datasets.py

import torch
import torch.utils.data as data
import os, math, random
from os.path import *
import numpy as np
from glob import glob
import utils.frame_utils as frame_utils
from scipy.misc import imread, imresize

class StaticRandomCrop(object):

    def __init__(self, image_size, crop_size):
        self.th, self.tw = crop_size
        h, w = image_size
        self.h1 = random.randint(0, h - self.th)
        self.w1 = random.randint(0, w - self.tw)

    def __call__(self, img):
        return img[self.h1:(self.h1 + self.th), self.w1:(self.w1 + self.tw), :]


class StaticCenterCrop(object):

    def __init__(self, image_size, crop_size):
        self.th, self.tw = crop_size
        self.h, self.w = image_size

    def __call__(self, img):
        # yapf: disable
        return img[(self.h - self.th) // 2:(self.h + self.th) // 2,
                   (self.w - self.tw) // 2:(self.w + self.tw) // 2, :]
        # yapf: enable


class MpiSintel(data.Dataset):

    def __init__(self,
                 args,
                 is_cropped=False,
                 root='',
                 dstype='clean',
                 replicates=1):
        self.args = args
        self.is_cropped = is_cropped
        self.crop_size = args.crop_size
        self.render_size = args.inference_size
        self.replicates = replicates

        flow_root = join(root, 'flow')
        image_root = join(root, dstype)

        file_list = sorted(glob(join(flow_root, '*/*.flo')))

        self.flow_list = []
        self.image_list = []

        for file in file_list:
            if 'test' in file:
                # print file
                continue

            fbase = file[len(flow_root) + 1:]
            fprefix = fbase[:-8]
            fnum = int(fbase[-8:-4])

            img1 = join(image_root, fprefix + "%04d" % (fnum + 0) + '.png')
            img2 = join(image_root, fprefix + "%04d" % (fnum + 1) + '.png')

            if not isfile(img1) or not isfile(img2) or not isfile(file):
                continue

            self.image_list += [[img1, img2]]
            self.flow_list += [file]

        self.size = len(self.image_list)

        self.frame_size = frame_utils.read_gen(self.image_list[0][0]).shape

        if (self.render_size[0] < 0) or (self.render_size[1] < 0) or (
                self.frame_size[0] % 64) or (self.frame_size[1] % 64):
            self.render_size[0] = self.frame_size[0] // 64 * 64
            self.render_size[1] = self.frame_size[1] // 64 * 64

        args.inference_size = self.render_size

        assert (len(self.image_list) == len(self.flow_list))

    def __getitem__(self, index):

        index = index % self.size

        img1 = frame_utils.read_gen(self.image_list[index][0])
        img2 = frame_utils.read_gen(self.image_list[index][1])

        flow = frame_utils.read_gen(self.flow_list[index])

        images = [img1, img2]
        image_size = img1.shape[:2]

        if self.is_cropped:
            cropper = StaticRandomCrop(image_size, self.crop_size)
        else:
            cropper = StaticCenterCrop(image_size, self.render_size)
        images = list(map(cropper, images))
        flow = cropper(flow)

        images = np.array(images).transpose(3, 0, 1, 2)
        flow = flow.transpose(2, 0, 1)

        images = torch.from_numpy(images.astype(np.float32))
        flow = torch.from_numpy(flow.astype(np.float32))

        return [images], [flow]

    def __len__(self):
        return self.size * self.replicates


class MpiSintelClean(MpiSintel):

    def __init__(self, args, is_cropped=False, root='', replicates=1):
        super(MpiSintelClean, self).__init__(
            args,
            is_cropped=is_cropped,
            root=root,
            dstype='clean',
            replicates=replicates)


class MpiSintelFinal(MpiSintel):

    def __init__(self, args, is_cropped=False, root='', replicates=1):
        super(MpiSintelFinal, self).__init__(
            args,
            is_cropped=is_cropped,
            root=root,
            dstype='final',
            replicates=replicates)


class FlyingChairs(data.Dataset):

    def __init__(self,
                 args,
                 is_cropped,
                 root='/path/to/FlyingChairs_release/data',
                 replicates=1):
        self.args = args
        self.is_cropped = is_cropped
        self.crop_size = args.crop_size
        self.render_size = args.inference_size
        self.replicates = replicates

        images = sorted(glob(join(root, '*.ppm')))

        self.flow_list = sorted(glob(join(root, '*.flo')))

        assert (len(images) / 2 == len(self.flow_list))

        self.image_list = []
        for i in range(len(self.flow_list)):
            im1 = images[2 * i]
            im2 = images[2 * i + 1]
            self.image_list += [[im1, im2]]

