import torch
import os
from skimage import io
from skimage import feature
import numpy as np
from torch.utils.data import Dataset
import random
import re
import albumentations as A

def validatePath(path):
    if not os.path.exists(path):
        raise ValueError(f"{path} is not a valild path")


def fgbg_test_train(folder, train=0.8, limit=1):
    # we have fixed file names from image000001 to image 400000, same for depth and mask
    # so we can easily shuffle and load
    validatePath(folder)
    images_path = os.path.join(folder, 'images')
    depths_path = os.path.join(folder, 'depth')
    masks_path = os.path.join(folder, 'masks')
    validatePath(images_path)
    validatePath(depths_path)
    validatePath(masks_path)

    # we are expecting the files in a particular format
    images = sorted(filter(lambda x: re.match(r'^fgbg[\d]{6}\.jpg$', x), os.listdir(images_path)))
    depths = sorted(filter(lambda x: re.match(r'^fgbg[\d]{6}\.jpg$', x), os.listdir(depths_path)))
    masks = sorted(filter(lambda x: re.match(r'^mask[\d]{6}\.jpg$', x), os.listdir(masks_path)))

    images = list(map(lambda x: os.path.join(images_path, x), images))
    depths = list(map(lambda x: os.path.join(depths_path, x), depths))
    masks = list(map(lambda x: os.path.join(masks_path, x), masks))


    if(len(images) != len(depths) != len(masks)):
        raise ValueError("image counts do not match in images, depth and masks")

    l = len(images)
    if limit<1:
        l = int(len(images)*limit)

    dataset = list(zip(images, masks, depths))
    random.shuffle(dataset)

    ts = int(l * train)
    return dataset[:ts], dataset[ts:l]

def make_one_hot(labels, num_classes):
    '''
    Converts an integer label torch.autograd.Variable to a one-hot Variable.

    Parameters
    ----------
    labels : torch.autograd.Variable of torch.cuda.LongTensor
        N x 1 x H x W, where N is batch size.
        Each value is an integer representing correct classification.
    Returns
    -------
    target : torch.autograd.Variable of torch.cuda.FloatTensor
        N x C x H x W, where C is class number. One-hot encoded.
    '''
    one_hot = torch.FloatTensor(labels.size(0), num_classes, labels.size(2), labels.size(3)).zero_()
    target = one_hot.scatter_(1, labels.data, 1) 
    return target

def combine_classes(labels, onehotclasses, device):
  l = (labels[:, :1, :, :]*onehotclasses[0]).long()
  v = make_one_hot(l, onehotclasses[0], device)[:, 1:, :, :]
  j = 1
  for i in range(1, len(onehotclasses)):
    l = (labels[:, j:j+1, :, :]*onehotclasses[i]).long()
    x = make_one_hot(l, onehotclasses[i], device)[:, 1:, :, :]
    j = j+1
    v = torch.cat((v,x), 1)

  return v

    
class FGBGDataset(Dataset):
    """Tine Imagenet dataset reader."""

    def __init__(self, data, common_transforms, means, stdevs, specific_transforms = None):
        """
        Args:
            data (string): zipped images and labels.
        """
        self.common_transforms = A.Compose(common_transforms, additional_targets = {'image': 'image', 'mask': 'image', 'depth':'image'}) if common_transforms else None
        self.specific_transforms = A.Compose(specific_transforms) if specific_transforms else None
        self.means = np.array(means)
        self.stdevs = np.array(stdevs)
        self.images, self.masks, self.depths = zip(*data)

    def __len__(self):
        return len(self.images)

    def __getitem__(self, idx):
        if torch.is_tensor(idx):
            idx = idx.tolist()

        image = io.imread(self.images[idx], as_gray=False, pilmode="RGB")
        # for grayscale images tensor will need to transpose it.
        mask = io.imread(self.masks[idx], as_gray=True, pilmode="1") 
        depth = io.imread(self.depths[idx], as_gray=True, pilmode="L")

        if self.common_transforms:
            augmented = self.common_transforms(image=image, mask=mask, depth=depth)
            image, mask, depth = augmented["image"], augmented["mask"], augmented["depth"]

        if self.specific_transforms:
          image = self.specific_transforms(image=image)['image']

        image = image/255.0
        image = (image - self.means)/self.stdevs
        image = np.transpose(image, (2, 0, 1))
        image = torch.from_numpy(image)
        mask = torch.from_numpy(mask)/255.0
        depth = torch.from_numpy(depth)/255.0

