deepness.processing.models.regressor.Regressor

class Regressor(model_file_path: str)

Bases: ModelBase

Class implements regression model.

Regression model is used to predict metric per pixel of the image.

Methods

check_loaded_model_outputs	Check if the model has correct output layers
get_channel_name	Get channel name by id if exists in model metadata
get_class_display_name	Returns display name of the model class
get_detector_type	Get detector type from metadata if exists
get_input_shape	Get shape of the input for the model
get_input_size_in_pixels	Get number of input pixels in x and y direction (the same value)
get_metadata_detection_confidence	Get detection confidence from metadata if exists
get_metadata_detection_iou_threshold	Get detection iou threshold from metadata if exists
get_metadata_model_type	Get model type from metadata
get_metadata_regression_output_scaling	Get regression output scaling from metadata if exists
get_metadata_resolution	Get resolution from metadata if exists
get_metadata_segmentation_small_segment	Get segmentation small segment from metadata if exists
get_metadata_segmentation_threshold	Get segmentation threshold from metadata if exists
get_metadata_standarization_parameters	Get standardization parameters from metadata if exists
get_metadata_tile_size	Get tile size from metadata if exists
get_metadata_tiles_overlap	Get tiles overlap from metadata if exists
get_model_batch_size	Get batch size of the model
get_model_type_from_metadata	Get model type from metadata
get_number_of_channels	Returns number of channels in the input layer
get_number_of_output_channels	Returns number of channels in the output layer
get_output_shapes	Get shapes of the outputs for the model
get_outputs_channel_names	Get class names from metadata
postprocessing	Postprocess the model output.
preprocessing	Preprocess the batch of images for the model (resize, normalization, etc)
process	Process a single tile image

check_loaded_model_outputs()

Check if the model has correct output layers

Correct means that: - there is at least one output layer - batch size is 1 or parameter - each output layer regresses only one channel - output resolution is square

get_channel_name(layer_id: int, channel_id: int) → str

Get channel name by id if exists in model metadata

Parameters:

channel_id (int) – Channel id (means index in the output tensor)

Returns:

Channel name or empty string if not found

Return type:

str

classmethod get_class_display_name() → str

Returns display name of the model class

Returns:

Display name of the model class

Return type:

str

get_detector_type() → str | None

Get detector type from metadata if exists

Returns string value of DetectorType enum or None if not found

Optional[str]

Detector type or None if not found

get_input_shape() → tuple

Get shape of the input for the model

Returns:

Shape of the input (batch_size, channels, height, width)

Return type:

tuple

get_input_size_in_pixels() → int

Get number of input pixels in x and y direction (the same value)

Returns:

Number of pixels in x and y direction

Return type:

int

get_metadata_detection_confidence() → float | None

Get detection confidence from metadata if exists

Returns:

Detection confidence or None if not found

Return type:

Optional[float]

get_metadata_detection_iou_threshold() → float | None

Get detection iou threshold from metadata if exists

Returns:

Detection iou threshold or None if not found

Return type:

Optional[float]

get_metadata_model_type() → str | None

Get model type from metadata

Returns:

Model type or None if not found

Return type:

Optional[str]

get_metadata_regression_output_scaling() → float | None

Get regression output scaling from metadata if exists

Returns:

Regression output scaling or None if not found

Return type:

Optional[float]

get_metadata_resolution() → float | None

Get resolution from metadata if exists

Returns:

Resolution or None if not found

Return type:

Optional[float]

get_metadata_segmentation_small_segment() → int | None

Get segmentation small segment from metadata if exists

Returns:

Segmentation small segment or None if not found

Return type:

Optional[int]

get_metadata_segmentation_threshold() → float | None

Get segmentation threshold from metadata if exists

Returns:

Segmentation threshold or None if not found

Return type:

Optional[float]

get_metadata_standarization_parameters() → StandardizationParameters | None

Get standardization parameters from metadata if exists

Returns:

Standardization parameters or None if not found

Return type:

Optional[StandardizationParameters]

get_metadata_tile_size() → int | None

Get tile size from metadata if exists

Returns:

Tile size or None if not found

Return type:

Optional[int]

get_metadata_tiles_overlap() → int | None

Get tiles overlap from metadata if exists

Returns:

Tiles overlap or None if not found

Return type:

Optional[int]

get_model_batch_size() → int | None

Get batch size of the model

Returns:

Batch size or None if not found (dynamic batch size)

Return type:

Optional[int] | None

classmethod get_model_type_from_metadata(model_file_path: str) → str | None

Get model type from metadata

Parameters:

model_file_path (str) – Path to the model file

Returns:

Model type or None if not found

Return type:

Optional[str]

get_number_of_channels() → int

Returns number of channels in the input layer

Returns:

Number of channels in the input layer

Return type:

int

get_number_of_output_channels() → List[int]

Returns number of channels in the output layer

Returns:

Number of channels in the output layer

Return type:

int

get_output_shapes() → List[tuple]

Get shapes of the outputs for the model

Returns:

Shapes of the outputs (batch_size, channels, height, width)

Return type:

List[tuple]

get_outputs_channel_names() → List[List[str]] | None

Get class names from metadata

Returns:

List of class names for each model output or None if not found

Return type:

List[List[str]] | None

postprocessing(model_output: List) → ndarray

Postprocess the model output.

Parameters:

model_output (List) – Output from the (Regression) model

Returns:

Output from the (Regression) model

Return type:

np.ndarray

preprocessing(tiles_batched: ndarray) → ndarray

Preprocess the batch of images for the model (resize, normalization, etc)

Parameters:

image (np.ndarray) – Batch of images to preprocess (N,H,W,C), RGB, 0-255

Returns:

Preprocessed batch of image (N,C,H,W), RGB, 0-1

Return type:

np.ndarray

process(tiles_batched: ndarray)

Process a single tile image

Parameters:

img (np.ndarray) – Image to process ([TILE_SIZE x TILE_SIZE x channels], type uint8, values 0 to 255)

Returns:

Single prediction

Return type:

np.ndarray