Python dlib.get_frontal_face_detector方法代码示例

# 需要导入模块: import dlib [as 别名]
# 或者: from dlib import get_frontal_face_detector [as 别名]
def test(image_path, model_path):
    '''Test the given model by showing the detected landmarks.
        - image_path: the path of an image. Should contain a face.
        - model_path: the path of a shape predictor model.
    '''
    image = cv2.imread(image_path)
    face_detector = dlib.get_frontal_face_detector()
    dets = face_detector(image, 1)
    predictor = dlib.shape_predictor(model_path)

    for d in dets:
      cv2.rectangle(image, (d.left(), d.top()), (d.right(), d.bottom()), 255, 1)
      shape = predictor(image, d)
      for i in range(shape.num_parts):
        p = shape.part(i)
        cv2.circle(image, (p.x, p.y), 2, 255, 1)
        cv2.putText(image, str(i), (p.x + 4, p.y), cv2.FONT_HERSHEY_SIMPLEX, 0.25, (255, 255, 255))

    cv2.imshow("window", image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()


# uncomment to test
# test("test_image.jpg", "shape_predictor.dat") 

# 需要导入模块: import dlib [as 别名]
# 或者: from dlib import get_frontal_face_detector [as 别名]
def detect_face_rect_of_pts(img_path,pts):
    detector = dlib.get_frontal_face_detector()
   # detect face using dlib
    im = cv2.imread(img_path);
    dets = detector(im, 1)
    selected_det = None
    pts_center = np.mean(pts,axis=0)
    for det in dets:
        if pts_center[0]>det.left() and pts_center[0]<det.right() and\
        pts_center[1]>det.top() and pts_center[1]<det.bottom():
            selected_det = det
    if selected_det is None:
        return None
    left = selected_det.left()
    top = selected_det.top()
    width = selected_det.width()
    height = selected_det.height()
    return [left,top,width,height] 

# 需要导入模块: import dlib [as 别名]
# 或者: from dlib import get_frontal_face_detector [as 别名]
def __init__(self, usage):
        self.usage = usage
        if self.usage == 'train':
            print('loading train samples')
            self.image_folder = image_folder
            if os.path.isfile('data/train_triplets.json'):
                with open('data/train_triplets.json', 'r') as file:
                    self.samples = json.load(file)
            else:
                self.samples = get_random_triplets('train')
        else:
            print('loading valid samples(LFW)')
            self.image_folder = lfw_folder
            with open('data/lfw_val_triplets.json', 'r') as file:
                self.samples = json.load(file)

        self.detector = dlib.get_frontal_face_detector()
        self.sp = dlib.shape_predictor(predictor_path) 

# 需要导入模块: import dlib [as 别名]
# 或者: from dlib import get_frontal_face_detector [as 别名]
def detect_fiducial_points(img, predictor_path):
    """
    Detect face landmarks and return the mean points of left and right eyes.
    If there are multiple faces in one image, only select the first one.
    """
    detector = dlib.get_frontal_face_detector()
    predictor = dlib.shape_predictor(predictor_path)
    dets = detector(img, 1)
    if len(dets) < 1:
        return [] 
    for k, d in enumerate(dets):
        shape = predictor(img, d)
        break
    landmarks = []
    for i in range(68):
        landmarks.append([shape.part(i).x, shape.part(i).y])
    landmarks = np.array(landmarks)
    left_eye = landmarks[36:42]
    right_eye = landmarks[42:48]
    mouth = landmarks[48:68]
    return np.array([np.mean(left_eye, 0), np.mean(right_eye, 0)]).astype('int') 

