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global detector initializer
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serengil
parent
69acbacf88
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@ -56,8 +56,7 @@ def verify(img1_path, img2_path = '', model_name='VGG-Face', distance_metric='co
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#------------------------------
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if detector_backend == 'mtcnn':
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functions.load_mtcnn()
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functions.initialize_detector(detector_backend = detector_backend)
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resp_objects = []
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@ -355,9 +354,7 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True, detec
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#---------------------------------
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#build mtcnn model once
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if detector_backend == 'mtcnn':
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functions.load_mtcnn()
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functions.initialize_detector(detector_backend = detector_backend)
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#---------------------------------
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@ -520,9 +517,7 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True, detec
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def detectFace(img_path, detector_backend = 'opencv'):
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#build mtcnn model once
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if detector_backend == 'mtcnn':
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functions.load_mtcnn()
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functions.initialize_detector(detector_backend = detector_backend)
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img = functions.preprocess_face(img = img_path, detector_backend = detector_backend)[0] #preprocess_face returns (1, 224, 224, 3)
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return img[:, :, ::-1] #bgr to rgb
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@ -543,9 +538,7 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
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#-------------------------------
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#build mtcnn model once
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if detector_backend == 'mtcnn':
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functions.load_mtcnn()
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functions.initialize_detector(detector_backend = detector_backend)
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#-------------------------------
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@ -20,9 +20,83 @@ import bz2
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from deepface.commons import distance
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from mtcnn import MTCNN #0.1.0
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def load_mtcnn():
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global mtcnn_detector
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mtcnn_detector = MTCNN()
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def initialize_detector(detector_backend):
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global face_detector
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home = str(Path.home())
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if detector_backend == 'opencv':
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opencv_path = get_opencv_path()
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face_detector_path = opencv_path+"haarcascade_frontalface_default.xml"
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eye_detector_path = opencv_path+"haarcascade_eye.xml"
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if os.path.isfile(face_detector_path) != True:
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raise ValueError("Confirm that opencv is installed on your environment! Expected path ",face_detector_path," violated.")
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face_detector = cv2.CascadeClassifier(face_detector_path)
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global eye_detector
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eye_detector = cv2.CascadeClassifier(eye_detector_path)
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elif detector_backend == 'ssd':
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#check required ssd model exists in the home/.deepface/weights folder
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#model structure
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if os.path.isfile(home+'/.deepface/weights/deploy.prototxt') != True:
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print("deploy.prototxt will be downloaded...")
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url = "https://github.com/opencv/opencv/raw/3.4.0/samples/dnn/face_detector/deploy.prototxt"
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output = home+'/.deepface/weights/deploy.prototxt'
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gdown.download(url, output, quiet=False)
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#pre-trained weights
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if os.path.isfile(home+'/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel') != True:
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print("res10_300x300_ssd_iter_140000.caffemodel will be downloaded...")
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url = "https://github.com/opencv/opencv_3rdparty/raw/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel"
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output = home+'/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel'
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gdown.download(url, output, quiet=False)
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face_detector = cv2.dnn.readNetFromCaffe(
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home+"/.deepface/weights/deploy.prototxt",
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home+"/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel"
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)
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elif detector_backend == 'dlib':
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import dlib #this is not a must library within deepface. that's why, I didn't put this import to a global level. version: 19.20.0
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global sp
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face_detector = dlib.get_frontal_face_detector()
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#check required file exists in the home/.deepface/weights folder
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if os.path.isfile(home+'/.deepface/weights/shape_predictor_5_face_landmarks.dat') != True:
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print("shape_predictor_5_face_landmarks.dat.bz2 is going to be downloaded")
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url = "http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2"
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output = home+'/.deepface/weights/'+url.split("/")[-1]
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gdown.download(url, output, quiet=False)
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zipfile = bz2.BZ2File(output)
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data = zipfile.read()
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newfilepath = output[:-4] #discard .bz2 extension
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open(newfilepath, 'wb').write(data)
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sp = dlib.shape_predictor(home+"/.deepface/weights/shape_predictor_5_face_landmarks.dat")
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elif detector_backend == 'mtcnn':
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face_detector = MTCNN()
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def loadBase64Img(uri):
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encoded_data = uri.split(',')[1]
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@ -98,17 +172,6 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
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if detector_backend == 'opencv':
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#get opencv configuration up first
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opencv_path = get_opencv_path()
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face_detector_path = opencv_path+"haarcascade_frontalface_default.xml"
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if os.path.isfile(face_detector_path) != True:
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raise ValueError("Confirm that opencv is installed on your environment! Expected path ",face_detector_path," violated.")
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face_detector = cv2.CascadeClassifier(face_detector_path)
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#--------------------------
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faces = []
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try:
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@ -131,39 +194,6 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
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elif detector_backend == 'ssd':
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#---------------------------
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#check required ssd model exists in the home/.deepface/weights folder
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#model structure
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if os.path.isfile(home+'/.deepface/weights/deploy.prototxt') != True:
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print("deploy.prototxt will be downloaded...")
