook-deepface-in-keras/) models are supported in deepface. The default configuration verifies faces with **VGG-Face** model. You can set the base model while verification as illustared below. Accuracy and speed show difference based on the performing model.
@@ -66,6 +76,8 @@ Deepface also offers facial attribute analysis including [`age`](https://sefiks.
 from deepface import DeepFace
 demography = DeepFace.analyze("img4.jpg") #passing nothing as 2nd argument will find everything
 #demography = DeepFace.analyze("img4.jpg", ['age', 'gender', 'race', 'emotion']) #identical to the line above
+
+demographies = DeepFace.analyze(["img1.jpg", "img2.jpg", "img3.jpg"])
 ```
 
 <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/tests/dataset/test-case-1.jpg" width="20%" height="20%"></p>

deepface/DeepFace.py

@@ -17,23 +17,19 @@ from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
 from deepface.extendedmodels import Age, Gender, Race, Emotion
 from deepface.commons import functions, distance as dst
 
-def verify(img1_path, img2_path
+def verify(img1_path, img2_path=''
 	, model_name ='VGG-Face', distance_metric = 'cosine', plot = False):
 	
 	tic = time.time()
 	
-	if os.path.isfile(img1_path) != True:
+	if type(img1_path) == list:
-		raise ValueError("Confirm that ",img1_path," exists")
+		bulkProcess = True
+		img_list = img1_path.copy()
+	else:
+		bulkProcess = False
+		img_list = [[img1_path, img2_path]]
 		
-	if os.path.isfile(img2_path) != True:
+	#------------------------------
-		raise ValueError("Confirm that ",img2_path," exists")
-	
-	#-------------------------
-	
-	#tuned thresholds for model and metric pair
-	threshold = functions.findThreshold(model_name, distance_metric)
-	
-	#-------------------------
 	
 	if model_name == 'VGG-Face':
 		print("Using VGG-Face model backend and", distance_metric,"distance.")
@@ -58,19 +54,39 @@ def verify(img1_path, img2_path
 	else:
 		raise ValueError("Invalid model_name passed - ", model_name)
 	
-	#-------------------------
+	#------------------------------
-	#crop face
+	
+	#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]
+			
+			#----------------------
+			
+			if os.path.isfile(img1_path) != True:
+				raise ValueError("Confirm that ",img1_path," exists")
+			
+			if os.path.isfile(img2_path) != True:
+				raise ValueError("Confirm that ",img2_path," exists")
+			
+			#----------------------
+			#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':
@@ -82,16 +98,14 @@ def verify(img1_path, img2_path
 			else:
 				raise ValueError("Invalid distance_metric passed - ", distance_metric)
 			
-	#-------------------------
+			#----------------------
 			#decision
 			
 			if distance <= threshold:
 				identified =  "true"
 			else:
 				identified =  "false"
-	
+			#----------------------
-	#-------------------------
-	
 			if plot:
 				label = "Verified: "+identified
 				label += "\nThreshold: "+str(round(distance, 2))
@@ -109,9 +123,8 @@ def verify(img1_path, img2_path
 				fig.suptitle(label, fontsize=17)
 				plt.show(block=True)
 				
-	#-------------------------
+			#----------------------
-	
+			#response object
-	toc = time.time()
 			
 			resp_obj = "{"
 			resp_obj += "\"verified\": "+identified
@@ -123,16 +136,34 @@ def verify(img1_path, img2_path
 			
 			resp_obj = json.loads(resp_obj) #string to json
 			
+			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()
+	
 	#print("identification lasts ",toc-tic," seconds")
 	
-	return resp_obj
+	if bulkProcess == True:
+		return resp_objects
 
 def analyze(img_path, actions= []):
 	
-	if os.path.isfile(img_path) != True:
+	if type(img_path) == list:
-		raise ValueError("Confirm that ",img_path," exists")
+		img_paths = img_path.copy()
+		bulkProcess = True
+	else:
+		img_paths = [img_path]
+		bulkProcess = False
 	
-	resp_obj = "{"
+	#---------------------------------
 	
 	#if a specific target is not passed, then find them all
 	if len(actions) == 0:
@@ -140,6 +171,32 @@ def analyze(img_path, actions= []):
 	
 	print("Actions to do: ", actions)
 	