# 需要导入模块: import dlib [as 别名]
# 或者: from dlib import get_frontal_face_detector [as 别名]
def startCapture(self):
        self.setText("请稍候，正在初始化数据和摄像头。。。")
        try:
            # 检测相关
            self.detector = dlib.get_frontal_face_detector()
            self.predictor = dlib.shape_predictor(
                "Data/shape_predictor_68_face_landmarks.dat")
            cascade_fn = "Data/lbpcascades/lbpcascade_frontalface.xml"
            self.cascade = cv2.CascadeClassifier(cascade_fn)
            if not self.cascade:
                return QMessageBox.critical(self, "错误", cascade_fn + " 无法找到")
            self.cap = cv2.VideoCapture(0)
            if not self.cap or not self.cap.isOpened():
                return QMessageBox.critical(self, "错误", "打开摄像头失败")
            # 开启定时器定时捕获
            self.timer = QTimer(self, timeout=self.onCapture)
            self.timer.start(1000 / self.fps)
        except Exception as e:
            QMessageBox.critical(self, "错误", str(e)) 

# 需要导入模块: import dlib [as 别名]
# 或者: from dlib import get_frontal_face_detector [as 别名]
def get_landmarks(img, resource_dir, verbose=False):
    # if not automatically downloaded, get it from:
    # http://sourceforge.net/projects/dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2
    predictor_path = resource_dir + "/dlib_models/shape_predictor_68_face_landmarks.dat"
    detector = dlib.get_frontal_face_detector()
    predictor = dlib.shape_predictor(predictor_path)

    lmarks = []
    dets = detector(img, 1)
    if verbose:
        print("Number of faces detected: {}".format(len(dets)))
    shapes = []
    for k, det in enumerate(dets):
        shape = predictor(img, det)
        shapes.append(shape)
        xy = _shape_to_np(shape)
        lmarks.append(xy)

    lmarks = np.asarray(lmarks, dtype='float32')
    # display_landmarks(img, dets, shapes)
    return lmarks 

# 需要导入模块: import dlib [as 别名]
# 或者: from dlib import get_frontal_face_detector [as 别名]
def face_detection(self, frame):
        '''
        Detect faces in a frame
        
        Args:
            frame (cv2 image): a normal frame grab from camera or video
            
        Outputs:
            rects (array): detected faces as rectangles
        '''
        if self.detector is None:
            self.detector = dlib.get_frontal_face_detector()
        
        if frame is None:
            return
            
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        #get all faces in the frame
        rects = self.detector(gray, 0)
        # to get the coords from a rect, use: (x, y, w, h) = face_utils.rect_to_bb(rects[0])
        
        return rects 

# 需要导入模块: import dlib [as 别名]
# 或者: from dlib import get_frontal_face_detector [as 别名]
def __init__(self, facePredictor):
        """
        Instantiate an 'AlignDlib' object.

        :param facePredictor: The path to dlib's
        :type facePredictor: str
        """
        assert facePredictor is not None

        #pylint: disable=no-member
        self.detector = dlib.get_frontal_face_detector()
        self.predictor = dlib.shape_predictor(facePredictor) 

# 需要导入模块: import dlib [as 别名]
# 或者: from dlib import get_frontal_face_detector [as 别名]
def __init__(self, device, path_to_detector=None, verbose=False):
        super().__init__(device, verbose)

        base_path = os.path.join(appdata_dir('face_alignment'), "data")

        # Initialise the face detector
        if 'cuda' in device:
            if path_to_detector is None:
                path_to_detector = os.path.join(
                    base_path, "mmod_human_face_detector.dat")

                if not os.path.isfile(path_to_detector):
                    print("Downloading the face detection CNN. Please wait...")

                    path_to_temp_detector = os.path.join(
                        base_path, "mmod_human_face_detector.dat.download")

                    if os.path.isfile(path_to_temp_detector):
                        os.remove(os.path.join(path_to_temp_detector))

                    request_file.urlretrieve(
                        "https://www.adrianbulat.com/downloads/dlib/mmod_human_face_detector.dat",
                        os.path.join(path_to_temp_detector))

                    os.rename(os.path.join(path_to_temp_detector), os.path.join(path_to_detector))

            self.face_detector = dlib.cnn_face_detection_model_v1(path_to_detector)
        else:
            self.face_detector = dlib.get_frontal_face_detector() 

