      """ Manual Detector """
      def __init__(self, **kwargs):
          super().__init__(**kwargs)
  
      def initialize(self, *args, **kwargs):
          """ Create the mtcnn detector """
          super().initialize(*args, **kwargs)
          logger.info("Initializing Manual Detector...")
          self.init.set()
          logger.info("Initialized Manual Detector.")
  
      def detect_faces(self, *args, **kwargs):
          """ Return the given bounding box in a bounding box dict """
          super().detect_faces(*args, **kwargs)
          while True:
              item = self.get_item()
              if item == "EOF":
                  break
              face = item["face"]
  
              bounding_box = [self.to_bounding_box_dict(face[0], face[1], face[2], face[3])]
              item["detected_faces"] = bounding_box
              self.finalize(item)
  
          self.queues["out"].put("EOF")
 
</s> remove     def initialize(self, *args, **kwargs):
        """ Create the mtcnn detector """
        try:
            super().initialize(*args, **kwargs)
            logger.info("Initializing MTCNN Detector...")
            is_gpu = False

            # Must import tensorflow inside the spawned process
            # for Windows machines
            import_tensorflow()
            _, vram_free, _ = self.get_vram_free()
            mtcnn_graph = tf.Graph()

            # Windows machines sometimes misreport available vram, and overuse
            # causing OOM. Allow growth fixes that
            config = tf.ConfigProto()
            config.gpu_options.allow_growth = True  # pylint: disable=no-member

            with mtcnn_graph.as_default():  # pylint: disable=not-context-manager
                sess = tf.Session(config=config)
                with sess.as_default():  # pylint: disable=not-context-manager
                    pnet, rnet, onet = create_mtcnn(sess, self.model_path)

                if any("gpu" in str(device).lower()
                       for device in sess.list_devices()):
                    logger.debug("Using GPU")
                    is_gpu = True
            mtcnn_graph.finalize()

            if not is_gpu:
                alloc = 2048
                logger.warning("Using CPU")
            else:
                alloc = vram_free
            logger.debug("Allocated for Tensorflow: %sMB", alloc)

            self.batch_size = int(alloc / self.vram)

            if self.batch_size < 1:
                self.error.set()
                raise ValueError("Insufficient VRAM available to continue "
                                 "({}MB)".format(int(alloc)))

            logger.verbose("Processing in %s threads", self.batch_size)

            self.kwargs["pnet"] = pnet
            self.kwargs["rnet"] = rnet
            self.kwargs["onet"] = onet

            self.init.set()
            logger.info("Initialized MTCNN Detector.")
        except Exception as err:
            self.error.set()
            raise err

    def detect_faces(self, *args, **kwargs):
        """ Detect faces in Multiple Threads """
        super().detect_faces(*args, **kwargs)
        workers = MultiThread(target=self.detect_thread, thread_count=self.batch_size)
        workers.start()
        workers.join()
        sentinel = self.queues["in"].get()
        self.queues["out"].put(sentinel)
        logger.debug("Detecting Faces complete")

    def detect_thread(self):
        """ Detect faces in rgb image """
        logger.debug("Launching Detect")
        while True:
            item = self.get_item()
            if item == "EOF":
                break
            logger.trace("Detecting faces: '%s'", item["filename"])
            [detect_image, scale] = self.compile_detection_image(item["image"], to_rgb=True)

            for angle in self.rotation:
                current_image, rotmat = self.rotate_image(detect_image, angle)
                faces, points = detect_face(current_image, **self.kwargs)
                if angle != 0 and faces.any():
                    logger.verbose("found face(s) by rotating image %s degrees", angle)
                if faces.any():
                    break

            detected_faces = self.process_output(faces, points, rotmat, scale)
            item["detected_faces"] = detected_faces
            self.finalize(item)

        logger.debug("Thread Completed Detect")

