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import os |
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import numpy as np |
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from skimage import io |
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from glob import glob |
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from tqdm import tqdm |
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import cv2 |
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import torch |
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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 ISNetDIS |
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if __name__ == "__main__": |
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dataset_path="input_images" |
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model_path="model.pth" |
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result_path="output_results" |
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if not os.path.exists(result_path): |
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os.makedirs(result_path) |
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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")+glob(dataset_path+"/*.JPG")+glob(dataset_path+"/*.jpeg")+glob(dataset_path+"/*.JPEG")+glob(dataset_path+"/*.png")+glob(dataset_path+"/*.PNG")+glob(dataset_path+"/*.bmp")+glob(dataset_path+"/*.BMP")+glob(dataset_path+"/*.tiff")+glob(dataset_path+"/*.TIFF") |
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with torch.no_grad(): |
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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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cv2.imwrite(os.path.join(result_path,im_name+".png"),(result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8)) |
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