File size: 11,164 Bytes
7f1f1cb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 |
import os
from PIL import Image
import cv2
import torch
from torch.utils import data
from torchvision import transforms
from torchvision.transforms import functional as F
import numbers
import numpy as np
import random
#re_size = (256, 256)
#cr_size = (224, 224)
class ImageDataTrain(data.Dataset):
def __init__(self):
self.sal_root = '/home/liuj/dataset/DUTS/DUTS-TR'
self.sal_source = '/home/liuj/dataset/DUTS/DUTS-TR/train_pair_edge.lst'
with open(self.sal_source, 'r') as f:
self.sal_list = [x.strip() for x in f.readlines()]
self.sal_num = len(self.sal_list)
def __getitem__(self, item):
sal_image = load_image(os.path.join(self.sal_root, self.sal_list[item%self.sal_num].split()[0]))
sal_label = load_sal_label(os.path.join(self.sal_root, self.sal_list[item%self.sal_num].split()[1]))
sal_edge = load_edge_label(os.path.join(self.sal_root, self.sal_list[item%self.sal_num].split()[2]))
sal_image, sal_label, sal_edge = cv_random_flip(sal_image, sal_label, sal_edge)
sal_image = torch.Tensor(sal_image)
sal_label = torch.Tensor(sal_label)
sal_edge = torch.Tensor(sal_edge)
sample = {'sal_image': sal_image, 'sal_label': sal_label, 'sal_edge': sal_edge}
return sample
def __len__(self):
# return max(max(self.edge_num, self.sal_num), self.skel_num)
return self.sal_num
class ImageDataTest(data.Dataset):
def __init__(self, test_mode=1, sal_mode='e'):
if test_mode == 0:
# self.image_root = '/home/liuj/dataset/saliency_test/ECSSD/Imgs/'
# self.image_source = '/home/liuj/dataset/saliency_test/ECSSD/test.lst'
self.image_root = '/home/liuj/dataset/HED-BSDS_PASCAL/HED-BSDS/test/'
self.image_source = '/home/liuj/dataset/HED-BSDS_PASCAL/HED-BSDS/test.lst'
elif test_mode == 1:
if sal_mode == 'e':
self.image_root = '/home/liuj/dataset/saliency_test/ECSSD/Imgs/'
self.image_source = '/home/liuj/dataset/saliency_test/ECSSD/test.lst'
self.test_fold = '/media/ubuntu/disk/Result/saliency/ECSSD/'
elif sal_mode == 'p':
self.image_root = '/home/liuj/dataset/saliency_test/PASCALS/Imgs/'
self.image_source = '/home/liuj/dataset/saliency_test/PASCALS/test.lst'
self.test_fold = '/media/ubuntu/disk/Result/saliency/PASCALS/'
elif sal_mode == 'd':
self.image_root = '/home/liuj/dataset/saliency_test/DUTOMRON/Imgs/'
self.image_source = '/home/liuj/dataset/saliency_test/DUTOMRON/test.lst'
self.test_fold = '/media/ubuntu/disk/Result/saliency/DUTOMRON/'
elif sal_mode == 'h':
self.image_root = '/home/liuj/dataset/saliency_test/HKU-IS/Imgs/'
self.image_source = '/home/liuj/dataset/saliency_test/HKU-IS/test.lst'
self.test_fold = '/media/ubuntu/disk/Result/saliency/HKU-IS/'
elif sal_mode == 's':
self.image_root = '/home/liuj/dataset/saliency_test/SOD/Imgs/'
self.image_source = '/home/liuj/dataset/saliency_test/SOD/test.lst'
self.test_fold = '/media/ubuntu/disk/Result/saliency/SOD/'
elif sal_mode == 'm':
self.image_root = '/home/liuj/dataset/saliency_test/MSRA/Imgs/'
self.image_source = '/home/liuj/dataset/saliency_test/MSRA/test.lst'
elif sal_mode == 'o':
self.image_root = '/home/liuj/dataset/saliency_test/SOC/TestSet/Imgs/'
self.image_source = '/home/liuj/dataset/saliency_test/SOC/TestSet/test.lst'
