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#CUDA_VISIBLE_DEVICES=1 python run.py --config log_TH1K/finetune-th1k-spade.yml --device_ids 0 --checkpoint log_TH1K/00000001-checkpoint.pth.tar
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 functools import partial
from skimage.transform import resize
import torch
import random
import numpy as np
from torch.utils.data import Dataset
import pandas as pd
from augmentation import AllAugmentationTransform
import glob
import math
import pickle
from basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels
from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
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, 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
tmp_file = open(root_dir + 'train_file_list.pickle','rb')
self.train_files_list = pickle.load(tmp_file)
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)
train_videos = list(self.train_files_list.keys())
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:
self.transform = AllAugmentationTransform(**augmentation_params)
#### for degradation ####
self.kernel_range = [2 * v + 1 for v in range(1,3)]
self.pulse_tensor = torch.zeros(11, 11).float()
self.pulse_tensor[5, 5] = 1
self.resize_range = [0.15, 1.5]
# blur settings for the first degradation
self.blur_kernel_size = 7
self.kernel_list = ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
self.kernel_prob = [0.45, 0.25, 0.12, 0.03, 0.12, 0.03] # a list for each kernel probability
self.blur_sigma = [0.1, 0.5]
self.betag_range = [0.2, 1] # betag used in generalized Gaussian blur kernels
self.betap_range = [0.5, 1.2] # betap used in plateau blur kernels
self.sinc_prob = 0.1 # the probability for sinc filters
# blur settings for the second degradation
self.blur_kernel_size2 = 7
self.kernel_list2 = ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
self.kernel_prob2 = [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
self.blur_sigma2 = [0.1, 0.5]
self.betag_range2 = [0.2, 1]
self.betap_range2 = [1, 1.2]
self.sinc_prob2 = 0.1
else:
self.transform = 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')))
name = self.videos[idx]
choice_list = self.train_files_list[name]
# if len(choice_list) == 0:
# name = self.videos[idx-1]
# choice_list = self.train_files_list[name]
paths = np.random.choice(choice_list)
else:
name = self.videos[idx]
paths = os.path.join(self.root_dir, name)
video_name = os.path.basename(paths)
if self.is_train and os.path.isdir(paths):
frames = os.listdir(paths)
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
video_array = [resize_fn(img_as_float32(io.imread(paths + '/' + '%06d.jpg'%(idx) ))) for idx in frame_idx]
else:
video_array = read_video(paths, 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 = self.transform(video_array)
out = {}
if self.is_train:
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))
# if self.degradation:
############ run degradation ############
# ---- Generate kernels (used in the first degradation) ---- #
kernel_size = random.choice(self.kernel_range)
if np.random.uniform() < 0.1:
# this sinc filter setting is for kernels ranging from [7, 21]
if kernel_size < 11:
omega_c = np.random.uniform(np.pi / 3, np.pi)
else:
omega_c = np.random.uniform(np.pi / 5, np.pi)
kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)
else:
kernel = random_mixed_kernels(
self.kernel_list,
self.kernel_prob,
kernel_size,
self.blur_sigma,
self.blur_sigma, [-math.pi, math.pi],
self.betag_range,
self.betap_range,
noise_range=None)
# pad kernel
pad_size = (21 - kernel_size) // 2
kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size)))
# ----- Generate kernels (used in the second degradation) ---- #
kernel_size = random.choice(self.kernel_range)
if np.random.uniform() < 0.1:
if kernel_size < 13:
omega_c = np.random.uniform(np.pi / 3, np.pi)
else:
omega_c = np.random.uniform(np.pi / 5, np.pi)
kernel2 = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)
else:
kernel2 = random_mixed_kernels(
self.kernel_list2,
self.kernel_prob2,
kernel_size,
self.blur_sigma2,
self.blur_sigma2, [-math.pi, math.pi],
self.betag_range2,
self.betap_range2,
noise_range=None)
# pad kernel
pad_size = (21 - kernel_size) // 2
kernel2 = np.pad(kernel2, ((pad_size, pad_size), (pad_size, pad_size)))
# ---- the final sinc kernel ---- #
if np.random.uniform() < 0.8:
kernel_size = random.choice(self.kernel_range)
omega_c = np.random.uniform(np.pi / 3, np.pi)
sinc_kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=11)
sinc_kernel = torch.FloatTensor(sinc_kernel)
else:
sinc_kernel = self.pulse_tensor
# BGR to RGB, HWC to CHW, numpy to tensor
# img_gt = img2tensor([img_gt], bgr2rgb=True, float32=True)[0]
kernel = torch.FloatTensor(kernel)
kernel2 = torch.FloatTensor(kernel2)
#########################################
out['kernel'] = kernel
out['kernel2']= kernel2
out['sinc_kernel'] = sinc_kernel
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__()]
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