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data.py
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data.py
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import os
from PIL import Image
import torch.utils.data as data
import torchvision.transforms as transforms
import random
import numpy as np
from PIL import ImageEnhance
#several data augumentation strategies
def cv_random_flip(img, label):
flip_flag = random.randint(0, 1)
# flip_flag2= random.randint(0,1)
#left right flip
if flip_flag == 1:
img = img.transpose(Image.FLIP_LEFT_RIGHT)
label = label.transpose(Image.FLIP_LEFT_RIGHT)
#top bottom flip
# if flip_flag2==1:
# img = img.transpose(Image.FLIP_TOP_BOTTOM)
# label = label.transpose(Image.FLIP_TOP_BOTTOM)
# depth = depth.transpose(Image.FLIP_TOP_BOTTOM)
return img, label
def randomCrop(image, label):
border=30
image_width = image.size[0]
image_height = image.size[1]
crop_win_width = np.random.randint(image_width-border , image_width)
crop_win_height = np.random.randint(image_height-border , image_height)
random_region = (
(image_width - crop_win_width) >> 1, (image_height - crop_win_height) >> 1, (image_width + crop_win_width) >> 1,
(image_height + crop_win_height) >> 1)
return image.crop(random_region), label.crop(random_region)
def randomRotation(image,label):
mode=Image.BICUBIC
if random.random()>0.8:
random_angle = np.random.randint(-15, 15)
image=image.rotate(random_angle, mode)
label=label.rotate(random_angle, mode)
return image,label
def colorEnhance(image):
bright_intensity=random.randint(5,15)/10.0
image=ImageEnhance.Brightness(image).enhance(bright_intensity)
contrast_intensity=random.randint(5,15)/10.0
image=ImageEnhance.Contrast(image).enhance(contrast_intensity)
color_intensity=random.randint(0,20)/10.0
image=ImageEnhance.Color(image).enhance(color_intensity)
sharp_intensity=random.randint(0,30)/10.0
image=ImageEnhance.Sharpness(image).enhance(sharp_intensity)
return image
def randomGaussian(image, mean=0.1, sigma=0.35):
def gaussianNoisy(im, mean=mean, sigma=sigma):
for _i in range(len(im)):
im[_i] += random.gauss(mean, sigma)
return im
img = np.asarray(image)
width, height = img.shape
img = gaussianNoisy(img[:].flatten(), mean, sigma)
img = img.reshape([width, height])
return Image.fromarray(np.uint8(img))
def randomPeper(img):
img=np.array(img)
noiseNum=int(0.0015*img.shape[0]*img.shape[1])
for i in range(noiseNum):
randX=random.randint(0,img.shape[0]-1)
randY=random.randint(0,img.shape[1]-1)
if random.randint(0,1)==0:
img[randX,randY]=0
else:
img[randX,randY]=255
return Image.fromarray(img)
class SalObjDataset(data.Dataset):
def __init__(self, image_root, gt_root, trainsize):
self.trainsize = trainsize
self.images = [image_root + f for f in os.listdir(image_root) if f.endswith('.jpg')]
self.gts = [gt_root + f for f in os.listdir(gt_root) if f.endswith('.jpg')
or f.endswith('.png')]
self.images = sorted(self.images)
# self.depths = sorted(self.depths)
self.gts = sorted(self.gts)
self.filter_files()
self.size = len(self.images)
self.img_transform = transforms.Compose([
transforms.Resize((self.trainsize, self.trainsize)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
self.gt_transform = transforms.Compose([
transforms.Resize((self.trainsize, self.trainsize)),
transforms.ToTensor()])
def __getitem__(self, index):
image = self.rgb_loader(self.images[index])
gt = self.binary_loader(self.gts[index])
# image,gt =cv_random_flip(image,gt)
# image,gt =randomCrop(image, gt)
# image,gt =randomRotation(image, gt)
# image=colorEnhance(image)
# gt=randomPeper(gt)
image = self.img_transform(image)
gt = self.gt_transform(gt)
return image, gt
def filter_files(self):
assert len(self.images) == len(self.gts)
images = []
# depths = []
gts = []
for img_path, gt_path in zip(self.images, self.gts):
img = Image.open(img_path)
gt = Image.open(gt_path)
if img.size == gt.size:
images.append(img_path)
gts.append(gt_path)
self.images = images
self.gts = gts
def rgb_loader(self, path):
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('RGB')
def binary_loader(self, path):
with open(path, 'rb') as f:
img = Image.open(f)
# return img.convert('1')
return img.convert('L')
def resize(self, img, gt):
assert img.size == gt.size
w, h = img.size
if h < self.trainsize or w < self.trainsize:
h = max(h, self.trainsize)
w = max(w, self.trainsize)
return img.resize((w, h), Image.BILINEAR), gt.resize((w, h), Image.NEAREST)
else:
return img, gt
def __len__(self):
return self.size
def get_loader(image_root, gt_root, batchsize, trainsize, shuffle=True, num_workers=12, pin_memory=True):
dataset = SalObjDataset(image_root, gt_root, trainsize)
data_loader = data.DataLoader(dataset=dataset,
batch_size=batchsize,
shuffle=shuffle,
num_workers=num_workers,
pin_memory=pin_memory)
return data_loader
class test_dataset:
def __init__(self, image_root, gt_root, testsize):
self.testsize = testsize
self.images = [image_root + f for f in os.listdir(image_root) if f.endswith('.jpg')]
self.gts = [gt_root + f for f in os.listdir(gt_root) if f.endswith('.jpg')
or f.endswith('.png')]
self.images = sorted(self.images)
# self.depths = sorted(self.depths)
self.gts = sorted(self.gts)
self.img_transform = transforms.Compose([
transforms.Resize((self.testsize, self.testsize)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
self.gt_transform = transforms.ToTensor()
self.size = len(self.images)
self.index = 0
def load_data(self):
image = self.rgb_loader(self.images[self.index])
image = self.img_transform(image).unsqueeze(0)
gt = self.binary_loader(self.gts[self.index])
name = self.images[self.index].split('/')[-1]
if name.endswith('.jpg'):
name = name.split('.jpg')[0] + '.png'
self.index = (self.index+1)%self.size
return image, gt, name
def rgb_loader(self, path):
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('RGB')
def binary_loader(self, path):
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('L')