Source code for Stoner.Image.imagefuncs

# -*- coding: utf-8 -*-
"""Functions for manipulating Kerr (or any other) images.

All of these functions are accessible through the :class:`ImageArray` attributes e.g.:

    k=ImageArray('myfile'); k.level_image().

The first 'im' function argument is automatically added in this case.

If you want to add new functions that's great. There's a few important points:

    * Please make sure they take an image as the first argument

    * Don't give them the same name as functions from the numpy library or
          skimage library if you don't want to override them.

    * The function should not change the shape of the array. Please use crop_image
          before doing the function if you want to do that.

    * After that you're free to treat im as a ImageArray
          or numpy array, it should all behave the same.
__all__ = [
import warnings
import os
import io

import numpy as np
from scipy.interpolate import griddata
from scipy.ndimage import gaussian_filter
from scipy import signal
from matplotlib.colors import to_rgba
import as cm
from matplotlib import pyplot as plt
from skimage import feature, measure, transform, filters, registration
from PIL import Image, PngImagePlugin

from import isTuple, isiterable, make_Data

# from .core import ImageArray
from ..core.base import metadataObject
from .util import sign_loss, _dtype2, _supported_types, prec_loss, dtype_range, _dtype, _scale as im_scale
from import changes_size, keep_return_type
from .widgets import LineSelect
from ..compat import string_types, get_filedialog  # Some things to help with Python2 and Python3 compatibility
from ..plot.utils import auto_fit_fontsize

    from PyQt5.QtGui import QImage
    from PyQt5.QtWidgets import QApplication
except ImportError:
    QImage = None
    QApplication = None

try:  # Make OpenCV an optional import
    import cv2
except ImportError:
    cv2 = None

try:  # Make imreg_dfft2 optional
    import imreg_dft
except ImportError:
    imreg_dft = None

try:  # image_registration module
    from image_registration import chi2_shift
except ImportError:
    chi2_shift = None

IMAGE_FILES = [("Tiff File", "*.tif;*.tiff"), ("PNG files", "*.png", "Numpy Files", "*.npy")]

def _scale(coord, scale=1.0, to_pixel=True):
    """Convert pixel cordinates to scaled co-ordinates or visa versa.

        coord(int,float or iterable): Coordinates to be scaled

    Keyword Arguments:
        scale(float): Microns per Pixel scale of image
        to_pixel(bool): Force the conversion to be to pixels

        scaled co-ordinates.
    if isinstance(coord, int):
        if not to_pixel:
            coord = float(coord) * scale
    elif isinstance(coord, float):
        if to_pixel:
            coord = int(round(coord / scale))
    elif isiterable(coord):
        coord = tuple([_scale(c, scale, to_pixel) for c in coord])
        raise ValueError("coord should be an integer or a float or an iterable of integers and floats")
    return coord

