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import torch
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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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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(torch.unsqueeze(im_tensor,0), size=model_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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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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