implement multi-faces detections

deepface/DeepFace.py

@@ -103,8 +103,8 @@ def verify(img1_path, img2_path = '', model_name ='VGG-Face', distance_metric =
 						input_shape = input_shape[1:3]
 
 
-					img1 = functions.preprocess_face(img = img1_path, target_size = input_shape, enforce_detection = enforce_detection, detector_backend = detector_backend)
-					img2 = functions.preprocess_face(img = img2_path, target_size = input_shape, enforce_detection = enforce_detection, detector_backend = detector_backend)
+					img1 = functions.preprocess_face(img = img1_path, target_size = input_shape, enforce_detection = enforce_detection, detector_backend = detector_backend)['processed']
+					img2 = functions.preprocess_face(img = img2_path, target_size = input_shape, enforce_detection = enforce_detection, detector_backend = detector_backend)['processed']
 
 					img1_representation = custom_model.predict(img1)[0,:]
 					img2_representation = custom_model.predict(img2)[0,:]
@@ -271,8 +271,8 @@ def verify(img1_path, img2_path = '', model_name ='VGG-Face', distance_metric =
 			#----------------------
 			#crop 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)
+			img1 = functions.preprocess_face(img=img1_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)['processed']
+			img2 = functions.preprocess_face(img=img2_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)['processed']
 
 			#----------------------
 			#find embeddings
@@ -358,7 +358,8 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True, detec
 
 	#if a specific target is not passed, then find them all
 	if len(actions) == 0:
-		actions= ['emotion', 'age', 'gender', 'race']
+		# actions= ['emotion', 'age', 'gender', 'race']
+		actions = ['emotion', 'age', 'gender']
 
 	#print("Actions to do: ", actions)
 
@@ -403,14 +404,21 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True, detec
 	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)
 
+		# preprocess images
+		emotion_imgs = functions.preprocess_face(img=img_path, target_size=(48, 48), grayscale=True, enforce_detection=enforce_detection, detector_backend=detector_backend)['processed']
+		imgs_224 = functions.preprocess_face(img_path, target_size=(224, 224), grayscale=False, enforce_detection=enforce_detection)  # just emotion model expects grayscale images
+		orig_faces = imgs_224['original']
+		imgs_224 = imgs_224['processed']
+
+		for i in range(len(imgs_224)):
+
+			resp_obj = "{"
 			action_idx = 0
-		img_224 = None # Set to prevent re-detection
+
 			#for action in actions:
 			for index in pbar:
 				action = actions[index]
@@ -421,9 +429,8 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True, detec
 
 				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 = emotion_model.predict(img)[0,:]
+					emotion_predictions = emotion_model.predict(emotion_imgs[i])[0,:]
 
 					sum_of_predictions = emotion_predictions.sum()
 
@@ -443,20 +450,16 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True, detec
 					resp_obj += emotion_obj
 
 				elif action == 'age':
-				if img_224 is None:
-					img_224 = functions.preprocess_face(img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection) #just emotion model expects grayscale images
 					#print("age prediction")
-				age_predictions = age_model.predict(img_224)[0,:]
+					age_predictions = age_model.predict(imgs_224[i])[0,:]
 					apparent_age = Age.findApparentAge(age_predictions)
 
 					resp_obj += "\"age\": %s" % (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) #just emotion model expects grayscale images
 					#print("gender prediction")
 
-				gender_prediction = gender_model.predict(img_224)[0,:]
+					gender_prediction = gender_model.predict(imgs_224[i])[0,:]
 
 					if np.argmax(gender_prediction) == 0:
 						gender = "Woman"
@@ -466,9 +469,7 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True, detec
 					resp_obj += "\"gender\": \"%s\"" % (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 = race_model.predict(img_224)[0,:]
+					race_predictions = race_model.predict(imgs_224[i])[0,:]
 					race_labels = ['asian', 'indian', 'black', 'white', 'middle eastern', 'latino hispanic']
 
 					sum_of_predictions = race_predictions.sum()
@@ -493,30 +494,27 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True, detec
 
