ToFloat

Targets:

image

volume

Image Types:uint8, uint16, uint32, float32

Convert the input image to a floating-point representation.

This transform divides pixel values by max_value to get a float32 output array where all values lie in the range [0, 1.0]. It's useful for normalizing image data before feeding it into neural networks or other algorithms that expect float input.

Arguments

max_value

float | None

The maximum possible input value. If None, the transform will try to infer the maximum value by inspecting the data type of the input image:

uint8: 255
uint16: 65535
uint32: 4294967295
float32: 1.0 Default: None.

p

float

Probability of applying the transform. Default: 1.0.

Examples

>>> import numpy as np
>>> import albumentations as A
>>>
# Convert uint8 image to float
>>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
>>> transform = A.ToFloat(max_value=None)
>>> float_image = transform(image=image)['image']
>>> assert float_image.dtype == np.float32
>>> assert 0 <= float_image.min() <= float_image.max() <= 1.0
>>>
# Convert uint16 image to float with custom max_value
>>> image = np.random.randint(0, 4096, (100, 100, 3), dtype=np.uint16)
>>> transform = A.ToFloat(max_value=4095)
>>> float_image = transform(image=image)['image']
>>> assert float_image.dtype == np.float32
>>> assert 0 <= float_image.min() <= float_image.max() <= 1.0

Returns

np.ndarray

Image in floating point representation, with values in range [0, 1.0].

Notes

If the input image is already float32 with values in [0, 1], it will be returned unchanged.
For integer types (uint8, uint16, uint32), the function will scale the values to [0, 1] range.
The output will always be float32, regardless of the input type.
This transform is often used as a preprocessing step before applying other transformations or feeding the image into a neural network.