import logging

import numpy as np
import pandas as pd
from PIL import Image
from torch.utils.data import Dataset


class COVIDChestXRayDataset(Dataset):
    def __init__(self, path, size=128, augment=None):
        super(COVIDChestXRayDataset, self).__init__()
        self.logger = logging.getLogger(__name__)

        self.path = path
        self.size = size
        self.augment = augment
        self.labels = None
        self.df = None

    def build(self):
        self.logger.info(f"{self.__class__.__name__} initialized with size={self.size}, augment={self.augment}")
        self.logger.info(f"Dataset is located in {self.path}")

        image_dir = self.path / "images"
        metadata_path = self.path / "metadata.csv"

        df_metadata = pd.read_csv(metadata_path, header=0)
        # Drop CT scans
        df_metadata = df_metadata[df_metadata["modality"] == "X-ray"]
        # Keep only PA/AP/AP Supine, drop Axial, L (lateral)
        allowed_views = ["PA", "AP", "AP Supine"]
        df_metadata = df_metadata[df_metadata["view"].isin(allowed_views)]

        # COVID-19 = 1, SARS/ARDS/Pneumocystis/Streptococcus/No finding = 0
        self.labels = (df_metadata.finding == "COVID-19").values.reshape(-1, 1)
        images = df_metadata.filename
        images = images.apply(lambda x: image_dir / x).values.reshape(-1, 1)

        self.df = pd.DataFrame(np.concatenate((images, self.labels), axis=1), columns=["image", "label"])

        del images

        self.logger.debug(f"Dataset: {self.df}")

    @staticmethod
    def _load_image(path, size):
        # Load image
        im = Image.open(path)
        im = im.resize((size, size))
        im = np.array(im).astype("float32") / 255.0

        # If image not RGB, replicate image in three channels
        if im.ndim == 2:
            im = np.expand_dims(im, axis=-1)
            im = np.tile(im, 3)

        # Remove transparency channel when present
        if im.shape[-1] == 4:
            im = im[:, :, :3]

        # size, size, chan -> chan, size, size
        img = np.transpose(im, axes=[2, 0, 1])

        return img

    def __getitem__(self, index):
        row = self.df.iloc[index]
        img = self._load_image(row["image"], self.size)
        label = row["label"]

        if self.augment is not None:
            img = self.augment(img)

        return img, label

    def __len__(self):
        return self.df.shape[0]

66108a32bb33bd9c1127c947465b5030a5b4b83b
<mask token>


class COVIDChestXRayDataset(Dataset):
    <mask token>
    <mask token>

    @staticmethod
    def _load_image(path, size):
        im = Image.open(path)
        im = im.resize((size, size))
        im = np.array(im).astype('float32') / 255.0
        if im.ndim == 2:
            im = np.expand_dims(im, axis=-1)
            im = np.tile(im, 3)
        if im.shape[-1] == 4:
            im = im[:, :, :3]
        img = np.transpose(im, axes=[2, 0, 1])
        return img
    <mask token>
    <mask token>

<mask token>


class COVIDChestXRayDataset(Dataset):
    <mask token>

    def build(self):
        self.logger.info(
            f'{self.__class__.__name__} initialized with size={self.size}, augment={self.augment}'
            )
        self.logger.info(f'Dataset is located in {self.path}')
        image_dir = self.path / 'images'
        metadata_path = self.path / 'metadata.csv'
        df_metadata = pd.read_csv(metadata_path, header=0)
        df_metadata = df_metadata[df_metadata['modality'] == 'X-ray']
        allowed_views = ['PA', 'AP', 'AP Supine']
        df_metadata = df_metadata[df_metadata['view'].isin(allowed_views)]
        self.labels = (df_metadata.finding == 'COVID-19').values.reshape(-1, 1)
        images = df_metadata.filename
        images = images.apply(lambda x: image_dir / x).values.reshape(-1, 1)
        self.df = pd.DataFrame(np.concatenate((images, self.labels), axis=1
            ), columns=['image', 'label'])
        del images
        self.logger.debug(f'Dataset: {self.df}')

    @staticmethod
    def _load_image(path, size):
        im = Image.open(path)
        im = im.resize((size, size))
        im = np.array(im).astype('float32') / 255.0
        if im.ndim == 2:
            im = np.expand_dims(im, axis=-1)
            im = np.tile(im, 3)
        if im.shape[-1] == 4:
            im = im[:, :, :3]
        img = np.transpose(im, axes=[2, 0, 1])
        return img

    def __getitem__(self, index):
        row = self.df.iloc[index]
        img = self._load_image(row['image'], self.size)
        label = row['label']
        if self.augment is not None:
            img = self.augment(img)
        return img, label

    def __len__(self):
        return self.df.shape[0]

<mask token>


class COVIDChestXRayDataset(Dataset):

    def __init__(self, path, size=128, augment=None):
        super(COVIDChestXRayDataset, self).__init__()
        self.logger = logging.getLogger(__name__)
        self.path = path
        self.size = size
        self.augment = augment
        self.labels = None
        self.df = None

    def build(self):
        self.logger.info(
            f'{self.__class__.__name__} initialized with size={self.size}, augment={self.augment}'
            )
        self.logger.info(f'Dataset is located in {self.path}')
        image_dir = self.path / 'images'
        metadata_path = self.path / 'metadata.csv'
        df_metadata = pd.read_csv(metadata_path, header=0)
        df_metadata = df_metadata[df_metadata['modality'] == 'X-ray']
        allowed_views = ['PA', 'AP', 'AP Supine']
        df_metadata = df_metadata[df_metadata['view'].isin(allowed_views)]
        self.labels = (df_metadata.finding == 'COVID-19').values.reshape(-1, 1)
        images = df_metadata.filename
        images = images.apply(lambda x: image_dir / x).values.reshape(-1, 1)
        self.df = pd.DataFrame(np.concatenate((images, self.labels), axis=1
            ), columns=['image', 'label'])
        del images
        self.logger.debug(f'Dataset: {self.df}')