+	#---------------------------------
+	
+	if 'emotion' in actions:
+		emotion_model = Emotion.loadModel()
+	
+	if 'age' in actions:
+		age_model = Age.loadModel()
+	
+	if 'gender' in actions:
+		gender_model = Gender.loadModel()
+	
+	if 'race' in actions:
+		race_model = Race.loadModel()
+	#---------------------------------
+	
+	resp_objects = []
+	for img_path in img_paths:
+		
+		if type(img_path) != str:
+			raise ValueError("You should pass string data type for image paths but you passed ", type(img_path))
+		
+		if os.path.isfile(img_path) != True:
+			raise ValueError("Confirm that ",img_path," exists")
+		
+		resp_obj = "{"
+		
 		#TO-DO: do this in parallel
 		
 		pbar = tqdm(range(0,len(actions)), desc='Finding actions')
@@ -157,8 +214,7 @@ def analyze(img_path, actions= []):
 				emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
 				img = functions.detectFace(img_path, (48, 48), True)
 				
-			model = Emotion.loadModel()
+				emotion_predictions = emotion_model.predict(img)[0,:]
-			emotion_predictions = model.predict(img)[0,:]
 				
 				sum_of_predictions = emotion_predictions.sum()
 				
@@ -180,8 +236,7 @@ def analyze(img_path, actions= []):
 			elif action == 'age':
 				img = functions.detectFace(img_path, (224, 224), False) #just emotion model expects grayscale images
 				#print("age prediction")
-			model = Age.loadModel()
+				age_predictions = age_model.predict(img)[0,:]
-			age_predictions = model.predict(img)[0,:]
 				apparent_age = Age.findApparentAge(age_predictions)
 				
 				resp_obj += "\"age\": %s" % (apparent_age)
@@ -190,8 +245,7 @@ def analyze(img_path, actions= []):
 				img = functions.detectFace(img_path, (224, 224), False) #just emotion model expects grayscale images
 				#print("gender prediction")
 				
-			model = Gender.loadModel()
+				gender_prediction = gender_model.predict(img)[0,:]
-			gender_prediction = model.predict(img)[0,:]
 				
 				if np.argmax(gender_prediction) == 0:
 					gender = "Woman"
@@ -202,8 +256,7 @@ def analyze(img_path, actions= []):
 				
 			elif action == 'race':
 				img = functions.detectFace(img_path, (224, 224), False) #just emotion model expects grayscale images
-			model = Race.loadModel()
+				race_predictions = race_model.predict(img)[0,:]
-			race_predictions = model.predict(img)[0,:]
 				race_labels = ['asian', 'indian', 'black', 'white', 'middle eastern', 'latino hispanic']
 				
 				sum_of_predictions = race_predictions.sum()
@@ -228,8 +281,14 @@ def analyze(img_path, actions= []):
 		
 		resp_obj = json.loads(resp_obj)
 		
+		if bulkProcess == True:
+			resp_objects.append(resp_obj)
+		else:
 			return resp_obj
 	
+	if bulkProcess == True:
+		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

tests/unit_tests.py

@@ -5,6 +5,43 @@ import os
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
 #-----------------------------------------
 
+print("Bulk tests")
+
+print("-----------------------------------------")
+
+print("Bulk face recognition tests")
+
+dataset = [
+	['dataset/img1.jpg', 'dataset/img2.jpg', True],
+	['dataset/img5.jpg', 'dataset/img6.jpg', True]
+]
+
+resp_obj = DeepFace.verify(dataset)
+print(resp_obj[0]["verified"] == True)
+print(resp_obj[1]["verified"] == True)
+
+print("-----------------------------------------")
+
+print("Bulk facial analysis tests")
+
+dataset = [
+	'dataset/img1.jpg',
+	'dataset/img2.jpg',
+	'dataset/img5.jpg',
+	'dataset/img6.jpg'
+]
+
+resp_obj = DeepFace.analyze(dataset)
+print(resp_obj[0]["age"]," years old ", resp_obj[0]["dominant_emotion"], " ",resp_obj[0]["gender"])
+print(resp_obj[1]["age"]," years old ", resp_obj[1]["dominant_emotion"], " ",resp_obj[1]["gender"])
+print(resp_obj[2]["age"]," years old ", resp_obj[2]["dominant_emotion"], " ",resp_obj[2]["gender"])
+print(resp_obj[3]["age"]," years old ", resp_obj[3]["dominant_emotion"], " ",resp_obj[3]["gender"])
+
+
+print("-----------------------------------------")
+
+#-----------------------------------------
+
 print("Facial analysis test. Passing nothing as an action")
 
 img = "dataset/img4.jpg"