clean codes

README.md

@@ -175,12 +175,6 @@ models["race"] = DeepFace.build_model('Race')
 DeepFace.analyze("img1.jpg", models=models)
 ```
 
-## Disclaimer
-
-Reference face recognition models have different type of licenses. This framework is just a wrapper for those models. That's why, licence types are inherited as well. You should check the licenses for the face recognition models before use.
-
-Herein, [OpenFace](https://github.com/cmusatyalab/openface/blob/master/LICENSE) is licensed under Apache License 2.0. [FB DeepFace](https://github.com/swghosh/DeepFace) and [Facenet](https://github.com/davidsandberg/facenet/blob/master/LICENSE.md) is licensed under MIT License. [Dlib](https://github.com/davisking/dlib/blob/master/dlib/LICENSE.txt) is licensed under Boost Software License. They all allow you to use for personal and commercial purpose free. On the other hand, [VGG-Face](http://www.robots.ox.ac.uk/~vgg/software/vgg_face/) is licensed under Creative Commons Attribution License. That's why, it is restricted to adopt VGG-Face for commercial use.
-
 ## Support
 
 There are many ways to support a project - starring⭐️ the GitHub repos is just one.
@@ -189,11 +183,12 @@ There are many ways to support a project - starring⭐️ the GitHub repos is ju
 
 Please cite [deepface](https://ieeexplore.ieee.org/document/9259802) in your publications if it helps your research. Here is an example BibTeX entry:
 
-```
+```BibTeX
-@inproceedings{9259802,
+@inproceedings{serengil2020lightface,
   title={LightFace: A Hybrid Deep Face Recognition Framework},
   author={Serengil, Sefik Ilkin and Ozpinar, Alper},
   booktitle={2020 Innovations in Intelligent Systems and Applications Conference (ASYU)},
+  pages={23--27},
   year={2020},
   doi={10.1109/ASYU50717.2020.9259802},
   organization={IEEE}
@@ -202,6 +197,6 @@ Please cite [deepface](https://ieeexplore.ieee.org/document/9259802) in your pub
 
 ## Licence
 
-Deepface is licensed under the MIT License - see [`LICENSE`](https://github.com/serengil/deepface/blob/master/LICENSE) for more details.
+Deepface is licensed under the MIT License - see [`LICENSE`](https://github.com/serengil/deepface/blob/master/LICENSE) for more details. However, the library wraps some face recognition models: [VGG-Face](http://www.robots.ox.ac.uk/~vgg/software/vgg_face/), [Facenet](https://github.com/davidsandberg/facenet/blob/master/LICENSE.md), [OpenFace](https://github.com/cmusatyalab/openface/blob/master/LICENSE), [DeepFace](https://github.com/swghosh/DeepFace) and [Dlib](https://github.com/davisking/dlib/blob/master/dlib/LICENSE.txt). Licence types will be inherited if you are going to use those models.
 
 [Logo](https://thenounproject.com/term/face-recognition/2965879/) is created by [Adrien Coquet](https://thenounproject.com/coquet_adrien/). Licensed under [Creative Commons: By Attribution 3.0 License](https://creativecommons.org/licenses/by/3.0/).

deepface/DeepFace.py

@@ -58,7 +58,6 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric =
 		metrics.append(distance_metric)
 			
 	#--------------------------------
-	#ensemble learning disabled
 	
 	if model == None:
 		if model_name == 'Ensemble':
@@ -135,7 +134,7 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric =
 					
 					if model_name != 'Ensemble':
 						
-						threshold = functions.findThreshold(i, j)
+						threshold = dst.findThreshold(i, j)
 
 						if distance <= threshold:
 							identified = True
@@ -197,8 +196,6 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric =
 
 	toc = time.time()
 	
-	#print("identification lasts ",toc-tic," seconds")
-	
 	if bulkProcess == True:
 		
 		resp_obj = {}
@@ -520,7 +517,7 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
 						df["%s_%s" % (j, k)] = distances
 						
 						if model_name != 'Ensemble':
-							threshold = functions.findThreshold(j, k)
+							threshold = dst.findThreshold(j, k)
 							df = df.drop(columns = ["%s_representation" % (j)])
 							df = df[df["%s_%s" % (j, k)] <= threshold]
 							

deepface/basemodels/Boosting.py

@@ -9,24 +9,19 @@ import lightgbm as lgb #lightgbm==2.3.1
 from deepface.commons import functions, distance as dst
 
