ContourDiff/dataset.py at main · mazurowski-lab/ContourDiff · GitHub

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import os
import random
import torch

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

class ContourDiffDataset(Dataset):
    def __init__(self, df_meta, image_directory, contour_directory, transform_img=None, transform_contour=None, generator_seed=None, config=None):
        self.df_meta = df_meta
        self.image_directory = image_directory
        self.contour_directory = contour_directory

        self.transform_img = transform_img
        self.transform_contour = transform_contour
        self.generator_seed = generator_seed
        if self.generator_seed is not None:
            self.seed_generator = torch.Generator().manual_seed(self.generator_seed)
        self.length = self.df_meta.shape[0]
        self.config = config

    def __len__(self):
        return self.length

    def __getitem__(self, index):
        img_name = self.df_meta.iloc[index, :]["image_name"]
        if self.config is None or self.config.in_channels == 1:
            img = Image.open(os.path.join(self.image_directory, img_name)).convert("L")
        elif self.config.in_channels == 3:
            img = Image.open(os.path.join(self.image_directory, img_name)).convert("RGB")

        contour_name = self.df_meta.iloc[index, :]["contour_name"]
        contour = Image.open(os.path.join(self.contour_directory, contour_name))

        near_img_name = None
        near_img = None

        if self.config.near_guided:
            if random.random() < self.config.near_guided_ratio:
                slice_num = self.df_meta.iloc[index, :]["slice"]
                specifier = self.df_meta.iloc[index, :]["specifier"]

                if random.random() < 0.5:
                    row = self.df_meta[(self.df_meta["specifier"] == specifier) & (self.df_meta["slice"] == slice_num - 1)]
                    if len(row) > 0:
                        near_img_name = row["image_name"].item()
                    else:
                        row = self.df_meta[(self.df_meta["specifier"] == specifier) & (self.df_meta["slice"] == slice_num + 1)]
                        if len(row) > 0:
                            near_img_name = row["image_name"].item()
                else:
                    row = self.df_meta[(self.df_meta["specifier"] == specifier) & (self.df_meta["slice"] == slice_num + 1)]
                    if len(row) > 0:
                        near_img_name = row["image_name"].item()
                    else:
                        row = self.df_meta[(self.df_meta["specifier"] == specifier) & (self.df_meta["slice"] == slice_num - 1)]
                        if len(row) > 0:
                            near_img_name = row["image_name"].item()

        if near_img_name is not None:
            near_img = Image.open(os.path.join(self.image_directory, near_img_name)).convert("L")
        else:
            near_img = Image.new('L', (img.size(1), img.size(2)), 0)

        if self.generator_seed is not None:
            seed = self.seed_generator.seed()

        if self.transform_img is not None:
            if self.generator_seed is not None:
                torch.manual_seed(seed)
            img = self.transform_img(img)

            if self.generator_seed is not None:
                torch.manual_seed(seed)
            near_img = self.transform_img(near_img)

        if self.transform_contour is not None:
            if self.generator_seed is not None:
                torch.manual_seed(seed)
            contour = self.transform_contour(contour)

        return {
            "images": img,
            "contours": contour,
            "near_images": near_img,
            "image_name": img_name,
            "contour_name": contour_name,
        }

class SegmentationDataset(Dataset):
    def __init__(self, df_meta, image_directory, mask_directory=None, class_specifier=None, generator_seed=None, transform_img=None, train_pre_transform_img=None, transform_mask=None):
        self.df_meta = df_meta

        self.image_directory = image_directory

        if mask_directory:
            self.mask_directory = mask_directory
        else:
            self.mask_directory = image_directory

        self.length = len(self.df_meta)
        self.class_specifier = class_specifier

        self.generator_seed = generator_seed
        if self.generator_seed is not None:
            self.seed_generator = torch.Generator().manual_seed(self.generator_seed)

        self.transform_img = transform_img
        self.train_pre_transform_img = train_pre_transform_img
        self.transform_mask = transform_mask

    def __len__(self):
        return self.length

    def __getitem__(self, index):
        img_name = self.df_meta.iloc[index]["image_name"]
        img = Image.open(os.path.join(self.image_directory, img_name)).convert("RGB")

        mask_name = self.df_meta.iloc[index]["mask_name"]
        mask = Image.open(os.path.join(self.mask_directory, mask_name))

        np_mask = np.array(mask)

        if self.class_specifier is not None:
            np_mask = np.isin(np_mask, self.class_specifier).astype("uint8") * 255

        mask = Image.fromarray(np_mask)

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

        if self.generator_seed is not None:
            seed = self.seed_generator.seed()

        if self.transform_img is not None:
            if self.generator_seed is not None:
                torch.manual_seed(seed)
                torch.random.manual_seed(seed)
                random.seed(seed)
            img = self.transform_img(img)

        if self.transform_mask is not None:
            if self.generator_seed is not None:
                torch.manual_seed(seed)
                torch.random.manual_seed(seed)
                random.seed(seed)
            mask = self.transform_mask(mask)

        return {
            "image": img,
            "mask": mask,
            "image_name": img_name,
            "mask_name": img_name
        }