    def process_output(self, faces, points, rotation_matrix, scale):
        """ Compile found faces for output """
        logger.trace("Processing Output: (faces: %s, points: %s, rotation_matrix: %s)",
                     faces, points, rotation_matrix)
        faces = self.recalculate_bounding_box(faces, points)
        faces = [self.to_bounding_box_dict(face[0], face[1], face[2], face[3]) for face in faces]
        if isinstance(rotation_matrix, np.ndarray):
            faces = [self.rotate_rect(face, rotation_matrix)
                     for face in faces]
        detected = [self.to_bounding_box_dict(face["left"] / scale, face["top"] / scale,
                                              face["right"] / scale, face["bottom"] / scale)
                    for face in faces]
        logger.trace("Processed Output: %s", detected)
        return detected
 </s> add     def init_model(self):
        """ Initialize S3FD Model"""
        self.model = MTCNN(self.model_path, **self.kwargs) </s> remove     def initialize(self, *args, **kwargs):
        """ Calculate batch size """
        super().initialize(*args, **kwargs)
        logger.info("Initializing cv2 DNN Detector...")
        logger.verbose("Using CPU for detection")
        self.detector = cv2.dnn.readNetFromCaffe(self.model_path[1],  # pylint: disable=no-member
                                                 self.model_path[0])
        self.detector.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)  # pylint: disable=no-member
        self.init.set()
        logger.info("Initialized cv2 DNN Detector.")

    def detect_faces(self, *args, **kwargs):
        """ Detect faces in grayscale image """
        super().detect_faces(*args, **kwargs)
        while True:
            item = self.get_item()
            if item == "EOF":
                break
            logger.trace("Detecting faces: %s", item["filename"])
            [detect_image, scale] = self.compile_detection_image(item["image"],
                                                                 is_square=True,
                                                                 scale_up=True)
            height, width = detect_image.shape[:2]
            for angle in self.rotation:
                current_image, rotmat = self.rotate_image(detect_image, angle)
                logger.trace("Detecting faces")

                blob = cv2.dnn.blobFromImage(current_image,  # pylint: disable=no-member
                                             1.0,
                                             self.target,
                                             [104, 117, 123],
                                             False,
                                             False)
                self.detector.setInput(blob)
                detected = self.detector.forward()
                faces = list()
                for i in range(detected.shape[2]):
                    confidence = detected[0, 0, i, 2]
                    if confidence >= self.confidence:
                        logger.trace("Accepting due to confidence %s >= %s",
                                     confidence, self.confidence)
                        faces.append([(detected[0, 0, i, 3] * width),
                                      (detected[0, 0, i, 4] * height),
                                      (detected[0, 0, i, 5] * width),
                                      (detected[0, 0, i, 6] * height)])

                logger.trace("Detected faces: %s", [face for face in faces])

                if angle != 0 and faces:
                    logger.verbose("found face(s) by rotating image %s degrees", angle)

                if faces:
                    break

            detected_faces = self.process_output(faces, rotmat, scale)
            item["detected_faces"] = detected_faces
            self.finalize(item)

        self.queues["out"].put("EOF")
        logger.debug("Detecting Faces Complete")

    def process_output(self, faces, rotation_matrix, scale):
 </s> add     def init_model(self):
        """ Initialize CV2 DNN Detector Model"""
        self.model = cv2.dnn.readNetFromCaffe(self.model_path[1],  # pylint: disable=no-member
                                              self.model_path[0])
        self.model.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)  # pylint: disable=no-member

    def process_input(self, batch):
        """ Compile the detection image(s) for prediction """
        batch["feed"] = cv2.dnn.blobFromImages(batch["scaled_image"],  # pylint: disable=no-member
                                               scalefactor=1.0,
                                               size=(self.input_size, self.input_size),
                                               mean=[104, 117, 123],
                                               swapRB=False,
                                               crop=False)
        return batch

    def predict(self, batch):
        """ Run model to get predictions """
        self.model.setInput(batch["feed"])
        predictions = self.model.forward()
        batch["prediction"] = self.finalize_predictions(predictions)
        return batch

    def finalize_predictions(self, predictions):
        """ Filter faces based on confidence level """
        faces = list()
        for i in range(predictions.shape[2]):
            confidence = predictions[0, 0, i, 2]
            if confidence >= self.confidence:
                logger.trace("Accepting due to confidence %s >= %s",
                             confidence, self.confidence)
                faces.append([(predictions[0, 0, i, 3] * self.input_size),
                              (predictions[0, 0, i, 4] * self.input_size),
                              (predictions[0, 0, i, 5] * self.input_size),
                              (predictions[0, 0, i, 6] * self.input_size)])
        logger.trace("faces: %s", faces)
        return [np.array(faces)]

    def process_output(self, batch): </s> remove     def initialize(self, *args, **kwargs):
        """ Initialization tasks to run prior to alignments """
        try:
            super().initialize(*args, **kwargs)
            logger.info("Initializing Face Alignment Network...")
            logger.debug("fan initialize: (args: %s kwargs: %s)", args, kwargs)

