Source code for fastdeploy.vision.matting.contrib.rvm

# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import absolute_import
import logging
from .... import FastDeployModel, ModelFormat
from .... import c_lib_wrap as C


class RobustVideoMatting(FastDeployModel):
    def __init__(self,
                 model_file,
                 params_file="",
                 runtime_option=None,
                 model_format=ModelFormat.ONNX):
        """Load a video matting model exported by RobustVideoMatting.

        :param model_file: (str)Path of model file, e.g rvm/rvm_mobilenetv3_fp32.onnx
        :param params_file: (str)Path of parameters file, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model, default is ONNX
        """
        super(RobustVideoMatting, self).__init__(runtime_option)

        self._model = C.vision.matting.RobustVideoMatting(
            model_file, params_file, self._runtime_option, model_format)
        assert self.initialized, "RobustVideoMatting initialize failed."

    def predict(self, input_image):
        """Matting an input image

        :param im: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
        :return: MattingResult
        """
        return self._model.predict(input_image)

    @property
    def size(self):
        """
        Returns the preprocess image size
        """
        return self._model.size

    @property
    def video_mode(self):
        """
        Whether to open the video mode, if there are some irrelevant pictures, set it to fasle, the default is true
        """
        return self._model.video_mode

    @property
    def swap_rb(self):
        """
        Whether convert to RGB, Set to false if you have converted YUV format images to RGB outside the model, dafault true
        """
        return self._model.swap_rb

    @size.setter
    def size(self, wh):
        """
        Set the preprocess image size
        """
        assert isinstance(wh, (list, tuple)),\
            "The value to set `size` must be type of tuple or list."
        assert len(wh) == 2,\
            "The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
            len(wh))
        self._model.size = wh

    @video_mode.setter
    def video_mode(self, value):
        """
        Set video_mode property, the default is true
        """
        assert isinstance(
            value, bool), "The value to set `video_mode` must be type of bool."
        self._model.video_mode = value

    @swap_rb.setter
    def swap_rb(self, value):
        """
        Set swap_rb property, the default is true
        """
        assert isinstance(
            value, bool), "The value to set `swap_rb` must be type of bool."
        self._model.swap_rb = value