detector argument for stream

deepface/DeepFace.py

@@ -746,7 +746,7 @@ def represent(img_path, model_name = 'VGG-Face', model = None, enforce_detection
 
 	return embedding
 
-def stream(db_path = '', model_name ='VGG-Face', distance_metric = 'cosine', enable_face_analysis = True, source = 0, time_threshold = 5, frame_threshold = 5):
+def stream(db_path = '', model_name ='VGG-Face', detector_backend = 'opencv', distance_metric = 'cosine', enable_face_analysis = True, source = 0, time_threshold = 5, frame_threshold = 5):
 
 	"""
 	This function applies real time face recognition and facial attribute analysis
@@ -756,6 +756,8 @@ def stream(db_path = '', model_name ='VGG-Face', distance_metric = 'cosine', ena
 
 		model_name (string): VGG-Face, Facenet, OpenFace, DeepFace, DeepID, Dlib or Ensemble
 
+		detector_backend (string): opencv, ssd, mtcnn, dlib, retinaface
+
 		distance_metric (string): cosine, euclidean, euclidean_l2
 
 		enable_facial_analysis (boolean): Set this to False to just run face recognition
@@ -774,7 +776,7 @@ def stream(db_path = '', model_name ='VGG-Face', distance_metric = 'cosine', ena
 	if frame_threshold < 1:
 		raise ValueError("frame_threshold must be greater than the value 1 but you passed "+str(frame_threshold))
 
-	realtime.analysis(db_path, model_name, distance_metric, enable_face_analysis
+	realtime.analysis(db_path, model_name, detector_backend, distance_metric, enable_face_analysis
 						, source = source, time_threshold = time_threshold, frame_threshold = frame_threshold)
 
 def detectFace(img_path, detector_backend = 'opencv', enforce_detection = True):

deepface/commons/distance.py

@@ -26,14 +26,16 @@ 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},
+		'VGG-Face': {'cosine': 0.40, 'euclidean': 0.60, 'euclidean_l2': 0.86},
-		'OpenFace': {'cosine': 0.10, 'euclidean': 0.55, 'euclidean_l2': 0.55},
         'Facenet':  {'cosine': 0.40, 'euclidean': 10, 'euclidean_l2': 0.80},
-        'Facenet512':  {'cosine': 0.3088582207770799, 'euclidean': 23.564685968740186, 'euclidean_l2': 1.0461709266148662},
+        'Facenet512':  {'cosine': 0.30, 'euclidean': 23.56, 'euclidean_l2': 1.04},
+        'ArcFace':  {'cosine': 0.68, 'euclidean': 4.15, 'euclidean_l2': 1.13},
+        'Dlib': 	{'cosine': 0.07, 'euclidean': 0.6, 'euclidean_l2': 0.4},
+
+		'OpenFace': {'cosine': 0.10, 'euclidean': 0.55, 'euclidean_l2': 0.55},
 		'DeepFace': {'cosine': 0.23, 'euclidean': 64, 'euclidean_l2': 0.64},
-		'DeepID': 	{'cosine': 0.015, 'euclidean': 45, 'euclidean_l2': 0.17},
+		'DeepID': 	{'cosine': 0.015, 'euclidean': 45, 'euclidean_l2': 0.17}
-		'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)

deepface/commons/functions.py

@@ -129,9 +129,6 @@ def preprocess_face(img, target_size=(224, 224), grayscale = False, enforce_dete
 
 	# img = cv2.resize(img, target_size) #resize causes transformation on base image, adding black pixels to resize will not deform the base image
 
-	# First resize the longer side to the target size
-	#factor = target_size[0] / max(img.shape)
-	
 	factor_0 = target_size[0] / img.shape[0]
 	factor_1 = target_size[1] / img.shape[1]
 	factor = min(factor_0, factor_1)

deepface/commons/realtime.py

@@ -12,10 +12,16 @@ os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
 from deepface import DeepFace
 from deepface.extendedmodels import Age
 from deepface.commons import functions, realtime, distance as dst
-from deepface.detectors import OpenCvWrapper
+from deepface.detectors import FaceDetector
 
