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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 |