        # now we need to take 4 images and put them with one another
        # also we need to shuffle every time
        # we need to use 
        return image.float(), torch.stack([mask, depth]).float()
6843b0e3c8be409a3f60ad970e9f150ef7242a92
<mask token>


def fgbg_test_train(folder, train=0.8, limit=1):
    validatePath(folder)
    images_path = os.path.join(folder, 'images')
    depths_path = os.path.join(folder, 'depth')
    masks_path = os.path.join(folder, 'masks')
    validatePath(images_path)
    validatePath(depths_path)
    validatePath(masks_path)
    images = sorted(filter(lambda x: re.match('^fgbg[\\d]{6}\\.jpg$', x),
        os.listdir(images_path)))
    depths = sorted(filter(lambda x: re.match('^fgbg[\\d]{6}\\.jpg$', x),
        os.listdir(depths_path)))
    masks = sorted(filter(lambda x: re.match('^mask[\\d]{6}\\.jpg$', x), os
        .listdir(masks_path)))
    images = list(map(lambda x: os.path.join(images_path, x), images))
    depths = list(map(lambda x: os.path.join(depths_path, x), depths))
    masks = list(map(lambda x: os.path.join(masks_path, x), masks))
    if len(images) != len(depths) != len(masks):
        raise ValueError('image counts do not match in images, depth and masks'
            )
    l = len(images)
    if limit < 1:
        l = int(len(images) * limit)
    dataset = list(zip(images, masks, depths))
    random.shuffle(dataset)
    ts = int(l * train)
    return dataset[:ts], dataset[ts:l]


def make_one_hot(labels, num_classes):
    """
    Converts an integer label torch.autograd.Variable to a one-hot Variable.

    Parameters
    ----------
    labels : torch.autograd.Variable of torch.cuda.LongTensor
        N x 1 x H x W, where N is batch size.
        Each value is an integer representing correct classification.
    Returns
    -------
    target : torch.autograd.Variable of torch.cuda.FloatTensor
        N x C x H x W, where C is class number. One-hot encoded.
    """
    one_hot = torch.FloatTensor(labels.size(0), num_classes, labels.size(2),
        labels.size(3)).zero_()
    target = one_hot.scatter_(1, labels.data, 1)
    return target


<mask token>


class FGBGDataset(Dataset):
    """Tine Imagenet dataset reader."""

    def __init__(self, data, common_transforms, means, stdevs,
        specific_transforms=None):
        """
        Args:
            data (string): zipped images and labels.
        """
        self.common_transforms = A.Compose(common_transforms,
            additional_targets={'image': 'image', 'mask': 'image', 'depth':
            'image'}) if common_transforms else None
        self.specific_transforms = A.Compose(specific_transforms
            ) if specific_transforms else None
        self.means = np.array(means)
        self.stdevs = np.array(stdevs)
        self.images, self.masks, self.depths = zip(*data)

    def __len__(self):
        return len(self.images)

    def __getitem__(self, idx):
        if torch.is_tensor(idx):
            idx = idx.tolist()
        image = io.imread(self.images[idx], as_gray=False, pilmode='RGB')
        mask = io.imread(self.masks[idx], as_gray=True, pilmode='1')
        depth = io.imread(self.depths[idx], as_gray=True, pilmode='L')
        if self.common_transforms:
            augmented = self.common_transforms(image=image, mask=mask,
                depth=depth)
            image, mask, depth = augmented['image'], augmented['mask'
                ], augmented['depth']
        if self.specific_transforms:
            image = self.specific_transforms(image=image)['image']
        image = image / 255.0
        image = (image - self.means) / self.stdevs
        image = np.transpose(image, (2, 0, 1))
        image = torch.from_numpy(image)
        mask = torch.from_numpy(mask) / 255.0
        depth = torch.from_numpy(depth) / 255.0
        return image.float(), torch.stack([mask, depth]).float()