-def analysis(db_path, model_name, distance_metric, enable_face_analysis = True
+def analysis(db_path, model_name = 'VGG-Face', detector_backend = 'opencv', distance_metric = 'cosine', enable_face_analysis = True, source = 0, time_threshold = 5, frame_threshold = 5):
-				, source = 0, time_threshold = 5, frame_threshold = 5):
+
+	#------------------------
+
+	face_detector = FaceDetector.build_model(detector_backend)
+	print("Detector backend is ", detector_backend)
+
+	#------------------------
 
 	input_shape = (224, 224); input_shape_x = input_shape[0]; input_shape_y = input_shape[1]
 
@@ -45,8 +51,7 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True
 		#------------------------
 
 		input_shape = functions.find_input_shape(model)
-		input_shape_x = input_shape[0]
+		input_shape_x = input_shape[0]; input_shape_y = input_shape[1]
-		input_shape_y = input_shape[1]
 
 		#tuned thresholds for model and metric pair
 		threshold = dst.findThreshold(model_name, distance_metric)
@@ -77,15 +82,21 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True
 
 	tic = time.time()
 
+	#-----------------------
+
 	pbar = tqdm(range(0, len(employees)), desc='Finding embeddings')
 
+	#TODO: why don't you store those embeddings in a pickle file similar to find function?
+
 	embeddings = []
 	#for employee in employees:
 	for index in pbar:
 		employee = employees[index]
 		pbar.set_description("Finding embedding for %s" % (employee.split("/")[-1]))
 		embedding = []
-		img = functions.preprocess_face(img = employee, target_size = (input_shape_y, input_shape_x), enforce_detection = False, detector_backend = 'opencv')
+
+		#preprocess_face returns single face. this is expected for source images in db.
+		img = functions.preprocess_face(img = employee, target_size = (input_shape_y, input_shape_x), enforce_detection = False, detector_backend = detector_backend)
 		img_representation = model.predict(img)[0,:]
 
 		embedding.append(employee)
@@ -105,12 +116,6 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True
 
 	#-----------------------
 
-	opencv_path = OpenCvWrapper.get_opencv_path()
-	face_detector_path = opencv_path+"haarcascade_frontalface_default.xml"
-	face_cascade = cv2.CascadeClassifier(face_detector_path)
-
-	#-----------------------
-
 	freeze = False
 	face_detected = False
 	face_included_frames = 0 #freeze screen if face detected sequantially 5 frames
@@ -129,12 +134,13 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True
 		#cv2.setWindowProperty('img', cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)
 
 		raw_img = img.copy()
-		resolution = img.shape
+		resolution = img.shape; resolution_x = img.shape[1]; resolution_y = img.shape[0]
-
-		resolution_x = img.shape[1]; resolution_y = img.shape[0]
 
 		if freeze == False:
-			faces = face_cascade.detectMultiScale(img, 1.3, 5)
+			#faces = face_cascade.detectMultiScale(img, 1.3, 5)
+
+			#faces stores list of detected_face and region pair
+			faces = FaceDetector.detect_faces(face_detector, detector_backend, img, align = False)
 
 			if len(faces) == 0:
 				face_included_frames = 0
@@ -143,7 +149,7 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True
 
 		detected_faces = []
 		face_index = 0
-		for (x,y,w,h) in faces:
+		for face, (x, y, w, h) in faces:
 			if w > 130: #discard small detected faces
 
 				face_detected = True

deepface/detectors/DlibWrapper.py

@@ -36,6 +36,8 @@ def detect_face(detector, img, align = True):
 
 	import dlib #this requirement is not a must that's why imported here
 
+	resp = []
+
 	home = str(Path.home())
 
 	sp = detector["sp"]
@@ -53,10 +55,12 @@ def detect_face(detector, img, align = True):
 			top = d.top(); bottom = d.bottom()
 			detected_face = img[top:bottom, left:right]
 			img_region = [left, top, right - left, bottom - top]
-			break #get the first one
 
 			if align:
-			img_shape = sp(img, detections[0])
+				img_shape = sp(img, detections[idx])
 				detected_face = dlib.get_face_chip(img, img_shape, size = detected_face.shape[0])
 