[docs]def adjust_contrast(im, lims=(0.1, 0.9), percent=True): """Rescale the intensity of the image. Mostly a call through to skimage.exposure.rescale_intensity. The absolute limits of contrast are added to the metadata as 'adjust_contrast' Parameters ---------- lims: 2-tuple limits of rescaling the intensity percent: bool if True then lims are the give the percentile of the image intensity histogram, otherwise lims are absolute Returns ------- image: ImageArray rescaled image """ if percent: vmin, vmax = np.percentile(im, np.array(lims) * 100) else: vmin, vmax = lims[0], lims[1] im.metadata["adjust_contrast"] = (vmin, vmax) im = im.rescale_intensity(in_range=(vmin, vmax)) # clip the intensity return im
def _align_scharr(im, ref, **kargs): """Return the translation vector to shirt im to align to ref using the Scharr method.""" scale = np.ceil(np.max(im.shape) / 500.0) ref1 = filters.edges.scharr(gaussian_filter(ref, sigma=scale, mode="wrap")) im1 = filters.edges.scharr(gaussian_filter(im, sigma=scale, mode="wrap")) return _align_imreg_dft(im1, ref1, **kargs) def _align_chi2_shift(im, ref, **kargs): """Return the translation vector to shirt im to align to ref using the chi^2 shift method.""" results = np.array(chi2_shift(ref, im, **kargs)) return -results[1::-1], {} def _align_imreg_dft(im, ref, **kargs): """Return the translation vector to shirt im to align to ref using the imreg_dft method.""" constraints = kargs.pop("constraints", {"angle": [0.0, 0.0], "scale": [1.0, 0.0]}) with warnings.catch_warnings(): # This causes a warning due to the masking warnings.simplefilter("ignore") result = imreg_dft.similarity(ref, im, constraints=constraints) tvec = result["tvec"] return np.array(tvec), result def _align_cv2(im, ref, **kargs): # pylint: disable=unused-argument """Return the translation vector to shirt im to align to ref using the cv2 method.""" im1_gray = ref.convert("uint8", force_copy=True) im2_gray = im.convert("uint8", force_copy=True) # Define the motion model warp_mode = cv2.MOTION_TRANSLATION warp_matrix = np.eye(2, 3, dtype=np.float32) # Specify the number of iterations. number_of_iterations = 5000 # Specify the threshold of the increment # in the correlation coefficient between two iterations termination_eps = 1e-10 # Define termination criteria criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, number_of_iterations, termination_eps) # Run the ECC algorithm. The results are stored in warp_matrix. (_, warp_matrix) = cv2.findTransformECC( im1_gray, im2_gray, warp_matrix, warp_mode, criteria, inputMask=None, gaussFiltSize=1 ) return -warp_matrix[1::-1, 2], {}
[docs]def align(im, ref, method="scharr", **kargs): """Use one of a variety of algroithms to align two images. Args: im (ndarray) image to align ref (ndarray) reference array Keyword Args: method (str or None): If given specifies which module to try and use. Options: 'scharr', 'chi2_shift', 'imreg_dft', 'cv2' _box (integer, float, tuple of images or floats): Used with ImageArray.crop to select a subset of the image to use for the aligning process. scale (int): Rescale the image and reference image by constant factor before finding the translation vector. prefilter (callable): A method to apply to the image before carrying out the translation to the align to the reference. **kargs (various): All other keyword arguments are passed to the specific algorithm. Returns (ImageArray or ndarray) aligned image Notes: Currently three algorithms are supported: - image_registration module's chi^2 shift: This uses a dft with an automatic up-sampling of the fourier transform for sub-pixel alignment. The metadata key *chi2_shift* contains the translation vector and errors. - imreg_dft module's similarity function. This implements a full scale, rotation, translation algorithm (by default cosntrained for just translation). It's unclear how much sub-pixel translation is accomodated. - cv2 module based affine transform on a gray scale image. from: """ # To be consistent with x-y co-ordinate systems align_methods = { "scharr": (_align_scharr, imreg_dft), "chi2_shift": (_align_chi2_shift, chi2_shift), "imreg_dft": (_align_imreg_dft, imreg_dft), "cv2": (_align_cv2, cv2), } cls = type(im) for meth in list(align_methods.keys()): mod = align_methods[meth][1] if mod is None: del align_methods[meth] method = method.lower() if not len(align_methods): raise ImportError("align requires one of imreg_dft, chi2_shift or cv2 modules to be available.") if method not in align_methods: raise ValueError(f"{method} is not available either because it is not recognised or there is a missing module") if "box" in kargs or "_box" in kargs: box = kargs.pop("box", kargs.pop("_box")) if not isiterable(box): box = [box] working = im.crop(*box, copy=True) if ref.shape != working.shape: ref = ref.view(cls).crop(*box, copy=True) else: working = im scale = kargs.pop("scale", None) mode = kargs.pop("mode", "mirror") cval = kargs.pop("cval", im.mean()) if mode == "mean": mode = "constant" cval = im.mean() if scale: working = working.rescale(scale, order=3) ref = transform.rescale(ref, scale, order=3) prefilter = kargs.pop("prefilter", True) try: tvec, data = align_methods[method][0](working, ref, **kargs) except (ValueError, TypeError): tvec = (0, 0) data = im data.pop("timg", None) if scale: tvec /= scale new_im = im.shift((tvec[1], tvec[0]), prefilter=prefilter, mode=mode, cval=cval) new_im.metadata.update(data) new_im["tvec"] = tuple(tvec) new_im["translation_limits"] = new_im.translate_limits("tvec") return new_im
[docs]def convert(image, dtype, force_copy=False, uniform=False, normalise=True): """Convert an image to the requested data-type. Warnings are issued in case of precision loss, or when negative values are clipped during conversion to unsigned integer types (sign loss). Floating point values are expected to be normalized and will be clipped to the range [0.0, 1.0] or [-1.0, 1.0] when converting to unsigned or signed integers respectively. Numbers are not shifted to the negative side when converting from unsigned to signed integer types. Negative values will be clipped when converting to unsigned integers. Parameters ---------- image : ndarray Input image. dtype : dtype Target data-type. force_copy : bool Force a copy of the data, irrespective of its current dtype. uniform : bool Uniformly quantize the floating point range to the integer range. By default (uniform=False) floating point values are scaled and rounded to the nearest integers, which minimizes back and forth conversion errors. normalise : bool When converting from int types to float normalise the resulting array by the maximum allowed value of the int type. References ---------- (1) DirectX data conversion rules. (2) Data Conversions. In "OpenGL ES 2.0 Specification v2.0.25", pp 7-8. Khronos Group, 2010. (3) Proper treatment of pixels as integers. A.W. Paeth. In "Graphics Gems I", pp 249-256. Morgan Kaufmann, 1990. (4) Dirty Pixels. J. Blinn. In "Jim Blinn's