self.test_fold = '/media/ubuntu/disk/Result/saliency/SOC/'
elif sal_mode == 't':
self.image_root = '/home/liuj/dataset/DUTS/DUTS-TE/DUTS-TE-Image/'
self.image_source = '/home/liuj/dataset/DUTS/DUTS-TE/test.lst'
self.test_fold = '/media/ubuntu/disk/Result/saliency/DUTS/'
elif test_mode == 2:
self.image_root = '/home/liuj/dataset/SK-LARGE/images/test/'
self.image_source = '/home/liuj/dataset/SK-LARGE/test.lst'
with open(self.image_source, 'r') as f:
self.image_list = [x.strip() for x in f.readlines()]
self.image_num = len(self.image_list)
def __getitem__(self, item):
image, im_size = load_image_test(os.path.join(self.image_root, self.image_list[item]))
image = torch.Tensor(image)
return {'image': image, 'name': self.image_list[item%self.image_num], 'size': im_size}
def save_folder(self):
return self.test_fold
def __len__(self):
# return max(max(self.edge_num, self.skel_num), self.sal_num)
return self.image_num
# get the dataloader (Note: without data augmentation, except saliency with random flip)
def get_loader(batch_size, mode='train', num_thread=1, test_mode=0, sal_mode='e'):
shuffle = False
if mode == 'train':
shuffle = True
dataset = ImageDataTrain()
else:
dataset = ImageDataTest(test_mode=test_mode, sal_mode=sal_mode)
data_loader = data.DataLoader(dataset=dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_thread)
return data_loader, dataset
def load_image(pah):
if not os.path.exists(pah):
print('File Not Exists')
im = cv2.imread(pah)
in_ = np.array(im, dtype=np.float32)
# in_ = cv2.resize(in_, im_sz, interpolation=cv2.INTER_CUBIC)
# in_ = in_[:,:,::-1] # only if use PIL to load image
in_ -= np.array((104.00699, 116.66877, 122.67892))
in_ = in_.transpose((2,0,1))
return in_
def load_image_test(pah):
if not os.path.exists(pah):
print('File Not Exists')
im = cv2.imread(pah)
in_ = np.array(im, dtype=np.float32)
im_size = tuple(in_.shape[:2])
# in_ = cv2.resize(in_, (cr_size[1], cr_size[0]), interpolation=cv2.INTER_LINEAR)
# in_ = in_[:,:,::-1] # only if use PIL to load image
in_ -= np.array((104.00699, 116.66877, 122.67892))
in_ = in_.transpose((2,0,1))
return in_, im_size
def load_edge_label(pah):
"""
pixels > 0.5 -> 1
Load label image as 1 x height x width integer array of label indices.
The leading singleton dimension is required by the loss.
"""
if not os.path.exists(pah):
print('File Not Exists')
im = Image.open(pah)
label = np.array(im, dtype=np.float32)
if len(label.shape) == 3:
label = label[:,:,0]
# label = cv2.resize(label, im_sz, interpolation=cv2.INTER_NEAREST)
label = label / 255.
label[np.where(label > 0.5)] = 1.
label = label[np.newaxis, ...]
return label
def load_skel_label(pah):
"""
pixels > 0 -> 1
Load label image as 1 x height x width integer array of label indices.
The leading singleton dimension is required by the loss.
"""
if not os.path.exists(pah):
print('File Not Exists')
im = Image.open(pah)
label = np.array(im, dtype=np.float32)
if len(label.shape) == 3:
label = label[:,:,0]
# label = cv2.resize(label, im_sz, interpolation=cv2.INTER_NEAREST)
label = label / 255.
label[np.where(label > 0.)] = 1.
label = label[np.newaxis, ...]
return label
def load_sal_label(pah):
"""
Load label image as 1 x height x width integer array of label indices.
