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Zero
| # Multi-HMR | |
| # Copyright (c) 2024-present NAVER Corp. | |
| # CC BY-NC-SA 4.0 license | |
| 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. | |
| 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. | |
| 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. | |
| img = img.astype(np.uint8) | |
| return img | |
| def unpatch(data, patch_size=14, c=3, img_size=224): | |
| # c = 3 | |
| 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**.5) | |
| p = q = int(HW**.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)) # pad with zero on the smallest side | |
| # Go to numpy | |
| resize_img = np.asarray(img_pil) | |
| # Normalize and go to torch. | |
| 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) | |
| # Normalize and go to torch. | |
| resize_img = normalize_rgb_tensor(img) | |
| return resize_img, img |