version 0.0.52

api/api.py

@@ -42,7 +42,7 @@ print("Loading Face Recognition Models...")
 pbar = tqdm(range(0, 6), desc='Loading Face Recognition Models...')
 
 for index in pbar:
-	
+
 	if index == 0:
 		pbar.set_description("Loading VGG-Face")
 		vggface_model = DeepFace.build_model("VGG-Face")
@@ -61,7 +61,7 @@ for index in pbar:
 	elif index == 5:
 		pbar.set_description("Loading ArcFace DeepFace")
 		arcface_model = DeepFace.build_model("ArcFace")
-		
+
 toc = time.time()
 
 print("Face recognition models are built in ", toc-tic," seconds")
@@ -112,21 +112,21 @@ def index():
 
 @app.route('/analyze', methods=['POST'])
 def analyze():
-	
+
 	global graph
-	
+
 	tic = time.time()
 	req = request.get_json()
 	trx_id = uuid.uuid4()
 
 	#---------------------------
-	
+
 	if tf_version == 1:
 		with graph.as_default():
 			resp_obj = analyzeWrapper(req, trx_id)
 	elif tf_version == 2:
 		resp_obj = analyzeWrapper(req, trx_id)
-		
+
 	#---------------------------
 
 	toc = time.time()
@@ -145,10 +145,10 @@ def analyzeWrapper(req, trx_id = 0):
 
 		for item in raw_content: #item is in type of dict
 			instances.append(item)
-	
+
 	if len(instances) == 0:
 		return jsonify({'success': False, 'error': 'you must pass at least one img object in your request'}), 205
-	
+
 	print("Analyzing ", len(instances)," instances")
 
 	#---------------------------
@@ -156,52 +156,52 @@ def analyzeWrapper(req, trx_id = 0):
 	actions= ['emotion', 'age', 'gender', 'race']
 	if "actions" in list(req.keys()):
 		actions = req["actions"]
-	
+
 	#---------------------------
 
 	#resp_obj = DeepFace.analyze(instances, actions=actions)
 	resp_obj = DeepFace.analyze(instances, actions=actions, models=facial_attribute_models)
-	
+
 	return resp_obj
-	
+
 @app.route('/verify', methods=['POST'])
 def verify():
-	
+
 	global graph
-	
+
 	tic = time.time()
 	req = request.get_json()
 	trx_id = uuid.uuid4()
-	
+
 	resp_obj = jsonify({'success': False})
-	
+
 	if tf_version == 1:
 		with graph.as_default():
 			resp_obj = verifyWrapper(req, trx_id)
 	elif tf_version == 2:
 		resp_obj = verifyWrapper(req, trx_id)
-		
+
 	#--------------------------
-	
+
 	toc =  time.time()
-	
+
 	resp_obj["trx_id"] = trx_id
 	resp_obj["seconds"] = toc-tic
-	
+
 	return resp_obj, 200
 
 def verifyWrapper(req, trx_id = 0):
-	
+
 	resp_obj = jsonify({'success': False})
-	
+
 	model_name = "VGG-Face"; distance_metric = "cosine"
 	if "model_name" in list(req.keys()):
 		model_name = req["model_name"]
 	if "distance_metric" in list(req.keys()):
 		distance_metric = req["distance_metric"]
-	
+
 	#----------------------
-	
+
 	instances = []
 	if "img" in list(req.keys()):
 		raw_content = req["img"] #list
@@ -213,7 +213,7 @@ def verifyWrapper(req, trx_id = 0):
 			validate_img1 = False
 			if len(img1) > 11 and img1[0:11] == "data:image/":
 				validate_img1 = True
-			
+
 			validate_img2 = False
 			if len(img2) > 11 and img2[0:11] == "data:image/":
 				validate_img2 = True
@@ -223,16 +223,16 @@ def verifyWrapper(req, trx_id = 0):
 
 			instance.append(img1); instance.append(img2)
 			instances.append(instance)
-		
+
 	#--------------------------
 
 	if len(instances) == 0:
 		return jsonify({'success': False, 'error': 'you must pass at least one img object in your request'}), 205
-	
+
 	print("Input request of ", trx_id, " has ",len(instances)," pairs to verify")
-	
+
 	#--------------------------
-	
+
 	if model_name == "VGG-Face":
 		resp_obj = DeepFace.verify(instances, model_name = model_name, distance_metric = distance_metric, model = vggface_model)
 	elif model_name == "Facenet":
@@ -252,13 +252,93 @@ def verifyWrapper(req, trx_id = 0):
 		models["OpenFace"] = openface_model
 		models["DeepFace"] = deepface_model
 		resp_obj = DeepFace.verify(instances, model_name = model_name, model = models)
-		
+
 		for key in resp_obj: #issue 198.
 			resp_obj[key]['verified'] = bool(resp_obj[key]['verified'])
-				
+
 	else:
 		resp_obj = jsonify({'success': False, 'error': 'You must pass a valid model name. You passed %s' % (model_name)}), 205
+
+	return resp_obj
+
+@app.route('/represent', methods=['POST'])
+def represent():
+
+	global graph
+
+	tic = time.time()
+	req = request.get_json()
+	trx_id = uuid.uuid4()
+
+	resp_obj = jsonify({'success': False})
+
+	if tf_version == 1:
+		with graph.as_default():
+			resp_obj = representWrapper(req, trx_id)
+	elif tf_version == 2:
+		resp_obj = representWrapper(req, trx_id)
+
+	#--------------------------
+
+	toc =  time.time()
 	