corner: Dirty Pixels", pp 47-57. Morgan Kaufmann, 1998. """ image = np.asarray(image) dtypeobj = np.dtype(dtype) dtypeobj_in = image.dtype dtype = dtypeobj.type dtype_in = dtypeobj_in.type if dtype_in == dtype: if force_copy: image = image.clone return image if not (dtype_in in _supported_types and dtype in _supported_types): raise ValueError(f"can not convert {dtype_in} to {dtype}.") kind = dtypeobj.kind kind_in = dtypeobj_in.kind itemsize = dtypeobj.itemsize itemsize_in = dtypeobj_in.itemsize if kind == "b": # to binary image if kind_in in "fi": sign_loss(dtype_in, dtypeobj) prec_loss(dtypeobj_in, dtypeobj) return image > dtype_in(dtype_range[dtype_in][1] / 2) if kind_in == "b": # from binary image, to float and to integer result = np.where(~image, *dtype_range[dtype]) return result if kind in "ui": imin = np.iinfo(dtype).min imax = np.iinfo(dtype).max if kind_in in "ui": imin_in = np.iinfo(dtype_in).min imax_in = np.iinfo(dtype_in).max if kind_in == "f": if np.min(image) < -1.0 or np.max(image) > 1.0: raise ValueError("Images of type float must be between -1 and 1.") if kind == "f": # floating point -> floating point if itemsize_in > itemsize: prec_loss(dtypeobj_in, dtypeobj) return image.astype(dtype) # floating point -> integer prec_loss(dtypeobj_in, dtypeobj) # use float type that can represent output integer type image = np.array(image, _dtype(itemsize, dtype_in, np.float32, np.float64)) if not uniform: if kind == "u": image *= imax elif itemsize_in <= itemsize and itemsize == 8: # f64->int64 needs care to avoid overruns! image *= 2 ** 54 # float64 has 52bits of mantissa, so this will avoid precission loss for a +/-1 range np.rint(image, out=image) image = image.astype(dtype) np.clip(image, -(2 ** 54), 2 ** 54 - 1, out=image) image *= 512 else: image *= imax - imin image -= 1.0 image /= 2.0 np.rint(image, out=image) np.clip(image, imin, imax, out=image) elif kind == "u": image *= imax + 1 np.clip(image, 0, imax, out=image) else: image *= (imax - imin + 1.0) / 2.0 np.floor(image, out=image) np.clip(image, imin, imax, out=image) return image.astype(dtype) if kind == "f": # integer -> floating point if itemsize_in >= itemsize: prec_loss(dtypeobj_in, dtypeobj) # use float type that can exactly represent input integers image = np.array(image, _dtype(itemsize_in, dtype, np.float32, np.float64)) if normalise: # normalise floats by maximum value of int type if kind_in == "u": image /= imax_in # DirectX uses this conversion also for signed ints # if imin_in: # np.maximum(image, -1.0, out=image) else: image *= 2.0 image += 1.0 image /= imax_in - imin_in return image.astype(dtype) if kind_in == "u": if kind == "i": # unsigned integer -> signed integer image = im_scale(image, 8 * itemsize_in, 8 * itemsize - 1, dtypeobj_in, dtypeobj) return image.view(dtype) # unsigned integer -> unsigned integer return im_scale(image, 8 * itemsize_in, 8 * itemsize, dtypeobj_in, dtypeobj) if kind == "u": # signed integer -> unsigned integer sign_loss(dtype_in, dtypeobj) image = im_scale(image, 8 * itemsize_in - 1, 8 * itemsize, dtypeobj_in, dtypeobj) result = np.empty(image.shape, dtype) np.maximum(image, 0, out=result, dtype=image.dtype, casting="unsafe") return result # signed integer -> signed integer if itemsize_in > itemsize: return im_scale(image, 8 * itemsize_in - 1, 8 * itemsize - 1, dtypeobj_in, dtypeobj) image = image.astype(_dtype2("i", itemsize * 8)) image -= imin_in image = im_scale(image, 8 * itemsize_in, 8 * itemsize, dtypeobj_in, dtypeobj, copy=False) image += imin return image.astype(dtype)
[docs]def correct_drift(im, ref, **kargs): """Align images to correct for image drift. Args: ref (ImageArray): Reference image with assumed zero drift Keyword Arguments: threshold (float): threshold for detecting imperfections in images (see skimage.feature.corner_fast for details) upsample_factor (float): the resolution for the shift 1/upsample_factor pixels registered. see skimage.feature.register_translation for more details box (sequence of 4 ints): defines a region of the image to use for identifyign the drift defaults to the whol image. Use this to avoid drift calculations being confused by the scale bar/annotation region. do_shift (bool): Shift the image, or just calculate the drift and store in metadata (default True, shit) Returns: A shifted iamge with the image shift added to the metadata as 'correct drift'. Detects common features on the images and tracks them moving. Adds 'drift_shift' to the metadata as the (x,y) vector that translated the image back to it's origin. """ do_shift = kargs.pop("do_shift", True) kargs["scale"] = kargs.pop("upscale", kargs.get("scale", 5.0)) kargs.setdefault("meothd", "scharr") ret = align(im, ref, **kargs) if do_shift: im = ret im["correct_drift"] = -np.array(ret["tvec"])[::-1] return im
[docs]def subtract_image(im, background, contrast=16, clip=True, offset=0.5): """Subtract a background image from the ImageArray. Multiply the contrast by the contrast parameter. If clip is on then clip the intensity after for the maximum allowed data range. """ im = im.asfloat(normalise=False, clip_negative=False) im = contrast * (im - background) + offset if clip: im = im.clip_intensity() return im
[docs]def fft(im, shift=True, phase=False, remove_dc=False, gaussian=None, window=None): """Perform a 2d fft of the image and shift the result to get zero frequency in the centre. Keyword Args: shift (bool): Shift the fft so that zero order is in the centre of the image. Default True phase (bool, None): If true, return the phase angle rather than the magnitude if False. If None, return the raw fft. Default False - return magnitude of fft. remove_dc(bool): Replace the points around the dc offset with the mean of the fft to avoid dc offset artefacts. Default False gaussian (None or float): Apply a gaussian blur to the fft where this parameter is the width of the blue in px. Default None for off. window (None or str): If not None (default) the image is multiplied by the given window function before the fft is calculated. This avpoids leaking some signal into the higher frequency bands due to discontinuities at the image edges. Return: fft of the image, preserving metadata. """ if window: window = filters.window(window, im.shape) im = im.clone * window r = np.fft.fft2(im) if remove_dc: fill = r.mean() r[0, 0] = fill r[-1, 0] = fill r[-1, -1] = fill r[0, -1] = fill if shift: r = np.fft.fftshift(r) if phase is None: pass if not phase: r = np.abs(r) else: r = np.angle(r) r = r.view(type(im)) if isinstance(gaussian, (float, int)): r.gaussian(gaussian) r.metadata.update(im.metadata) return r
[docs]def filter_image(im, sigma=2): """Alias for skimage.filters.gaussian.""" return im.gaussian(sigma=sigma)