            _, _, vram_total = self.get_vram_free()

            if vram_total <= self.vram:
                tf_ratio = 1.0
            else:
                tf_ratio = self.vram / vram_total
            logger.verbose("Reserving %sMB for face alignments", self.vram)

            self.model = FAN(self.model_path, ratio=tf_ratio)

            self.init.set()
            logger.info("Initialized Face Alignment Network.")
        except Exception as err:
            self.error.set()
            raise err

    # DETECTED FACE BOUNDING BOX PROCESSING
    def align_image(self, detected_face, image):
        """ Get center and scale, crop and align image around center """
        logger.trace("Aligning image around center")
        center, scale = self.get_center_scale(detected_face)
        image = self.crop(image, center, scale)
 </s> add     def init_model(self):
        """ Initialize FAN model """
        model_kwargs = dict(custom_objects={'TorchBatchNorm2D': TorchBatchNorm2D})
        self.model = KSession(self.name, self.model_path, model_kwargs=model_kwargs)
        self.model.load_model()
        # Feed a placeholder so Aligner is primed for Manual tool
        placeholder = np.zeros((self.batchsize, 3, self.input_size, self.input_size),
                               dtype="float32")
        self.model.predict(placeholder)

    def process_input(self, batch):
        """ Compile the detected faces for prediction """
        # TODO Batching
        logger.trace("Aligning faces around center")
        batch["center_scale"] = self.get_center_scale(batch["detected_faces"])
        faces = self.crop(batch) </s> remove         self.model = None

    def initialize(self, *args, **kwargs):
        """ Initialization tasks to run prior to alignments """
        try:
            super().initialize(*args, **kwargs)
            logger.info("Initializing cv2 DNN Aligner...")
            logger.debug("cv2 DNN initialize: (args: %s kwargs: %s)", args, kwargs)
            logger.verbose("Using CPU for alignment")

            self.model = cv2.dnn.readNetFromTensorflow(  # pylint: disable=no-member
                self.model_path)
            self.model.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)  # pylint: disable=no-member
            self.init.set()
            logger.info("Initialized cv2 DNN Aligner.")
        except Exception as err:
            self.error.set()
            raise err

    def align_image(self, detected_face, image):
 </s> add         self.batchsize = 1

    def init_model(self):
        """ Initialize CV2 DNN Detector Model"""
        self.model = cv2.dnn.readNetFromTensorflow(self.model_path)  # pylint: disable=no-member
        self.model.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)  # pylint: disable=no-member

    def process_input(self, batch):
        """ Compile the detected faces for prediction """
        faces, batch["roi"] = self.align_image(batch["detected_faces"])
        faces = self._normalize_faces(faces)
        batch["feed"] = np.array(faces, dtype="float32").transpose((0, 3, 1, 2))
        return batch

    def align_image(self, detected_faces): </s> remove             if not self.model.is_gpu:
                alloc = 2048
                logger.warning("Using CPU")
            else:
                logger.debug("Using GPU")
                alloc = vram_free
            logger.debug("Allocated for Tensorflow: %sMB", alloc)

            if self.min_vram < alloc < self.vram:
                self.batch_size = 1
                logger.warning("You are running s3fd with %sMB VRAM. The model is optimized for "
                               "%sMB VRAM. Detection should still run but you may get "
                               "warnings/errors", int(alloc), self.vram)
            else:
                self.batch_size = int(alloc / self.vram)
            if self.batch_size < 1:
                raise ValueError("Insufficient VRAM available to continue "
                                 "({}MB)".format(int(alloc)))

            logger.verbose("Processing in %s threads", self.batch_size)

            self.init.set()
            logger.info("Initialized S3FD Detector.")
        except Exception as err:
            self.error.set()
            raise err

    def detect_faces(self, *args, **kwargs):
        """ Detect faces in Multiple Threads """
        super().detect_faces(*args, **kwargs)
        workers = MultiThread(target=self.detect_thread, thread_count=self.batch_size)
        workers.start()
        workers.join()
        sentinel = self.queues["in"].get()
        self.queues["out"].put(sentinel)
        logger.debug("Detecting Faces complete")