+	resp_obj["trx_id"] = trx_id
+	resp_obj["seconds"] = toc-tic
+
+	return resp_obj, 200
+
+def representWrapper(req, trx_id = 0):
+
+	resp_obj = jsonify({'success': False})
+
+	#-------------------------------------
+	#find out model
+
+	model_name = "VGG-Face"; distance_metric = "cosine"
+
+	if "model_name" in list(req.keys()):
+		model_name = req["model_name"]
+
+	#-------------------------------------
+	#retrieve images from request
+
+	img = ""
+	if "img" in list(req.keys()):
+		img = req["img"] #list
+		#print("img: ", img)
+
+	validate_img = False
+	if len(img) > 11 and img[0:11] == "data:image/":
+		validate_img = True
+
+	if validate_img != True:
+		print("invalid image passed!")
+		return jsonify({'success': False, 'error': 'you must pass img as base64 encoded string'}), 205
+
+	#-------------------------------------
+	#cal represent function from the interface
+
+	embedding = []
+	if model_name == "VGG-Face":
+		embedding = DeepFace.represent(img, model_name = model_name, model = vggface_model)
+	elif model_name == "Facenet":
+		embedding = DeepFace.represent(img, model_name = model_name, model = facenet_model)
+	elif model_name == "OpenFace":
+		embedding = DeepFace.represent(img, model_name = model_name, model = openface_model)
+	elif model_name == "DeepFace":
+		embedding = DeepFace.represent(img, model_name = model_name, model = deepface_model)
+	elif model_name == "DeepID":
+		embedding = DeepFace.represent(img, model_name = model_name, model = deepid_model)
+	elif model_name == "ArcFace":
+		embedding = DeepFace.represent(img, model_name = model_name, model = arcface_model)
+	else:
+		resp_obj = jsonify({'success': False, 'error': 'You must pass a valid model name. You passed %s' % (model_name)}), 205
+
+	#print("embedding is ", len(embedding)," dimensional vector")
+	resp_obj = {}
+	resp_obj["embedding"] = embedding
+	#-------------------------------------
+
 	return resp_obj
 
 if __name__ == '__main__':

deepface/extendedmodels/Age.py

@@ -10,48 +10,47 @@ tf_version = int(tf.__version__.split(".")[0])
 if tf_version == 1:
 	import keras
 	from keras.models import Model, Sequential
-	from keras.layers import Convolution2D, Flatten, Activation	
+	from keras.layers import Convolution2D, Flatten, Activation
 elif tf_version == 2:
 	from tensorflow import keras
 	from tensorflow.keras.models import Model, Sequential
 	from tensorflow.keras.layers import Convolution2D, Flatten, Activation
 
-def loadModel():
+def loadModel(url = 'https://drive.google.com/uc?id=1YCox_4kJ-BYeXq27uUbasu--yz28zUMV'):
-	
+
 	model = VGGFace.baseModel()
-	
+
 	#--------------------------
-	
+
 	classes = 101
 	base_model_output = Sequential()
 	base_model_output = Convolution2D(classes, (1, 1), name='predictions')(model.layers[-4].output)
 	base_model_output = Flatten()(base_model_output)
 	base_model_output = Activation('softmax')(base_model_output)
-	
+
 	#--------------------------
 
 	age_model = Model(inputs=model.input, outputs=base_model_output)
-	
+
 	#--------------------------
-	
+
 	#load weights
-	
+
 	home = str(Path.home())
-	
+
 	if os.path.isfile(home+'/.deepface/weights/age_model_weights.h5') != True:
 		print("age_model_weights.h5 will be downloaded...")
-		
+
-		url = 'https://drive.google.com/uc?id=1YCox_4kJ-BYeXq27uUbasu--yz28zUMV'
 		output = home+'/.deepface/weights/age_model_weights.h5'
 		gdown.download(url, output, quiet=False)
-	
+
 	age_model.load_weights(home+'/.deepface/weights/age_model_weights.h5')
-	
+
 	return age_model
-	
+
 	#--------------------------
 
 def findApparentAge(age_predictions):
 	output_indexes = np.array([i for i in range(0, 101)])
 	apparent_age = np.sum(age_predictions * output_indexes)
-	return apparent_age
+	return apparent_age

deepface/extendedmodels/Emotion.py

@@ -14,11 +14,11 @@ elif tf_version == 2:
 	from tensorflow import keras
 	from tensorflow.keras.models import Model, Sequential
 	from tensorflow.keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Flatten, Dense, Dropout
-	
+
-def loadModel():
+def loadModel(url = 'https://drive.google.com/uc?id=13iUHHP3SlNg53qSuQZDdHDSDNdBP9nwy'):
-	
+
 	num_classes = 7
-	
+
 	model = Sequential()
 