 			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])
-
-			if i > 0:
-				resp_obj += ", "
-
-			resp_obj += "\"instance_"+str(i+1)+"\": "+resp_item
-		resp_obj += "}"
-		resp_obj = json.loads(resp_obj)
-		return resp_obj
-		#return resp_objects
 
+	# resp_obj = "{"
+	#
+	# for i in range(0, len(resp_objects)):
+	# 	resp_item = json.dumps(resp_objects[i])
+	#
+	# 	if i > 0:
+	# 		resp_obj += ", "
+	#
+	# 	resp_obj += "\"instance_"+str(i+1)+"\": "+resp_item
+	# resp_obj += "}"
+	# resp_obj = json.loads(resp_obj)
+	# return resp_obj
+	return resp_objects, orig_faces
 
 def detectFace(img_path, detector_backend='opencv'):
-	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
+	imgs = functions.preprocess_face(img=img_path, detector_backend=detector_backend)['processed'] #preprocess_face returns (1, 224, 224, 3)
+	for i in range(len(imgs)):
+		imgs[i] = imgs[i][0][:, :, ::-1] #bgr to rgb
+	return imgs
 
 def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'opencv'):
 
@@ -659,7 +657,7 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
 
 					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)
+					img = functions.preprocess_face(img = employee, target_size = (input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)['processed']
 					representation = model.predict(img)[0,:]
 
 					instance = []
@@ -685,7 +683,7 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
 
 						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)
+						img = functions.preprocess_face(img = employee, target_size = (input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)['processed']
 						representation = ensemble_model.predict(img)[0,:]
 						instance.append(representation)
 
@@ -730,7 +728,7 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
 					else:
 						input_shape = input_shape[1:3]
 
-					img = functions.preprocess_face(img = img_path, target_size = input_shape, enforce_detection = enforce_detection, detector_backend = detector_backend)
+					img = functions.preprocess_face(img = img_path, target_size = input_shape, enforce_detection = enforce_detection, detector_backend = detector_backend)['processed']
 					target_representation = ensemble_model.predict(img)[0,:]
 
 					for k in metric_names:
@@ -822,7 +820,7 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
 
 				input_shape_x = input_shape[0]; input_shape_y = input_shape[1]
 
-				img = functions.preprocess_face(img = img_path, target_size = (input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
+				img = functions.preprocess_face(img = img_path, target_size = (input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)['processed']
 				target_representation = model.predict(img)[0,:]
 
 				distances = []

deepface/commons/functions.py

@@ -20,14 +20,15 @@ import bz2
 from deepface.commons import distance
 from mtcnn import MTCNN  # 0.1.0
 
+
 def loadBase64Img(uri):
     encoded_data = uri.split(',')[1]
     nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8)
     img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
     return img
 
-def initializeFolder():
 
+def initializeFolder():
     home = str(Path.home())
 
     if not os.path.exists(home + "/.deepface"):
@@ -38,8 +39,8 @@ def initializeFolder():
         os.mkdir(home + "/.deepface/weights")
         print("Directory ", home, "/.deepface/weights created")
 
-def findThreshold(model_name, distance_metric):
 
+def findThreshold(model_name, distance_metric):
     threshold = 0.40
 
     if model_name == 'VGG-Face':
@@ -92,6 +93,7 @@ def findThreshold(model_name, distance_metric):
 
     return threshold
 
+
 def get_opencv_path():
     opencv_home = cv2.__file__
     folders = opencv_home.split(os.path.sep)[0:-1]
@@ -102,8 +104,8 @@ def get_opencv_path():
 
     return path + "/data/"
 
-def load_image(img):
 
+def load_image(img):
     exact_image = False
     if type(img).__module__ == np.__name__:
         exact_image = True
@@ -125,8 +127,8 @@ def load_image(img):
 
     return img
 
-def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_detection = True):
 