        assert len(self.image_list) == len(self.flow_list)

        self.size = len(self.image_list)

        self.frame_size = frame_utils.read_gen(self.image_list[0][0]).shape

        if (self.render_size[0] < 0) or (self.render_size[1] < 0) or (
                self.frame_size[0] % 64) or (self.frame_size[1] % 64):
            self.render_size[0] = self.frame_size[0] // 64 * 64
            self.render_size[1] = self.frame_size[1] // 64 * 64

        args.inference_size = self.render_size

    def __getitem__(self, index):
        index = index % self.size

        img1 = frame_utils.read_gen(self.image_list[index][0])
        img2 = frame_utils.read_gen(self.image_list[index][1])

        flow = frame_utils.read_gen(self.flow_list[index])

        images = [img1, img2]
        image_size = img1.shape[:2]
        if self.is_cropped:
            cropper = StaticRandomCrop(image_size, self.crop_size)
        else:
            cropper = StaticCenterCrop(image_size, self.render_size)
        images = map(cropper, images)
        flow = cropper(flow)

        images = np.array(images).transpose(3, 0, 1, 2)
        flow = flow.transpose(2, 0, 1)

        images = torch.from_numpy(images.astype(np.float32))
        flow = torch.from_numpy(flow.astype(np.float32))

        return [images], [flow]

    def __len__(self):
        return self.size * self.replicates


class FlyingThings(data.Dataset):

    def __init__(self,
                 args,
                 is_cropped,
                 root='/path/to/flyingthings3d',
                 dstype='frames_cleanpass',
                 replicates=1):
        self.args = args
        self.is_cropped = is_cropped
        self.crop_size = args.crop_size
        self.render_size = args.inference_size
        self.replicates = replicates

        image_dirs = sorted(glob(join(root, dstype, 'TRAIN/*/*')))
        image_dirs = sorted([join(f, 'left') for f in image_dirs] +
                            [join(f, 'right') for f in image_dirs])

        flow_dirs = sorted(
            glob(join(root, 'optical_flow_flo_format/TRAIN/*/*')))
        flow_dirs = sorted([join(f, 'into_future/left') for f in flow_dirs] +
                           [join(f, 'into_future/right') for f in flow_dirs])

        assert (len(image_dirs) == len(flow_dirs))

        self.image_list = []
        self.flow_list = []

        for idir, fdir in zip(image_dirs, flow_dirs):
            images = sorted(glob(join(idir, '*.png')))
            flows = sorted(glob(join(fdir, '*.flo')))
            for i in range(len(flows)):
                self.image_list += [[images[i], images[i + 1]]]
                self.flow_list += [flows[i]]

        assert len(self.image_list) == len(self.flow_list)

        self.size = len(self.image_list)

        self.frame_size = frame_utils.read_gen(self.image_list[0][0]).shape

        if (self.render_size[0] < 0) or (self.render_size[1] < 0) or (
                self.frame_size[0] % 64) or (self.frame_size[1] % 64):
            self.render_size[0] = self.frame_size[0] // 64 * 64
            self.render_size[1] = self.frame_size[1] // 64 * 64

        args.inference_size = self.render_size

    def __getitem__(self, index):
        index = index % self.size

        img1 = frame_utils.read_gen(self.image_list[index][0])
        img2 = frame_utils.read_gen(self.image_list[index][1])

        flow = frame_utils.read_gen(self.flow_list[index])

        images = [img1, img2]
        image_size = img1.shape[:2]
        if self.is_cropped:
            cropper = StaticRandomCrop(image_size, self.crop_size)
        else:
            cropper = StaticCenterCrop(image_size, self.render_size)
        images = map(cropper, images)
        flow = cropper(flow)

        images = np.array(images).transpose(3, 0, 1, 2)
        flow = flow.transpose(2, 0, 1)

        images = torch.from_numpy(images.astype(np.float32))
        flow = torch.from_numpy(flow.astype(np.float32))

        return [images], [flow]

    def __len__(self):
        return self.size * self.replicates


class FlyingThingsClean(FlyingThings):

    def __init__(self, args, is_cropped=False, root='', replicates=1):
        super(FlyingThingsClean, self).__init__(
            args,
            is_cropped=is_cropped,
            root=root,
            dstype='frames_cleanpass',
            replicates=replicates)


class FlyingThingsFinal(FlyingThings):

    def __init__(self, args, is_cropped=False, root='', replicates=1):
        super(FlyingThingsFinal, self).__init__(
            args,
            is_cropped=is_cropped,
            root=root,
            dstype='frames_finalpass',
            replicates=replicates)