<mask token>


def fgbg_test_train(folder, train=0.8, limit=1):
    validatePath(folder)
    images_path = os.path.join(folder, 'images')
    depths_path = os.path.join(folder, 'depth')
    masks_path = os.path.join(folder, 'masks')
    validatePath(images_path)
    validatePath(depths_path)
    validatePath(masks_path)
    images = sorted(filter(lambda x: re.match('^fgbg[\\d]{6}\\.jpg$', x),
        os.listdir(images_path)))
    depths = sorted(filter(lambda x: re.match('^fgbg[\\d]{6}\\.jpg$', x),
        os.listdir(depths_path)))
    masks = sorted(filter(lambda x: re.match('^mask[\\d]{6}\\.jpg$', x), os
        .listdir(masks_path)))
    images = list(map(lambda x: os.path.join(images_path, x), images))
    depths = list(map(lambda x: os.path.join(depths_path, x), depths))
    masks = list(map(lambda x: os.path.join(masks_path, x), masks))
    if len(images) != len(depths) != len(masks):
        raise ValueError('image counts do not match in images, depth and masks'
            )
    l = len(images)
    if limit < 1:
        l = int(len(images) * limit)
    dataset = list(zip(images, masks, depths))
    random.shuffle(dataset)
    ts = int(l * train)
    return dataset[:ts], dataset[ts:l]


def make_one_hot(labels, num_classes):
    """
    Converts an integer label torch.autograd.Variable to a one-hot Variable.

    Parameters
    ----------
    labels : torch.autograd.Variable of torch.cuda.LongTensor
        N x 1 x H x W, where N is batch size.
        Each value is an integer representing correct classification.
    Returns
    -------
    target : torch.autograd.Variable of torch.cuda.FloatTensor
        N x C x H x W, where C is class number. One-hot encoded.
    """
    one_hot = torch.FloatTensor(labels.size(0), num_classes, labels.size(2),
        labels.size(3)).zero_()
    target = one_hot.scatter_(1, labels.data, 1)
    return target


def combine_classes(labels, onehotclasses, device):
    l = (labels[:, :1, :, :] * onehotclasses[0]).long()
    v = make_one_hot(l, onehotclasses[0], device)[:, 1:, :, :]
    j = 1
    for i in range(1, len(onehotclasses)):
        l = (labels[:, j:j + 1, :, :] * onehotclasses[i]).long()
        x = make_one_hot(l, onehotclasses[i], device)[:, 1:, :, :]
        j = j + 1
        v = torch.cat((v, x), 1)
    return v


class FGBGDataset(Dataset):
    """Tine Imagenet dataset reader."""

    def __init__(self, data, common_transforms, means, stdevs,
        specific_transforms=None):
        """
        Args:
            data (string): zipped images and labels.
        """
        self.common_transforms = A.Compose(common_transforms,
            additional_targets={'image': 'image', 'mask': 'image', 'depth':
            'image'}) if common_transforms else None
        self.specific_transforms = A.Compose(specific_transforms
            ) if specific_transforms else None
        self.means = np.array(means)
        self.stdevs = np.array(stdevs)
        self.images, self.masks, self.depths = zip(*data)

    def __len__(self):
        return len(self.images)

    def __getitem__(self, idx):
        if torch.is_tensor(idx):
            idx = idx.tolist()
        image = io.imread(self.images[idx], as_gray=False, pilmode='RGB')
        mask = io.imread(self.masks[idx], as_gray=True, pilmode='1')
        depth = io.imread(self.depths[idx], as_gray=True, pilmode='L')
        if self.common_transforms:
            augmented = self.common_transforms(image=image, mask=mask,
                depth=depth)
            image, mask, depth = augmented['image'], augmented['mask'
                ], augmented['depth']
        if self.specific_transforms:
            image = self.specific_transforms(image=image)['image']
        image = image / 255.0
        image = (image - self.means) / self.stdevs
        image = np.transpose(image, (2, 0, 1))
        image = torch.from_numpy(image)
        mask = torch.from_numpy(mask) / 255.0
        depth = torch.from_numpy(depth) / 255.0
        return image.float(), torch.stack([mask, depth]).float()