-	return detected_face, img_region
+			resp.append((detected_face, img_region))
+
+
+	return resp

deepface/detectors/FaceDetector.py

@@ -33,6 +33,18 @@ def build_model(detector_backend):
 
 def detect_face(face_detector, detector_backend, img, align = True):
 
+    obj = detect_faces(face_detector, detector_backend, img, align)
+
+    if len(obj) > 0:
+        face, region = obj[0] #discard multiple faces
+    else: #len(obj) == 0
+        face = None
+        region = [0, 0, img.shape[0], img.shape[1]]
+
+    return face, region
+
+def detect_faces(face_detector, detector_backend, img, align = True):
+
     backends = {
         'opencv': OpenCvWrapper.detect_face,
         'ssd': SsdWrapper.detect_face,
@@ -44,12 +56,13 @@ def detect_face(face_detector, detector_backend, img, align = True):
     detect_face = backends.get(detector_backend)
 
     if detect_face:
-        face, region = detect_face(face_detector, img, align)
+        obj = detect_face(face_detector, img, align)
+        #obj stores list of detected_face and region pair
+
+        return obj
     else:
         raise ValueError("invalid detector_backend passed - " + detector_backend)
 
-    return face, region
-
 def alignment_procedure(img, left_eye, right_eye):
 
 	#this function aligns given face in img based on left and right eye coordinates

deepface/detectors/MtcnnWrapper.py

@@ -8,6 +8,8 @@ def build_model():
 
 def detect_face(face_detector, img, align = True):
 
+	resp = []
+
 	detected_face = None
 	img_region = [0, 0, img.shape[0], img.shape[1]]
 
@@ -15,16 +17,18 @@ def detect_face(face_detector, img, align = True):
 	detections = face_detector.detect_faces(img_rgb)
 
 	if len(detections) > 0:
-		detection = detections[0]
+
+		for detection in detections:
 			x, y, w, h = detection["box"]
 			detected_face = img[int(y):int(y+h), int(x):int(x+w)]
 			img_region = [x, y, w, h]
 
+			if align:
 				keypoints = detection["keypoints"]
 				left_eye = keypoints["left_eye"]
 				right_eye = keypoints["right_eye"]
-
-		if align:
 				detected_face = FaceDetector.alignment_procedure(detected_face, left_eye, right_eye)
 
-	return detected_face, img_region
+			resp.append((detected_face, img_region))
+
+	return resp

deepface/detectors/OpenCvWrapper.py

@@ -37,6 +37,8 @@ def build_cascade(model_name = 'haarcascade'):
 
 def detect_face(detector, img, align = True):
 
+	resp = []
+
 	detected_face = None
 	img_region = [0, 0, img.shape[0], img.shape[1]]
 
@@ -48,14 +50,18 @@ def detect_face(detector, img, align = True):
 		pass
 
 	if len(faces) > 0:
-		x,y,w,h = faces[0] #focus on the 1st face found in the image
+
+		for x,y,w,h in faces:
 			detected_face = img[int(y):int(y+h), int(x):int(x+w)]
 
 			if align:
 				detected_face = align_face(detector["eye_detector"], detected_face)
+
 			img_region = [x, y, w, h]
 
