File size: 2,146 Bytes
a924c15 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 |
import os
import numpy as np
from skimage import io
from glob import glob
from tqdm import tqdm
import cv2
import torch
import torch.nn.functional as F
from torchvision.transforms.functional import normalize
from models import ISNetDIS
if __name__ == "__main__":
dataset_path="input_images" #Your dataset path
model_path="model.pth"
result_path="output_results" #The folder path that you want to save the results
if not os.path.exists(result_path):
os.makedirs(result_path)
input_size=[1024,1024]
net=ISNetDIS()
if torch.cuda.is_available():
net.load_state_dict(torch.load(model_path))
net=net.cuda()
else:
net.load_state_dict(torch.load(model_path,map_location="cpu"))
net.eval()
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")
with torch.no_grad():
for i, im_path in tqdm(enumerate(im_list), total=len(im_list)):
print("im_path: ", im_path)
im = io.imread(im_path)
if len(im.shape) < 3:
im = im[:, :, np.newaxis]
im_shp=im.shape[0:2]
im_tensor = torch.tensor(im, dtype=torch.float32).permute(2,0,1)
im_tensor = F.upsample(torch.unsqueeze(im_tensor,0), input_size, mode="bilinear").type(torch.uint8)
image = torch.divide(im_tensor,255.0)
image = normalize(image,[0.5,0.5,0.5],[1.0,1.0,1.0])
if torch.cuda.is_available():
image=image.cuda()
result=net(image)
result=torch.squeeze(F.upsample(result[0][0],im_shp,mode='bilinear'),0)
ma = torch.max(result)
mi = torch.min(result)
result = (result-mi)/(ma-mi)
im_name=im_path.split('/')[-1].split('.')[0]
cv2.imwrite(os.path.join(result_path,im_name+".png"),(result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8))
|