class ChairsSDHom(data.Dataset):

    def __init__(self,
                 args,
                 is_cropped,
                 root='/path/to/chairssdhom/data',
                 dstype='train',
                 replicates=1):
        self.args = args
        self.is_cropped = is_cropped
        self.crop_size = args.crop_size
        self.render_size = args.inference_size
        self.replicates = replicates

        image1 = sorted(glob(join(root, dstype, 't0/*.png')))
        image2 = sorted(glob(join(root, dstype, 't1/*.png')))
        self.flow_list = sorted(glob(join(root, dstype, 'flow/*.flo')))

        assert (len(image1) == len(self.flow_list))

        self.image_list = []
        for i in range(len(self.flow_list)):
            im1 = image1[i]
            im2 = image2[i]
            self.image_list += [[im1, im2]]

        assert len(self.image_list) == len(self.flow_list)

        self.size = len(self.image_list)

        self.frame_size = frame_utils.read_gen(self.image_list[0][0]).shape

        if (self.render_size[0] < 0) or (self.render_size[1] < 0) or (
                self.frame_size[0] % 64) or (self.frame_size[1] % 64):
            self.render_size[0] = self.frame_size[0] // 64 * 64
            self.render_size[1] = self.frame_size[1] // 64 * 64

        args.inference_size = self.render_size

    def __getitem__(self, index):
        index = index % self.size

        img1 = frame_utils.read_gen(self.image_list[index][0])
        img2 = frame_utils.read_gen(self.image_list[index][1])

        flow = frame_utils.read_gen(self.flow_list[index])
        flow = flow[::-1, :, :]

        images = [img1, img2]
        image_size = img1.shape[:2]
        if self.is_cropped:
            cropper = StaticRandomCrop(image_size, self.crop_size)
        else:
            cropper = StaticCenterCrop(image_size, self.render_size)
        images = map(cropper, images)
        flow = cropper(flow)

        images = np.array(images).transpose(3, 0, 1, 2)
        flow = flow.transpose(2, 0, 1)

        images = torch.from_numpy(images.astype(np.float32))
        flow = torch.from_numpy(flow.astype(np.float32))

        return [images], [flow]

    def __len__(self):
        return self.size * self.replicates


class ChairsSDHomTrain(ChairsSDHom):

    def __init__(self, args, is_cropped=False, root='', replicates=1):
        super(ChairsSDHomTrain, self).__init__(
            args,
            is_cropped=is_cropped,
            root=root,
            dstype='train',
            replicates=replicates)


class ChairsSDHomTest(ChairsSDHom):

    def __init__(self, args, is_cropped=False, root='', replicates=1):
        super(ChairsSDHomTest, self).__init__(
            args,
            is_cropped=is_cropped,
            root=root,
            dstype='test',
            replicates=replicates)


class ImagesFromFolder(data.Dataset):

    def __init__(self,
                 args,
                 is_cropped,
                 root='/path/to/frames/only/folder',
                 iext='png',
                 replicates=1):
        self.args = args
        self.is_cropped = is_cropped
        self.crop_size = args.crop_size
        self.render_size = args.inference_size
        self.replicates = replicates

        images = sorted(glob(join(root, '*.' + iext)))
        self.image_list = []
        for i in range(len(images) - 1):
            im1 = images[i]
            im2 = images[i + 1]
            self.image_list += [[im1, im2]]

        self.size = len(self.image_list)

        self.frame_size = frame_utils.read_gen(self.image_list[0][0]).shape

        if (self.render_size[0] < 0) or (self.render_size[1] < 0) or (
                self.frame_size[0] % 64) or (self.frame_size[1] % 64):
            self.render_size[0] = self.frame_size[0] // 64 * 64
            self.render_size[1] = self.frame_size[1] // 64 * 64

        args.inference_size = self.render_size

    def __getitem__(self, index):
        index = index % self.size

        img1 = frame_utils.read_gen(self.image_list[index][0])
        img2 = frame_utils.read_gen(self.image_list[index][1])

        images = [img1, img2]
        image_size = img1.shape[:2]
        if self.is_cropped:
            cropper = StaticRandomCrop(image_size, self.crop_size)
        else:
            cropper = StaticCenterCrop(image_size, self.render_size)
        images = map(cropper, images)

        images = np.array(images).transpose(3, 0, 1, 2)
        images = torch.from_numpy(images.astype(np.float32))

        return [images], [
            torch.zeros(images.size()[0:1] + (2, ) + images.size()[-2:])
        ]

    def __len__(self):
        return self.size * self.replicates