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data.py
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import os
from PIL import Image
import torch.utils.data as data
import numpy as np
import torch
import torchvision.transforms as transforms
import cv2
from fast_slic import Slic
class SalObjDataset(data.Dataset):
def __init__(self, image_root,depth_root, gt_root, mask_root, gray_root,edge_root,trainsize):
self.trainsize = trainsize
self.images = [image_root + f for f in os.listdir(image_root) if f.endswith('.jpg')]
self.depths = [depth_root + f for f in os.listdir(depth_root) if f.endswith('.png') or f.endswith('.jpg')]
self.gts = [gt_root + f for f in os.listdir(gt_root) if f.endswith('.jpg')
or f.endswith('.png')]
self.masks = [mask_root + f for f in os.listdir(mask_root) if f.endswith('.png')]
self.grays = [gray_root + f for f in os.listdir(gray_root) if f.endswith('.png')]
self.edges = [edge_root + f for f in os.listdir(edge_root) if f.endswith('.png')]
self.images = sorted(self.images)
self.gts = sorted(self.gts)
self.masks = sorted(self.masks)
self.grays = sorted(self.grays)
self.edges = sorted(self.edges)
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.depth_transform = transforms.Compose([
transforms.Resize((self.trainsize, self.trainsize)),
transforms.ToTensor()])
self.gt_transform = transforms.Compose([
transforms.Resize((self.trainsize, self.trainsize)),
transforms.ToTensor()])
self.mask_transform = transforms.Compose([
transforms.Resize((self.trainsize, self.trainsize)),
transforms.ToTensor()])
self.gray_transform = transforms.Compose([
transforms.Resize((self.trainsize, self.trainsize)),
transforms.ToTensor()])
self.edge_transform = transforms.Compose([
transforms.Resize((self.trainsize, self.trainsize)),
transforms.ToTensor()])
def __getitem__(self, index):
image = self.rgb_loader(self.images[index])
depth = self.rgb_loader(self.depths[index])
gt = self.binary_loader(self.gts[index])
mask = self.binary_loader(self.masks[index])
gray = self.binary_loader(self.grays[index])
edge = self.binary_loader(self.edges[index])
name = self.images[index].split('/')[-1]
np_img = np.array(image)
np_img = cv2.resize(np_img, dsize=(self.trainsize, self.trainsize), interpolation=cv2.INTER_LINEAR)
np_depth = np.array(depth)
np_depth = cv2.resize(np_depth, dsize=(self.trainsize, self.trainsize), interpolation=cv2.INTER_LINEAR)
np_gt = np.array(gt)
np_gt = cv2.resize(np_gt, dsize=(self.trainsize, self.trainsize), interpolation=cv2.INTER_LINEAR) / 255
np_mask = np.array(mask)
np_mask = cv2.resize(np_mask, dsize=(self.trainsize, self.trainsize), interpolation=cv2.INTER_LINEAR) / 255
slic = Slic(num_components=40, compactness=10)
SS_map = slic.iterate(np_img)
SS_map_depth = slic.iterate(np_depth)
SS_map = SS_map + 1
SS_map_depth = SS_map_depth + 1
SS_maps_label = []
SS_maps = []
# SS_maps_label_mask = []
# SS_maps_mask = []
SS_maps_label_depth = []
SS_maps_depth = []
# SS_maps_label_mask_depth = []
# SS_maps_mask_depth = []
label_gt = np.zeros((1, self.trainsize, self.trainsize))
# label_mask = np.zeros((1, self.trainsize, self.trainsize))
label_gt_depth = np.zeros((1, self.trainsize, self.trainsize))
# label_mask_depth = np.zeros((1, self.trainsize, self.trainsize))
for i in range(1, 40+ 1):
buffer = np.copy(SS_map)
buffer[buffer != i] = 0
buffer[buffer == i] = 1
if np.sum(buffer) != 0:
if np.sum(buffer * np_gt) > 1:
label_gt = label_gt+buffer
SS_maps_label.append(1)
else:
SS_maps_label.append(0)
else:
SS_maps_label.append(0)
SS_maps.append(buffer)
label_gt = torch.tensor(label_gt)
label_gt = label_gt.to(torch.float32)
for i in range(1, 40 + 1):
buffer = np.copy(SS_map_depth)
buffer[buffer != i] = 0
buffer[buffer == i] = 1
if np.sum(buffer) != 0:
if np.sum(buffer * np_gt) > 1:
label_gt_depth = label_gt_depth+buffer
SS_maps_label_depth.append(1)
else:
SS_maps_label_depth.append(0)
else:
SS_maps_label_depth.append(0)
SS_maps_depth.append(buffer)
label_gt_depth = torch.tensor(label_gt_depth)
label_gt_depth = label_gt_depth.to(torch.float32)
image = self.img_transform(image)
depth = self.depth_transform(depth)
gt = self.gt_transform(gt)
mask = self.mask_transform(mask)
gray = self.gray_transform(gray)
edge = self.edge_transform(edge)
return image, depth, gt, mask, gray, edge, label_gt, label_gt_depth,name
def filter_files(self):
assert len(self.images) == len(self.gts)
images = []
depths = []
gts = []
masks = []
grays = []
edges = []
for img_path,depth_path, gt_path, mask_path, gray_path,edge_path in zip(self.images, self.depths, self.gts, self.masks, self.grays,self.edges):
img = Image.open(img_path)
depth = Image.open(depth_path)
gt = Image.open(gt_path)
mask = Image.open(mask_path)
gray = Image.open(gray_path)
edge = Image.open(edge_path)
if img.size == gt.size:
images.append(img_path)
depths.append(depth_path)
gts.append(gt_path)
masks.append(mask_path)
grays.append(gray_path)
edges.append(edge_path)
self.images = images
self.depths = depths
self.gts = gts
self.masks = masks
self.grays = grays
self.edges = edges
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,depth_root, gt_root, mask_root, gray_root,edge_root, batchsize, trainsize, shuffle=True, num_workers=0, pin_memory=True):
dataset = SalObjDataset(image_root, depth_root, gt_root, mask_root, gray_root, edge_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,depth_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.depths=[depth_root + f for f in os.listdir(depth_root) if f.endswith('.bmp')
or f.endswith('.jpg')]
self.images = sorted(self.images)
self.gts = sorted(self.gts)
self.depths=sorted(self.depths)
self.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.gt_transform = transforms.Compose([
# transforms.Resize((self.trainsize, self.trainsize)),
# transforms.ToTensor()])
self.depths_transform = transforms.Compose([transforms.Resize((self.testsize, self.testsize)),transforms.ToTensor()])
self.size = len(self.images)
self.index = 0
def load_data(self):
image = self.rgb_loader(self.images[self.index])
image = self.transform(image).unsqueeze(0)
gt = self.binary_loader(self.gts[self.index])
depth=self.rgb_loader(self.depths[self.index])
depth=self.depths_transform(depth).unsqueeze(0)
name = self.images[self.index].split('/')[-1]
image_for_post=self.rgb_loader(self.images[self.index])
image_for_post=image_for_post.resize(gt.size)
if name.endswith('.jpg'):
name = name.split('.jpg')[0] + '.jpg'
self.index += 1
self.index = self.index % self.size
return image, gt,depth, name,np.array(image_for_post)
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')