arcface added

api/api.py

@@ -10,13 +10,6 @@ import tensorflow as tf
 tf_version = int(tf.__version__.split(".")[0])
 
 from deepface import DeepFace
-from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID
-from deepface.basemodels.DlibResNet import DlibResNet
-from deepface.extendedmodels import Age, Gender, Race, Emotion
-
-#import DeepFace
-#from basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
-#from extendedmodels import Age, Gender, Race, Emotion
 
 #------------------------------
 
@@ -28,28 +21,29 @@ tic = time.time()
 
 print("Loading Face Recognition Models...")
 
-pbar = tqdm(range(0,6), desc='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 = VGGFace.loadModel()
+		vggface_model = DeepFace.build_model("VGG-Face")
 	elif index == 1:
 		pbar.set_description("Loading OpenFace")
-		openface_model = OpenFace.loadModel()
+		openface_model = DeepFace.build_model("OpenFace")
 	elif index == 2:
 		pbar.set_description("Loading Google FaceNet")
-		facenet_model = Facenet.loadModel()
+		facenet_model = DeepFace.build_model("Facenet")
 	elif index == 3:
 		pbar.set_description("Loading Facebook DeepFace")
-		deepface_model = FbDeepFace.loadModel()
+		deepface_model = DeepFace.build_model("DeepFace")
 	elif index == 4:
 		pbar.set_description("Loading DeepID DeepFace")
-		deepid_model = DeepID.loadModel()
+		deepid_model = DeepFace.build_model("DeepID")
 	elif index == 5:
-		pbar.set_description("Loading Dlib ResNet DeepFace")
+		pbar.set_description("Loading ArcFace DeepFace")
-		dlib_model = DlibResNet()
+		arcface_model = DeepFace.build_model("ArcFace")
-
+		
 toc = time.time()
 
 print("Face recognition models are built in ", toc-tic," seconds")
@@ -65,16 +59,16 @@ pbar = tqdm(range(0,4), desc='Loading Facial Attribute Analysis Models...')
 for index in pbar:
 	if index == 0:
 		pbar.set_description("Loading emotion analysis model")
-		emotion_model = Emotion.loadModel()
+		emotion_model = DeepFace.build_model('Emotion')
 	elif index == 1:
 		pbar.set_description("Loading age prediction model")
-		age_model = Age.loadModel()
+		age_model = DeepFace.build_model('Age')
 	elif index == 2:
 		pbar.set_description("Loading gender prediction model")
-		gender_model = Gender.loadModel()
+		gender_model = DeepFace.build_model('Gender')
 	elif index == 3:
 		pbar.set_description("Loading race prediction model")
-		race_model = Race.loadModel()
+		race_model = DeepFace.build_model('Race')
 
 toc = time.time()
 
@@ -231,19 +225,17 @@ def verifyWrapper(req, trx_id = 0):
 		resp_obj = DeepFace.verify(instances, model_name = model_name, distance_metric = distance_metric, model = deepface_model)
 	elif model_name == "DeepID":
 		resp_obj = DeepFace.verify(instances, model_name = model_name, distance_metric = distance_metric, model = deepid_model)
-	elif model_name == "Dlib":
+	elif model_name == "ArcFace":
-		resp_obj = DeepFace.verify(instances, model_name = model_name, distance_metric = distance_metric, model = dlib_model)
+		resp_obj = DeepFace.verify(instances, model_name = model_name, distance_metric = distance_metric, model = arcface_model)
 	elif model_name == "Ensemble":
 		models =  {}
 		models["VGG-Face"] = vggface_model
 		models["Facenet"] = facenet_model
 		models["OpenFace"] = openface_model
 		models["DeepFace"] = deepface_model
-		
 		resp_obj = DeepFace.verify(instances, model_name = model_name, model = models)
-		
 	else:
-		resp_obj = jsonify({'success': False, 'error': 'You must pass a valid model name. Available models are VGG-Face, Facenet, OpenFace, DeepFace but you passed %s' % (model_name)}), 205
+		resp_obj = jsonify({'success': False, 'error': 'You must pass a valid model name. You passed %s' % (model_name)}), 205
 	
 	return resp_obj
 

deepface/DeepFace.py

@@ -9,7 +9,7 @@ import pandas as pd
 from tqdm import tqdm
 import pickle
 
-from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID, DlibWrapper, Boosting
+from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID, DlibWrapper, ArcFace, Boosting
 from deepface.extendedmodels import Age, Gender, Race, Emotion
 from deepface.commons import functions, realtime, distance as dst
 
