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frames_dataset.py
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import os
from skimage import io, img_as_float32
from skimage.color import gray2rgb
from sklearn.model_selection import train_test_split
from imageio import mimread
from skimage.transform import resize
import numpy as np
from torch.utils.data import Dataset
from augmentation import AllAugmentationTransform
import glob
from functools import partial
import pandas as pd
def read_video(name, frame_shape):
"""
Read video which can be:
- an image of concatenated frames
- '.mp4' and'.gif'
- folder with videos
"""
if os.path.isdir(name):
frames = sorted(os.listdir(name))
num_frames = len(frames)
video_array = np.array(
[img_as_float32(io.imread(os.path.join(name, frames[idx]))) for idx in range(num_frames)])
elif name.lower().endswith('.png') or name.lower().endswith('.jpg'):
image = io.imread(name)
if len(image.shape) == 2 or image.shape[2] == 1:
image = gray2rgb(image)
if image.shape[2] == 4:
image = image[..., :3]
image = img_as_float32(image)
video_array = np.moveaxis(image, 1, 0)
video_array = video_array.reshape((-1,) + frame_shape)
video_array = np.moveaxis(video_array, 1, 2)
elif name.lower().endswith('.gif') or name.lower().endswith('.mp4') or name.lower().endswith('.mov'):
video = mimread(name)
if len(video[0].shape) == 2:
video = [gray2rgb(frame) for frame in video]
if frame_shape is not None:
video = np.array([resize(frame, frame_shape) for frame in video])
video = np.array(video)
if video.shape[-1] == 4:
video = video[..., :3]
video_array = img_as_float32(video)
else:
raise Exception("Unknown file extensions %s" % name)
return video_array
class FramesDataset(Dataset):
"""
Dataset of videos, each video can be represented as:
- an image of concatenated frames
- '.mp4' or '.gif'
- folder with all frames
"""
def __init__(self, root_dir, frame_shape=(256, 256, 3), id_sampling=False, is_train=True,
random_seed=0, pairs_list=None, augmentation_params=None):
self.root = root_dir
self.videos = os.listdir(root_dir)
self.frame_shape = frame_shape
print("Frame_shape: {}".format(self.frame_shape))
self.pairs_list = pairs_list
self.id_sampling = id_sampling
if os.path.exists(os.path.join(root_dir, 'train')):
assert os.path.exists(os.path.join(root_dir, 'test'))
print("Use predefined train-test split.")
if id_sampling:
train_videos = {os.path.basename(video).split('#')[0] for video in
os.listdir(os.path.join(root_dir, 'train'))}
train_videos = list(train_videos)
else:
train_videos = os.listdir(os.path.join(root_dir, 'train'))
test_videos = os.listdir(os.path.join(root_dir, 'test'))
mask_video = os.listdir(os.path.join(root_dir, 'train_mask'))
self.root_dir = os.path.join(root_dir, 'train' if is_train else 'test')
else:
print("Use random train-test split.")
train_videos, test_videos = train_test_split(self.videos, random_state=random_seed, test_size=0.2)
if is_train:
self.videos = train_videos
else:
self.videos = test_videos
self.mask_video = mask_video
self.is_train = is_train
if self.is_train:
self.transform1 = AllAugmentationTransform(**augmentation_params["group1"]) ## flip & perspective
self.transform2 = AllAugmentationTransform(**augmentation_params["group2"]) ## jitter
else:
self.transform1 = None
self.transform2 = None
def __len__(self):
return len(self.videos)
def __getitem__(self, idx):
if self.is_train and self.id_sampling:
name = self.videos[idx]
path = np.random.choice(glob.glob(os.path.join(self.root_dir, name + '*.mp4')))
else:
name = self.videos[idx]
path = os.path.join(self.root_dir, name)
video_name = os.path.basename(path)
if self.is_train and os.path.isdir(path):
frames = os.listdir(path)
num_frames = len(frames)
frame_idx = np.sort(np.random.choice(num_frames, replace=True, size=2))
if self.frame_shape is not None:
