openvino.inference_engine.PreProcessInfo¶

class openvino.inference_engine.PreProcessInfo¶

This class stores pre-process information for the input

__init__()¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`()	Initialize self.
`get_number_of_channels`(self)	Returns a number of channels to preprocess
`init`(self, size_t number_of_channels)	Initializes with given number of channels
`set_mean_image`(self, Blob mean_image)	Sets mean image values if operation is applicable.
`set_mean_image_for_channel`(self, …)	Sets mean image values if operation is applicable.

Attributes

`color_format`	Color format to be used in on-demand color conversions applied to input before inference
`mean_variant`	Mean Variant to be applied for input before inference if needed.
`resize_algorithm`	Resize Algorithm to be applied for input before inference if needed. .. note::.

color_format¶

Color format to be used in on-demand color conversions applied to input before inference

Usage example:

net = ie_core.read_network(model=path_to_xml_file, weights=path_to_bin_file)
net.input_info['data'].preprocess_info.color_format = ColorFormat.BGR

get_number_of_channels(self)¶: Returns a number of channels to preprocess

init(self, size_t number_of_channels)¶: Initializes with given number of channels

mean_variant¶

Mean Variant to be applied for input before inference if needed.

Usage example:

net = ie_core.read_network(model=path_to_xml_file, weights=path_to_bin_file)
net.input_info['data'].preprocess_info.mean_variant = MeanVariant.MEAN_IMAGE

resize_algorithm¶

Resize Algorithm to be applied for input before inference if needed. .. note:

It's need to set your input via the set_blob method.

Usage example:

net = ie_core.read_network(model=path_to_xml_file, weights=path_to_bin_file)
net.input_info['data'].preprocess_info.resize_algorithm = ResizeAlgorithm.RESIZE_BILINEAR
exec_net = ie_core.load_network(net, 'CPU')
tensor_desc = ie.TensorDesc("FP32", [1, 3, image.shape[2], image.shape[3]], "NCHW")
img_blob = ie.Blob(tensor_desc, image)
request = exec_net.requests[0]
request.set_blob('data', img_blob)
request.infer()

set_mean_image(self, Blob mean_image)¶: Sets mean image values if operation is applicable. Also sets the mean type to MEAN_IMAGE for all channels

set_mean_image_for_channel(self, Blob mean_image, size_t channel)¶: Sets mean image values if operation is applicable. Also sets the mean type to MEAN_IMAGE for a particular channel

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