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import os
import gradio as gr
import sys
sys.path.insert(0, 'U-2-Net')
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 model import U2NET # full size version 173.6 MB
from model 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')
return d_dir+'/'+imidx+'.png'
# --------- 1. get image path and name ---------
model_name='u2net_portrait'#u2netp
image_dir = 'portrait_im'
prediction_dir = 'portrait_results'
if(not os.path.exists(prediction_dir)):
os.mkdir(prediction_dir)
model_dir = os.path.join(os.path.abspath(os.path.dirname(__file__)), 'U-2-Net/saved_models/u2net_portrait/u2net_portrait.pth')
# --------- 3. model define ---------
print("...load U2NET---173.6 MB")
net = U2NET(3,1)
net.load_state_dict(torch.load(model_dir, map_location='cpu'))
# if torch.cuda.is_available():
# net.cuda()
net.eval()
def process(im):
img_name_list = glob.glob(im.name)
print("Number of images: ", len(img_name_list))
# --------- 2. dataloader ---------
# 1. dataloader
test_salobj_dataset = SalObjDataset(img_name_list=img_name_list,
lbl_name_list=[],
transform=transforms.Compose([RescaleT(512),
ToTensorLab(flag=0)])
)
test_salobj_dataloader = DataLoader(test_salobj_dataset,
batch_size=1,
shuffle=False,
num_workers=1)
results = []
# --------- 4. inference for each image ---------
for i_test, data_test in enumerate(test_salobj_dataloader):
print("inferencing:", img_name_list[i_test].split(os.sep)[-1])
inputs_test = data_test['image']
inputs_test = inputs_test.type(torch.FloatTensor)
# if torch.cuda.is_available():
# inputs_test = Variable(inputs_test.cuda())
# else:
inputs_test = Variable(inputs_test)
d1, d2, d3, d4, d5, d6, d7 = net(inputs_test)
# normalization
pred = 1.0 - d1[:, 0, :, :]
pred = normPRED(pred)
# save results to test_results folder
results.append(save_output(img_name_list[i_test], pred, prediction_dir))
del d1, d2, d3, d4, d5, d6, d7
print(results)
return Image.open(results[0])
title = "U-2-Net"
description = "Gradio demo for U-2-Net, https://github.com/xuebinqin/U-2-Net"
article = ""
gr.Interface(
process,
[gr.inputs.Image(type="file", label="Input")
],
gr.outputs.Image(type="pil", label="Output"),
title=title,
description=description,
article=article,
examples=[],
allow_flagging=False,
allow_screenshot=False
).launch(enable_queue=True,cache_examples=True)
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