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dataset.py
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import torch, torchvision
from torch.utils import data
from torchvision import transforms
import math, random, os, scipy
import numpy as np
from PIL import Image
from utils_data import *
#########################################################################
# Images TRAINING SETTINGS
#########################################################################
img_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
map_transform = transforms.Compose([
transforms.ToTensor()
])
fix_transform = transforms.Compose([
transforms.ToTensor()
])
class SALICON(data.Dataset):
def __init__(self, root, classes='train',
img_transform=img_transform, map_transform=map_transform, fix_transform=fix_transform):
self.root = os.path.expanduser(root)
self.img_transform = img_transform
self.map_transform = map_transform
self.fix_transform = fix_transform
# dset_opts = ['train', 'val', 'test']
self.classes = classes
imgs_path = os.path.join(self.root, self.classes, 'images/')
self.imgs_list = [imgs_path + f for f in os.listdir(imgs_path) if f.endswith(('.jpg', '.jpeg', '.png'))]
self.imgs_list.sort()
if self.classes == 'test':
self.maps_list = []
self.fixs_list = []
else:
maps_path = os.path.join(self.root, self.classes, 'maps/')
fixs_path = os.path.join(self.root, self.classes, 'fixations', 'maps/')
self.maps_list = [maps_path + f for f in os.listdir(maps_path) if f.endswith(('.jpg', '.jpeg', '.png'))]
self.fixs_list = [fixs_path + f for f in os.listdir(fixs_path) if f.endswith('.mat')]
self.maps_list.sort()
self.fixs_list.sort()
def __getitem__(self, index):
img_path = self.imgs_list[index]
img = Image.open(img_path).convert('RGB')
img_name = os.path.split(img_path)[1][:-4]
img_size = (img.size[1], img.size[0])
if self.img_transform is not None:
img = self.img_transform(img)
if self.classes == 'test':
return img, img_name, img_size
else:
map_path = self.maps_list[index]
map = Image.open(map_path).convert('L')
fix_path = self.fixs_list[index]
fix = scipy.io.loadmat(fix_path)["I"]
if self.map_transform is not None:
map = self.map_transform(map)
if self.fix_transform is not None:
fix = self.fix_transform(fix)
return img, map, fix, img_name, img_size
def __len__(self):
return len(self.imgs_list)
def get_datasize(self):
return len(self.imgs_list)
def salicon_loader(datapath, classes='train', iosize=[480, 640, 60, 80], batch_size=4, num_workers=0):
input_h, input_w, target_h, target_w = iosize
img_transform = transforms.Compose([
transforms.Resize((input_h, input_w)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
map_transform = transforms.Compose([
transforms.Resize((target_h, target_w)),
transforms.ToTensor()
])
fix_transform = transforms.Compose([
transforms.Lambda(lambda x: padding_fixation(x, shape_r=target_h, shape_c=target_w)),
transforms.Lambda(lambda x: np.expand_dims(x, axis=2)),
transforms.ToTensor()
])
if classes == 'train':
shuffle = True
else:
shuffle = False
dataset = SALICON(root=datapath, classes=classes, img_transform=img_transform, map_transform=map_transform,
fix_transform=fix_transform)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
return dataloader
class VideoData(data.Dataset):
def __init__(self, root, classes='train', MaxFrame=float('inf'), iosize=[360, 640, 45, 80], ext='.avi'):
self.root = os.path.expanduser(root)
self.classes = classes
assert self.classes.lower() in ['train', 'val', 'test']
self.MaxFrame = MaxFrame
self.iosize = iosize
videos_list, vidmaps_list, vidfixs_list = read_video_list(root, classes, shuffle=False, ext=ext)
self.vids_list = videos_list
self.maps_list = vidmaps_list
self.fixs_list = vidfixs_list
def __getitem__(self, index):
vidname = self.vids_list[index]
shape_r, shape_c, shape_r_out, shape_c_out = self.iosize
