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import torch, os |
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import torch.nn.functional as F |
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from torchvision.transforms.functional import normalize |
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import numpy as np |
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from transformers import Pipeline |
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from transformers.image_utils import load_image |
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from skimage import io |
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from PIL import Image |
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class RMBGPipe(Pipeline): |
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def __init__(self,**kwargs): |
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Pipeline.__init__(self,**kwargs) |
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self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
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self.model.to(self.device) |
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self.model.eval() |
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def _sanitize_parameters(self, **kwargs): |
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preprocess_kwargs = {} |
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postprocess_kwargs = {} |
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if "model_input_size" in kwargs : |
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preprocess_kwargs["model_input_size"] = kwargs["model_input_size"] |
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if "return_mask" in kwargs: |
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postprocess_kwargs["return_mask"] = kwargs["return_mask"] |
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return preprocess_kwargs, {}, postprocess_kwargs |
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def preprocess(self,input_image,model_input_size: list=[1024,1024]): |
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orig_im = load_image(input_image) |
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orig_im = np.array(orig_im) |
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orig_im_size = orig_im.shape[0:2] |
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preprocessed_image = self.preprocess_image(orig_im, model_input_size).to(self.device) |
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inputs = { |
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"preprocessed_image":preprocessed_image, |
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"orig_im_size":orig_im_size, |
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"input_image" : input_image |
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} |
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return inputs |
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def _forward(self,inputs): |
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result = self.model(inputs.pop("preprocessed_image")) |
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inputs["result"] = result |
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return inputs |
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def postprocess(self,inputs,return_mask:bool=False ): |
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result = inputs.pop("result") |
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orig_im_size = inputs.pop("orig_im_size") |
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input_image = inputs.pop("input_image") |
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result_image = self.postprocess_image(result[0][0], orig_im_size) |
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pil_im = Image.fromarray(result_image) |
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if return_mask ==True : |
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return pil_im |
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no_bg_image = Image.new("RGBA", pil_im.size, (0,0,0,0)) |
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input_image = load_image(input_image) |
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no_bg_image.paste(input_image, mask=pil_im) |
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return no_bg_image |
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def preprocess_image(self,im: np.ndarray, model_input_size: list=[1024,1024]) -> 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(torch.unsqueeze(im_tensor,0), size=model_input_size, mode='bilinear') |
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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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return image |
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def postprocess_image(self,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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