+def detect_face(img, detector_backend='opencv', grayscale=False, enforce_detection=True):
     home = str(Path.home())
 
     if detector_backend == 'opencv':
@@ -136,7 +138,8 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
         face_detector_path = opencv_path + "haarcascade_frontalface_default.xml"
 
         if os.path.isfile(face_detector_path) != True:
-			raise ValueError("Confirm that opencv is installed on your environment! Expected path ",face_detector_path," violated.")
+            raise ValueError("Confirm that opencv is installed on your environment! Expected path ", face_detector_path,
+                             " violated.")
 
         face_detector = cv2.CascadeClassifier(face_detector_path)
 
@@ -150,9 +153,13 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
             pass
 
         if len(faces) > 0:
-			x,y,w,h = faces[0] #focus on the 1st face found in the image
+            detected_faces = []
+            for face in faces:
+                print(face)
+                x, y, w, h = face
                 detected_face = img[int(y):int(y + h), int(x):int(x + w)]
-			return detected_face
+                detected_faces.append(detected_face)
+            return detected_faces
 
         else:  # if no face detected
 
@@ -160,7 +167,8 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
                 return img
 
             else:
-				raise ValueError("Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.")
+                raise ValueError(
+                    "Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.")
 
     elif detector_backend == 'ssd':
 
@@ -169,7 +177,6 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
 
         # model structure
         if os.path.isfile(home + '/.deepface/weights/deploy.prototxt') != True:
-			
             print("deploy.prototxt will be downloaded...")
 
             url = "https://github.com/opencv/opencv/raw/3.4.0/samples/dnn/face_detector/deploy.prototxt"
@@ -178,10 +185,8 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
 
             gdown.download(url, output, quiet=False)
 
-		
         # pre-trained weights
         if os.path.isfile(home + '/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel') != True:
-			
             print("res10_300x300_ssd_iter_140000.caffemodel will be downloaded...")
 
             url = "https://github.com/opencv/opencv_3rdparty/raw/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel"
@@ -237,7 +242,8 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
             bottom = instance["bottom"]
             top = instance["top"]
 
-			detected_face = base_img[int(top*aspect_ratio_y):int(bottom*aspect_ratio_y), int(left*aspect_ratio_x):int(right*aspect_ratio_x)]
+            detected_face = base_img[int(top * aspect_ratio_y):int(bottom * aspect_ratio_y),
+                            int(left * aspect_ratio_x):int(right * aspect_ratio_x)]
 
             return detected_face
 
@@ -248,10 +254,12 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
                 return img
 
             else:
-				raise ValueError("Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.")
+                raise ValueError(
+                    "Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.")
 
     elif detector_backend == 'dlib':
-		import dlib #this is not a must library within deepface. that's why, I didn't put this import to a global level. version: 19.20.0
+        import \
+            dlib  # this is not a must library within deepface. that's why, I didn't put this import to a global level. version: 19.20.0
 
         detector = dlib.get_frontal_face_detector()
 
@@ -260,8 +268,10 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
         if len(detections) > 0:
 
             for idx, d in enumerate(detections):
-				left = d.left(); right = d.right()
-				top = d.top(); bottom = d.bottom()
+                left = d.left();
+                right = d.right()
+                top = d.top();
+                bottom = d.bottom()
 
                 detected_face = img[top:bottom, left:right]
 
@@ -273,7 +283,8 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
                 return img
 
             else:
-				raise ValueError("Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.") 
+                raise ValueError(
+                    "Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.")
 
     elif detector_backend == 'mtcnn':
 
@@ -282,17 +293,20 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
         detections = mtcnn_detector.detect_faces(img)
 
         if len(detections) > 0:
-			detection = detections[0]
+            detected_faces = []
+            for detection in detections:
                 x, y, w, h = detection["box"]
                 detected_face = img[int(y):int(y + h), int(x):int(x + w)]
-			return detected_face
+                detected_faces.append(detected_face)
+            return detected_faces
 
         else:  # if no face detected
             if enforce_detection != True:
                 return img
 
             else:
-				raise ValueError("Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.")
+                raise ValueError(
+                    "Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.")
 
     else:
         detectors = ['opencv', 'ssd', 'dlib', 'mtcnn']
@@ -300,8 +314,8 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
 
     return 0
 
-def alignment_procedure(img, left_eye, right_eye):
 
