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import os |
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import torch |
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import argparse |
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import numpy as np |
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from PIL import Image |
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from skimage import io |
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from ormbg import ORMBG |
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import torch.nn.functional as F |
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def parse_args(): |
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parser = argparse.ArgumentParser( |
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description="Remove background from images using ORMBG model." |
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) |
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parser.add_argument( |
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"--input", |
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type=str, |
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default=os.path.join("examples", "example1.png"), |
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help="Path to the input image file.", |
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) |
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parser.add_argument( |
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"--output", |
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type=str, |
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default=os.path.join("examples", "no-background1.png"), |
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help="Path to the output image file.", |
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) |
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parser.add_argument( |
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"--model-path", |
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type=str, |
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default=os.path.join("models", "ormbg.pth"), |
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help="Path to the model file.", |
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) |
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return parser.parse_args() |
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def preprocess_image(im: np.ndarray, model_input_size: list) -> torch.Tensor: |
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if len(im.shape) < 3: |
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im = im[:, :, np.newaxis] |
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im_tensor = torch.tensor(im, dtype=torch.float32).permute(2, 0, 1) |
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im_tensor = F.interpolate( |
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torch.unsqueeze(im_tensor, 0), size=model_input_size, mode="bilinear" |
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).type(torch.uint8) |
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image = torch.divide(im_tensor, 255.0) |
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return image |
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def postprocess_image(result: torch.Tensor, im_size: list) -> np.ndarray: |
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result = torch.squeeze(F.interpolate(result, 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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im_array = (result * 255).permute(1, 2, 0).cpu().data.numpy().astype(np.uint8) |
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im_array = np.squeeze(im_array) |
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return im_array |
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def inference(args): |
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image_path = args.input |
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result_name = args.output |
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model_path = args.model_path |
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net = ORMBG() |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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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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model_input_size = [1024, 1024] |
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orig_im = io.imread(image_path) |
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orig_im_size = orig_im.shape[0:2] |
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image = preprocess_image(orig_im, model_input_size).to(device) |
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result = net(image) |
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result_image = postprocess_image(result[0][0], orig_im_size) |
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pil_im = Image.fromarray(result_image) |
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no_bg_image = Image.new("RGBA", pil_im.size, (0, 0, 0, 0)) |
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orig_image = Image.open(image_path) |
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no_bg_image.paste(orig_image, mask=pil_im) |
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no_bg_image.save(result_name) |
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if __name__ == "__main__": |
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inference(parse_args()) |
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