caffe transformer预处理

  def preprocess(self, in_, data):
        """
        Format input for Caffe:
        - convert to single
        - resize to input dimensions (preserving number of channels)
        - transpose dimensions to K x H x W
        - reorder channels (for instance color to BGR)
        - scale raw input (e.g. from [0, 1] to [0, 255] for ImageNet models)
        - subtract mean
        - scale feature
        Parameters
        ----------
        in_ : name of input blob to preprocess for
        data : (H' x W' x K) ndarray
        Returns
        -------
        caffe_in : (K x H x W) ndarray for input to a Net
        """
        self.__check_input(in_)
        caffe_in = data.astype(np.float32, copy=False)
        transpose = self.transpose.get(in_)
        channel_swap = self.channel_swap.get(in_)
        raw_scale = self.raw_scale.get(in_)
        mean = self.mean.get(in_)
        input_scale = self.input_scale.get(in_)
        in_dims = self.inputs[in_][2:]
        if caffe_in.shape[:2] != in_dims:
            caffe_in = resize_image(caffe_in, in_dims)
        if transpose is not None:
            caffe_in = caffe_in.transpose(transpose)
        if channel_swap is not None:
            caffe_in = caffe_in[channel_swap, :, :]
        if raw_scale is not None:
            caffe_in *= raw_scale
        if mean is not None:
            caffe_in -= mean
        if input_scale is not None:
            caffe_in *= input_scale
        return caffe_in
        #input_img:(256,256,3)
        #resize:(227,227,3)
        #transpose:(3,227,227)
        #channel_swap:(3,227,227) 在shape[0]进行swap
        #*=raw_scale:raw_scale=255则 value from 0-1 to 0-255
        #-=mean：去均值
        #*=input_scale: blob = blob * scale扩容