face region fixes

.gitignore

@@ -15,4 +15,3 @@ deepface/basemodels/__pycache__/*
 deepface/extendedmodels/__pycache__/*
 deepface/subsidiarymodels/__pycache__/*
 tests/dataset/*.pkl
-Age_Gender_Retail_Analysis.ipynb

deepface/DeepFace.py

@@ -282,12 +282,7 @@ def analyze(img_path, actions = ['emotion', 'age', 'gender', 'race']
 		The function returns a dictionary. If img_path is a list, then it will return list of dictionary.
 		
 		{
-			"region": {
-				'x': 230,
-				'y': 120,
-				'w': 36,
-				'h': 45
-			}
+			"region": {'x': 230, 'y': 120, 'w': 36, 'h': 45},
 			"age": 28.66,
 			"gender": "woman",
 			"dominant_emotion": "neutral",
@@ -370,7 +365,6 @@ def analyze(img_path, actions = ['emotion', 'age', 'gender', 'race']
 		img_224 = None # Set to prevent re-detection
 
 		region = [] # x, y, w, h of the detected face region
-
 		region_labels = ['x', 'y', 'w', 'h']
 		
 		#facial attribute analysis

deepface/commons/functions.py

@@ -190,6 +190,8 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
 	
 	home = str(Path.home())
 	
+	img_region = [0, 0, img.shape[0], img.shape[1]]
+	
 	#if functions.preproces_face is called directly, then face_detector global variable might not been initialized.
 	if not "face_detector" in globals():
 		initialize_detector(detector_backend = detector_backend)
@@ -211,7 +213,7 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
 		else: #if no face detected
 	
 			if enforce_detection != True:			
-				return img
+				return img, img_region
 	
 			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.")
@@ -266,7 +268,7 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
 	
 			if enforce_detection != True:
 				img = base_img.copy()
-				return img
+				return img, img_region
 	
 			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.")
@@ -288,7 +290,7 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
 		else: #if no face detected
 	
 			if enforce_detection != True:			
-				return img
+				return img, img_region
 	
 			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.") 
@@ -306,7 +308,7 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
 		
 		else: #if no face detected
 			if not enforce_detection:			
-				return img
+				return img, img_region
 	
 			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.")

tests/unit_tests.py

@@ -21,6 +21,9 @@ if tf_version == 2:
 
 print("Running unit tests for TF ", tf.__version__)
 
+from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
+from deepface.extendedmodels import Age, Gender, Race, Emotion
+
 #-----------------------------------------
 
 dataset = [
@@ -200,27 +203,10 @@ else:
 	raise ValueError("Unit test score does not satisfy the minimum required accuracy. Minimum expected score is ",threshold,"% but this got ",accuracy,"%")
 
 #-----------------------------------
-
-# api tests - already built models will be passed to the functions
-
-from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
-
 #-----------------------------------
-print("--------------------------")
-
-print("Verify function with passing pre-trained model")
-
-vggface_model = VGGFace.loadModel()
-resp_obj = DeepFace.verify("dataset/img1.jpg", "dataset/img2.jpg", model_name = "VGG-Face", model = vggface_model)
-print(resp_obj)
-
-#-----------------------------------
-print("--------------------------")
 
 print("Analyze function with passing pre-trained model")
 
-from deepface.extendedmodels import Age, Gender, Race, Emotion
-
 emotion_model = Emotion.loadModel()
 age_model = Age.loadModel()
 gender_model = Gender.loadModel()
@@ -257,7 +243,7 @@ for i in range(0, len(dataset)):
 #-----------------------------------
 print("--------------------------")
 
-print("Pre-trained ensemble method")
+print("Pre-trained ensemble method - find")
 
 from deepface import DeepFace
 from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
@@ -272,10 +258,18 @@ print("OpenFace loaded")
 model["DeepFace"] = FbDeepFace.loadModel()
 print("DeepFace loaded")
 
-df = DeepFace.find("dataset/img1.jpg", db_path = "dataset", model_name = 'Ensemble', model=model, enforce_detection=False)
+df = DeepFace.find("dataset/img1.jpg", db_path = "dataset", model_name = 'Ensemble', model = model, enforce_detection=False)
 
 print(df)
 
+#-----------------------------------
+print("--------------------------")
+
+print("Pre-trained ensemble method - verify")
+res = DeepFace.verify(dataset, model_name = "Ensemble", model = model)
+print(res)
+
+#-----------------------------------
 print("--------------------------")
 
 import cv2