passing built models for analysis from api

api/api.py

@@ -3,37 +3,82 @@ from flask import Flask, jsonify, request, make_response
 import uuid
 import json
 import time
+from tqdm import tqdm
 
 import tensorflow as tf
 
 from deepface import DeepFace
 from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
+from deepface.extendedmodels import Age, Gender, Race, Emotion
 
 #import DeepFace
 #from basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
+#from extendedmodels import Age, Gender, Race, Emotion
 
 #------------------------------
 
 app = Flask(__name__)
 
+#------------------------------
+
 tic = time.time()
 
+print("Loading Face Recognition Models...")
+
+pbar = tqdm(range(0,4), desc='Loading Face Recognition Models...')
+
+for index in pbar:
+	if index == 0:
+		pbar.set_description("Loading VGG-Face")
 		vggface_model = VGGFace.loadModel()
-print("VGG-Face model is built.")
+	elif index == 1:
-
+		pbar.set_description("Loading OpenFace")
 		openface_model = OpenFace.loadModel()
-print("OpenFace model is built")
+	elif index == 2:
-
+		pbar.set_description("Loading Google FaceNet")
 		facenet_model = Facenet.loadModel()
-print("FaceNet model is built")
+	elif index == 3:
-
+		pbar.set_description("Loading Facebook DeepFace")
 		deepface_model = FbDeepFace.loadModel()
-print("DeepFace model is built")
 
 toc = time.time()
 
 print("Face recognition models are built in ", toc-tic," seconds")
 
+#------------------------------
+
+tic = time.time()
+
+print("Loading Facial Attribute Analysis Models...")
+
+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()
+	elif index == 1:
+		pbar.set_description("Loading age prediction model")
+		age_model = Age.loadModel()
+	elif index == 2:
+		pbar.set_description("Loading gender prediction model")
+		gender_model = Gender.loadModel()
+	elif index == 3:
+		pbar.set_description("Loading race prediction model")
+		race_model = Race.loadModel()
+
+toc = time.time()
+
+facial_attribute_models = {}
+facial_attribute_models["emotion"] = emotion_model
+facial_attribute_models["age"] = age_model
+facial_attribute_models["gender"] = gender_model
+facial_attribute_models["race"] = race_model
+
+print("Facial attribute analysis models are built in ", toc-tic," seconds")
+
+#------------------------------
+
 graph = tf.get_default_graph()
 
 #------------------------------
@@ -76,7 +121,8 @@ def analyze():
 		
 		#---------------------------
 
-		resp_obj = DeepFace.analyze(instances, actions=actions)
+		#resp_obj = DeepFace.analyze(instances, actions=actions)
+		resp_obj = DeepFace.analyze(instances, actions=actions, models=facial_attribute_models)
 		
 		#---------------------------
 
@@ -85,7 +131,7 @@ def analyze():
 	resp_obj["trx_id"] = trx_id
 	resp_obj["seconds"] = toc-tic
 
-	return resp_obj
+	return resp_obj, 200
 
 @app.route('/verify', methods=['POST'])
 

deepface/DeepFace.py

@@ -20,7 +20,6 @@ from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
 from deepface.extendedmodels import Age, Gender, Race, Emotion
 from deepface.commons import functions, realtime, distance as dst
 
-
 def verify(img1_path, img2_path=''
 	, model_name ='VGG-Face', distance_metric = 'cosine', model = None):
 
@@ -171,24 +170,28 @@ def analyze(img_path, actions= [], models= {}):
 
 	if 'emotion' in actions:
 		if 'emotion' in models:
+			print("already built emotion model is passed")
 			emotion_model = models['emotion']
 		else:
 			emotion_model = Emotion.loadModel()
 
 	if 'age' in actions:
 		if 'age' in models:
+			print("already built age model is passed")
 			age_model = models['age']
 		else:
 			age_model = Age.loadModel()
 
 	if 'gender' in actions:
 		if 'gender' in models:
+			print("already built gender model is passed")
 			gender_model = models['gender']
 		else:
 			gender_model = Gender.loadModel()
 
 	if 'race' in actions:
 		if 'race' in models:
+			print("already built race model is passed")
 			race_model = models['race']
 		else:
 			race_model = Race.loadModel()

tests/unit_tests.py

@@ -125,3 +125,32 @@ if accuracy > 75:
 	print("Unit tests are completed successfully. Score: ",accuracy,"%")
 else:
 	raise ValueError("Unit test score does not satisfy the minimum required accuracy. Minimum expected score is 80% but this got ",accuracy,"%")
+
+#-----------------------------------
+
+# api tests - already built models will be passed to the functions
+
+from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
+
+#-----------------------------------
+
+vggface_model = VGGFace.loadModel()
+resp_obj = DeepFace.verify("dataset/img1.jpg", "dataset/img2.jpg", model_name = "VGG-Face", model = vggface_model)
+print(resp_obj)
+
+#-----------------------------------
+
+from deepface.extendedmodels import Age, Gender, Race, Emotion
+
+emotion_model = Emotion.loadModel()
+age_model = Age.loadModel()
+gender_model = Gender.loadModel()
+race_model = Race.loadModel()
+
+facial_attribute_models = {}
+facial_attribute_models["emotion"] = emotion_model
+facial_attribute_models["age"] = age_model
+facial_attribute_models["gender"] = gender_model
+facial_attribute_models["race"] = race_model
+
+resp_obj = DeepFace.analyze("dataset/img1.jpg", models=facial_attribute_models)