# 需要导入模块: import dlib [as 别名]
# 或者: from dlib import get_frontal_face_detector [as 别名]
def monkey_patch_face_detector(_):
    detector = dlib.get_frontal_face_detector()
    class Rect(object):
        def __init__(self,rect):
            self.rect=rect
    def detect( *args ):
        return [ Rect(x) for x in detector(*args) ]
    return detect 

# 需要导入模块: import dlib [as 别名]
# 或者: from dlib import get_frontal_face_detector [as 别名]
def get_one_image(self, x, detector):
        path_name_x = self.path + self.path_array[x-1]
        try:
            img_x = cv2.imread(path_name_x)
        except IndexError:
            print(path_name_x)
            print('error')
        else:
            img_x_rgb = cv2.cvtColor(img_x, cv2.COLOR_BGR2RGB)  # 转为RGB图片
            face_align_rgb_x = detector.align(size, img_x_rgb)
            if face_align_rgb_x is None:
                det = dlib.get_frontal_face_detector()
                gray_img = cv2.cvtColor(img_x, cv2.COLOR_BGR2GRAY)
                # 使用detector进行人脸检测
                dets = det(gray_img, 1)
                if len(dets) > 0:
                    x1 = dets[0].top() if dets[0].top() > 0 else 0
                    y1 = dets[0].bottom() if dets[0].bottom() > 0 else 0
                    x2 = dets[0].left() if dets[0].left() > 0 else 0
                    y2 = dets[0].right() if dets[0].right() > 0 else 0
                    face = img_x[x1:y1, x2:y2]
                else:
                    face = cv2.resize(img_x, (size, size))
                face_align_x = cv2.resize(face, (size, size))
            else:
                face_align_x = cv2.cvtColor(face_align_rgb_x, cv2.COLOR_RGB2BGR)  # 转为BGR图片
            x_img = np.array(face_align_x)
            x_img = x_img.astype('float32') / 255.0
            return x_img 

# 需要导入模块: import dlib [as 别名]
# 或者: from dlib import get_frontal_face_detector [as 别名]
def __init__(self, facePredictor):
        """
        Instantiate an 'AlignDlib' object.
        :param facePredictor: The path to dlib's
        :type facePredictor: str
        """
        assert facePredictor is not None

        self.detector = dlib.get_frontal_face_detector()
        self.predictor = dlib.shape_predictor(facePredictor) 

# 需要导入模块: import dlib [as 别名]
# 或者: from dlib import get_frontal_face_detector [as 别名]
def __init__(self, MDE: MainData):
		self.md = MDE

		self.detector = dlib.get_frontal_face_detector()
		self.sp = dlib.shape_predictor("shape_predictor_5_face_landmarks.dat") 

# 需要导入模块: import dlib [as 别名]
# 或者: from dlib import get_frontal_face_detector [as 别名]
def detector(self):
        """
        检测是否有人脸
        :return:
        """
        return get_frontal_face_detector() 

# 需要导入模块: import dlib [as 别名]
# 或者: from dlib import get_frontal_face_detector [as 别名]
def __init__(self, saved=False):
        self.saved = saved  # 是否保存图片
        self.listener = True  # 启动参数
        self.video_capture = cv2.VideoCapture(0)  # 调用本地摄像头
        self.doing = False  # 是否进行面部面具
        self.speed = 0.1  # 面具移动速度
        self.detector = get_frontal_face_detector()  # 面部识别器
        self.predictor = shape_predictor("shape_predictor_68_face_landmarks.dat")  # 面部分析器
        self.fps = 4  # 面具存在时间基础时间
        self.animation_time = 0  # 动画周期初始值
        self.duration = self.fps * 4  # 动画周期最大值
        self.fixed_time = 4  # 画图之后，停留时间
        self.max_width = 500  # 图像大小
        self.deal, self.text, self.cigarette = None, None, None  # 面具对象