Update README.md

README.md

@@ -1,3 +1,70 @@
 ---
 license: apache-2.0
+tags:
+- background-removal
+- Pytorch
+- vision
 ---
+
+# BRIA Background Removal v1.3
+
+## Usage
+
+```python
+import os
+import numpy as np
+from skimage import io
+from glob import glob
+from tqdm import tqdm
+import cv2
+import torch.nn.functional as F
+from torchvision.transforms.functional import normalize
+from models import BriaRMBG
+
+input_size=[1024,1024]
+net=BriaRMBG()
+
+model_path = "./model.pth"
+im_path = "./example_image.jpg"
+result_path = "."
+
+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()    
+
+# prepare input
+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)
+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()
+
+# inference 
+result=net(image)
+
+# post process
+result = torch.squeeze(F.interpolate(result[0][0], size=im_size, mode='bilinear') ,0)
+ma = torch.max(result)
+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)
+```
+
+## Training data
+Bria-RMBG model was trained over 12000 high quality, high resolution images. 
+All images were manualy labeled pixel-wise accuratly. The images belong to veriety of categories, the majority of them inclues people. 
+
+## Qualitative Evaluation