The leading singleton dimension is required by the loss.
"""
if not os.path.exists(pah):
print('File Not Exists')
im = Image.open(pah)
label = np.array(im, dtype=np.float32)
if len(label.shape) == 3:
label = label[:,:,0]
# label = cv2.resize(label, im_sz, interpolation=cv2.INTER_NEAREST)
label = label / 255.
label = label[np.newaxis, ...]
return label
def load_sem_label(pah):
"""
Load label image as 1 x height x width integer array of label indices.
The leading singleton dimension is required by the loss.
"""
if not os.path.exists(pah):
print('File Not Exists')
im = Image.open(pah)
label = np.array(im, dtype=np.float32)
if len(label.shape) == 3:
label = label[:,:,0]
# label = cv2.resize(label, im_sz, interpolation=cv2.INTER_NEAREST)
# label = label / 255.
label = label[np.newaxis, ...]
return label
def edge_thres_transform(x, thres):
# y0 = torch.zeros(x.size())
y1 = torch.ones(x.size())
x = torch.where(x >= thres, y1, x)
return x
def skel_thres_transform(x, thres):
y0 = torch.zeros(x.size())
y1 = torch.ones(x.size())
x = torch.where(x > thres, y1, y0)
return x
def cv_random_flip(img, label, edge):
flip_flag = random.randint(0, 1)
if flip_flag == 1:
img = img[:,:,::-1].copy()
label = label[:,:,::-1].copy()
edge = edge[:,:,::-1].copy()
return img, label, edge
def cv_random_crop_flip(img, label, resize_size, crop_size, random_flip=True):
def get_params(img_size, output_size):
h, w = img_size
th, tw = output_size
if w == tw and h == th:
return 0, 0, h, w
i = random.randint(0, h - th)
j = random.randint(0, w - tw)
return i, j, th, tw
if random_flip:
flip_flag = random.randint(0, 1)
img = img.transpose((1,2,0)) # H, W, C
label = label[0,:,:] # H, W
img = cv2.resize(img, (resize_size[1], resize_size[0]), interpolation=cv2.INTER_LINEAR)
label = cv2.resize(label, (resize_size[1], resize_size[0]), interpolation=cv2.INTER_NEAREST)
i, j, h, w = get_params(resize_size, crop_size)
img = img[i:i+h, j:j+w, :].transpose((2,0,1)) # C, H, W
label = label[i:i+h, j:j+w][np.newaxis, ...] # 1, H, W
if flip_flag == 1:
img = img[:,:,::-1].copy()
label = label[:,:,::-1].copy()
return img, label
def random_crop(img, label, size, padding=None, pad_if_needed=True, fill_img=(123, 116, 103), fill_label=0, padding_mode='constant'):
def get_params(img, output_size):
w, h = img.size
th, tw = output_size
if w == tw and h == th:
return 0, 0, h, w
i = random.randint(0, h - th)
j = random.randint(0, w - tw)
return i, j, th, tw
if isinstance(size, numbers.Number):
size = (int(size), int(size))
if padding is not None:
img = F.pad(img, padding, fill_img, padding_mode)
label = F.pad(label, padding, fill_label, padding_mode)
# pad the width if needed
if pad_if_needed and img.size[0] < size[1]:
img = F.pad(img, (int((1 + size[1] - img.size[0]) / 2), 0), fill_img, padding_mode)
label = F.pad(label, (int((1 + size[1] - label.size[0]) / 2), 0), fill_label, padding_mode)
# pad the height if needed
if pad_if_needed and img.size[1] < size[0]:
img = F.pad(img, (0, int((1 + size[0] - img.size[1]) / 2)), fill_img, padding_mode)
label = F.pad(label, (0, int((1 + size[0] - label.size[1]) / 2)), fill_label, padding_mode)
i, j, h, w = get_params(img, size)
return [F.crop(img, i, j, h, w), F.crop(label, i, j, h, w)]
|