-	return detected_face, img_region
+			resp.append((detected_face, img_region))
+
+	return resp
 
 def align_face(eye_detector, img):
 

deepface/detectors/RetinaFaceWrapper.py

@@ -1,4 +1,4 @@
-#from retinaface import RetinaFace
+#from retinaface import RetinaFace #this is not a must dependency
 import cv2
 
 def build_model():
@@ -9,10 +9,17 @@ def build_model():
 def detect_face(face_detector, img, align = True):
 
     from retinaface import RetinaFace
+    from retinaface.commons import postprocess
+
+    #---------------------------------
+
+    resp = []
+
     img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) #retinaface expects RGB but OpenCV read BGR
 
+    """
     face = None
-    img_region = [0, 0, img.shape[0], img.shape[1]]
+    img_region = [0, 0, img.shape[0], img.shape[1]] #Really?
 
     faces = RetinaFace.extract_faces(img_rgb, model = face_detector, align = align)
 
@@ -20,3 +27,36 @@ def detect_face(face_detector, img, align = True):
         face = faces[0][:, :, ::-1]
 
     return face, img_region
+    """
+
+    #--------------------------
+
+    obj = RetinaFace.detect_faces(img_rgb, model = face_detector, threshold = 0.9)
+
+    if type(obj) == dict:
+        for key in obj:
+            identity = obj[key]
+            facial_area = identity["facial_area"]
+
+            y = facial_area[1]
+            h = facial_area[3] - y
+            x = facial_area[0]
+            w = facial_area[2] - x
+            img_region = [x, y, w, h]
+
+            #detected_face = img[int(y):int(y+h), int(x):int(x+w)] #opencv
+            detected_face = img[facial_area[1]: facial_area[3], facial_area[0]: facial_area[2]]
+
+            if align:
+                landmarks = identity["landmarks"]
+                left_eye = landmarks["left_eye"]
+                right_eye = landmarks["right_eye"]
+                nose = landmarks["nose"]
+                #mouth_right = landmarks["mouth_right"]
+                #mouth_left = landmarks["mouth_left"]
+
+                detected_face = postprocess.alignment_procedure(detected_face, right_eye, left_eye, nose)
+
+            resp.append((detected_face, img_region))
+
+    return resp

deepface/detectors/SsdWrapper.py

@@ -47,6 +47,8 @@ def build_model():
 
 def detect_face(detector, img, align = True):
 
+	resp = []
+
 	detected_face = None
 	img_region = [0, 0, img.shape[0], img.shape[1]]
 
@@ -81,10 +83,7 @@ def detect_face(detector, img, align = True):
 
 	if detections_df.shape[0] > 0:
 
-		#TODO: sort detections_df
+		for index, instance in detections_df.iterrows():
-
-		#get the first face in the image
-		instance = detections_df.iloc[0]
 
 			left = instance["left"]
 			right = instance["right"]
@@ -97,4 +96,6 @@ def detect_face(detector, img, align = True):
 			if align:
 				detected_face = OpenCvWrapper.align_face(detector["eye_detector"], detected_face)
 
-	return detected_face, img_region
+			resp.append((detected_face, img_region))
+
+	return resp

tests/stream.py

@@ -1,3 +1,8 @@
 from deepface import DeepFace
 
-DeepFace.stream("dataset")
+DeepFace.stream("dataset") #opencv
+#DeepFace.stream("dataset", detector_backend = 'opencv')
+#DeepFace.stream("dataset", detector_backend = 'ssd')
+#DeepFace.stream("dataset", detector_backend = 'mtcnn')
+#DeepFace.stream("dataset", detector_backend = 'dlib')
+#DeepFace.stream("dataset", detector_backend = 'retinaface')

tests/unit_tests.py

@@ -170,7 +170,6 @@ dataset = [
 	['dataset/img5.jpg', 'dataset/img6.jpg', True],
 	['dataset/img6.jpg', 'dataset/img7.jpg', True],
 	['dataset/img8.jpg', 'dataset/img9.jpg', True],
-
 	['dataset/img1.jpg', 'dataset/img11.jpg', True],
 	['dataset/img2.jpg', 'dataset/img11.jpg', True],