[docs]def gridimage(im, points=None, xi=None, method="linear", fill_value=None, rescale=False): """Use :py:func:`scipy.interpolate.griddata` to shift the image to a regular grid of co-ordinates. Args: points (tuple of (x-co-ords,yco-ordsa)): The actual sampled data co-ordinates xi (tupe of (2D array,2D array)): The regular grid co-ordinates (as generated by e.g. :py:func:`np.meshgrid`) Keyword Arguments: method ("linear","cubic","nearest"): how to interpolate, default is linear fill_value (folat, Callable, None): What to put when the co-ordinates go out of range (default is None). May be a callable in which case the initial image is presented as the only argument. If None, use the mean value. rescale (bool): If the x and y co-ordinates are very different in scale, set this to True. Returns: A copy of the modified image. The image data is interpolated and metadata kets "actual_x","actual_y","sample_ x","samp[le_y" are set to give co-ordinates of new grid. Notes: If points and or xi are missed out then we try to construct them from the metadata. For points, the metadata keys "actual-x" and "actual_y" are looked for and for xi, the metadata keys "sample_x" and "sample_y" are used. These are set, for example, by the :py:class:`Stoner.HDF5.SXTMImage` loader if the interformeter stage data was found in the file. The metadata used in this case is then adjusted as well to ensure that repeated application of this method doesn't change the image after it has been corrected once. """ if points is None: points = np.column_stack((im["actual_x"].ravel(), im["actual_y"].ravel())) if xi is None: xi = xi = (im["sample_x"], im["sample_y"]) if fill_value is None: fill_value = np.mean if callable(fill_value): fill_value = fill_value(im) im2 = griddata(points, im.ravel(), xi, method, fill_value, rescale) im2 = type(im)(im2) im2.metadata = im.metadata im2.metadata["actual_x"] = xi[0] im2.metadata["actual_y"] = xi[1] return im2
[docs]def hist(im, *args, **kargs): """Pass through to :py:func:`matplotlib.pyplot.hist` function.""" if isinstance(im, im_data = im[~im.mask] else: im_data = im.ravel() counts, edges = np.histogram(im_data, *args, **kargs) centres = (edges[1:] + edges[:-1]) / 2 new = make_Data(np.column_stack((centres, counts))) new.column_headers = ["Intensity", "Frequency"] new.setas = "xy" return new
[docs]def imshow(im, **kwargs): """Quickly plot of image. Keyword Arguments: figure (int, str or matplotlib.figure): if int then use figure number given, if figure is 'new' then create a new figure, if None then use whatever default figure is available show_axis (bool): If True, show the axis otherwise don't (default)' title (str,None,False): Title for plot - defaults to False (no title). None will take the title from the filename if present title_args (dict): Arguments to pass to the title function to control formatting. cmap (str,matplotlib.cmap): Colour scheme for plot, defaults to gray Any masked areas are set to NaN which stops them being plotted at all. """ figure = kwargs.pop("figure", "new") ax = kwargs.pop("ax", None) # Get a title - from keyword argument, from title attr or filename attr title = os.path.split(getattr(im, "title", getattr(im, "filename", "")))[-1] title = kwargs.pop("title", title) title_args = kwargs.pop("title_args", {}) cmap = kwargs.pop("cmap", "gray") mask_alpha = kwargs.pop("mask_alpha", getattr(im, "_mask_alpha", 0.5)) mask_col = kwargs.pop("mask_color", getattr(im, "_mask_color", "red")) if isinstance(cmap, string_types): cmap = getattr(cm, cmap) im_data = im if figure is not None and isinstance(figure, int): fig = plt.figure(figure) elif figure is not None and figure == "new": fig = plt.figure() elif figure is not None: # matplotlib.figure instance fig = plt.figure(figure.number) else: fig = plt.figure() if ax is not None: else: ax = plt.gca() ax.imshow(im_data.view(np.ndarray), cmap=cmap, **kwargs) if mask_col = list(to_rgba(mask_col)) mask_col[-1] = mask_alpha mask_data = np.zeros(im.shape + (4,)) mask_data[im.mask] = mask_col ax.imshow(mask_data) if title is None: if "filename" in im.metadata.keys(): title = os.path.split(im["filename"])[1] elif hasattr(im, "filename"): title = os.path.split(im.filename)[1] else: title = " " elif isinstance(title, bool) and not title: title = "" txt = ax.set_title(title, **title_args) if not title_args.get("fontdict", {}).get("fontsize", False): gs = ax.get_gridspec() width = 0.9 / gs.ncols height = 0.09 / gs.nrows auto_fit_fontsize(txt, width, height) ax.axis("on" if kwargs.get("show_axis", False) else "off") if QImage is None: # No Qt5 return fig def add_figure_to_clipboard(event): if event.key == "ctrl+c": with io.BytesIO() as buffer: fig.savefig(buffer) QApplication.clipboard().setImage(QImage.fromData(buffer.getvalue())) fig.canvas.mpl_connect("key_press_event", add_figure_to_clipboard) return fig
[docs]def level_image(im, poly_vert=1, poly_horiz=1, box=None, poly=None, mode="clip"): """Subtract a polynomial background from image. Keword Arguments: poly_vert (int): fit a polynomial in the vertical direction for the image of order given. If 0 do not fit or subtract in the vertical direction poly_horiz (int): fit a polynomial of order poly_horiz to the image. If 0 given do not subtract box (array, list or tuple of int): [xmin,xmax,ymin,ymax] define region for fitting. IF None use entire image poly (list or None): [pvert, phoriz] pvert and phoriz are arrays of polynomial coefficients (highest power first) to subtract in the horizontal and vertical directions. If None function defaults to fitting its own polynomial. mode (str): Either 'clip' or 'norm' - specifies how to handle intensitry values that end up being outside of the accepted range for the image. Returns: A new copy of the processed images. Fit and subtract a background to the image. Fits a polynomial of order given in the horizontal and vertical directions and subtracts. If box is defined then level the *entire* image according to the gradient within the box. The polynomial subtracted is added to the metadata as 'poly_vert_subtract' and 'poly_horiz_subtract' """ if box is None: box = im.max_box cim = im.crop(box=box) (vertl, horizl) = cim.shape p_horiz = 0 p_vert = 0 if poly_horiz > 0: comp_vert = np.average(cim, axis=0) # average (compress) the vertical values if poly is not None: p_horiz = poly[0] else: p_horiz = np.polyfit(np.arange(horizl), comp_vert, poly_horiz) # fit to the horizontal av = np.average(comp_vert) # get the average pixel height p_horiz[-1] = p_horiz[-1] - av # maintain the average image height horizcoord = np.indices(im.shape)[1] # now apply level to whole image for i, c in enumerate(p_horiz): im = im - c * horizcoord ** (len(p_horiz) - i - 1) if poly_vert > 0: comp_horiz = np.average(cim, axis=1) # average the horizontal values if poly is not None: p_vert = poly[1] else: p_vert = np.polyfit(np.arange(vertl), comp_horiz, poly_vert) av = np.average(comp_horiz) p_vert[-1] = p_vert[-1] - av # maintain the average image height vertcoord = np.indices(im.shape)[0] for i, c in enumerate(p_vert): im = im - c * vertcoord ** (len(p_vert) - i - 1) im.metadata["poly_sub"] = (p_horiz, p_vert) if mode == "clip": im = im.clip_intensity() # saturate any pixels outside allowed range elif mode == "norm": im = im.normalise() return im
