global eye detector for opencv and ssd

deepface/detectors/DlibWrapper.py

@@ -25,19 +25,25 @@ def build_model():
 		open(newfilepath, 'wb').write(data)
 
 	face_detector = dlib.get_frontal_face_detector()
-	return face_detector
+	sp = dlib.shape_predictor(home+"/.deepface/weights/shape_predictor_5_face_landmarks.dat")
 
-def detect_face(face_detector, img):
+	detector = {}
+	detector["face_detector"] = face_detector
+	detector["sp"] = sp
+	return detector
+
+def detect_face(detector, img):
 
 	import dlib #this requirement is not a must that's why imported here
 
 	home = str(Path.home())
 
-	sp = dlib.shape_predictor(home+"/.deepface/weights/shape_predictor_5_face_landmarks.dat")
+	sp = detector["sp"]
 
 	detected_face = None
 	img_region = [0, 0, img.shape[0], img.shape[1]]
 
+	face_detector = detector["face_detector"]
 	detections = face_detector(img, 1)
 
 	if len(detections) > 0:

deepface/detectors/OpenCvWrapper.py

@@ -3,7 +3,16 @@ import os
 import pandas as pd
 from deepface.detectors import FaceDetector
 
-def build_model(model_name = 'haarcascade'):
+def build_model():
+
+	detector ={}
+
+	detector["face_detector"] = build_cascade('haarcascade')
+	detector["eye_detector"] = build_cascade('haarcascade_eye')
+
+	return detector
+
+def build_cascade(model_name = 'haarcascade'):
 	opencv_path = get_opencv_path()
 
 	if model_name == 'haarcascade':
@@ -16,6 +25,7 @@ def build_model(model_name = 'haarcascade'):
 
 		face_detector = cv2.CascadeClassifier(face_detector_path)
 		return face_detector
+
 	elif model_name == 'haarcascade_eye':
 		eye_detector_path = opencv_path+"haarcascade_eye.xml"
 
@@ -25,15 +35,14 @@ def build_model(model_name = 'haarcascade'):
 		eye_detector = cv2.CascadeClassifier(eye_detector_path)
 		return eye_detector
 
-
+def detect_face(detector, img):
-def detect_face(face_detector, img):
 
 	detected_face = None
 	img_region = [0, 0, img.shape[0], img.shape[1]]
 
 	faces = []
 	try:
-		faces = face_detector.detectMultiScale(img, 1.3, 5)
+		faces = detector["face_detector"].detectMultiScale(img, 1.3, 5)
 	except:
 		pass
 
@@ -41,15 +50,13 @@ def detect_face(face_detector, img):
 		x,y,w,h = faces[0] #focus on the 1st face found in the image
 		detected_face = img[int(y):int(y+h), int(x):int(x+w)]
 
-		detected_face = align_face(detected_face)
+		detected_face = align_face(detector["eye_detector"], detected_face)
 		img_region = [x, y, w, h]
 
 	return detected_face, img_region
 
-def align_face(img):
+def align_face(eye_detector, img):
-
+	
-	eye_detector = build_model(model_name = 'haarcascade_eye')
-
 	detected_face_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #eye detector expects gray scale image
 
 	eyes = eye_detector.detectMultiScale(detected_face_gray)

deepface/detectors/SsdWrapper.py

@@ -37,9 +37,15 @@ def build_model():
 		home+"/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel"
 	)
 
-	return face_detector
+	eye_detector = OpenCvWrapper.build_cascade("haarcascade_eye")
 
-def detect_face(face_detector, img):
+	detector = {}
+	detector["face_detector"] = face_detector
+	detector["eye_detector"] = eye_detector
+
+	return detector
+
+def detect_face(detector, img):
 
 	detected_face = None
 	img_region = [0, 0, img.shape[0], img.shape[1]]
@@ -59,6 +65,7 @@ def detect_face(face_detector, img):
 
 	imageBlob = cv2.dnn.blobFromImage(image = img)
 
+	face_detector = detector["face_detector"]
 	face_detector.setInput(imageBlob)
 	detections = face_detector.forward()
 
@@ -87,6 +94,6 @@ def detect_face(face_detector, img):
 		detected_face = base_img[int(top*aspect_ratio_y):int(bottom*aspect_ratio_y), int(left*aspect_ratio_x):int(right*aspect_ratio_x)]
 		img_region = [int(left*aspect_ratio_x), int(top*aspect_ratio_y), int(right*aspect_ratio_x) - int(left*aspect_ratio_x), int(bottom*aspect_ratio_y) - int(top*aspect_ratio_y)]
 
-		detected_face = OpenCvWrapper.align_face(detected_face)	
+		detected_face = OpenCvWrapper.align_face(detector["eye_detector"], detected_face)
 
 	return detected_face, img_region

tests/unit_tests.py

@@ -26,6 +26,19 @@ from deepface.extendedmodels import Age, Gender, Race, Emotion
 
 #-----------------------------------------
 
+if False:
+	print("Detector tests")
+	import matplotlib.pyplot as plt
+	detectors = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface']
+	for detector in detectors:
+		img = DeepFace.detectFace("dataset/img11.jpg", detector_backend = detector)
+		plt.imshow(img)
+		plt.show()
+
+#-----------------------------------------
+print("-----------------------------------------")
+
+
 img_path = "dataset/img1.jpg"
 embedding = DeepFace.represent(img_path)
 print("Function returned ", len(embedding), "dimensional vector")
@@ -45,18 +58,6 @@ dataset = [
 
 print("-----------------------------------------")
 
-if False:
-	print("Detector tests")
-	import matplotlib.pyplot as plt
-	detectors = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface']
-	#detectors = ['retinaface']
-	for detector in detectors:
-		img = DeepFace.detectFace("dataset/img1.jpg", detector_backend = detector)
-		plt.imshow(img)
-		plt.show()
-
-print("-----------------------------------------")
-
 print("Face detectors test")
 
 print("retinaface detector")