    def detect_thread(self):
        """ Detect faces in rgb image """
        logger.debug("Launching Detect")
        while True:
            item = self.get_item()
            if item == "EOF":
                break
            logger.trace("Detecting faces: '%s'", item["filename"])
            detect_image, scale = self.compile_detection_image(item["image"], is_square=True)
            for angle in self.rotation:
                current_image, rotmat = self.rotate_image(detect_image, angle)
                faces = self.model.detect_face(current_image)
                if angle != 0 and faces.any():
                    logger.verbose("found face(s) by rotating image %s degrees", angle)
                if faces.any():
                    break

            detected_faces = self.process_output(faces, rotmat, scale)
            item["detected_faces"] = detected_faces
            self.finalize(item)

        logger.debug("Thread Completed Detect")

    def process_output(self, faces, rotation_matrix, scale):
        """ Compile found faces for output """
        logger.trace("Processing Output: (faces: %s, rotation_matrix: %s)", faces, rotation_matrix)
        faces = [self.to_bounding_box_dict(face[0], face[1], face[2], face[3]) for face in faces]
        if isinstance(rotation_matrix, np.ndarray):
            faces = [self.rotate_rect(face, rotation_matrix)
                     for face in faces]
        detected = [self.to_bounding_box_dict(face["left"] / scale, face["top"] / scale,
                                              face["right"] / scale, face["bottom"] / scale)
                    for face in faces]
        logger.trace("Processed Output: %s", detected)
        return detected


class S3fd():
    """ Tensorflow Network """
    def __init__(self, model_path, target_size, vram_ratio, card_id, confidence):
        logger.debug("Initializing: %s: (model_path: '%s', target_size: %s, vram_ratio: %s, "
                     "card_id: %s)",
                     self.__class__.__name__, model_path, target_size, vram_ratio, card_id)
        # Must import tensorflow inside the spawned process for Windows machines
        import tensorflow as tf
        self.is_gpu = False
        self.tf = tf  # pylint: disable=invalid-name
        self.model_path = model_path
 </s> add class O2K_Sum(O2K_ReduceLayer):
    def call(self, x, *args):
        return K.sum(x, self._axes, self._keepdims)


class O2K_Sqrt(keras.engine.Layer):
    def call(self, x, *args):
        return K.sqrt(x)


class O2K_Pow(keras.engine.Layer):
    def call(self, x, *args):
        return K.pow(*x)


class O2K_ConstantLayer(keras.engine.Layer):
    def __init__(self, constant_obj, dtype, **kwargs):
        self._dtype = np.dtype(dtype).name
        self._constant = np.array(constant_obj, dtype=self._dtype)
        super(O2K_ConstantLayer, self).__init__(**kwargs)

    def call(self, *args):
        # pylint:disable=arguments-differ
        data = K.constant(self._constant, dtype=self._dtype)
        return data

    def compute_output_shape(self, input_shape):
        return self._constant.shape

    def get_config(self):
        config = super(O2K_ConstantLayer, self).get_config()
        config.update({
            'constant_obj': self._constant,
            'dtype': self._dtype
        })
        return config


class O2K_Div(O2K_ElementwiseLayer):
    # pylint:disable=arguments-differ
    def call(self, x, *args):
        return x[0] / x[1]


class S3fd(KSession):
    """ Keras Network """
    def __init__(self, model_path, model_kwargs, confidence):
        logger.debug("Initializing: %s: (model_path: '%s')",
                     self.__class__.__name__, model_path)
        super().__init__("S3FD", model_path, model_kwargs)
        self.load_model()
    def _compile_detection_image(self, input_image):
        """ Override compile detection image for manual. No face is actually fed into a model """
        return input_image, 1, (0, 0)

    def init_model(self):
        """ No model for Manual """
        return

    def process_input(self, batch):
        """ No pre-processing for Manual. Just set a dummy feed """
        batch["feed"] = batch["scaled_image"]
        return batch

    def predict(self, batch):
        """ No prediction for Manual """
        batch["prediction"] = [np.array(batch["manual_face"])]
        return batch

    def process_output(self, batch):
        """ Post process the detected faces """
        return batch
      """ Manual Detector """
      def __init__(self, **kwargs):
          super().__init__(**kwargs)
  
    def _compile_detection_image(self, input_image):
        """ Override compile detection image for manual. No face is actually fed into a model """
        return input_image, 1, (0, 0)

    def init_model(self):
        """ No model for Manual """
        return

    def process_input(self, batch):
        """ No pre-processing for Manual. Just set a dummy feed """
        batch["feed"] = batch["scaled_image"]
        return batch