    @staticmethod
    def _load_image(path, size):
        im = Image.open(path)
        im = im.resize((size, size))
        im = np.array(im).astype('float32') / 255.0
        if im.ndim == 2:
            im = np.expand_dims(im, axis=-1)
            im = np.tile(im, 3)
        if im.shape[-1] == 4:
            im = im[:, :, :3]
        img = np.transpose(im, axes=[2, 0, 1])
        return img

    def __getitem__(self, index):
        row = self.df.iloc[index]
        img = self._load_image(row['image'], self.size)
        label = row['label']
        if self.augment is not None:
            img = self.augment(img)
        return img, label

    def __len__(self):
        return self.df.shape[0]

import logging
import numpy as np
import pandas as pd
from PIL import Image
from torch.utils.data import Dataset


class COVIDChestXRayDataset(Dataset):

    def __init__(self, path, size=128, augment=None):
        super(COVIDChestXRayDataset, self).__init__()
        self.logger = logging.getLogger(__name__)
        self.path = path
        self.size = size
        self.augment = augment
        self.labels = None
        self.df = None

    def build(self):
        self.logger.info(
            f'{self.__class__.__name__} initialized with size={self.size}, augment={self.augment}'
            )
        self.logger.info(f'Dataset is located in {self.path}')
        image_dir = self.path / 'images'
        metadata_path = self.path / 'metadata.csv'
        df_metadata = pd.read_csv(metadata_path, header=0)
        df_metadata = df_metadata[df_metadata['modality'] == 'X-ray']
        allowed_views = ['PA', 'AP', 'AP Supine']
        df_metadata = df_metadata[df_metadata['view'].isin(allowed_views)]
        self.labels = (df_metadata.finding == 'COVID-19').values.reshape(-1, 1)
        images = df_metadata.filename
        images = images.apply(lambda x: image_dir / x).values.reshape(-1, 1)
        self.df = pd.DataFrame(np.concatenate((images, self.labels), axis=1
            ), columns=['image', 'label'])
        del images
        self.logger.debug(f'Dataset: {self.df}')

    @staticmethod
    def _load_image(path, size):
        im = Image.open(path)
        im = im.resize((size, size))
        im = np.array(im).astype('float32') / 255.0
        if im.ndim == 2:
            im = np.expand_dims(im, axis=-1)
            im = np.tile(im, 3)
        if im.shape[-1] == 4:
            im = im[:, :, :3]
        img = np.transpose(im, axes=[2, 0, 1])
        return img

    def __getitem__(self, index):
        row = self.df.iloc[index]
        img = self._load_image(row['image'], self.size)
        label = row['label']
        if self.augment is not None:
            img = self.augment(img)
        return img, label

    def __len__(self):
        return self.df.shape[0]

import logging

import numpy as np
import pandas as pd
from PIL import Image
from torch.utils.data import Dataset


class COVIDChestXRayDataset(Dataset):
    def __init__(self, path, size=128, augment=None):
        super(COVIDChestXRayDataset, self).__init__()
        self.logger = logging.getLogger(__name__)

        self.path = path
        self.size = size
        self.augment = augment
        self.labels = None
        self.df = None

    def build(self):
        self.logger.info(f"{self.__class__.__name__} initialized with size={self.size}, augment={self.augment}")
        self.logger.info(f"Dataset is located in {self.path}")

        image_dir = self.path / "images"
        metadata_path = self.path / "metadata.csv"

        df_metadata = pd.read_csv(metadata_path, header=0)
        # Drop CT scans
        df_metadata = df_metadata[df_metadata["modality"] == "X-ray"]
        # Keep only PA/AP/AP Supine, drop Axial, L (lateral)
        allowed_views = ["PA", "AP", "AP Supine"]
        df_metadata = df_metadata[df_metadata["view"].isin(allowed_views)]

        # COVID-19 = 1, SARS/ARDS/Pneumocystis/Streptococcus/No finding = 0
        self.labels = (df_metadata.finding == "COVID-19").values.reshape(-1, 1)
        images = df_metadata.filename
        images = images.apply(lambda x: image_dir / x).values.reshape(-1, 1)

        self.df = pd.DataFrame(np.concatenate((images, self.labels), axis=1), columns=["image", "label"])

        del images

        self.logger.debug(f"Dataset: {self.df}")

    @staticmethod
    def _load_image(path, size):
        # Load image
        im = Image.open(path)
        im = im.resize((size, size))
        im = np.array(im).astype("float32") / 255.0

        # If image not RGB, replicate image in three channels
        if im.ndim == 2:
            im = np.expand_dims(im, axis=-1)
            im = np.tile(im, 3)

        # Remove transparency channel when present
        if im.shape[-1] == 4:
            im = im[:, :, :3]

        # size, size, chan -> chan, size, size
        img = np.transpose(im, axes=[2, 0, 1])

        return img

    def __getitem__(self, index):
        row = self.df.iloc[index]
        img = self._load_image(row["image"], self.size)
        label = row["label"]

        if self.augment is not None:
            img = self.augment(img)

        return img, label

    def __len__(self):
        return self.df.shape[0]
