ml.preprocessing — ML Preprocessing

The ml.preprocessing module contains classes for preprocessing images for use with machine learning models.

class Normalization – Image Normalization

The Normalization object converts image.Image objects into ndarray input tensors for use with ml.Model.predict(). It is automatically created by the ml.Model object when an image is passed to ml.Model.predict(), but may be instantiated manually to control the conversion (scale, mean/stdev, ROI).

class ml.preprocessing.Normalization(scale: tuple[float, float] = (0.0, 1.0), mean: tuple[float, float, float] = (0.0, 0.0, 0.0), stdev: tuple[float, float, float] = (1.0, 1.0, 1.0), roi: tuple[int, int, int, int] = None)

Creates a Normalization object.

scale is the (min, max) range of values that floating-point input tensors expect after normalization (e.g. (0.0, 1.0) or (-1.0, 1.0)). Ignored for uint8 and int8 input tensors.

mean is the per-channel mean (R, G, B) subtracted from the image after scaling. For grayscale tensors the mean is reduced to a single luma value using 0.299*R + 0.587*G + 0.114*B. Ignored for uint8 and int8 input tensors.

stdev is the per-channel standard deviation (R, G, B) the image is divided by after the mean is subtracted. For grayscale tensors the stdev is reduced to a single luma value using 0.299*R + 0.587*G + 0.114*B. Ignored for uint8 and int8 input tensors.

roi is an optional (x, y, w, h) region of interest within the input image to crop. If None, the full image is used. The cropped region is centered, bilinearly scaled (preserving aspect ratio with black padding) to the model’s input tensor dimensions.

__call__(image: image.Image) → Normalization

__call__(buffer: bytearray, shape: tuple[int, int, int, int], dtype: int) → None

When called with a single image.Image argument, returns a new Normalization object bound to that image. The bound object is what ml.Model.predict() invokes internally to fill the input tensor. If roi was not set on the original instance, it is initialized to the full image size.

When called with (buffer, shape, dtype), fills buffer in-place with the normalized input tensor for the previously bound image. shape must be (1, H, W, C) with C equal to 1 (grayscale) or 3 (RGB). dtype is the ulab numpy type code (e.g. ord('f') for float32, ord('b') for int8, ord('B') for uint8). Float tensors apply scale, mean, and stdev; integer tensors are written directly (with an offset for int8).