    def predict(self, batch):
        """ No prediction for Manual """
        batch["prediction"] = [np.array(batch["manual_face"])]
        return batch

    def process_output(self, batch):
        """ Post process the detected faces """
        return batch
    def _compile_detection_image(self, input_image):
        """ Override compile detection image for manual. No face is actually fed into a model """
        return input_image, 1, (0, 0)

    def init_model(self):
        """ No model for Manual """
        return

    def process_input(self, batch):
        """ No pre-processing for Manual. Just set a dummy feed """
        batch["feed"] = batch["scaled_image"]
        return batch

    def predict(self, batch):
        """ No prediction for Manual """
        batch["prediction"] = [np.array(batch["manual_face"])]
        return batch

    def process_output(self, batch):
        """ Post process the detected faces """
        return batch
    def _compile_detection_image(self, input_image):
        """ Override compile detection image for manual. No face is actually fed into a model """
        return input_image, 1, (0, 0)

    def init_model(self):
        """ No model for Manual """
        return

    def process_input(self, batch):
        """ No pre-processing for Manual. Just set a dummy feed """
        batch["feed"] = batch["scaled_image"]
        return batch

    def predict(self, batch):
        """ No prediction for Manual """
        batch["prediction"] = [np.array(batch["manual_face"])]
        return batch

    def process_output(self, batch):
        """ Post process the detected faces """
        return batch
    def _compile_detection_image(self, input_image):
        """ Override compile detection image for manual. No face is actually fed into a model """
        return input_image, 1, (0, 0)

    def init_model(self):
        """ No model for Manual """
        return

    def process_input(self, batch):
        """ No pre-processing for Manual. Just set a dummy feed """
        batch["feed"] = batch["scaled_image"]
        return batch

    def predict(self, batch):
        """ No prediction for Manual """
        batch["prediction"] = [np.array(batch["manual_face"])]
        return batch

    def process_output(self, batch):
        """ Post process the detected faces """
        return batch
    def _compile_detection_image(self, input_image):
        """ Override compile detection image for manual. No face is actually fed into a model """
        return input_image, 1, (0, 0)

    def init_model(self):
        """ No model for Manual """
        return

    def process_input(self, batch):
        """ No pre-processing for Manual. Just set a dummy feed """
        batch["feed"] = batch["scaled_image"]
        return batch

    def predict(self, batch):
        """ No prediction for Manual """
        batch["prediction"] = [np.array(batch["manual_face"])]
        return batch

    def process_output(self, batch):
        """ Post process the detected faces """
        return batch
    def _compile_detection_image(self, input_image):
        """ Override compile detection image for manual. No face is actually fed into a model """
        return input_image, 1, (0, 0)

    def init_model(self):
        """ No model for Manual """
        return

    def process_input(self, batch):
        """ No pre-processing for Manual. Just set a dummy feed """
        batch["feed"] = batch["scaled_image"]
        return batch

    def predict(self, batch):
        """ No prediction for Manual """
        batch["prediction"] = [np.array(batch["manual_face"])]
        return batch

    def process_output(self, batch):
        """ Post process the detected faces """
        return batch
    def _compile_detection_image(self, input_image):
        """ Override compile detection image for manual. No face is actually fed into a model """
        return input_image, 1, (0, 0)

    def init_model(self):
        """ No model for Manual """
        return

    def process_input(self, batch):
        """ No pre-processing for Manual. Just set a dummy feed """
        batch["feed"] = batch["scaled_image"]
        return batch

    def predict(self, batch):
        """ No prediction for Manual """
        batch["prediction"] = [np.array(batch["manual_face"])]
        return batch

    def process_output(self, batch):
        """ Post process the detected faces """
        return batch
    def _compile_detection_image(self, input_image):
        """ Override compile detection image for manual. No face is actually fed into a model """
        return input_image, 1, (0, 0)

    def init_model(self):
        """ No model for Manual """
        return

    def process_input(self, batch):
        """ No pre-processing for Manual. Just set a dummy feed """
        batch["feed"] = batch["scaled_image"]
        return batch

    def predict(self, batch):
        """ No prediction for Manual """
        batch["prediction"] = [np.array(batch["manual_face"])]
        return batch

    def process_output(self, batch):
        """ Post process the detected faces """
        return batch
    def _compile_detection_image(self, input_image):
        """ Override compile detection image for manual. No face is actually fed into a model """
        return input_image, 1, (0, 0)