[docs]def normalise(im, scale=None, sample=False, limits=(0.0, 1.0), scale_masked=False): """Norm alise the data to a fixed scale. Keyword Arguements: scale (2-tuple): The range to scale the image to, defaults to -1 to 1. saple (box): Only use a section of the input image to calculate the new scale over. Default is False - whole image limits (low,high): Take the input range from the *high* and *low* fraction of the input when sorted. scale_masked (bool): If True then the masked region is also scaled, otherwise any masked region is ignored. Default, False. Returns: A scaled version of the data. The ndarray min and max methods are used to allow masked images to be operated on only on the unmasked areas. Notes: The *sample* keyword controls the area in which the range of input values is calculated, the actual scaling is done on the whole image. The *limits* parameter is used to set the input scale being normalised from - if an image has a few outliers then this setting can be used to clip the input range before normalising. The parameters in the limit are the values at the *low* and *high* fractions of the cumulative distribution functions. """ mask = im.mask cls = type(im) im = im.astype(float) if scale is None: scale = (-1.0, 1.0) section = im[im._box(sample)] section = section[~section.mask] if limits != (0.0, 1.0): low, high = limits low = np.sort(section.ravel())[int(low * section.size)] high = np.sort(section.ravel())[int(high * section.size)] im.clip_intensity(limits=(low, high)) else: high = section.max() low = section.min() if not isTuple(scale, float, float, strict=False): raise ValueError("scale should be a 2-tuple of floats.") scaled = ( - low) / (high - low) delta = scale[1] - scale[0] offset = scale[0] scaled = scaled * delta + offset if not scale_masked: im = np.where(im.mask, im, scaled).view(cls) else: im = scaled.view(cls) im.mask = mask return im
def clip_neg(im): """Clip negative pixels to 0. Most useful for float where pixels above 1 are reduced to 1.0 and -ve pixels are changed to 0. """ im[im < 0] = 0 return im
[docs]def profile_line(img, src=None, dst=None, linewidth=1, order=1, mode="constant", cval=0.0, constrain=True, **kargs): """Wrap sckit-image method of the same name to get a line_profile. Parameters: img(ImageArray): Image data to take line section of Keyword Parameters: src, dst (2-tuple of int or float): start and end of line profile. If the co-ordinates are given as intergers then they are assumed to be pxiel co-ordinates, floats are assumed to be real-space co-ordinates using the embedded metadata. linewidth (int): the wideth of the profile to be taken. order (int 1-3): Order of interpolation used to find image data when not aligned to a point mode (str): How to handle data outside of the image. cval (float): The constant value to assume for data outside of the image is mode is "constant" constrain (bool): Ensure the src and dst are within the image (default True). no_scale (bool): Do not attempt to scale values by the image scale and work in pixels throughout. (default False) Returns: A :py:class:`Stoner.Data` object containing the line profile data and the metadata from the image. """ scale = 1.0 if kargs.get("no_scale", False) else img.get("MicronsPerPixel", 1.0) r, c = img.shape fast_mode = False if src is None and dst is None: if "x" in kargs: src = (0, kargs["x"]) dst = (r, kargs["x"]) fast_mode = kargs.get("no_scale", False) if "y" in kargs: src = (kargs["y"], 0) dst = (kargs["y"], c) fast_mode = kargs.get("no_scale", False) if src is None and dst is None: src, dst = LineSelect()(img) if isinstance(src, float): src = (src, src) if isinstance(dst, float): dst = (dst, dst) dst = _scale(dst, scale) src = _scale(src, scale) if not isTuple(src, int, int): raise ValueError("src co-ordinates are not a 2-tuple of ints.") if not isTuple(dst, int, int): raise ValueError("dst co-ordinates are not a 2-tuple of ints.") if constrain: fix = lambda x, mx: int(round(sorted([0, x, mx])[1])) r, c = img.shape src = list(src) src = (fix(src[0], r), fix(src[1], c)) dst = (fix(dst[0], r), fix(dst[1], c)) if fast_mode: if "x" in kargs: result = img[:, src[1]] points = np.row_stack((np.ones(img.shape[0]) * src[1], np.arange(img.shape[0]))) else: result = img[src[0], :] points = np.row_stack((np.arange(img.shape[1]), np.ones(img.shape[1]) * src[0])) else: result = measure.profile_line(img, src, dst, linewidth, order, mode, cval) points = measure.profile._line_profile_coordinates(src, dst, linewidth)[:, :, 0] ret = make_Data() = points.T ret.setas = "xy" x, y = points x -= x[0] y -= y[0] ret &= np.sqrt(x ** 2 + y ** 2) * scale ret &= result ret.column_headers = ["X", "Y", "Distance", "Intensity"] ret.setas = "..xy" ret.metadata = img.metadata.copy() return ret
[docs]def radial_coordinates(im, centre=(None, None), pixel_size=(1, 1), angle=False): """Rerurn a map of the radial co-ordinates of an image from a given centre, with adjustments for pixel size. Keyword Arguments: centre (2-tuple): Co-ordinates of centre point in terms of the orginal pixels. Defaults to(None,None) for the middle of the image. pixel_size (2-tuple): The size of one pixel in (dx by dy) - defaults to 1,1 angle (bool, None): Whether to return the angles (in radians, True), distances (False) o a complex number (None). Returns: An array of the same class as the input, but with values corresponding to the radial co-ordinates. """ cx, cy = centre r, c = im.shape dx, dy = pixel_size cx = c / 2 if cx is None else cx cy = r / 2 if cy is None else cy x_r = dx * (np.linspace(0, c - 1, c) - cx) y_r = dy * (np.linspace(0, r - 1, r) - cy) Y, X = np.meshgrid(x_r, y_r) Z = -Y + (0 + 1j) * X if angle is None: pass elif not angle: Z = np.abs(Z) else: Z = np.angle(Z) Z = Z.view(type(im)) Z.metadata = im.metadata return Z
[docs]def radial_profile(im, angle=None, r=None, centre=(None, None), pixel_size=(1, 1)): """Extract a radial profile line from an image. Keyword Paramaters: angle (float, tuple, None): Select the radial angle to include: - float selects a single angle - tuple selects a range of angles - None integrates over all angles r (array, None): Edges of the bins in the radual direction - will return r.size-1 points. Default is None which uses the minimum r value found on the edges of the image. centre (2-tuple): Co-ordinates of centre point in terms of the orginal pixels. Defaults to(None,None) for the middle of the image. pixel_size (2-tuple): The size of one pixel in (dx by dy) - defaults to 1,1 Retunrs: (Data): A py:class:`Stoner.Data` object with a column for r and columns for mean, std, and number of pixels. """ coords = im.radial_coordinates(centre=centre, pixel_size=pixel_size, angle=None) if r is None: # Identify the minimum edge value edges = np.append(coords[:, 0], coords[-1, :]) edges = np.append(edges, coords[:, -1]) edges = np.append(edges, coords[0, :]) r_limit = np.abs(edges).min() r = np.linspace(0, np.ceil(r_limit), int(np.ceil(r_limit) + 1)) r_l = r[:-1] r_h = r[1:] r_m = (r_l + r_h) / 2 if angle is None: angle_select = np.ones_like(coords).astype(bool) elif isinstance(angle, tuple): angle_low = np.angle(coords) >= angle[0] angle_high = np.angle(coords) <= angle[1] angle_select = np.logical_and(angle_low, angle_high) elif isinstance(angle, (int, float)): angle_select = np.isclose(np.angle(coords), angle) else: raise TypeError(f"angle should be a float, tuple of two floats or None not a {type(angle)}") ret = make_Data() for low, high, mid in zip(r_l, r_h, r_m): r_select = np.logical_and(np.abs(coords) >= low, np.abs(coords) < high) data = im[np.logical_and(r_select, angle_select)] if data.size == 0: continue if data.size == 1: avg = data[0] std = np.nan else: avg = data.mean() std = data.std() num = data.size ret += np.array([low, mid, high, avg, std, num]) ret.column_headers = ["Low_r", "r", "high_r", "mean", "std", "number"] ret.setas = "" ret.metadata = im.metadata ret.filename = im.filename[:-4] + "_profile.txt" return ret
[docs]def quantize(im, output, levels=None): """Quantise the image data into fixed levels given by a mapping. Args: output (list,array,tuple): Output levels to return. Keyword Arguments: levels (list, array or None): The input band markers. If None is constructed from the data. The number of levels should be one less than the number of output levels given. Notes: The routine will ignore all masked pixels and will preserve the mask. """ section = im[~im.mask] mask = im.mask lmin, lmax = section.min(), section.max() # Dudge to ensure that the bottom and top elements are included. delta = (lmax - lmin) / 100 if levels is None: levels = np.linspace(lmin - delta, lmax + delta, len(output) + 1) elif len(levels) == len(output) + 1: pass elif len(levels) == len(output) - 1: lvl = np.zeros(len(output) + 1) lvl[1:-1] = levels lvl[0] = section.min() - delta lvl[-1] = section.max() + delta levels = lvl else: raise RuntimeError(f"{len(output)} output levels and {len(levels)} input levels") ret = im.clone ret.mask = False for lvl, lvh, val in zip(levels[:-1], levels[1:], output): select = np.logical_and(np.less_equal(im, lvh), np.greater(im, lvl)) ret[select] = val ret[mask] = im.view(np.ndarray)[mask] # Put back the original image values where they are masked. ret.mask = mask # restore mask return ret
[docs]def remove_outliers(im, percentiles=(0.01, 0.99), replace=None): """Find values of the data that are beyond a percentile of the overall distribution and replace them. Keyword Parameters: percentile (2 tuple): Fraction percentiles to consider to be outliers (default is (0.01,0.99) for 1% limits) replace (2 tuple or None): Values to set outliers to. If None, then the pixel values at the percentile limits are used. Returns: (ndarray): Tje modified array. Use this method if you have an image with a small number of pixels with extreme values that are out of range. """ from scipy.interpolate import interp1d cdf, bins = im.cumulative_distribution(nbins=min(1000, im.count())) cdf = interp1d(cdf, bins, kind="linear") limits = [cdf(x) for x in percentiles] if replace is None: replace = limits im[im < limits[0]] = replace[0] im[im > limits[1]] = replace[1] return im
[docs]def rotate(im, angle, resize=False, center=None, order=1, mode="constant", cval=0, clip=True, preserve_range=False): """Rotate image by a certain angle around its center. Parameters: angle (float): Rotation angle in **radians** in clockwise direction. Keyword Parameters: resize (bool): Determine whether the shape of the output image will be automatically calculated, so the complete rotated image exactly fits. Default is False. center (iterable of length 2): The rotation center. If ``center=None``, the image is rotated around its center, i.e. ``center=(cols / 2 - 0.5, rows / 2 - 0.5)``. Please note that this parameter is (cols, rows), contrary to normal skimage ordering. order (int): The order of the spline interpolation, default is 1. The order has to be in the range 0-5. See `skimage.transform.warp` for detail. mode ({'constant', 'edge', 'symmetric', 'reflect', 'wrap'}): Points outside the boundaries of the input are filled according to the given mode. Modes match the behaviour of `numpy.pad`. cval (float): Used in conjunction with mode 'constant', the value outside the image boundaries. clip (bool): Whether to clip the output to the range of values of the input image. This is enabled by default, since higher order interpolation may produce values outside the given input range. preserve_range (bool): Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of `Stpomer.Image.ImageArray.as_float`. Returns: (ImageFile/ImageArray): Rotated image Notes: (pass through to the skimage.transform.warps.rotate function) """ ang = np.rad2deg(-angle) data = transform.rotate( im, ang, resize=resize, center=center, order=order, mode=mode, cval=cval, clip=clip, preserve_range=preserve_range, ) print(type(im), type(data)) ret = data.view(type(im)) try: ret.metadata = im.metadata ret.metadata["transform:rotation"] = angle except AttributeError: pass return ret
def sgolay2d(img, points=15, poly=1, derivative=None): """Implements a 2D Savitsky Golay Filter for a 2D array (e.g. image). Arguments: img (ImageArray or ImageFile): image to be filtered Keyword Arguments: points (int): The number of points in the window aperture. Must be an odd number. (default 15) poly (int): Degree of polynomial to use in the filter. (defatult 1) derivative (str or None): Type of defivative to calculate. Can be: None - smooth only (default) "x","y" - calculate dIntentity/dx or dIntensity/dy "both" - calculate the full derivative and return magnitud and angle. ReturnsL (imageArray or ImageFile): filtered image. Raises: ValueError if points, order or derivative are incorrect. Notes: Adapted from code on the scipy cookbook : """ # number of terms in the polynomial expression n_terms = (poly + 1) * (poly + 2) / 2.0 if points % 2 == 0: raise ValueError("window_size must be odd") if points ** 2 < n_terms: raise ValueError("order is too high for the window size") half_size = points // 2 # exponents of the polynomial. # p(x,y) = a0 + a1*x + a2*y + a3*x^2 + a4*y^2 + a5*x*y + ... # this line gives a list of two item tuple. Each tuple contains # the exponents of the k-th term. First element of tuple is for x # second element for y. # Ex. exps = [(0,0), (1,0), (0,1), (2,0), (1,1), (0,2), ...] exps = [(k - n, n) for k in range(poly + 1) for n in range(k + 1)] # coordinates of points ind = np.arange(-half_size, half_size + 1, dtype=np.float64) dx = np.repeat(ind, points) dy = np.tile(ind, [points, 1]).reshape(points ** 2) # build matrix of system of equation A = np.empty((points ** 2, len(exps))) for i, exp in enumerate(exps): A[:, i] = (dx ** exp[0]) * (dy ** exp[1]) # pad input array with appropriate values at the four borders new_shape = img.shape[0] + 2 * half_size, img.shape[1] + 2 * half_size Z = np.zeros((new_shape)) # top