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README.md
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---
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license: apache-2.0
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---
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---
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license: apache-2.0
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tags:
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- background-removal
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- Pytorch
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- vision
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---
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# BRIA Background Removal v1.3
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## Usage
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```python
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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.nn.functional as F
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from torchvision.transforms.functional import normalize
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from models import BriaRMBG
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input_size=[1024,1024]
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net=BriaRMBG()
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model_path = "./model.pth"
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im_path = "./example_image.jpg"
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result_path = "."
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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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# prepare input
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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_size=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.interpolate(torch.unsqueeze(im_tensor,0), size=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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# inference
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result=net(image)
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# post process
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result = torch.squeeze(F.interpolate(result[0][0], size=im_size, 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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# save result
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im_name=im_path.split('/')[-1].split('.')[0]
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im_array = (result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8)
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cv2.imwrite(os.path.join(result_path, im_name+".png"), im_array)
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```
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## Training data
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Bria-RMBG model was trained over 12000 high quality, high resolution images.
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All images were manualy labeled pixel-wise accuratly. The images belong to veriety of categories, the majority of them inclues people.
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## Qualitative Evaluation
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