NormalizePreprocessor
Bases: DataPreprocessor
Data preprocessor that applies min-max normalization
Examples:
Assume the data is a 3-channel image of data type uint8.
You can scale the data to a range of 0 to 1 by normalizing the data.
>>> normalize_preprocessor = NormalizePreprocessor(
... min_values=[0.] * 3,
... max_values=[255.] * 3,
... )
>>> preprocessed_data = normalize_preprocessor(data)
| PARAMETER | DESCRIPTION |
|---|---|
min_values
|
minimum values of the data (per channel)
TYPE:
|
max_values
|
maximum values of the data (per channel)
TYPE:
|
from_config
classmethod
Creates a normalize preprocessor from the configuration.
| PARAMETER | DESCRIPTION |
|---|---|
config
|
configuration
TYPE:
|
| RETURNS | DESCRIPTION |
|---|---|
NormalizePreprocessor
|
normalize preprocessor |
__call__
Preprocesses the data by applying min-max normalization.
| PARAMETER | DESCRIPTION |
|---|---|
data
|
data
TYPE:
|
| RETURNS | DESCRIPTION |
|---|---|
npt.NDArray[np.float32]
|
preprocessed data |
NormalizePreprocessorConfig
Bases: pydantic.BaseModel
Configuration for the from_config class method of NormalizePreprocessor
| ATTRIBUTE | DESCRIPTION |
|---|---|
min_values |
minimum values of the data (per channel)
TYPE:
|
max_values |
maximum values of the data (per channel)
TYPE:
|