@@ -33,6 +33,7 @@ def build_model(model_name):
 		'DeepFace': FbDeepFace.loadModel,
 		'DeepID': DeepID.loadModel,
 		'Dlib': DlibWrapper.loadModel,
+		'ArcFace': ArcFace.loadModel,
 		'Emotion': Emotion.loadModel,
 		'Age': Age.loadModel,
 		'Gender': Gender.loadModel,
@@ -62,7 +63,7 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric =
 			['img2.jpg', 'img3.jpg']
 		]
 		
-		model_name (string): VGG-Face, Facenet, OpenFace, DeepFace, DeepID, Dlib or Ensemble
+		model_name (string): VGG-Face, Facenet, OpenFace, DeepFace, DeepID, Dlib, ArcFace or Ensemble
 		
 		distance_metric (string): cosine, euclidean, euclidean_l2
 		

deepface/basemodels/ArcFace.py

@@ -0,0 +1,94 @@
+from tensorflow.python.keras import backend
+from tensorflow.python.keras.engine import training
+from tensorflow.python.keras.utils import data_utils
+from tensorflow.python.keras.utils import layer_utils
+from tensorflow.python.lib.io import file_io
+import tensorflow
+from tensorflow import keras
+
+import os
+from pathlib import Path
+import gdown
+
+def loadModel():
+	base_model = ResNet34()
+	inputs = base_model.inputs[0]
+	arcface_model = base_model.outputs[0]
+	arcface_model = keras.layers.BatchNormalization(momentum=0.9, epsilon=2e-5)(arcface_model)
+	arcface_model = keras.layers.Dropout(0.4)(arcface_model)
+	arcface_model = keras.layers.Flatten()(arcface_model)
+	arcface_model = keras.layers.Dense(512, activation=None, use_bias=True, kernel_initializer="glorot_normal")(arcface_model)
+	embedding = keras.layers.BatchNormalization(momentum=0.9, epsilon=2e-5, name="embedding", scale=True)(arcface_model)
+	model = keras.models.Model(inputs, embedding, name=base_model.name)
+	
+	#---------------------------------------
+	#check the availability of pre-trained weights
+	
+	home = str(Path.home())
+	
+	url = "https://drive.google.com/uc?id=1LVB3CdVejpmGHM28BpqqkbZP5hDEcdZY"
+	file_name = "arcface_weights.h5"
+	output = home+'/.deepface/weights/'+file_name
+	
+	if os.path.isfile(output) != True:
+
+		print(file_name," will be downloaded to ",output)
+		gdown.download(url, output, quiet=False)	
+	
+	#---------------------------------------
+	
+	try:
+		model.load_weights(output)
+	except:
+		print("pre-trained weights could not be loaded.")
+		print("You might try to download it from the url ", url," and copy to ",output," manually")
+	
+	return model
+	
+def ResNet34():
+	
+	img_input = tensorflow.keras.layers.Input(shape=(112, 112, 3))
+	
+	x = tensorflow.keras.layers.ZeroPadding2D(padding=1, name='conv1_pad')(img_input)
+	x = tensorflow.keras.layers.Conv2D(64, 3, strides=1, use_bias=False, kernel_initializer='glorot_normal', name='conv1_conv')(x)
+	x = tensorflow.keras.layers.BatchNormalization(axis=3, epsilon=2e-5, momentum=0.9, name='conv1_bn')(x)
+	x = tensorflow.keras.layers.PReLU(shared_axes=[1, 2], name='conv1_prelu')(x)
+	x = stack_fn(x)
+		
+	model = training.Model(img_input, x, name='ResNet34')
+
+	return model
+
+def block1(x, filters, kernel_size=3, stride=1, conv_shortcut=True, name=None):
+	bn_axis = 3
+
+	if conv_shortcut:
+		shortcut = tensorflow.keras.layers.Conv2D(filters, 1, strides=stride, use_bias=False, kernel_initializer='glorot_normal', name=name + '_0_conv')(x)
+		shortcut = tensorflow.keras.layers.BatchNormalization(axis=bn_axis, epsilon=2e-5, momentum=0.9, name=name + '_0_bn')(shortcut)
+	else:
+		shortcut = x
+
+	x = tensorflow.keras.layers.BatchNormalization(axis=bn_axis, epsilon=2e-5, momentum=0.9, name=name + '_1_bn')(x)
+	x = tensorflow.keras.layers.ZeroPadding2D(padding=1, name=name + '_1_pad')(x)
+	x = tensorflow.keras.layers.Conv2D(filters, 3, strides=1, kernel_initializer='glorot_normal', use_bias=False, name=name + '_1_conv')(x)
+	x = tensorflow.keras.layers.BatchNormalization(axis=bn_axis, epsilon=2e-5, momentum=0.9, name=name + '_2_bn')(x)
+	x = tensorflow.keras.layers.PReLU(shared_axes=[1, 2], name=name + '_1_prelu')(x)
+
+	x = tensorflow.keras.layers.ZeroPadding2D(padding=1, name=name + '_2_pad')(x)
+	x = tensorflow.keras.layers.Conv2D(filters, kernel_size, strides=stride, kernel_initializer='glorot_normal', use_bias=False, name=name + '_2_conv')(x)
+	x = tensorflow.keras.layers.BatchNormalization(axis=bn_axis, epsilon=2e-5, momentum=0.9, name=name + '_3_bn')(x)
+
+	x = tensorflow.keras.layers.Add(name=name + '_add')([shortcut, x])
+	return x
+
+def stack1(x, filters, blocks, stride1=2, name=None):
+	x = block1(x, filters, stride=stride1, name=name + '_block1')
+	for i in range(2, blocks + 1):
+		x = block1(x, filters, conv_shortcut=False, name=name + '_block' + str(i))
+	return x
+
+def stack_fn(x):
+	x = stack1(x, 64, 3, name='conv2')
+	x = stack1(x, 128, 4, name='conv3')
+	x = stack1(x, 256, 6, name='conv4')
+	return stack1(x, 512, 3, name='conv5')

