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frames_dataset.py
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frames_dataset.py
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import os
import cv2
from skimage import io, img_as_float32
from skimage.color import gray2rgb
from sklearn.model_selection import train_test_split
from imageio import mimread
import numpy as np
from torch.utils.data import Dataset
import pandas as pd
from augmentation import AllAugmentationTransform
import glob
import json
import pickle
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 = np.array(mimread(name))
if len(video.shape) == 3:
video = np.array([gray2rgb(frame) for frame in 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, meta_dir, frame_shape=(256, 256, 3), id_sampling=False, is_train=True,
random_seed=0, pairs_list=None, augmentation_params=None):
self.root_dir = root_dir
self.videos = os.listdir(root_dir)
self.frame_shape = tuple(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'))
self.root_dir = os.path.join(self.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.is_train = is_train
if self.is_train and augmentation_params is not None:
self.transform = AllAugmentationTransform(**augmentation_params)
else:
self.transform = None
self.meta_dir = meta_dir
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))
video_array = [img_as_float32(io.imread(os.path.join(path, frames[idx]))) for idx in frame_idx]
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.transform is not None:
video_array, t_flip, h_flip = self.transform(video_array)
if t_flip:
frame_idx = frame_idx[::-1]
out = {}
if self.is_train:
f = open(os.path.join(self.meta_dir, video_name.split('.')[0] + '.pkl'), 'rb')
video_meta = pickle.load(f)
f.close()
source = np.array(video_array[0], dtype='float32')
driving = np.array(video_array[1], dtype='float32')
out['driving'] = driving.transpose((2, 0, 1))
out['source'] = source.transpose((2, 0, 1))
out['source_ldmk_2d'] = video_meta[frame_idx[0]]['ldmk']
out['driving_ldmk_2d'] = video_meta[frame_idx[1]]['ldmk']
if h_flip:
out['source_ldmk_2d'][:, 0] = source.shape[1] - out['source_ldmk_2d'][:, 0]
out['driving_ldmk_2d'][:, 0] = driving.shape[1] - out['driving_ldmk_2d'][:, 0]
else:
video = np.array(video_array, dtype='float32')
out['video'] = video.transpose((3, 0, 1, 2))
out['name'] = video_name
return out
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}
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 ImageDataset(Dataset):
'''
Dataset of images for the pre-training of tdmm estimator
'''
def __init__(self, data_dir, meta_dir=None, augmentation_params=None):
if augmentation_params:
self.transform = AllAugmentationTransform(**augmentation_params)
else:
self.transform = None
self.data_list = glob.glob(os.path.join(data_dir, '*', '*.png'))
self.meta_dir = meta_dir
def __len__(self):
return len(self.data_list)
def __getitem__(self, idx):
image_pth = self.data_list[idx]
frame_idx = int(os.path.basename(image_pth).split('.')[0])
video_name = image_pth.split('/')[-2]
image = [img_as_float32(io.imread(image_pth))]
if self.transform:
image, _, f_flip = self.transform(image)
image = np.array(image[0], dtype='float32')
out = {}
out['image'] = image.transpose((2, 0, 1))
if self.meta_dir is not None:
f = open(os.path.join(self.meta_dir, video_name.split('.')[0] + '.pkl'), 'rb')
video_meta = pickle.load(f)
f.close()
out['ldmk'] = video_meta[frame_idx]['ldmk']
if f_flip:
out['ldmk'][:, 0] = image.shape[1] - out['ldmk'][:, 0]
return out