band band = img[0, :] Z[:half_size, half_size:-half_size] = band - np.abs(np.flipud(img[1 : half_size + 1, :]) - band) # bottom band band = img[-1, :] Z[-half_size:, half_size:-half_size] = band + np.abs(np.flipud(img[-half_size - 1 : -1, :]) - band) # left band band = np.tile(img[:, 0].reshape(-1, 1), [1, half_size]) Z[half_size:-half_size, :half_size] = band - np.abs(np.fliplr(img[:, 1 : half_size + 1]) - band) # right band band = np.tile(img[:, -1].reshape(-1, 1), [1, half_size]) Z[half_size:-half_size, -half_size:] = band + np.abs(np.fliplr(img[:, -half_size - 1 : -1]) - band) # central band Z[half_size:-half_size, half_size:-half_size] = img # top left corner band = img[0, 0] Z[:half_size, :half_size] = band - np.abs(np.flipud(np.fliplr(img[1 : half_size + 1, 1 : half_size + 1])) - band) # bottom right corner band = img[-1, -1] Z[-half_size:, -half_size:] = band + np.abs( np.flipud(np.fliplr(img[-half_size - 1 : -1, -half_size - 1 : -1])) - band ) # top right corner band = Z[half_size, -half_size:] Z[:half_size, -half_size:] = band - np.abs(np.flipud(Z[half_size + 1 : 2 * half_size + 1, -half_size:]) - band) # bottom left corner band = Z[-half_size:, half_size].reshape(-1, 1) Z[-half_size:, :half_size] = band - np.abs(np.fliplr(Z[-half_size:, half_size + 1 : 2 * half_size + 1]) - band) # solve system and convolve if derivative is None: m = np.linalg.pinv(A)[0].reshape((points, -1)) ret = signal.fftconvolve(Z, m, mode="valid").view(type(img)) elif derivative == "x": c = np.linalg.pinv(A)[1].reshape((points, -1)) ret = signal.fftconvolve(Z, -c, mode="valid").view(type(img)) elif derivative == "y": r = np.linalg.pinv(A)[2].reshape((points, -1)) ret = signal.fftconvolve(Z, -r, mode="valid").view(type(img)) elif derivative == "both": c = np.linalg.pinv(A)[1].reshape((points, -1)) r = np.linalg.pinv(A)[2].reshape((points, -1)) ret = signal.fftconvolve(Z, -r, mode="valid"), signal.fftconvolve(Z, -c, mode="valid").view(type(img)) else: raise ValueError(f"Unknown derivative mode {derivative}") ret.metadata.update(img.metadata) return ret def span(im): """Return the minimum and maximum values in the image.""" return np.min(im), np.max(im)
[docs]def translate(im, translation, add_metadata=False, order=3, mode="wrap", cval=None): """Translate the image. Areas lost by move are cropped, and areas gained are made black (0) The area not lost or cropped is added as a metadata parameter 'translation_limits' Args: translate (2-tuple): translation (x,y) Keyword Arguments: add_metadata (bool): Record the shift in the image metadata order (int): Interpolation order (default, 3, bi-cubic) mode (str): How to handle points outside the original image. See :py:func:`skimage.transform.warp`. Defaults to "wrap" cval (float): The value to fill with if *mode* is constant. If not speficied or None, defaults to the mean pixcel value. Returns: im (ImageArray): translated image """ translation = [-x for x in translation] trans = transform.SimilarityTransform(translation=translation) if cval is None: cval = im.mean() im = im.warp(trans, order=order, mode=mode, cval=cval) if add_metadata: im.metadata["translation"] = translation im.metadata["translation_limits"] = translate_limits(im, translation) return im
[docs]def translate_limits(im, translation, reverse=False): """Find the limits of an image after a translation. After using ImageArray.translate some areas will be black, this finds the max area that still has original pixels in Args: translation: 2-tuple the (x,y) translation applied to the image Keyword Args: reverse (bool): whether to reverse the translation vector (default False, no) Returns: limits: 4-tuple (xmin,xmax,ymin,ymax) the maximum coordinates of the image with original information """ if isinstance(translation, string_types): translation = im[translation] translation = np.array(translation) if reverse: translation *= -1 shape = im.shape xmin = max(0, translation[0]) xmax = min(shape[0], shape[0] + translation[0]) ymin = max(0, translation[1]) ymax = min(shape[1], shape[1] + translation[1]) return (xmin, xmax, ymin, ymax)
[docs]def plot_histogram(im, bins=256): """Plot the histogram and cumulative distribution for the image.""" hist, bins = np.histogram(im, bins) cum, bins = im.cumulative_distribution(nbins=bins) cum = cum * np.max(hist) / np.max(cum) plt.figure() plt.plot(bins, hist, "k-") plt.plot(bins, cum, "r-")
[docs]def threshold_minmax(im, threshmin=0.1, threshmax=0.9): """Return a boolean array which is thresholded between threshmin and threshmax. (ie True if value is between threshmin and threshmax) """ im = im.convert(np.float) return np.logical_and(im > threshmin, im < threshmax)
[docs]def denoise(im, weight=0.1): """Rename the skimage restore function.""" return im.denoise_tv_chambolle(weight=weight)
[docs]def do_nothing(self): """Nulop function for testing the integration into ImageArray.""" return self
@changes_size def crop(self, *args, **kargs): """Crop the image according to a box. Args: box(tuple) or 4 separate args or None: (xmin,xmax,ymin,ymax) If None image will be shown and user will be asked to select a box (bit experimental) Keyword Arguments: copy(bool): If True return a copy of ImageFile with the cropped image Returns: (ImageArray): view or copy of array asked for Notes: This is essentially like taking a view onto the array but uses image x,y coords (x,y --> col,row) Returns a view according to the coords given. If box is None it will allow the user to select a rectangle. If a tuple is given with None included then max extent is used for that coord (analagous to slice). If copy then return a copy of self with the cropped image. The box can be specified in a number of ways: - (int): A border around all sides of the given number pixels is ignored. - (float 0.0-1.0): A border of the given fraction of the images height and width is ignored - (string): A correspoinding item of metadata is located and used to specify the box - (tuple of 4 ints or floats): For each item in the tuple it is interpreted as foloows: - (int): A pixel co-ordinate in either the x or y direction - (float 0.0-1.0): A fraction of the width or height in from the left, right, top, bottom sides - (float > 1.0): Is rounded to the nearest integer and used a pixel cordinate. - None: The extent of the image is used. Example: a=ImageFile(np.arange(12).reshape(3,4)) a.crop(1,3,None,None) """ box = self._box(*args, **kargs) ret = self[box] if "copy" in kargs.keys() and kargs["copy"]: ret = ret.clone return ret def dtype_limits(self, clip_negative=True): """Return intensity limits, i.e. (min, max) tuple, of the image's dtype. Args: image(ndarray): Input image. clip_negative(bool): If True, clip the negative range (i.e. return 0 for min intensity) even if the image dtype allows negative values. Returns: (imin, imax : tuple): Lower and upper intensity limits. """ if clip_negative is None: clip_negative = True imin, imax = dtype_range[self.dtype.type] if clip_negative: imin = 0 return imin, imax @keep_return_type def asarray(self): """Provide a consistent way to get at the underlying array data in both ImageArray and ImageFile objects.""" return self def asfloat(self, normalise=True, clip=False, clip_negative=False): """Return the image converted to floating point type. If currently an int type and normalise then floats will be normalised to the maximum allowed value of the int type. If currently a float type then no change occurs. If clip then clip values outside the range -1,1 If clip_negative then further clip values to range 0,1 Keyword Arguments: normalise(bool): normalise the image to the max value of current int type clip_negative(bool): clip negative intensity to 0 """ if self.dtype.kind == "f": ret = self else: ret = self.convert(dtype=np.float64, normalise=normalise).view(type(self)) # preserve metadata tmp = metadataObject.