| import torch
|
| import torch.nn.functional as F
|
| from torchvision.transforms.functional import normalize
|
| import numpy as np
|
|
|
| def preprocess_image(im: np.ndarray, model_input_size: list) -> torch.Tensor:
|
| if len(im.shape) < 3:
|
| im = im[:, :, np.newaxis]
|
|
|
| im_tensor = torch.tensor(im, dtype=torch.float32).permute(2,0,1)
|
| im_tensor = F.interpolate(torch.unsqueeze(im_tensor,0), size=model_input_size, mode='bilinear').type(torch.uint8)
|
| image = torch.divide(im_tensor,255.0)
|
| image = normalize(image,[0.5,0.5,0.5],[1.0,1.0,1.0])
|
| return image
|
|
|
|
|
| def postprocess_image(result: torch.Tensor, im_size: list)-> np.ndarray:
|
| result = torch.squeeze(F.interpolate(result, size=im_size, mode='bilinear') ,0)
|
| ma = torch.max(result)
|
| mi = torch.min(result)
|
| result = (result-mi)/(ma-mi)
|
| im_array = (result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8)
|
| im_array = np.squeeze(im_array)
|
| return im_array
|
| |
