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|
| | import torch |
| | import numpy as np |
| | from PIL import Image, ImageOps |
| | import torch.nn.functional as F |
| | import cv2 |
| | import time |
| |
|
| | IMG_NORM_MEAN = [0.485, 0.456, 0.406] |
| | IMG_NORM_STD = [0.229, 0.224, 0.225] |
| |
|
| |
|
| | def normalize_rgb_tensor(img, imgenet_normalization=True): |
| | img = img / 255.0 |
| | if imgenet_normalization: |
| | img = ( |
| | img - torch.tensor(IMG_NORM_MEAN, device=img.device).view(1, 3, 1, 1) |
| | ) / torch.tensor(IMG_NORM_STD, device=img.device).view(1, 3, 1, 1) |
| | return img |
| |
|
| |
|
| | def normalize_rgb(img, imagenet_normalization=True): |
| | """ |
| | Args: |
| | - img: np.array - (W,H,3) - np.uint8 - 0/255 |
| | Return: |
| | - img: np.array - (3,W,H) - np.float - -3/3 |
| | """ |
| | img = img.astype(np.float32) / 255.0 |
| | img = np.transpose(img, (2, 0, 1)) |
| | if imagenet_normalization: |
| | img = (img - np.asarray(IMG_NORM_MEAN).reshape(3, 1, 1)) / np.asarray( |
| | IMG_NORM_STD |
| | ).reshape(3, 1, 1) |
| | img = img.astype(np.float32) |
| | return img |
| |
|
| |
|
| | def denormalize_rgb(img, imagenet_normalization=True): |
| | """ |
| | Args: |
| | - img: np.array - (3,W,H) - np.float - -3/3 |
| | Return: |
| | - img: np.array - (W,H,3) - np.uint8 - 0/255 |
| | """ |
| | if imagenet_normalization: |
| | img = (img * np.asarray(IMG_NORM_STD).reshape(3, 1, 1)) + np.asarray( |
| | IMG_NORM_MEAN |
| | ).reshape(3, 1, 1) |
| | img = np.transpose(img, (1, 2, 0)) * 255.0 |
| | img = img.astype(np.uint8) |
| | return img |
| |
|
| |
|
| | def unpatch(data, patch_size=14, c=3, img_size=224): |
| | |
| | if len(data.shape) == 2: |
| | c = 1 |
| | data = data[:, :, None].repeat([1, 1, patch_size**2]) |
| |
|
| | B, N, HWC = data.shape |
| | HW = patch_size**2 |
| | c = int(HWC / HW) |
| | h = w = int(N**0.5) |
| | p = q = int(HW**0.5) |
| | data = data.reshape([B, h, w, p, q, c]) |
| | data = torch.einsum("nhwpqc->nchpwq", data) |
| | return data.reshape([B, c, img_size, img_size]) |
| |
|
| |
|
| | def image_pad(img, img_size, device=torch.device("cuda")): |
| | img_pil = ImageOps.contain(img, (img_size, img_size)) |
| | img_pil_bis = ImageOps.pad( |
| | img_pil.copy(), size=(img_size, img_size), color=(255, 255, 255) |
| | ) |
| | img_pil = ImageOps.pad( |
| | img_pil, size=(img_size, img_size) |
| | ) |
| |
|
| | |
| | resize_img = np.asarray(img_pil) |
| |
|
| | |
| | resize_img = normalize_rgb(resize_img) |
| |
|
| | x = torch.from_numpy(resize_img).unsqueeze(0).to(device) |
| | return x, img_pil_bis |
| |
|
| |
|
| | def image_pad_cuda(img, img_size, rot=0, device=torch.device("cuda"), vis=False): |
| | img = torch.Tensor(img).to(device) |
| | img = torch.flip(img, dims=[2]).unsqueeze(0).permute(0, 3, 1, 2) |
| | if rot != 0: |
| | img = torch.rot90(img, rot, [2, 3]) |
| |
|
| | if vis: |
| | image = img.clone()[0].permute(1, 2, 0).cpu().numpy() |
| | if image.dtype != np.uint8: |
| | image = image.astype(np.uint8) |
| | cv2.imshow("k4a", image[..., ::-1]) |
| | cv2.waitKey(1) |
| | _, _, h, w = img.shape |
| | scale_factor = min(img_size / w, img_size / h) |
| |
|
| | img = F.interpolate(img, scale_factor=scale_factor, mode="bilinear") |
| |
|
| | _, _, h, w = img.shape |
| |
|
| | pad_w = (img_size - w) // 2 |
| | pad_h = (img_size - h) // 2 |
| |
|
| | img = F.pad(img, (pad_w, pad_w, pad_h, pad_h), mode="constant", value=255) |
| |
|
| | |
| | resize_img = normalize_rgb_tensor(img) |
| | return resize_img, img |
| |
|
