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53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
import os
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import time
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import numpy as np
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from skimage import io
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import time
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from glob import glob
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from tqdm import tqdm
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import torch, gc
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import torch.nn as nn
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from torch.autograd import Variable
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import torch.optim as optim
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import torch.nn.functional as F
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from torchvision.transforms.functional import normalize
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from models import *
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if __name__ == "__main__":
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dataset_path="" #Your dataset path
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model_path="" #Your model path
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result_path="" #The folder path that you want to save the results
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input_size=[1024,1024]
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net=ISNetDIS()
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if torch.cuda.is_available():
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net.load_state_dict(torch.load(model_path))
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net=net.cuda()
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else:
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net.load_state_dict(torch.load(model_path,map_location="cpu"))
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net.eval()
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im_list = glob(dataset_path+"/*.jpg")
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for i, im_path in tqdm(enumerate(im_list), total=len(im_list)):
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print("im_path: ", im_path)
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im = io.imread(im_path)
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if len(im.shape) < 3:
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im = im[:, :, np.newaxis]
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im_shp=im.shape[0:2]
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im_tensor = torch.tensor(im, dtype=torch.float32).permute(2,0,1)
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im_tensor = F.upsample(torch.unsqueeze(im_tensor,0), input_size, mode="bilinear").type(torch.uint8)
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image = torch.divide(im_tensor,255.0)
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image = normalize(image,[0.5,0.5,0.5],[1.0,1.0,1.0])
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if torch.cuda.is_available():
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image=image.cuda()
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result=net(image)
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result=torch.squeeze(F.upsample(result[0][0],im_shp,mode='bilinear'),0)
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ma = torch.max(result)
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mi = torch.min(result)
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result = (result-mi)/(ma-mi)
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im_name=im_path.split('/')[-1].split('.')[0]
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io.imsave(os.path.join(result_path,im_name+".png"),(result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8))
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