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import os
from typing import Union
from skimage import io, transform
import torch
import torchvision
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms#, utils
# import torch.optim as optim
import numpy as np
from PIL import Image
import glob
from .data_loader import RescaleT
from .data_loader import ToTensor
from .data_loader import ToTensorLab
from .data_loader import SalObjDataset
from .u2net import U2NET # full size version 173.6 MB
from .u2net import U2NETP # small version u2net 4.7 MB
# normalize the predicted SOD probability map
def normPRED(d):
ma = torch.max(d)
mi = torch.min(d)
dn = (d-mi)/(ma-mi)
return dn
def save_output(image_name,pred,d_dir):
predict = pred
predict = predict.squeeze()
predict_np = predict.cpu().data.numpy()
im = Image.fromarray(predict_np*255).convert('RGB')
img_name = image_name.split(os.sep)[-1]
image = io.imread(image_name)
imo = im.resize((image.shape[1],image.shape[0]),resample=Image.BILINEAR)
pb_np = np.array(imo)
aaa = img_name.split(".")
bbb = aaa[0:-1]
imidx = bbb[0]
for i in range(1,len(bbb)):
imidx = imidx + "." + bbb[i]
imo.save(d_dir+imidx+'.png')
def get_u2net_model():
model_pth = "models/u2net.pth"
net = U2NET(3,1)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net.load_state_dict(torch.load(model_pth, map_location=device))
net.eval()
return net
def get_saliency_mask(model, image_or_image_path : Union[str, np.array]):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if isinstance(image_or_image_path, str):
image = io.imread(image_or_image_path)
else:
image = image_or_image_path
transform = transforms.Compose([RescaleT(320), ToTensorLab(flag=0)])
sample = transform({
'imidx' : np.array([0]),
'image' : image,
'label' : np.expand_dims(np.zeros(image.shape[:-1]), -1)
})
input_test = sample["image"].unsqueeze(0).type(torch.FloatTensor).to(device)
d1,d2,d3,d4,d5,d6,d7= model(input_test)
pred = d1[:,0,:,:]
pred = normPRED(pred)
pred = pred.squeeze()
predict_np = pred.cpu().data.numpy()
im = Image.fromarray(predict_np * 255).convert("RGB")
return im |