    def init_model(self):
        """ No model for Manual """
        return

    def process_input(self, batch):
        """ No pre-processing for Manual. Just set a dummy feed """
        batch["feed"] = batch["scaled_image"]
        return batch

    def predict(self, batch):
        """ No prediction for Manual """
        batch["prediction"] = [np.array(batch["manual_face"])]
        return batch

    def process_output(self, batch):
        """ Post process the detected faces """
        return batch
    def _compile_detection_image(self, input_image):
        """ Override compile detection image for manual. No face is actually fed into a model """
        return input_image, 1, (0, 0)

    def init_model(self):
        """ No model for Manual """
        return

    def process_input(self, batch):
        """ No pre-processing for Manual. Just set a dummy feed """
        batch["feed"] = batch["scaled_image"]
        return batch

    def predict(self, batch):
        """ No prediction for Manual """
        batch["prediction"] = [np.array(batch["manual_face"])]
        return batch

    def process_output(self, batch):
        """ Post process the detected faces """
        return batch
    def _compile_detection_image(self, input_image):
        """ Override compile detection image for manual. No face is actually fed into a model """
        return input_image, 1, (0, 0)

    def init_model(self):
        """ No model for Manual """
        return

    def process_input(self, batch):
        """ No pre-processing for Manual. Just set a dummy feed """
        batch["feed"] = batch["scaled_image"]
        return batch

    def predict(self, batch):
        """ No prediction for Manual """
        batch["prediction"] = [np.array(batch["manual_face"])]
        return batch

    def process_output(self, batch):
        """ Post process the detected faces """
        return batch
    def _compile_detection_image(self, input_image):
        """ Override compile detection image for manual. No face is actually fed into a model """
        return input_image, 1, (0, 0)

    def init_model(self):
        """ No model for Manual """
        return

    def process_input(self, batch):
        """ No pre-processing for Manual. Just set a dummy feed """
        batch["feed"] = batch["scaled_image"]
        return batch

    def predict(self, batch):
        """ No prediction for Manual """
        batch["prediction"] = [np.array(batch["manual_face"])]
        return batch

    def process_output(self, batch):
        """ Post process the detected faces """
        return batch
    def _compile_detection_image(self, input_image):
        """ Override compile detection image for manual. No face is actually fed into a model """
        return input_image, 1, (0, 0)

    def init_model(self):
        """ No model for Manual """
        return

    def process_input(self, batch):
        """ No pre-processing for Manual. Just set a dummy feed """
        batch["feed"] = batch["scaled_image"]
        return batch

    def predict(self, batch):
        """ No prediction for Manual """
        batch["prediction"] = [np.array(batch["manual_face"])]
        return batch

    def process_output(self, batch):
        """ Post process the detected faces """
        return batch
    def _compile_detection_image(self, input_image):
        """ Override compile detection image for manual. No face is actually fed into a model """
        return input_image, 1, (0, 0)

    def init_model(self):
        """ No model for Manual """
        return

    def process_input(self, batch):
        """ No pre-processing for Manual. Just set a dummy feed """
        batch["feed"] = batch["scaled_image"]
        return batch

    def predict(self, batch):
        """ No prediction for Manual """
        batch["prediction"] = [np.array(batch["manual_face"])]
        return batch

    def process_output(self, batch):
        """ Post process the detected faces """
        return batch
    def _compile_detection_image(self, input_image):
        """ Override compile detection image for manual. No face is actually fed into a model """
        return input_image, 1, (0, 0)

    def init_model(self):
        """ No model for Manual """
        return

    def process_input(self, batch):
        """ No pre-processing for Manual. Just set a dummy feed """
        batch["feed"] = batch["scaled_image"]
        return batch

    def predict(self, batch):
        """ No prediction for Manual """
        batch["prediction"] = [np.array(batch["manual_face"])]
        return batch

    def process_output(self, batch):
        """ Post process the detected faces """
        return batch
    def _compile_detection_image(self, input_image):
        """ Override compile detection image for manual. No face is actually fed into a model """
        return input_image, 1, (0, 0)

    def init_model(self):
        """ No model for Manual """
        return

    def process_input(self, batch):
        """ No pre-processing for Manual. Just set a dummy feed """
        batch["feed"] = batch["scaled_image"]
        return batch

    def predict(self, batch):
        """ No prediction for Manual """
        batch["prediction"] = [np.array(batch["manual_face"])]
        return batch