vidimgs, nframes, height, width = preprocess_videos(self.vids_list[index], shape_r, shape_c,
self.MaxFrame, mode='RGB', normalize=False)
if self.classes.lower() in ['test']:
return vidname, vidimgs, nframes, height, width
vidmaps = preprocess_vidmaps(self.maps_list[index], shape_r_out, shape_c_out, self.MaxFrame)
vidfixs = preprocess_vidfixs(self.fixs_list[index], shape_r_out, shape_c_out, self.MaxFrame)
nframes = min(min(vidfixs.shape[0], vidmaps.shape[0]), nframes)
vidimgs = vidimgs[:nframes].transpose((0, 3, 1, 2))
vidgaze = np.concatenate((vidmaps[:nframes], vidfixs[:nframes]), axis=-1).transpose((0, 3, 1, 2))
return vidname, vidimgs, vidgaze
def __len__(self):
return len(self.vids_list)
def get_datasize(self):
return len(self.vids_list)
def video_loader(datapath, classes='train', iosize=[360, 640, 45, 80], MaxFrame=float('inf'), batch_size=4,
shuffle=False, num_workers=0, ext='.avi'):
dataset = VideoData(root=datapath, classes=classes, MaxFrame=MaxFrame, iosize=iosize, ext=ext)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
return dataloader
def read_traindata_list(datapath, phase_gen='train', shuffle=True):
if phase_gen in ['train', 'val', 'test']:
txt_path = datapath + '/txt/' + phase_gen + '.txt'
videos_path = datapath + '/vidmat/'
vidgaze_path = datapath + '/labels/'
else:
raise NotImplementedError
f = open(txt_path)
lines = f.readlines()
lines.sort()
if shuffle:
random.shuffle(lines)
videos = [videos_path + f.strip('\n') + '.mat' for f in lines]
labels = [vidgaze_path + f.strip('\n') + '.mat' for f in lines]
f.close()
return videos, labels
class TrainData(data.Dataset):
def __init__(self, root, classes='train', MaxFrame=float('inf')):
self.root = os.path.expanduser(root)
self.classes = classes
assert self.classes.lower() in ['train', 'val', 'test']
self.MaxFrame = MaxFrame
videos_list, labels_list = read_traindata_list(root, classes, shuffle=False)
self.vids_list = videos_list
self.labs_list = labels_list
def __getitem__(self, index):
vidname = self.vids_list[index]
viddata = h5io.loadmat(self.vids_list[index])
vidimgs = viddata["videos"]
if self.classes.lower() in ['test']:
return vidname, vidimgs, min(vidimgs.shape[0], self.MaxFrame), viddata["oh"], viddata["ow"]
vidgaze = h5io.loadmat(self.labs_list[index])["gazemap"]
nframes = min(min(vidimgs.shape[0], vidgaze.shape[0]), self.MaxFrame)
vidimgs = vidimgs[:nframes]
vidgaze = vidgaze[:nframes]
return vidname, vidimgs, vidgaze
def __len__(self):
return len(self.vids_list)
def get_datasize(self):
return len(self.vids_list)
def train_loader(datapath, classes='train', MaxFrame=float('inf'), batch_size=4, shuffle=False, num_workers=0):
dataset = TrainData(root=datapath, classes=classes, MaxFrame=MaxFrame)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
return dataloader
class TestData(data.Dataset):
def __init__(self, root, MaxFrame=float('inf'), iosize=[360, 640, 45, 80]):
self.root = os.path.expanduser(root)
self.MaxFrame = MaxFrame
self.iosize = iosize
videos_list = [root + f for f in os.listdir(root) if (f.endswith('.avi') or f.endswith('.AVI') or f.endswith('.mp4'))]
self.vids_list = videos_list
def __getitem__(self, index):
shape_r, shape_c, shape_r_out, shape_c_out = self.iosize
vidimgs, nframes, height, width = preprocess_videos(self.vids_list[index], shape_r, shape_c,
self.MaxFrame, mode='RGB', normalize=False)
vidimgs = vidimgs.transpose((0, 3, 1, 2))
vidname = self.vids_list[index]
return vidname, vidimgs, nframes, height, width
def __len__(self):
return len(self.vids_list)
def get_datasize(self):
return len(self.vids_list)
def test_loader(datapath, iosize=[360, 640, 45, 80], MaxFrame=float('inf'), batch_size=4,
shuffle=False, num_workers=0):
dataset = TestData(root=datapath, MaxFrame=MaxFrame, iosize=iosize)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
return dataloader