Update README.md

README.md

@@ -88,41 +88,40 @@ For clarity, we provide our data distribution according to different categories,
 
 RMBG v1.4 is developed on the [IS-Net](https://github.com/xuebinqin/DIS) enhanced with our unique training scheme and proprietary dataset. These modifications significantly improve the model’s accuracy and effectiveness in diverse image-processing scenarios.
 
+## installation
+git clone https://huggingface.co/briaai/RMBG-1.4
+cd RMBG-1.4/
+pip install -r requirements.txt
 
 ## Usage
 
 ```python
-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 BriaRMBG
-
-input_size=[1024,1024]
-net=BriaRMBG()
+from briarmbg import BriaRMBG
+from PIL import Image
 
 model_path = "./model.pth"
-im_path = "./example_image.jpg"
-result_path = "."
+im_path = "./example_input.jpg"
 
+net = BriaRMBG()
 if torch.cuda.is_available():
-    net.load_state_dict(torch.load(model_path))
-    net=net.cuda()
+    net.load_state_dict(torch.load(model_path)).cuda()
 else:
     net.load_state_dict(torch.load(model_path,map_location="cpu"))
 net.eval()    
 
 # prepare input
+model_input_size=[1024,1024]
 im = io.imread(im_path)
 if len(im.shape) < 3:
     im = im[:, :, np.newaxis]
 im_size=im.shape[0:2]
 im_tensor = torch.tensor(im, dtype=torch.float32).permute(2,0,1)
-im_tensor = F.interpolate(torch.unsqueeze(im_tensor,0), size=input_size, mode='bilinear').type(torch.uint8)
+im_tensor = F.interpolate(torch.unsqueeze(im_tensor,0), size=model_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])
 
@@ -139,7 +138,10 @@ mi = torch.min(result)
 result = (result-mi)/(ma-mi)
 
 # save result
-im_name=im_path.split('/')[-1].split('.')[0]
 im_array = (result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8)
-cv2.imwrite(os.path.join(result_path, im_name+".png"), im_array)
+pil_im = Image.fromarray(np.squeeze(im_array))
+no_bg_image = Image.new("RGBA", pil_im.size, (0,0,0,0))
+orig_image = Image.open(im_path)
+no_bg_image.paste(orig_image, mask=pil_im)
+no_bg_image.save("example_image_no_bg.png")
 ```