deepface/basemodels/VGGFace.py

@@ -70,16 +70,20 @@ def loadModel(url = 'https://drive.google.com/uc?id=1CPSeum3HpopfomUEK1gybeuIVoe
 	#-----------------------------------
 	
 	home = str(Path.home())
+	output = home+'/.deepface/weights/vgg_face_weights.h5'
 	
-	if os.path.isfile(home+'/.deepface/weights/vgg_face_weights.h5') != True:
+	if os.path.isfile(output) != True:
 		print("vgg_face_weights.h5 will be downloaded...")		
-		
-		output = home+'/.deepface/weights/vgg_face_weights.h5'
 		gdown.download(url, output, quiet=False)
 	
 	#-----------------------------------
 	
-	model.load_weights(home+'/.deepface/weights/vgg_face_weights.h5')
+	try:
+		model.load_weights(output)
+	except Exception as err:
+		print(str(err))
+		print("Pre-trained weight could not be loaded.")
+		print("You might try to download the pre-trained weights from the url ", url, " and copy it to the ", output)
 	
 	#-----------------------------------
 	

deepface/commons/distance.py

@@ -25,7 +25,8 @@ def findThreshold(model_name, distance_metric):
 		'Facenet':  {'cosine': 0.40, 'euclidean': 10, 'euclidean_l2': 0.80},
 		'DeepFace': {'cosine': 0.23, 'euclidean': 64, 'euclidean_l2': 0.64},
 		'DeepID': 	{'cosine': 0.015, 'euclidean': 45, 'euclidean_l2': 0.17},
-		'Dlib': 	{'cosine': 0.07, 'euclidean': 0.6, 'euclidean_l2': 0.6}
+		'Dlib': 	{'cosine': 0.07, 'euclidean': 0.6, 'euclidean_l2': 0.6},
+		'ArcFace':  {'cosine': 0.6871912959056619, 'euclidean': 4.1591468986978075, 'euclidean_l2': 1.1315718048269017}
 		}
 
 	threshold = thresholds.get(model_name, base_threshold).get(distance_metric, 0.4)

tests/unit_tests.py

@@ -148,7 +148,7 @@ dataset = [
 	['dataset/img6.jpg', 'dataset/img9.jpg', False],
 ]
 
-models = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace', 'DeepID', 'Dlib']
+models = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace', 'DeepID', 'Dlib', 'ArcFace']
 metrics = ['cosine', 'euclidean', 'euclidean_l2']
 
 passed_tests = 0; test_cases = 0