from .dataset import TileDataset

import reprlib

from collections import OrderedDict

import pathml.core.h5managers

import pathml.core.masks

import pathml.core.tile

                if tile.coords is None:

                coords = tile.coords

                tiledictionary[coords] = tile

            self._tiles = OrderedDict(tiledictionary)

import h5py

import numpy as np

import torch

            self.h5 = h5py.File(self.file_path, "r")

        k = self.tile_keys[ix]

        tile_image = self.h5["tiles"][str(k)]["array"][:]

from .masks import Masks

from .slide_data import (

from .slide_dataset import SlideDataset

from .tile import Tile

from .tiles import Tiles

from io import BytesIO

import numpy as np

import openslide

from javabridge.jutil import JavaException

from PIL import Image

from pydicom.dataset import Dataset

from pydicom.encaps import get_frame_offsets

from pydicom.uid import UID

from scipy.ndimage import zoom

try:

    import bioformats

    import javabridge

            try:

                self.pixel_dtype = pixel_dtype_map[ome_pixeltype]

                logger.info(f"Found corresponding dtype: {self.pixel_dtype}")

        if series_as_channels:

            return array_scaled.astype(np.uint8)

    def get_thumbnail(self, size=None):

        if size is not None:

            if len(size) != len(self.shape):

                size = size + self.shape[len(size) :]

            ratio = tuple([x / y for x, y in zip(size, self.shape)])

            assert (

            image_array = zoom(array, ratio)

        return image_array

        if isinstance(location, tuple):

        for i in range(self.n_frames):

            if not pad:

from pathlib import Path

import anndata

import dask.distributed

import h5py

import matplotlib.pyplot as plt

import numpy as np

import pathml.core

import pathml.preprocessing.pipeline

from pathml.core.slide_types import SlideType

        out.append(f"image shape: {self.shape}")

        try:

            nlevels = self.slide.level_count

            nlevels = 1

        out.append(f"number of levels: {nlevels}")

        out.append(repr(self.tiles))

        if self.counts:

        out = ",\n\t".join(out)

        out += ")"

            if not overwrite_existing_tiles:

                raise Exception(

    def write(self, path):

import numpy as np

def extract_tiles(arr, tile_size, stride=None):

import os

import anndata

import cv2

import numpy as np

import pandas as pd

import pathml.core

import pathml.core.slide_data

from pathml.utils import (

class Transform:

        if stain_estimation_method.lower() == "vahadane":

            try:

        self.target = target.lower()

        try:

            import spams

        image_OD = RGB_to_OD(image)

        try:

            import spams

        image_OD = RGB_to_OD(image).reshape(-1, 3)

        assert isinstance(

        assert isinstance(

        self.nuclear_channel = nuclear_channel

        self.cytoplasm_channel = cytoplasm_channel

        self.image_resolution = image_resolution

        if model.lower() == "mesmer":

            try:

            self.model = model.lower()

    def __repr__(self):

        return (

        counts.layers["max_intensity"] = max_intensities

        try:

            counts.obsm["spatial"] = np.array(counts.obs[["x", "y"]])

        return counts

        pass

    def __repr__(self):

    def F(self, image):

        image = np.squeeze(image)

from .pipeline import Pipeline

from .transforms import (

from .deepfocus import DeepFocusDataModule

import reprlib

import pathml.core.h5managers

import reprlib

from pathlib import Path

import dask.distributed

class SlideDataset:

                slide_path = d / (slide.name + ".h5path")

            slide.write(slide_path)

import torch

from torch.nn import functional as F

def center_crop_im_batch(batch, dims, batch_order="BCHW"):

    return batch_cropped

import functools

import sys

class PathMLLogger:

import os

import tempfile

import anndata

import h5py

import numpy as np

import pathml.core

import pathml.core.masks

import pathml.core.tile

        if h5path:

            assert (

            assert check_valid_h5path_format(

            for ds in h5path.keys():

                if ds in ["fields", "masks", "tiles"]:

            fieldsgroup = self.h5.create_group("fields")

            fieldsgroup.attrs["name"] = slidedata.name

            fieldsgroup.attrs["shape"] = slidedata.slide.get_image_shape()

            if slidedata.labels:

                for key, label in slidedata.labels.items():

                    self.h5["fields/labels"].attrs[key] = label

            if slidedata.slide_type:

                for key, val in slidedata.slide_type.asdict().items():

                    self.h5["fields/slide_type"].attrs[key] = val

            tilesgroup.attrs["tile_stride"] = b"(0, 0)"

        slide_type_dict = {

        if tile.masks:

            if "masks" not in self.h5["tiles"][str(tile.coords)].keys():

            for key, mask in tile.masks.items():

        self.h5["tiles"][str(tile.coords)].attrs["name"] = (

        if tile.labels:

            for key, val in tile.labels.items():

                self.h5["tiles"][str(tile.coords)]["labels"].attrs[key] = val

        if isinstance(item, bool):

        if isinstance(item, (str, tuple)):

            item = str(item)

            if item not in self.h5["tiles"].keys():

        if not isinstance(key, (str, tuple)):

        if str(key) not in self.h5["tiles"].keys():

            raise KeyError(f"key {key} is not in Tiles")

        del self.h5["tiles"][str(key)]

    def update_mask(self, key, mask):

        if key not in self.h5["masks"].keys():

        del self.h5["masks"][key]

    def get_slidetype(self):

import cv2

import matplotlib.pyplot as plt

from matplotlib.colors import TABLEAU_COLORS

from pathml.ml.utils import center_crop_im_batch, dice_loss, get_sobel_kernels

class _BatchNormRelu(nn.Module):

    try:

        inst_list.remove(0)  # 0 is background

        logger.warning(

import hashlib

import os

from pathlib import Path

import h5py

from pathml.datasets.base_data_module import BaseDataModule

from pathml.utils import download_from_url

class DeepFocusDataModule(BaseDataModule):

            assert (

import tempfile

import anndata

import h5py

import numpy as np

import pickle

import pathml.core.tile

from pathml.preprocessing.transforms import Transform

    def __init__(self, transform_sequence=None):

        assert transform_sequence is None or all(

        self.transforms = transform_sequence

    def __len__(self):

        if self.transforms is None:

            return "Pipeline()"

            for t in self.transforms:

                out += f"\t{repr(t)},\n"

            out += "])"

class SlideType:

import shutil

import zipfile

from pathlib import Path

import cv2

import numpy as np

import torch

import torch.utils.data as data

from pathml.datasets.base_data_module import BaseDataModule

from pathml.datasets.utils import pannuke_multiclass_mask_to_nucleus_mask

from pathml.ml.hovernet import compute_hv_map

import numpy as np

def pannuke_multiclass_mask_to_nucleus_mask(multiclass_mask):

from collections import OrderedDict

import h5py

import pathml.core.masks

        if self.counts:

        out = ",\n\t".join(out)

        out += ")"

        return out