__new__(metadataObject) for k, v in tmp.__dict__.items(): if k not in ret.__dict__: ret.__dict__[k] = v c = self.clone # copy formatting and apply to new array for k, v in c._optinfo.items(): setattr(ret, k, v) if clip or clip_negative: ret = ret.clip_intensity(clip_negative=clip_negative) return ret def clip_intensity(self, clip_negative=False, limits=None): """Clip intensity outside the range -1,1 or 0,1. Keyword Arguments: clip_negative(bool): if True clip to range 0,1 else range -1,1 limits (low,high): Clip the intensity between low and high rather than zero and 1. Ensure data range is -1 to 1 or 0 to 1 if clip_negative is True. """ if limits is None: dl = self.dtype_limits(clip_negative=clip_negative) else: dl = list(limits) np.clip(self, dl[0], dl[1], out=self) return self def asint(self, dtype=np.uint16): """Convert the image to unsigned integer format. May raise warnings about loss of precision. """ if self.dtype.kind == "f" and (np.max(self) > 1 or np.min(self) < -1): self = self.normalise() ret = self.convert(dtype) ret = ret.view(type(self)) tmp = metadataObject.__new__(metadataObject) for k, v in tmp.__dict__.items(): if k not in ret.__dict__: ret.__dict__[k] = v for k, v in self._optinfo.items(): setattr(ret, k, v) return ret def save(self, filename=None, **kargs): """Save the image into the file 'filename'. Args: filename (string, bool or None): Filename to save data as, if this is None then the current filename for the object is used If this is not set, then then a file dialog is used. If filename is False then a file dialog is forced. Keyword Args: fmt (string or list): format to save data as. 'tif', 'png' or 'npy' or a list of them. If not included will guess from filename. forcetype (bool): integer data will be converted to np.float32 type for saving. if forcetype then preserve and save as int type (will be unsigned). Notes: Metadata will be preserved in .png and .tif format. fmt can be 'png', 'npy', 'tif', 'tiff' or a list of more than one of those. tif is recommended since metadata is lost in .npy format but data is converted to integer format for png so that definition cannot be saved. Since Stoner.Image is meant to be a general 2d array often with negative and floating point data this poses a problem for saving images. Images are naturally saved as 8 or more bit unsigned integer values representing colour. The only obvious way to save an image and preserve negative data is to save as a float32 tif. This has the advantage over the npy data type which cannot be opened by external programs and will not save metadata. """ # Standard filename block if filename is None: filename = getattr(self, "filename", None) if filename is None or (isinstance(filename, bool) and not filename): # now go and ask for one filename = get_filedialog(what="file", filetypes=IMAGE_FILES) def_fmt = os.path.splitext(filename)[1][1:] # Get a default format from the filename if def_fmt not in self.fmts: # Default to png if nothing else def_fmt = "png" fmt = kargs.pop("fmt", [def_fmt]) if not isinstance(fmt, list): fmt = [fmt] if set(fmt) & set(self.fmts) == set([]): raise ValueError(f"fmt must be {','.join(self.fmts)}") fmt = ["tiff" if f == "tif" else f for f in fmt] self.filename = filename for fm in fmt: saver = getattr(self, f"save_{fm}", "save_tif") if fm == "tiff": forcetype = kargs.pop("forcetype", False) saver(filename, forcetype) else: saver(filename) def save_png(self, filename): """Save the ImageArray with metadata in a png file. This can only save as 8bit unsigned integer so there is likely to be a loss of precision on floating point data""" pngname = os.path.splitext(filename)[0] + ".png" meta = PngImagePlugin.PngInfo() info = self.metadata.export_all() info = [(i.split("=")[0], "=".join(i.split("=")[1:])) for i in info] for k, v in info: meta.add_text(k, v) s = (self - self.min()) * 256 / (self.max() - self.min()) im = Image.fromarray(s.astype("uint8"), mode="L"), pnginfo=meta) def save_npy(self, filename): """Save the ImageArray as a numpy array.""" npyname = os.path.splitext(filename)[0] + ".npy", np.array(self)) def save_tiff(self, filename, forcetype=False): """Save the ImageArray as a tiff image with metadata. Args: filename (str): Filename to save file as. Keyword Args: forcetype(bool): (depricated) if forcetype then preserve data type as best as possible on save. Otherwise we let the underlying pillow library choose the best data type. Note: PIL can save in modes "L" (8bit unsigned int), "I" (32bit signed int), or "F" (32bit signed float). In general max info is preserved for "F" type so if forcetype is not specified then this is the default. For boolean type data mode "L" will suffice and this is chosen in all cases. The type name is added as a string to the metadata before saving. """ from PIL.TiffImagePlugin import ImageFileDirectory_v2 import json dtype = np.dtype(self.dtype).name # string representation of dtype we can save self["ImageArray.dtype"] = dtype # add the dtype to the metadata for saving. if forcetype: # PIL supports uint8, int32 and float32, try to find the best match if self.dtype == np.uint8 or self.dtype.kind == "b": # uint8 or boolean im = Image.fromarray(self, mode="L") elif self.dtype.kind in ["i", "u"]: im = Image.fromarray(self.astype("int32"), mode="I") else: # default to float32 im = Image.fromarray(self.astype(np.float32), mode="F") else: if ( self.dtype.kind == "b" ): # boolean we're not going to lose data by saving as unsigned int im = Image.fromarray(self) im = Image.fromarray(self, mode="L") else: # try to convert everything else to float32 which can has maximum preservation of info try: im = Image.fromarray(self) except TypeError: im = Image.fromarray(self.astype("float32")) ifd = ImageFileDirectory_v2() ifd[270] = json.dumps( {"type": type(self).__name__, "module": type(self).__module__, "metadata": self.metadata.export_all()} ) ext = os.path.splitext(filename)[1] if ext in [".tif", ".tiff"]: # ensure extension is preserved in save pass else: # default to tiff ext = ".tiff" tiffname = os.path.splitext(filename)[0] + ext, tiffinfo=ifd)