<mask token>


def validatePath(path):
    if not os.path.exists(path):
        raise ValueError(f'{path} is not a valild path')


def fgbg_test_train(folder, train=0.8, limit=1):
    validatePath(folder)
    images_path = os.path.join(folder, 'images')
    depths_path = os.path.join(folder, 'depth')
    masks_path = os.path.join(folder, 'masks')
    validatePath(images_path)
    validatePath(depths_path)
    validatePath(masks_path)
    images = sorted(filter(lambda x: re.match('^fgbg[\\d]{6}\\.jpg$', x),
        os.listdir(images_path)))
    depths = sorted(filter(lambda x: re.match('^fgbg[\\d]{6}\\.jpg$', x),
        os.listdir(depths_path)))
    masks = sorted(filter(lambda x: re.match('^mask[\\d]{6}\\.jpg$', x), os
        .listdir(masks_path)))
    images = list(map(lambda x: os.path.join(images_path, x), images))
    depths = list(map(lambda x: os.path.join(depths_path, x), depths))
    masks = list(map(lambda x: os.path.join(masks_path, x), masks))
    if len(images) != len(depths) != len(masks):
        raise ValueError('image counts do not match in images, depth and masks'
            )
    l = len(images)
    if limit < 1:
        l = int(len(images) * limit)
    dataset = list(zip(images, masks, depths))
    random.shuffle(dataset)
    ts = int(l * train)
    return dataset[:ts], dataset[ts:l]


def make_one_hot(labels, num_classes):
    """
    Converts an integer label torch.autograd.Variable to a one-hot Variable.

    Parameters
    ----------
    labels : torch.autograd.Variable of torch.cuda.LongTensor
        N x 1 x H x W, where N is batch size.
        Each value is an integer representing correct classification.
    Returns
    -------
    target : torch.autograd.Variable of torch.cuda.FloatTensor
        N x C x H x W, where C is class number. One-hot encoded.
    """
    one_hot = torch.FloatTensor(labels.size(0), num_classes, labels.size(2),
        labels.size(3)).zero_()
    target = one_hot.scatter_(1, labels.data, 1)
    return target


def combine_classes(labels, onehotclasses, device):
    l = (labels[:, :1, :, :] * onehotclasses[0]).long()
    v = make_one_hot(l, onehotclasses[0], device)[:, 1:, :, :]
    j = 1
    for i in range(1, len(onehotclasses)):
        l = (labels[:, j:j + 1, :, :] * onehotclasses[i]).long()
        x = make_one_hot(l, onehotclasses[i], device)[:, 1:, :, :]
        j = j + 1
        v = torch.cat((v, x), 1)
    return v


class FGBGDataset(Dataset):
    """Tine Imagenet dataset reader."""

    def __init__(self, data, common_transforms, means, stdevs,
        specific_transforms=None):
        """
        Args:
            data (string): zipped images and labels.
        """
        self.common_transforms = A.Compose(common_transforms,
            additional_targets={'image': 'image', 'mask': 'image', 'depth':
            'image'}) if common_transforms else None
        self.specific_transforms = A.Compose(specific_transforms
            ) if specific_transforms else None
        self.means = np.array(means)
        self.stdevs = np.array(stdevs)
        self.images, self.masks, self.depths = zip(*data)

    def __len__(self):
        return len(self.images)

    def __getitem__(self, idx):
        if torch.is_tensor(idx):
            idx = idx.tolist()
        image = io.imread(self.images[idx], as_gray=False, pilmode='RGB')
        mask = io.imread(self.masks[idx], as_gray=True, pilmode='1')
        depth = io.imread(self.depths[idx], as_gray=True, pilmode='L')
        if self.common_transforms:
            augmented = self.common_transforms(image=image, mask=mask,
                depth=depth)
            image, mask, depth = augmented['image'], augmented['mask'
                ], augmented['depth']
        if self.specific_transforms:
            image = self.specific_transforms(image=image)['image']
        image = image / 255.0
        image = (image - self.means) / self.stdevs
        image = np.transpose(image, (2, 0, 1))
        image = torch.from_numpy(image)
        mask = torch.from_numpy(mask) / 255.0
        depth = torch.from_numpy(depth) / 255.0
        return image.float(), torch.stack([mask, depth]).float()

import torch
import os
from skimage import io
from skimage import feature
import numpy as np
from torch.utils.data import Dataset
import random
import re
import albumentations as A


def validatePath(path):
    if not os.path.exists(path):
        raise ValueError(f'{path} is not a valild path')