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url = "https://github.com/opencv/opencv/raw/3.4.0/samples/dnn/face_detector/deploy.prototxt"
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output = home+'/.deepface/weights/deploy.prototxt'
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gdown.download(url, output, quiet=False)
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#pre-trained weights
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if os.path.isfile(home+'/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel') != True:
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print("res10_300x300_ssd_iter_140000.caffemodel will be downloaded...")
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url = "https://github.com/opencv/opencv_3rdparty/raw/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel"
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output = home+'/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel'
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gdown.download(url, output, quiet=False)
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#---------------------------
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ssd_detector = cv2.dnn.readNetFromCaffe(
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home+"/.deepface/weights/deploy.prototxt",
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home+"/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel"
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)
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ssd_labels = ["img_id", "is_face", "confidence", "left", "top", "right", "bottom"]
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target_size = (300, 300)
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@ -179,8 +209,8 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
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imageBlob = cv2.dnn.blobFromImage(image = img)
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ssd_detector.setInput(imageBlob)
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detections = ssd_detector.forward()
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face_detector.setInput(imageBlob)
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detections = face_detector.forward()
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detections_df = pd.DataFrame(detections[0][0], columns = ssd_labels)
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@ -218,11 +248,8 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
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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.")
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elif detector_backend == 'dlib':
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import dlib #this is not a must library within deepface. that's why, I didn't put this import to a global level. version: 19.20.0
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detector = dlib.get_frontal_face_detector()
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detections = detector(img, 1)
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detections = face_detector(img, 1)
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if len(detections) > 0:
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@ -244,9 +271,8 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
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elif detector_backend == 'mtcnn':
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# mtcnn_detector = MTCNN() #this is a global variable now
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img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) #mtcnn expects RGB but OpenCV read BGR
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detections = mtcnn_detector.detect_faces(img_rgb)
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detections = face_detector.detect_faces(img_rgb)
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if len(detections) > 0:
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detection = detections[0]
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@ -265,8 +291,6 @@ def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_det
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detectors = ['opencv', 'ssd', 'dlib', 'mtcnn']
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raise ValueError("Valid backends are ", detectors," but you passed ", detector_backend)
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return 0
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def alignment_procedure(img, left_eye, right_eye):
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#this function aligns given face in img based on left and right eye coordinates
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@ -320,10 +344,6 @@ def align_face(img, detector_backend = 'opencv'):
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if (detector_backend == 'opencv') or (detector_backend == 'ssd'):
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opencv_path = get_opencv_path()
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eye_detector_path = opencv_path+"haarcascade_eye.xml"
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eye_detector = cv2.CascadeClassifier(eye_detector_path)
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detected_face_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #eye detector expects gray scale image
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eyes = eye_detector.detectMultiScale(detected_face_gray)
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@ -365,30 +385,9 @@ def align_face(img, detector_backend = 'opencv'):
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elif detector_backend == 'dlib':
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#check required file exists in the home/.deepface/weights folder
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if os.path.isfile(home+'/.deepface/weights/shape_predictor_5_face_landmarks.dat') != True:
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print("shape_predictor_5_face_landmarks.dat.bz2 is going to be downloaded")
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url = "http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2"
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output = home+'/.deepface/weights/'+url.split("/")[-1]
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gdown.download(url, output, quiet=False)
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zipfile = bz2.BZ2File(output)
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data = zipfile.read()
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newfilepath = output[:-4] #discard .bz2 extension
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open(newfilepath, 'wb').write(data)
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#------------------------------
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import dlib #this is not a must dependency in deepface
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detector = dlib.get_frontal_face_detector()
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sp = dlib.shape_predictor(home+"/.deepface/weights/shape_predictor_5_face_landmarks.dat")
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detections = detector(img, 1)
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detections = face_detector(img, 1)
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if len(detections) > 0:
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detected_face = detections[0]
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@ -399,9 +398,8 @@ def align_face(img, detector_backend = 'opencv'):
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elif detector_backend == 'mtcnn':
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# mtcnn_detector = MTCNN() #this is a global variable now
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img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) #mtcnn expects RGB but OpenCV read BGR
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detections = mtcnn_detector.detect_faces(img_rgb)
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detections = face_detector.detect_faces(img_rgb)
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if len(detections) > 0:
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detection = detections[0]
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@ -15,6 +15,26 @@ dataset = [
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print("-----------------------------------------")
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print("Face detectors test")
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print("opencv detector")
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res = DeepFace.verify(dataset, detector_backend = 'opencv')
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print(res)
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print("ssd detector")
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res = DeepFace.verify(dataset, detector_backend = 'ssd')
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print(res)
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print("dlib detector")
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res = DeepFace.verify(dataset, detector_backend = 'dlib')
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print(res)
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print("mtcnn detector")
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res = DeepFace.verify(dataset, detector_backend = 'mtcnn')
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print(res)
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print("-----------------------------------------")
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print("Large scale face recognition")
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df = DeepFace.find(img_path = "dataset/img1.jpg", db_path = "dataset"
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