 	#1st convolution layer
@@ -44,29 +44,24 @@ def loadModel():
 	model.add(Dropout(0.2))
 
 	model.add(Dense(num_classes, activation='softmax'))
-	
+
 	#----------------------------
-	
+
 	home = str(Path.home())
-	
+
 	if os.path.isfile(home+'/.deepface/weights/facial_expression_model_weights.h5') != True:
 		print("facial_expression_model_weights.h5 will be downloaded...")
-		
+
 		#TO-DO: upload weights to google drive
-		
+
 		#zip
-		url = 'https://drive.google.com/uc?id=13iUHHP3SlNg53qSuQZDdHDSDNdBP9nwy'
 		output = home+'/.deepface/weights/facial_expression_model_weights.zip'
 		gdown.download(url, output, quiet=False)
-		
+
 		#unzip facial_expression_model_weights.zip
 		with zipfile.ZipFile(output, 'r') as zip_ref:
 			zip_ref.extractall(home+'/.deepface/weights/')
-		
+
 	model.load_weights(home+'/.deepface/weights/facial_expression_model_weights.h5')
-	
+
 	return model
-	
-	#----------------------------
-	
-	return 0

deepface/extendedmodels/Gender.py

@@ -14,37 +14,36 @@ elif tf_version == 2:
 	from tensorflow.keras.models import Model, Sequential
 	from tensorflow.keras.layers import Convolution2D, Flatten, Activation
 
-def loadModel():
+def loadModel(url = 'https://drive.google.com/uc?id=1wUXRVlbsni2FN9-jkS_f4UTUrm1bRLyk'):
-	
+
 	model = VGGFace.baseModel()
-	
+
 	#--------------------------
-	
+
 	classes = 2
 	base_model_output = Sequential()
 	base_model_output = Convolution2D(classes, (1, 1), name='predictions')(model.layers[-4].output)
 	base_model_output = Flatten()(base_model_output)
 	base_model_output = Activation('softmax')(base_model_output)
-	
+
 	#--------------------------
 
 	gender_model = Model(inputs=model.input, outputs=base_model_output)
-	
+
 	#--------------------------
-	
+
 	#load weights
-	
+
 	home = str(Path.home())
-	
+
 	if os.path.isfile(home+'/.deepface/weights/gender_model_weights.h5') != True:
 		print("gender_model_weights.h5 will be downloaded...")
-		
+
-		url = 'https://drive.google.com/uc?id=1wUXRVlbsni2FN9-jkS_f4UTUrm1bRLyk'
 		output = home+'/.deepface/weights/gender_model_weights.h5'
 		gdown.download(url, output, quiet=False)
-	
+
 	gender_model.load_weights(home+'/.deepface/weights/gender_model_weights.h5')
-	
+
 	return gender_model
-	
+
-	#--------------------------
+	#--------------------------

deepface/extendedmodels/Race.py

@@ -16,42 +16,41 @@ elif tf_version == 2:
 	from tensorflow.keras.models import Model, Sequential
 	from tensorflow.keras.layers import Convolution2D, Flatten, Activation
 
-def loadModel():
+def loadModel(url = 'https://drive.google.com/uc?id=1nz-WDhghGQBC4biwShQ9kYjvQMpO6smj'):
-	
+
 	model = VGGFace.baseModel()
-	
+
 	#--------------------------
-	
+
 	classes = 6
 	base_model_output = Sequential()
 	base_model_output = Convolution2D(classes, (1, 1), name='predictions')(model.layers[-4].output)
 	base_model_output = Flatten()(base_model_output)
 	base_model_output = Activation('softmax')(base_model_output)
-	
+
 	#--------------------------
 
 	race_model = Model(inputs=model.input, outputs=base_model_output)
-	
+
 	#--------------------------
-	
+
 	#load weights
-	
+
 	home = str(Path.home())
-	
+
 	if os.path.isfile(home+'/.deepface/weights/race_model_single_batch.h5') != True:
 		print("race_model_single_batch.h5 will be downloaded...")
-		
+
 		#zip
-		url = 'https://drive.google.com/uc?id=1nz-WDhghGQBC4biwShQ9kYjvQMpO6smj'
 		output = home+'/.deepface/weights/race_model_single_batch.zip'
 		gdown.download(url, output, quiet=False)
-		
+
 		#unzip race_model_single_batch.zip
 		with zipfile.ZipFile(output, 'r') as zip_ref:
 			zip_ref.extractall(home+'/.deepface/weights/')
-	
+
 	race_model.load_weights(home+'/.deepface/weights/race_model_single_batch.h5')
-	
+
 	return race_model
-	
+
 	#--------------------------