u2net/u2net_inference.py

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