Utils#

class model_api.adapters.utils.InputTransform(reverse_input_channels=False, mean_values=None, scale_values=None)#

Bases: object

__call__(inputs)#

Call self as a function.

class model_api.adapters.utils.Layout(layout='')#

Bases: object

static from_openvino(input)#

Create Layout from openvino input

static from_shape(shape)#

Create Layout from given shape

Return type:

str

static from_user_layouts(input_names, user_layouts)#

Create Layout for input based on user info

static parse_layouts(layout_string)#

Parse layout parameter in format “input0:NCHW,input1:NC” or “NCHW” (applied to all inputs)

Return type:

dict | None

model_api.adapters.utils.change_layout(image, layout)#

Changes the input image layout to fit the layout of the model input layer.

Parameters:

  • image (ndarray) – a single image as 3D array in HWC layout

  • layout (str) – target layout

Returns:

the image with layout aligned with the model layout

Return type:

ndarray

model_api.adapters.utils.crop_resize(size, interpolation, pad_value)#
Return type:

Callable

model_api.adapters.utils.crop_resize_graph(input, size)#
Return type:

Node

model_api.adapters.utils.crop_resize_ocv(image, size)#
Return type:

ndarray

model_api.adapters.utils.get_rt_info_from_dict(rt_info_dict, path)#
Return type:

OVAny

model_api.adapters.utils.load_parameters_from_onnx(onnx_model)#
Return type:

dict[str, Any]

model_api.adapters.utils.resize_image(size, interpolation, pad_value)#
Return type:

Callable

model_api.adapters.utils.resize_image_graph(input, size, keep_aspect_ratio, interpolation, pad_value)#
Return type:

Node

model_api.adapters.utils.resize_image_letterbox(size, interpolation, pad_value)#
Return type:

Callable

model_api.adapters.utils.resize_image_letterbox_graph(input, size, interpolation, pad_value)#
Return type:

Node

model_api.adapters.utils.resize_image_letterbox_ocv(image, size, interpolation=1, pad_value=0)#
Return type:

ndarray

model_api.adapters.utils.resize_image_ocv(image, size, keep_aspect_ratio=False, is_pad=False, pad_value=0, interpolation=1)#
Return type:

ndarray

model_api.adapters.utils.resize_image_with_aspect(size, interpolation, pad_value)#
Return type:

Callable

model_api.adapters.utils.resize_image_with_aspect_ocv(image, size, interpolation=1)#
Return type:

ndarray

model_api.adapters.utils.setup_python_preprocessing_pipeline(layout, resize_mode, interpolation_mode, target_shape, pad_value, dtype=<class 'int'>, brg2rgb=False, mean=None, scale=None, input_idx=0)#

Sets up a Python preprocessing pipeline for model adapters.

Parameters:

  • layout (str) – Target layout for the input (e.g., “NCHW”, “NHWC”)

  • resize_mode (str) – Type of resizing (“crop”, “standard”, “fit_to_window”, “fit_to_window_letterbox”)

  • interpolation_mode (str) – Interpolation method (“LINEAR”, “CUBIC”, “NEAREST”)

  • target_shape (tuple[int, ...]) – Target shape for resizing

  • pad_value (int) – Padding value for letterbox resizing

  • dtype (type) – Data type for preprocessing

  • brg2rgb (bool) – Whether to convert BGR to RGB

  • mean (Optional[list[Any]]) – Mean values for normalization

  • scale (Optional[list[Any]]) – Scale values for normalization

  • input_idx (int) – Input index (unused but kept for compatibility)

Returns:

A preprocessing function that can be applied to input data

Return type:

Callable