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dataset.py
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dataset.py
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import numpy as np
import random
from PIL import Image
from torch.utils.data import Dataset
import os
def make_dataset(image_list, label_list, au_relation=None):
len_ = len(image_list)
if au_relation is not None:
images = [(image_list[i].strip(), label_list[i, :],au_relation[i,:]) for i in range(len_)]
else:
images = [(image_list[i].strip(), label_list[i, :]) for i in range(len_)]
return images
def pil_loader(path):
with open(path, 'rb') as f:
with Image.open(f) as img:
return img.convert('RGB')
def default_loader(path):
return pil_loader(path)
class BP4D(Dataset):
def __init__(self, root_path, train=True, fold = 1, transform=None, crop_size = 224, stage=1, loader=default_loader):
assert fold>0 and fold <=3, 'The fold num must be restricted from 1 to 3'
assert stage>0 and stage <=2, 'The stage num must be restricted from 1 to 2'
self._root_path = root_path
self._train = train
self._stage = stage
self._transform = transform
self.crop_size = crop_size
self.loader = loader
self.img_folder_path = os.path.join(root_path,'img')
if self._train:
# img
train_image_list_path = os.path.join(root_path, 'list', 'BP4D_train_img_path_fold' + str(fold) +'.txt')
train_image_list = open(train_image_list_path).readlines()
# img labels
train_label_list_path = os.path.join(root_path, 'list', 'BP4D_train_label_fold' + str(fold) + '.txt')
train_label_list = np.loadtxt(train_label_list_path)
# AU relation
if self._stage == 2:
au_relation_list_path = os.path.join(root_path, 'list', 'BP4D_train_AU_relation_fold' + str(fold) + '.txt')
au_relation_list = np.loadtxt(au_relation_list_path)
self.data_list = make_dataset(train_image_list, train_label_list, au_relation_list)
else:
self.data_list = make_dataset(train_image_list, train_label_list)
else:
# img
test_image_list_path = os.path.join(root_path, 'list', 'BP4D_test_img_path_fold' + str(fold) + '.txt')
test_image_list = open(test_image_list_path).readlines()
# img labels
test_label_list_path = os.path.join(root_path, 'list', 'BP4D_test_label_fold' + str(fold) + '.txt')
test_label_list = np.loadtxt(test_label_list_path)
self.data_list = make_dataset(test_image_list, test_label_list)
def __getitem__(self, index):
if self._stage == 2 and self._train:
img, label, au_relation = self.data_list[index]
img = self.loader(os.path.join(self.img_folder_path, img))
w, h = img.size
offset_y = random.randint(0, h - self.crop_size)
offset_x = random.randint(0, w - self.crop_size)
flip = random.randint(0, 1)
if self._transform is not None:
img = self._transform(img, flip, offset_x, offset_y)
return img, label, au_relation
else:
img, label = self.data_list[index]
img = self.loader(os.path.join(self.img_folder_path, img))
if self._train:
w, h = img.size
offset_y = random.randint(0, h - self.crop_size)
offset_x = random.randint(0, w - self.crop_size)
flip = random.randint(0, 1)
if self._transform is not None:
img = self._transform(img, flip, offset_x, offset_y)
else:
if self._transform is not None:
img = self._transform(img)
return img, label
def __len__(self):
return len(self.data_list)
class DISFA(Dataset):
def __init__(self, root_path, train=True, fold = 1, transform=None, crop_size = 224, stage=1, loader=default_loader):
assert fold>0 and fold <=3, 'The fold num must be restricted from 1 to 3'
assert stage>0 and stage <=2, 'The stage num must be restricted from 1 to 2'
self._root_path = root_path
self._train = train
self._stage = stage
self._transform = transform
self.crop_size = crop_size
self.loader = loader
self.img_folder_path = os.path.join(root_path,'img')
if self._train:
# img
train_image_list_path = os.path.join(root_path, 'list', 'DISFA_train_img_path_fold' + str(fold) + '.txt')
train_image_list = open(train_image_list_path).readlines()
# img labels
train_label_list_path = os.path.join(root_path, 'list', 'DISFA_train_label_fold' + str(fold) + '.txt')
train_label_list = np.loadtxt(train_label_list_path)
# AU relation
if self._stage == 2:
au_relation_list_path = os.path.join(root_path, 'list', 'DISFA_train_AU_relation_fold' + str(fold) + '.txt')
au_relation_list = np.loadtxt(au_relation_list_path)
self.data_list = make_dataset(train_image_list, train_label_list, au_relation_list)
else:
self.data_list = make_dataset(train_image_list, train_label_list)
else:
# img
test_image_list_path = os.path.join(root_path, 'list', 'DISFA_test_img_path_fold' + str(fold) + '.txt')
test_image_list = open(test_image_list_path).readlines()
# img labels
test_label_list_path = os.path.join(root_path, 'list', 'DISFA_test_label_fold' + str(fold) + '.txt')
test_label_list = np.loadtxt(test_label_list_path)
self.data_list = make_dataset(test_image_list, test_label_list)
def __getitem__(self, index):
if self._stage == 2 and self._train:
img, label, au_relation = self.data_list[index]
img = self.loader(os.path.join(self.img_folder_path, img))
w, h = img.size
offset_y = random.randint(0, h - self.crop_size)
offset_x = random.randint(0, w - self.crop_size)
flip = random.randint(0, 1)
if self._transform is not None:
img = self._transform(img, flip, offset_x, offset_y)
return img, label, au_relation
else:
img, label = self.data_list[index]
img = self.loader(os.path.join(self.img_folder_path,img))
if self._train:
w, h = img.size
offset_y = random.randint(0, h - self.crop_size)
offset_x = random.randint(0, w - self.crop_size)
flip = random.randint(0, 1)
if self._transform is not None:
img = self._transform(img, flip, offset_x, offset_y)
else:
if self._transform is not None:
img = self._transform(img)
return img, label
def __len__(self):
return len(self.data_list)