image_util

Module Contents

Functions

check_versions()
show_image(image)
show_image_gray(image)
int2float_image(image)
float2int_image(image[, target_dtype])
ensure_unsigned_type(→ numpy.dtype)
ensure_unsigned_image(→ numpy.ndarray)
convert_image_sign_type(→ numpy.ndarray)
redimension_data(data, old_order, new_order, **indices)
get_numpy_slicing(dimension_order, **slicing)
get_image_quantile(→ float)
normalise_values(→ numpy.ndarray)
get_image_size_info(→ str)
pilmode_to_pixelinfo(→ tuple)
calc_pyramid(→ list)
image_reshape(→ numpy.ndarray)
image_resize(→ numpy.ndarray)
precise_resize(→ numpy.ndarray)
create_compression_filter(→ tuple)
get_tiff_pages(→ list)
tags_to_dict(→ dict)
tiff_info(→ str)
tiff_info_short(→ str)
get_pil_metadata(→ dict)
compare_image(→ float)
compare_image_dist(→ tuple)
compare_image_dist_simple(→ dict)
calc_fraction_used(→ float)
blur_image_single(image, sigma)
blur_image(image, sigma)
calc_tiles_median(images)
calc_tiles_quantiles(images, quantiles)
save_image(image, filename[, output_params])

image_util.check_versions()

image_util.show_image(image: numpy.ndarray)

image_util.show_image_gray(image: numpy.ndarray)

image_util.int2float_image(image)

image_util.float2int_image(image, target_dtype=np.dtype(np.uint8))

image_util.ensure_unsigned_type(dtype: numpy.dtype) → numpy.dtype

image_util.ensure_unsigned_image(image: numpy.ndarray) → numpy.ndarray

image_util.convert_image_sign_type(image: numpy.ndarray, target_dtype: numpy.dtype) → numpy.ndarray

image_util.redimension_data(data, old_order, new_order, **indices)

image_util.get_numpy_slicing(dimension_order, **slicing)

image_util.get_image_quantile(image: numpy.ndarray, quantile: float, axis=None) → float

image_util.normalise_values(image: numpy.ndarray, min_value: float, max_value: float) → numpy.ndarray

image_util.get_image_size_info(sizes_xyzct: list, pixel_nbytes: int, pixel_type: numpy.dtype, channels: list) → str

image_util.pilmode_to_pixelinfo(mode: str) → tuple

image_util.calc_pyramid(xyzct: tuple, npyramid_add: int = 0, pyramid_downsample: float = 2, volumetric_resize: bool = False) → list

image_util.image_reshape(image: numpy.ndarray, target_size: tuple) → numpy.ndarray

image_util.image_resize(image: numpy.ndarray, target_size0: tuple, dimension_order: str = 'yxc') → numpy.ndarray

image_util.precise_resize(image: numpy.ndarray, factors) → numpy.ndarray

image_util.create_compression_filter(compression: list) → tuple

image_util.get_tiff_pages(tiff: tifffile.TiffFile) → list

image_util.tags_to_dict(tags: tifffile.TiffTags) → dict

image_util.tiff_info(filename: str) → str

image_util.tiff_info_short(filename: str) → str

image_util.get_pil_metadata(image: PIL.Image) → dict

image_util.compare_image(image0, image1, show=False) → float

image_util.compare_image_dist(image0: numpy.ndarray, image1: numpy.ndarray) → tuple

image_util.compare_image_dist_simple(image0: numpy.ndarray, image1: numpy.ndarray) → dict

image_util.calc_fraction_used(image: numpy.ndarray, threshold: float = 0.1) → float

image_util.blur_image_single(image, sigma)

image_util.blur_image(image, sigma)

image_util.calc_tiles_median(images)

image_util.calc_tiles_quantiles(images, quantiles)

image_util.save_image(image: numpy.ndarray, filename: str, output_params: dict = {})