 def loadModel():
+	
+	model_names = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace']
+
 	model = {}
 	
 	model_pbar = tqdm(range(0, 4), desc='Face recognition models')
 	
 	for index in model_pbar:
 		
-		if index == 0:
+		model_name = model_names[index]
-			model_pbar.set_description("Loading VGG-Face")
+		
-			model["VGG-Face"] = DeepFace.build_model('VGG-Face')
+		model_pbar.set_description("Loading %s" % (model_name))
-		elif index == 1:
+		model[model_name] = DeepFace.build_model(model_name)
-			model_pbar.set_description("Loading Google FaceNet")
-			model["Facenet"] = DeepFace.build_model('Facenet')
-		elif index == 2:
-			model_pbar.set_description("Loading OpenFace")
-			model["OpenFace"] = DeepFace.build_model('OpenFace')
-		elif index == 3:
-			model_pbar.set_description("Loading Facebook DeepFace")
-			model["DeepFace"] = DeepFace.build_model('DeepFace')
 	
 	return model
 
@@ -37,7 +32,8 @@ def validate_model(model):
 		found_models.append(key)
 	
 	if ('VGG-Face' in found_models) and ('Facenet' in found_models) and ('OpenFace' in found_models) and ('DeepFace' in found_models):
-		print("Ensemble learning will be applied for ", found_models," models")
+		#print("Ensemble learning will be applied for ", found_models," models")
+		valid = True
 	else:
 		raise ValueError("You would like to apply ensemble learning and pass pre-built models but models must contain [VGG-Face, Facenet, OpenFace, DeepFace] but you passed "+found_models)
 

deepface/commons/distance.py

@@ -14,3 +14,20 @@ def findEuclideanDistance(source_representation, test_representation):
 
 def l2_normalize(x):
     return x / np.sqrt(np.sum(np.multiply(x, x)))	
+
+def findThreshold(model_name, distance_metric):
+	
+	base_threshold = {'cosine': 0.40, 'euclidean': 0.55, 'euclidean_l2': 0.75}
+	
+	thresholds = {
+		'VGG-Face': {'cosine': 0.40, 'euclidean': 0.55, 'euclidean_l2': 0.75},
+		'OpenFace': {'cosine': 0.10, 'euclidean': 0.55, 'euclidean_l2': 0.55},
+		'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}
+		}
+
+	threshold = thresholds.get(model_name, base_threshold).get(distance_metric, 0.4)
+	
+	return threshold

deepface/commons/functions.py

@@ -98,12 +98,6 @@ def initialize_detector(detector_backend):
 	elif detector_backend == 'mtcnn':
 		face_detector = MTCNN()
 
-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():
 	
 	home = str(Path.home())
@@ -116,22 +110,11 @@ def initializeFolder():
 		os.mkdir(home+"/.deepface/weights")
 		print("Directory ",home,"/.deepface/weights created")
 
-def findThreshold(model_name, distance_metric):
+def loadBase64Img(uri):
-	
+   encoded_data = uri.split(',')[1]
-	base_threshold = {'cosine': 0.40, 'euclidean': 0.55, 'euclidean_l2': 0.75}
+   nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8)
-	
+   img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
-	thresholds = {
+   return img
-		'VGG-Face': {'cosine': 0.40, 'euclidean': 0.55, 'euclidean_l2': 0.75},
-		'OpenFace': {'cosine': 0.10, 'euclidean': 0.55, 'euclidean_l2': 0.55},
-		'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}
-		}
-
-	threshold = thresholds.get(model_name, base_threshold).get(distance_metric, 0.4)
-	
-	return threshold
 
 def get_opencv_path():
 	opencv_home = cv2.__file__

deepface/commons/realtime.py

@@ -76,7 +76,7 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True):
 		input_shape_y = input_shape[1]
 		
 		#tuned thresholds for model and metric pair
-		threshold = functions.findThreshold(model_name, distance_metric)
+		threshold = dst.findThreshold(model_name, distance_metric)
 		
 	#------------------------
 	#facial attribute analysis models

tests/unit_tests.py

@@ -177,13 +177,15 @@ for model in models:
 
 print("Passed unit tests: ",passed_tests," / ",test_cases)
 
+threshold = 70
+
 accuracy = 100 * passed_tests / test_cases
 accuracy = round(accuracy, 2)
 
-if accuracy > 75:
+if accuracy >= threshold:
 	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,"%")
+	raise ValueError("Unit test score does not satisfy the minimum required accuracy. Minimum expected score is ",threshold,"% but this got ",accuracy,"%")
 
 #-----------------------------------