    def process_output(self, batch):
        """ Post process the detected faces """
        return batch
    def _compile_detection_image(self, input_image):
        """ Override compile detection image for manual. No face is actually fed into a model """
        return input_image, 1, (0, 0)

    def init_model(self):
        """ No model for Manual """
        return

    def process_input(self, batch):
        """ No pre-processing for Manual. Just set a dummy feed """
        batch["feed"] = batch["scaled_image"]
        return batch

    def predict(self, batch):
        """ No prediction for Manual """
        batch["prediction"] = [np.array(batch["manual_face"])]
        return batch

    def process_output(self, batch):
        """ Post process the detected faces """
        return batch
    def _compile_detection_image(self, input_image):
        """ Override compile detection image for manual. No face is actually fed into a model """
        return input_image, 1, (0, 0)

    def init_model(self):
        """ No model for Manual """
        return

    def process_input(self, batch):
        """ No pre-processing for Manual. Just set a dummy feed """
        batch["feed"] = batch["scaled_image"]
        return batch

    def predict(self, batch):
        """ No prediction for Manual """
        batch["prediction"] = [np.array(batch["manual_face"])]
        return batch

    def process_output(self, batch):
        """ Post process the detected faces """
        return batch
    def _compile_detection_image(self, input_image):
        """ Override compile detection image for manual. No face is actually fed into a model """
        return input_image, 1, (0, 0)

    def init_model(self):
        """ No model for Manual """
        return

    def process_input(self, batch):
        """ No pre-processing for Manual. Just set a dummy feed """
        batch["feed"] = batch["scaled_image"]
        return batch

    def predict(self, batch):
        """ No prediction for Manual """
        batch["prediction"] = [np.array(batch["manual_face"])]
        return batch

    def process_output(self, batch):
        """ Post process the detected faces """
        return batch
    def _compile_detection_image(self, input_image):
        """ Override compile detection image for manual. No face is actually fed into a model """
        return input_image, 1, (0, 0)

    def init_model(self):
        """ No model for Manual """
        return

    def process_input(self, batch):
        """ No pre-processing for Manual. Just set a dummy feed """
        batch["feed"] = batch["scaled_image"]
        return batch

    def predict(self, batch):
        """ No prediction for Manual """
        batch["prediction"] = [np.array(batch["manual_face"])]
        return batch

    def process_output(self, batch):
        """ Post process the detected faces """
        return batch
    def _compile_detection_image(self, input_image):
        """ Override compile detection image for manual. No face is actually fed into a model """
        return input_image, 1, (0, 0)

    def init_model(self):
        """ No model for Manual """
        return

    def process_input(self, batch):
        """ No pre-processing for Manual. Just set a dummy feed """
        batch["feed"] = batch["scaled_image"]
        return batch

    def predict(self, batch):
        """ No prediction for Manual """
        batch["prediction"] = [np.array(batch["manual_face"])]
        return batch

    def process_output(self, batch):
        """ Post process the detected faces """
        return batch
keep keep keep keep replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace
 De-Multiprocess Extract (#871)

* requirements.txt: - Pin opencv to 4.1.1 (fixes cv2-dnn error)

* lib.face_detect.DetectedFace: change LandmarksXY to landmarks_xy. Add left, right, top, bottom attributes

* lib.model.session: Session manager for loading models into different graphs (for Nvidia + CPU)

* plugins.extract._base: New parent class for all extract plugins

* plugins.extract.pipeline. Remove MultiProcessing. Dynamically limit batchsize for Nvidia cards. Remove loglevel input

* S3FD + FAN plugins. Standardise to Keras version for all backends

* Standardize all extract plugins to new threaded codebase

* Documentation. Start implementing Numpy style docstrings for Sphinx Documentation

* Remove s3fd_amd. Change convert OTF to expect DetectedFace object

* faces_detect - clean up and documentation

* Remove PoolProcess

* Migrate manual tool to new extract workflow

* Remove AMD specific extractor code from cli and plugins

* Sort tool to new extract workflow

* Remove multiprocessing from project

* Remove multiprocessing queues from QueueManager

* Remove multiprocessing support from logger

* Move face_filter to new extraction pipeline

* Alignments landmarksXY > landmarks_xy and legacy handling

* Intercept get_backend for sphinx doc build

# Add Sphinx documentation 
plugins/extract/detect/manual.py