def fgbg_test_train(folder, train=0.8, limit=1):
    validatePath(folder)
    images_path = os.path.join(folder, 'images')
    depths_path = os.path.join(folder, 'depth')
    masks_path = os.path.join(folder, 'masks')
    validatePath(images_path)
    validatePath(depths_path)
    validatePath(masks_path)
    images = sorted(filter(lambda x: re.match('^fgbg[\\d]{6}\\.jpg$', x),
        os.listdir(images_path)))
    depths = sorted(filter(lambda x: re.match('^fgbg[\\d]{6}\\.jpg$', x),
        os.listdir(depths_path)))
    masks = sorted(filter(lambda x: re.match('^mask[\\d]{6}\\.jpg$', x), os
        .listdir(masks_path)))
    images = list(map(lambda x: os.path.join(images_path, x), images))
    depths = list(map(lambda x: os.path.join(depths_path, x), depths))
    masks = list(map(lambda x: os.path.join(masks_path, x), masks))
    if len(images) != len(depths) != len(masks):
        raise ValueError('image counts do not match in images, depth and masks'
            )
    l = len(images)
    if limit < 1:
        l = int(len(images) * limit)
    dataset = list(zip(images, masks, depths))
    random.shuffle(dataset)
    ts = int(l * train)
    return dataset[:ts], dataset[ts:l]


def make_one_hot(labels, num_classes):
    """
    Converts an integer label torch.autograd.Variable to a one-hot Variable.

    Parameters
    ----------
    labels : torch.autograd.Variable of torch.cuda.LongTensor
        N x 1 x H x W, where N is batch size.
        Each value is an integer representing correct classification.
    Returns
    -------
    target : torch.autograd.Variable of torch.cuda.FloatTensor
        N x C x H x W, where C is class number. One-hot encoded.
    """
    one_hot = torch.FloatTensor(labels.size(0), num_classes, labels.size(2),
        labels.size(3)).zero_()
    target = one_hot.scatter_(1, labels.data, 1)
    return target


def combine_classes(labels, onehotclasses, device):
    l = (labels[:, :1, :, :] * onehotclasses[0]).long()
    v = make_one_hot(l, onehotclasses[0], device)[:, 1:, :, :]
    j = 1
    for i in range(1, len(onehotclasses)):
        l = (labels[:, j:j + 1, :, :] * onehotclasses[i]).long()
        x = make_one_hot(l, onehotclasses[i], device)[:, 1:, :, :]
        j = j + 1
        v = torch.cat((v, x), 1)
    return v


class FGBGDataset(Dataset):
    """Tine Imagenet dataset reader."""

    def __init__(self, data, common_transforms, means, stdevs,
        specific_transforms=None):
        """
        Args:
            data (string): zipped images and labels.
        """
        self.common_transforms = A.Compose(common_transforms,
            additional_targets={'image': 'image', 'mask': 'image', 'depth':
            'image'}) if common_transforms else None
        self.specific_transforms = A.Compose(specific_transforms
            ) if specific_transforms else None
        self.means = np.array(means)
        self.stdevs = np.array(stdevs)
        self.images, self.masks, self.depths = zip(*data)

    def __len__(self):
        return len(self.images)

    def __getitem__(self, idx):
        if torch.is_tensor(idx):
            idx = idx.tolist()
        image = io.imread(self.images[idx], as_gray=False, pilmode='RGB')
        mask = io.imread(self.masks[idx], as_gray=True, pilmode='1')
        depth = io.imread(self.depths[idx], as_gray=True, pilmode='L')
        if self.common_transforms:
            augmented = self.common_transforms(image=image, mask=mask,
                depth=depth)
            image, mask, depth = augmented['image'], augmented['mask'
                ], augmented['depth']
        if self.specific_transforms:
            image = self.specific_transforms(image=image)['image']
        image = image / 255.0
        image = (image - self.means) / self.stdevs
        image = np.transpose(image, (2, 0, 1))
        image = torch.from_numpy(image)
        mask = torch.from_numpy(mask) / 255.0
        depth = torch.from_numpy(depth) / 255.0
        return image.float(), torch.stack([mask, depth]).float()

import torch
import os
from skimage import io
from skimage import feature
import numpy as np
from torch.utils.data import Dataset
import random
import re
import albumentations as A

def validatePath(path):
    if not os.path.exists(path):
        raise ValueError(f"{path} is not a valild path")


def fgbg_test_train(folder, train=0.8, limit=1):
    # we have fixed file names from image000001 to image 400000, same for depth and mask
    # so we can easily shuffle and load
    validatePath(folder)
    images_path = os.path.join(folder, 'images')
    depths_path = os.path.join(folder, 'depth')
    masks_path = os.path.join(folder, 'masks')
    validatePath(images_path)
    validatePath(depths_path)
    validatePath(masks_path)