+def alignment_procedure(img, left_eye, right_eye):
     # this function aligns given face in img based on left and right eye coordinates
 
     left_eye_x, left_eye_y = left_eye
@@ -347,8 +361,8 @@ def alignment_procedure(img, left_eye, right_eye):
 
     return img  # return img anyway
 
-def align_face(img, detector_backend = 'opencv'):
 
+def align_face(img, detector_backend='opencv'):
     home = str(Path.home())
 
     if (detector_backend == 'opencv') or (detector_backend == 'ssd'):
@@ -379,12 +393,15 @@ def align_face(img, detector_backend = 'opencv'):
             # -----------------------
             # decide left and right eye
 
-			eye_1 = eyes[0]; eye_2 = eyes[1]
+            eye_1 = eyes[0];
+            eye_2 = eyes[1]
 
             if eye_1[0] < eye_2[0]:
-				left_eye = eye_1; right_eye = eye_2
+                left_eye = eye_1;
+                right_eye = eye_2
             else:
-				left_eye = eye_2; right_eye = eye_1
+                left_eye = eye_2;
+                right_eye = eye_1
 
             # -----------------------
             # find center of eyes
@@ -401,7 +418,6 @@ def align_face(img, detector_backend = 'opencv'):
         # check required file exists in the home/.deepface/weights folder
 
         if os.path.isfile(home + '/.deepface/weights/shape_predictor_5_face_landmarks.dat') != True:
-			
             print("shape_predictor_5_face_landmarks.dat.bz2 is going to be downloaded")
 
             url = "http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2"
@@ -446,28 +462,39 @@ def align_face(img, detector_backend = 'opencv'):
 
         return img  # return img anyway
 
-def preprocess_face(img, target_size=(224, 224), grayscale = False, enforce_detection = True, detector_backend = 'opencv'):
 
+def preprocess_face(img, target_size=(224, 224), grayscale=False, enforce_detection=True, detector_backend='opencv'):
     # img might be path, base64 or numpy array. Convert it to numpy whatever it is.
     img = load_image(img)
     base_img = img.copy()
 
-	img = detect_face(img = img, detector_backend = detector_backend, grayscale = grayscale, enforce_detection = enforce_detection)
+    imgs = detect_face(img=img, detector_backend=detector_backend, grayscale=grayscale,
+                       enforce_detection=enforce_detection)
 
     # --------------------------
 
+    for i in range(len(imgs)):
+
+        img = imgs[i]
+
         if img.shape[0] > 0 and img.shape[1] > 0:
-		img = align_face(img = img, detector_backend = detector_backend)
+            imgs[i] = align_face(img=img, detector_backend=detector_backend)
         else:
 
             if enforce_detection == True:
-			raise ValueError("Detected face shape is ", img.shape,". Consider to set enforce_detection argument to False.")
+                raise ValueError("Detected face shape is ", img.shape,
+                                 ". Consider to set enforce_detection argument to False.")
             else:  # restore base image
-			img = base_img.copy()
+                imgs[i] = base_img.copy()
 
     # --------------------------
 
     # post-processing
+
+    pixels = []
+
+    for img in imgs:
+
         if grayscale == True:
             img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
 
@@ -476,14 +503,20 @@ def preprocess_face(img, target_size=(224, 224), grayscale = False, enforce_dete
         img_pixels = np.expand_dims(img_pixels, axis=0)
         img_pixels /= 255  # normalize input in [0, 1]
 
-	return img_pixels
+        pixels.append(img_pixels)
+
+    return {'processed': pixels, 'original': imgs}
+
 
 def allocateMemory():
-	
     # find allocated memories
     gpu_indexes = []
-	memory_usage_percentages = []; available_memories = []; total_memories = []; utilizations = []
-	power_usages = []; power_capacities = []
+    memory_usage_percentages = [];
+    available_memories = [];
+    total_memories = [];
+    utilizations = []
+    power_usages = [];
+    power_capacities = []
 
     try:
         result = subprocess.check_output(['nvidia-smi'])