resize_fn = partial(resize, output_shape=self.frame_shape)
else:
resize_fn = img_as_float32
if type(frames[0]) is bytes:
video_array = [resize_fn(io.imread(os.path.join(path, frames[idx].decode('utf-8')))) for idx in frame_idx]
if video_name in self.mask_video:
mask_array = [resize_fn(io.imread(os.path.join(self.root, 'train_mask', video_name, frames[idx].decode('utf-8').split('.')[0]+'.png'))[:,:,None]) for idx in frame_idx]
else:
mask_array = [np.zeros_like(img)[:,:,:1] for img in video_array]
else:
video_array = [resize_fn(io.imread(os.path.join(path, frames[idx]))) for idx in frame_idx]
if video_name in self.mask_video:
mask_array = [resize_fn(io.imread(os.path.join(self.root, 'train_mask', video_name, frames[idx].split('.')[0]+'.png'))[:,:,None]) for idx in frame_idx]
else:
mask_array = [np.zeros_like(img)[:,:,:1] for img in video_array]
# print("##",video_array[0].shape, mask_array[0].shape)
else:
video_array = read_video(path, frame_shape=self.frame_shape)
num_frames = len(video_array)
frame_idx = np.sort(np.random.choice(num_frames, replace=True, size=2)) if self.is_train else range(
num_frames)
video_array = video_array[frame_idx]
if self.transform1 is not None:
video_mask_cat = [np.concatenate((video_array[i], mask_array[i]), axis=2) for i in range(2)]
video_mask_cat = self.transform1(video_mask_cat)
video_array = [vimg[:,:,:3] for vimg in video_mask_cat]
mask_array = [vimg[:,:,3:] for vimg in video_mask_cat]
if self.transform2:
video_array = self.transform2(video_array)
# print(mask_array[0].sum(), mask_array[1].sum())
out = {}
if self.is_train:
source = np.array(video_array[0], dtype='float32')
driving = np.array(video_array[1], dtype='float32')
source_mask = np.array(mask_array[0], dtype='float32')
driving_mask = np.array(mask_array[1], dtype='float32')
out['driving'] = driving.transpose((2, 0, 1))
out['source'] = source.transpose((2, 0, 1))
out['driving_mask'] = driving_mask.transpose((2, 0, 1))
out['source_mask'] = source_mask.transpose((2, 0, 1))
else:
video = np.array(video_array, dtype='float32')
out['video'] = video.transpose((3, 0, 1, 2))
out['name'] = video_name
return out
class DatasetRepeater(Dataset):
"""
Pass several times over the same dataset for better i/o performance
"""
def __init__(self, dataset, num_repeats=100):
self.dataset = dataset
self.num_repeats = num_repeats
def __len__(self):
return self.num_repeats * self.dataset.__len__()
def __getitem__(self, idx):
return self.dataset[idx % self.dataset.__len__()]
class PairedDataset(Dataset):
"""
Dataset of pairs for animation.
"""
def __init__(self, initial_dataset, number_of_pairs, seed=0):
self.initial_dataset = initial_dataset
pairs_list = self.initial_dataset.pairs_list
np.random.seed(seed)
if pairs_list is None:
max_idx = min(number_of_pairs, len(initial_dataset))
nx, ny = max_idx, max_idx
xy = np.mgrid[:nx, :ny].reshape(2, -1).T
number_of_pairs = min(xy.shape[0], number_of_pairs)
self.pairs = xy.take(np.random.choice(xy.shape[0], number_of_pairs, replace=False), axis=0)
else:
videos = self.initial_dataset.videos
name_to_index = {name: index for index, name in enumerate(videos)}
pairs = pd.read_csv(pairs_list)
pairs = pairs[np.logical_and(pairs['source'].isin(videos), pairs['driving'].isin(videos))]
number_of_pairs = min(pairs.shape[0], number_of_pairs)
self.pairs = []
self.start_frames = []
for ind in range(number_of_pairs):
self.pairs.append(
(name_to_index[pairs['driving'].iloc[ind]], name_to_index[pairs['source'].iloc[ind]]))
def __len__(self):
return len(self.pairs)
def __getitem__(self, idx):
pair = self.pairs[idx]
first = self.initial_dataset[pair[0]]
second = self.initial_dataset[pair[1]]
first = {'driving_' + key: value for key, value in first.items()}
second = {'source_' + key: value for key, value in second.items()}
return {**first, **second}