    # we are expecting the files in a particular format
    images = sorted(filter(lambda x: re.match(r'^fgbg[\d]{6}\.jpg$', x), os.listdir(images_path)))
    depths = sorted(filter(lambda x: re.match(r'^fgbg[\d]{6}\.jpg$', x), os.listdir(depths_path)))
    masks = sorted(filter(lambda x: re.match(r'^mask[\d]{6}\.jpg$', x), os.listdir(masks_path)))

    images = list(map(lambda x: os.path.join(images_path, x), images))
    depths = list(map(lambda x: os.path.join(depths_path, x), depths))
    masks = list(map(lambda x: os.path.join(masks_path, x), masks))


    if(len(images) != len(depths) != len(masks)):
        raise ValueError("image counts do not match in images, depth and masks")

    l = len(images)
    if limit<1:
        l = int(len(images)*limit)

    dataset = list(zip(images, masks, depths))
    random.shuffle(dataset)

    ts = int(l * train)
    return dataset[:ts], dataset[ts:l]

def make_one_hot(labels, num_classes):
    '''
    Converts an integer label torch.autograd.Variable to a one-hot Variable.

    Parameters
    ----------
    labels : torch.autograd.Variable of torch.cuda.LongTensor
        N x 1 x H x W, where N is batch size.
        Each value is an integer representing correct classification.
    Returns
    -------
    target : torch.autograd.Variable of torch.cuda.FloatTensor
        N x C x H x W, where C is class number. One-hot encoded.
    '''
    one_hot = torch.FloatTensor(labels.size(0), num_classes, labels.size(2), labels.size(3)).zero_()
    target = one_hot.scatter_(1, labels.data, 1) 
    return target

def combine_classes(labels, onehotclasses, device):
  l = (labels[:, :1, :, :]*onehotclasses[0]).long()
  v = make_one_hot(l, onehotclasses[0], device)[:, 1:, :, :]
  j = 1
  for i in range(1, len(onehotclasses)):
    l = (labels[:, j:j+1, :, :]*onehotclasses[i]).long()
    x = make_one_hot(l, onehotclasses[i], device)[:, 1:, :, :]
    j = j+1
    v = torch.cat((v,x), 1)

  return v

    
class FGBGDataset(Dataset):
    """Tine Imagenet dataset reader."""

    def __init__(self, data, common_transforms, means, stdevs, specific_transforms = None):
        """
        Args:
            data (string): zipped images and labels.
        """
        self.common_transforms = A.Compose(common_transforms, additional_targets = {'image': 'image', 'mask': 'image', 'depth':'image'}) if common_transforms else None
        self.specific_transforms = A.Compose(specific_transforms) if specific_transforms else None
        self.means = np.array(means)
        self.stdevs = np.array(stdevs)
        self.images, self.masks, self.depths = zip(*data)

    def __len__(self):
        return len(self.images)

    def __getitem__(self, idx):
        if torch.is_tensor(idx):
            idx = idx.tolist()

        image = io.imread(self.images[idx], as_gray=False, pilmode="RGB")
        # for grayscale images tensor will need to transpose it.
        mask = io.imread(self.masks[idx], as_gray=True, pilmode="1") 
        depth = io.imread(self.depths[idx], as_gray=True, pilmode="L")

        if self.common_transforms:
            augmented = self.common_transforms(image=image, mask=mask, depth=depth)
            image, mask, depth = augmented["image"], augmented["mask"], augmented["depth"]

        if self.specific_transforms:
          image = self.specific_transforms(image=image)['image']

        image = image/255.0
        image = (image - self.means)/self.stdevs
        image = np.transpose(image, (2, 0, 1))
        image = torch.from_numpy(image)
        mask = torch.from_numpy(mask)/255.0
        depth = torch.from_numpy(depth)/255.0

        # now we need to take 4 images and put them with one another
        # also we need to shuffle every time
        # we need to use 
        return image.float(), torch.stack([mask, depth]).float()