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data_new.py
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import os, random
from PIL import Image
import torch.utils.data as data
import torchvision.transforms as transforms
import numpy as np
import torch
#mean = np.array((104.00699, 116.66877, 122.67892)).reshape((1, 1, 3))
mean = np.array([0.485, 0.456, 0.406]).reshape([1, 1, 3])
std = np.array([0.229, 0.224, 0.225]).reshape([1, 1, 3])
def rgb_loader(path):
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('RGB')
def binary_loader(path):
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('L')
def get_image_list(name, config, phase):
images = []
gts = []
image_root = os.path.join(config['data_path'], name, 'images')
if phase == 'train' and name == 'MSB-TR':
#tag = 'imagenet'
tag = 'moco'
#tag = 'segmentations'
#gt_root = os.path.join('data', name, 'crf')
else:
tag = 'segmentations'
print(tag)
gt_root = os.path.join(config['data_path'], name, tag)
'''
if name == 'MSB-TR':
with open(os.path.join(config['data_path'], name, '{}.txt'.format('train'))) as f:
lines = f.readlines()
images = sorted([os.path.join(image_root, '.'.join(line.split('.')[:-1]) + '.jpg') for line in lines])
gts = sorted([os.path.join(gt_root, '.'.join(line.split('.')[:-1]) + '.png') for line in lines])
else:
'''
images = sorted([os.path.join(image_root, f) for f in os.listdir(image_root) if f.endswith('.jpg')])
gts = sorted([os.path.join(gt_root, f) for f in os.listdir(gt_root) if f.endswith('.png')])
#print(len(images), len(gts))
'''
if name in ('DUTS-TR', 'SOD', 'PASCAL-S', 'ECSSD', 'HKU-IS', 'DUTS-TE', 'DUT-OMRON'):
image_root = os.path.join(config['data_path'], name, 'images')
gt_root = os.path.join(config['data_path'], name, 'segmentations')
#if os.path.exists(os.path.join('data/pseudo', name)):
# gt_root = os.path.join('data/pseudo', name)
#else:
# gt_root = os.path.join(config['data_path'], name, 'segmentations')
images = sorted([os.path.join(image_root, f) for f in os.listdir(image_root) if f.endswith('.jpg')])
gts = sorted([os.path.join(gt_root, f) for f in os.listdir(gt_root) if f.endswith('.png')])
#print(len(images), len(gts))
'''
return images, gts
def get_loader(config):
dataset = Train_Dataset(config['trset'], config)
data_loader = data.DataLoader(dataset=dataset,
batch_size=config['batch'],
shuffle=True,
num_workers=4,
pin_memory=True,
drop_last=True)
return data_loader
def random_light(x):
contrast = np.random.rand(1)+0.5
light = np.random.randint(-20,20)
x = contrast*x + light
return np.clip(x,0,255)
def rotate(img, gt):
angle = np.random.randint(-25,25)
img = img.rotate(angle)
gt = gt.rotate(angle)
return img, gt
class Train_Dataset(data.Dataset):
def __init__(self, config):
self.config = config
self.images_list, self.gts_list = get_image_list(config['trset'], config, 'train')
self.size = len(self.images_list)
self.dataset_name = config['trset']
self.images, self.gts = self.load_data(self.images_list, self.gts_list)
#self.images, self.gts = self.load_data(self.images_list[:10], self.gts_list[:10])
print(len(self.images), len(self.gts))
def load_data(self, images_list, gts_list):
images = []
gts = []
for image_path, gt_path in zip(images_list, gts_list):
image = Image.open(image_path).convert('RGB')
gt = Image.open(gt_path).convert('L')
if self.config['data_aug']:
image, gt = rotate(image, gt)
image = random_light(image)
img_size = self.config['size']
image = image.resize((img_size, img_size))
gt = gt.resize((img_size, img_size))
image = np.array(image).astype(np.float32)
gt = np.array(gt)
#print(image.shape, gt.shape)
if random.random() > 0.5:
image = image[:, ::-1]
gt = gt[:, ::-1]
image = ((image / 255.) - mean) / std
image = image.transpose((2, 0, 1))
gt = np.expand_dims(gt / 255., axis=0)
images.append(image)
gts.append(gt)
return torch.tensor(np.array(images)).float().cuda(), torch.tensor(np.array(gts)).float().cuda()
# return torch.tensor(np.array(images)).float(), torch.tensor(np.array(gts)).float()
def __len__(self):
return self.size
class Test_Dataset:
def __init__(self, name, config=None):
self.config = config
self.images, self.gts = get_image_list(name, config, 'test')
self.size = len(self.images)
self.dataset_name = name
def load_data(self, index):
image = Image.open(self.images[index]).convert('RGB')
if not self.config['orig_size']:
image = image.resize((self.config['size'], self.config['size']))
image = np.array(image).astype(np.float32)
gt = np.array(Image.open(self.gts[index]).convert('L'))
name = self.images[index].split('/')[-1].split('.')[0]
image = ((image / 255.) - mean) / std
image = image.transpose((2, 0, 1))
image = torch.tensor(np.expand_dims(image, 0)).float()
gt = (gt - np.min(gt)) / (np.max(gt) - np.min(gt))
#gt = (gt > 0.5).astype(np.float32)
#gt = (gt > 128).astype(np.float32)
return image, gt, name
def test_data():
config = {'orig_size': True, 'size': 288, 'data_path': '../dataset'}
dataset = 'SOD'
'''
data_loader = Test_Dataset(dataset, config)
#data_loader = Train_Dataset(dataset, config)
data_size = data_loader.size
for i in range(data_size):
img, gt, name = data_loader.load_data(i)
#img, gt = data_loader.__getitem__(i)
new_img = (img * std + mean) * 255.
#new_img = gt * 255
print(np.min(new_img), np.max(new_img))
new_img = (new_img).astype(np.uint8)
#print(new_img.shape).astype(np.)
im = Image.fromarray(new_img)
#im.save('temp/' + name + '.jpg')
im.save('temp/' + str(i) + '.jpg')
'''
data_loader = Val_Dataset(dataset, config)
imgs, gts, names = data_loader.load_all_data()
print(imgs.shape, gts.shape, len(names))
if __name__ == "__main__":
test_data()