neuralib.imglib.norm

neuralib.imglib.norm.normalize_sequences(frames, handle_invalid=True, gamma_correction=False, gamma_value=0.5, to_8bit=False)

Do the normalization for the image sequences

Parameters:

• frames (list[np.ndarray] | np.ndarray) – list of image array

• handle_invalid (bool) – handle Nan and negative value

• gamma_correction (bool) – to the gamma correction

• gamma_value (float) – gamma correction value

• to_8bit (bool) – to 8bit images

Returns:

list of normalized image array

Return type:

list[np.ndarray]

neuralib.imglib.norm.handle_invalid_value(frames)

Handle NaN and negative values and ensure all values are >= 0

Parameters:

frames (list[np.ndarray] | np.ndarray)

Return type:

list[np.ndarray]

neuralib.imglib.norm.get_percentile_value(im, perc_interval=(10, 100))

Get the central distribution boundary value for imaging enhancement by changing the scaling of array.

Parameters:

• im (ndarray) – image array

• perc_interval (tuple[float, float]) – percentile

Returns:

lower_bound and upper_bound based on value distribution

Return type:

tuple[float, float]